Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 63581

2 ‘‘Updated Estimates of the Subsidies to the 

Housing GSEs,’’ attachment to a letter from Douglas 

Holtz-Eakin, Director, Congressional Budget Office, 

to the Honorable Richard C. Shelby, Chairman, 

Committee on Banking, Houseing, and Urban 

Affairs, United States Senate, April 8, 2004. A 

related recent study is Wayne Passmore, ‘‘The GSE 

Implicit subsidy and Value of Government 

Ambiguity,’’ Board of Governors of the Federal 

Reserve System. Finance and Economics Discussion 

Series, FEDS Working Paper 2003–64, December 

2003.

shareholders, while the remainder 

accrued to borrowers.2

In return for the public benefits they 

receive, Congress has mandated in the 

GSEs’ Charter Acts that the GSEs carry 

out public purposes not required of 

other private sector entities in the 

housing finance industry. These 

statutory mandates obligate the GSEs to 

work to ensure that everyone in the 

nation has a reasonable opportunity to 

enjoy access to the mortgage financing 

benefits resulting from the activities of 

these enterprises. 

With respect to these public purposes, 

Congress does not simply expect the 

GSEs to strive toward achievement of 

these purposes but rather to ‘‘lead the 

mortgage finance industry’’ and to 

‘‘ensure that citizens throughout the 

country enjoy access to the public 

benefits provided by these federally 

related entities.’’ (See S. Rep. No. 102–

282, at 34 (1992).) 

C. Statutory and Regulatory Background 

The statutory and regulatory 

background applicable to the chartering 

of Fannie Mae and Freddie Mac and 

HUD’s regulatory authority over these 

two GSEs were set out in detail in the 

preamble to HUD’s proposed rule 

published on May 3, 2004 (69 FR 

24228). Therefore, this background 

information is not repeated here in the 

preamble to this final rule. Interested 

members of the public should refer to 

Section I.A. of the preamble to the 

proposed rule at pages 69 FR 24228 

through 69 FR 24230 for this 

information. 

D. The Proposed Rule 

On May 3, 2004, HUD published a 

proposed rule setting forth new housing 

goal levels for Fannie Mae and Freddie 

Mac. (See 69 FR 24228.) HUD’s rule 

proposed to increase the level of the 

housing goals (‘‘Housing Goals’’) for the 

purchase by Fannie Mae and Freddie 

Mac of mortgages financing low- and 

moderate-income housing, special 

affordable housing, and housing in 

central cities, rural areas, and other 

underserved areas. The rule also 

proposed to establish new subgoals for 

the GSEs’ acquisitions of home purchase 

loans that qualify for each of the 

housing goals. 

In addition to soliciting public 

comments on the proposed goal levels 

and new subgoals, the rule solicited 

public comments on several other issues 

related to the housing goals, including: 

(1) Provisions relating to GSE data 

integrity, such as verification, 

certification, treatment of errors, 

omissions or discrepancies, and other 

enforcement authority; (2) amended 

definitions of ‘‘underserved area,’’

‘‘metropolitan area’’ and ‘‘minority,’’

and a new definition of the term ‘‘home 

purchase mortgage’’; (3) a method for 

imputing the distribution of GSEpurchased mortgages that lack income 

data; and (4) other changes related to the 

GSEs’ bulk purchases of seasoned loans. 

More detailed information about HUD’s 

proposals can be found in the preamble 

to HUD’s May 3, 2004, proposed rule. 

E. This Final Rule—Overview 

Under this 2004 rulemaking, the 

Department is setting new, higher levels 

for the Housing Goals, accompanied by 

subgoals under each of the Housing 

Goals for purchases of home purchase 

mortgages (i.e., excluding refinance 

mortgages) on owner-occupied 

properties in metropolitan areas. (The 

subgoals are referred to in this rule as 

the ‘‘Home Purchase Subgoals.’’) 

The Department’s purpose in setting 

higher Housing Goals and in 

establishing new Home Purchase 

Subgoals in this final rule is to 

encourage the GSEs to facilitate greater 

financing and homeownership 

opportunities for families and 

neighborhoods targeted by the Housing 

Goals. The final rule establishes levels 

of the Housing Goals that will bring the 

GSEs to a position of market leadership 

in a range of foreseeable economic 

circumstances related to the future 

course of interest rates and consequent 

fluctuations in origination rates on 

home purchase and refinance 

mortgages—both multifamily and 

single-family.

For each goal, HUD has projected 

goal-qualifying percentages of mortgage 

originations in terms of ranges that 

cover a variety of economic scenarios. 

The objective of HUD’s Housing Goals is 

to bring the GSEs’ performance to the 

upper end of HUD’s market range 

estimate for each goal, consistent with 

the requirement in FHEFSSA that HUD 

should consider the GSEs’ ability to lead 

the market for each goal. 

To enable the GSEs to achieve this 

leadership, the Department has 

established staged increases in Housing 

Goal levels for 2005, which will 

increase further, year-by-year through 

2008, to achieve the ultimate objective 

for the GSEs to lead the market under 

a range of foreseeable economic 

circumstances by 2008. 

The staged increases established by 

this rule, are consistent with the 

statutory requirement that HUD 

consider the past performance of the 

GSEs in setting the Housing Goals. 

Staged annual increases in the Goals 

will provide the GSEs with the 

opportunity to adjust their business 

models, so as to meet the required 2008 

levels without compromising other 

business objectives and requirements. 

The Department believes that the 

Home Purchase Subgoals established by 

this final rule are necessary and 

warranted. Increasing homeownership 

is a national priority. The past average 

performance of the GSEs in the home 

purchase market has been below market 

levels. As further discussed below, the 

GSEs must apply greater efforts to 

increasing homeownership for low- and 

moderate-income families, families 

living in underserved areas, and verylow income families and low-income 

families living in low-income areas. The 

addition of Home Purchase Subgoals to 

the regulatory structure will serve to 

better focus the GSEs’ efforts in a clear 

and transparent manner. The Home 

Purchase Subgoals will better allow the 

government and public alike to monitor 

the GSEs’ efforts in meeting the nation’s 

homeownership needs. The increases in 

the levels of the Housing Goals, and the 

addition of the new Home Purchase 

Subgoals, are predicated upon the 

Department’s recognition that the GSEs 

not only have the ability to achieve 

these Housing Goals and Subgoals but, 

also, that they are fully consistent with 

the statutory factors established under 

FHEFSSA. In addition, this rule is 

supported by the Department’s 

comprehensive analyses of the size of 

the mortgage market, the opportunities 

available to the GSEs, America’s unmet 

housing needs, and identified credit 

gaps. 

In addition to the establishment of 

higher Housing Goals for the years 2005 

through 2008, and the establishment of 

Home Purchase Subgoals, specific 

changes included in the final rule from 

the provisions included in the May 3, 

2004, proposed rule are as follows: 

(1) The final rule expands the existing 

provisions to permit the GSEs to impute 

incomes or rents when data are missing 

for some purchases, addressing the 

market’s expanding use of low 

documentation mortgages; 

(2) The final rule provides that goals 

credit is available for purchases of loans 

in transactions involving seller 

dissolution options, such as repurchase 

63582 Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 

agreements, only when the option 

provides for a minimum one-year 

lockout period; 

(3) The final rule clarifies the 

proposed provisions regarding HUD’s 

procedures for correcting errors, 

omissions and discrepancies in current 

year-end data and in remedying material 

overstatements of housing goals 

performance for prior years; 

(4) The final rule changes the scope of 

the proposed certification statement that 

the GSEs must provide to make it closer 

to the certification used by the Office of 

Federal Housing Enterprise Oversight 

(OFHEO), the GSEs’ financial safety and 

soundness regulator; and 

(5) The final rule makes a technical 

correction to the special counting rules 

prohibiting double counting of GSE 

purchases of seasoned mortgages toward 

the housing goals. 

In developing these regulations, the 

Department was guided by, and reaffirms, the following principles 

established in the Housing Goals 1995 

final rule (published on December 1, 

1995 at 60 FR 1846): 

(1) The GSEs should fulfill 

FHEFSSA’s intent that they lead the 

industry in ensuring that access to 

mortgage credit is made available for 

very low-, low- and moderate-income 

families and residents of underserved 

areas. HUD recognizes that, to lead the 

mortgage industry over time, the GSEs 

will have to stretch to reach certain 

Housing Goals and to close gaps 

between the secondary mortgage market 

and the primary mortgage market for 

various categories of loans. This 

recognition is consistent with the 

Congressional directive that ‘‘the 

enterprises will need to stretch their 

efforts to achieve’’ the goals. (See S. 

Rep. No. 102–282, at 35 (1992).) 

(2) The Department’s role as a 

regulator is to set broad performance 

standards for the GSEs through the 

Housing Goals, but not to dictate the 

specific products or delivery 

mechanisms the GSEs will use to 

achieve a Housing Goal. Regulating two 

exceedingly large financial enterprises 

in a dynamic market requires that HUD 

provide the GSEs with sufficient 

latitude to use their innovative 

capacities to determine how best to 

develop products to carry out their 

respective missions. HUD’s regulations 

are intended to allow the GSEs the 

flexibility to respond quickly to market 

opportunities. At the same time, the 

Department must ensure that the GSEs’

strategies address national credit needs, 

especially as they relate to housing for 

low- and moderate-income families and 

housing located in underserved 

geographical areas. The addition of 

Home Purchase Subgoals to the 

regulatory structure provides an 

additional means of encouraging the 

GSEs’ affordable housing activities to 

address identified, persistent credit 

needs while leaving to the GSEs the 

specific approaches used to meet these 

needs.

(3) Discrimination in lending 

continues to limit access to credit for 

purchasing homes by racial and ethnic 

minorities. Troublesome gaps in 

homeownership remain for minorities 

even after record growth in affordable 

lending and homeownership during the 

nineties. Studies indicate that, over the 

next few years, minorities will account 

for a growing share of the families 

seeking to buy their first home. HUD’s 

analyses indicate, however, that Fannie 

Mae and Freddie Mac account for a 

disproportionately small share of the 

minority first-time homebuyer market. 

The GSEs have a responsibility to 

promote access to capital for minorities 

and others who are seeking their first 

homes, and to demonstrate the benefits 

of such lending to industry and 

borrowers alike. The GSEs also have an 

integral role in eliminating predatory 

mortgage lending practices. 

(4) In addition to the GSEs’ purchases 

of single-family home mortgages, the 

GSEs also must continue to assist in the 

creation of an active secondary market 

for mortgages on multifamily rental 

housing. Affordable rental housing is 

essential for those families who cannot 

afford to become, or who choose not to 

become, homeowners. For this reason, 

the GSEs must assist in making capital 

available to assure the continued 

development of single-family and 

multifamily rental housing. 

II. Discussion of Public Comments 

A. Overview of Public Comments 

At the close of the public comment 

period on July 16, 2004, which was 

extended an additional two weeks 

beyond the original public comment 

deadline of July 2, 2004, HUD had 

received 302 comments, which are in 

HUD’s docket file for this rule. In 

addition to the public comments 

received on the rule, during the public 

comment period, HUD met with 

representatives of several organizations, 

including Fannie Mae and Freddie Mac, 

to accommodate oral presentation of 

concerns about the rule. HUD’s docket 

file for this rule contains information on 

the dates of these meetings, the 

attendees, and the subject discussed. 

Of the public comments received on 

the proposed rule, the most detailed 

comments were those submitted by the 

two directly affected GSEs, Fannie Mae 

and Freddie Mac. Neither GSE was 

supportive of the higher goal levels 

proposed for 2005–2008, nor did either 

support the creation of HUD’s proposed 

Home Purchase Subgoals. The GSEs 

stated, among other comments that they 

made on the rule, that the effect of many 

goals and subgoals would be 

micromanagement of the GSEs. With 

their comments, the GSEs provided 

several appendices that provided 

alternative analyses of data and 

questioned the Department’s 

methodology in determining market 

share for the three affordable housing 

goals, a key component for establishing 

the appropriate level of the housing 

goals and the subgoals. 

The GSEs did not object to HUD’s 

special affordable multifamily subgoal 

levels for 2005–2008, but other 

commenters (mostly public advocacy 

groups) recommended that HUD 

increase the levels of these subgoals. 

In addition to the GSEs, the 

commenters included national and 

regional housing industry organizations, 

nonprofit organizations, alliances, 

councils, and advocacy organizations 

involved in housing or housing issues, 

lenders, academic researchers, Members 

of Congress, state and local government 

officials, and two individuals. 

In large measure, except for several 

nonprofit organizations and public 

advocacy groups that favored higher 

goals, the majority of commenters were 

not supportive of HUD’s proposed goals, 

especially in the outer years when the 

goal levels would reach their highest 

levels. A particular concern cited by a 

number of commenters was the 

potential for adverse impact on middleincome borrowers, particularly higher 

interest rates and fees. Another concern 

raised by the commenters was the 

possibility of unintended consequences 

for the industry. Many commenters, 

including the GSEs, urged HUD to 

exclude all single-family refinances 

from the calculation of the goals. 

The Department received fewer 

comments that addressed other 

proposals in the rule, such as those 

regarding data integrity, large-scale 

transactions involving seasoned loans, 

the treatment of missing income data, 

and modifying the definition of rural 

underserved areas. For those 

commenters who submitted comments 

on these proposals, the reactions were 

generally mixed. 

With respect to HUD’s proposals for 

new data integrity provisions, the 

majority of those who commented on 

the new data integrity proposals were 

generally supportive of the concept and 

acknowledged the need for some sort of 

data verification process. However, two 

Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 63583

industry-related commenters expressed 

concern about the potential for HUD’s 

proposals to result in increased 

reporting burdens for lenders. The 

GSEs’ comments also reflected several 

concerns about the data integrity 

provisions, mainly with respect to 

definitions, procedures, and 

enforcement. 

The GSEs favored generous proxy 

provisions for the treatment of missing 

income data and submitted several 

suggestions. The majority of 

commenters on this issue, consisting 

chiefly of nonprofit and advocacy 

organizations, opposed using proxies, 

and several favored an outright ban on 

purchasing ‘‘no income’’ subprime 

mortgages. 

With regard to large-scale transactions 

involving seasoned loans, the GSEs 

commented that they should receive 

housing goals credit and that no change 

in HUD’s current definition of 

‘‘mortgage purchase’’ was warranted. 

However, a group of industry-related 

organizations opposed providing goals 

credit for seasoned loans, as did several 

advocacy organizations. Commenters 

offered no alternative definitions for 

‘‘mortgage purchase’’ in HUD’s 

regulations.

All but one commenter who 

addressed the issue of HUD’s rural 

underserved area definition favored 

changing this definition to one that is 

census tract-based, rather than countybased. Those commenters favoring 

conversion to a tract-based definition 

believed that county-level data do not 

show disparities in service that the 

GSEs should address. The dissenting 

commenter felt that lenders serving 

rural areas would face operational 

difficulties and expenses in shifting to 

a tract-based orientation. 

In addition to comments on its 

proposals related to housing goals, HUD 

received other comments on subjects 

pertaining to HUD’s regulatory authority 

over the GSEs but which were not 

related to the rule’s proposals on 

housing goals (for example, comments 

on new program authority, monitoring 

and reporting procedures, and public 

access to GSE mortgage data). Because 

these comments raised issues outside 

the scope of the May 3, 2004, proposed 

rule, they are not addressed in this final 

rule. 

A discussion of the general and 

specific comments on the rule, as well 

as HUD’s responses to these comments, 

follows in subsequent sections in this 

preamble, as well as in the Appendices 

to this Final Rule. While comments are 

summarized, not all the comments are 

addressed explicitly in this preamble. 

HUD is appreciative of the full range of 

public comments received and 

acknowledges the value of all of the 

comments submitted in response to the 

proposed rule. 

B. Subpart A—General 

In the May 3, 2004, rule, HUD 

proposed to add a definition of ‘‘home 

purchase mortgage’’ in connection with 

its proposal to specify Home Purchase 

Subgoals under each of the three 

Housing Goals, to revise the definitions 

of ‘‘metropolitan area’’ and ‘‘minority’’

to conform HUD’s regulations to 

changes in data collection practices 

made by the Office of Management and 

Budget (OMB), and to modify the 

current definition of ‘‘underserved area’’

with respect to the delineation of 

underserved portions of nonmetropolitan areas. 

1. Home Purchase Mortgage 

HUD proposed to insert a definition of 

‘‘home purchase mortgage’’ for purposes 

of specifying the Home Purchase 

Mortgage Subgoals. Since no comments 

bearing directly on this definition were 

received and the Department has 

retained the subgoal concept in this 

final rule, the definition is adopted. 

2. Metropolitan Area 

HUD proposed to alter the definition 

of ‘‘metropolitan area’’ to reflect a 

change in the definition of 

‘‘metropolitan area’’ recently 

promulgated by OMB, in which the 

concept of ‘‘Primary Metropolitan 

Statistical Area’’ was removed. No 

comments were received on this 

proposed change; accordingly, it is 

adopted. 

3. Minority 

HUD proposed to alter the definition 

of ‘‘minority’’ to reflect changes in 

standards for the classification of federal 

data on race and ethnicity previously 

promulgated by OMB and implemented 

in the 2000 census and in data 

collection under the Home Mortgage 

Disclosure Act in 2004. No comments 

were received on this proposed change; 

accordingly, it is adopted. 

4. Underserved Area 

HUD proposed to alter the definition 

of ‘‘underserved area’’ to provide for the 

specification of underserved areas 

outside of metropolitan areas at the 

census tract level rather than at the 

county level. 

For properties in non-metropolitan 

(rural) areas, mortgage purchases have 

counted toward the Underserved Areas 

Housing Goal where such purchases 

finance properties that are located in 

underserved counties. This final rule 

incorporates a determination that 

mortgage purchases will count toward 

the Underserved Areas Housing Goal 

where such purchases finance 

properties that are located in 

underserved census tracts. These are 

defined as census tracts where either: 

(1) the median income in the tract does 

not exceed 95 percent of the greater of 

the median income for the nonmetropolitan portions of the state or the 

median income of the non-metropolitan 

portions of the nation as a whole; or (2) 

minorities comprise at least 30 percent 

of the residents and the median income 

in the tract does not exceed 120 percent 

of the greater of the median incomes for 

the non-metropolitan portions of the 

state or of the nation as a whole. 

HUD originally adopted its current 

county-based definition for targeting 

GSE purchases to underserved nonmetropolitan areas primarily based on 

information that rural lenders did not 

perceive their market areas in terms of 

census tracts, but rather, in terms of 

counties. A further concern was an 

apparent lack of reliability of geocoding 

software applied to non-metropolitan 

areas. 

Thirteen commenters endorsed HUD’s 

proposed change in definition, 

observing that the change would 

produce more precise targeting and 

improved service toward underserved 

segments of the market within counties. 

One banking trade association 

advocated continuation of a countybased definition, stating that because 

the business perspective of community 

banks in rural areas is geared toward 

entire counties, there would be costs 

associated with monitoring the tract 

location of loans, and therefore, 

marketing toward borrowers at the tract 

level would be difficult. 

Recent research summarized in 

Appendix B to this rule indicates that a 

tract-based system will improve the 

extent to which the underserved area 

definition distinguishes areas by key 

socioeconomic and demographic 

characteristics such as median family 

income, poverty, unemployment, school 

dropout rates, and minority 

populations. Under a tract-based 

definition underserved areas stand out 

more as areas of lower income and low 

economic activity and as having 

somewhat larger minority population 

proportions. A tract-based definition 

will also improve the targeting of the 

goal to areas with relatively greater 

housing needs. Based on these findings, 

which are detailed in Appendix B to 

this rule, HUD is adopting a respecification of underserved areas 

within non-metropolitan (rural) areas to 

63584 Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 

3Fiscal Year 2005 Budget of the U.S. 

Government: Mid-Session review (July 30, 2004). 

Office of Management and Budget, also posted at 

http://www.whitehouse.gov/omb. The Budget and 

Economic Outlook: An Update, Washington, 

Congressional Budget Office, September 2004, also 

posted on http://www.cbo.gov.

4Mortgage Bankers Association of America, MBA 

Mortgage Finance Forecast, September 17, 2004.

be based on census tracts rather than 

counties. 

C. Subpart B—Housing Goals 

1. Overview 

A substantial majority of the 

comments received criticized HUD’s 

proposed levels of the housing goals on 

the basis that they would be difficult for 

the GSEs to achieve, particularly in 

periods of high refinance activity when 

higher-income borrowers comprise a 

relatively high proportion of mortgage 

borrowers. Several types of adverse 

consequences of such high goals were 

forecast, including diminution of 

availability of mortgage credit to some 

sectors of the mortgage market, 

unfavorable effects on neighborhood 

housing quality, and other adverse 

effects discussed below. This section of 

the final rule reviews the statutory 

factors the Department must consider in 

setting the level of the housing goals 

and the Department’s determinations 

with regard to the levels of each of the 

housing goals as well as the proposed 

Home Purchase Subgoals. 

2. Statutorily Required Factors in 

Setting the Levels of the Housing Goals 

and Subgoals

The Housing Goals and Home 

Purchase Subgoals being implemented 

by this final rule were established 

following consideration of the six 

factors required by statute to be 

considered in establishing goal levels 

and establishing subgoals. A summary 

of HUD’s findings relative to each of the 

six statutory factors follows. More 

detailed discussion of these points is 

included in Appendices A, B, and C to 

this rule. 

a. Demographic, Economic, and Housing 

Conditions 

(i) Demographic Trends 

Changing population demographics 

will result in a need for the primary and 

secondary mortgage markets to meet 

nontraditional credit needs, respond to 

diverse housing preferences and 

overcome information and other barriers 

that many immigrants and minorities 

currently face. 

The U.S. Census Bureau has projected 

that the U.S. population will grow by an 

average of 2.5 million persons per year 

between 2000 and 2025, resulting in 

about 1.2 million new households per 

year. The aging of the baby-boom 

generation and the entry of the babybust generation into prime home-buying 

age will have a dampening effect on 

housing demand. Growing housing 

demand from minorities, immigrants 

and non-traditional homebuyers will 

help offset declines in the demand for 

housing caused by the aging of the 

population. 

The continued influx of immigrants 

will increase the demand for rental 

housing, while those who immigrated 

during the 1980s and 1990s will be in 

the market for homeownership. 

Immigrants and minorities—who 

accounted for nearly 40 percent of the 

growth in the nation’s homeownership 

rate over the past five years—will be 

responsible for almost two-thirds of the 

growth in the number of new 

households over the next 10 years. 

Non-traditional households have 

become more important, as overall 

household formation rates have slowed. 

With later marriages, divorce, and nontraditional living arrangements, the 

fastest growing household groups are 

single-parent and single-person 

households. By 2025, non-family 

households will make up one-third of 

all households. The role of traditional 

25-to-34 year-old married, first-time 

homebuyers in the housing market will 

be smaller in the current decade due to 

the aging of the population. Between 

2000 and 2025, the Census Bureau 

projects that the largest growth in 

households will occur among 

householders who are age 65 and older. 

As these demographic factors play 

out, the overall effect on housing 

demand will likely be continued growth 

and an increasingly diverse household 

population from which to draw new 

renters and homeowners. A greater 

diversity in the housing market will, in 

turn, require greater adaptation by the 

primary and secondary mortgage 

markets. 

(ii) Economic and Housing Conditions 

While most other sectors of the 

economy were weak or declining during 

2001 and 2002, the housing sector 

showed remarkable strength. The 

housing market continued at a record 

pace during 2003. 

In 2002, the U.S. economy moved into 

recovery, with real Gross Domestic 

Product (GDP) growing 2.2 percent, 

although measures of unemployment 

continued to rise before declining again 

in 2003. In October 2002, the average 

30-year home mortgage interest rate 

slipped below 6 percent for the first 

time since the mid-1960s. Favorable 

financing conditions and solid increases 

in house prices were the key supports 

to record housing markets during both 

2002 and 2003. By the end of 2003, the 

industry had set new records in singlefamily home permits, new home sales, 

existing home sales, low interest rates, 

and rates of homeownership. Other 

indicators—total permits, starts, 

completions, and affordability—reached 

levels that were among the highest in 

the past two decades. 

The Administration’s forecast for real 

GDP growth is 3.7 percent for 2005 and 

3.1–3.4 percent in 2006–2009, while 

CBO projects that real GDP will grow at 

an average rate of 4.1 percent in 2005 

and annual rates of 2.9–3.2 percent in 

2006 through 2009.3 The 

Administration projects the 10-year 

Treasury rate to average 5.1 percent in 

2005 and 5.4–5.8 percent between 2006 

and 2009 compared to its average of 4.6 

percent in 2002 and 4.0 percent in 2003.

Standard & Poor’s expects housing 

starts to average 1.8 million units in 

2004–2005. Fannie Mae projects 

existing home sales for 2004 at 6.1 

million units, and for 2005 at 5.8 

million, compared to their record level 

in 2003 of 6 million units. 

(iii) Mortgage Market Conditions 

Low interest rates and record levels of 

refinancing caused mortgage 

originations to soar from $2.0 trillion in 

2001 to $2.6 trillion in 2002 and around 

$3.8 trillion in 2003. The Mortgage 

Bankers Association projects that 

mortgage originations will drop to $2.7 

trillion in 2004 and $1.8 trillion in 2005, 

as refinancing returns to more normal 

levels.4

The volume of home purchase 

mortgages was $910 billion to $1.1 

trillion between 1999 and 2001 before 

jumping to $1.2 trillion in 2002 and $1.3 

trillion in 2003. As with housing starts, 

the home purchase origination market is 

expected to exhibit sustained growth. 

b. National Housing Needs 

(i) Affordability Problems

Data from the 2000 Census and the 

American Housing Survey demonstrate 

that there are substantial housing needs 

among low- and moderate-income 

families. Many of these households are 

burdened by high homeownership costs 

or rent payments and, consequently, are 

facing serious housing affordability 

problems. There is evidence of 

persistent housing problems for 

Americans with the lowest incomes. 

Since 1977 the percentage of U.S. 

households with worst case needs has 

hovered around five percent, with the 

worst year being 1983 (6.03 percent) and 

the best being 1999 (4.72 percent). The 

Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 63585

5Margery Austin Turner, All Other Things Being 

Equal: A Paired Testing Study of Mortgage Lending 

Institutions, The Urban Institute Press, April 2002. 

Appendix A includes further discussion of this 

study.

6These studies are discussed in section B.1 of 

Appendix B.

proportion in 2001 was 4.77 percent, 

which is not significantly different from 

the 1999 figure. HUD’s analysis of 

American Housing Survey data reveals 

that, in 2001, 5.1 million unassisted 

very-low-income renter households had 

‘‘worst case’’ housing needs, defined as 

housing costs greater than 50 percent of 

household income or severely 

inadequate housing. Among these 

households, 90 percent had a severe 

rent burden, 6 percent lived in severely 

inadequate housing, and 4 percent 

suffered from both problems. Among the 

34 million renters in all income 

categories, 6.3 million (19 percent) had 

a severe rent burden and over one 

million renters (3 percent) lived in 

housing that was severely inadequate. 

(ii) Disparities in Housing and Mortgage 

Markets 

Despite the strong growth in 

affordable lending over the past ten 

years, there are families who are not 

being adequately served by the nation’s 

housing and mortgage markets. Serious 

racial and income disparities remain. 

The homeownership rate for minorities 

is 25 percentage points below that for 

whites. A major HUD-funded study of 

discrimination in the sales and rental 

markets found that discrimination still 

persists in both rental and sales markets 

of large metropolitan areas nationwide, 

although its incidence has generally 

declined since 1989. The most prevalent 

form of discrimination observed in the 

study against Hispanic and AfricanAmerican home seekers was Hispanics 

and African Americans being told that 

housing units were unavailable when 

non-Hispanic whites found them to be 

available. Levels of consistent adverse 

treatment experienced by the nation’s 

largest minority groups when they 

inquire about a unit advertised for sale 

in metropolitan areas nationwide in 

2000–2001 were: African Americans 

16.8 percent, Hispanics 18.3 percent, 

and Asians and Pacific Islanders 20.4 

percent. 

The study also found other worrisome 

trends of discrimination in metropolitan 

housing markets that persisted in 2000. 

Examples include geographical steering 

experienced by African-American 

homebuyers, and real estate agents who 

provided less assistance in obtaining 

financing for Hispanic homebuyers than 

for non-Hispanic whites.5 Racial 

disparities in mortgage lending are also 

well documented. HUD-sponsored 

studies of the pre-qualification process 

conclude that African Americans and 

Hispanics risk unequal treatment when 

they visit mainstream mortgage lenders. 

Studies reveal higher mortgage denial 

rates for African Americans and 

Hispanics, even after controlling for 

applicant income and a host of 

underwriting characteristics, such as the 

credit record of the applicant.6

However, substantial progress has been 

made since 1989.

The existence of substantial 

neighborhood disparities in 

homeownership and mortgage credit is 

also well documented for metropolitan 

areas. HUD’s analysis of Home Mortgage 

Disclosure Act (HMDA) data shows that 

mortgage credit flows in metropolitan 

areas are substantially lower in highminority and low-income 

neighborhoods and that mortgage denial 

rates are much higher for residents of 

these neighborhoods. Studies have also 

documented that mainstream lenders 

often do not operate in inner-city 

minority neighborhoods, leaving their 

residents with only high-cost lenders as 

options. Too often, residents of these 

same neighborhoods have been 

subjected to the abusive practices of 

predatory lenders. 

These troublesome disparities mostly 

affect those families (minorities and 

immigrants) who are projected to 

account for almost two-thirds of the 

growth in the number of new 

households over the next 10 years. 

(iii) Single-Family Market: Trends in 

Affordable Lending and 

Homeownership 

Many younger, minority and lowerincome families did not become 

homeowners during the 1980s due to 

slow growth of some earnings, high real 

interest rates, lower inflation, and 

continued increases in housing prices. 

Over the past 10 years, economic 

expansion, accompanied by low interest 

rates and increased outreach on the part 

of the mortgage industry, has improved 

affordability conditions for these 

families. 

As this preamble and the appendices 

note, there has been a ‘‘revolution in 

affordable lending’’ that has extended 

homeownership opportunities to 

historically underserved households. 

The mortgage industry, including the 

GSEs, has offered more customized 

mortgage products, more flexible 

underwriting, and expanded outreach to 

low-income and minority borrowers. 

HMDA data suggest that the industry 

and GSE initiatives are increasing the 

flow of credit to underserved borrowers. 

Between 1993 and 2002, conventional 

loans to low-income and minority 

families increased at much faster rates 

than loans to upper-income and nonminority families. Conventional home 

purchase originations to African 

Americans more than doubled between 

1993 and 2002, and those to Hispanic 

borrowers more than tripled during this 

period. Home loans to low-income 

borrowers and to low-income and highminority census tracts also more than 

doubled during this period. 

Thus, the 1990s and the early part of 

the current decade have seen the 

development of a strong affordable 

lending market. Homeownership 

statistics show similar trends. After 

declining during the 1980s, the 

homeownership rate has increased 

every year since 1994, reaching a record 

mark of 69.2 percent in the second 

quarter of 2004. 

The number of households owning 

their own home increased by 13.3 

million between 1994 and 2003. Gains 

in homeownership rates during the 

period of 1994 to 2003 have been 

widespread, with the homeownership 

rate for African-American households 

increasing from 42.5 percent to 48.8 

percent, for Hispanic households from 

41.2 percent to 46.7 percent, for nonHispanic white households from 70.0 

percent to 75.4 percent, and for central 

city residents from 48.5 percent to 52.3 

percent. 

Despite the record gains in 

homeownership since 1994, a gap of 

approximately 25 percent in the 

homeownership rate prevails for 

African-American and Hispanic 

households as compared to white nonHispanic households. Studies show that 

these lower homeownership rates are 

only partly accounted for by differences 

in income, age, and other 

socioeconomic factors. 

In addition to low income, barriers to 

homeownership that disproportionately 

affect minorities and immigrants 

include: lack of capital for 

downpayment and closing costs; poor 

credit history; lack of access to 

mainstream lenders; little 

understanding of the home buying 

process; a limited supply of modestly 

priced homes in locations where these 

populations reside; and continued 

discrimination in housing markets and 

mortgage lending. These barriers are 

discussed in Appendix A to this rule. 

(iv) Single-Family Market: Potential 

Homeowners 

As already noted, the potential 

homeowner population over the next 

decade will be highly diverse, as 

growing housing demand from 

63586 Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 

immigrants (both those who are already 

in this country and those who are 

projected to arrive), minorities, and nontraditional homebuyers will help to 

offset declines in the demand for 

housing caused by the aging of the 

population.

Studies cited in Appendix A to this 

rule reveal that increased immigration 

during the 1990s directly accounted for 

35 percent of the nation’s rise in 

population during that decade, as a 

result of which the foreign-born 

population of the United States was 31.1 

million in 2000. These trends do not 

depend on the future inflow of new 

immigrants, as immigrants do not, on 

average, enter the home purchase 

market until they have been in this 

country for eleven years. Fannie Mae 

staff have noted that there are enough 

immigrants already in this country to 

keep housing demand strong for several 

years. 

Thus, the need for the GSEs and other 

industry participants to meet 

nontraditional credit needs, respond to 

diverse housing preferences, and to 

overcome the information barriers that 

many immigrants face will take on 

added importance. A new or recent 

immigrant may have no credit history 

or, at least, may not have a credit history 

that can be documented by traditional 

methods. In order to address these 

needs, the GSEs and the mortgage 

industry have been developing 

innovative products and seeking to 

extend their outreach efforts to attract 

these homebuyers, as discussed in 

Appendix A to this rule. 

In addition, the current low 

homeownership rates in inner cities (as 

compared with the suburbs) also suggest 

that urban areas may be a potential 

growth market for lenders. As explained 

in Appendix A to this rule, lenders are 

beginning to recognize that urban 

borrowers and properties have different 

needs than suburban borrowers and 

properties. 

Surveys indicate that these 

demographic trends will be reinforced 

by the fact that most Americans desire, 

and plan, to become homeowners. 

According to Fannie Mae’s 2002 

National Housing Survey, Americans 

rate homeownership as the best 

investment they can make, far ahead of 

401(k) plans, other retirement accounts, 

and stocks. Forty-two percent of 

African-American families reported that 

they were ‘‘very or fairly likely’’ to buy 

a home in the next three years, up from 

38 percent in 1998 and 25 percent in 

1997. Among Hispanics and Hispanic 

immigrants, the numbers reached 37 

percent and 34 percent, respectively. 

The survey also reported that more than 

half of Hispanic renters cite 

homeownership as being ‘‘one of their 

top priorities.’’

Despite these trends, potential 

minority and immigrant homebuyers see 

more obstacles to buying a home than 

does the general public. Typically, the 

primary barriers to homeownership are 

credit issues and a lack of funds for a 

downpayment and closing costs. 

However, other barriers also exist, such 

as a lack of affordable housing, little 

understanding of the home buying 

process, and language barriers. Thus, 

the new group of potential homeowners 

will have unique needs. 

The GSEs can play an important role 

in tapping this potential homeowner 

population. Along with others in the 

industry, they can address these needs 

on several fronts, such as expanding 

education and outreach efforts, 

introducing new products, and 

adjusting current underwriting 

standards to better reflect the special 

circumstances of these new households. 

These efforts are necessary for achieving 

the Administration’s goal of expanding 

minority homeownership by 5.5 million 

families by the end of the decade. 

The single-family mortgage market 

has been very dynamic over the past few 

years, experiencing volatile swings in 

originations (with the 1998 and 2001–

2003 refinancing waves), witnessing the 

rapid growth in new types of lending 

(such as subprime lending), 

incorporating new technologies (such as 

automated underwriting systems), and 

facing serious challenges (such as 

predatory lending). Fannie Mae and 

Freddie Mac have played a major role in 

the ongoing changes in the single-family 

market and in helping the industry 

address the problems and challenges 

that have arisen. 

The appendices to this final rule 

discuss the various roles that Fannie 

Mae and Freddie Mac have played in 

the single-family market. A wide range 

of topics is examined, including the 

GSEs’ automated underwriting 

technology used throughout the 

industry, their many affordable lending 

partnerships and underwriting 

initiatives aimed at extending credit to 

underserved borrowers, their 

development of new targeted lowdownpayment products, their entry into 

new markets such as the subprime 

market, and their attempts to reduce 

predatory lending. As that discussion 

emphasizes, the GSEs have the ability to 

bring increased efficiencies to a market 

and to attract mainstream lenders into 

markets. (Readers are referred to 

Appendices A, B, and C to this rule for 

further discussion of the GSEs’ role in 

different segments of the single-family 

mortgage market.) 

(v) Multifamily Mortgage Market 

The market for financing of 

multifamily apartments has reached 

record volume. The favorable long-term 

prospects for apartments, combined 

with record low interest rates, have kept 

investor demand for apartments strong 

and have supported property prices 

despite recently high vacancy rates. 

Fannie Mae and Freddie Mac have 

been among those boosting their 

volumes of multifamily financing and 

both have introduced new programs to 

serve the multifamily market. Fannie 

Mae and, especially (considering its 

earlier withdrawal from the market), 

Freddie Mac have rapidly expanded 

their presence in the multifamily 

mortgage market under the Housing 

Goals.

Freddie Mac has successfully rebuilt 

its multifamily acquisition program, as 

reflected by the increase in its purchases 

of multifamily mortgages: from $27 

million in 1992 to $3.9 billion in 1998 

and then rising to $9.5 billion in 2001, 

$10.7 billion in 2002, and $21.5 billion 

in 2003. Multifamily units accounted for 

9.0 percent of all dwelling units (both 

owner and rental) financed by Freddie 

Mac between 1999 and 2003. Concerns 

regarding multifamily capabilities no 

longer constrain Freddie Mac’s 

performance with regard to the Housing 

Goals. 

Although Fannie Mae never withdrew 

from the multifamily market, it has 

stepped up its activities in this area 

substantially, with multifamily 

purchases rising from $3.0 billion in 

1992 to $10.0 billion in 1999, and $19.1 

billion in 2001, then declining slightly 

to $16.6 billion in 2002, and then rising 

markedly to $30.9 billion in 2003. 

Multifamily units accounted for 8.8 

percent of all dwelling units (both 

owner and rental) financed by Fannie 

Mae between 1999 and 2003. 

The increased role of Fannie Mae and 

Freddie Mac in the multifamily market 

has major implications for the Low- and 

Moderate-Income Housing and Special 

Affordable Housing Goals, since high 

percentages of multifamily units have 

affordable-level rents and can count 

toward one or both of these Housing 

Goals. However, the potential of the 

GSEs to lead the multifamily mortgage 

industry has not been fully developed. 

The GSEs’ purchases between 1999 and 

2002 accounted for less than 40 percent 

of the multifamily units that received 

financing during this period. Certainly 

there are ample opportunities and room 

for expansion of the GSEs’ share of the 

multifamily mortgage market. 

Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 63587

7Unless otherwise noted, the conventional 

conforming market data reported in this section 

exclude an estimate of B&C loans; the less-risky Aminus portion of the subprime market is included 

in the market definition. See section d below and 

Appendix D for a discussion of primary market 

definitions and the uncertainty surrounding 

estimates of the number of B&C loans in HMDA 

data. As noted there, B&C loans are much more 

Continued

The GSEs’ size and market position 

between loan originators and mortgage 

investors make them the logical 

institutions to identify and promote 

needed innovations and to establish 

standards that will improve market 

efficiency. As their role in the 

multifamily market continues to grow, 

the GSEs will have the knowledge and 

market presence to push simultaneously 

for standardization and for 

programmatic flexibility to meet special 

needs and circumstances, with the 

ultimate goal of increasing the 

availability and reducing the cost of 

financing for affordable and other 

multifamily rental properties. 

The long-term outlook for the 

multifamily rental market is sustained, 

moderate growth, based on favorable 

demographics. The minority population, 

especially Hispanics, provides a 

growing source of demand for affordable 

rental housing. ‘‘Lifestyle renters’’

(older, middle-income households) are 

also a fast-growing segment of the rental 

population. 

At the same time, the provision of 

affordable housing units will continue 

to challenge suppliers of multifamily 

rental housing as well as policy makers 

at all levels of government. Low 

incomes, combined with high housing 

expenses, define the difficult situation 

of millions of renter households. 

Housing cost reductions are constrained 

by high land prices and construction 

costs in many markets. Regulatory 

barriers at the state and local level have 

an enormous impact on the 

development of affordable rental 

housing. Government action—through 

land use regulation, building codes, and 

occupancy standards—is a major 

contributor to high housing costs. 

Since the early 1990s, the multifamily 

mortgage market has become more 

closely interconnected with global 

capital markets, although not to the 

same degree as the single-family 

mortgage market. Loans on multifamily 

properties are still viewed as riskier by 

some than are mortgages on singlefamily properties, despite delinquency 

rates that in recent quarters have been 

lower than those on single-family 

mortgages. 

There is a need for an ongoing GSE 

presence in the multifamily secondary 

market, both to increase liquidity and to 

advance affordable housing efforts. The 

potential for an increased GSE presence 

is enhanced by the fact that an 

increasing proportion of multifamily 

mortgages are now originated in 

accordance with secondary market 

standards. Small multifamily properties, 

and multifamily properties with 

significant rehabilitation needs, have 

historically experienced difficulty 

gaining access to mortgage financing, 

and the flow of capital into multifamily 

housing for seniors has been historically 

characterized by volatility. The GSEs 

can play a role in promoting liquidity 

for multifamily mortgages and 

increasing the availability of long-term, 

fixed-rate financing for these properties. 

c. GSEs’ Past Performance and Effort 

Toward Achieving the Housing Goals 

Since the enactment of FHEFSSA and 

HUD’s establishment in 1993 of the 

Housing Goals, both Fannie Mae and 

Freddie Mac have improved their 

affordable housing loan performance. 

However, the GSEs’ mortgage purchases 

have generally lagged, and not led, the 

overall primary market in providing 

financing for affordable housing to lowand moderate-income families and 

underserved borrowers and their 

neighborhoods, indicating that there is 

more that the GSEs can do to improve 

their performance. 

(i) Performance on the Housing Goals 

The year 2001 was the first year under 

the higher levels of the Housing Goals 

established in the Housing Goals 2000 

final rule. Fannie Mae met all three 

Housing Goals in 2001, 2002, and 2003. 

Freddie Mac met all three Housing 

Goals in 2001 and 2003. However, in 

2002 HUD discovered that Freddie Mac 

had counted 22,371 housing units 

towards the Low- and Moderate Income 

Goal even though it had previously 

counted these same housing units 

towards the same goal in 2001. Freddie 

Mac also counted 22,424 housing units 

towards the Underserved Area Goal 

even though these units had also been 

credited towards the same goal in 2001. 

HUD’s regulations prohibit double 

counting. To correct for these doublecounting errors, the Department has 

adjusted its official performance results 

for Freddie Mac in 2002 by deducting 

the double-counted housing units, 

including all bonus point credit that had 

been awarded for most of these units, 

from the official performance results it 

had previously reported publicly. As a 

result of these adjustments, Freddie Mac 

continued to meet the Low- and 

Moderate-Income Goal in 2002. 

However, Freddie Mac fell short of the 

31 percent target for the Underserved 

Areas goal by 90 units or 0.002 percent. 

Freddie Mac’s 2002 goal performance 

results are described more fully in 

Tables 4, 6 and 8 in this preamble, 

including the accompanying footnotes. 

(ii) The GSEs’ Efforts in the Home 

Purchase Mortgage Market 

The Appendices to this final rule 

include a comprehensive analysis of 

each GSE’s performance in funding 

home purchase mortgages for borrowers 

and neighborhoods targeted by the three 

Housing Goals—special affordable and 

low- and moderate-income borrowers 

and underserved areas. The GSEs’ role 

in the first-time homebuyer market is 

also analyzed. Because homeownership 

opportunities are integrally tied to the 

ready availability of affordable home 

purchase loans, the main findings from 

that analysis are provided below. 

• Both Fannie Mae and Freddie Mac 

have increased their purchases of 

affordable home purchase mortgages 

since the Housing Goals were put into 

effect, as indicated by the increasing 

share of their business going to the three 

goals-qualifying categories. Between 

1992 and 2003, the special affordable 

share of Fannie Mae’s business almost 

tripled, rising from 6.3 percent to 17.1 

percent, while the underserved areas 

share increased more modestly, from 

18.3 percent to 26.8 percent. The figures 

for Freddie Mac are similar. The special 

affordable share of Freddie Mac’s 

business rose from 6.5 percent to 15.6 

percent, while the underserved areas 

share also increased but more modestly, 

from 18.6 percent to 24.0 percent.

• While both GSEs improved their 

performance, they have historically 

lagged the primary market in providing 

affordable home purchase mortgage 

loans to low-income borrowers and 

underserved neighborhoods. Freddie 

Mac’s average performance, in 

particular, fell far short of market 

performance during the 1990s. Fannie 

Mae’s average performance was better 

than Freddie Mac’s during the 1993–

2003 period as well as during the 1996–

2003 period, which covers the period 

under HUD’s currently-defined Housing 

Goals. Between 1993 and 2003, 12.2 

percent of Freddie Mac’s mortgage 

purchases were for special affordable 

borrowers, compared with 13.3 percent 

of Fannie Mae’s purchases, 15.4 percent 

of loans originated by depositories, and 

15.5 percent of loans originated in the 

conventional conforming market 

(without estimated B&C subprime 

loans).7

63588 Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 

likely to be refinance loans rather than home 

purchase loans.

• Between 2001 and 2003, both 

Fannie Mae and Freddie Mac fell 

significantly below the market in 

funding affordable home purchase 

mortgage loans. During this period, 

special affordable loans accounted for 

15.1 percent of Fannie Mae’s purchases, 

14.7 percent of Freddie Mac’s 

purchases, and 16.2 percent of loans 

originated in the market; thus, the 

‘‘Fannie-Mae-to-market’’ ratio was 0.93 

and the ‘‘Freddie-Mac-to-market’’ ratio 

was also 0.91. During the same period, 

underserved area loans accounted for 

24.7 percent of Fannie Mae’s purchases, 

23.1 percent of Freddie Mac’s 

purchases, and 26.2 percent of loans 

originated in the market; the ‘‘FannieMae-to-market’’ ratio was 0.94 and the 

‘‘Freddie-Mac-to-market’’ ratio was only 

0.88. 

• While Freddie Mac has improved 

its affordable lending performance in 

the past two years, it has continued to 

lag the conventional conforming market 

in funding affordable home purchase 

loans for special affordable and lowand moderate-income borrowers and 

underserved neighborhoods targeted by 

the Housing Goals. In 2003, Freddie 

Mac’s performance on the underserved 

areas goal was particularly low relative 

to both the performances of Fannie Mae 

and the market; in that year, 

underserved area loans accounted for 

only 24.0 percent of Freddie Mac’s 

purchases compared with 26.8 percent 

of Fannie Mae’s purchases and 27.6 

percent of market originations. 

• As noted above, Fannie Mae’s 

average performance during past 

periods (e.g., 1993–2003, 1996–2003, 

1999–2003) has been below market 

levels. However, it is encouraging that 

Fannie Mae markedly improved its 

affordable lending performance relative 

to the market during 2001, 2002, and 

2003, the first three years under the 

higher housing goal targets that HUD 

established in the GSE Final Rule dated 

October 2000. Over this three-year 

period, Fannie Mae led the primary 

market in funding special affordable and 

low- and moderate-income home 

purchase mortgage loans but lagged the 

market in funding underserved areas 

home purchase loans. In 2003, Fannie 

Mae’s increased performance placed it 

significantly above the special 

affordable market (a 17.1 percent share 

for Fannie Mae compared with a 15.9 

percent share for the market) and the 

low-mod market (a 47.0 percent share 

for Fannie Mae compared with a 44.6 

percent share for the market). However, 

Fannie Mae continued to lag the 

underserved areas market in 2003 (a 

26.8 percent share for Fannie Mae 

compared with a 27.6 percent share for 

the market). These data are based on the 

‘‘purchase year’’ approach, that is, 

Fannie Mae’s performance is based on 

comparing its purchases of all home 

purchase loans (both seasoned loans 

and newly-originated mortgages) during 

a particular year with loans originated 

in the market in that year. When Fannie 

Mae’s performance is measured on an 

‘‘origination year’’ basis (that is, 

allocating Fannie Mae’s purchases in a 

particular year to the year that the 

purchased loan was originated), Fannie 

Mae also led the 2003 market in funding 

special affordable and low- and 

moderate-income loans, and lagged the 

market in funding underserved area 

loans. 

• Appendix A compares the GSEs’

funding of first-time homebuyers with 

that of primary lenders in the 

conventional conforming market. Both 

Fannie Mae and Freddie Mac lag the 

market in funding first-time 

homebuyers, and by a rather wide 

margin. Between 1999 and 2002, firsttime homebuyers accounted for 27 

percent of each GSE’s purchases of 

home purchase mortgages, compared 

with 38 percent for home purchase 

mortgages originated in the 

conventional conforming market. For 

minority first-time homebuyers, the GSE 

ratio was 6.2 percent, compared to a 

market originations ratio of 10.6 

percent. For African-American and 

Hispanic first-time homebuyers, the 

GSE ratio was 3.8 percent, compared to 

a market originations ratio of 6.9 

percent. For first-time homebuyers, 

particularly first-time minority 

homebuyers, both GSEs substantially lag 

the private conventional conforming 

market. 

• The GSEs account for a small share 

of the market for important groups such 

as minority first-time homebuyers. 

Considering all mortgage originations 

(both government and conventional) 

between 1999 and 2001, it is estimated 

that the GSEs purchased only 14 percent 

of all loans originated for AfricanAmerican and Hispanic first-time 

homebuyers, or one-third of their share 

(42 percent) of all home purchase loans 

originated during that period. 

Considering conventional conforming 

originations during the same time 

period, it is estimated that the GSEs 

purchased only 31 percent of loans for 

African-American and Hispanic firsttime homebuyers, or about one-half of 

their share (57 percent) of all home 

purchase loans in that market. A large 

percentage of the lower-income loans 

purchased by the GSEs had relatively 

low loan-to-value ratios and 

consequently high downpayments, 

which may explain the GSEs’ limited 

role in the first-time homebuyer market. 

Appendix A to this rule provides 

evidence that there is a significant 

population of potential homebuyers 

who are likely to respond well to 

increased homeownership opportunities 

produced by increased GSE purchases 

in this area. Immigrants and minorities, 

in particular, are expected to be a major 

source of future homebuyers. 

d. Size of the Mortgage Market That 

Qualifies for the Housing Goals 

The Department has estimated the 

size of the conventional, conforming 

market for loans that would qualify 

under each Housing Goal category based 

on 2000 Census data and geography. 

These estimates, which are changed 

slightly from estimates reported in the 

proposed rule, are as follows:

• 51–56 percent for the Low- and 

Moderate-Income Housing Goal 

• 23–27 percent for the Special 

Affordable Housing Goal 

• 35–39 percent for the Underserved 

Areas Housing Goal

These market estimates exclude the 

B&C (i.e., subprime loans that are not Aminus grade) portion of the subprime 

market. The estimates, expressed as 

ranges, allow for economic and market 

affordability conditions that are more 

adverse than recent conditions. The 

market estimates are based on several 

mortgage market databases such as 

HMDA and American Housing Survey 

data. The Department’s estimates of the 

size of the conventional mortgage 

market for each Housing Goal are 

discussed in detail in Appendix D to 

this rule.

The GSEs have room for growth in 

serving the affordable housing mortgage 

market. The Department estimates that 

the two GSEs’ mortgage purchases 

accounted for 55 percent of the total 

(single-family and multifamily) 

conventional, conforming mortgage 

market between 1999 and 2002. In 

contrast, GSE purchases comprised 48 

percent of the low- and moderateincome market, 48 percent of the 

underserved areas market, and a still 

smaller 41 percent of the special 

affordable market. Thus, the remaining 

52–59 percent of the Goals-qualifying 

markets have not yet been touched by 

the GSEs. 

The GSEs’ presence in mortgage 

markets for rental properties, where 

much of the nation’s affordable housing 

is concentrated, is below that in the 

single-family-owner market. The GSEs’

share of the total rental market 

Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 63589

(including both single-family and 

multifamily) was also less than 40 

percent between 1999 and 2002. 

Obviously, there is room for the GSEs to 

increase their presence in the singlefamily rental and multifamily rental 

markets. 

Table 1 summarizes the Department’s 

findings regarding GSE performance 

relative to market projections for 2005–

2008 and the Housing Goal levels for 

2005–2008. 

BILLING CODE 4210–27–P

63590 Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 


ER02NO04.000</GPH><FNP>
Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 63591

The analysis for 2005 and later 

reflected in Table 1 is based on 2000 

Census data on area median incomes 

and minority concentrations, using the 

metropolitan area boundaries specified 

by OMB in June 2003. This affects the 

market percentages for all three Housing 

Goals, as well as the figures on area 

median incomes and minority 

percentage figures that will be used to 

measure GSE performance on the 

Housing Goals beginning in 2005. The 

greatest effect of the updated data is on 

the Underserved Areas Housing Goal. 

Expressing this goal in terms of 2000 

Census data adds approximately 5 

percentage points to the Housing Goal 

and market levels, compared with 

analysis using 1990 Census data with 

Metropolitan Statistical Areas (MSAs) as 

defined prior to 2000. 

The GSEs’ baseline performance 

figures in Table 1 exclude the effects of 

the bonus points for small multifamily 

and single-family two-to-four unit 

owner-occupied properties and the 

Temporary Adjustment Factor (TAF) for 

Freddie Mac that were applied in 

official scoring toward the Housing 

Goals in 2001–2003. The Department 

did not extend these adjustments 

beyond 2003. 

Table 1 reveals several features of 

HUD’s Housing Goals. First, it is evident 

from this table that the 2005 level (22 

percent) for the Special Affordable 

Housing Goal is below the low end (23 

percent) of HUD’s projected market 

range for 2005–2008. The 2005 level (52 

percent) of the Low- and ModerateIncome Housing Goal is slightly above 

the low-end (51 percent) of HUD’s 

market estimate range. 

Second, the 2005 Underserved Areas 

Housing Goal level (37 percent) is 

consistent with the market range (35–39 

percent) now projected by HUD for the 

Housing Goals using 2000 Census data.

Third, the GSEs’ performance on all of 

the Housing Goals was significantly 

below the market averages for 1999–

2002. Appendix D to this rule provides 

market estimates for the years 1999–

2002 under different assumptions about 

the multifamily mix (i.e., newlymortgaged multifamily units as a share 

of all financed dwelling units). The 

estimates differ between the two home 

purchase years (1999 and 2000) and the 

heavy refinance years (2001 and 2002). 

For the low-mod goal, the estimates 

average approximately 56 percent for 

the two home purchase years and 52 

percent for the two heavy refinance 

years, with an overall 1999–2002 lowmod average of 54 percent (five 

percentage points above Fannie Mae’s 

performance and seven percentage 

points above Freddie Mac’s 

performance). The market estimates for 

the underserved areas goal average 

slightly over 37 percent (38 percent 

during the two home purchase years 

and 36 percent during the two heavy 

refinance years), or approximately 2–4 

percentage points above the GSEs’

performance (see Table 1). The higher 

Housing Goals are intended to move the 

GSEs closer to or within the market 

range for 2005, and to the upper end of 

the market range projection by 2008. 

An analysis of the GSEs’ mortgage 

purchases by property type shows that 

they have had much less presence in the 

‘‘goals-rich’’ rental segments of the 

market, as compared with the ‘‘lessgoals-rich’’ owner segment of the 

market. As shown in Figure 1, GSE 

mortgage purchases represented 37 

percent of single-family and multifamily 

rental units financed between 1999 and 

2002. This figure is much lower than 

their 61 percent market share for singlefamily owner-occupied properties. 

(Figure 2 provides unit-level detail 

comparing the GSEs’ purchases with 

originations in the conventional 

conforming market.) 

Typically, about 90 percent of rental 

units in single-family rental and 

multifamily properties qualify for the 

Low- and Moderate-Income Housing 

Goal, compared with about 44 percent 

of owner units. Corresponding figures 

for the Special Affordable Housing Goal 

are almost 60 percent of rental units and 

16.4 percent of owner units. Thus, one 

reason that the GSEs’ performance 

under the Low- and Moderate-Income 

Housing and Special Affordable 

Housing Goals has fallen short of HUD’s 

market estimates is that the GSEs have 

had a relatively small presence in the 

two rental market segments, 

notwithstanding that these market 

segments are important sources of 

affordable housing and important 

components in HUD’s market estimates. 

BILLING CODE 4210–27–P

63592 Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 

BILLING CODE 4210–27–C


ER02NO04.001</GPH>
Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 63593

8These percentage shares are computed from 

Table A.30 in Appendix A. Note that B&C loans are 

excluded from these data. See also Table A.31b in 

Appendix A.

9As discussed in Appendix D, the GSEs 

questioned HUD’s historical estimates of the 

multifamily market as too high. Section C of 

Appendix D discusses these comments and 

responds. As indicated in Table A.30, multifamily 

loans accounted for 14.8 percent of all financed 

units in the market, excluding B&C loans. As 

reported in Section G of Appendix A and Sections 

F–H of Appendix D, HUD also conducted 

sensitivity analyses that reduced its 1999–2002 

multifamily shares for the market by approximately 

two percentage points. As a result, 1999–2002 

multifamily units decreased from 7,018,044 units to 

5,991,036 units (reducing the multifamily share 

from 14.8 percent to 12.6 percent). With these 

reduced multifamily market numbers, the GSEs’

share of the multifamily market increased from 35 

percent to 41 percent. The GSEs also accounted for 

higher shares of the goals-qualifying multifamily

market: 42 percent for low-mod units, 34 percent 

for underserved area units, and 37 percent for 

special affordable units. In this case, the GSEs’

shares of the overall goals-qualifying markets 

(including single-family-owner, single-familyrental, and multifamily mortgages) increased as 

follows: low-mod—from 48 percent (see right 

column of Table A.30 in Appendix A) to 50 percent 

(see right column of Table A.31b in Appendix A); 

underserved areas—from 48 percent to 50 percent; 

and special affordable—from 41 percent to 43 

percent.

In the overall conventional 

conforming mortgage market, rental 

units in single-family properties and in 

multifamily properties represented 

approximately 25 percent of the overall 

mortgage market between 1999 and 

2002, 42 percent of the units that 

collateralize mortgages qualifying for 

the Low- and Moderate-Income Housing 

Goal, and 56 percent of the units that 

collateralize mortgages qualifying for 

the Special Affordable Housing Goal. 

Yet between 1999 and 2002, units in 

such properties accounted for only 17 

percent of the GSEs’ overall purchases, 

32 percent of the GSEs’ purchases 

meeting the Low- and Moderate-Income 

Housing Goal, and 44 percent of the 

GSEs’ purchases meeting the Special 

Affordable Housing Goal.8 Continuing 

weakness in GSE purchases of 

mortgages on single-family rental and 

multifamily properties has been a 

significant factor underlying the 

shortfall between GSE performance and 

that of the primary mortgage market.

e. Ability of the GSEs To Lead the 

Industry 

An important factor in determining 

the overall Housing Goal level is the 

ability of the GSEs to lead the industry 

in making mortgage credit available for 

Housing Goals—qualifying populations 

and areas. 

The legislative history of FHEFSSA 

reflects Congress’s strong concern that 

the GSEs need to do more to benefit 

low- and moderate-income families and 

residents of underserved areas that lack 

access to credit. (See, e.g., S. Rep. No. 

102–282, at 34.) The Senate Report on 

FHEFSSA emphasized that the GSEs 

should ‘‘lead the mortgage finance 

industry in making mortgage credit 

available for low- and moderate-income 

families.’’ (Id.) 

Thus, FHEFSSA specifically requires 

that HUD consider the ability of the 

GSEs to lead the industry in establishing 

the level of the Housing Goals. 

FHEFSSA also clarified the GSEs’

responsibility to complement the 

requirements of the CRA (see section 

1335(a)(3)(B) of FHEFSSA, 12 U.S.C. 

4565(a)(3)(B)), and fair lending laws (see

section 1325 of FHEFSSA, 12 U.S.C. 

4545) in order to expand access to 

capital to those historically underserved 

by the housing finance market. 

While leadership may be exhibited 

through the GSEs’ introduction of 

innovative products, technology, and 

processes, and through their 

establishment of partnerships and 

alliances with local communities and 

community groups, leadership must 

always involve increasing the 

availability of financing for 

homeownership and affordable rental 

housing. Thus, the GSEs’ obligation to 

‘‘lead the industry’’ entails leadership in 

facilitating access to affordable credit in 

the primary market for borrowers at 

different income levels, and with 

different housing needs, as well as in 

underserved urban and rural areas. 

Because the GSEs’ market presence 

varies significantly by property type, the 

Department examined whether the GSEs 

have led the industry in three different 

market sectors served by the GSEs: 

single-family-owner, single-family 

rental (those with at least one rental unit 

and no more than four units in total), 

and multifamily rental. 

The GSEs’ purchases between 1999 

and 2002 financed almost 61 percent of 

the approximately 36 million owneroccupied units financed in the 

conventional conforming market during 

that period. The GSEs’ state-of-the-art 

technology, staff resources, share of the 

total conventional conforming market, 

and financial strength strongly suggest 

that they have the ability to lead the 

industry in making home purchase 

credit available for low-income families 

and underserved neighborhoods. From 

the analysis in Appendices A-D to this 

rule, it is clear that the GSEs are able to 

improve their performance and lead the 

primary market in financing Housing 

Goals—qualifying home purchase 

mortgages. In fact, Fannie Mae’s 

improved performance in 2003 is 

evidence of this potential, as it led the 

market in funding home purchase loans 

for special affordable and low- and 

moderate-income families.

As discussed in Appendix A to this 

rule, there are a wide variety of 

quantitative and qualitative indicators 

that demonstrate that the GSEs have 

ample, indeed robust, financial strength 

to improve their affordable lending 

performance. For example, the 

combined net income of the GSEs has 

risen steadily over the last 15 years, 

from $888 million in 1988 to $12.7 

billion in 2003. This financial strength 

provides the GSEs with the resources to 

lead the industry in making mortgage 

financing available for families and 

neighborhoods targeted by the Housing 

Goals.9

As noted above, the GSEs have been 

much less active in providing financing 

for the rental housing market. Between 

1999 and 2002, the GSEs financed 4.5 

million rental dwelling units, which 

represented 37 percent of the 12 million 

single-family and multifamily rental 

dwelling units that were financed in the 

conventional market during this period. 

Thus, the GSEs’ share of the rental 

mortgage market was just three-fifths of 

their share of the market for mortgages 

on single-family owner-occupied 

properties. 

Clearly there is room for the GSEs to 

increase their presence in the singlefamily rental and multifamily rental 

markets. As explained above, these 

markets are an important source of lowand moderate-income housing since 

these units qualify for the Housing 

Goals in a greater proportion than do 

single-family owner-occupied 

properties. Thus, Fannie Mae and 

Freddie Mac can improve their 

performance on each of the three 

Housing Goals if they increase their 

purchases of mortgages on rental 

properties. 

As discussed below in Section II.C.4 

of this preamble with respect to the 

Home Purchase Subgoals, both GSEs 

should be able to lead the market for 

single-family owner-occupied properties 

in all three housing goal categories—

special affordable, low- and moderateincome, and underserved areas. The 

GSEs are already dominant players in 

this market, which, unlike the rental 

markets, is their main business activity. 

However, as already discussed, research 

studies conducted by HUD and 

academic researchers conclude that 

except for Fannie Mae’s recent 

performance on the special affordable 

and low- and moderate-income 

categories, the GSEs have not led the 

primary market in financing ownerVerDate jul<14>2003 18:20 Nov 01, 2004 Jkt 205001 PO 00000 Frm 00015 Fmt 4701 Sfmt 4700 C:\02NOR2.SGM 02NOR2
63594 Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 

occupied housing for low-income 

families, for first-time homebuyers, or 

for properties located in underserved 

areas. 

As discussed above, the Housing 

Goals established by this rule are 

quantitative measures of how well the 

GSEs are serving low- and moderateincome homebuyers. HUD received 

comments on this factor from Freddie 

Mac and one other commenter. The 

commenter stated that, in addition to 

measuring leadership through the 

purchase of goal-qualifying mortgages, 

Fannie Mae and Freddie Mac’s 

leadership should be measured in more 

qualitative ways such as their 

‘‘development of products and 

technologies that the private sector may 

not be willing or able to do as well.’’

This commenter asserted that through 

the qualitative leadership of the GSEs, 

homeownership opportunities are 

expanded and costs lowered for all 

potential purchasers, including those in 

more affordable markets.

With respect to the issue of 

leadership, Freddie Mac contended in 

its comments on the proposed rule that 

HUD misinterpreted the ‘‘leading the 

industry’’ statutory factor and asserted 

that ‘‘[t]here is no intimation in the Act 

or its legislative history that Congress 

intended industry leadership to be 

determined based on the enterprises 

purchases of goal-qualifying mortgages.’’

Moreover, Freddie Mac commented that 

the GSEs are statutorily mandated to 

‘‘facilitate the financing of affordable 

housing for low- and moderate-income 

families in a manner consistent with 

their overall public purposes.’’ Freddie 

Mac stated that the overall public 

purpose of the GSEs is to facilitate the 

operation of, and provide ongoing 

assistance to, the secondary market for 

residential mortgages. To the extent that 

the proposed goals inhibit or endanger 

Freddie Mac’s ability to accomplish its 

general purpose of bringing liquidity 

and stability to the residential mortgage 

market, Freddie Mac contended that its 

ability to ‘‘lead the market’’ is in 

jeopardy. While the Department 

recognizes the degree of qualitative 

leadership provided by the GSEs, the 

Department also believes that their 

expertise and substantial financial 

resources allow them to lead 

quantitatively as well. 

f. Need To Maintain the Sound 

Financial Condition of the GSEs 

Based on HUD’s economic analysis 

prepared for this final rule (Economic 

Analysis) and review by OFHEO, the 

Department has concluded that the 

Housing Goals in this final rule will not 

adversely affect the sound financial 

condition of the GSEs. Further 

discussion of this issue is found in the 

Economic Analysis. 

3. Determinations Regarding the Levels 

of the Housing Goals 

There are several reasons why the 

Department, having considered all the 

statutory factors as well as the 

comments on the May 3, 2004, proposed 

rule, is increasing the levels of the 

Housing Goals. The following sections 

describe these reasons and discuss and 

respond to comments received by HUD 

regarding the levels of the housing 

goals. 

a. HUD’s Market Analysis 

Summary of Comments and HUD’s 

Determination. As part of the process of 

establishing goals, HUD estimates the 

size of the conventional conforming 

mortgage market. In this process, HUD 

separately analyzes the markets for 

several different categories of mortgage 

loans: single-family owner-occupied 

housing units, rental units in two-tofour unit properties where the owner 

occupies one unit, rental units in oneto-four-unit investor-owned properties, 

and rental units in multifamily (five or 

more units) properties. This 

categorization is necessary because the 

data sources differ for the various 

categories, and it is also desirable 

because goals-qualifying shares of units 

vary markedly by category. HUD 

described its methodology for analyzing 

each category in Appendix D to the 

proposed rule, and the GSEs 

commented on that analysis. Other 

commenters expressed concern about 

the magnitude of the goals, but did not 

discuss the analysis on which the goals 

calculations were based. 

(i) Multifamily Share of the Mortgage 

Market 

An important component of HUD’s 

calculation process is estimating the 

number of multifamily units financed 

each year as a percentage share of the 

total number of dwelling units financed 

(often referred to as the ‘‘multifamily 

mix’’); this is important because of the 

high proportions of multifamily units, 

which qualify for credit under all three 

goals. Section C of Appendix D to this 

Final Rule provides a detailed 

discussion of estimates of the size of the 

multifamily mortgage market, including 

estimates by HUD, the GSEs, and other 

researchers. As explained in Appendix 

D, comprehensive data on the annual 

volume of multifamily mortgage 

originations are much less available 

than similar data on single-family 

mortgage originations. This introduces a 

degree of uncertainty into the market 

sizing analysis and highlights the need 

for sensitivity analyses to show the 

effects of different multifamily mixes on 

the size of the goals-qualifying markets. 

As explained below, HUD’s market 

analysis focused on multifamily mixes 

between 13.5 percent and 16.0 percent, 

with a baseline of 15 percent. This range 

and baseline is consistent with HUD’s 

historical estimates of the multifamily 

mix reported in Table D.5b of Appendix 

D. For example, between 1995 and 2002, 

HUD estimated that the multifamily mix 

was in the 14–16 percent range. 

In its comments, Fannie Mae 

estimated a multifamily mix of 12.3 

percent, stating that HUD’s range is too 

high for current conditions in the 

multifamily market. Fannie Mae cited 

the current high vacancy rates for 

multifamily properties and the fact that 

the population aged 20 to 34 will not 

begin to increase until after 2007; this 

age group tends to be predominantly 

renters. Fannie Mae also projected a low 

multifamily refinance volume, because 

of a recent peak in multifamily 

originations; these recent originations 

will not be able to refinance easily 

under their current contracts until 2008 

or later. 

At Freddie Mac’s request, ICF 

Consulting also calculated the 

multifamily mix. In its best estimate, 

ICF projected an average of 14.2 percent 

over the 2005–2008 period, ranging 

between 13.7 percent and 14.7 percent 

in individual years, while recognizing 

that the actual outcomes may be higher 

or lower. ICF projected multifamily 

refinancings based on the number of 

units financed eight, nine, and ten years 

ago, because 10-year balloon mortgages 

are the most common multifamily 

mortgages, and prepayment possibilities 

are limited by yield maintenance 

agreements in their current mortgage 

contracts. 

In Appendix D to this rule, HUD 

reviews the evidence provided by the 

GSEs in their comments. HUD notes that 

the 2001 Residential Finance Survey 

(RFS) has recently been published by 

the Census Bureau, and that the RFS 

provides higher estimates of the 

multifamily mix for 1999–2001 (the 

most recent years available) than either 

Fannie Mae or ICF. The RFS data and 

other data analyzed in Appendix D to 

this rule suggest that 15.0 percent is a 

reasonable baseline, particularly in a 

home purchase mortgage market 

environment, with a relatively small 

volume of refinanced mortgage 

originations. HUD also notes that the 

ICF average of 14.2 percent is fairly 

close to HUD’s estimate of 15.0 percent. 

HUD therefore continues to use 15.0 

percent as the best estimate of the 

Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 63595

projected share of multifamily 

mortgages over the 2005–2008 period. 

HUD reports the goals-qualifying shares 

of mortgage originations on the basis of 

this estimate in Appendix D to this rule. 

HUD also publishes sensitivity analyses 

using other estimates of the multifamily 

mix, including 12.3 percent (Fannie 

Mae estimate), 13.5 percent (low end of 

HUD’s range), 14.2 percent (ICF’s best 

estimate), and 16.0 percent (high end of 

HUD’s range). Using this range of 

multifamily mix estimates, the estimate 

of the goals-qualifying share of mortgage 

originations varies by about 1.5 to 2.5 

percentage points for the low-mod goal, 

by about 1.0 percentage point for the 

underserved areas goal, and by about 1.2 

to 1.7 percentage points for the special 

affordable housing goal. The estimate 

varies depending on other market 

factors. 

As also discussed in Appendix D to 

this final rule, the multifamily mix is 

even lower during heavy refinance 

environments, as single-family owner 

refinance loans dominate both the 

market and the GSEs’ purchases. This 

makes it more difficult for the GSEs to 

meet specific Housing Goal targets. As 

discussed in section b below of this 

preamble, HUD is soliciting public 

comments on how to structure and 

implement a regulatory provision to 

take account of the effects of high 

volumes of refinance loans in some 

years on the GSEs’ ability to achieve the 

Housing Goals.

(ii) Single-Family Rental Share of the 

Mortgage Market 

HUD also estimated the distribution 

of mortgage originations for singlefamily properties, defined as structures 

with one-to-four units. In Appendix D to 

this rule, HUD disaggregates singlefamily mortgage originations into three 

categories: those on owner-occupied 

single-family homes, those on structures 

with two to four units having one unit 

owner-occupied, and those on 

structures with one to four rental units 

owned by investors. HUD bases this 

categorization on the fact that the rental 

units in the latter two categories qualify 

at much higher rates for the housing 

goals. 

HUD uses two data sources in 

Appendix D to estimate the size of the 

investor category, the Residential 

Finance Survey (RFS) and the Home 

Mortgage Disclosure Act database 

(HMDA). HMDA provides data only on 

the investor category. The investor share 

of HMDA single-family loans averaged 

7.8 percent over 1993–2003, and 8.3 

percent over the recent period of 1999–

2003. The share of investor loans has 

also been rising for home purchase 

loans; it was 9.6 percent over 1993–2003 

and 11.2 percent over 1999–2003. The 

RFS for 2001 reported a larger share of 

investor loans than HMDA, 13.4 percent 

compared to 7.8 percent. The RFS also 

reported larger investor shares for 1999 

and 2000. 

In the proposed rule, HUD estimated 

the investor share of the single-family 

market at 10 percent, based on HMDA 

data and the 2001 RFS, which was then 

the most recent available. HUD also 

considered alternatives of 8 percent and 

12 percent. Both GSEs and ICF 

commented that HUD should use 

HMDA data rather than RFS data, and 

should use a lower investor share in 

setting the goals. While they agreed with 

HUD that the RFS provides the most 

accurate estimate of the true investor 

share of the market, they stated that 

lender reporting of investor loans to the 

GSEs is conceptually closer to HMDA 

data, which are based on lender reports. 

They commented that the actual 

opportunities available to the GSEs in 

the single-family investor loan market 

are best measured by data that lenders 

report, based on actual loan 

applications. 

Fannie Mae stated that HUD’s two 

highest alternatives exceed the highest 

investor share ever reported in HMDA. 

Fannie Mae cited research indicating a 

reporting bias in HMDA, due to ‘‘hidden 

investors.’’ At the time of loan 

origination, a property may be owneroccupied or intended for owneroccupancy, but may become rental 

shortly after origination. Fannie Mae 

stated that the same bias exists in its 

own reporting. The hidden investors 

cannot be identified at the time of 

origination. 

Freddie Mac stated that investors 

have an incentive to claim falsely that 

they are owner-occupants because 

investor properties are subject to higher 

underwriting standards and loans tend 

to carry higher interest rates. Freddie 

Mac concluded that HUD should 

measure the opportunities that are 

actually available in the market to the 

GSEs, which are best measured by 

lender-reported HMDA data. 

In this rule, HUD has adopted HMDA 

data as the basis for its calculation of the 

investor share of single-family mortgage 

originations. The GSEs make a valid 

argument that lender-reported data at 

the time of origination measures the 

investor loans that are available for 

them to purchase; HMDA provides that 

data. As discussed in Appendix D to 

this rule, HUD projects the investor 

share to be 8.5 to 9.0 percent (based on 

HMDA) during the 2005–2008 home 

purchase environments, rather than 10 

percent. HUD also reports sensitivity 

analyses for higher and lower investor 

shares of 8.0 and 9.5 percent. Using this 

range of single-family investor share 

estimates, the estimate of the goalsqualifying share of mortgage 

originations varies by about 1.5 

percentage points for the low-mod goal, 

and by 0.5 percent or less for the other 

two goals. The estimate varies 

depending on other market factors. 

In the proposed rule, HUD estimated 

that the share of the single-family 

market consisting of two-to-four units 

properties with one unit owneroccupied was 2.0 percent of all singlefamily mortgages. This category is 

reported only in the RFS. The 2001 RFS 

reports that this category comprised 1.5 

percent of all single-family mortgages. 

Because the RFS calculates a higher 

share of investor mortgages in the 

single-family market (13.4 percent) than 

HUD employs in this rule (8.5 to 9.0 

percent), it is necessary to adjust the 

2001 RFS figure upward. 

The RFS reports that 85.1 percent of 

all single-family mortgages were for 

owner-occupied homes. The estimated 

share of two-to-four units properties 

with one unit owner-occupied in the 

single-family market is calculated at 

1.73 percent (i.e., 1.5 percent/[1.5 

percent + 85.1 percent]). This figure lies 

between Fannie Mae’s share of about 2.0 

percent over 1999–2003 and Freddie 

Mac’s share of about 1.5 percent. In this 

final rule, HUD uses a share of 1.6 

percent. Sensitivity analyses for 2.0 

percent are reported in Appendix D to 

this rule. 

Similarly, the single-family owneroccupied share is adjusted upward to 

take account of the lower share of 

investor loans, from 85.1 percent to 89.9 

percent. 

The estimated market share range for 

each of the three goals categories is as 

follows: 51–56 percent for the Low- and 

Moderate-Income Goal, 35–39 percent 

for the Underserved Areas Goal, and 23–

27 percent for the Special Affordable 

Goal. These estimates are one 

percentage point below the market 

ranges reported in the Proposed Rule, 

for the reasons discussed above and 

detailed in Sections F–H of Appendix D. 

The top ends of the market ranges were 

reduced as follows: from 57 percent to 

56 percent for the low- and moderateincome market; from 40 percent to 39 

percent for the underserved areas 

market; and from 28 percent 27 percent 

for the special affordable market. 

Accordingly, the 2008 goals were also 

reduced by one percentage point from 

those included in the Proposed Rule. In 

the Final Rule, the Low- and ModerateIncome Goal increases from 52 percent 

in 2005 to 56 percent in 2008, as 

63596 Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 

10By way of comparison, the refinance rate was 

29 percent for both Fannie Mae and Freddie Mac 

in 2000.

compared with an increase of 52 percent 

to 57 percent in the Proposed Rule. In 

the Final Rule, the Underserved Areas 

Goal increases from 37 percent in 2005 

to 39 percent in 2008, as compared with 

an increase of 38 percent to 40 percent 

in the Proposed Rule. In the Final Rule, 

the Special Affordable Goal increases 

from 22 percent in 2005 to 27 percent 

in 2008, as compared with an increase 

of 22 percent to 28 percent in the 

Proposed Rule. 

b. Attainability of the Goals in a High 

Refinance Environment 

Summary of Comments. A common 

theme of many of the public comments 

was concern about the volatility of the 

mortgage market and how such 

volatility makes setting Housing Goals a 

delicate and risky proposition. 

These commenters indicated that the 

goals proposed by HUD would be 

unattainable, particularly in a high 

refinance environment when a large 

portion of the mortgage market is 

comprised of refinance loans rather than 

home purchase mortgages. 

Fannie Mae and others suggested that 

including single-family refinance 

mortgages in goals calculations creates 

tension between liquidity goals and 

affordable housing goals by taking the 

emphasis away from increasing 

purchase money mortgages (and 

therefore homeownership) and placing 

the focus instead on meeting high goals. 

Freddie Mac, several trade 

associations, a financial organization 

and consumer advocacy groups also 

expressed concern that inclusion of 

single-family refinances jeopardizes the 

GSEs’ abilities to increase 

homeownership through acquisitions of 

purchase money mortgages because the 

focus would be on attaining goals rather 

than providing affordable home 

purchases for the target population.

One trade association, however, 

asserted that removing refinance 

mortgages from the goals calculations 

would only serve to encourage the GSEs 

to buy refinance loans instead of home 

purchase loans. By buying refinance 

loans, the GSEs could effectively ignore 

housing goals and both ‘‘jeopardize the 

safety and soundness of the GSEs due to 

the higher default rate of refinance loans 

and increase the minority housing gap 

due to the lower rate of minority 

borrowers for refinance loans.’’

Other commenters suggested that the 

final rule should include mechanisms 

for making adjustments to the goals if 

there are changes in market conditions 

including a surge or drop in refinance 

volume. These commenters asserted that 

the GSEs’ ability to successfully meet 

the goals should not be contingent upon 

interest rate stability. One suggestion 

that was offered for dealing with market 

mix fluctuations (i.e., between home 

purchase and refinance loans) was to 

remove from both the numerator and 

denominator ‘‘any mortgage activity in 

excess of the percentage of home 

refinance loans used by HUD for 

estimating the size of this market (i.e.,

above 35%).’’

Another commenter stated that ‘‘HUD 

should simply set goals that require the 

GSEs to lead the market, whatever the 

market turns out to be.’’ This 

commenter explained that ‘‘if 50% of 

home purchase loans are to lowmoderate income borrowers in 2005, 

then HUD should expect that a slightly 

higher percentage than this, say 51%, of 

Fannie’s and Freddie’s home purchase 

loans should fit in the purchase category 

of loans to low-moderate income 

borrowers.’’

HUD’s Determination. This final rule 

retains the approach of the May 3, 2004, 

proposed rule, in which the level of 

each Housing Goal will increase yearby-year so that by 2008 each goal will 

match the top of the market range 

established in section 2.d, above. 

The last three years have shown 

unprecedented volumes of refinance 

activity. For both GSEs, refinance loans 

accounted for 64 percent of all loans on 

single-family owner-occupied properties 

in 2001.10 The refinance shares 

increased to 70 percent for Fannie Mae 

and 73 percent for Freddie Mac in 2002, 

and rose even further last year, to 79 

percent for Fannie Mae and 82 percent 

for Freddie Mac. These unexpected 

record refinance rates made it more 

challenging for the GSEs to attain the 

housing goals in the past few years, as 

discussed elsewhere in this Preamble. 

The goals in HUD’s proposed rule for 

the latter part of the 2005–2008 period 

would be even more challenging if 

(contrary to current expectations) very 

high refinance rates are experienced in 

those years.

HUD received a number of public 

comments seeking a regulatory solution 

to the issue of the ability of the GSEs to 

meet the housing goals during a period 

when refinances of home mortgages 

constitute an unusually large share of 

the mortgage market. HUD is not 

addressing the refinance issue as a 

regulatory change in this final rule. 

Elsewhere in today’s Federal Register,

HUD is publishing an Advance Notice 

of Proposed Rulemaking that advises the 

public of HUD’s intention to consider by 

separate rulemaking a provision that 

recognizes and takes into consideration 

the impact of high volumes of refinance 

transactions on the GSEs’ ability to 

achieve the housing goals in certain 

years, and solicits proposals on how 

such a provision should be structured 

and implemented. HUD believes that it 

would benefit from further 

consideration and additional public 

input on this issue. HUD also notes that 

FHEFSSA provides a mechanism by 

which HUD can take into consideration 

market and economic conditions that 

may make the achievement of housing 

goals infeasible in a given year. (See 12 

U.S.C. 4566(b).) 

c. Bonus Points 

The Housing Goals 2000 final rule 

provided for the award of bonus points 

(double credit) toward the Housing 

Goals for both GSEs’ mortgage 

purchases that financed single-family, 

owner-occupied two-to-four unit 

properties and 5–50 unit multifamily 

properties. The rule also established a 

temporary adjustment factor (TAF) that 

awarded Freddie Mac 1.2 units credit 

for each multifamily unit in properties 

over 50 units for calendar years 2001 

through 2003. (Congress increased the 

level of the TAF to 1.35 per unit under 

section 1002 of Public Law 106–554.) 

The Housing Goals 2000 final rule 

made clear that both of these measures 

were temporary, intended to encourage 

the GSEs to increase their efforts to meet 

financing needs that had not been well 

served. During the three years for which 

the temporary bonus points and TAF 

were established, HUD expected the 

GSEs to develop new, sustainable 

business relationships and purchasing 

strategies for the targeted needs. Data 

indicate that, because both GSEs did 

increase their financing of units targeted 

by the bonus points and the TAF, the 

original objectives were met. The 

Department determined at the end of the 

three years (2001–2003) not to extend 

the bonus points or the TAF. 

Summary of Comments. A number of 

non-GSE commenters, including 

organizations representing affordable 

housing and consumer groups, trade 

associations, organizations representing 

racial and ethnic minorities, other 

organizations, and both Fannie Mae and 

Freddie Mac, recommended that the 

Department reinstate the award of bonus 

points for the GSEs which were 

established for 2001–2003 but which the 

Department did not continue after the 

end of 2003. Various non-GSE 

commenters, in addition to 

recommending reinstatement, also 

suggested that HUD develop new bonus 

point incentives for other unmet 

housing needs, such as manufactured 

Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 63597

housing, rural housing, or tax credit 

properties or for particular groups, e.g., 

Native Americans, other minority 

populations, or persons with 

disabilities. 

Fannie Mae recommended that HUD 

provide bonuses for targeted business 

such as extremely low-income 

households, i.e., those with incomes 

less than 30% of area median income 

(AMI); first-time homebuyers; 

manufactured housing; rural areas; and 

small multifamily properties. Freddie 

Mac suggested that instead of purchase 

money subgoals, the Department could 

provide bonus point incentives for these 

mortgages. Freddie Mac stated that the 

bonus points for two-to-four unit and 5–

50 unit properties provided an 

extremely effective incentive. Freddie 

Mac indicated that other markets that 

could be assisted by bonus points are 

rural and manufactured housing. 

Freddie Mac noted that the 

Department’s concern that bonus points 

obfuscate the GSEs’ actual goalsqualifying performance is easily 

remedied by having the GSEs report two 

numbers, one with and one without the 

bonuses. 

HUD’s Determination. The 

Department has fully considered the 

comments suggesting the reintroduction of bonus points, as well as 

other types of targeted incentives for the 

GSEs’ mortgage purchases, and has 

determined not to reinstate the bonus 

points for the years covered by this rule. 

The position of the Department 

discussed in the preamble of the 

proposed rule (see 69 FR 24228, 24232) 

remains unchanged; that is, the 

continued use of the bonus points 

‘‘would only result in misleading 

information about the extent to which 

the GSEs are, in fact, meeting the 

Housing Goals.’’ In addition, the 

Department reiterates that the ‘‘decision 

to increase the levels of the Housing 

Goals substantially in a staged manner 

* * * and, at the same time, not renew 

the bonus points or TAF, will ensure 

that the GSEs continue to address the 

areas formerly targeted by these 

measures’’ (see 69 FR 24228, 24232). 

d. Appropriate Levels of the Goals

In the May 3, 2004, proposed rule, 

HUD set the Goals to increase to levels 

at or near the high end of the estimated 

market range for each goal category by 

2008. A large number of commenters 

expressed concern that these goal levels 

were set too high, and could have 

deleterious consequences for the 

mortgage market as a whole, or for 

specific sectors of the market. 

Fannie Mae commented that a high 

allocation of affordable mortgage credit 

will take away from the broad middle 

class, especially in high-housing cost 

regions. For example, Fannie Mae 

asserted that if the special affordable 

housing goal had been set at 28 percent 

in 2003, then it would have needed to 

greatly curtail support to the overall 

market to meet that goal. Fannie Mae 

concluded that such manipulation does 

not promote stability and limits 

liquidity, and that it can shut out 

working middle class borrowers, 

contribute to higher interest rates and 

lower conforming loan limits. 

Many commenters, including Freddie 

Mac, also claimed that setting the goals 

at a high percentage may lead to 

denominator management. They state 

that denominator management would 

occur if a GSE purposely abstained from 

buying mortgages in the markets that are 

not goals-eligible, rather than increasing 

its purchases in markets that are goalseligible. Freddie Mac contended that 

this may be necessary if goals are set 

above the market percentage of available 

goal-qualifying loans. One financial 

institution observed that denominator 

management ‘‘will be exacerbated by the 

fact that the GSEs do not operate in the 

primary market and do not have any 

direct control over the origination 

strategies of their customers.’’

In addition to the allocation problems 

discussed above, the GSEs stated that 

the liquidity requirements in their 

charters imply that they must stand 

ready to buy any and all conventional, 

conforming residential mortgages. They 

contend that denominator management 

is in direct conflict with these 

provisions, and goals set higher than 

market originations could force the 

GSEs to refuse to purchase mortgages 

that are not eligible. This, in turn, could 

reduce liquidity in the market. Knowing 

that the GSEs would no longer stand 

willing and able to purchase all 

conventional, conforming mortgages, 

other market participants might be less 

willing to hold these mortgages in their 

portfolios, and general liquidity would 

decline. The GSEs further asserted that 

changing market forces could cause 

swings in prices and trading volumes, 

and these temporary disturbances could 

create unstable markets, increase risk, 

and reduce the willingness of investors 

to invest in the sector. Thus, the GSEs 

maintained that denominator 

management decreases market stability. 

The GSEs pointed to specific 

historical examples that describe their 

positive influence on stability. They 

maintained that during the 1990–1991 

recession, the GSEs advised that they 

stood ready to purchase mortgages 

while many industry participants 

curtailed their purchase programs. 

Using historical trends in prices, the 

GSEs asserted that their presence in the 

mortgage market explains why 

mortgage-backed securities have a more 

stable price trend than commodity 

markets. They warned that because of 

denominator management resulting 

from unrealistic goals, they could not 

buy mortgage-backed securities and 

encourage stability in a financial crisis. 

The GSEs further contended that if 

they reduce their willingness to buy 

non-goals eligible mortgages, it will be 

harder for borrowers whose incomes 

marginally exceed goals eligibility 

requirements to obtain financing since 

the two income-based Housing Goals 

compare the incomes of the borrower or 

resident to area median income. For 

example, the combined incomes in a 

working family may just disqualify that 

family’s loan for eligibility under the 

low- and moderate-income goal even 

though each individual’s income would 

not be considered to be affluent. The 

GSEs and other commenters provided 

examples of working families in the 

middle class, such as ‘‘teacher/fireman’’

households, that could encounter 

difficulties in financing a home. 

Moreover, the commenters asserted 

that non-goal qualifying households 

may have higher costs associated with 

available financing since these 

mortgages would be less likely to be 

purchased by a GSE. Freddie Mac 

asserted that HUD did not take this into 

account in its cost/benefit analysis. 

Furthermore, commenters claimed 

that denominator management may 

contribute to higher interest rates and, 

as a result, harm the precise borrowers 

that HUD is trying to help. These 

commenters stated that if denominator 

management reduces liquidity then the 

supply of mortgage funds will decline 

and interest rates will rise. The GSEs 

contended that if they are less willing to 

buy mortgages under all conditions, 

then investors will be less willing to 

provide funds to the market. As a result, 

the GSEs claimed that as investors seek 

out safer instruments, home mortgage 

interest rates will rise, and this rise in 

home mortgage rates will harm even 

those borrowers that are still goalseligible. 

Several commenters expressed 

concern about the effect of the goals on 

high cost markets. One commenter 

explained that while the goals are set 

with a national standard, a market level 

analysis ‘‘reveals a pronounced shortage 

of affordable mortgages in high cost 

housing markets.’’ Commenters stated 

that the GSEs’ current loan purchasing 

patterns demonstrate that market 

affordability already has an impact on 

goals-related purchases. The 

63598 Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 

commenters expressed concern that 

high cost markets could see even tighter 

credit if the proposed goals are enacted. 

The GSEs note that under HUD’s May 

3, 2004, proposed rule, the goal levels 

rise to levels at the top of HUD’s market 

range in 2008 and stabilize there. They 

state that the projected market range 

concept is one in which HUD projects 

market levels of loans generated within 

each goal category will fluctuate within 

the range, depending on relative 

volumes of single-family refinance loans 

relative to other loans, interest rates and 

other macroeconomic and housing 

market conditions. The GSEs express 

the concern that, in any particular year, 

they could be confronted with goal 

levels that are several percentage points 

higher than the market percentages of 

goal-qualifying loans, or goal levels that 

are at the market percentages. The GSEs 

state that if HUD’s proposed Housing 

Goals are retained, they foresee years 

when the goal levels will be attainable 

only by means of ‘‘denominator 

management’’ in which they limit their 

purchases of loans that do not qualify 

for the goals. 

HUD’s Determination. Many of the 

comments expressed concern about the 

goal levels established for the last year 

or two of the period covered by this 

rule. In these years, the goals are set at 

the market levels estimated by HUD. 

Also, since they are the later years, 

market projections are necessarily more 

imprecise. In particular, the possibility 

of a decline in mortgage interest rates in 

those years raises the possibility of 

another boom in refinancing, and thus 

greater difficulty for the GSEs to meet 

the housing goals without denominator 

management. The comments relating to 

middle-income borrowers are 

predicated on the difficulty of 

foreseeing refinance volatility. Recent 

years have seen large unexpected home 

refinance rates. Since higher income 

homeowners disproportionately engage 

in refinancing, inclusion of refinance 

loans in the denominator increases the 

difficulty of GSE goals performance. A 

middle-income borrower just above the 

low/mod bracket would be less 

attractive to the GSEs in high refinance 

years. As noted in section II.C.3.b., HUD 

is considering in a separate rulemaking 

a provision that recognizes and takes 

into consideration the impact of high 

volumes of refinance transactions on the 

GSEs’ ability to achieve the housing 

goals in certain years. HUD also notes 

that FHEFSSA provides a mechanism by 

which HUD can take into consideration 

market and economic conditions that 

may make the achievement of housing 

goals infeasible in a given year. (See 12 

U.S.C. 4566(b).)

With regard to the effects of the goals 

on high-cost markets, HUD notes that 

the overall presence of the GSEs in these 

markets depends on the conforming 

loan limit, which has been established 

by Congress for all states, including 

states deemed to be ‘‘high-cost areas.’’

With regard to HUD’s housing goals 

more specifically, the low- and 

moderate-income and special affordable 

goals are based on borrower income 

relative to area median income, thus a 

mortgage for a lower-income family in a 

high-income metropolitan area will 

count towards the goals in the same 

manner as a mortgage for a lowerincome family in a low-income area. 

Underserved areas are defined in terms 

of median family income in a census 

tract relative for median income in the 

area; thus a mortgage for a family living 

in a lower-income tract in a highincome metropolitan area will count 

towards the goals in the same manner as 

a mortgage for a family living in a lowerincome tract in a low-income area. Thus 

HUD concludes that its housing goals 

will have no adverse impact on 

borrowers or neighborhoods in areas 

with high housing costs. 

e. Consequences of the Goals for FHA 

Fannie Mae, Freddie Mac, several 

trade associations, two advocacy groups 

and two financial institutions expressed 

concern over the impact HUD’s 

proposed goals would have on the 

future solvency of the FHA program. 

One trade association asserted that 

‘‘excessive goals will push GSEs to 

expand into the least-risky part of the 

FHA market and put into question 

FHA’s long-term viability.’’

The aforementioned commenters 

reiterated this point by stating that 

unrealistically high goals would compel 

the GSEs to increase competition with 

FHA for higher credit quality borrowers 

and would therefore further undermine 

the FHA program in the long-run. One 

advocacy group asserted that not only 

will these goals encourage the GSEs to 

compete with FHA more in the single 

family sector but in the multifamily 

sector as well. 

Freddie Mac and Fannie Mae agreed 

that they would be compelled to more 

aggressively compete with FHA in 

procuring top-quality borrowers. 

Freddie Mac stated that the GSEs would 

take as many as ‘‘1⁄3 of all FHA 

borrowers.’’ Freddie Mac and two trade 

associations further contended that such 

a loss to the FHA program would be 

seen in the increasing expenses to the 

remaining FHA borrowers. As the FHA 

program loses better quality loans to the 

GSEs, the result would be ‘‘higher fees 

to FHA borrowers or government 

subsidies to pay claims, effectively 

making FHA the lender of last resort,’’ 

said one trade association. 

One financial institution stated that 

the so-called competition for goalsqualifying loans would not be between 

traditional conventional lenders vying 

for loans with a separate group of 

traditional FHA lenders, but rather an 

acceleration of product competition 

within a single group of existing lenders 

who originate for both markets. This 

commenter stated that 12 of the top 15 

(by volume) FHA/VA lenders are also 

among the top 15 conventional lenders 

and indicated that the increased product 

competition would not result in a net 

increase in goals-qualifying loans, but in 

a shift from FHA to the GSEs of FHA’s 

relatively lower risks. 

HUD’s Determination. The 

Department agrees with many of these 

commenters that improvements in 

technology, such as the widespread use 

of commercial credit scores, mortgage 

scores, and automated underwriting 

systems, have fundamentally changed 

the way lenders process loan 

applications in recent years. Where once 

rules-based underwriting distinctions 

between prime conventional and FHA 

loans were fairly clear, in recent years, 

with the new technology, these 

distinctions have become blurred. For 

example, loan applications with 

payment-to-income ratios above 

conventional market guidelines were 

once clearly candidates for FHA 

financing because FHA would accept 

applicants with higher payment-toincome ratios. However, today, the same 

application would be processed using 

an automated underwriting system 

(AUS) that scores the application based 

on the totality of the application’s risk 

factors. What once may have been an 

unacceptable payment-to-income ratio 

for a prime conventional loan may now 

be acceptable if the application contains 

offsetting low risks in other key areas 

such as borrower cash reserves, loan-tovalue ratio, or commercial credit scores. 

In addition to these technological 

changes, FHA made several changes to 

its underwriting guidelines in FY 1995 

in order to promote increased 

homeownership opportunities among 

low-income and minority homebuyers. 

By doing so, FHA modestly increased 

the risk characteristics of its post-1995 

books of business, but it succeeded in 

raising FHA’s proportion of first-time 

homebuyers from 60.9 percent in fiscal 

year 1994 to 73.0 percent in fiscal year 

2003. During the same period (fiscal 

years 1994 to 2003), FHA’s proportion 

of minority borrowers increased from 

24.8 percent to 33.0 percent, and has 

since remained at this level, or higher.

Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 63599

11 ‘‘Economic value’’ is the net present value of 

the fund’s reserves plus expected future cash flows, 

and the ‘‘capital ratio’’ is economic value divided 

by insurance-in-force.

12A negative credit subsidy of 3.0 percent means 

that the net present value of FHA’s revenues 

(premiums, fees, recoveries from claims paid, etc.) 

will exceed the net present value of FHA’s program 

costs (claims and related expenses) by 3.0 percent 

of the total insured mortgage amount.

The new technology may allow the 

conventional market to identify lower 

risk loan applications that historically 

have come to FHA. However, the ability 

to identify risks does not, in and of 

itself, equate to shifts in market share 

from FHA to conventional lenders. 

Better pricing for borrowers by the 

conventional market is required to lure 

lower risk borrowers from FHA. If 

conventional lenders use the new 

technology to not only evaluate risks but 

also to price according to risk, then 

there may be some shift from FHA to the 

conventional market. Such a shift can 

produce tangible benefits for borrowers 

in the form of lower cost mortgage 

financing. 

The Department does not believe it is 

FHA’s mission to compete with the 

private sector. Rather, FHA’s mission is 

to complement the conventional market, 

using FHA’s cost of capital advantage 

where it can have the most benefit in 

creating homeownership opportunities 

for those households who might not 

otherwise be served by the prime 

conventional market. 

HUD gauges the soundness of FHA’s 

insurance funds in several ways. The 

statutorily mandated annual 

independent actuarial review of FHA’s 

principal single-family insurance fund, 

the Mutual Mortgage Insurance Fund 

(MMIF), provides the Department, and 

the public, with an outside expert’s 

estimate of the capital ratio of the 

overall fund, and the economic value of 

new business coming into the fund. The 

capital ratio indicates whether the 

existing books of business (current 

portfolio) are financially sound, while 

the economic value estimates of new 

business show whether if the marginal 

impact of new loans insured is adding 

or detracting from the financial health of 

the fund.11 Specifically, the Fiscal Year 

2003 actuarial review estimated the 

economic value of the MMIF at the end 

of Fiscal Year 2003 to be $22.7 billion 

and the fund’s capital ratio to be 5.21 

percent—the eighth full year this ratio 

has exceeded the Congressionally 

mandated minimum of 2.0 percent. The 

economic value of new loans endorsed 

for insurance during 2003 was estimated 

by FHA’s independent actuary to be 

$2.8 billion, indicating new business 

coming into FHA is further contributing 

to FHA’s reserves.

In comparison, the Fiscal Year 2002 

actuarial review estimated the economic 

value and capital ratio of the MMIF at 

$22.6 billion and 4.52 percent, 

respectively. The increases in both 

measures for Fiscal Year 2003 were 

driven by the large positive economic 

value the actuary placed on a record 

dollar volume of new loans FHA 

insured in FY 2003 along with the rapid 

prepayment of older loans, keeping the 

end-of-year insurance-in-force 

(denominator of the capital ratio) down.

With regard to the GSEs taking 

multifamily business away from FHA, 

the Department notes that there are 

many differences between the types of 

multifamily mortgages FHA insures and 

those the GSEs purchase. For newly 

constructed multifamily properties, 

FHA insures the loan from the start of 

construction while GSE multifamily 

loan products generally do not. The 

GSEs do have forward commitment 

programs that can be used for new 

construction, but the purchase of the 

permanent loan by the GSEs generally 

requires the property to achieve 

minimum sustained occupancy levels, 

whereas FHA does not have this 

requirement. However, it is possible that 

the new goals will provide incentives 

for the GSEs to expand and refine their 

forward commitment products to be 

more attractive in the market for new 

multifamily housing. This could be a 

benefit to the market. 

The greatest potential impact of the 

higher housing goals on FHA’s 

multifamily business may come from a 

reduction in two of FHA’s programs that 

address the purchase or refinance of 

existing properties. The first is the 

Section 223(f) program, which insures 

mortgages for the purchase or refinance 

of existing (over three year old) 

properties that are not currently 

financed with an FHA mortgage. This 

program accounted for about $0.8 

billion in endorsements for FHA during 

Fiscal Year 2003, and is expected to 

produce about $0.5 billion in 

endorsements during Fiscal Year 2004. 

FHA’s 223(f) business is estimated to be 

profitable to FHA—it is estimated to 

have a credit subsidy (net present value 

of all cash flows from the insurance 

contract at the time of endorsement) of 

negative 3.0%.12 The second is the 

Section 223(a)(7) program, which 

insures mortgages for FHA-to-FHA 

refinances—that is, the refinance of an 

existing FHA-insured mortgage. Section 

223(a)(7) is used, for example, to 

refinance loans previously insured 

under FHA’s most used programs—i.e., 

Section 221(d)(4) new construction/

substantial rehabilitation, and Section 

223(f). FHA endorsed over $2.1 billion 

in Section 223(a)(7) loans during Fiscal 

Year 2003, and is expected to endorse 

about $1.4 billion during Fiscal Year 

2004. As with the Section 223(f) 

program, FHA’s Section 223(a)(7) 

program is also profitable to FHA—

operating with an estimated negative 

credit subsidy of 2.2%.

If FHA does lose some multifamily 

market share from its purchase or 

refinance programs for existing housing 

as a result of the goals, it would not 

likely have any significant impact on 

FHA overall. 

f. Consequences of the Goals for the 

Multifamily Market 

Summary of Comments. Several 

organizations commented on potential 

adverse consequences if the housing 

goals are set too high. Fannie Mae and 

Freddie Mac, among others, cited the 

recent high vacancy rates for 

multifamily rental housing as an 

example that increased lending by the 

GSEs at this time would encourage 

overbuilding. 

Others stated that the multifamily 

market is already flush with capital and 

that inappropriate goals could promote 

overly aggressive bidding for loans and 

reckless lending. 

One trade association stated that the 

increased presence of the two GSEs 

would promote a duopsony (a market 

with only two buyers) that would 

hinder competition in the multifamily 

mortgage market. 

Other commenters suggested that 

increased loan purchases by the GSEs 

would skim the highest credit-quality 

loan from other mortgage lenders, and 

reduce the credit quality of multifamily 

loans remaining in the portfolios of 

pension funds or insured through FHA. 

Another commenter stated that 

increased goals pressure on the GSEs 

would cause them to concentrate on 

large properties, where a single loan 

would contribute more toward goal 

attainment.

HUD’s Determination. One of HUD’s 

objectives in promulgating this final 

rule is to promote the availability of 

mortgage credit to affordable properties 

at the lowest possible cost. It is not the 

intent of this rule to promote the 

maximum flow of credit to this market, 

regardless of housing and mortgage 

market conditions. 

Increased competition for business, as 

intended by the rule, should bring 

benefits to borrowers, and therefore 

renters, through lower interest rates and 

more attractive non-price terms. Such 

increased competition does not imply 

impaired credit quality or lax 

63600 Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 

13This is suggested by recent experience of 

below-average multifamily mix in years where the 

volume of single-family refinancings has been high. 

Further support is provided by evidence of a 

relationship between interest rates and the 

multifamily share of the net change in residential 

mortgage debt between 1975 and 2002.

underwriting. As the GSEs compete 

more aggressively for multifamily 

business and gain market share, the 

market will not necessarily grow onefor-one with every additional loan 

purchased by the GSEs. It is likely that 

the market impacts will be more on the 

pricing of multifamily credit and less on 

the volume of credit supplied. Lower 

pricing of credit in and of itself does not 

promote overbuilding; its one 

unambiguous effect is to reduce the cost 

of supplying housing to consumers. 

Demand for multifamily mortgages 

will be responsive to cyclical 

macroeconomic factors. Beyond these 

influences, demand for multifamily 

housing will be supported by favorable 

demographics. In its comments on the 

proposed rule, Fannie Mae highlighted 

the prospective growth in the number of 

people ages 20 through 34 in arguing 

that the demographics do not become 

clearly favorable to rental demand until 

late in this decade. But fewer than half 

of all renter households are headed by 

someone of this age, and more 

comprehensive estimates and 

projections suggest a steadier path of 

moderate growth in the demographic 

component of demand for multifamily 

housing. 

Interest rates clearly will be important 

for the future path of mortgage lending, 

as noted by Fannie Mae, Freddie Mac, 

and other commenters. The historically 

low interest rates of recent years have 

spurred lending in both the multifamily 

and single-family markets. If interest 

rates should rise in the future, the 

volume of mortgage lending presumably 

would be lower than if rates were to 

remain at current levels. But the effect 

of higher rates on the GSEs’ ability to 

achieve the housing goals is less clear. 

Because the goals are established in 

terms of shares of the GSEs’ business, 

rather than levels, a key question is how 

higher interest rates would affect the 

relative demand for single-family and 

multifamily mortgage credit. Because of 

differences in prepayment provisions 

and other characteristics between 

single-family and multifamily mortgage 

lending, multifamily credit demand 

might drop off proportionally less than 

would single-family credit demand in 

response to higher rates.13 This in turn 

would make it easier to attain the goal 

levels if interest rates were to increase 

from current levels.

Regarding the market structure 

implications of increased GSE 

multifamily activity, HUD estimates that 

the GSEs purchased slightly less than 

one-third of the dollar volume of 

conventional multifamily loan 

originations during 2001–2003 (see 

Table D.2). There is room for increasing 

this market share without producing the 

duopsony alluded to in the previously 

cited comments. Furthermore, if the 

GSEs do increase their market 

penetration, it is because they are 

offering multifamily borrowers more 

attractive products or pricing than are 

their competitors, including the pension 

funds and FHA programs alluded to by 

some commenting organizations. The 

borrower and, ultimately, the rentpaying affordable housing resident 

benefit from these more attractive 

products and pricing. 

In summary, the Department’s 

determination is that the Housing Goal 

levels established by this rule are 

prudent and will improve the 

availability and pricing of credit for 

affordable multifamily properties. For 

the reasons stated above, it is the 

Department’s view that the rule will not 

have the adverse consequences 

mentioned in some comments on the 

proposed rule. 

g. Consequences of the Goals for the 

Single-Family Rental Market 

Summary of Comments. Several 

community organizations raised 

concerns about encouraging the singlefamily rental market. They asserted that 

the goals should target families who 

want to live in the financed houses, as 

opposed to the investors who purchase 

these homes. In these commenters’

view, investors take affordable housing 

stock off the market, which raises the 

price for low and moderate-income firsttime homebuyers. They claimed that 

homeownership should be stressed 

because home equity is a large 

component of the disparity that exists in 

household net wealth between ethnic 

groups. 

Some commenters cited studies that 

suggest homeownership has beneficial 

neighborhood effects relative to 

investor-owned properties. According to 

one cited study, absentee landlords are 

much more likely to let housing stock 

decline but homeowners are much more 

likely to invest in the upkeep of their 

homes. In the view of one of these 

organizations, the incentives that the 

GSEs receive for rental housing should 

be to promote multifamily 

developments, not single-family homes. 

HUD’s Determination. HUD 

considered many factors related to the 

single-family rental market. Singlefamily rentals are another source of 

affordable housing. Also, the capital 

provided by investors can help maintain 

demand for single-family homes in 

underserved neighborhoods. While 

some commenters complained that this 

raises the cost to first-time homebuyers, 

investors also help to maintain the 

liquidity and value of owner-occupied 

homes. Further, there are some investors 

who make it their business to renovate 

the housing stock and resell the 

properties. On balance, HUD found no 

compelling evidence that single-family 

rentals should be excluded from goals 

eligibility. 

h. Consequences of the Goals for the 

Subprime Market 

Summary of Comments. Both GSEs 

indicated that they would need to 

increase their purchase of subprime 

loans to meet the higher goals. Freddie 

Mac stated that the increased affordable 

housing goals created tension in its 

business practices between meeting the 

goals and conducting responsible 

lending practices. 

In the past, Fannie Mae and Freddie 

Mac have voluntarily decided not to 

purchase subprime loans with features 

such as single-premium life insurance 

and prepayment penalty terms that 

exceed three years, or to purchase loans 

subject to the Home Ownership and 

Equity Protection Act (HOEPA). Freddie 

Mac indicated that the increased goals 

would limit its ability to influence 

subprime lending practices. More 

specifically, Freddie Mac claimed that, 

to meet the higher housing goals, it 

might not have the option in the future 

of turning away subprime loans that 

have less desirable loan terms than the 

subprime business it currently 

purchases. 

Several commenters suggested that if 

the GSEs are pushed to serve more of 

the subprime market, they will skim a 

significant portion of the lower-risk 

borrowers from that market. The 

resulting smaller subprime market 

would include the neediest borrowers. 

The commenters stated that these higher 

risk borrowers would pay more because 

lower risk borrowers would not be 

present to subsidize them, and the 

market’s high fixed costs would be 

distributed across fewer borrowers. 

One industry group also suggested 

that a significantly smaller subprime 

market for private lenders would drive 

some lenders out of business and 

translate into less competition.

While some industry commenters 

welcomed the entrance of the GSEs into 

the subprime market because their 

presence would bring stability and 

standardize business practices, the 

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commenters also expressed concern that 

unrealistically high goals could force 

the GSEs to jump into the market in a 

manner that negatively distorts 

underwriting and pricing. These 

commenters contended that the GSEs 

could bring capital and standards up, 

but that they must gradually and 

carefully enter the subprime market to 

have a positive effect. They strongly 

urged HUD to lower the goals to 

encourage the GSEs to expand their 

subprime activities at a measured pace. 

Some commenters suggested that 

bonus points, or other incentives for the 

GSEs’ purchases of certain nonprime 

loans, could foster more deliberate and 

prudent purchases by the GSEs of 

subprime loans. One lender also 

suggested that incentives could be 

granted to the GSEs for other 

underserved market segments, such as 

manufactured homes, minority first time 

buyers, and nonprime first-time buyers. 

HUD’s Determination. To date, the 

GSEs’ involvement in the subprime 

market has benefited two types of 

borrowers: ‘‘A’’ risk and ‘‘near A’’ risk. 

The first group consists of borrowers 

with risk profiles similar to ‘‘A’’

borrowers, but receive mortgages from a 

subprime lender. The GSEs’’ outreach 

and education efforts increase the 

likelihood that ‘‘A’’ borrowers will use 

cheaper prime lenders for refinance 

mortgages, and reduce their reliance on 

subprime firms. The second group, 

borrowers who are near A credit risks, 

has growing access to mortgage products 

offered by the GSEs as these borrowers 

are increasingly served by GSE seller/

servicers. 

The GSEs have been prudent in their 

pursuit of subprime lending, focusing 

on the top part of the market, the ‘‘Aminus’’ and ‘‘Alt A’’ segments. A-minus 

mortgages are typically those where 

borrowers have less than perfect credit. 

Alt A mortgages are originated to 

borrowers who cannot document all of 

the underwriting information in the 

application but generally have FICO 

scores similar to those in the prime 

market. The GSEs’ subprime products 

are integrated into their automated 

underwriting systems and are approved 

based on mortgage scoring models. 

These models have proven over the 

years to be an effective tool in limiting 

risk layering. The GSEs charge lenders 

higher fees for guaranteeing these loans. 

As a result these higher risk loans are 

priced above those offered to prime 

borrowers but below what subprime 

lenders would otherwise charge for 

these loans. 

The GSEs’ presence in the subprime 

mortgage market benefits many lowincome and minority borrowers whose 

risk profiles differ markedly from 

borrowers who qualify for prime 

mortgage products. Millions of 

Americans with less than perfect credit 

or who cannot meet some of the tougher 

underwriting requirements of the prime 

market for reasons such as inadequate 

income documentation, limited 

downpayment or cash reserves, or the 

desire to take more cash out in a 

refinancing than conventional loans 

allow, rely on subprime lenders for 

access to mortgage financing. If the 

GSEs reach deeper into the subprime 

market, more borrowers will benefit 

from the advantages that greater stability 

and standardization create. 

i. Consequences of the Goals for 

Mortgage Defaults; Neighborhood 

Impacts 

Summary of Comments. HUD 

received several comments concerning 

the impact of mortgage default rates on 

neighborhoods. Comments from 

mortgage insurance companies 

highlighted that the higher goals will 

likely lead to more expanded affordable 

housing products as well as higher 

foreclosures. Affordable products 

present challenges to borrowers and 

lenders. For borrowers, qualifying for an 

affordable mortgage does not insure they 

have a clear understanding of the risks 

of homeownership. Where aggressive 

affordable products are aimed at 

qualifying borrowers for home loans 

rather than qualifying families for 

homeownership, lenders need to be 

cautious of products that test the limits 

of borrowers’ credit capabilities. 

Affordable products that have been 

introduced into the market under 

favorable economic conditions can 

experience increasing defaults and 

foreclosures during periods of higher 

interest rates, higher unemployment 

and/or lower house price appreciation 

rates. One commenter indicated that 15 

percent or more of borrowers in some 

affordable housing products could 

experience default in an economic 

downturn. 

As defaults on affordable products 

rise, inner city neighborhoods can be 

especially hard hit. A large number of 

foreclosures in an area may lead to 

abandoned properties. While 

foreclosures devastate borrowers who 

lose their homes and damage borrowers’

credit history, foreclosures also weaken 

the neighborhoods where the properties 

are located. 

The potential for affordable lending 

products to result in higher foreclosure 

during a less prosperous economic 

environment was echoed in Freddie 

Mac’s comments. Its comment discussed 

how too many defaults in one 

neighborhood can lead to serious blight 

and disinvestment in the community. 

One commenter recommended that 

HUD establish safeguards against 

aggressive affordable products. The 

commenter suggested that HUD deny 

Housing Goal credit for GSE mortgage 

purchases that experience early-term 

serious defaults (e.g., delinquent 90 

days or longer within 12 months of the 

date of origination). 

The GSEs and community groups 

cautioned that the struggle to meet high 

goals for low-income groups could 

cause the GSEs to relax underwriting 

standards and/or extend loans to people 

who are unprepared. For example, the 

commenters pointed out that FHA 

default rates are higher than the 

conventional conforming market. High 

goals would encourage the GSEs to enter 

markets served by FHA. This incentive 

to extend credit to unprepared lowincome people would rise if unexpected 

refinances decreased the proportion of 

goals-eligible units produced in the 

market.

HUD’s Determination. HUD carefully 

reviewed the comments regarding 

mortgage default rates. The Department 

believes that the GSEs’ presence in 

underserved markets will be beneficial 

for neighborhoods. The GSEs have 

improved their underwriting methods to 

better identify risks in these markets, 

and also have instituted homebuyer 

education programs. An increased role 

for the GSEs’ seller-services in innercity neighborhoods will improve 

competition, reduce high-cost lending, 

and reduce predatory lending. As 

described in Appendix A, families 

living in inner-city, high-minority 

neighborhoods often have to rely on 

subprime lenders as their main source 

of mortgage credit. Studies indicate that 

many of these borrowers obtaining highcost loans could qualify for lower-cost, 

prime mortgage credit. An active GSE 

effort in these neighborhoods will 

encourage traditional, mainstream 

lenders to increase their lending 

activities in these historically 

underserved areas. This will offer 

additional funding options for those 

lower-income and minority borrowers 

who today may have to take out a highcost loan in order to purchase or 

renovate a home or to refinance an 

existing mortgage. Reductions in 

predatory lending reduce the costs of 

mortgages and the chances of default. 

As a result, the Department believes that 

GSE participation is a net benefit to 

lower income neighborhoods. 

63602 Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 

j. Consequences of the Goals for 

Residents of Puerto Rico 

Summary of Comments. Several 

associations stated that HUD’s proposed 

affordable housing goals could be 

disadvantageous to residents of Puerto 

Rico, alleging that less than 10 percent 

of loans that are originated in the Puerto 

Rican market would qualify for the 

goals. These commenters were 

concerned that the GSEs might be 

unable to buy loans from Puerto Rico, 

and urged HUD to take special measures 

to ensure that owner and rental housing 

production are not deleteriously 

affected by the demographic and 

economic differences that exist between 

the mainland markets and the Puerto 

Rico market. 

HUD’s Determination. Loans 

purchased by the GSEs for properties in 

Puerto Rico are counted in the same 

manner as loans purchased on 

properties in any other location. Since 

underserved areas are defined as lowincome and/or high-minority census 

tracts in metropolitan areas or counties 

in non-metropolitan areas, the 

overwhelming majority of loans 

purchased by the GSEs on properties in 

Puerto Rico count toward that goal. In 

fact, in 2003, Fannie Mae reported that 

95 percent of the units it financed in 

Puerto Rico qualified for the 

underserved areas goal; the 

corresponding figure for Freddie Mac 

was 98 percent. 

Relatively few of the loans in Puerto 

Rico that are purchased by the GSEs 

qualify for the two income-based goals. 

Despite this, HUD does not believe that 

the final housing goals will adversely 

affect Puerto Rico. In 2003, Puerto Rico 

accounted for only 0.2 percent of all 

units financed by Fannie Mae and only 

0.1 percent of all units financed by 

Freddie Mac. Thus overall performance 

on these broad national goals is not 

materially affected by the characteristics 

of loans purchased by the GSEs in 

Puerto Rico. 

Apparently many lower-income 

families in Puerto Rico rely on 

consumer finance companies for 

financing their homes. Since such 

financing is typically more expensive to 

borrowers than traditional mortgages, 

this suggests that the GSEs could play 

an important role, working with 

mortgage originators, to better develop 

the mortgage market in Puerto Rico. 

4. Determinations Regarding the 

Specification and Levels of the Home 

Purchase Subgoals 

a. Overview 

Given that the past average 

performance of the GSEs in the home 

purchase market has been below market 

levels, and the Administration’s 

emphasis on increasing homeownership 

opportunities, including those for lowand moderate-income and minority 

borrowers, HUD proposed to set Home 

Purchase Subgoals for GSE mortgage 

purchase activities to increase financing 

opportunities for low- and moderateincome, underserved, and special 

affordable borrowers who are 

purchasing single-family homes. 

Specifically, the Department proposed 

Home Purchase Subgoals for home 

purchase loans that qualify for the 

Housing Goals. The purpose of the 

Home Purchase Subgoals is to ensure 

that the GSEs focus on financing home 

purchases for the homeowners targeted 

by the Housing Goals. The Department 

believes that the establishment of Home 

Purchase Subgoals will place the GSEs 

in an important leadership position in 

the Housing Goals categories, while also 

facilitating homeownership. The GSEs 

have years of experience in providing 

secondary market financing for singlefamily properties and are fully capable 

of exerting such leadership. 

The focus of these Subgoals on home 

purchase loans meeting the Housing 

Goals will also help address the racial 

and income disparities in 

homeownership that exist today. As 

noted earlier, although minority 

homeownership has grown, the 

homeownership rate for AfricanAmerican and Hispanic families is still 

approximately 25 percentage points 

below that for non-Hispanic white 

families. The focus of the Subgoals on 

home purchase will also increase the 

GSEs’ support of first-time homebuyers, 

a market segment where they have 

lagged primary lenders. 

Summary of Comments. Fannie Mae 

claimed that the proposed Subgoals are 

not necessary and are, in fact, 

duplicative of the broader goals 

structure. Fannie Mae asserted that it is 

already a leader in financing home 

purchases, even in a period of 

aggressive refinancings. In addition, 

Fannie Mae stated that subgoals add 

complexity to the mortgage market and 

contribute to a loss of liquidity, and 

suggested that the proposed Subgoals do 

not reflect recent market experience 

because affordability may decline and 

HUD may mistreat missing data when 

formulating subgoals. Fannie Mae also 

stated that HUD improperly exercised 

its authority in proposing the Subgoals. 

Specifically, Fannie Mae contended 

that a complex subgoal structure harms 

liquidity and that when Fannie Mae 

needs to stretch in one market to meet 

a goal, it may have to reduce its 

willingness to purchase mortgages in 

another market. Fannie Mae stated that 

conflicts between the goals arise 

because the goals are set as a percentage 

of business, and fulfilling the numerator 

of one goal adds to the denominator of 

the other goals. Fannie Mae asserted 

that the GSEs could be forced to abstain 

from buying non-goal eligible mortgages 

that would count in the denominator, 

but that would not benefit its 

calculation of goals performance in the 

numerator. In Fannie Mae’s view, its 

own abstention from buying implies an 

illiquid market. 

Other commenters affirmed Fannie 

Mae’s comments and expressed concern 

that, given the market leadership of the 

GSEs, the manner in which home 

purchases are counted toward the 

Subgoals could distort the lending 

market. 

In addition, both Fannie Mae and 

Freddie Mac asserted that FHEFSSA 

requires that HUD consider each of the 

six statutory factors set forth in sections 

1332(b) and 1334(b) of the statute in 

setting the levels of any Subgoals within 

those Housing Goals. Freddie Mac 

objected to the home purchase Subgoals 

because it claimed these Subgoals 

would constitute micromanagement of 

the GSEs’ business decisions. Freddie 

Mac also noted that, in the past, HUD 

has declined to implement subgoals for 

that very reason. 

Several commenters expressed the 

view that HUD had overestimated 

available purchase money mortgages 

and noted that if Subgoals on these 

types of mortgages are set too high, 

adverse market distortions will occur.

Other commenters contended that, 

regardless of the level of the Subgoals, 

a subgoal that targets home purchase 

mortgages unfairly allocates credit 

toward home buying rather than 

mortgage refinances. These commenters 

asserted that this credit allocation is 

unfair in that it penalizes borrowers 

who want to lower mortgage costs or 

improve their homes. They also 

contended that credit allocation that 

promotes purchase mortgages could 

push refinance borrowers into high-cost 

loans rather than conforming, GSEeligible mortgages. To combat such 

effects, one organization suggested 

separate subgoals for both purchase 

money mortgages and refinances, with 

the overall low- and moderate-income 

goal as the weighted average of the 

different subgoals. 

Commenters also objected to mortgage 

purchase subgoals targeting only those 

loans originated in metropolitan areas 

because this geographic limitation 

allocates credit at the expense of 

residents of rural communities. The 

commenters stated that Congress 

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charged the GSEs in their charters to 

‘‘promote access to mortgage credit 

throughout the Nation (including 

central cities, rural areas, and 

underserved areas).’’ One commenter 

stated that the lack of detailed HMDA 

data in rural areas makes market size 

estimates difficult, but suggested that 

other data from private vendors could 

provide acceptable measures (without 

offering any specific sources). 

HUD’s Determination. Home purchase 

is a high national priority. The 

comments received and research 

reviewed document many studies 

revealing the desire of Americans to 

own their own home. HUD finds that 

the proposed home purchase subgoal 

furthers the statutory objectives of 

FHEFSSA. HUD set the level of the 

home purchase subgoal prudently. 

Details of HUD’s methodology are found 

in Appendices A and D of this final rule 

and in chapter 3 of the Economic 

Analysis that accompanies the rule. 

Rather than distorting the market, the 

home purchase subgoal facilitates the 

desire of many Americans to use the 

market to acquire their own home. 

Several commenters asked HUD to 

extend the counting for the home 

purchase subgoal to rural areas even 

though data for rural areas is sparse. 

HUD disagrees. Although HMDA data 

for rural areas has improved, it is still 

too incomplete to support extending the 

counting system. Alternative sources 

from private lenders are similarly 

flawed. While HMDA’s reporting of 

non-metropolitan areas has improved 

over the years, it continues to be 

unreliable. In 2001, 3,757 (3,280 of 

which were small banks) of the 4,394 

non-metropolitan-area banks did not 

report under HMDA. In that same year, 

324 (246 of which were small thrifts) of 

the 458 non-metropolitan-area thrift 

institutions did not report under 

HMDA. 

Except for Fannie Mae’s recent 

performance in the Special Affordable 

and Low- and Moderate-Income 

categories, the GSEs have lagged the 

market in purchasing single-family, 

owner-occupied loans that qualify for 

the Housing Goals. In 2003, Fannie Mae 

continued to lag the market in financing 

properties located in underserved areas 

while Freddie Mac lagged the market in 

all three goals-qualifying categories. The 

Department’s analysis reveals that there 

is ample room for both Fannie Mae and 

Freddie Mac to improve their 

performance in purchasing home loans 

that qualify for the Housing Goals, 

particularly in important market 

segments such as the minority, first-time 

homebuyer market. 

Both GSEs’ funding of mortgages for 

first-time homebuyers lags the market’s 

provision of funding for these families, 

and the lag is particularly large for firsttime minority homebuyers. Table 2 

compares the GSEs’ funding of 

mortgages for first-time homebuyers 

with market loan originations for firsttime homebuyers. This table shows that 

first-time homebuyers represented 37.6 

percent of market loan originations, 

compared with 26.5 percent of the 

GSEs’ purchases; thus, the GSEs fell 

substantially short of the market 

originations ratio for first-time 

homebuyers, over the period 1999–

2001. 

BILLING CODE 4210–27–P

63604 Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 

BILLING CODE 4210–27–C


ER02NO04.002</GPH>
Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 63605

For minority first-time homebuyers, 

the GSE ratio was 6.2 percent, compared 

to a market originations ratio of 10.6 

percent. For African-American and 

Hispanic first-time homebuyers, the 

GSE ratio was 3.8 percent, compared to 

a market originations ratio of 6.9 

percent. For first-time homebuyers, 

particularly first-time minority 

homebuyers, both GSEs substantially lag 

the private conventional conforming 

market. 

As detailed in Appendix A to this 

rule, evidence suggests that there is a 

significant population of potential 

homebuyers who are likely to respond 

well to increased homeownership 

opportunities produced by increased 

GSE purchases in this area. Immigrants 

and minorities, in particular, are 

expected to be a major source of future 

homebuyers. Furthermore, studies 

indicate the existence of a large 

untapped pool of potential homeowners 

among the rental population. Indeed, 

the GSEs’ recent experience with new 

outreach and affordable housing 

initiatives confirms the existence of this 

potential. 

The Department therefore is 

establishing through this rule Subgoals 

for home purchase loans that qualify for 

the three Housing Goals to encourage 

the GSEs to take a leadership position 

in creating homeownership financing 

opportunities within the categories that 

Congress expressly targeted with the 

Housing Goals. 

b. HUD’s Determinations Regarding the 

Home Purchase Subgoals 

Under FHEFSSA, HUD is authorized 

to establish nonenforceable Subgoals 

within the Low- and Moderate-Income 

Housing Goal and the Underserved 

Areas Housing Goal. HUD also is 

authorized under FHEFSSA to establish 

enforceable Subgoals within the Special 

Affordable Housing Goal. The 

Administration has proposed, as part of 

GSE regulatory reform, that Congress 

authorize HUD to establish a separate 

Home Purchase Goal that would include 

enforceable components. Pending the 

enactment of any such legislation, HUD 

is establishing the Home Purchase 

Subgoals described in this final rule 

under its current statutory authority. 

HUD stated in the preamble to the 

proposed rule that in setting a subgoal, 

‘‘[c]urrent law does not require that 

HUD consider the statutory factors set 

forth in FHEFSSA prior to establishing 

or setting the level of Subgoals.’’ (69 FR 

24244.) HUD’s interpretation of this 

portion of FHEFSSA is unchanged. Each 

of the subsections identifying the factors 

for consideration indicates that the 

factors are to be considered in setting 

each respective goal; no mention is 

made of the subgoals. However, despite 

the absence of any statutory requirement 

to consider the listed factors in setting 

the levels of the subgoals, HUD has 

nevertheless carefully considered each 

of these factors in setting the subgoal 

levels in this final rule. 

The following sections provide an 

overview of HUD’s reasons for 

establishing the Subgoals, which are 

detailed in the Appendices to this rule. 

(i) The GSEs Have the Ability To Lead 

the Market 

The GSEs have the ability to lead the 

primary market for mortgages on singlefamily owner-occupied properties, 

which are the GSEs’ principal line of 

business. Both GSEs have long 

experience in the home purchase 

mortgage market, and therefore there is 

no issue of the degree to which they 

have penetrated this market. In 

addition, because the Subgoals focus on 

homeownership opportunities and, 

thus, do not include refinance loans, 

there is no issue regarding potentially 

large year-to-year changes in refinance 

mortgage volumes, which affect the 

magnitude of the denominator in 

calculating performance percentages 

under the Housing Goals, as 

experienced in the heavy refinance 

years of 1998 and 2001–2003. 

Both GSEs have not only been 

operating in the single-family owner 

mortgage market for years, they have 

been the dominant players in that 

market, funding 57 percent of mortgages 

on single-family owner-occupied 

residences financed between 1999 and 

2002. As discussed in Section G of 

Appendix A to this rule, their 

underwriting guidelines are industry 

standards and their AUS are widely 

used in the mortgage industry. 

(ii) The GSEs’ Performance Relative to 

the Market 

Even though the GSEs have had the 

ability to lead the home purchase 

market, their past average performance 

(1993–2003, 1996–2003, and 1999–

2003) has been below market levels. 

During 2002 and 2003, Fannie Mae 

improved its performance enough to 

lead the special affordable and low-mod 

markets for home purchase loans, but 

Fannie Mae continued to lag the 

primary market in funding homes in 

underserved areas. The subgoals will 

ensure that Fannie Mae maintains and 

further improves its above-market 

performance in the special affordable 

and low-mod markets, and also becomes 

a market leader in funding underserved 

areas. Freddie Mac, although it has also 

improved its recent performance, 

continues to lag behind the primary 

market on all housing goal categories. 

The subgoals will ensure that Freddie 

Mac erases its gaps with the market and 

takes a leadership position as well. The 

type of improvement needed for Freddie 

Mac to meet these new subgoals was 

demonstrated by Fannie Mae during 

2001–2003. For example, Fannie Mae 

increased its low-mod purchases from 

40.8 percent of its single-family-owner 

business in 2000 to 45.3 percent in 2002 

to 47.0 percent in 2003. 

(iii) Disparities in Homeownership and 

Credit Access Remain 

HUD notes that there remain 

troublesome disparities in our housing 

and mortgage markets, even after the 

‘‘revolution in affordable lending’’ and 

the growth in homeownership that has 

taken place since the mid-1990s. As 

noted previously in the discussion of 

the goals, the homeownership rate for 

African-American and Hispanic 

households remains 25 percentage 

points below that of white households. 

In 2002, the mortgage denial rate for 

African-American borrowers was over 

twice that for white borrowers, even 

after controlling for the income of the 

borrower. 

HUD also notes that there is growing 

evidence that inner city neighborhoods 

are not always being adequately served 

by mainstream lenders. Some have 

concluded that a dual mortgage market 

has developed in our nation, with 

conventional mainstream lenders 

serving mainly white families living in 

the suburbs and FHA and subprime 

lenders serving minority families 

concentrated in inner city 

neighborhoods. In addition to the 

unavailability of mainstream lenders, 

families living in high-minority 

neighborhoods generally face many 

additional hurdles, such as lack of cash 

for a downpayment, credit problems, 

and discrimination. 

Immigrants and minorities are 

projected to account for almost twothirds of the growth in the number of 

new households over the next ten years. 

As emphasized throughout this 

preamble and the Appendices to this 

rule, changing population demographics 

will result in a need for the primary and 

secondary mortgage markets to meet 

nontraditional credit needs, respond to 

diverse housing preferences and 

overcome information and other barriers 

that many immigrants and minorities 

face. HUD finds that the GSEs must 

increase their efforts towards providing 

financing for these families. 

63606 Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 

(iv) There Are Ample Opportunities for 

the GSEs To Improve Their Performance 

in the Home Purchase Market 

Home purchase loans that qualify for 

the Housing Goals are available for the 

GSEs to purchase, which means they 

can improve their performance and lead 

the primary market in purchasing loans 

for lower-income borrowers and 

properties in underserved areas. Three 

indicators of this have already been 

discussed.

First, the affordable lending market 

has shown an underlying strength over 

the past few years that is unlikely to 

vanish (without a significant increase in 

interest rates or a decline in the 

economy). Since 1999, the shares of the 

home purchase market accounted for by 

the three Housing Goal categories are as 

follows: 16.3 percent for special 

affordable, 31.4 percent for underserved 

areas, and 44.1 percent for low- and 

moderate-income. 

Second, market share data reported in 

section G of Appendix A to this rule 

show that almost half of newlyoriginated loans that qualify for the 

Housing Goals are not purchased by the 

GSEs. As noted above, the situation is 

even more extreme for special submarkets, such as the minority first-time 

homebuyer market where the GSEs have 

only a minimal presence. In terms of the 

overall mortgage market (both 

conventional and government), the 

GSEs funded only 24 percent of all firsttime homebuyers and 17 percent of 

minority first-time homebuyers between 

1999 and 2001. Similarly, during the 

same period, the GSEs funded only 40 

percent of first-time homebuyers in the 

conventional conforming market, and 

only 33 percent of minority first-time 

homebuyers in that market. 

Finally, the GSEs’ purchases that can 

count toward the Subgoal are not 

limited to new mortgages that are 

originated in the current calendar year. 

The GSEs can purchase loans from the 

substantial, existing stock of affordable 

loans held in lenders’ portfolios, after 

these loans have seasoned and the GSEs 

have had the opportunity to observe 

their payment performance. In fact, 

based on Fannie Mae’s recent 

experience, the purchase of seasoned 

loans is at present one strategy 

employed for purchasing Housing 

Goals-qualifying loans and meeting the 

goals. 

The current low homeownership rate 

of minorities and others living in inner 

cities suggests that there will be 

considerable growth in the origination 

of CRA loans in urban areas. For banks 

and thrifts, selling their CRA 

originations will free up capital to make 

new CRA loans. As a result, the CRA 

market segment provides an opportunity 

for the GSEs to expand their affordable 

lending programs. As explained in 

Appendix A to this rule, Fannie Mae 

and Freddie Mac have already started 

developing programs to purchase CRAtype loans on a flow basis as well as 

after they have seasoned. 

While the GSEs can choose any 

strategy for leading the market, this 

leadership role can likely be 

accomplished by building on the many 

initiatives and programs that the 

enterprises have already started, 

including: (1) Their outreach to 

underserved markets and their 

partnership efforts that encourage 

mainstream lenders to move into these 

markets; (2) their incorporation of 

greater flexibility into their purchase 

and underwriting guidelines, (3) their 

development of new products for 

borrowers with little cash for a 

downpayment and for borrowers with 

credit blemishes or non-traditional 

credit histories; (4) their targeting of 

important markets where they have had 

only a limited presence in the past, such 

as the markets for minority first-time 

homebuyers; (5) their purchases of both 

newly-originated and seasoned CRA 

loans; and (6) their use of automated 

underwriting technology to qualify 

creditworthy borrowers that would have 

been deemed not creditworthy under 

traditional underwriting rules. 

The experience of Fannie Mae and 

Freddie Mac in the subprime market 

indicates that they have the expertise 

and experience to develop technologies 

and new products that allow them to 

enter new markets in a prudent manner. 

Given the innovativeness of Fannie Mae 

and Freddie Mac, other strategies will 

be available as well. In fact, a wide 

variety of quantitative and qualitative 

indicators suggest that the GSEs have 

the expertise, resources and financial 

strength to improve their affordable 

lending performance enough to lead the 

home purchase market for special 

affordable, low- and moderate-income, 

and underserved areas loans. The recent 

improvement in the affordable lending 

performance of the GSEs, and 

particularly Fannie Mae, further 

demonstrates the GSEs’ capacity to lead 

the home purchase market. 

c. Structure and Levels of the Home 

Purchase Subgoals 

Under this rule, performance on the 

Home Purchase Subgoals will be 

calculated as Housing Goal-qualifying 

percentages of the GSEs’ total purchases 

of mortgages that finance purchases of 

single-family, owner-occupied 

properties located in metropolitan areas, 

based on the owner’s income and the 

location of the property. Specifically, for 

each GSE the following Subgoals would 

apply. (A ‘‘home purchase mortgage’’ is 

defined as a residential mortgage for the 

purchase of an owner-occupied singlefamily property.) 

• 45 percent of home purchase 

mortgages purchased by the GSE in 

metropolitan areas must qualify under 

the Low- and Moderate-Income Housing 

Goal in 2005, with this share rising to 

46 percent in 2006 and 47 percent in 

both 2007 and 2008; 

• 32 percent of home purchase 

mortgages purchased by the GSE in 

metropolitan areas must qualify under 

the Underserved Areas Housing Goal in 

2005, with this share rising to 33 

percent in both 2006 and 2007 and 34 

percent in 2008; and 

• 17 percent of home purchase 

mortgages purchased by the GSE in 

metropolitan areas must qualify under 

the Special Affordable Housing Goal in 

both 2005 and 2006, with this share 

rising to 18 percent in both 2007 and 

2008. 

Calculation of performance under the 

Home Purchase Subgoals will be in 

terms of numbers of mortgages, not 

numbers of units. This is consistent 

with the basis of reporting in HMDA 

data, which were HUD’s point of 

reference in establishing the Home 

Purchase Subgoal levels. HMDA data 

are reported in terms of numbers of 

mortgages in metropolitan areas.

These Home Purchase Subgoals are 

shown in Table 3, along with 

information on what the GSEs’

performance on the Subgoals would 

have been if they had been in effect for 

1999–2003 (under the proposed 

counting rules for 2005–2008). Table 3 

also presents HUD’s estimates of the 

average shares of mortgages on owneroccupied single-family properties in 

metropolitan areas that were originated 

in 1999–2003 that would have qualified 

for these Home Purchase Subgoals. 

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14The Freddie Mac 2002 figures in Table 4 differ 

from the corresponding figures in Table 3 in HUD’s 

Proposed Rule. Subsequent to publication of the 

Proposed Rule, HUD discovered that HUD had 

credited some units toward Freddie Mac’s Low- and 

Moderate-Income Housing Goal in 2002 that had 

been previously counted toward the goal in 2001. 

The units were associated with a large year-end 

Freddie Mac mortgage purchase transaction in 

2002. Because HUD’s regulations prohibit double 

counting, HUD has recalculated Freddie Mac’s 2002 

Low- and Moderate-Income Housing Goal 

performance. The recalculation also reflects 

correction of some coding errors discovered in 

HUD’s recent review.

d. Counting Mortgages Toward the 

Home Purchase Subgoals 

The Department is amending 24 CFR 

81.15 to add a new paragraph (i) that 

would clarify that the procedures in 

§ 81.15 generally govern the counting of 

home purchase mortgages toward the 

Home Purchase Subgoals in §§ 81.12, 

81.13 and 81.14. The new paragraph 

provides, however, that the numerator 

and denominator for purposes of 

counting performance under the 

Subgoals are comprised of numbers of 

home purchase mortgages in 

metropolitan areas, rather than numbers 

of dwelling units. Paragraph (i) also 

provides that, for purposes of 

addressing missing data or information 

for each Subgoal, the procedures in 

§ 81.15(d) shall be implemented using 

numbers of home purchase mortgages in 

metropolitan areas and not singlefamily, owner-occupied dwelling units. 

Finally, the new paragraph provides 

that where a single home purchase 

mortgage finances the purchase of two 

or more owner-occupied units, the 

mortgage shall count once toward each 

Subgoal that applies to the GSE’s 

mortgage purchase. 

5. Low- and Moderate-Income Housing 

Goal, § 81.12 

This section discusses the 

Department’s consideration of the 

statutory factors in arriving at, and the 

comments received on, the new housing 

goal level for the Low- and ModerateIncome Housing Goal, which targets 

mortgages on housing for families with 

incomes at or below the area median 

income. After consideration of these 

factors, this final rule establishes the 

goal for the percentage of dwelling units 

to be financed by each GSE’s mortgage 

purchases at 52 percent for 2005, 53 

percent for 2006, 55 percent for 2007, 

and 56 percent for 2008. 

Additional information analyzing 

each of the statutory factors is provided 

in Appendix A, ‘‘Departmental 

Considerations to Establish the Lowand Moderate-Income Housing Goal,’’

and Appendix D, ‘‘Estimating the Size 

of the Conventional Conforming Market 

for each Housing Goal.’’

a. Market Estimate for the Low- and 

Moderate-Income Housing Goal 

The Department estimates that 

dwelling units serving low- and 

moderate-income families will account 

for 51–56 percent of total units financed 

in the overall conventional conforming 

mortgage market during the period 2005 

through 2008. HUD has developed this 

range, rather than a specific point 

estimate, to account for the projected 

effects of different economic and 

affordability conditions that can 

reasonably be anticipated. HUD 

estimates that the low-and-moderateincome share of the market averaged 57 

percent between 1999 and 2002. 

b. Past Performance of the GSEs Under 

the Low- and Moderate-Income Housing 

Goal 

A number of changes in goal-counting 

procedures were adopted as part of 

HUD’s Housing Goals final rule 

published on October 31, 2000 (65 FR 

65044) (Housing Goals 2000 final rule). 

Thus, it is necessary to provide 

information using several different 

measures in order to track performance 

on the Low- and Moderate-Income 

Housing Goal over the 1996–2003 

period. Table 4 shows performance 

under these measures.14

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Specifically, the following changes 

were made in counting procedures for 

measuring performance on the Low- and 

Moderate-Income Housing Goal for 

2001–2003. HUD: 

(1) Established ‘‘bonus points’’

(awarding double credit) for purchases 

of low- and moderate-income mortgages 

on small (5–50 unit) multifamily 

properties and, above a threshold level, 

mortgages on two-to-four unit owneroccupied properties; 

(2) Established a ‘‘temporary 

adjustment factor’’ (1.35 units credit, as 

revised by Congress for 2001–2003 from 

HUD’s 1.2 unit credits in the Housing 

Goals 2000 final rule) that applied to 

Freddie Mac’s purchases (but not 

Fannie Mae’s purchases) of low- and 

moderate-income mortgages on large 

(more than 50-unit) multifamily 

properties; and 

(3) Revised procedures that HUD had 

instituted regarding the treatment of 

missing data on unit affordability, the 

use of imputed or proxy rents for 

determining goal credit for multifamily 

mortgages, and the eligibility for goals 

credit for certain qualifying governmentbacked loans. 

Based on the counting rules in effect 

at that time for 1996–2000, as shown 

under ‘‘official performance’’ for 1996–

2000 in Table 4, Low- and ModerateIncome Housing Goal performance for 

Fannie Mae was consistently in the 44–

46 percent range over the 1996–1999 

period, before jumping to a peak of 49.5 

percent in 2000. Freddie Mac’s 

performance started at a lower level, but 

then increased in several steps, from 

41–43 percent in 1996–1998 to 46.1 

percent in 1999, and a record level of 

49.9 percent in 2000. That was the only 

year prior to 2001 in which Freddie 

Mac’s performance exceeded Fannie 

Mae’s performance on this goal. 

Based on the then current counting 

rules, including the bonus points and 

TAF, as shown under ‘‘official 

performance’’ in Table 4, Low- and 

Moderate-Income Housing Goal 

performance was 51.5 percent for 

Fannie Mae in 2001, 51.8 percent in 

2002, and 52.3 percent in 2003. For 

Freddie Mac, performance was 53.2 

percent in 2001, 50.5 percent in 2002, 

and 51.2 percent in 2003. 

Immediately beneath the official Lowand Moderate-Income Housing Goal 

performance percentages in Table 4 are 

figures showing the GSEs’ low- and 

moderate-income purchase percentages 

on a consistent basis for the entire 

1996–2003 period. The assumptions 

used were the counting rules 

established in HUD’s Housing Goals 

2000 final rule except that bonus points 

and the Freddie Mac TAF (which were 

terminated at the end of 2003) are not 

applied. These figures are termed the 

‘‘2001–2003 baseline assumptions.’’ For 

1996–2000 these figures differ from the 

official performance figures because 

they incorporate the revised counting 

procedures described under point (c), 

above, which were not reflected in the 

official performance figures at that time. 

For 2001–2003 both sets of figures 

incorporate the revised counting 

procedures, but the baseline does not 

incorporate the bonus points and the 

Freddie Mac TAF. 

In terms of the 2001–2003 baseline 

measure, both Fannie Mae’s and Freddie 

Mac’s low- and moderate-income 

performance reached its maximum in 

2000 (Fannie Mae at 51.3 percent and 

Freddie Mac at 50.6 percent). Baseline 

performance fell somewhat for both 

GSEs in 2001, 2002, and 2003. Fannie 

Mae’s baseline performance last year 

exceeded the level attained in 1999, but 

Freddie Mac’s performance fell to the 

lowest level since 1998. 

Overall, both GSEs’ performance 

exceeded HUD’s Low- and ModerateIncome Housing Goals by significant 

margins in 1996–1999, and by wide 

margins in 2000. New, higher goals were 

established for 2001–2003, and despite 

somewhat lower performance than the 

level attained in 2000, both GSEs’

official performance exceeded the new 

goal levels in each year 2001–2003, with 

the inclusion of the bonus points and 

the TAF. 

The decline in baseline performance 

in 2001–2003 can be attributed in large 

measure to the mortgage refinance wave 

that occurred in those years. Fannie 

Mae’s overall volume of mortgage 

purchases (in terms of numbers of 

housing units) rose from 2.2 million in 

2000 to 4.7 million in 2001, 6.4 million 

in 2002, and then to 10.1 million in 

2003. Similarly, Freddie Mac’s volume 

rose from 1.6 million in 2000 to 3.3 

million in 2001, 4.3 million in 2002, 

and then to 5.8 million in 2003. For 

each GSE the increase in volume each 

year can be largely attributed to 

increases in purchase volumes for 

refinance mortgages relative to home 

purchase mortgages. For each GSE, the 

fraction of mortgages that qualified as 

Low- and Moderate-Income was less for 

refinance mortgages than for home 

purchase mortgages. 

For 2005–2008, HUD is expanding the 

affordability estimation of units with 

missing affordability information. In 

addition to multifamily units, the GSEs 

will also be able to use estimates of 

affordability for single-family rental 

units with missing rents and owneroccupied units with missing borrower 

incomes for determining goal credit. 

HUD is also increasing the amount of 

the maximum allowed for affordability 

estimation for multifamily units. 

Beneath the 2001–2003 baseline 

figures in Table 4 is another row of 

figures designated ‘‘With 2005 

Assumptions.’’ These figures show the 

effects of applying 2000 Census data 

and the new specification of MSAs 

released by OMB in 2003 to the 

measurement of Low- and ModerateIncome purchase percentages with the 

same counting rules that were used for 

the 2001–2003 baseline in Table 4. The 

effect is to reduce the Goal-qualifying 

percentage by an average of 0.6 

percentage points for Fannie Mae and 

0.7 percentage points for Freddie Mac, 

over the 1999–2002 period. 

However, for 2003, the effects are just 

the opposite—these assumptions 

increased Fannie Mae’s performance by 

0.8 percentage point (from 48.7 percent 

to 49.5 percent) and Freddie Mac’s 

performance by 0.3 percentage point 

(from 45.0 percent to 45.3 percent). The 

difference in the direction of this impact 

between 1999–2002 and 2003 may be 

due to the need to apply estimation 

techniques in 1999–2002 but not in 

2003. For 1999–2002 HUD had to 

estimate the effect based on data 

geocoded according to 1990 census tract 

definitions, while for 2003 the data were 

geocoded to 2000 census tracts. Further 

insight will be provided by analysis of 

data for 2004 and further years. 

c. Low- and Moderate-Income Home 

Purchase Subgoal 

The Department has determined to 

establish a Subgoal of 45 percent for 

each GSE’s purchases of home purchase 

mortgages on single-family owneroccupied properties in metropolitan 

areas which are for low- and moderateincome families in 2005, with this 

Subgoal rising to 46 percent in 2006 and 

47 percent in both 2007 and 2008. 

The purpose of this Subgoal is to 

encourage the GSEs to increase their 

acquisitions of home purchase loans for 

low- and moderate-income families, 

many of whom are expected to enter the 

homeownership market over the next 

few years. Table 5 provides basic 

information on both the GSEs’ low-mod 

performance and the primary market’s 

low-mod performance for the years 1999 

to 2003. Since the same format will be 

followed for the other housing subgoals, 

several points are made about the 

information in the Table 5, prior to 

discussing the low-mod subgoal. 

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Average Performance Data. In 

addition to individual year data, various 

averages of annual performance are 

provided at the bottom of Table 5 

(1999–2003, 2001–2003, and 2002–

2003); these averages provide a useful 

context for examining the feasibility of 

the subgoals and the degree to which 

they call for performance that is above 

past market levels. This table provides 

a picture of how much the low-mod 

subgoal targets move the GSEs above 

past market levels and how much of a 

stretch each subgoal will be for each 

GSE (as compared with that GSE’s past 

performance). As will become clear 

below, Fannie Mae and Freddie Mac 

have shown different past performances, 

which means that the subgoal targets 

will appear to have different impacts on 

these two institutions. 

Definitions of Primary Market. HUD’s 

basic market definition is the 

conventional conforming market 

without B&C loans; in other words, the 

A-minus loans in the subprime market 

are included in the market definition 

but the more risky B&C portion is not 

included (see Appendix D of the final 

rule for further discussion of this). In its 

report for Freddie Mac, ICF indicated 

that small loans (those less than 

$15,000) should be excluded from any 

analysis that dealt with loans that might 

be available for purchase by the GSEs. 

Therefore, data are provided in Table 5 

for (a) the market without B&C loans 

and (b) the market without both B&C 

and small loans less than $15,000. As 

shown in Table 5, dropping small loans 

reduces the low-mod share of the 

conventional conforming market by 

about one-half percentage point. 

Projected 2000-Based Data. Table 5 is 

based on projected data that 

incorporates both 2000 Census 

geography and the new OMB 

definitions. Thus, the goals-qualifying 

percentages in this table differ from 

those reported earlier in this Preamble, 

the latter being historical, 1990-Censusbased percentages. HUD had to 

reapportion the data for the years prior 

to 2003. For 2003, both HMDA and GSE 

data were defined in terms of 2000 

Census geography, so no 

reapportionment was necessary; for this 

reason, the 2003 data are probably the 

most accurate. With these basics, the 

results for the low-mod subgoal can now 

be briefly summarized as follows: 

Low-Mod Subgoals Compared With 

Market. The 45-percent subgoal for the 

first year (2005) is approximately two 

percentage points above 1999–2003 and 

2001–2003 average market performance, 

one percentage point above 2002–2003 

average market performance, and 0.6 

percent (market without B&C loans) to 

0.2 percent (market without both B&C 

and small loans) below peak market 

performance. The 46-percent subgoal for 

2006 would add one percentage point to 

these comparisons, while the 47-percent 

subgoal for 2007 and 2008 would add 

two percentage points. For example, the 

47-percent subgoal is approximately 

three percentage points above 2002–

2003 average market performance, and 

1.4 percent (market without B&C loans) 

to 1.8 percent (market without both B&C 

and small loans) above peak market 

performance.

Low-Mod Subgoals Compared With Past 

Freddie Mac Performance. To reach the 45-

percent 2005 subgoal, Freddie Mac would 

have to improve its performance by 3.0 

percentage points over its 2001–2003 average 

low-mod performance of 42.0 percent, by 1.8 

percentage points over its 2002–2003 average 

low-mod performance of 43.2 percent, and by 

0.8 percent over its previous peak 

performance of 44.2 percent in 2003. To 

reach the 47-percent subgoal, Freddie Mac 

would have to improve its performance by 

3.8 percentage points over its 2002–2003 

average low-mod performance, and by 2.8 

percent over its previous peak performance. 

Low-Mod Subgoals Compared With Past 

Fannie Mae Performance. To reach the 45-

percent 2005 subgoal, Fannie Mae would 

have to improve its performance by 0.7 

percentage points over its 2001–2003 average 

low-mod performance of 44.3 percent; Fannie 

Mae would meet the 45-percent subgoal 

based on its 2002–2003 average low-mod 

performance of 45.6 percent and its previous 

peak low-mod performance of 47.5 percent in 

2003. To reach the 47-percent subgoal, 

Fannie Mae would have to improve its 

performance by 2.7 percent over its 2001–

2003 average performance and by 1.4 

percentage points over its 2002–2003 average 

performance; Fannie Mae would meet the 47-

percent subgoal based on its previous peak 

performance of 47.5 percent in 2003.

The low-mod subgoal targets will be 

more challenging for Freddie Mac than 

Fannie Mae. The type of improvement 

needed to meet the new low-mod 

subgoal targets was demonstrated by 

Fannie Mae during 2001–2003, as 

Fannie Mae increased its low-mod 

purchases from 40.1 percent of its 

single-family-owner business in 2000 to 

43.6 percent in 2002 to 47.5 percent in 

2003, as shown in Table 5. The 

approach taken is for the GSEs to obtain 

their leadership position by staged 

increases in the subgoals; this will 

enable the GSEs to take new initiatives 

in a correspondingly staged manner to 

achieve the new subgoals each year. 

Thus, the increases in the housing 

subgoals are sequenced so that the GSEs 

can gain experience as they improve 

and move toward the new higher 

subgoal targets. 

Section 4.b. above of this preamble, 

and Section I.3 of Appendix A to this 

rule, discuss the reasons why the 

Department is establishing the Subgoal 

for low- and moderate-income loans, as 

follows: (1) The GSEs have the resources 

and the ability to lead the market in 

providing mortgage funding for lowand moderate-income families; (2) 

except for Fannie Mae’s recent 

performance, the GSEs have historically 

(over periods such as 1993–2003, 1996–

2003, and 1999–2003) not led the 

market, even though they have had the 

ability to do so; (3) troublesome 

disparities in our housing and mortgage 

markets indicate a continuing need for 

increased GSE activity; and (4) there are 

ample opportunities for the GSEs to 

improve their low- and moderateincome performance in the home 

purchase market.

Although single-family owneroccupied mortgages comprise their 

principal line of business, Freddie Mac 

has always lagged behind the primary 

market in financing mortgages for lowand moderate-income families. Over the 

past three years Fannie Mae has closed 

its historical gap with the market and 

now leads the primary market in 

funding mortgages for low- and 

moderate-income families. Because 

home purchase loans account for a 

major share of the GSEs’ purchases, the 

establishment of this Subgoal will aid 

their performance under the overall 

Low- and Moderate-Income Housing 

Goal. 

For the foregoing reasons, the 

Department believes that the GSEs, and 

particularly Freddie Mac, can do more 

to raise the share of their home loan 

purchases serving low- and moderateincome families. This can be 

accomplished by building on efforts that 

the enterprises have already started, 

including their new affordable lending 

products, their many partnership efforts, 

their outreach to inner city 

neighborhoods, their incorporation of 

greater flexibility into their 

underwriting guidelines, and their 

purchases of seasoned CRA loans. A 

wide variety of quantitative and 

qualitative indicators indicate that the 

GSEs have the resources and financial 

strength to improve their affordable 

lending performance enough to lead the 

market serving low- and moderateincome families. 

d. Summary of Comments 

The majority of comments that 

addressed the housing goals focused on 

the highest goal in year 2008 for the 

Low- and Moderate-Income Housing 

Goal. While some commenters, such as 

affordable housing policy advocacy 

groups and housing and consumer 

coalitions, expressed support for more 

Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 63613

aggressive goals, stating that the goals 

should be set to challenge the GSEs to 

do more, most commenters expressed 

concerns about possible adverse affects 

on middle-income borrowers, including 

the potential for higher costs and for 

unrealistic goals to lead to credit 

allocation to the lower end of the 

housing market, thereby hindering the 

GSEs’ ability to serve all homebuyers. 

Other concerns included issues related 

to HUD’s market share methodology 

analysis and the effects of single-family 

refinance loans in high refinance years 

on the GSEs’ ability to meet the higher 

goals. Many commenters recommended 

that HUD exempt refinances from the 

goals performance calculation. As 

described earlier in this rule, HUD is 

seeking public comments on how to 

address the effects of refinance loans 

when this annual volume is high. In 

addition, some expressed the belief that 

overly aggressive goals could weaken 

the FHA insurance program and could 

encourage over-investment in rental 

housing at a time when multifamily 

vacancy rates are high. HUD has 

addressed these concerns in earlier 

sections of this final rule preamble. 

Others felt that higher goal levels will 

encourage more investor-owned rental 

units that harm communities. Both 

Fannie Mae and Freddie Mac objected 

to the higher goal level for the Low- and 

Moderate-Income Goal. Each disputed 

HUD’s market share analysis, citing the 

uncertainty of data, for example the size 

of the multifamily market, and the 

uncertainty about future economic 

conditions. Freddie Mac stated that 

HUD overestimated the low/mod market 

share by 4 percent. Both GSEs also 

stated that it was inappropriate to base 

the goals at the high end of market share 

ranges. Freddie Mac stated that this 

approach ignores the year-to-year 

variability of the market. Appendix D to 

this rule responds to these market issues 

raised by the GSEs. 

With regard to the Low- and 

Moderate-Income Home Purchase 

Subgoal, most commenters did not 

address the subgoal levels proposed by 

HUD, and none specifically addressed 

the proposal levels for the Low- and 

Moderate-Income Subgoal. For those 

that did mention the subgoals, the 

comments were mixed with about half 

supportive of the subgoal proposals in 

general and half believing the subgoal 

levels were too high. Both GSEs 

commented on HUD’s proposed 

subgoals. Fannie Mae stated that the 

levels were higher than any values 

observed in HMDA from 1999–2002, 

and that the concept was duplicative of 

the overall goal structure. Freddie Mac 

stated that HUD should withdraw the 

home purchase subgoals or HUD should 

re-estimate the market using reasonable 

assumptions and set both the goal and 

subgoal levels no higher than the 

midpoint of the resulting ranges. 

e. HUD’s Determination 

The Low- and Moderate-Income 

Housing Goal established in this final 

rule is reasonable and appropriate 

having considered the factors set forth 

in FHEFSSA. For 2001–2003, HUD set 

the level of the housing goal 

conservatively, relative to the 

Department’s market share estimates, in 

order to accommodate a variety of 

economic scenarios. Moreover, current 

examination of the gaps in the mortgage 

markets, along with the estimated size 

of the market available to the GSEs, 

demonstrate that the number of 

mortgages secured by housing for lowand moderate-income families is more 

than sufficient for the GSEs to achieve 

the new goal. 

Therefore, having considered all the 

statutory factors including housing 

needs, projected economic and 

demographic conditions for 2005 to 

2008, the GSEs’ past performance, the 

size of the market serving low- and 

moderate-income families, and the 

GSEs’ ability to lead the market while 

maintaining a sound financial 

condition, HUD has determined that the 

annual goal for mortgage purchases 

qualifying under the Low- and 

Moderate-Income Housing Goal will be 

52 percent for 2005, 53 percent in 2006, 

55 percent in 2007, and 56 percent in 

2008. This reflects a reduction in the 

upper end of the market share range 

from 57 percent to 56 percent since 

HUD’s publication of its proposed rule, 

resulting from changes in estimating 

market share as described at the end of 

section 3 (a), above, and in section F of 

Appendix D to this rule.

Further, the Department is 

establishing a Subgoal for each GSE’s 

purchases of home purchase mortgages 

on single-family owner-occupied 

properties in metropolitan areas which 

are for low- and moderate-income 

families of 45 percent in 2005, with this 

Subgoal rising to 46 percent in 2006, 

and 47 percent in both 2007 and 2008. 

The reasons for increasing the Low- and 

Moderate-Income Housing Goal are 

discussed in sections a and b, above, 

and the reasons for establishing a Home 

Purchase Subgoal at the stated levels are 

set forth in section c. 

While the GSEs have lagged the 

primary market in financing owner and 

rental housing for low- and moderateincome families, they appear to have 

ample room to improve their 

performance in that market. A wide 

variety of quantitative and qualitative 

indicators demonstrate that the GSEs 

have the expertise, resources and 

financial strength to improve their lowand moderate-income lending 

performance, including lending for lowand moderate-income home purchases, 

and achieve the levels of the goals being 

established. 

6. Central Cities, Rural Areas, and Other 

Underserved Areas Housing Goal, 

§ 81.13 

This section discusses the 

Department’s consideration of the 

statutory factors in arriving at, and the 

comments received on, the new housing 

goal levels for the Central Cities, Rural 

Areas, and Other Underserved Areas 

Goal, which focuses on areas currently 

underserved by the mortgage finance 

system. After consideration of the 

factors and the comments received, this 

final rule establishes the goal for the 

percentage of dwelling units to be 

financed by each GSE’s mortgage 

purchases at 37 percent in 2005, 38 

percent in 2006 and 2007, and 39 

percent in 2008. 

The 1995 final rule provided that 

mortgage purchases count toward the 

Underserved Areas Housing Goal if such 

purchases finance properties that are 

located in underserved census tracts. At 

24 CFR 81.2 of HUD’s current 

regulations, HUD defines ‘‘underserved 

areas’’ for metropolitan areas (in central 

cities and other underserved areas) as 

census tracts where either: (1) The tract 

median income is at or below 90 percent 

of the area median income (AMI); or (2) 

the minority population is at least 30 

percent and the tract median income is 

at or below 120 percent of AMI. The 

AMI ratio is calculated by dividing the 

tract median income by the MSA 

median income. The minority 

percentage of a tract’s population is 

calculated by dividing the tract’s 

minority population by its total 

population. For properties in nonmetropolitan (rural) areas, mortgage 

purchases have counted toward the 

Underserved Areas Housing Goal where 

such purchases finance properties that 

are located in underserved counties. As 

discussed above under the heading 

‘‘Definitions’’ in this final rule, HUD is 

changing this specification from the 

county level to the census tract level. 

Mortgages will count toward the 

Underserved Areas Housing Goal where 

such purchases finance properties that 

are located in census tracts were either 

(1) the median income in the tract does 

not exceed 95 percent of the greater of 

the median incomes for the nonmetropolitan portions of the state or the 

63614 Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 

15The Freddie Mac 2002 figures in Table 6 differ 

from the corresponding figures in Table 4 in HUD’s 

Proposed Rule. Subsequent to publication of the 

Proposed Rule, HUD discovered that HUD had 

credited some units toward Freddie Mac’s 

Underserved Areas Housing Goal in 2002 that had 

been previously counted toward the goal in 2001. 

The units were associated with a large year-end 

Freddie Mac mortgage purchase transaction in 

2002. Because HUD’s regulations prohibit double 

counting, HUD has recalculated Freddie Mac’s 2002 

Underserved Areas Housing Goal performance. The 

recalculation also reflects correction of some coding 

errors discovered in HUD’s recent review. With the 

recalculation, Freddie Mac fell slightly short of its 

2002 Underserved Areas Housing Goal.

non-metropolitan portions of the nation 

as a whole, or (2) minorities comprise at 

least 30 percent of the residents of the 

tract and the median income in the tract 

does not exceed 120 percent of the 

greater of the median incomes for the 

non-metropolitan portions of the state or 

the non-metropolitan portions of the 

nation as a whole. 

The level for the Underserved Areas 

Housing Goal is based on 2000 Census 

data on area median incomes and 

minority percentages for census tracts, 

MSAs, and the non-metropolitan 

portions of states and of the entire 

nation. HUD’s analysis, which is set 

forth below and described in greater 

detail in Appendix B to this rule, is 

based on 2000 census data. The effect of 

using 2000 census data rather than 1990 

data to determine whether areas are 

underserved increases the percentage of 

the GSEs’ mortgage purchases in 

underserved areas by an estimated 

average of 5 percentage points for 

Fannie Mae and 4 percentage points for 

Freddie Mac, based on the geographic 

locations of properties financed by the 

GSEs’ mortgage purchases in 1999 

through 2003. This change reflects 

geographical shifts in population 

concentrations by income and minority 

status from 1990 to 2000. 

After analyzing the statutory factors, 

HUD is: (a) establishing a Goal of 37 

percent for the percentage of the total 

number of dwelling units financed by 

each GSE’s mortgage purchases for 

properties located in underserved areas 

for 2005, 38 percent for 2006 and 2007, 

and 39 percent for 2008; (b) establishing 

census tracts as the spatial basis for 

establishing whether properties in nonmetropolitan (rural) areas count toward 

the Underserved Areas Housing Goal, in 

place of counties as in the definition 

stated above, for the reasons described 

below; and (c) also establishing a 

Subgoal of 32 percent of the total 

number of dwelling units financed by 

each GSE’s purchases of home purchase 

mortgages in metropolitan areas for 

properties located in underserved areas 

of metropolitan areas for 2005, rising to 

33 percent for 2006 and 2007, and 34 

percent for 2008. 

A short discussion of the statutory 

factors reviewed follows. Additional 

information analyzing each of the 

statutory factors is provided in 

Appendix B to this rule, ‘‘Departmental 

Considerations to Establish the 

Underserved Areas Housing Goal,’’ and 

Appendix D to this rule, ‘‘Estimating the 

Size of the Conventional Conforming 

Market for each Housing Goal.’’

a. Market Estimate for the Underserved 

Areas Housing Goal 

The Department estimates that 

dwelling units in underserved areas will 

account for 35–39 percent of total units 

financed in the overall conventional 

conforming mortgage market during the 

period 2005 through 2008. HUD has 

developed this range, rather than a 

specific point estimate, to accommodate 

the projected effects of different 

economic and affordability conditions 

that can reasonably be anticipated. HUD 

estimates that the underserved areas 

market averaged 39 percent between 

1999 and 2002. 

b. Past Performance of the GSEs Under 

the Underserved Areas Housing Goal 

As discussed above, a number of 

changes in goal-counting procedures 

were adopted as part of HUD’s Housing 

Goals 2000 final rule. Thus it is 

necessary to provide information using 

several different measures in order to 

track changes in the GSEs’ performance 

on the Underserved Areas Housing Goal 

over the 1996–2003 period. These are 

shown in Table 6.15 The same changes 

in counting rules described for the Lowand Moderate-Income Housing Goal are 

applicable to the Underserved Areas 

Housing Goal.

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Based on the counting rules in effect 

at that time, as shown under ‘‘official 

performance’’ for 1996–2000 in Table 6, 

Underserved Areas Housing Goal 

performance for Fannie Mae generally 

fluctuated between 27 and 29 percent 

over the 1996–1999 period, before rising 

to a peak of 31.0 percent in 2000. 

Freddie Mac’s performance started at a 

lower level, but then increased in 

several steps, from 25–26 percent in 

1996–1998, to 27.5 percent in 1999, and 

a record level of 29.2 percent in 2000. 

Freddie Mac’s performance in 1999 was 

the only year prior to 2001 in which it 

exceeded Fannie Mae’s performance on 

this Goal. 

Based on counting rules in effect for 

2001–2003, including the bonus points 

and the TAF, as shown under ‘‘official 

performance’’ in Table 6, Underserved 

Areas Housing Goal performance for 

Fannie Mae was 32.6 percent in 2001, 

32.8 percent in 2002, and 32.1 percent 

in 2003. Performance for Freddie Mac 

was 31.7 percent in 2001, slightly less 

than 31.0 percent in 2002, and 32.7 

percent in 2003. 

Immediately beneath the official 

Underserved Areas Housing Goal 

performance percentages in Table 6 are 

figures showing the GSEs’ purchase 

percentages under this Goal on a 

consistent basis for the entire 1996–

2003 period. The assumptions used 

were the counting rules established in 

HUD’s Housing Goals 2000 final rule, 

except that bonus points and the 

Freddie Mac TAF (which terminated at 

the end of 2003) are not applied. These 

figures are termed the ‘‘2001–2003 

baseline’’ assumptions. For 1996–2000 

these figures differ from the official 

performance figures because they 

incorporate the revised counting 

procedures, which were not reflected in 

the official performance figures at that 

time. For 2001–2003 both sets of figures 

incorporate the revised counting 

procedures, but the baseline does not 

incorporate the bonus points and 

Freddie Mac TAF. 

In terms of the 2001–2003 baseline 

measure, both Fannie Mae and Freddie 

Mac’s Underserved Areas Housing Goal 

performance reached its maximum in 

2000 (Fannie Mae at 31.0 percent and 

Freddie Mac at 29.2 percent) before 

declining somewhat over the 2001–2003 

period. Both GSEs’ baseline 

performance in 2001–2003 exceeded the 

level attained in 1999. 

Overall, both GSEs’ official 

performance exceeded their 

Underserved Areas Housing Goal by 

significant margins in 1996–1999, and 

by wide margins in 2000. New, higher 

Goals were established for 2001–2003, 

and despite somewhat lower 

performance than the level attained in 

2000 (largely due to the 2001–2003 

refinance wave), both GSEs’

performance exceeded the new Goal 

levels in 2001 and 2003; Fannie Mae 

also exceeded its goal in 2002, while 

Freddie Mac fell slightly short. 

Appendix B to this rule includes a 

comprehensive analysis of the GSEs’

performance in funding mortgages for 

single-family-owner properties in 

underserved areas. (The data reported 

there are based on 2000 Census 

geography, which produces underserved 

area figures slightly over five percentage 

points higher than 1990-based 

geography.) Both GSEs have lagged the 

market in funding properties located in 

underserved neighborhoods. Between 

1999 and 2003, 28.3 percent of Freddie 

Mac’s purchases of home loans financed 

properties in underserved 

neighborhoods, as did 30.0 percent of 

Fannie Mae’s purchases—compared 

with 31.4 percent of home purchase 

loans originated in the conventional 

conforming market (excluding B&C 

loans). Thus, Freddie Mac performed at 

90 percent of the market level, while 

Fannie Mae performed at 96 percent of 

the market level. In 2003, underserved 

areas accounted for 29.0 percent of 

Freddie Mac’s purchases, 32.0 percent 

of Fannie Mae’s purchases, and 32.5 

percent of market originations.

In evaluating the GSEs’ past 

performance, it should be noted that 

while borrowers in underserved 

metropolitan areas tend to have much 

lower incomes than borrowers in other 

areas, this does not mean that GSE 

mortgage purchases in underserved 

areas must necessarily be mortgages on 

housing for lower income families. 

Between 1999 and 2001, housing for 

above median-income households 

accounted for nearly 60 percent of the 

single-family owner-occupied mortgages 

that the GSEs purchased in underserved 

areas. 

Beneath the 2001–2003 baseline 

figures in Table 6 are two additional 

rows of figures designated ‘‘2005 

Assumptions.’’ These figures show the 

effects of applying 2000 census data and 

the new specification of MSAs released 

by OMB in 2003 to the identification of 

underserved areas for purposes of 

measuring historical GSE goal 

performance. The second of the two 

rows also incorporates the effects of the 

Department’s proposed change from 

counties to census tracts as the basis for 

identifying underserved areas outside of 

metropolitan areas beginning in 2005. 

HUD’s determination of underserved 

areas for purposes of computing the 

GSEs’ performance on the Underserved 

Areas Housing Goal has, through 2003, 

been based on area median incomes and 

area minority percentages from the 1990 

Census. HUD applied the existing 

numerical thresholds for minority 

percentages and median incomes to 

2000 Census data and ascertained that 

the proportion of underserved census 

tracts and the proportion of housing 

units in underserved census tracts in 

metropolitan areas both have increased 

significantly from 1990 levels: from 47.6 

percent to 51.3 percent of census tracts 

underserved and from 44.3 percent to 

48.7 percent of population in 

underserved census tracts (including the 

effects of the 2003 re-specification of 

Metropolitan Statistical Areas). 

Comparable shifts at the county level 

in non-metropolitan areas were found to 

be of much smaller magnitude. Further, 

HUD estimated the spatial distribution 

of GSE mortgage purchases across 

metropolitan census tracts and nonmetropolitan counties for recent years. 

The findings were that for 2000, 2001, 

2002, and 2003, Fannie Mae’s 

performance figures are an estimated 7.2 

percentage points, 6.0 percentage 

points, 5.5 percentage points, and 5.1 

percentage points higher in terms of 

2000 Census geography than with 1990 

Census geography. The corresponding 

figures for Freddie Mac are 5.6 

percentage points, 5.1 percentage 

points, 5.1 percentage points, and 3.9 

percentage points larger, respectively. 

With a further shift to tract-based 

definitions, the figures for Fannie Mae 

are reduced by 0.7 percentage point in 

2000, 2001, and 2002, and for Freddie 

Mac by 0.7, 0.8, and 0.7 percentage 

point, respectively. The differences 

between county-based performance and 

tract-based performance were much 

smaller in 2003, with the latter falling 

below the former by only 0.2 percentage 

point for Fannie Mae and exceeding the 

former by 0.1 percentage point for 

Freddie Mac last year. As previously 

noted in the discussion of the Low- and 

Moderate-Income Housing Goals, the 

smaller differences between these two 

approaches in 2003 than in 2000–2002 

may be due to the need to apply 

estimation techniques in 2000–2002 but 

not in 2003. 

c. Underserved Areas Home Purchase 

Subgoal 

The Department believes the GSEs 

can play a leadership role in 

underserved markets. To facilitate this 

leadership, the Department is 

establishing a Subgoal of 32 percent for 

each GSE’s acquisitions of home 

purchase mortgages on properties 

located in the underserved census tracts 

of metropolitan areas for 2005, rising to 

33 percent in 2006 and 2007, and 34 

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16HUD will begin defining underserved areas 

based on 2000 Census geography and new OMB 

definitions of metropolitan areas in 2005, the first 

year of the proposed rule. As explained in 

Appendix B of the proposed GSE Rule, the 2000-

based definition of underserved areas includes 

5,372 more census tracts in metropolitan areas than 

the 1990-based definition, which means the GSEmarket comparisons had to be updated to 

incorporate tract designations from the 2000 

Census. Therefore, for the years 1999, 2000, 2001, 

and 2002, HUD used various apportionment 

techniques to re-allocate 1990-based GSE and 

HMDA data into census tracts as defined by the 

2000 Census. (Since 2003 HMDA and GSE data 

were gathered in terms of 2000 Census geography, 

no apportionment was required for that year.) 

Switching to the 2000-based tracts increases the 

underserved area share of market originations by 

about five percentage points. Between 1999 and 

2002, 30.3 percent of mortgage originations 

(without B&C loans) were originated in underserved 

tracts based on 2000 geography, compared with 

25.2 percent based on 1990 geography. As shown 

in Table B.8 of Appendix B of this Final Rule, the 

underserved areas share of each GSE’s purchases 

also rises by approximately five percentage points. 

Thus, conclusions about the GSEs’ performance 

relative to the market are similar whether the 

analysis is conducted in terms of 2000 Census 

geography or 1990 Census geography.

percent in 2008. The purpose of this 

Subgoal is to encourage the GSEs to 

improve their purchases of mortgages 

for homeownership in underserved 

areas, thus providing additional credit 

and capital for neighborhoods that 

historically have not been adequately 

served. As discussed in Appendix A to 

this rule, the GSEs have the ability to 

lead the primary market for singlefamily-owner loans, which is their 

‘‘bread-and-butter’’ business. Both GSEs 

have been dominant players in the 

home purchase market for years, 

funding 61 percent of the single-familyowner mortgages financed between 1999 

and 2002. Through their many new 

product offerings and their various 

partnership initiatives, the GSEs have 

shown that they have the capacity to 

operate in underserved neighborhoods. 

Even though they have the ability to 

lead the market, they have not done so, 

as both GSEs have lagged behind the 

primary market in serving underserved 

areas. As shown in Table 7, underserved 

areas (based on 2000 Census geography) 

accounted for 29.4 percent of Freddie 

Mac’s purchases of home purchase 

mortgages in 2003, 32.0 percent of 

Fannie Mae’’ purchases, and 32.5 

percent of market originations.16 The 

following points can be made about the 

data presented in Table 7 regarding the 

underserved areas subgoal:

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Underserved Areas Subgoals 

Compared With Market. The 32-percent 

subgoal for the first year (2005) is 

approximately one percentage point 

above 1999–2003 and 2001–2003 

average market performance (based on 

the market defined without B&C and 

small loans) and approximately at the 

2002–2003 average market performance 

and the previous peak market 

performance. The 33-percent subgoal for 

2006 and 2007 would add one 

percentage point to these comparisons, 

while the 34-percent subgoal for 2008 

would add two percentage points. For 

example, the 34-percent subgoal is 

approximately three percentage points 

above both 1999–2003 and 2001–2003 

average market performance, 1.8 percent 

(market without B&C loans) to 2.4 

percent (market without both B&C and 

small loans) above 2002–2003 average 

market performance, and 1.5 percent 

(market without B&C loans) to 1.8 

percent (market without both B&C and 

small loans) the market’s previous peak 

performance in 2003.

Underserved Areas Subgoals Compared 

With Past Freddie Mac Performance. To 

reach the 32-percent 2005 subgoal, Freddie 

Mac would have to improve its performance 

by 2.7 percentage points over its 2001–2003 

average underserved areas performance of 

29.3 percent, by 1.6 percentage points over 

its 2002–2003 average underserved areas 

performance of 30.4 percent, and by 0.3 

percent over its previous peak performance 

of 31.7 percent in 2002. To reach the 34-

percent subgoal, Freddie Mac would have to 

improve its performance by 3.6 percentage 

points over its 2002–2003 average 

underserved areas performance, and by 2.3 

percent over its previous peak performance. 

As noted in Table 7, Freddie Mac’s 

performance jumped from 27.3 percent in 

2001 to 31.7 percent in 2002, only to fall back 

to 29.0 percent in 2003. Thus, the 32-percent 

subgoal for 2005 is three percentage points 

above Freddie Mac’s most recent experience 

(29.0 percent). However, as noted above, 

Freddie Mac’s 31.7-percent performance in 

2002 is only 0.3 percentage points below the 

32-percent subgoal for 2005. 

Underserved Areas Subgoals Compared 

With Past Fannie Mae Performance. To reach 

the 32-percent 2005 subgoal, Fannie Mae 

would have to improve its performance by 

0.6 percentage points over its 2001–2003 

average underserved areas performance of 

31.4 percent; Fannie Mae would meet the 32-

percent subgoal based on its 2002–2003 

average underserved areas performance of 

32.2 percent and its previous peak 

underserved areas performance of 32.3 

percent in 2002. To reach the 34-percent 

subgoal, Fannie Mae would have to improve 

its performance by 2.6 percent over its 2001–

2003 average performance, by 1.8 percentage 

points over its 2002–2003 average 

performance, and by 1.7 percent over its 

previous peak performance of 32.3 percent in 

2003.

As with the other two home purchase 

subgoals, the underserved areas subgoal 

targets will be more challenging for 

Freddie Mac than Fannie Mae, 

particularly given Freddie Mac’s low 

performance (29.0 percent) during the 

most recent year (2003). Again, the type 

of improvement needed to meet the new 

underserved areas subgoal targets was 

demonstrated by Fannie Mae during 

2001–2003, as Fannie Mae increased its 

underserved areas purchases from 29.0 

percent of its single-family-owner 

business in 2000 to approximately 32 

percent in both 2002 and 2003. As noted 

above for the low-mod subgoals, staged 

increases in the underserved areas 

subgoal enable the GSEs to obtain their 

leadership position by gaining 

experience as they improve and move 

toward the new higher subgoal targets. 

The type of improvement needed to 

meet this new underserved area subgoal 

was demonstrated by Fannie Mae 

during 2001 and 2002. During 2001, 

underserved area loans declined as a 

percentage of primary market 

originations (from 31.7 to 30.7 percent), 

but they increased as a percentage of 

Fannie Mae’s purchases (from 29.0 to 

29.8 percent); and during 2002, they 

increased further as a percentage of 

Fannie Mae’s purchases (from 29.8 to 

32.3 percent), placing Fannie Mae at the 

market level. 

Section 4.b. above of this preamble 

and Section I.4 of Appendix B to this 

rule discuss the reasons why the 

Department is establishing a Subgoal for 

home purchase mortgages in 

underserved areas, namely: (1) the GSEs 

have the resources and the ability to 

lead the market in providing funding in 

underserved neighborhoods; (2) the 

GSEs lag the underserved areas market, 

even though they have the ability to 

lead; (3) troublesome disparities in our 

housing and mortgage markets indicate 

a continuing need for increased GSE 

activity; and (4) there are ample 

opportunities for the GSEs to improve 

their underserved area performance in 

the home purchase market. 

Although single-family owneroccupied mortgages are the GSEs’

principal line of business, the GSEs 

have lagged behind the primary market 

in financing properties in underserved 

areas. For the foregoing reasons, HUD 

believes that the GSEs can do more to 

raise the share of their home loan 

purchases in underserved areas. This 

can be accomplished by building on 

efforts that the GSEs have already 

started, including their new affordable 

lending products, their many 

partnership efforts, their outreach to 

inner city neighborhoods, their 

incorporation of greater flexibility into 

their underwriting guidelines, and their 

purchases of seasoned CRA loans. 

A wide variety of quantitative and 

qualitative indicators demonstrate that 

the GSEs have the resources and 

financial strength to improve their 

affordable lending performance enough 

to lead the market in underserved areas. 

d. Summary of Comments

The Department received no 

comments that specifically addressed 

the level of the Underserved Areas Goal. 

The majority of commenters that offered 

opinions on the level of the housing 

goals focused on the high year (2008) of 

the Low- and Moderate-Income Goal. 

Where commenters did mention the 

Underserved Area Goal, their remarks 

were in the context of better targeting 

through changes in the definition of 

underserved areas. HUD also received 

no comments specific to the 

Underserved Area Home Purchase 

Subgoal. Both Fannie Mae and Freddie 

Mac commented on the level of the 

Underserved Area Goal. Fannie Mae 

stated that its replication of HUD’s 

market sizing assumptions did not 

justify an Underserved Area Goal of 38 

or 40 percent. For example, Fannie Mae 

noted that in reaching a goal level of 40 

percent, HUD relied on the most 

unlikely owner-occupied underserved 

share of 30 percent, a level reached only 

once in the past 11 years. With respect 

to the Underserved Area Subgoal, 

Fannie Mae stated generally that 

subgoals risk unintended consequences 

and that HUD has proposed subgoals in 

excess of the opportunity and business 

mix seen in the market. Freddie Mac 

commented in general that all the goals 

and subgoals were set beyond what the 

primary market is likely to originate. 

With respect to the underserved areas 

market share, Freddie Mac estimates 

that the core ranges are 3–4 percentage 

points below the upper limits of the 

Department’s projected ranges. 

e. HUD’s Determination 

The Underserved Areas Housing Goal 

established in this final rule is 

reasonable and appropriate having 

considered the factors set forth in 

FHEFSSA. For 2001–2003, HUD set the 

level of the housing goal conservatively, 

relative to the Department’s market 

share estimates, in order to 

accommodate a variety of economic 

scenarios. Moreover, current 

examination of the gaps in the mortgage 

markets, along with the estimated size 

of the market available to the GSEs, 

demonstrate that the number of 

mortgages secured by housing in 

underserved areas is more than 

63620 Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 

17The Freddie Mac 2002 figures in Table 8 differ 

from the corresponding figures in Table 5 in HUD’s 

Proposed Rule. Subsequent to publication of the 

Proposed Rule, HUD discovered that HUD had 

credited some units toward Freddie Mac’s Special 

Affordable Housing Goal in 2002 that had been 

previously counted toward the goal in 2001. The 

units were associated with a large year-end Freddie 

Mac mortgage purchase transaction in 2002. 

Because HUD’s regulations prohibit double 

counting, HUD has recalculated Freddie Mac’s 2002 

Special Affordable Housing Goal performance. The 

recalculation also reflects correction of some coding 

errors discovered in HUD’s recent review.

sufficient for the GSEs to achieve the 

new goal. 

Therefore, having considered all the 

statutory factors including housing 

needs, projected economic and 

demographic conditions for 2005 to 

2008, the GSEs’ past performance, the 

size of the market serving low- and 

moderate-income families, and the 

GSEs’ ability to lead the market while 

maintaining a sound financial 

condition, HUD has determined that the 

annual goal for mortgage purchases 

qualifying under the Underserved Areas 

Housing Goal will be 37 percent for 

2005, 38 percent for 2006 and 2007, and 

39 percent for 2008. 

Further, the Department is 

establishing a Subgoal of 32 percent for 

each GSE’s acquisitions of home 

purchase mortgages on properties 

located in the underserved census tracts 

of metropolitan areas for 2005, rising to 

33 percent in 2006 and 2007, and 34 

percent in 2008. This reflects a 

reduction in the upper end of the 

market share range from 35 percent to 

34 percent since HUD’s publication of 

its proposed rule, resulting from 

changes in estimating market share as 

described at the end of Section 3.a. 

above, and in Section G of Appendix D 

to this rule. 

The reasons for increasing the 

Underserved Areas Housing Goal are 

discussed in Sections a. and b. above, 

and for establishing a Home Purchase 

Subgoal at the stated levels in section c. 

While the GSEs have lagged the primary 

market in funding loans in underserved 

areas, they appear to have ample room 

to improve their performance in that 

market. A wide variety of quantitative 

and qualitative indicators demonstrate 

that the GSEs have the expertise, 

resources, and financial strength to 

improve their low- and moderateincome lending performance, including 

lending for home purchases in 

underserved areas, and achieve the 

levels of the goals being established. 

7. Special Affordable Housing Goal, 

§ 81.14 

This section discusses the 

Department’s consideration of the 

statutory factors in arriving at, and the 

comments received on, the new housing 

goal level for the Special Affordable 

Housing Goal, which targets mortgages 

on housing for very low-income families 

and low-income families in low-income 

areas. After consideration of these 

statutory factors and the comments 

received, this final rule establishes the 

goal for the percentage of dwelling units 

to be financed by each GSE’s mortgage 

purchases at 22 percent in 2005, 23 

percent in 2006, 25 percent in 2007, and 

27 percent in 2008. 

After analyzing the statutory factors, 

HUD has determined to establish: (a) a 

Goal of 22 percent for the percentage of 

the total number of dwelling units 

financed by each GSE’s mortgage 

purchases that are for special affordable 

housing, affordable to very low-income 

families and families living in lowincome areas for 2005, rising to 23 

percent in 2006, 25 percent in 2007, and 

27 percent in 2008; (b) a Subgoal of 17 

percent of the total number of each 

GSE’s purchases of home purchase 

mortgages in metropolitan areas that are 

for housing affordable to very lowincome families and low-income 

families in low-income areas for 2005 

and 2006, rising to 18 percent in 2007 

and 2008; and (c) a Subgoal of 1 percent 

of each GSE’s combined annual average 

mortgage purchases in 2000, 2001, and 

2002, for each GSE’s special affordable 

mortgage purchases that are for 

multifamily housing in 2005–2008. 

A short discussion of the statutory 

factors for establishing the Special 

Affordable Housing Goal follows. 

Additional information analyzing each 

of the statutory factors is provided in 

Appendix C, ‘‘Departmental 

Considerations to Establish the Special 

Affordable Housing Goal,’’ and 

Appendix D, ‘‘Estimating the Size of the 

Conventional Conforming Market for 

each Housing Goal.’’

a. Market Estimate for the Special 

Affordable Housing Goal

The Department estimates that 

dwelling units serving very low-income 

families and low-income families living 

in low-income areas will account for 

23–27 percent of total units financed in 

the overall conventional conforming 

mortgage market during the period 2005 

through 2008. HUD has developed this 

range, rather than a point estimate, to 

account for the projected effects of 

different economic conditions that can 

reasonably be anticipated. HUD also 

estimates that the special affordable 

market averaged 28 percent between 

1999 and 2002. 

b. Past Performance of the GSEs under 

the Special Affordable Housing Goal 

As discussed above, a number of 

changes in goal-counting procedures 

were adopted as part of HUD’s Housing 

Goals 2000 final rule. Thus, it is 

necessary to provide information using 

several different measures in order to 

track changes in performance on the 

Special Affordable Housing Goal over 

the 1996–2003 period. These are shown 

in Table 8.17

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Based on the counting rules in effect 

at that time, as shown under ‘‘official 

performance’’ for 1996–2000 in Table 8, 

Special Affordable Housing Goal 

performance for Fannie Mae generally 

fluctuated in the range between 14 and 

17 percent over the 1996–1999 period, 

before rising to a peak of 19.2 percent 

in 2000. Freddie Mac’s performance 

started at a lower level, but then 

increased in several steps, from 14–16 

percent in 1996–1998 to 17.2 percent in 

1999, and to a record level of 20.7 

percent in 2000. That was the only year 

prior to 2001 in which Freddie Mac’s 

performance exceeded Fannie Mae’s 

performance on the Special Affordable 

9Housing Goal. 

Based on counting rules in effect for 

2001–2003, as shown under ‘‘official 

performance’’ in Table 8, Special 

Affordable Housing Goal performance 

for Fannie Mae was 21.6 percent in 

2001, 21.4 percent in 2002, and 21.2 

percent in 2003. Official performance 

for Freddie Mac was 22.6 percent in 

2001, 20.4 percent in 2002, and 21.4 

percent in 2003. 

Immediately beneath the official 

Special Affordable Housing Goal 

performance percentages in Table 8 are 

figures showing the GSEs’ special 

affordable purchase percentages on a 

consistent basis for the entire 1996–

2003 period. The assumptions used 

were the counting rules established in 

HUD’s Housing Goals 2000 final rule, 

except that bonus points and the 

Freddie Mac TAF (which were 

terminated at the end of 2003) are not 

applied. These are termed the ‘‘2001–

2003 baseline’’ assumptions. In terms of 

this measure, both Fannie Mae and 

Freddie Mac’s special affordable 

performance reached its maximum in 

2000 (Fannie Mae at 21.4, percent and 

Freddie Mac at 21.0 percent) before 

declining somewhat in 2001, and then 

declining further in 2002 and 2003. 

Both GSEs’ baseline performance in 

2003 exceeded the level attained in 

1999. 

Overall, both GSEs’ performance 

exceeded HUD’s Special Affordable 

Housing Goals by significant margins in 

1996–1999, and by wide margins in 

2000. New, higher Goals were 

established for 2001–2003, and despite 

somewhat lower performance than the 

level attained in 2000 (largely due to the 

2001–2003 refinance wave, as discussed 

under the Low- and Moderate-Income 

Housing Goal), both GSEs’ performance 

exceeded the new Goal levels in 2001–

2003. 

The Special Affordable Housing Goal 

is designed, in part, to ensure that the 

GSEs maintain a consistent focus on 

serving the low- and very low-income 

portion of the housing market where 

housing needs are greatest. Appendices 

A and C to this rule use HMDA data and 

GSE loan-level data for home purchase 

mortgages on single-family owneroccupied properties in metropolitan 

areas to compare the GSEs’ performance 

in special affordable lending to the 

performance of depositories and other 

lenders in the conventional conforming 

market. There are two main findings 

with respect to the special affordable 

category. 

First, Freddie Mac and Fannie Mae 

have historically lagged depositories 

and the overall market in providing 

mortgage funds for special affordable 

borrowers over periods, such as 1993–

2003, 1996–2003, and 1999–2003. 

Between 1993 and 2003, 12.2 percent of 

Freddie Mac’s mortgage purchases were 

for special affordable borrowers, 13.3 

percent of Fannie Mae’s purchases, 15.4 

percent of loans originated by 

depositories, and 15.5 percent of loans 

originated in the conventional 

conforming market (without estimated 

B&C loans). During the period between 

1999 and 2003, the GSEs’ performance 

was approximately 90 percent of the 

market’special affordable loans 

accounted for 15.1 percent of Fannie 

Mae’s purchases, 14.5 percent of 

Freddie Mac’s purchases, and 16.2 

percent of loans originated in the 

conforming market. (See Table 9, which 

is based on 2000 Census geography.)

Second, while both GSEs have 

improved their performance over the 

past few years, Fannie Mae has made 

more progress than Freddie Mac in 

erasing its gap with the market. During 

2003, the special affordable share of 

Fannie Mae’s purchases was 17.7 

percent, which was above the market 

share of 16.8 percent. In 2003, the 

special affordable share of Freddie 

Mac’s purchases was 16.2 percent. 

Section G in Appendix A to this rule 

discusses the role of the GSEs both in 

the overall special affordable market 

and in the different segments (singlefamily owner, single-family rental, and 

multifamily rental) of the special 

affordable market. The GSEs’ special 

affordable purchases accounted for 41 

percent of all special affordable owner 

and rental units that were financed in 

the conventional conforming market 

between 1999 and 2002. The GSEs’ 41-

percent share of the special affordable 

market was below their 55-percent share 

of the overall market. Even in the owner 

market, where the GSEs account for 61 

percent of the market, their share of the 

special affordable market was only 52 

percent. As noted above, Fannie Mae 

led the primary market in funding 

special affordable home loans during 

2003. On the other hand, Freddie Mac 

continued to lag that market in 2003. 

The data indicate that there is room for 

Freddie Mac to improve its performance 

in purchasing affordable home loans at 

the lower-income end of the market. 

The rental market (including both 1-

to 4-family rental properties and 

multifamily rental properties) is 

especially important in the 

establishment of the Special Affordable 

Housing Goal for Fannie Mae and 

Freddie Mac because of the relatively 

high percentage of rental units meeting 

the Special Affordable Housing Goal. 

For example, between 1999 and 2002, 

51 percent of units financed by Fannie 

Mae’s rental mortgage purchases met the 

Special Affordable Housing Goal, 

representing 46 percent of units counted 

toward the Special Affordable Housing 

Goal, during a period when rental units 

represented only 18 percent of its total 

purchase volume. For Freddie Mac, 50 

percent of units financed by rental 

mortgage purchases met the Special 

Affordable Housing Goal, representing 

41 percent of units counted toward the 

Special Affordable Housing Goal, during 

a period when rental units represented 

only 16 percent of its total purchase 

volume. 

c. Special Affordable Home Purchase 

Subgoal 

The Department believes the GSEs 

can play a leadership role in the special 

affordable market generally, and the 

home purchase special affordable 

market in particular. Thus, the 

Department is establishing a Subgoal of 

17 percent for each GSE’s purchases of 

home purchase mortgages for special 

affordable housing located in 

metropolitan areas for 2005 and 2006, 

rising to 18 percent in 2007 and 2008. 

The purpose of this Subgoal is to 

encourage the GSEs to improve their 

purchases of home purchase mortgages 

on special affordable housing, thus 

expanding homeownership 

opportunities for very-low-income 

borrowers and low-income borrowers in 

low-income areas, including minority 

first-time homebuyers who are expected 

to enter the housing market over the 

next few years. Table 9 provides 

information needed to compare the 

special affordable subgoal targets with 

past market and GSE performance. 

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63624 Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 

Special Affordable Subgoals 

Compared With Market. The 17-percent 

subgoal for the first year (2005) is 

approximately one percentage point 

above the 1999–2003, 2001–2003, and 

2002–2003 average market performance. 

The 17-percent subgoal is at the 

previous peak market performance (the 

1999, 2000, and 2003 markets without 

B&C loans were about 17 percent) or 

slightly below the previous peak market 

performance (based on 2003 market 

without both B&C and small loans). The 

18-percent subgoal for 2007 and 2008 

would add one percentage point to these 

figures. Thus, the 18-percent subgoal is 

approximately two percentage points 

above the 1999–2003, 2001–2003, and 

2002–2003 average market performance 

of approximately 16 percent. The 18-

percent subgoal is one percentage point 

above the previous peak market 

performance (the 1999, 2000, and 2003 

markets without B&C loans were about 

17 percent) or 1.5 percentage points 

above the previous peak market 

performance based on the 2003 market 

without both B&C and small loans. 

Special Affordable Subgoals 

Compared With Past Freddie Mac 

Performance. To reach the 17-percent 

2005 subgoal, Freddie Mac would have 

to improve its performance by 1.9 

percentage points over its 2001–2003 

average special affordable performance 

of 15.1 percent, by 1.3 percentage points 

over its 2002–2003 average special 

affordable performance of 15.7 percent, 

and by 0.8 percent over its previous 

peak performance of 16.2 percent in 

2003. To reach the 18-percent subgoal, 

Freddie Mac would have to improve its 

performance by 2.9 percentage points 

over its 2001–2003 average special 

affordable performance, 2.3 percent over 

its 2002–2003 average performance, and 

by about 1.8 percent over its previous 

peak performance. 

Special Affordable Subgoals 

Compared With Past Fannie Mae 

Performance. To reach the 17-percent 

2005 subgoal, Fannie Mae would have 

to improve its performance by 0.9 

percentage points over its 2001–2003 

average special affordable performance 

of 16.1 percent; Fannie Mae would 

essentially meet the 17-percent subgoal 

based on its 2002–2003 average special 

affordable performance of 16.8 percent 

and would surpass the 17-percent 

subgoal based on its peak special 

affordable performance of 17.7 percent 

in 2003. To reach the 18-percent 

subgoal, Fannie Mae would have to 

improve its performance by 1.9 percent 

over its 2001–2003 average performance 

and by 1.2 percentage points over its 

2002–2003 average performance; Fannie 

Mae would meet the 18-percent subgoal 

based on its peak performance of 17.7 

percent in 2003. 

As with the low-mod and 

underserved areas subgoals, the special 

affordable subgoal targets will be more 

challenging for Freddie Mac than 

Fannie Mae. But, as with other goals, 

the type of improvement needed to meet 

the new special affordable subgoal 

targets was demonstrated by Fannie Mae 

during 2001–2003, as Fannie Mae 

increased its special affordable 

purchases from 13.4 percent of its 

single-family-owner business in 2000, to 

15.8 percent in 2002, to 17.7 percent in 

2003, as shown in Table 9. This subgoal 

is designed to encourage Fannie Mae 

and Freddie Mac to lead the special 

affordable market. As noted earlier, the 

approach taken is for the GSEs to obtain 

their leadership position by staged 

increases in the subgoals to enable the 

GSEs to gain experience as they improve 

and move toward the new higher 

subgoal targets. 

The section above on considerations 

in establishing the Low- and ModerateIncome Home Purchase Subgoal and 

Section D of Appendix C to this rule 

further discuss reasons why the 

Department set the Subgoal for special 

affordable loans. 

Both Fannie Mae and Freddie Mac 

questioned HUD’s authority under 

FHEFSSA to establish any subgoals 

within the Special Affordable Housing 

Goal. The GSEs noted that both sections 

establishing the Low- and ModerateIncome and the Underserved Areas 

Housing Goals include language that 

HUD ‘‘may establish separate specific 

subgoals within the goal under this 

section and such subgoals shall not be 

enforceable * * * .’’ No such language 

appears in the section establishing the 

Special Affordable Housing Goal. The 

GSEs asserted that this omission is an 

indication that Congress intended to 

prohibit HUD from establishing any 

subgoals within the Special Affordable 

Housing Goal. 

HUD has also considered the GSEs’

claim that HUD lacks the statutory 

authority to impose any subgoals within 

the Special Affordable Housing Goal. 

These same arguments were presented 

by the GSEs during HUD’s 1995 

rulemaking establishing the housing 

goals. (See Housing Goals 1995 

proposed rule published on February 

16, 1995 at 60 FR 9154, and the final 

rule published on December 1, 1995 at 

60 FR 1846.) 

At that time, HUD stated that the 

absence of a similar subgoal provision 

under the Special Affordable Housing 

Goal section ‘‘is not an indication that 

subgoals or subcategories within the 

overall goal are prohibited; rather, such 

omission indicates that to the extent 

that subgoals or subcategories are 

promulgated for the Special Affordable 

Housing Goal, no bar exists to enforcing 

them.’’ (60 FR 61860.) The 1995 

Housing Goals final rule established an 

enforceable subgoal for multifamily 

mortgages within the Special Affordable 

Housing Goal; this subgoal has been in 

place each year since then. This final 

rule does not change this longstanding 

agency interpretation. 

d. Special Affordable Housing Goal: 

Multifamily Subgoals 

Based on the GSEs’ past performance 

on the Special Affordable Multifamily 

Subgoals, and on the outlook for the 

multifamily mortgage market, HUD 

proposed that these Subgoals be 

retained for the 2005–2008 period.

Unlike the overall Goals, which are 

expressed in terms of minimum Goalqualifying percentages of total units 

financed, these Subgoals for 2001–2003 

and in prior years have been expressed 

in terms of minimum dollar volumes of 

Goal-qualifying multifamily mortgage 

purchases. Specifically, each GSE’s 

special affordable multifamily Subgoal 

is currently equal to 1.0 percent of its 

average total (single-family plus 

multifamily) mortgage volume over the 

1997–1999 period. Under the proposal, 

the GSEs’ purchases of mortgages 

financing dwelling units in multifamily 

housing for calendar years 2005–2008 

will be 1.0 percent of the GSEs’ average 

annual dollar volume of mortgage 

purchases in the calendar years 2000, 

2001, and 2002. The proposal would 

increase the subgoal levels by roughly 

90 percent compared to their current 

levels. Specifically, Fannie Mae’s total 

eligible multifamily mortgage purchase 

volume increased from $4.6 billion in 

1993 to $12.5 billion in 1998, and then 

jumped sharply to $18.7 billion in 2001, 

$18.3 billion in 2002, and $33.3 billion 

in 2003. As shown in Table 8, special 

affordable multifamily mortgage 

purchases followed a similar path, 

rising from $1.7 billion in 1993 to $3.5 

billion in 1998 and $4.1 billion in 1999, 

and also jumping sharply to $7.4 billion 

in 2001, $7.6 billion in 2002, and $12.2 

billion in 2003. As a result of its strong 

performance, Fannie Mae’s purchases 

have been at least twice its minimum 

subgoal in every year since 1997—247 

percent of the Subgoal in that year, 274 

percent in 1998, 315 percent in 1999, 

294 percent in 2000, and, under the new 

Subgoal level, 258 percent in 2001, 266 

percent in 2002, and 426 percent in 

2003. 

Freddie Mac’s total eligible 

multifamily mortgage purchase volume 

increased even more sharply, from $0.2 

Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 63625

billion in 1993 to $6.6 billion in 1998, 

and then jumped further to $11.8 billion 

in 2001, $18.3 billion in 2002, and $21.5 

billion in 2003. As shown in Table 8, 

special affordable multifamily mortgage 

purchases followed a similar path, 

rising from $0.1 billion in 1993 to $2.7 

billion in 1998, and also jumping 

sharply to $4.6 billion in 2001, $5.2 

billion in 2002, and $8.8 billion in 2003. 

As a result of its strong performance, 

Freddie Mac’s purchases have also been 

at least twice its minimum Subgoal in 

every year since 1998—272 percent of 

the Subgoal in that year, 228 percent in 

1999, 242 percent in 2000, and, under 

the new Subgoal level, 220 percent in 

2001, 247 percent in 2002, and 417 

percent in 2003. 

The Special Affordable Multifamily 

Subgoals set forth in this final rule are 

reasonable and appropriate based on the 

Department’s analysis of this market. 

The Department’s decision to retain 

these Subgoals is based on HUD’s 

analysis, which indicates that 

multifamily housing still serves the 

housing needs of lower-income families 

and families in low-income areas to a 

greater extent than single-family 

housing. By retaining the Special 

Affordable Multifamily Subgoal, the 

Department ensures that the GSEs 

continue their activity in this market, 

and that they achieve at least a 

minimum level of special affordable 

multifamily mortgage purchases that are 

affordable to lower-income families. 

e. Summary of Comments 

Comments regarding the Special 

Affordable Goal were received from 

numerous public advocacy groups and 

one trade association; however, only 

one public advocacy group commented 

on the level of the goal. The 

commenting group recommended that 

the 2004 Special Affordable Goal be 

maintained for the years 2005–2008. 

No comments specific to the Special 

Affordable Home Purchase Subgoal 

were received from the public. Fannie 

Mae provided an analysis as part of its 

comments that illustrated, for the years 

1999 through 2002, that the market did 

not perform up to the level of HUD’s 

proposed Special Affordable Home 

Purchase Subgoal.

Regarding the Multifamily Special 

Affordable Subgoal, neither GSE 

objected to HUD’s proposed subgoal 

levels for 2005–2008. One trade 

organization suggested that the subgoal 

has outlived its original purpose and 

should be discontinued. This 

organization stated that the subgoal was 

established to induce the GSEs to 

purchase multifamily loans at a time 

when heavy credit losses had caused 

them to back away from this market, 

and that the situation had changed 

greatly since then. The organization 

stated that the overall goals now 

provided sufficient incentive for the 

GSEs to focus on multifamily mortgage 

purchases. One multifamily lender 

expressed concern that increasing the 

Multifamily Special Affordable Subgoal 

will push the GSEs to extend credit to 

unqualified borrowers with poor quality 

properties that should not be eligible for 

long-term, low-cost financing. However, 

other commenters, including multiple 

public advocacy groups and a local 

government official, recommended that 

HUD increase the level of this subgoal. 

Several commenters specifically 

recommended that HUD set this subgoal 

between 2.5 percent and 3 percent of the 

GSEs’ purchases in preceding years. 

They noted that the GSEs have far 

exceeded the subgoal levels in recent 

years and said that a higher subgoal 

level is needed to promote additional 

multifamily lending. 

f. HUD’s Determination 

HUD concludes that the Special 

Affordable Housing Goal established in 

this final rule is reasonable and 

appropriate having considered the 

factors set forth in FHEFSSA. Current 

examination of the gaps in the mortgage 

markets, along with the estimated size 

of the market available to the GSEs, 

demonstrates that the number of 

mortgages secured by special affordable 

housing is more than sufficient for the 

GSEs to achieve the new goal. 

Therefore, having considered all the 

statutory factors including housing 

needs, projected economic and 

demographic conditions, the GSEs’ past 

performance, the size of the market 

serving low- and moderate-income 

families, and the GSEs’ ability to lead 

the market while maintaining a sound 

financial condition, HUD has 

determined that the Special Affordable 

Housing Goal will be 22 percent for 

2005, 23 percent for 2006, 25 percent for 

2007, and 27 percent for 2008. This 

reflects a reduction in the upper end of 

the market share range from 28 percent 

to 27 percent since HUD’s publication of 

its proposed rule, resulting from 

changes in estimating market share as 

described at the end of section 3.a, 

above, and in Section H of Appendix D 

to this rule. 

The reasons for increasing the Special 

Affordable Housing Goal are discussed 

above in this preamble. Since the GSEs 

have historically lagged the primary 

market in purchasing loans on owner 

and rental properties that qualify as 

special affordable, they have ample 

room to improve their performance in 

that market. The GSEs’ mortgage 

purchases between 1999 and 2002 

accounted for 55 percent of the total 

(single-family and multifamily) 

conforming mortgage market, but they 

accounted for only 41 percent of the 

special affordable market. A wide 

variety of quantitative and qualitative 

indicators demonstrate that the GSEs 

have the expertise, resources, and 

financial strength to improve their 

special affordable lending performance 

and to close their gap with the market. 

Further, the Department is 

establishing a Subgoal of 17 percent for 

each GSE’s acquisitions of home 

purchase mortgages for special 

affordable housing in 2005 and 2006, 

rising to 18 percent in 2007 and 2008. 

The special affordable home purchase 

subgoal will ensure that Freddie Mac 

improves its performance enough not 

only to close its current gap with the 

primary market but also to place itself 

in a leadership position. The subgoal 

will also encourage Fannie Mae to 

improve further its current marketleading performance. A wide variety of 

quantitative and qualitative indicators 

demonstrate that the GSEs have the 

expertise, resources, and financial 

strength to improve their special 

affordable lending performance, 

including lending for home purchases 

for special affordable housing, and to 

achieve the levels of the subgoals being 

established. 

Finally, the Department is 

establishing each GSE’s Special 

Affordable Multifamily Subgoal at 1.0 

percent of its average annual dollar 

volume of total (single-family and 

multifamily) mortgage purchases over 

the 2000–2002 period. In dollar terms, 

the level of the subgoal is $5.49 billion 

per year in special affordable 

multifamily mortgage purchases for 

Fannie Mae and $3.92 billion per year 

in special affordable multifamily 

mortgage purchases for Freddie Mac. 

These Subgoals would be less than the 

actual special affordable multifamily 

mortgage purchase volume in 2001–

2003 for both GSEs. Thus, the 

Department believes that they would be 

feasible for the 2005–2008 period. 

HUD believes that the proposed 

increase in the dollar level of the 

Special Affordable Multifamily Subgoal 

balances the need to promote GSE 

activity in this segment with the need to 

provide some protection in the event of 

a decline in overall mortgage market 

activity. Because this goal is set as a 

dollar amount rather than as a share of 

business, overall declines in residential 

mortgage lending would make this goal 

harder to achieve. Setting the subgoal 

level based on the GSEs’ record 

63626 Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 

18For rental units, the 2000 Housing Goals Final 

Rule also established counting rules that allow the 

GSEs to estimate rents or exclude units from the 

denominator when rent data are missing. See 24 

CFR 81.15(e)(6)(i) on the rules applicable to 

multifamily units and 24 CFR 81.15(e)(6)(ii) on the 

rules for single-family rental units.

multifamily loan purchases during 

2000–2002 sets an appropriately high 

level for the next several years, in the 

Department’s view. In recent years 

Fannie Mae and Freddie Mac have each 

purchased multifamily mortgages in at 

least twice the subgoal amount. The 

increase in that subgoal dollar level 

should serve to provide a more 

meaningful floor to the level of 

multifamily lending during the 2005–

2008 period. 

8. Missing Data/No-Doc Loans 

Overview. Accurate measurement of 

the GSEs’ performance under the three 

Housing Goals depends on the 

completeness of data on borrower 

income (or, in the case of non-owneroccupied units, the rent) and property 

location. With respect to property 

location data, there was a less than one 

percent incidence of missing or 

incomplete geographical data between 

2000 and 2002 for mortgages purchased 

by the GSEs. The incidence of missing 

borrower income data has been 

greater—on the order of several percent 

each year. 

One reason for the increase in missing 

income data is the market’s recent 

increased use of mortgages, commonly 

called low documentation (Low Doc) 

and no documentation (No Doc) loans. 

These loans do not require the borrower 

to provide income information. In some 

cases, the borrower provides 

information on assets but not income 

because of circumstances that make 

assets easier to document. In other 

instances, mortgages are originated 

entirely on the basis of a credit report, 

property appraisal, and cash for the 

downpayment. These mortgages 

typically require relatively large 

downpayments and may also require a 

higher interest rate than fully 

documented mortgages. 

The Housing Goals 2000 Final Rule 

provided that the GSEs may exclude 

from the denominator owner-occupied 

units which lack mortgagor income data 

and which are located in low- or 

moderate-income census tracts, i.e.,

tracts whose median income is no 

greater than the median income of the 

metropolitan area, or for properties 

located outside of metropolitan areas, 

the larger of the median incomes of the 

county or the statewide nonmetropolitan area (see 24 CFR 

81.15(d)).18

In view of the increasing use of loans 

made without obtaining income 

information from the borrower, there is 

a question whether HUD’s existing 

counting rules for missing-data 

situations are adequately reliable and 

create no more than a negligible 

statistical bias in the GSEs’ Housing 

Goals performance figures relative to the 

values that they would have if complete 

income data could be obtained, and 

whether a more precise method for 

imputing incomes could be employed. 

For this reason, HUD requested 

comments from the public about the 

desirability and feasibility of 

implementing a standard 

econometrically based method for 

imputing the income distribution of 

mortgages purchased by each GSE that 

lack income data, based on known 

characteristics of the loan and the 

census tract. 

Summary of Comments. Fannie Mae 

supported expanding affordability 

estimation to single-family rental and 

owner-occupied goal performance 

calculations and favored a more 

complex econometrically based 

affordability estimation methodology. 

For owner-occupied units Fannie Mae 

suggested a method based on the 

probability of mortgages/units 

qualifying for a goal based on census 

tract location. Fannie Mae stated that 

the multifamily affordability estimation 

methodology could also be applied to 

single-family rental units. Fannie Mae 

commented that if HUD were to adopt 

an econometrically based methodology, 

no limit should be placed on its 

implementation. With the current 

methodology, Fannie Mae requested 

that the limit for rental units be 

increased to 10 percent of total rental 

unit acquisitions. 

Freddie Mac commented that HUD 

should adopt a simpler approach to 

missing data. For example, HUD should 

allow the GSEs to remove units with 

missing incomes from the calculation of 

the housing goals. Freddie Mac 

reasoned that the market numbers used 

in establishing the Housing Goals omit 

missing data and that omitting missing 

data from a GSE’s performance would 

be consistent. Also, Freddie Mac stated 

that it historically has had a lower 

missing data rate than the market and 

that it has sufficient business related 

incentives to reduce missing data. 

Freddie Mac commented that any limits 

on adjustments for missing data should 

be related to overall missing data rates 

in the market, estimation parameters 

should be available at the beginning of 

the performance year, and estimation 

procedures should be simple and 

straightforward to implement. 

Several other organizations endorsed 

a standardized procedure for estimating 

affordability for those units missing rent 

or income data, including an 

econometrically based methodology. 

Two commenters stated that HUD 

should require only actual data for 

determining whether a unit is affordable 

or not. In addition, some commenters 

strongly recommended that HUD 

disallow goals credit for all nodocumentation subprime loans because 

such loans are likely to be predatory. 

HUD’s Determination. Having 

considered the comments received, 

HUD has determined that permitting 

some level of estimation for affordability 

data is reasonable and consistent with 

statutory intent that the GSEs serve the 

affordable housing needs of families 

even if actual data are not available. 

With regard to some commenters’

objections that HUD should not permit 

the use of estimated data for—or even 

allow goals credit for—any loans that 

were underwritten for approval without 

borrower income data due to the 

potential for these loans to have 

predatory features, the Department does 

not find that these loans are inherently 

predatory in nature. Also, both GSEs 

have publicly announced that they will 

not finance any loans with predatory 

features, and the Department expects 

that they will continue to vigorously 

enforce these policies. Accordingly, this 

final rule implements several changes to 

the treatment of missing data. The first 

change amends § 81.15(d) of the General 

Requirements to provide an alternative 

treatment for single-family owneroccupied units where the mortgagor’s 

income is missing. As provided in 

§ 81.15(d), the GSEs may continue to 

exclude such units from the 

denominator as well as the numerator 

when they are located in census tracts 

with median income less than or equal 

to area median income according to the 

most recent census, up to a ceiling of 

one percent of total eligible units. 

Purchases in excess of the ceiling will 

be included in the denominator and 

excluded from the numerator if they are 

missing data. 

However, in lieu of using this 

procedure, HUD is making available to 

the GSEs in § 81.15(d) an alternative 

method for missing income treatment 

that provides the GSEs with the ability 

to apply a HUD-approved affordability 

estimation methodology to all singlefamily owner-occupied units with 

missing borrower income data up to a 

specified maximum. This alternative 

provision specifies an approach that 

recognizes the distribution of borrower 

incomes within census tracts in 

determining how to treat loans with 

Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 63627

missing income data. Goal-qualifying 

units, by census tract, are estimated by 

multiplying the number of single-family 

owner-occupied units with missing 

borrower income information in 

properties securing mortgages 

purchased by the GSE, by the 

percentage of all single-family owneroccupied units from originations that 

would count toward achievement of the 

goal, as determined by HUD based on 

the most recent HMDA data available, 

for each census tract where the GSE 

acquired mortgage units. In establishing 

the maximum number of units where 

borrower income may be estimated 

under this alternative provision, HUD 

will apply two factors. The first of these 

is the rate of missing borrower income 

data for each census tract. This is 

calculated using HMDA data for the 

most recent years for which comparable 

data are available. The second factor is 

the number of single-family owneroccupied units purchased by a GSE 

during the performance year, by census 

tract. The maximum is calculated by 

multiplying the HMDA percentage of 

missing income data by the number of 

units that a GSE purchased in each tract. 

This number is summed up for all tracts 

to obtain the overall nationwide 

maximum for that GSE. HUD will 

provide each GSE with a dataset 

containing applicable tract-based 

HMDA missing income rates prior to the 

start of each year. The GSEs may choose 

which provision of § 81.15(d) they will 

use in any year. However, they may not 

combine the options available under 

this provision. If the maximum on 

missing single-family owner-occupied 

unit incomes is exceeded, the estimated 

goal-qualifying units will be adjusted by 

the ratio of the maximum amount 

divided by the total number of units 

with missing income information. 

Under each provision of § 81.15(d), 

units in excess of the specified 

maximum as well as units where 

affordability information is not available 

will remain in the denominator when 

calculating goal performance.

HUD is also in this final rule revising 

§ 81.15(e)(6) to change the current 

maximum on the use of HUD-approved 

multifamily rent estimation data from 5 

percent to 10 percent. In analyzing the 

GSEs’ multifamily purchases for the 

past several years, HUD has determined 

that this change is statistically 

insignificant and will serve to promote 

further the financing of rental units that 

would otherwise be eligible for credit 

under the Housing Goals. In this final 

rule, HUD is also specifying a 

methodology that may be used to 

estimate affordability data for 

multifamily properties with missing 

rent data. This methodology is the same 

methodology that has been used in past 

years to estimate affordability data for 

multifamily properties with missing 

rent data. 

With regard to single-family one-tofour unit rental properties financed with 

loans that are missing affordability data, 

the Department finds that a lack of data 

should not act as a disincentive for the 

GSEs to serve markets that historically 

are important sources of affordable 

housing. Under HUD’s 2000 Rule, 

§ 81.15(e)(6)(ii) permits the GSEs to 

exclude these units from both the 

numerator and the denominator when 

neither income nor rental data are 

available. While this provision does not 

penalize the GSEs for financing these 

properties by requiring that they be 

counted in the denominator towards 

goal calculation, it also does not allow 

them to obtain Housing Goals credit for 

financing mortgages that tend 

disproportionately to serve affordable 

housing. In this final rule, HUD is 

retaining the exclusion provision at 

§ 81.15(e)(6)(ii) but is also adding an 

alternative provision that will permit 

the use of the same estimation 

methodology now used for multifamily 

loans with missing rent data. However, 

HUD is imposing separate maximum 

rates for the new provision as follows: 

a 5 percent maximum on unseasoned 

single-family rental units originated in 

the current year and a 20 percent 

maximum for seasoned loan units, that 

is, for loans that were originated more 

than 365 days prior to the date of 

acquisition by the GSE. HUD recognizes 

the greater difficulty of obtaining rent 

information on units from mortgages 

originated a year or more prior to 

acquisition by the GSE. Therefore, HUD 

is allowing the higher maximum on 

affordability estimation for these units. 

As with the estimating provisions 

permitted under § 81.15(d), the GSEs 

may use only one of the provisions 

permitted under § 81.15(e)(6)(ii) in any 

year. 

In addition to the changes described 

herein, HUD is adding a provision to 

§§ 81.15(d)(2)(i), 81.15(e)(6)(i) and (ii) 

that permits the use of such other data 

source or methodology as may be 

approved by HUD. HUD is also 

clarifying that owner occupied units 

that exceed the maximum established 

under § 81.15(d)(2) for using any 

estimation methodology will remain in 

the denominator of the respective goal 

calculation. 

9. Double Counting of Seasoned 

Mortgages 

In addition to the preceding changes 

being made at this final rule stage, HUD 

is making a technical change to 

§ 81.16(c)(6) for purposes of clarity. 

Paragraph (c)(6) addresses the treatment 

of seasoned mortgages. The paragraph, 

as currently codified, is a long onesentence paragraph. HUD believes that 

dividing this paragraph into two 

subparagraphs would improve 

comprehensibility and clarity. This 

change is intended to clarify the 

restriction on double counting of 

seasoned mortgages in § 81.16(c)(6), i.e.,

the restriction that prohibits the 

counting of a GSE’s purchase of a 

seasoned mortgage toward a goal where 

such mortgage has already been counted 

by the GSE toward the goal. This change 

makes clear that the restriction applies 

to all seasoned mortgages, regardless of 

whether any other counting rules under 

§ 81.16(c) also apply. Section 81.16(c)(6) 

in this final rule reflects this technical 

change. 

10. Bulk Purchases/Counting of 

Seasoned Loans 

Overview. In its May 3, 2004, 

proposed rule, HUD sought comment on 

whether its current definition of a 

‘‘mortgage purchase’’ should be revised 

to ensure that transactions, especially 

large transactions, are appropriately 

counted under the law and in 

accordance with the purposes of 

FHEFSSA and the GSEs’ charter acts. 

HUD also sought comment on whether 

it should amend its counting rules at 24 

CFR 81.15 and 81.16 to ensure that the 

GSEs’ large-scale transactions further 

the requirements and purposes of the 

Housing Goals. 

For example, HUD asked if 

commenters believe the current 

counting rules are specific enough to 

determine which seasoned mortgage 

transactions, including large-scale 

transactions, are substantially 

equivalent to mortgage purchases. HUD 

sought these comments primarily in 

response to certain large-scale 

transactions of seasoned loans 

undertaken by both GSEs in late 2003 

for the purpose of meeting the 2003 

Housing Goals. HUD questioned 

whether such transactions furthered the 

purposes of FHEFSSA, especially since 

the transactions, including a transaction 

between Freddie Mac and Washington 

Mutual Bank (WaMu), contained an 

option for dissolution in the following 

year. HUD sought public comment on 

its counting rules and definitions to 

ascertain the effect of the GSEs’ bulk 

purchases, including those with special 

63628 Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 

terms or conditions, on the market and 

on affordable housing.

Summary of Comments. HUD 

received several suggestions for revising 

its current definitions and counting 

rules. A trade association commented 

that HUD should specify the definition 

of mortgage purchase so as not to count 

transactions that are goals-oriented in 

form but not in substance. Some 

organizations commented that seasoned 

loans should be excluded from counting 

towards the goals altogether because 

they do not directly fund new housing 

supply. Likewise, some commenters 

believed that these transactions are 

contrary to the Charter requirement that 

the GSEs provide assistance to the 

secondary market on an on-going basis. 

One policy group asked that HUD 

exclude loans with recourse clauses 

because these purchases do not alleviate 

risk from the market. Other commenters 

took the opportunity to request that the 

definitions and counting rules more 

closely match CRA loan definitions. 

These commenters did not suggest 

specific regulatory language for the 

definitions. 

HUD also received comments that 

supported counting bulk purchases that 

occur late in the year towards the goals. 

One trade association described the 

efficiencies gained from large-scale 

transactions. For example, the market 

for multifamily units is large and 

fragmented, and seasoned portfolio 

transactions are an efficient means for 

the GSEs to acquire smaller loans in the 

under 50-unit segment of the market. 

Some commenters cautioned that 

changing the definition of mortgage 

purchase or the counting rules to clarify 

the treatment of large-scale seasoned 

mortgage transactions could have 

negative unintended consequences. 

The GSEs responded to this issue 

with detailed comments. Fannie Mae 

stated that every mortgage purchase, 

whether executed through flow, large or 

seasoned transactions, contributes to its 

housing mission, and therefore, HUD 

should not change the qualification of 

mortgage purchases either for the size of 

the transaction or for the amount of 

seasoning involved. Fannie Mae also 

stated that large-scale mortgage 

purchases lower transactions costs for 

both the buyers and sellers of mortgages. 

Some lenders offer to sell the GSEs 

mortgages on a flow basis, but others 

prefer to bundle mortgages together and 

sell to the GSEs from their portfolios. 

Bulk transactions also serve the 

business needs of lenders who do not 

have a direct relationship with Fannie 

Mae. Fannie Mae said that two-thirds of 

its bulk purchases between 2001 and 

2003 were not for seasoned loans. 

Fannie Mae characterized the purchase 

of seasoned loans as an important 

component of the liquidity of current 

mortgages. Knowing that there is a ready 

market allows financial institutions to 

hold some of their assets in the form of 

mortgages, and affords them the 

opportunity to sell these mortgages later 

to manage liquidity, improve 

profitability, strengthen their capital 

position, and manage certain risks. 

In addition to the market benefits of 

seasoned mortgages, Fannie Mae also 

discussed the practical relationship of 

seasoned loan treatment and goals 

performance. The GSEs need bulk 

purchases of seasoned loans to meet the 

goals in years when the mix of business 

in the primary market deviates from the 

business mix anticipated at the time the 

goals were set. Fannie Mae pointed out 

that HUD cited late-year purchases of 

seasoned loans in the proposed rule as 

a useful method to meet the goals when 

market conditions change unexpectedly. 

Fannie Mae also discussed the attributes 

of dissolvable securities, stating that 

lenders sometimes request the option to 

dissolve securities swapped with the 

GSEs. Fannie Mae said that dissolution 

options are common terms in the 

marketplace because dissolution options 

grant lenders greater control over their 

balance sheets, capital position, and 

other financial concerns. Fannie Mae 

indicated that lenders request these 

options because they obtain more 

favorable rates and can make more 

loans.

Freddie Mac made many of the same 

points about bulk purchases of seasoned 

purchases as Fannie Mae and also 

discussed its recent bulk transaction 

with WaMu. For example, Freddie Mac 

commented that bulk purchases and 

dissolution options are common 

industry practices. Freddie Mac also 

stated that counting seasoned loans 

increased the value and liquidity of 

current loans. Knowledge that the GSEs 

stand ready to purchase mortgages 

under all market conditions gives other 

investors greater confidence because 

they have a viable exit strategy when 

providing funds to the real estate 

market. 

Freddie Mac indicated that bulk 

purchases are an essential means of 

achieving the goals when market 

conditions take an unexpected turn, 

such as the conditions leading to its 

transaction with WaMu in 2003. Freddie 

Mac pointed out that, unlike FHA, 

which can manage its business to the 

cap on insurance commitments set 

annually by Congress, Freddie Mac 

instead must respond to a dynamic 

market in which the nature and 

magnitude of loan originations are 

volatile. In real time, it is extremely 

difficult to predict the volume and 

‘‘mix’’ or proportion of goals-eligible 

mortgages those markets will produce. 

Market refinance forecasts for 2003 by 

Economy.com and Freddie Mac were off 

by over $2 trillion. Large transactions of 

mortgage purchases are essential 

because forecasts are not precise. 

With respect to its transaction in 2003 

with WaMu, Freddie Mac stated that it 

engaged in this transaction because 

HUD took a number of steps to strongly 

encourage the GSEs to participate in the 

small 5–50 multifamily mortgage 

market, including bonus points. The 

GSEs can only purchase on terms that 

sellers are willing to accept. Freddie 

Mac further stated that goals that force 

the GSEs to stretch their business mix 

in uncertain market conditions must 

eventually cause the GSEs to value some 

mortgages more than sellers do. Under 

these conditions, sellers will negotiate 

for more favorable terms. Freddie Mac 

stated that the seller ‘‘put’’ option in the 

WaMu transaction and a similar 

transaction with Citibank exemplify 

pro-seller terms and that these 

transactions advance the GSE’s 

regulatory purposes as well as meet the 

letter of the law. 

In response to concerns about the 

options included in the swap, Freddie 

Mac stated that ‘‘it is the GSE’s 

affordable housing goal requirements, 

among other things, that give the sellers 

the negotiating power to obtain such 

options.’’ Both Fannie Mae and Freddie 

Mac concluded that HUD’s definition of 

a mortgage purchase and the counting 

rules should not be changed. 

HUD’s Determination. HUD 

considered the comments received, with 

particular focus on the GSEs’ comments 

regarding transactions that include 

dissolution options. HUD is concerned 

that transactions of this type, which 

both GSEs undertook in 2003 to achieve 

their affordable housing goals, are not 

fully consistent with the purposes of 

FHEFSSA, which are to award goals 

credit for mortgage purchases that 

increase market liquidity for affordable 

housing. When a seller can exercise its 

option to reverse or unwind a 

transaction and take back the mortgages 

within a specified time period, the 

transaction appears temporary in nature, 

and the liquidity that might result from 

the transaction also appears transitory. 

The drafters of FHEFSSA intended 

that the GSEs provide liquidity for 

affordable housing where such liquidity 

would otherwise not exist or where it 

would be less reliable. HUD is aware 

that even short-term liquidity, as may 

occur with dissolution options, can be 

of value to mortgage sellers, especially 

Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 63629

for balance sheet management or other 

purposes, but sellers seeking such 

options are generally not constrained in 

locating short-term liquidity solutions, 

especially when these solutions are 

backed by seasoned mortgage loans. 

Further, HUD believes that placing no 

constraints on goals eligibility for 

transactions with dissolution options 

would have the effect of encouraging 

transactions that are so short-term as to 

be dissolvable almost immediately after 

they have been counted towards the 

housing goals. Such an outcome is 

clearly at odds with FHEFSSA. 

Therefore, HUD has determined to 

amend its counting rules to provide that 

for units acquired in transactions with 

seller dissolution options to count 

toward the housing goals, such options 

must provide for a lockout period that 

prohibits the exercise of the dissolution 

option for at least one year from the date 

on which the transaction was entered 

into and the transaction cannot be 

dissolved during the one-year period. 

The Secretary may grant an exception to 

the minimum lockout period, in 

response to a written request from a 

GSE, if the Secretary determines that the 

transaction furthers the GSE’s statutory 

purposes and the purposes of FHEFSSA. 

Where a mortgage purchase involving a 

seller dissolution option has been 

counted toward the housing goals under 

a transaction subject to this provision, 

the transaction may not be dissolved 

(either by the exercise of the seller 

dissolution option, or by separate 

agreement entered into by the GSE and 

the seller) during the one-year minimum 

lockout period. If the seller of the 

mortgages and the GSE dissolve the 

transaction before that time, the 

transaction may no longer be counted 

toward the housing goals and the GSE’s 

performance must be adjusted in 

accordance with this rule. 

The Department defines seller 

dissolution option as an option for a 

seller of mortgages to the GSEs to 

dissolve or otherwise cancel a mortgage 

purchase agreement or loan sale. The 

Department, however, wishes to fully 

distinguish the arrangements 

established in these seller dissolution 

options from other types of agreements 

involving repurchases of securities or 

mortgages that involve the GSEs. For 

example, the GSE, as seller of a security, 

may agree to repurchase, or buy back, a 

previously sold mortgage-backed 

security on a negotiated basis from the 

holder of the security. HUD’s regulation 

does not address that practice. Likewise, 

it does not address arrangements 

whereby a mortgage lender agrees to 

repurchase or replace a mortgage upon 

demand of the GSE if the mortgage 

defaults. The provision also does not 

apply to repurchase and resale 

agreements where the GSE is the 

purchaser of the security. Rather, the 

transactions addressed by HUD’s 

regulation provide, as a term of the 

transaction, the mortgage lender/seller—

and not the GSE—with the option of 

dissolving the transaction and having 

the mortgages returned to the mortgage 

lender/seller. 

HUD believes the one-year lockout 

period will prevent potential misuse of 

these transactions but will still allow 

sellers of mortgages to manage their 

portfolios in the medium and long term. 

The limit on dissolution options applies 

to all transactions because it is the 

potential for misuse, not the size of the 

transaction that could conflict with 

FHEFSSA. HUD will continue to 

monitor the GSEs’ use of dissolution 

options to ensure that the one-year 

minimum lockout requirement is 

accomplishing its intended purpose. If 

there is a question about whether a 

particular transaction complies with the 

one-year minimum lockout requirement, 

HUD expects that the GSE will seek 

clarification from HUD regarding the 

appropriate treatment of that transaction 

under the counting rules. 

With regard to modifying its 

definition of a ‘‘mortgage purchase,’’

HUD has determined that defining 

mortgage purchases in terms of market 

effects would be cumbersome. The 

definition would have to be broad 

enough to encompass all of the statutory 

purposes, including market liquidity 

and market stability, and still narrow 

enough to exclude transactions that are 

legitimate in form but not in substance. 

Similarly, while some commenters 

suggested that HUD exclude seasoned 

mortgages from its definition or that 

HUD impose a credit risk threshold for 

awarding goals credit, HUD believes 

that these measures could have 

unintended consequences that could 

potentially harm market liquidity for 

affordable housing. For example, HUD 

has encouraged the GSEs to buy 

seasoned portfolios of CRA loans as an 

important source of liquidity for these 

loans.

11. Responses to Other Issues Raised by 

Commenters Relating to the Housing 

Goals 

a. Feasibility Determinations 

Overview. Section 1336(b) of 

FHEFSSA, together with HUD’s current 

regulations, provides a process for 

determining that one or more goal levels 

are infeasible. This process may be 

initiated either by HUD or by a GSE; 

nothing in FHEFSSA or in HUD’s 

regulations limits a GSE’s ability to 

request HUD to examine whether a 

particular goal may be infeasible. If 

HUD determines that a GSE has failed 

to meet a housing goal, or that there is 

a substantial probability that a GSE will 

fail to do so, HUD must notify the GSE 

and provide an opportunity for the GSE 

to respond. HUD must then determine 

whether or not the goal was feasible. If 

HUD determines that the goal was 

infeasible, then no further HUD action 

to enforce the goal is authorized. 

HUD’s proposed rule did not make 

any changes to the process for 

determining whether a goal was or was 

not feasible. However, HUD still 

received comments from both Fannie 

Mae and Freddie Mac regarding those 

provisions. 

Summary of Comments. Fannie Mae 

commented that ‘‘uncertainty regarding 

HUD’s potential feasibility 

determination would lead Fannie Mae 

and Freddie Mac to engage in whatever 

means necessary to meet the goals, 

potentially resulting in market 

distortions.’’ Fannie Mae recommended 

that the goals be set at levels that are 

more likely to be seen in the 

marketplace, rather than at the high end 

of market estimates. 

Freddie Mac commented that an afterthe-fact finding of ‘‘infeasibility’’ or an 

adjustment to the goals would not 

alleviate the burden imposed by 

unreasonable goals. Freddie Mac noted 

that it is very difficult to estimate the 

size and composition (or ‘‘goal mix’’) of 

the mortgage market in advance. 

Freddie Mac also expressed concern 

that an after-the-fact feasibility 

determination would require HUD to 

second-guess innumerable business 

decisions made by the GSEs, with no 

certainty as to how HUD would make 

such determinations. Finally, Freddie 

Mac stated that its reputation would 

suffer great harm during the time HUD 

considered its feasibility determination, 

and that this harm could not be undone. 

HUD’s Determination. The final rule 

does not make any changes to the 

process for determining whether a goal 

is infeasible for a particular year. 

Although HUD has never had to make 

a determination that a goal is infeasible, 

HUD believes that the process that is 

currently in place provides an effective 

framework for making a timely 

determination of infeasibility. If in the 

future it is necessary to make a 

determination of whether a goal is or 

was infeasible, HUD will make every 

effort to expedite the process in an effort 

to minimize any potential costs and 

uncertainty associated with the process. 

63630 Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 

b. Specification of Underserved Areas 

Summary of Comments. Several 

commenters suggested that HUD should 

redefine the Underserved Areas Goal. A 

consensus of these commenters stated 

that lowering the tract income criteria 

from 90 (120) percent to 80 (100) 

percent would make the Underserved 

Areas Goal consistent with CRA. Several 

of the commenters also stated that the 

current definition is too broad and that 

lowering the tract income criteria to 80 

percent or 100 percent when the 

minority population is greater than 50 

percent (as opposed to 30 percent 

currently) of the tract would focus the 

goal on truly underserved areas. One 

commenter suggested including a 

borrower income criteria, such as less 

than 80 percent of area median income, 

in the Underserved Areas Goal to 

further focus the goal on the 

underserved. 

HUD’s Determination. As discussed in 

Appendix B to this rule, HUD has 

determined not to go forward with 

redefining the Underserved Areas Goal 

at this time. 

c. Reconciling the CRA and the 

Affordable Housing Goals 

Summary of Comments. Several 

commenters from trade associations and 

policy organizations suggested that HUD 

could more sharply focus GSE activity 

on low- and moderate-income 

homebuyers by encouraging greater 

purchases of CRA loans. According to 

these commenters, this could be 

accomplished by establishing a new 

CRA goal or by establishing CRA 

subgoals under each of the current 

Housing Goals. 

The CRA requires depository 

institutions to help serve the credit 

needs of their communities and 

authorizes federal regulators to examine 

the level of lending, investment, and 

service that these institutions provide. 

Commenters noted that under section 

1335 of FHEFSSA, Fannie Mae and 

Freddie Mac are directed to ‘‘take 

affirmative steps to assist insured 

depository institutions to meet their 

obligations under the CRA which shall 

include developing appropriate and 

prudent underwriting standards, 

business practices, repurchase 

requirements, pricing, fees, and 

procedures.’’ These commenters noted, 

however, that under FHEFSSA, the 

definitions for key categories of 

borrowers served through affordable 

housing goals differ from those 

established for borrowers served under 

CRA. 

Under FHEFSSA, the definition for 

‘‘low income’’ is a borrower at or below 

80 percent of area median income, 

while for CRA purposes, the definition 

of ‘‘low-income’’ is a borrower at or 

below 50 percent of area median 

income. Similarly, the affordable 

housing goal definition of a ‘‘moderate 

income’’ borrower is at or below 100 

percent of area median income, while 

for CRA purposes, ‘‘moderate income’’

is defined as at or below 80 percent of 

median area income.

Commenters pointed out that these 

definitional discrepancies create a 

mismatch between the loans made by 

the primary market institutions and 

those purchased by the GSEs to meet 

affordable housing goals. The result is 

that the GSEs can meet their goals by 

purchasing loans to borrowers in higher 

income ranges than those mandated 

under CRA, which may result in less 

liquidity available to primary mortgage 

market lenders to make additional low 

and moderate income loans. 

These commenters recommended that 

HUD find a way to resolve the apparent 

contradiction between the definitions. 

One commenter suggested that HUD has 

the authority to align the affordable 

housing goals with the CRA definitions 

without additional legislation. This 

commenter recommended that HUD 

require the GSEs to report low-income 

loans in two categories—‘‘low income’’

and ‘‘very low income’’—and conform 

the definitions of low-income and 

moderate income to the CRA 

definitions. 

Other commenters however, indicated 

that legislative correction would be 

needed to accomplish such alignment. 

These commenters recommended that 

until that time, HUD should consult 

with federal bank and thrift regulators to 

determine the CRA-eligible market share 

and adjust the affordable housing goals 

for Fannie Mae and Freddie Mac 

accordingly. 

Several commenters recommended 

that HUD should consider establishing 

specific ‘‘CRA loan sub-goals’’ under the 

existing goals for the GSEs. One 

commenter suggested that HUD could 

create a new goal that requires the GSEs 

to purchase stated amounts of CRAeligible home purchase mortgages, with 

low and moderate income subgoals 

based on the CRA measures. 

HUD’s Determination. After close 

review of this issue, HUD has 

determined that full harmonization 

between affordable housing goals and 

CRA definitions will require legislative 

action. Income brackets for the goals 

under FHEFSSA and under CRA are 

statutorily defined, and CRA definitions 

allow for much greater discretion by 

examiners to determine CRA scoring. 

For example, under CRA, the distinction 

between home improvement loans and 

small business loans secured by housing 

may not match HUD’s definitions of 

mortgage purchases. In contrast, HUD 

does not use a system of examiners to 

determine the goals eligibility of sellers 

dealing with the GSEs, and comparison 

areas are established through regulation. 

In light of these legal constraints, 

HUD will not make any changes to the 

housing goals to address CRA concerns 

at this time. 

d. Predatory Lending 

Summary of Comments. Certain 

commenters urged the Department to 

adopt predatory lending safeguards in 

the final rule that would prohibit 

Housing Goals credit for purchases of 

loans that included mandatory 

arbitration clauses or loans with 

prepayment penalties beyond three 

years towards the goals. The GSEs did 

not specifically mention this issue in 

their comments to HUD. HUD’s 

proposed rule did not suggest changes 

to its existing GSE regulations that 

address predatory lending practices. 

HUD’s Determination. The 

Department continues to vigorously 

oppose specific lending practices that 

are predatory or abusive in nature. As 

stated in the 2000 rulemaking, the GSEs 

should seek to ensure that they do not 

purchase loans that actually harm 

borrowers and support unfair lending 

practices. In that rulemaking, the 

Department determined that the GSEs 

should not receive the incentive of goals 

credit for purchasing high cost 

mortgages, including mortgages with 

unacceptable features. 

The Department is authorized under 

24 CFR 81.16 to determine whether to 

provide full, partial, or no credit toward 

achievement of any of the housing goals 

for any transaction. The Department’s 

existing rules contain strong safeguards 

against abusive lending by excluding 

certain types of mortgages from 

counting towards the affordable housing 

goals. These include loans with 

excessive fees, and prepayment 

penalties in certain loans. 

The Department is aware that certain 

practices that were not enumerated in 

the regulations adopted in 2000, such as 

loans with prepayment penalties after 

three years and loans with mandatory 

arbitration clauses, often lock borrowers 

into disadvantageous loan products. The 

Department will rely on existing 

regulatory authorities to monitor the 

GSEs’ performance in this area. Should 

the Department later determine that 

there is a need to specifically enumerate 

additional prohibited predatory 

practices, it will address such practices 

at a future time. 

Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 63631

e. Minority Subgoals/Goals 

Summary of Comments. Among the 

many suggestions HUD received for 

subgoals and bonus points, several 

advocacy groups recommended that 

HUD directly target minority mortgage 

purchases such as those made to Native 

Americans. These groups note that 

homeownership rates are not equal 

across ethnic groups. Fewer Blacks and 

Hispanics own their own homes than 

the general population. Although the 

GSEs have made some progress in this 

area, the GSEs are still less likely to 

serve high minority areas than other 

lenders. In the view of these 

commenters, the absence of the GSEs 

has led to higher borrowing costs and 

harsher borrowing terms for minority 

borrowers because they are more likely 

to deal with nontraditional and 

predatory lenders. 

HUD’s Determination. Under 

FHEFSSA, HUD does not have statutory 

authority to establish goals beyond 

those enumerated in the statute. 

FHEFSSA directs HUD to establish a 

goal for underserved areas, and HUD’s 

goal includes census tracts with high 

concentrations of minority households 

(and with median income below a 

certain level) as one category of 

underserved area. The statute does not 

empower HUD to establish a goal based 

on the characteristics of borrowers, 

other than by income of borrower.

Even without an explicit subgoal, 

HUD believes that the goals structure 

will address the concerns of minority 

borrowers. As discussed in the 

introduction, minorities and immigrants 

are a growing percentage of homebuyers 

and many more aspire to home 

ownership. Demographics dictate that 

these buyers will become increasing 

shares of the conventional conforming 

market. Requiring the GSEs to lead the 

market will encourage them to do even 

more to reach out to minorities. 

f. Technical Change to § 81.16(c)(7) 

In addition to the preceding changes 

being made at this final rule stage, HUD 

is making a technical change to 

§ 81.6(c)(7) to correct a cross-reference. 

Paragraph (c)(7) addresses the treatment 

of refinanced mortgages. The paragraph 

includes a reference to § 81.14(f), which 

is not related to refinanced mortgages. 

Section 81.16(c)(7) in this final rule is 

revised to correct this cross-reference. 

D. Subpart I—Other Provisions 

1. Overview—Verification and 

Enforcement To Ensure GSE Data 

Integrity 

HUD proposed to amend § 81.102 

(Independent Verification Authority) of 

its regulations to incorporate certain 

data integrity procedures designed to 

ensure the accuracy, completeness, and 

timeliness of housing goal information 

submitted by the GSEs to the 

Department. These procedures 

included: (1) A requirement that the 

GSEs provide a certification with their 

Annual Housing Activity Reports 

(AHAR) and such other reports, data 

submissions, and information that the 

Department may request in writing be 

certified; (2) a procedure to adjust 

current year-end errors, omissions, and 

discrepancies in data submissions to 

HUD; and (3) a procedure for correcting 

prior year overstatements of 

performance due to reporting errors, 

omissions, or discrepancies in a GSE’s 

AHAR. HUD also restated in the 

proposed amendment to § 81.102 the 

enforcement options and remedies 

under FHEFSSA and HUD’s regulations 

that could result from a determination 

that a GSE’s data submissions, 

information, or reports were not current, 

were incomplete, or otherwise 

contained an untrue statement of 

material fact. 

In addition to comments provided by 

the GSEs, HUD received comments from 

groups that included mortgage lenders, 

non-profit and policy advocacy 

organizations, and trade associations. 

Most commenters supported the data 

verification provisions of the proposed 

rule. However, one mortgage lender 

stated that the proposed certification 

would impose a severe burden on the 

GSEs and lenders. Another suggested 

that the data integrity process should 

include some leeway for unintentional 

mistakes so that it does not become 

burdensome. A trade association stated 

that HUD should not enact regulations 

that would put additional data integrity 

burdens on lenders. Fannie Mae and 

Freddie Mac provided detailed 

comments on each proposal. These 

comments are discussed more fully in 

the following sections. 

2. Independent Verification Authority—

§ 81.102(a) 

As it proposed, the Department is 

retaining and recodifying the provisions 

of the current § 81.102(a) that provide 

that HUD may independently verify the 

accuracy and completeness of data, 

information and reports submitted by a 

GSE in addition to the Department’s 

existing authority to conduct on-site 

verifications and performance reviews. 

HUD is redesignating this section, as 

HUD proposed, as § 81.102(a). 

3. Certification—§ 81.102(b) 

To ensure the highest degree of 

corporate accountability, and to be 

consistent with the customary practice 

of regulators of financial institutions, 

the Department proposed that the GSEs 

be required to provide a certification 

with their AHAR reports and such other 

report(s), data submission(s), or 

information for which HUD requests 

certification in writing. HUD proposed a 

certification that consisted of the 

following four parts: (1) The GSE 

Certifying Official has reviewed the 

particular AHAR, other report(s), data 

submission(s) or information; (2) to the 

best of the GSE Certifying Official’s 

knowledge and belief, the particular 

AHAR, other report(s), data 

submission(s), or information are 

current, complete, and do not contain 

any untrue statement of a material fact; 

(3) to the best of the GSE Certifying 

Official’s knowledge and belief, the 

AHAR or other report(s), data 

submission(s), and information fairly 

present in all material respects the 

GSE’s performance, as required to be 

reported; and (4) to the best of the 

Certifying Official’s knowledge and 

belief, the GSE has identified in writing 

any areas in which the GSE’s particular 

AHAR, other report(s), data 

submission(s), or information may differ 

from HUD’s written articulations of its 

counting rules including, but not 

limited to, the regulations under 24 CFR 

part 81, and any other areas of 

ambiguity. 

Summary of Comments. Fannie Mae 

and Freddie Mac commented on this 

proposal. Each expressed many similar 

objections to the certification language 

as proposed and offered many similar 

recommendations. For example, both 

GSEs stated that the certification 

language was overly broad and should 

be modified to the form authorized in 

FHEFSSA for submissions to OFHEO; 

namely, that the report is true and 

correct to the best of such officer’s 

knowledge and belief. Each 

recommended that the words ‘‘fairly 

present’’ be deleted from the third 

proposed certification statement stating 

that these words are meaningful only in 

the context of Generally Accepted 

Accounting Practices (GAAP), which 

defines standards of determining 

‘‘fairness’’ in financial reporting, but not 

performance reporting.

In addition, both GSEs questioned 

HUD’s authority to impose a 

certification requirement, but stated that 

to the extent HUD does impose this 

requirement, it should be the 

certification used by OFHEO. They also 

stated that the phrases ‘‘errors, 

omissions, discrepancies, and 

ambiguities,’’ ‘‘written articulations of 

its counting rules,’’ and ‘‘any other areas 

of ambiguity’’ are vague and undefined, 

63632 Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 

and that this vagueness makes it 

possible for HUD to arbitrarily 

implement the certification provision by 

interpreting it in a way that is not 

known by the GSEs. Freddie Mac also 

stated that HUD’s informal written 

articulations are not enforceable and 

that it may not know about all of HUD’s 

informal articulations. Both GSEs also 

stated that it is difficult to certify to the 

accuracy of information that must be 

included in the reports that they receive 

from third parties. 

Freddie Mac suggested that the 

subject of the certification be limited to 

the year-end annual data tables and 

computerized loan-level data that it 

submits with its AHARs, and should not 

cover any narrative portions of the 

AHARs. Fannie Mae suggested that the 

certification should focus on the process 

it follows for generating its submissions 

and should cover only the final tables in 

the AHAR that it submits each year. 

Both GSEs stated that no certification 

should be required for reports-inprogress, such as the housing goals 

progress reports each submits to HUD 

on a quarterly basis. 

A policy advocacy group commented 

that the certification should be limited 

to reporting processes of the GSEs, not 

the accuracy of the underlying data 

obtained from individual lenders. A 

trade association commented that HUD 

should not put additional data integrity 

burdens on lenders. 

HUD’s Determination. HUD has 

considered the comments received and 

has determined to modify its proposal. 

HUD’s reasons for requiring a 

certification were not disputed by 

commenters. However, HUD has revised 

the proposed rule language to address 

commenters’ concerns regarding clarity. 

HUD has also included alternative 

language in the final rule that would 

specifically define terms as well as 

eliminate the language that the GSEs 

and others found to be ambiguous. As 

a result, the final rule includes a 

simplified certification that is much 

closer to the certification used by 

OFHEO. Section 81.102(b) has been 

amended to require the senior officer of 

each GSE who is responsible for 

submitting to HUD the fourth quarter 

Annual Mortgage Report and the AHAR 

under sections 309(m) and (n) of the 

Fannie Mae Charter Act or sections 

307(e) and (f) of the Freddie Mac Act, 

as applicable, or for submitting to the 

Secretary such other report(s), data 

submission(s), or information for which 

certification is requested in writing by 

the Secretary to state that: ‘‘To the best 

of my knowledge and belief, the 

information provided herein is true, 

correct and complete.’’

The Department has also included 

language to clarify that it may pursue 

enforcement action against a GSE that 

fails to provide the certification required 

under § 81.102(b). In addition, the 

Department may pursue enforcement 

action if a GSE submits the certification 

required under § 81.102(b), but the 

Secretary later determines that the data, 

information or report(s) are not true, 

correct and complete. For data, 

information and report(s) subject to 

§ 81.102(c) or (d), the final rule makes 

clear that the Department will only 

pursue enforcement action against a 

GSE in connection with material errors, 

omissions or discrepancies, as those 

terms are defined therein. 

The GSEs have asserted that HUD 

may not require certification of any 

information they submit because the 

Department has no express statutory 

authority to do so. The Department’s 

authority to require certification of 

information submitted by the GSEs is 

authorized under HUD’s general 

regulatory power over the GSEs under 

section 1321 of FHEFSSA as well as its 

authority to monitor and enforce the 

GSEs’ compliance with the housing 

goals under section 1336. (See the 

preamble of HUD’s proposed rule at 69 

FR 24247–24248 for a full discussion of 

HUD’s authority to require certification.)

In requiring this certification, HUD is 

fully aware that the GSEs collect 

millions of data elements from 

hundreds of sources and that the GSEs 

must depend upon these sources to 

provide accurate data. In requiring a 

certification, HUD intends that the GSEs 

will use and rely upon their internal 

controls and other due diligence 

processes and procedures for collecting, 

compiling, verifying the accuracy of, 

and reporting the data received from 

sellers. HUD will evaluate the 

sufficiency of this certification 

beginning with the 2005 fourth quarter 

Annual Mortgage Report and the AHAR 

to determine whether it is serving its 

function of providing adequate 

assurance as to the accuracy and 

completeness of information. 

With respect to the scope of the 

certification, HUD believes it is 

appropriate and reasonable that the 

certification statement apply to the 

entire AHAR submission, including the 

narrative text, data tables, and 

computerized loan-level data. Section 

309(n) of Fannie Mae’s Charter Act and 

section 307(f) of the Freddie Mac Act 

specify the types of information each 

GSE is required to report, including 

narrative descriptions as well as data. 

HUD expects that all of the required 

information, not just the data and data 

tables, will be subjected to appropriate 

internal review processes by the GSEs. 

A certification regarding the entire 

report helps to ensure the GSEs’

accountability for the information that 

they are required to report accurately 

under their charters. 

Although Fannie Mae recommended 

that the certification should apply only 

to the tables in the AHARs and Freddie 

Mac recommended that the certification 

should apply only to the data tables in 

the AHAR and the loan-level data it 

submits with its AHAR, from time to 

time HUD requires one or both GSEs to 

submit other report(s), information, or 

data submission(s) that rise to a 

sufficient level of importance to HUD’s 

oversight work that a certification 

statement is warranted. The final rule, 

therefore, retains this provision and 

further provides that the Secretary will 

issue a written notification to the GSE 

whenever such a certification is 

required. HUD expects that any 

additional certification requirements 

will be the exception rather than the 

rule to ensure that the routine and 

necessary flow of information is not 

impeded. 

Both GSEs recommended that HUD 

not impose a certification on any 

progress reporting, such as the quarterly 

housing goals performance reports each 

submits to HUD. HUD did not propose 

that such reports be certified and 

reiterates that certification statements 

will not be required for the GSEs’ first 

three quarterly housing goals reports 

and any other report(s), data 

submission(s) or information that 

represent incomplete ‘‘snapshots’’ or 

information that is being gathered but 

which is not in final form. Certification 

will be required for the fourth quarter 

report, i.e., the Annual Mortgage Report. 

4. Adjustment To Correct Current Yearend Errors, Omissions or 

Discrepancies—§ 81.102(c) 

HUD routinely conducts 

computerized consistency checks of 

loan-level data received from the GSEs 

as part of their AHAR reporting. This 

data are received on March 15th of each 

year for the previous year’s 

performance. These reviews verify that 

the GSEs have applied HUD’s counting 

rules and goals eligibility standards 

appropriately in determining their yearend performance. A key procedure 

involves applying HUD’s counting rules 

to the GSEs’ loan-level data for the 

purposes of replicating the performance 

figures computed by the GSEs in their 

AHARs. Also, in conjunction with other 

reports provided by the GSEs, including 

a report that reconciles all adjusted 

mortgage purchases (the denominator) 

with the GSE’s total business volume as 

Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 63633

reported in the annual report to 

shareholders or other information 

filings, HUD’s reviews also verify the 

completeness of the data. If HUD finds 

discrepancies between its results and 

those reported by the GSEs, HUD works 

with appropriate GSE staff to resolve the 

discrepancies after which HUD makes a 

final determination of year-end results 

and publishes these as HUD’s official 

performance figures for the year. 

HUD’s proposed rule provided for a 

timeframe within which the GSEs may 

comment or otherwise respond to 

HUD’s findings of errors, omissions, or 

discrepancies with additional 

information. If a GSE did not respond 

with information to correct or explain 

the error, omission, or discrepancy to 

HUD’s satisfaction within five working 

days of HUD’s initial notification, then 

HUD would notify the GSE in writing 

and seek clarification or additional 

information. At this point, the GSE 

would have 10 working days in which 

to respond and could request an 

extension of an additional 20 working 

days from HUD. If the GSE still did not 

respond in a manner that corrected the 

error, omission, or discrepancy, then 

HUD would determine the appropriate 

adjustment to the numerator and 

denominator of the applicable goal and/

or subgoal. Currently, there are no 

required time limits within which the 

GSEs must respond to HUD’s inquiries 

for additional information, and there is 

no procedure by which HUD can bring 

the process of reviewing a GSE’s current 

year submission to closure absent 

voluntary assistance from the GSEs. The 

practical effect of not codifying a 

timetable for completion of this process 

is that HUD could be delayed in 

fulfilling its responsibilities to issue a 

timely, official report on the GSEs’

performance for the year most recently 

ended and to produce the public use 

database. 

Summary of Comments. In addition to 

the GSEs, many organizations, including 

policy advocates, trade associations, and 

one non-profit group, commented on the 

data verification provisions of HUD’s 

proposed rule. Nearly all of these 

comments supported implementation of 

some type of data verification 

procedures. One trade group stated that 

data verification regulations should be 

enforced to get more accurate 

information. However, another trade 

group expressed concern that the data 

integrity process should include some 

leeway for unintentional mistakes to 

avoid becoming burdensome. Two 

advocacy organizations supported the 

proposed provisions regarding data 

verification but thought HUD should 

give the public the ability to comment 

on the GSEs’ AHARs. 

Both GSEs commented in detail on 

HUD’s proposal. Both expressed 

concerns about the scope of this 

provision and questioned what 

procedures, especially adjustment 

notification and enforcement 

procedures, would be associated with 

its implementation. Freddie Mac 

augmented its comments with a legal 

opinion from outside counsel. 

With respect to the words ‘‘errors, 

omissions and discrepancies,’’ the GSEs 

contended that these terms were vague 

and needed further definition. Freddie 

Mac stated that without such further 

definition, HUD could disallow 

counting of units based upon 

interpretations of its rules of which 

Freddie Mac was unaware, and thus 

violate the fair notice doctrine. Freddie 

Mac suggested that if HUD retained the 

use of these words in its regulation, it 

should explain how their meanings 

differ. Fannie Mae stated that potential 

adjustments should apply only to 

situations where the GSE failed to 

follow HUD’s rules for data collection 

and reporting, and not where it failed to 

follow its own rules for procedures in 

data collection and reporting. Fannie 

Mae also contended that adjustments 

should be made only where the error, 

omission or discrepancy was in a data 

field that affected scoring and where it 

also had a material effect on compliance 

with a housing goal. Freddie Mac stated 

that adjustments should be made only 

for material errors or omissions. Fannie 

Mae stated that a GSE should be subject 

to additional enforcement action only 

when an error, omission or discrepancy 

is due to intentional or bad faith action.

Both GSEs stated that HUD’s 

regulations should provide that HUD 

will issue a written determination to a 

GSE when it determines that an 

adjustment is necessary, that HUD 

should specify which official within 

HUD is authorized to issue orders under 

proposed § 81.102(c) and (d), and that 

the rule should provide for more lenient 

time frames for responding to HUD’s 

inquiries. In addition, Freddie Mac 

commented that the regulations should 

state that an order requiring an 

adjustment constitutes ‘‘final agency 

action’’ for purposes of judicial review 

under the Administrative Procedure Act 

and that judicial review is immediately 

available. 

Fannie Mae also commented on the 

title of HUD’s provision stating that a 

provision to correct ‘‘current year end 

errors’’ is confusing because HUD 

cannot correct errors for a current year 

when it does not receive the data about 

any current year until the next year. 

HUD’s Determination. HUD has 

considered the comments and 

determined that a provision specifying 

what procedures HUD will use in 

developing its official performance 

numbers for the immediately preceding 

year is necessary. HUD notes that many 

of the concerns expressed by 

commenters, especially the GSEs, 

involve the lack of definition of the 

terms ‘‘errors, omissions or 

discrepancies’’ and a lack of clarity 

regarding how the regulation will be 

enforced. Accordingly, in the final rule, 

HUD has added a paragraph that defines 

an ‘‘error’’ as a technical mistake, such 

as a mistake in coding or calculating 

data. Mistakes of this type may also 

include, but not be limited to, systems 

errors, such as those related to 

geocoding or misapplication of HUD’s 

most current data regarding median 

income or underserved areas. An 

‘‘omission’’ is defined as a GSE’s failure 

to count units in the denominator. A 

‘‘discrepancy’’ is defined as any 

difference between HUD’s analysis of 

data and the analysis contained in a 

GSE’s submission of data, including a 

discrepancy in goal and/or Special 

Affordable subgoal performance. 

The Department also clarifies in 

§ 81.102(c)(5) of this final rule that an 

error, omission or discrepancy is 

‘‘material’’ if it results in an 

overstatement of credit for a housing 

goal or Special Affordable subgoal and, 

without such overstatement, the GSE 

would have failed to meet such housing 

goal or Special Affordable subgoal for 

the immediately preceding year. Finally, 

the rule defines the term ‘‘year-end 

data’’ to mean data that HUD receives 

from the GSEs related to housing goals 

performance in the immediately 

preceding year and covering data 

reported in the fourth quarter Annual 

Mortgage Report and the GSE’s AHAR. 

With respect to procedures for 

notifying a GSE of any suspected error, 

omission or discrepancy, HUD is 

responding to the concerns raised by the 

commenters by amending the proposed 

rule to: (1) Provide that, with regard to 

each initial notification by HUD to a 

GSE, HUD may, in its own discretion, or 

upon a request by a GSE, extend the 

initial five working day response period 

for up to 20 additional working days; (2) 

establish that any person with delegated 

authority from the Secretary, or the 

Director of HUD’s Financial Institution 

Regulation Division, or his or her 

designee, is responsible for issuing 

initial notifications regarding errors, 

omissions, or discrepancies, making 

determinations on the adequacy of 

responses received, approving any 

extensions of time permitted under this 

63634 Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 

provision, and generally managing the 

data verification process; (3) establish 

that the Secretary or his designee will 

inform a GSE in writing of HUD’s 

determination of official performance 

figures, including any adjustments, five 

working days prior to HUD’s release of 

its official performance figures to the 

public; (4) provide that during the five 

working days prior to such public 

release, a GSE may request 

reconsideration in writing of HUD’s 

final determination of its performance 

in which case the Secretary will decide 

whether to grant the request for 

reconsideration, and if the request is 

granted, make a final determination on 

the request for reconsideration within 

10 working days of the Secretary’s 

granting of the GSE’s request for 

reconsideration; and (5) provide that, 

with the exception of the written 

determination of HUD’s official 

performance figures, all other 

notifications under this provision may 

be by electronic mail. 

HUD has also clarified through its 

definitions of errors, omissions and 

discrepancies, that an ‘‘adjustment’’ will 

be made in situations where a GSE 

failed to follow correct procedures in 

data compilation and reporting and/or 

where it failed to comply with HUD’s 

regulation for determining eligible units. 

As has been the case in the past, HUD 

expects that any adjustments that it may 

make to the numerator or denominator, 

that result in a difference between the 

GSE’s performance as stated in the 

GSE’s AHAR for the immediately 

preceding year and HUD’s official 

performance figures, will be well 

understood by the GSE because 

adjustments of this type occur routinely 

during HUD’s verification work. 

HUD is also clarifying that it intends 

to treat a GSE’s material errors, 

omissions or discrepancies in, or failure 

to certify, data submissions under 

§ 81.102(c) as a failure to submit 

information that the GSE is required to 

submit under its charter. Accordingly, 

the Department may pursue the 

additional enforcement remedies 

authorized under § 81.102(e). 

With respect to events that could 

trigger enforcement under this 

provision, HUD does not intend that 

routine technical errors or omissions 

would warrant such enforcement. In 

order to trigger the enforcement 

provision, errors, omissions or 

discrepancies discovered during review 

of the immediately preceding year’s 

performance must be material, as HUD 

has defined that term. The error, 

omission or discrepancy also must be 

one that indicates to HUD a serious 

problem in the GSE’s internal 

procedures. Examples of errors, 

omissions, or discrepancies that could 

rise to this level under these criteria 

include a GSE counting units that are 

not eligible under HUD’s rules for goals 

credit or a GSE underreporting units in 

the denominator. With respect to 

Freddie Mac’s suggestion that HUD’s 

regulations should state that this 

determination is ‘‘final agency action’’

for purposes of the Administrative 

Procedure Act and is immediately 

subject to judicial review, FHEFSSA 

already provides that the GSEs may 

obtain judicial review in connection 

with proceedings to enforce the housing 

goals, and that those proceedings shall 

be governed by the Administrative 

Procedure Act. Therefore, the 

Department declines to adopt Freddie 

Mac’s suggestion. 

To more clearly define the scope of 

this provision, HUD has renamed this 

provision in the final rule as 

Verification Procedure and Adjustment 

to Correct Errors, Omissions, or 

Discrepancies in AHAR Data for the 

Immediately Preceding Year. 

5. Procedures for Prior Year Reporting 

Errors—§ 81.102(d) 

The annual data verification review 

for the immediately preceding year 

described in § 81.102(c) was designed to 

ensure that reported goals performance 

was correctly calculated in accordance 

with HUD’s regulations. Although these 

reviews can test for the reasonableness 

of some reported data, the reviews 

cannot generally determine the accuracy 

of the underlying loan-level data. To 

monitor data accuracy, HUD has 

implemented a second type of 

procedure, called performance reviews. 

Performance reviews are especially 

necessary because housing goals are 

calculated from information (e.g.,

number of dwelling units) that is not 

reported in the GSEs’ financial 

statements and is, therefore, not subject 

to all GSE procedures designed to 

ensure the accuracy and completeness 

of reported financial information. HUD’s 

performance reviews ensure that 

rigorous audit procedures, either similar 

or identical to those used to monitor the 

integrity of financial data, are also used 

in monitoring the accuracy, 

completeness, and timeliness of the data 

each GSE submits to HUD. Performance 

reviews include, but are not limited to, 

evaluating the GSEs’ internal controls 

over the collection, management and 

reporting of loan-level mortgage data 

used in calculating housing goals 

performance. Performance reviews may 

also focus on the GSEs’ quality control 

standards and procedures for 

information received from loan sellers 

and securities issuers and dealers and 

may include additional procedures to 

test random samples of data for 

accuracy and completeness. To 

supplement HUD’s on-site performance 

review work, the Department has 

implemented specialized reporting by 

which each GSE informs HUD on a 

scheduled basis of key issues and 

findings relevant to goals reporting. For 

example, the GSEs report to HUD 

quarterly on the results of their own 

internal reviews and self-assessments 

related to housing goals. These reports 

cover all actions taken by the GSE to 

remove any findings related to 

weaknesses in controls or procedures, 

including those findings identified by 

HUD. 

Because of the complexity of each 

GSE’s business, as well as the 

complexity of many of the transactions 

that the GSEs undertake to meet their 

housing goals, there is a possibility that 

HUD may discover, during a 

performance review, that a serious 

overstatement of credit towards one or 

more housing goals occurred in the 

reported prior year under review. 

Currently, HUD has no procedure for 

ensuring that any such overstatement is 

corrected or otherwise adjusted in some 

manner unless the overstatement is 

discovered in the review of the 

immediately past year’s data during the 

replication review described in 

§ 81.102(c). To remedy this, HUD 

proposed a procedure that would adjust 

a GSE’s current year performance by 

deducting from the numerator of the 

relevant housing goal or subgoal the 

number of overstated units from a prior 

year. A prior year was any one of the 

two years preceding the current 

reporting year.

Summary of Comments. Many 

organizations commented on HUD’s 

data integrity provisions in general and 

nearly all of these organizations 

expressed support for data verification. 

The GSEs commented more specifically 

on HUD’s proposals for adjustments to 

make up prior years’ overstatements. 

The GSEs asserted that the Department 

does not have authority to either deduct 

credit from a current year’s purchase 

that is entitled to credit under HUD’s 

regulations or add to a current year’s 

housing goal to compensate for the 

GSE’s failure to meet its goals in a prior 

year. They also had other objections, 

including the objection that the only 

remedy provided in FHEFSSA for any 

failure to meet housing goals is the 

imposition of a housing plan, which 

may address only a probable failure to 

meet housing goals in the current year 

or actual failure to meet goals in a 

current year in the next calendar year. 

Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 63635

The GSEs stated that the Congress 

intended the statute to provide no 

remedy for their failure to meet a prior 

year’s housing goal and, therefore, that 

the Department has no authority to 

fashion such a remedy. Based on this 

line of reasoning, they concluded that 

HUD may not take any action against a 

GSE when it discovers that it failed to 

meet a housing goal in a prior year, even 

though HUD could have taken action if 

the failure had been discovered within 

one year after the year in which it 

occurred. 

Both GSEs also objected to the policy 

basis for HUD’s proposal. For instance, 

Fannie Mae wanted the time period 

within which HUD might impose a 

prior-year correction shortened from up 

to 24 months to three months after 

HUD’s receipt of AHAR loan-level data, 

which HUD receives on March 15th of 

each year. Fannie Mae cited OFHEO’s 

ability to render a decision on its final 

capital classification within 90 days of 

the reporting quarter as evidence that 

complex determinations can be made 

within short time frames. Freddie Mac 

saw no reason why the necessary 

evaluations could not be accomplished 

within six months after the close of the 

immediately preceding year. Freddie 

Mac stated that HUD’s policy 

justification does not support the 

proposal and that HUD did not point to 

any instance where the increasing 

complexity of transactions has led to 

overstatements in performance. Freddie 

Mac also commented that the 

Department already has the option of 

publicizing the discovery of any prior 

year mistakes—by press release, news 

conference or its Web site information—

and of making Congress aware of these 

mistakes. 

Freddie Mac requested that HUD 

withdraw the proposal entirely. If HUD 

opted to proceed to implement the 

proposal, then Freddie Mac suggested 

that HUD amend the provision to: (1) 

Limit application of the rule to large 

prior year overstatements that affect a 

material number of units under a goal 

(e.g., five percent); (2) provide that HUD 

will apply the rule only when a GSE 

acted in bad faith; (3) provide that HUD 

will not apply the rule cumulatively; 

that is, that HUD will not accumulate 

several years’ over-counts and then 

deduct a cumulative total from the 

current year; and (4) clarify in the final 

rule which official within HUD will 

make decisions under this provision 

and provide that the basis for decisions 

be explained. 

HUD’s Determination. HUD has 

carefully considered both GSEs’

comments, including their legal and 

policy arguments. The Department 

agrees that the only remedy Congress set 

out in FHEFSSA for failing to meet a 

housing goal is a housing plan under 

section 1336, and as the statute is 

written, the housing plan addresses only 

a current year’s failure, either in that 

year or in the next calendar year. 

Therefore, the statute does not 

specifically address a GSE’s failure to 

meet a housing goal in a prior year, i.e.,

a failure occurring in any one of the two 

years immediately preceding the latest 

year for which data on housing goals 

performance was reported to HUD. 

However, the Department does not agree 

that Congressional silence on this 

precise issue means either that Congress 

intended the GSEs to be allowed to fail 

to meet their housing goals as long as 

the Department does not discover that 

failure within a specific time or that the 

Department may not fashion a remedy 

to address this issue. This conclusion 

runs counter to Congress’s purposes in 

enacting FHEFSSA, which directs HUD 

to establish and monitor the GSEs’

compliance with the Housing Goals. 

Section 1336 of FHEFSSA provides 

that the Secretary shall ‘‘monitor and 

enforce’’ the GSEs’’ compliance with the 

housing goals set by the Department. 

According to FHEFSSA’s legislative 

history, in enacting FHEFSSA Congress 

intended ‘‘to establish a comprehensive

framework of goals, data collection, 

reporting requirements and enforcement 

provisions.’’ S. Rep. No. 102–282, at 34 

(1992)(emphasis added). 

When discussing the GSEs’ duties to 

meet housing goals set for low- and 

moderate-income housing and housing 

in central cities and rural areas, 

Congress stated:

The GSEs need to provide more leadership 

in all of these areas, and they have indicated 

a desire to do so. But direct and potentially 

forceful federal oversight is the only way to 

ensure that it will happen. Id. at 11.

Under the GSEs’ suggested 

construction of FHEFSSA, HUD’s ability 

to enforce the housing goals is totally 

dependent upon only one factor, namely 

how quickly HUD discovers that a GSE 

has failed to meet a goal. In order to 

determine whether a GSE has failed a 

goal, HUD must receive, verify and 

analyze massive amounts of data, as 

described above. Under the GSEs’

suggested construction of FHEFSSA, 

only if HUD discovers that a GSE has 

failed to meet a housing goal or subgoal 

in the nine month period that runs from 

March 15th, when the GSEs submit 

current year-end data, to the end of that 

year—may HUD enforce the housing 

goals for that year. Such a construction 

is not only unreasonable on its face but 

it is contrary to the plain intent of 

Congress as expressed in the FHEFSSA 

and its legislative history. FHEFSSA 

and its legislative history indicate that 

Congress established a comprehensive 

regulatory scheme under which HUD 

would establish and enforce the 

Housing Goals through strict and 

pervasive regulation. 

Furthermore, there is absolutely no 

statement in FHEFSSA or its legislative 

history to suggest that Congress 

intended that HUD must ignore or 

forgive a GSE’s failure to meet its 

housing goals in any year for any 

reason, including the passage of a 

certain amount of time before HUD 

discovers this failure. The fact that 

FHEFSSA is silent on the issue of how 

to address a GSE’s failure to meet a prior 

year’s housing goal means that there is 

a gap in FHEFSSA’s enforcement 

scheme regarding this precise issue. 

Under Chevron v. NRDC, 467 U.S. 837 

(1984), the Department has discretion to 

fashion an appropriate remedy to fill 

this gap, and it has done so in 

§ 81.102(d). Moreover, the Department 

has the discretion to fashion a remedy 

to correct prior year overstatements 

without which a GSE would have failed 

to meet a housing goal or Special 

Affordable subgoal under its general 

regulatory powers under section 1321 of 

FHEFSSA. 

However, in light of the objections 

raised to the proposed regulation in the 

comments discussed above, HUD has 

revised § 81.102(d) to remove provisions 

that either provide for deduction of 

Housing Goals credit in a current year 

from purchases that qualify for credit, or 

that add requirements to a current year’s 

Housing Goals due to errors, omissions 

or discrepancies in a prior year’s data 

submissions. The final rule provides 

instead that the Secretary may require 

the GSEs to make up any overstatements 

of goal performance without which a 

GSE would have failed to meet a prior 

year’s Housing Goal, no later than the 

year following the year in which HUD 

first notifies the GSE of this failure. (The 

rule now defines the term ‘‘prior year’’

to mean any one of the two years 

immediately preceding the latest year 

for which data on housing goals 

performance was reported to HUD.)

In order to remedy this failure, the 

Secretary may require the GSEs to 

purchase additional mortgages that 

finance a number of units that either (a) 

equal the number of units overstated in 

the prior year’s goal performance, or for 

the Special Affordable subgoals the 

number or dollar amount, as applicable, 

of mortgage purchases that the Secretary 

has determined were overstated, or (b) 

that equal the percentage of the 

overstatement in the prior year’s goal 

63636 Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 

performance as applied to the most 

current year-end performance, 

whichever is less. Units purchased to 

remedy an overstatement must be 

eligible to qualify under the same goal 

or goals for which the overstatement 

occurred in the prior year. For example, 

a GSE may have overstated a prior year’s 

performance by 5,000 units or .22 

percent under the Low- and ModerateIncome Goal. To make up this 

overstatement, a GSE may purchase an 

additional 5,000 units that are eligible 

under the Low- and Moderate-Income 

Goal in the year immediately following 

the year in which HUD notifies the GSE 

of the overstatement or it may multiply 

the current year’s total eligible 

purchases under the Low- and 

Moderate-Income Goal by the overstated 

percentage from a prior year (e.g., .22 

percent) to determine the number of 

units that must be purchased, provided 

this number is less than 5,000 units. The 

same requirement also applies to the 

Special Affordable Home Purchase 

Subgoal. When an overstatement occurs 

under this Subgoal, the Secretary may 

require the GSE to make up the number 

of mortgages that were overstated using 

the lesser of the two procedures 

previously described. For 

overstatements under the Special 

Affordable Multifamily Subgoal, the 

GSE may be required to make up the 

dollar amount of overstatement by 

purchasing qualifying multifamily 

mortgages in an amount equal to the 

overstatement. The GSEs will not be 

required to make up any errors, 

omissions or discrepancies in prior 

years that were not material. As 

previously noted, the final rule provides 

that an error, omission or discrepancy is 

material if it results in an overstatement 

of credit for a housing goal or Special 

Affordable subgoal and, without such 

overstatement, the GSE would have 

failed to meet such housing goal or 

Special Affordable subgoal for the prior 

year. 

Also, corrections for overstatement of 

goals performance under this provision 

will not be counted or reported under 

the GSEs’ Annual Housing Activity 

Report, including calculation of housing 

goal performance in any year, but rather 

will be managed separately from the 

housing goals as directed by HUD. 

If the GSE does not purchase a 

sufficient number of eligible units or 

mortgages, as described previously, then 

HUD may issue a notice that the GSE 

failed a housing goal or subgoal in a 

previous year, or seek additional 

enforcement remedies under § 81.102(e) 

or any other civil or administrative 

remedies that are available under 

applicable law. The Department is 

treating a GSE’s material errors, 

omissions or discrepancies in, or failure 

to certify, a prior year’s data submission 

as a failure to submit information that 

the GSE is required to submit under its 

charter. 

Both GSEs also questioned the need 

for an adjustment period that could 

extend for up to 24 months from the 

close of a calendar year’s performance, 

believing instead that any such review 

could be accomplished within six 

months of the close of the previous year, 

which is a time frame similar to that 

used by OFHEO to assess the adequacy 

of a GSE’s capital. As HUD has stated 

previously, reviews conducted 

immediately upon receipt of a GSE’s 

prior year loan-level data and pursuant 

to § 81.102(c) cannot generally gauge the 

accuracy of the data and cannot always 

determine whether the transaction itself 

complies with HUD’s regulations for 

counting units towards goals 

performance. Assessments of this type 

require the application of procedures, 

either in whole or in part, that are 

characteristic of audit engagements. For 

example, it is customary for audits of a 

previous year’s financial statements to 

require up to one year or more for 

completion due to the number of 

procedures involved and the volume of 

information to be reviewed, especially 

for exceedingly large and complex 

organizations. Similarly, the relatively 

new field of performance data auditing, 

including reviews based on some or all 

of these procedures, also requires a 

substantial commitment of time and 

resources if meaningful results are to be 

obtained. For these reasons, 

performance reviews are not analogous 

to OFHEO’s evaluations of capital 

adequacy. HUD believes that its original 

proposal of allowing for up to 24 

months after the close of the year under 

review is the appropriate time frame for 

completion of the performance review. 

The GSEs also expressed some 

concerns about the potential for HUD to 

make determinations of error after the 

fact and without any prior notice to a 

GSE that a type of transaction and/or 

housing unit would not be eligible for 

goals credit. HUD believes it is useful to 

more fully describe the types of errors 

likely to trigger a finding that units were 

overstated. In the context of 

performance reviews, the words ‘‘errors, 

omissions or discrepancies’’ connote 

serious mistakes, such as those 

associated with violations of HUD’s 

counting rules and other goals eligibility 

criteria as set forth in its regulations. 

HUD is aware that in collecting and 

reporting millions of data elements, 

some level of factual error is probably 

unavoidable. However, with regard to 

data accuracy in performance reviews, 

HUD is concerned with errors of a 

substantial nature, such as those that 

suggest a larger internal control 

problem, an example of which could be 

a pattern of incorrect rental data 

acquired from or generated by the same 

source. HUD is also concerned with 

types of transactions that are either 

expressly prohibited from goals 

eligibility, such as high cost mortgages, 

or for which HUD approval may have 

been required but not obtained prior to 

a GSE counting the units, such as the 

use of an affordability estimation 

methodology. Other similar types of 

problems may also trigger a HUD 

determination of error. In the event 

HUD supplements its regulations with 

letters to one or both GSEs regarding 

appropriate counting treatment, the GSE 

will be responsible for complying with 

only the specific directives it has 

received from HUD. In the final rule, 

HUD has reiterated that this procedure 

will apply only in those instances where 

an overstatement was material in nature; 

that is, the overstated units enabled the 

GSE to meet a housing goal that it 

otherwise would not have met. In the 

event that HUD undertakes a 

performance review that covers a twoyear period and determines that 

material misstatements of housing goals 

or Special Affordable subgoals 

performance occurred in both years, 

then HUD will apply the same 

procedures as described previously for 

making up the overstatements. Upon a 

written request from a GSE, the 

Secretary may, in his discretion, 

determine to grant an extension of 

additional time to correct or compensate 

for the overstatement. For example, if 

overstatements were discovered for 

years 2005 and 2006 and the GSE is 

notified of the overstatements for both 

years in 2007, then the GSE could be 

required to make up the overstatements 

for both years in 2008. Similarly, if the 

overstatement was discovered for one 

year, 2005, and the GSE was notified of 

the overstatement in 2006, then the GSE 

could be required to make up the 

overstated units or mortgages in 2007. In 

both examples, upon receipt of a GSE’s 

written request for an extension of time, 

the Secretary may grant an extension for 

completing make up of the overstated 

units or mortgages. 

With regard to HUD’s reasons for 

implementing a procedure that provides 

a mechanism by which overstated units 

of a material nature from a prior year 

can be made up in a subsequent year 

following the year a GSE is first notified 

of the overstated units, for reasons 

stated above, it is the Department’s view 

Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 63637

that it has authority to do so, and that 

the procedure is needed at this time. 

The procedure is the only tool by which 

HUD can meet its statutory 

responsibility to assure the integrity of 

all of the housing goal data reported to 

the public, including the data reported 

in the GSE public use database and its 

duty to enforce the housing goals.

6. Additional Enforcement Option 

§ 81.102(e) 

The Department proposed a new 

§ 81.102(e) that would provide HUD 

with additional enforcement options in 

the event it determines that a GSE has 

submitted data, information, or report(s) 

that are not current, are incomplete, or 

otherwise contain an untrue statement 

of material fact. Section 81.102(e) 

identified the data, information, or 

report(s) that would be subject to HUD’s 

additional enforcement authority as 

those required under section 307(e) and 

(f) of the Freddie Mac Act, section 

309(m) or (n) of the Fannie Mae Charter 

Act, or under 24 CFR part 81, subpart 

E. 

The Department indicated in 

proposed § 81.102(e) that it could make 

a determination—either under its 

independent verification authority in 

§ 81.102(a) or by ‘‘other means’’—that 

such data, information or report(s) are 

not current, are incomplete, or 

otherwise contain an untrue statement 

of material fact. This reference to ‘‘other 

means’’ was intended to encompass the 

Secretary’s authority under the three 

other provisions in § 81.102 that were 

also being proposed to ensure the 

accuracy, truthfulness and completeness 

of GSE submissions to HUD: (1) The 

proposed GSE certification in 

§ 81.102(b); (2) the proposed procedure 

established in § 81.102(c) to verify the 

accuracy and completeness of the GSE’s 

current year-end data; and (3) the 

proposed procedure established in 

§ 81.102(d) to ensure the accuracy and 

completeness of the GSE’s prior years’

data. 

The Department further provided in 

§ 81.102(e) that the Secretary could 

regard a GSE’s submission of data, 

information or report(s) that he or she 

determines under § 81.102(a), or by 

‘‘other means’’ (i.e., pursuant to 

paragraphs (b), (c) or (d) of § 81.102), are 

not current, are incomplete, or that 

otherwise contain an untrue statement 

of material fact to be equivalent to the 

GSE’s failure to submit such data. As a 

result of such failure of submission, 

proposed § 81.102(e) provided that the 

Department could initiate against the 

GSE, in accordance with the procedures 

in 24 CFR part 81, subpart G, an order 

to cease and desist, an action to seek 

civil money penalties, or any other 

remedies or penalties that may be 

available to the Secretary as a result of 

the GSE’s failure to provide data 

submissions, information, and/or 

report(s) in accordance with § 81.102. 

Summary of Comments. Several 

organizations commented, generally, on 

HUD’s proposed requirements in 

§ 81.102 for ensuring the accuracy and 

integrity of GSE data and other 

submissions, and almost all expressed 

support for HUD’s proposals relating to 

data verification. The GSEs commented 

more specifically on HUD’s proposal in 

§ 81.102(e) relating to additional 

enforcement options. 

Fannie Mae asserted that HUD’s 

proposed additional enforcement 

options were overly broad, and 

exceeded the Department’s authority 

under FHEFSSA to issue cease and 

desist orders, impose civil money 

penalties, and to punish GSE 

noncompliance by requiring the 

adoption of a housing plan. Fannie Mae 

stated that, if HUD decided to retain 

§ 81.102(e), this provision should be 

redrafted more narrowly. 

Freddie Mac, through a legal opinion 

prepared by outside counsel, asserted 

that sections 1341 and 1345 of 

FHEFSSA provide a two-step process 

before a GSE’s failure to submit a 

housing plan, or its failure to comply 

with a feasible housing plan, could 

result in the Department’s initiating an 

action for a cease and desist order or 

civil money penalties. Freddie Mac 

asserted that HUD’s proposal expanded 

its enforcement authority beyond the 

FHEFSSA statutory limits by 

eliminating this two-step process. 

Freddie Mac also contended that HUD’s 

enforcement powers under sections 

1341 and 1345 of FHEFSSA extend only 

to instances of intentional noncompliance by the GSE, and that 

§ 81.102 should be narrowed to reflect 

this limitation. 

HUD’s Determination. The 

Department has considered the GSEs’

and other comments on § 81.102(e) and 

is making several changes in this final 

rule in response to these comments. In 

addition, the Department is making a 

number of conforming changes to 

§ 81.102(e) to reflect changes that it has 

also decided to adopt in connection 

with the other provisions in § 81.102 

(primarily in paragraphs (b), (c) and (d)), 

and is also making minor editorial 

corrections. 

Specifically, the Department is 

providing in this final rule that: 

The Department may pursue 

additional enforcement remedies under 

paragraph (e) under either of the 

following circumstances: (1) When a 

GSE fails to submit the certification 

required by § 81.102(b) in connection 

with data, information or report(s) 

required by section 309(m) or (n) of the 

Fannie Mae Charter Act, section 307(e) 

or (f) of the Freddie Mac Act, or under 

24 CFR part 81, subpart E; or (2) when 

a GSE submits the certification required 

by § 81.102(b) in connection with such 

data, information or report(s), but the 

Secretary later determines that the data, 

information or report(s) are not ‘‘true, 

correct and complete’’ as provided in 

the certification. The final rule provides 

that, under either of the above two 

circumstances, the Secretary may regard 

a GSE’s actions as tantamount to a 

failure to submit the data, information 

or report(s) which, in turn, authorizes 

the Secretary to take the additional 

enforcement remedies described in 

§ 81.102(e). 

The final rule also clarifies that for 

data, information or report(s) that are 

subject to § 81.102(c) or (d), the 

Secretary may only pursue additional 

enforcement remedies in connection 

with material errors, omissions or 

discrepancies. Moreover, if the data, 

information or report(s) are subject to 

§ 81.102(d), the rule provides that the 

Secretary may only pursue additional 

enforcement remedies if the GSE has 

failed to purchase a sufficient amount or 

type of mortgages as required by the 

Secretary under § 81.102(d)(4). 

It is the Department’s view that 

§ 81.102(e) is needed so that it can take 

appropriate action to ensure the 

accuracy and completeness of the GSEs’

submissions to HUD, and also to 

implement the certification that is now 

established at § 81.102(b) of this final 

rule, while providing the Secretary with 

sufficient flexibility to exercise his or 

her discretion to determine whether 

enforcement action is appropriate in 

each instance. 

The final rule clarifies that the 

proposed rule’s reference in paragraph 

(e)(1) to ‘‘other means’’ by which the 

Secretary may determine that a GSE’s 

data submission(s), information or 

report(s) fail to meet the prescribed 

regulatory standards is meant to refer to 

the Secretary’s determinations under 

paragraphs (b), (c) or (d) of § 81.102 (i.e., 

the GSE certification in § 81.102(b), the 

procedure established in § 81.102(c) to 

verify the accuracy and completeness of 

the GSE’s data for the immediately 

preceding year, and the procedure 

established in § 81.102(d) to ensure the 

accuracy and completeness of the GSE’s 

prior years’ data). In the final rule, the 

Department has deleted the reference to 

‘‘other means’’ and has included a 

specific reference to paragraphs (b), (c) 

or (d) of § 81.102. 

63638 Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 

The final rule establishes a bifurcated 

approach with respect to the types of 

additional enforcement remedies that 

the Department may pursue under 

paragraph (e). This bifurcated approach 

recognizes that the Department’s ability 

to pursue a cease and desist order, or to 

levy civil money penalties, applies 

specifically to data required by section 

309(m) or (n) of the Fannie Mae Charter 

Act or section 307(e) or (f) of the 

Freddie Mac Act. The rule nevertheless 

provides that the Department may 

pursue other types of remedies against 

a GSE in connection with data that the 

GSE is required to submit under 24 CFR 

part 81, subpart E, but that the GSE is 

not required to submit under section 

309(m) or (n) of the Fannie Mae Charter 

Act or section 307(e) or (f) of the 

Freddie Mac Charter Act.

The final rule provides that, in 

connection with either of the two 

remedial approaches now described in 

§ 81.102(e)(2), the Secretary may pursue 

any civil or administrative remedies or 

penalties against the GSE that may be 

available to the Secretary by virtue of 

either of the circumstances described in 

81.102(e)(1). If the Department elects to 

pursue a cease-and-desist order or civil 

money penalties against a GSE under 

§ 81.102(e)(2)(i)(A) or (B), it will comply 

with the procedures applicable to such 

actions under 24 CFR part 81, subpart 

G. Alternatively, if the Department 

elects to pursue other civil or 

administrative remedies against a GSE 

under either §§ 81.102(e)(2)(i)(C) or 

81.102(e)(2)(ii), it will pursue such 

remedies in accordance with applicable 

law. 

Finally, the Department is replacing 

in paragraph (e) each reference to 

‘‘HUD’’ with a reference to ‘‘the 

Secretary.’’ This replacement is 

designed to ensure that any additional 

enforcement action that may be pursued 

under § 81.102(e) will be considered at 

the highest levels within the 

Department. 

III. Findings and Certifications 

Executive Order 12866 

The Office of Management and Budget 

(OMB) reviewed this final rule under 

Executive Order 12866, Regulatory 

Planning and Review, which the 

President issued on September 30, 1993. 

This rule was determined to be 

economically significant under E.O. 

12866. Any changes made to this rule 

subsequent to its submission to OMB 

are identified in the docket file, which 

is available for public inspection 

between 8 a.m. and 5 p.m. weekdays in 

the Office of the Rules Docket Clerk, 

Office of General Counsel, Room 10276, 

Department of Housing and Urban 

Development, 451 Seventh Street, SW., 

Washington, DC. The Economic 

Analysis prepared for this rule is also 

available for public inspection in the 

Office of the Rules Docket Clerk and on 

HUD’s Web site at http://www.hud.gov.

Congressional Review of Regulations 

This rule is a ‘‘major rule’’ as defined 

in Chapter 8 of 5 U.S.C. This rule will 

be submitted for Congressional review 

in accordance with this chapter. 

Paperwork Reduction Act 

HUD’s collection of information on 

the GSEs’ activities has been reviewed 

and authorized by OMB under the 

Paperwork Reduction Act of 1995 (44 

U.S.C. 3501–3520), as implemented by 

OMB in regulations at 5 CFR part 1320. 

The OMB control number is 2502–0514. 

Environmental Impact 

This final rule does not direct, 

provide for assistance or loan and 

mortgage insurance for, or otherwise 

govern or regulate real property 

acquisition, disposition, leasing, 

rehabilitation, alteration, demolition, or 

new construction; or establish, revise, or 

provide for standards for construction or 

construction materials, manufactured 

housing, or occupancy. Accordingly, 

under 24 CFR 50.19(c)(1), this rule is 

categorically excluded from 

environmental review under the 

National Environmental Policy Act of 

1969 (42 U.S.C. 4321). 

Regulatory Flexibility Act 

The Secretary, in accordance with the 

Regulatory Flexibility Act (5 U.S.C. 

605(b)), has reviewed this rule before 

publication and by approving it certifies 

that this rule would not have a 

significant economic impact on a 

substantial number of small entities. 

This rule is applicable only to the GSEs, 

which are not small entities for 

purposes of the Regulatory Flexibility 

Act. Therefore, the rule does not have a 

significant economic impact on a 

substantial number of small entities 

within the meaning of the Regulatory 

Flexibility Act. 

Executive Order 13132, Federalism 

Executive Order 13132 (‘‘Federalism’’) 

prohibits, to the extent practicable and 

permitted by law, an agency from 

promulgating a regulation that has 

federalism implications and either 

imposes substantial direct compliance 

costs on state and local governments 

and is not required by statute, or 

preempts state law, unless the relevant 

requirements of section 6 of the 

executive order are met. This rule does 

not have federalism implications and 

does not impose substantial direct 

compliance costs on state and local 

governments or preempt state law 

within the meaning of the executive 

order. 

Unfunded Mandates Reform Act 

Title II of the Unfunded Mandates 

Reform Act of 1995 (12 U.S.C. 1531–

1538) (UMRA) establishes requirements 

for federal agencies to assess the effects 

of their regulatory actions on state, 

local, and tribal governments, and the 

private sector. This rule would not 

impose any federal mandates on any 

state, local, or tribal government, or on 

the private sector, within the meaning of 

UMRA.

List of Subjects in 24 CFR Part 81 

Accounting, Federal Reserve System, 

Mortgages, Reporting and recordkeeping 

requirements, Securities.

■ For the reasons discussed in the 

preamble, HUD is amending 24 CFR part 

81 as follows:

PART 81—THE SECRETARY OF HUD’S 

REGULATION OF THE FEDERAL 

NATIONAL MORTGAGE ASSOCIATION 

(FANNIE MAE) AND THE FEDERAL 

HOME LOAN MORTGAGE 

CORPORATION (FREDDIE MAC)

■ 1. The authority citation for 24 CFR 

part 81 continues to read as follows:

Authority: 12 U.S.C. 1451 et seq., 1716–

1723h, and 4501–4641; 28 U.S.C. 2461 note; 

42 U.S.C. 3535(d) and 3601–3619.

■ 2. In § 81.2(b), revise the definitions of 

‘‘Metropolitan area’’ and ‘‘Minority,’’

and paragraph (2) of the definition of 

‘‘Underserved area,’’ and add a new 

definition of the term ‘‘Home Purchase 

Mortgage,’’ in alphabetical order, to read 

as follows:

§ 81.2 Definitions.

* * * * *

(b) * * * 

Home Purchase Mortgage means a 

residential mortgage for the purchase of 

an owner-occupied single-family 

property.

* * * * *

Metropolitan area means a 

metropolitan statistical area (‘‘MSA’’), or 

a portion of such an area for which 

median family income estimates are 

published annually by HUD. 

Minority means any individual who is 

included within any one or more of the 

following racial and ethnic categories:

(1) American Indian or Alaskan 

Native—a person having origins in any 

of the original peoples of North and 

South America (including Central 

Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 63639

America), and who maintains tribal 

affiliation or community attachment; 

(2) Asian—a person having origins in 

any of the original peoples of the Far 

East, Southeast Asia, or the Indian 

subcontinent, including, for example, 

Cambodia, China, India, Japan, Korea, 

Malaysia, Pakistan, the Philippine 

Islands, Thailand, and Vietnam; 

(3) Black or African American—a 

person having origins in any of the 

black racial groups of Africa; 

(4) Hispanic or Latino—a person of 

Cuban, Mexican, Puerto Rican, South or 

Central American, or other Spanish 

culture or origin, regardless of race; and 

(5) Native Hawaiian or Other Pacific 

Islander—a person having origins in any 

of the original peoples of Hawaii, Guam, 

Samoa, or other Pacific Islands.

* * * * *

Underserved area means * * * 

(2) For purposes of the definition of 

‘‘Rural area,’’ a whole census tract, a 

Federal or State American Indian 

reservation or tribal or individual trust 

land, or the balance of a census tract 

excluding the area within any Federal or 

State American Indian reservation or 

tribal or individual trust land, having: 

(i) A median income at or below 120 

percent of the greater of the State nonmetropolitan median income or the 

nationwide non-metropolitan median 

income and a minority population of 30 

percent or greater; or 

(ii) A median income at or below 95 

percent of the greater of the State nonmetropolitan median income or 

nationwide non-metropolitan median 

income.

* * * * *

■ 3. In § 81.12, revise the last sentence of 

paragraph (b) and revise paragraph (c), to 

read as follows:

§ 81.12 Low- and Moderate-Income 

Housing Goal.

* * * * *

(b) Factors. * * * A statement 

documenting HUD’s considerations and 

findings with respect to these factors, 

entitled ‘‘Departmental Considerations 

to Establish the Low- and ModerateIncome Housing Goal,’’ was published 

in the Federal Register on November 2, 

2004. 

(c) Goals. The annual goals for each 

GSE’s purchases of mortgages on 

housing for low- and moderate-income 

families are: 

(1) For the year 2005, 52 percent of 

the total number of dwelling units 

financed by that GSE’s mortgage 

purchases unless otherwise adjusted by 

HUD in accordance with FHEFSSA. In 

addition, as a Low- and ModerateIncome Housing Home Purchase 

Subgoal, 45 percent of the total number 

of home purchase mortgages in 

metropolitan areas financed by that 

GSE’s mortgage purchases shall be home 

purchase mortgages in metropolitan 

areas which count toward the Low- and 

Moderate-Income Housing Goal in the 

year 2005 unless otherwise adjusted by 

HUD in accordance with FHEFSSA; 

(2) For the year 2006, 53 percent of 

the total number of dwelling units 

financed by that GSE’s mortgage 

purchases unless otherwise adjusted by 

HUD in accordance with FHEFSSA. In 

addition, as a Low- and ModerateIncome Housing Home Purchase 

Subgoal, 46 percent of the total number 

of home purchase mortgages in 

metropolitan areas financed by that 

GSE’s mortgage purchases shall be home 

purchase mortgages in metropolitan 

areas which count toward the Low- and 

Moderate-Income Housing Goal in the 

year 2006 unless otherwise adjusted by 

HUD in accordance with FHEFSSA; 

(3) For the year 2007, 55 percent of 

the total number of dwelling units 

financed by that GSE’s mortgage 

purchases unless otherwise adjusted by 

HUD in accordance with FHEFSSA. In 

addition, as a Low- and ModerateIncome Housing Home Purchase 

Subgoal, 47 percent of the total number 

of home purchase mortgages in 

metropolitan areas financed by that 

GSE’s mortgage purchases shall be home 

purchase mortgages in metropolitan 

areas which count toward the Low- and 

Moderate-Income Housing Goal in the 

year 2007 unless otherwise adjusted by 

HUD in accordance with FHEFSSA; 

(4) For the year 2008, 56 percent of 

the total number of dwelling units 

financed by that GSE’s mortgage 

purchases unless otherwise adjusted by 

HUD in accordance with FHEFSSA. In 

addition, as a Low- and ModerateIncome Housing Home Purchase 

Subgoal, 47 percent of the total number 

of home purchase mortgages in 

metropolitan areas financed by that 

GSE’s mortgage purchases shall be home 

purchase mortgages in metropolitan 

areas which count toward the Low- and 

Moderate-Income Housing Goal in the 

year 2008 unless otherwise adjusted by 

HUD in accordance with FHEFSSA; and 

(5) For the year 2009 and thereafter 

HUD shall establish annual goals. 

Pending establishment of goals for the 

year 2009 and thereafter, the annual 

goal for each of those years shall be 56 

percent of the total number of dwelling 

units financed by that GSE’s mortgage 

purchases in each of those years. In 

addition, as a Low and Moderate 

Income Housing Home Purchase 

Subgoal, 47 percent of the total number 

of home purchase mortgages in 

metropolitan areas financed by that 

GSE’s mortgage purchases shall be home 

purchase mortgages in metropolitan 

areas which count toward the Low- and 

Moderate-Income Housing Goal in each 

of those years unless otherwise adjusted 

by HUD in accordance with FHEFSSA.

■ 4. In § 81.13, revise the last sentence of 

paragraph (b) and revise paragraph (c), to 

read as follows:

§ 81.13 Central Cities, Rural Areas, and 

Other Underserved Areas Housing Goal.

* * * * *

(b) Factors. * * * A statement 

documenting HUD’s considerations and 

findings with respect to these factors, 

entitled ‘‘Departmental Considerations 

to Establish the Central Cities, Rural 

Areas, and Other Underserved Areas 

Housing Goal,’’ was published in the 

Federal Register on November 2, 2004. 

(c) Goals. The annual goals for each 

GSE’s purchases of mortgages on 

housing located in central cities, rural 

areas, and other underserved areas are: 

(1) For the year 2005, 37 percent of 

the total number of dwelling units 

financed by that GSE’s mortgage 

purchases unless otherwise adjusted by 

HUD in accordance with FHEFSSA. In 

addition, as a Central Cities, Rural 

Areas, and Other Underserved Areas 

Home Purchase Subgoal, 32 percent of 

the total number of home purchase 

mortgages in metropolitan areas 

financed by that GSE’s mortgage 

purchases shall be home purchase 

mortgages in metropolitan areas which 

count toward the Central Cities, Rural 

Areas, and Other Underserved Areas 

Housing Goal in the year 2005 unless 

otherwise adjusted by HUD in 

accordance with FHEFSSA; 

(2) For the year 2006, 38 percent of 

the total number of dwelling units 

financed by that GSE’s mortgage 

purchases unless otherwise adjusted by 

HUD in accordance with FHEFSSA. In 

addition, as a Central Cities, Rural 

Areas, and Other Underserved Areas 

Home Purchase Subgoal, 33 percent of 

the total number of home purchase 

mortgages in metropolitan areas 

financed by that GSE’s mortgage 

purchases shall be home purchase 

mortgages in metropolitan areas which 

count toward the Central Cities, Rural 

Areas, and Other Underserved Areas 

Housing Goal in the year 2006 unless 

otherwise adjusted by HUD in 

accordance with FHEFSSA; 

(3) For the year 2007, 38 percent of 

the total number of dwelling units 

financed by that GSE’s mortgage 

purchases unless otherwise adjusted by 

HUD in accordance with FHEFSSA. In 

addition, as a Central Cities, Rural 

Areas, and Other Underserved Areas 

63640 Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 

Home Purchase Subgoal, 33 percent of 

the total number of home purchase 

mortgages in metropolitan areas 

financed by that GSE’s mortgage 

purchases shall be home purchase 

mortgages in metropolitan areas which 

count toward the Central Cities, Rural 

Areas, and Other Underserved Areas 

Housing Goal in the year 2007 unless 

otherwise adjusted by HUD in 

accordance with FHEFSSA; 

(4) For the year 2008, 39 percent of 

the total number of dwelling units 

financed by that GSE’s mortgage 

purchases unless otherwise adjusted by 

HUD in accordance with FHEFSSA. In 

addition, as a Central Cities, Rural 

Areas, and Other Underserved Areas 

Home Purchase Subgoal, 34 percent of 

the total number of home purchase 

mortgages in metropolitan areas 

financed by that GSE’s mortgage 

purchases shall be home purchase 

mortgages in metropolitan areas which 

count toward the Central Cities, Rural 

Areas, and Other Underserved Areas 

Housing Goal in the year 2008 unless 

otherwise adjusted by HUD in 

accordance with FHEFSSA; and 

(5) For the year 2009 and thereafter 

HUD shall establish annual goals. 

Pending establishment of goals for the 

year 2009 and thereafter, the annual 

goal for each of those years shall be 39 

percent of the total number of dwelling 

units financed by that GSE’s mortgage 

purchases in each of those years. In 

addition, as a Central Cities, Rural 

Areas, and Other Underserved Areas 

Home Purchase Subgoal, 34 percent of 

the total number of home purchase 

mortgages in metropolitan areas 

financed by that GSE’s mortgage 

purchases shall be home purchase 

mortgages in metropolitan areas which 

count toward the Central Cities, Rural 

Areas, and Other Underserved Areas 

Housing Goal in each of those years 

unless otherwise adjusted by HUD in 

accordance with FHEFSSA.

* * * * *

■ 5. In § 81.14, revise the last sentence of 

paragraph (b) and revise paragraph (c), to 

read as follows:

§ 81.14 Special Affordable Housing Goal.

* * * * *

(b) * * * A statement documenting 

HUD’s considerations and findings with 

respect to these factors, entitled 

‘‘Departmental Considerations to 

Establish the Special Affordable 

Housing Goal,’’ was published in the 

Federal Register on November 2, 2004. 

(c) Goals. The annual goals for each 

GSE’s purchases of mortgages on rental 

and owner-occupied housing meeting 

the then-existing, unaddressed needs of 

and affordable to low-income families in 

low-income areas and very low-income 

families are: 

(1) For the year 2005, 22 percent of 

the total number of dwelling units 

financed by each GSE’s mortgage 

purchases unless otherwise adjusted by 

HUD in accordance with FHEFSSA. The 

goal for the year 2005 shall include 

mortgage purchases financing dwelling 

units in multifamily housing totaling 

not less than 1.0 percent of the average 

annual dollar volume of combined 

(single-family and multifamily) 

mortgages purchased by the respective 

GSE in 2000, 2001, and 2002, unless 

otherwise adjusted by HUD in 

accordance with FHEFSSA. In addition, 

as a Special Affordable Housing Home 

Purchase Subgoal, 17 percent of the 

total number of home purchase 

mortgages in metropolitan areas 

financed by each GSE’s mortgage 

purchases shall be home purchase 

mortgages in metropolitan areas which 

count toward the Special Affordable 

Housing Goal in the year 2005 unless 

otherwise adjusted by HUD in 

accordance with FHEFSSA; 

(2) For the year 2006, 23 percent of 

the total number of dwelling units 

financed by each GSE’s mortgage 

purchases unless otherwise adjusted by 

HUD in accordance with FHEFSSA. The 

goal for the year 2006 shall include 

mortgage purchases financing dwelling 

units in multifamily housing totaling 

not less than 1.0 percent of the average 

annual dollar volume of combined 

(single-family and multifamily) 

mortgages purchased by the respective 

GSE in 2000, 2001, and 2002, unless 

otherwise adjusted by HUD in 

accordance with FHEFSSA. In addition, 

as a Special Affordable Housing Home 

Purchase Subgoal, 17 percent of the 

total number of home purchase 

mortgages in metropolitan areas 

financed by each GSE’s mortgage 

purchases shall be home purchase 

mortgages in metropolitan areas which 

count toward the Special Affordable 

Housing Goal in the year 2006 unless 

otherwise adjusted by HUD in 

accordance with FHEFSSA; 

(3) For the year 2007, 25 percent of 

the total number of dwelling units 

financed by each GSE’s mortgage 

purchases unless otherwise adjusted by 

HUD in accordance with FHEFSSA. The 

goal for the year 2007 shall include 

mortgage purchases financing dwelling 

units in multifamily housing totaling 

not less than 1.0 percent of the average 

annual dollar volume of combined 

(single-family and multifamily) 

mortgages purchased by the respective 

GSE in 2000, 2001, and 2002, unless 

otherwise adjusted by HUD in 

accordance with FHEFSSA. In addition, 

as a Special Affordable Housing Home 

Purchase Subgoal, 18 percent of the 

total number of home purchase 

mortgages in metropolitan areas 

financed by each GSE’s mortgage 

purchases shall be home purchase 

mortgages in metropolitan areas which 

count toward the Special Affordable 

Housing Goal in the year 2007 unless 

otherwise adjusted by HUD in 

accordance with FHEFSSA;

(4) For the year 2008, 27 percent of 

the total number of dwelling units 

financed by each GSE’s mortgage 

purchases unless otherwise adjusted by 

HUD in accordance with FHEFSSA. The 

goal for the year 2008 shall include 

mortgage purchases financing dwelling 

units in multifamily housing totaling 

not less than 1.0 percent of the average 

annual dollar volume of combined 

(single-family and multifamily) 

mortgages purchased by the respective 

GSE in 2000, 2001, and 2002, unless 

otherwise adjusted by HUD in 

accordance with FHEFSSA. In addition, 

as a Special Affordable Housing Home 

Purchase Subgoal, 18 percent of the 

total number of home purchase 

mortgages in metropolitan areas 

financed by each GSE’s mortgage 

purchases shall be home purchase 

mortgages in metropolitan areas which 

count toward the Special Affordable 

Housing Goal in the year 2008 unless 

otherwise adjusted by HUD in 

accordance with FHEFSSA; and 

(5) For the year 2009 and thereafter 

HUD shall establish annual goals. 

Pending establishment of goals for the 

year 2009 and thereafter, the annual 

goal for each of those years shall be 27 

percent of the total number of dwelling 

units financed by each GSE’s mortgage 

purchases in each of those years. The 

goal for each such year shall include 

mortgage purchases financing dwelling 

units in multifamily housing totaling 

not less than 1.0 percent of the annual 

average dollar volume of combined 

(single-family and multifamily) 

mortgages purchased by the respective 

GSE in the years 2000, 2001, and 2002. 

In addition, as a Special Affordable 

Housing Home Purchase Subgoal, 18 

percent of the total number of home 

purchase mortgages in metropolitan 

areas financed by each GSE’s mortgage 

purchases shall be home purchase 

mortgages in metropolitan areas which 

count toward the Special Affordable 

Housing Goal in each of those years 

unless otherwise adjusted by HUD in 

accordance with FHEFSSA.

* * * * *

Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 63641

■ 6. In § 81.15, revise paragraphs (d), 

(e)(6)(i), and (e)(6)(ii) and add a new 

paragraph (i), to read as follows:

§ 81.15 General requirements.

* * * * *

(d) Counting owner-occupied units.

(1) For purposes of counting owneroccupied units toward achievement of 

the Low- and Moderate-Income Housing 

Goal or the Special Affordable Housing 

Goal, mortgage purchases financing 

such units shall be evaluated based on 

the income of the mortgagors and the 

area median income at the time of 

origination of the mortgage. To 

determine whether mortgages may be 

counted under a particular family 

income level, i.e., especially low, very 

low, low or moderate income, the 

income of the mortgagors is compared to 

the median income for the area at the 

time of the mortgage application, using 

the appropriate percentage factor 

provided under § 81.17. 

(2)(i) When the income of the 

mortgagor(s) is not available to 

determine whether an owner-occupied 

unit in a property securing a singlefamily mortgage originated after 1992 

and purchased by a GSE counts toward 

achievement of the Low- and ModerateIncome Housing Goal or the Special 

Affordable Housing Goal, a GSE’s 

performance with respect to such unit 

may be evaluated using estimated 

affordability information in accordance 

with one of the following methods: 

(A) Excluding from the denominator 

and the numerator single-family owneroccupied units located in census tracts 

with median incomes less than, or equal 

to, area median income based on the 

most recent decennial census, up to a 

maximum of one percent of the total 

number of single-family owneroccupied dwelling units eligible to be 

counted toward the respective housing 

goal in the current year. Mortgage 

purchases with missing data in excess of 

the maximum will be included in the 

denominator and excluded from the 

numerator; 

(B) For home purchase mortgages and 

for refinance mortgages separately, 

multiplying the number of owneroccupied units with missing borrower 

income information in properties 

securing mortgages purchased by the 

GSE in each census tract by the 

percentage of all single-family owneroccupied mortgage originations in the 

respective tracts that would count 

toward achievement of each goal, as 

determined by HUD based on the most 

recent HMDA data available; or 

(C) Such other data source and 

methodology as may be approved by 

HUD. 

(ii) In any calendar year, a GSE may 

use only one of the methods specified 

in paragraph (d)(2)(i) of this section to 

estimate affordability information for 

single-family owner-occupied units. 

(iii) If a GSE chooses to use an 

estimation methodology under 

paragraph (d)(2)(i)(B) or (d)(2)(i)(C) of 

this section to determine affordability 

for owner-occupied units in properties 

securing single-family mortgage 

purchases eligible to be counted toward 

the respective housing goal, then that 

methodology may be used up to 

nationwide maximums for home 

purchase mortgages and for refinance 

mortgages that shall be calculated by 

multiplying, for each census tract, the 

percentage of all single-family owneroccupied mortgage originations with 

missing borrower incomes (as 

determined by HUD based on the most 

recent HMDA data available for home 

purchase and refinance mortgages, 

respectively) by the number of singlefamily owner-occupied units in 

properties securing mortgages 

purchased by the GSE for each census 

tract, summed up over all census tracts. 

If this nationwide maximum is 

exceeded, then the estimated number of 

goal-qualifying units will be adjusted by 

the ratio of the applicable nationwide 

maximum number of units for which 

income information may be estimated to 

the total number of single-family owneroccupied units with missing income 

information in properties securing 

mortgages purchased by the GSE. 

Owner-occupied units in excess of the 

nationwide maximum, and any units for 

which estimation information is not 

available, shall remain in the 

denominator of the respective goal 

calculation. 

(e) * * * 

(6) * * * 

(i) Multifamily. (A) When a GSE lacks 

sufficient information to determine 

whether a rental unit in a property 

securing a multifamily mortgage 

purchased by a GSE counts toward 

achievement of the Low- and ModerateIncome Housing Goal or the Special 

Affordable Housing Goal because 

neither the income of prospective or 

actual tenants, nor the actual or average 

rental data, are available, a GSE’s 

performance with respect to such unit 

may be evaluated using estimated 

affordability information in accordance 

with one of the following methods: 

(1) Multiplying the number of rental 

units with missing affordability 

information in properties securing 

multifamily mortgages purchased by the 

GSE in each census tract by the 

percentage of all rental dwelling units in 

the respective tracts that would count 

toward achievement of each goal, as 

determined by HUD based on the most 

recent decennial census. For units with 

missing affordability information in 

tracts for which such methodology is 

not possible, such units will be 

excluded from the denominator as well 

as the numerator in calculating 

performance under the respective 

housing goal(s); or

(2) Such other data source and 

methodology as may be approved by 

HUD. 

(B) In any calendar year, a GSE may 

use only one of the methods specified 

in paragraph (e)(6)(i)(A) of this section 

to estimate affordability information for 

multifamily rental units. 

(C) If a GSE chooses to use an 

estimation methodology under 

paragraph (e)(6)(i)(A) of this section to 

determine affordability for rental units 

in properties securing multifamily 

mortgage purchases eligible to be 

counted toward the respective housing 

goal, then that methodology may be 

used up to a nationwide maximum of 

ten percent of the total number of rental 

units in properties securing multifamily 

mortgages purchased by the GSE in the 

current year. If this maximum is 

exceeded, the estimated number of goalqualifying units will be adjusted by the 

ratio of the nationwide maximum 

number of units for which affordability 

information may be estimated to the 

total number of multifamily rental units 

with missing affordability information 

in properties securing mortgages 

purchased by the GSE. Multifamily 

rental units in excess of the maximum 

set forth in this paragraph (e)(6)(i)(C), 

and any units for which estimation 

information is not available, shall be 

removed from the denominator of the 

respective goal calculation. 

(ii) Rental units in 1–4 unit singlefamily properties. (A) When a GSE lacks 

sufficient information to determine 

whether a rental unit in a property 

securing a single-family mortgage 

purchased by a GSE counts toward 

achievement of the Low- and ModerateIncome Housing Goal or the Special 

Affordable Housing Goal because 

neither the income of prospective or 

actual tenants, nor the actual or average 

rental data, are available, a GSE’s 

performance with respect to such unit 

may be evaluated using estimated 

affordability information in accordance 

with one of the following methods: 

(1) Excluding rental units in 1-to 4-

unit properties with missing 

affordability information from the 

denominator as well as the numerator in 

calculating performance under those 

goals; 

63642 Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 

(2) Multiplying the number of rental 

units with missing affordability 

information in properties securing 

single family mortgages purchased by 

the GSE in each census tract by the 

percentage of all rental dwelling units in 

the respective tracts that would count 

toward achievement of each goal, as 

determined by HUD based on the most 

recent decennial census. For units with 

missing affordability information in 

tracts for which such methodology is 

not possible, such units will be 

excluded from the denominator as well 

as the numerator in calculating 

performance under the respective 

housing goal(s); or 

(3) Such other data source and 

methodology as may be approved by 

HUD. 

(B) In any calendar year, a GSE may 

use only one of the methods specified 

in paragraph (e)(6)(ii)(A) of this section 

to estimate affordability information for 

single-family rental units. 

(C) If a GSE chooses to use an 

estimation methodology under 

paragraph (e)(6)(ii)(A)(2) or 

(e)(6)(ii)(A)(3) of this section to 

determine affordability for rental units 

in properties securing single-family 

mortgage purchases eligible to be 

counted toward the respective housing 

goal, then that methodology may be 

used up to nationwide maximums of 

five percent of the total number of rental 

units in properties securing nonseasoned single-family mortgage 

purchases by the GSE in the current 

year and 20 percent of the total number 

of rental units in properties securing 

seasoned single-family mortgage 

purchases by the GSE in the current 

year. If either or both of these 

maximums are exceeded, the estimated 

number of goal-qualifying units will be 

adjusted by the ratio of the applicable 

nationwide maximum number of units 

for which affordability information may 

be estimated to the total number of 

single-family rental units with missing 

affordability information in properties 

securing seasoned or unseasoned 

mortgages purchased by the GSE, as 

applicable. Single-family rental units in 

excess of the maximums set forth in this 

paragraph (e)(6)(ii)(C), and any units for 

which estimation information is not 

available, shall be removed from the 

denominator of the respective goal 

calculation.

* * * * *

(i) Counting mortgages toward the 

Home Purchase Subgoals. (1) General.

The requirements of this section, except 

for paragraphs (b) and (e) of this section, 

shall apply to counting mortgages 

toward the Home Purchase Subgoals at 

§§ 81.12 through 81.14. However, 

performance under the subgoals shall be 

counted using a fraction that is 

converted into a percentage for each 

subgoal and the numerator of the 

fraction for each subgoal shall be the 

number of home purchase mortgages in 

metropolitan areas financed by each 

GSE’s mortgage purchases in a 

particular year that count towards 

achievement of the applicable housing 

goal. The denominator of each fraction 

shall be the total number of home 

purchase mortgages in metropolitan 

areas financed by each GSE’s mortgage 

purchases in a particular year. For 

purposes of each subgoal, the procedure 

for addressing missing data or 

information, as set forth in paragraph (d) 

of this section, shall be implemented 

using numbers of home purchase 

mortgages in metropolitan areas and not 

single-family owner-occupied dwelling 

units. 

(2) Special counting rule for 

mortgages with more than one owneroccupied unit. For purposes of counting 

mortgages toward the Home Purchase 

Subgoals, where a single home purchase 

mortgage finances the purchase of two 

or more owner-occupied units in a 

metropolitan area, the mortgage shall 

count once toward each subgoal that 

applies to the GSE’s mortgage purchase.

■ 7. In § 81.16, revise paragraphs (c)(6) 

and (c)(7), remove and reserve 

paragraphs (c)(10) and (c)(11), and add a 

paragraph (c)(14), to read as follows:

§ 81.16 Special counting requirements.

* * * * *

(c) * * * 

(6) Seasoned mortgages. A GSE’s 

purchase of a seasoned mortgage shall 

be treated as a mortgage purchase for 

purposes of these goals and shall be 

included in the numerator, as 

appropriate, and the denominator in 

calculating the GSE’s performance 

under the housing goals, except where: 

(i) The GSE has already counted the 

mortgage under a housing goal 

applicable to 1993 or any subsequent 

year; or 

(ii) HUD determines, based upon a 

written request by a GSE, that a 

seasoned mortgage or class of such 

mortgages should be excluded from the 

numerator and the denominator in order 

to further the purposes of the Special 

Affordable Housing Goal. 

(7) Purchase of refinanced mortgages.

Except as otherwise provided in this 

part, the purchase of a refinanced 

mortgage by a GSE is a mortgage 

purchase and shall count toward 

achievement of the housing goals to the 

extent the mortgage qualifies.

* * * * *

(14) Seller dissolution option. (i) 

Mortgages acquired through transactions 

involving seller dissolution options 

shall be treated as mortgage purchases, 

and receive credit toward the 

achievement of the housing goals, only 

when:

(A) The terms of the transaction 

provide for a lockout period that 

prohibits the exercise of the dissolution 

option for at least one year from the date 

on which the transaction was entered 

into by the GSE and the seller of the 

mortgages; and 

(B) The transaction is not dissolved 

during the one-year minimum lockout 

period. 

(ii) The Secretary may grant an 

exception to the one-year minimum 

lockout period described in paragraph 

(c)(14)(i)(A) and (B) of this section, in 

response to a written request from an 

enterprise, if the Secretary determines 

that the transaction furthers the 

purposes of FHEFSSA and the GSE’s 

charter act; 

(iii) For purposes of this paragraph 

(c)(14), ‘‘seller dissolution option’’

means an option for a seller of 

mortgages to the GSEs to dissolve or 

otherwise cancel a mortgage purchase 

agreement or loan sale.

* * * * *

■ 8. Revise § 81.102 to read as follows:

§ 81.102 Verification and enforcement to 

ensure GSE data integrity. 

(a) Independent verification authority.

The Secretary may independently verify 

the accuracy and completeness of the 

data, information, and reports provided 

by each GSE, including conducting onsite verification, when such steps are 

reasonably related to determining 

whether a GSE is complying with 12 

U.S.C. 4541–4589 and the GSE’s Charter 

Act. 

(b) Certification. (1) The senior officer 

of each GSE who is responsible for 

submitting to HUD the fourth quarter 

Annual Mortgage Report and the AHAR 

under sections 309(m) and (n) of the 

Fannie Mae Charter Act or sections 

307(e) and (f) of the Freddie Mac Act, 

as applicable, or for submitting to the 

Secretary such other report(s), data, or 

information for which certification is 

requested in writing by the Secretary, 

shall certify such report(s), data or 

information. 

(2) The certification shall state as 

follows: ‘‘To the best of my knowledge 

and belief, the information provided 

herein is true, correct and complete.’’

(3) If the Secretary determines that a 

GSE has failed to provide the 

certification required by paragraphs 

(b)(1) and (b)(2) of this section, or that 

a GSE has provided the certification 

Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 63643

required by paragraph (b) in connection 

with data, information or report(s) that 

the Secretary later determines are not 

true, correct and complete, the Secretary 

may pursue the enforcement remedies 

under paragraph (e) of this section. For 

data, information or report(s) subject to 

paragraphs (c) or (d) of this section, the 

Secretary may pursue the enforcement 

remedies described in paragraph (e) 

only in connection with material errors, 

omissions or discrepancies as those 

terms are defined in § 81.102(c) or (d). 

(c) Verification procedure and 

adjustment to correct errors, omissions 

or discrepancies in AHAR data for the 

immediately preceding year. (1) This 

paragraph (c) pertains to the GSEs’

submission of year-end data. For 

purposes of this paragraph, ‘‘year-end 

data’’ means data that HUD receives 

from the GSEs related to housing goals 

performance in the immediately 

preceding year and covering data 

reported in the fourth quarter Annual 

Mortgage Report and the GSE’s AHAR. 

An ‘‘error’’ means a technical mistake, 

such as a mistake in coding or 

calculating data. An ‘‘omission’’ means 

a GSE’s failure to count units in the 

denominator. A ‘‘discrepancy’’ means 

any difference between HUD’s analysis 

of data and the analysis contained in a 

GSE’s submission of data, including a 

discrepancy in goal or Special 

Affordable subgoal performance. 

(2) If HUD finds errors, omissions or 

discrepancies in a GSE’s year-end data 

submissions relative to HUD’s 

regulations, HUD will first notify the 

GSE by telephone or e-mail 

transmission of each such error, 

omission or discrepancy. The GSE must 

respond within five working days of 

each such notification. HUD may, in its 

discretion or upon a request by a GSE 

within the five working day period, 

extend the response period for up to an 

additional 20 working days. Information 

exchanges during the five working day 

period following initial notification, and 

any subsequent extensions of time that 

may be granted, may be by electronic 

mail. Any person with delegated 

authority from the Secretary, or the 

Director of HUD’s Financial Institution 

Regulation Division, or his or her 

designee, shall be responsible for 

issuing initial notifications regarding 

errors, omissions, or discrepancies; 

making determinations on the adequacy 

of responses received; approving any 

extensions of time permitted under this 

provision; and managing the data 

verification process. 

(3) If each error, omission or 

discrepancy is not resolved to HUD’s 

satisfaction during the initial five 

working day period from notification, 

and any extension period, the Secretary 

will notify the GSE in writing and seek 

clarification or additional information to 

correct the error, omission or 

discrepancy. The GSE shall have 10 

working days (or such longer period as 

the Secretary may establish, not to 

exceed 30 working days) from the date 

of the Secretary’s written notice to 

respond in writing to the notice. If the 

GSE fails to submit a written response 

to the Secretary within this period, or if 

the Secretary determines that the GSE’s 

written response fails to correct or 

otherwise resolve each error, omission 

or discrepancy in its reported year-end 

data to the Secretary’s satisfaction, the 

Secretary will determine the appropriate 

adjustments to the numerator and the 

denominator of the applicable housing 

goal(s) and Special Affordable 

subgoal(s) due to the GSE’s failure to 

provide the Secretary with accurate 

submissions of data. 

(4) The Secretary, or his or her 

designee, shall inform a GSE in writing, 

at least five working days prior to HUD’s 

release of its official performance figures 

to the public, of HUD’s determination of 

official goals performance figures, 

including any adjustments. During the 

five working days prior to such public 

release, a GSE may request, in writing, 

a reconsideration of HUD’s final 

determination of its performance and 

must provide the basis for requesting 

the reconsideration. If the request is 

granted, the Secretary will consider the 

GSE’s request for reconsideration of its 

determination of goals performance and 

make a final determination regarding 

the GSE’s performance, within 10 

working days of the Secretary’s granting 

of the GSE’s written request for 

reconsideration. 

(5) Should the Secretary determine 

that additional enforcement action 

against the GSE is warranted for 

material errors, omissions or 

discrepancies with regard to a housing 

goal or Special Affordable subgoal, it 

may pursue additional remedies under 

paragraph (e) of this section. An error, 

omission or discrepancy is material if it 

results in an overstatement of credit for 

a housing goal or Special Affordable 

subgoal, and, without such 

overstatement, the GSE would have 

failed to meet such housing goal or 

Special Affordable subgoal for the 

immediately preceding year. 

(d) Adjustment to correct prior year 

reporting errors, omissions or 

discrepancies. (1) General. The 

Secretary may require a GSE to correct 

a material error, omission or 

discrepancy in a GSE’s prior year’s data 

reported in the fourth quarter Annual 

Mortgage Report and the GSE’s AHAR 

under sections 309(m) and (n) of the 

Fannie Mae Charter Act or sections 

307(e) and (f) of the Freddie Mac Act, 

as applicable. An error, omission or 

discrepancy is material if it results in an 

overstatement of credit for a housing 

goal or Special Affordable subgoal and, 

without such overstatement, the GSE 

would have failed to meet such housing 

goal or Special Affordable subgoal for 

the prior year. A ‘‘prior year’’ for 

purposes of this section is any one of 

the two years immediately preceding 

the latest year for which data on 

housing goals performance was reported 

to HUD.

(2) Procedural requirements. In the 

event the Secretary determines that a 

GSE’s prior year’s fourth quarter Annual 

Mortgage Report or AHAR contain a 

material error, omission or discrepancy, 

the Secretary will provide the GSE with 

an initial letter containing written 

findings and determinations within 24 

months of the end of the relevant GSE 

reporting year. The GSE shall have an 

opportunity, not to exceed 30 days from 

the date of receipt of the Secretary’s 

initial letter, to respond in writing with 

supporting documentation, to contest 

the Secretary’s initial determination that 

there was a material error, omission or 

discrepancy in a prior year’s data. The 

Secretary shall then issue a final 

determination letter within 60 days of 

the date of HUD’s receipt of the GSE’s 

written response or, if no response is 

received, within 90 days of the date of 

the GSE’s receipt of the Secretary’s 

initial letter. The Secretary may extend 

the period for issuing a final 

determination letter by an additional 30 

days and may grant the GSE an 

opportunity, for a period not to exceed 

10 working days from the date of the 

GSE’s receipt of the determination letter 

to request that the determination be 

reconsidered. 

(3) If the Secretary determines that a 

GSE’s prior year’s fourth quarter Annual 

Mortgage Report or AHAR contained a 

material error, omission or discrepancy, 

the Secretary may direct the GSE to 

correct the overstatement by purchasing 

mortgages to finance the number of 

units that HUD has determined were 

overstated in the prior year’s goal 

performance (or, for the Special 

Affordable subgoal, the number or 

dollar amount, as applicable, of 

mortgage purchases that HUD has 

determined were overstated), or that 

equal the percentage of the 

overstatement in the prior year’s goal or 

Special Affordable subgoal performance 

as applied to the most current year-end 

performance, whichever is less. Units or 

mortgages purchased to remedy an 

overstatement in the housing goals or 

63644 Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 

the Special Affordable subgoal must be 

eligible to qualify under the same goal 

or Special Affordable subgoal that HUD 

has determined were overstated in the 

prior year. 

(4) If a GSE does not purchase a 

sufficient amount or type of mortgages 

to meet the requirements set forth in 

paragraph (d)(3) of this section as 

directed by the Secretary by no later 

than the end of the calendar year 

immediately following the year in 

which the Secretary notifies the GSE of 

such overstatement (unless, upon 

written request from the GSE, the 

Secretary, in his or her discretion, 

determines that a grant of additional 

time is appropriate to correct or 

compensate for the overstatement) the 

Department may pursue any or all of the 

following remedies: 

(i) Issue a notice that the GSE has 

failed a housing goal or Special 

Affordable subgoal in the prior year; 

(ii) Seek additional enforcement 

remedies under paragraph (e) of this 

section; 

(iii) Pursue any other civil or 

administrative remedies as are available 

to it. 

(e) Additional enforcement options.

(1) General. In the event the Secretary 

determines, either as a result of his or 

her independent verification authority 

described in paragraph (a) of this 

section, or by the authority set forth in 

paragraphs (b), (c) or (d) of this section, 

that any of the following circumstances 

has occurred with respect to data, 

information or report(s) required by 

sections 309(m) or (n) of the Fannie Mae 

Charter Act, sections 307(e) or (f) of the 

Freddie Mac Act, or subpart E of this 

part, the Secretary may regard this as a 

GSE’s failure to submit such data, 

information or report(s) and, 

accordingly, the Secretary may take the 

additional enforcement actions 

authorized by paragraph (e)(2) of this 

section: 

(i) A GSE fails to submit the 

certification required by paragraphs 

(b)(1) and (b)(2) of this section in 

connection with such data, information 

or report(s); or 

(ii) A GSE submits the certification 

required by paragraph (b) of this section, 

but the Secretary later determines that 

the data, information or report(s) are not 

true, correct and complete. For data, 

information or report(s) subject to 

paragraphs (c) or (d) of this section, the 

Secretary may pursue the additional 

enforcement remedies under paragraph 

(e)(2) only in connection with material 

errors, omissions or discrepancies, as 

those terms are defined in § 81.102(c) or 

(d). In addition, the Secretary may only 

pursue such remedies in connection 

with material errors, omissions or 

discrepancies arising under paragraph 

(d) of this section if the GSE has failed 

to purchase a sufficient amount or type 

of mortgages, as provided in paragraphs 

(d)(3) and (d)(4) of this section. 

(2) Remedies. (i) Submissions 

required under the GSE’s charter acts.

After the Secretary makes a 

determination under paragraph (e)(1) of 

this section that any of the 

circumstances described in paragraphs 

(e)(1)(i) or (ii) has occurred with respect 

to data, information, or report(s) 

required by sections 309(m) or (n) of the 

Fannie Mae Charter Act, or by sections 

307(e) or (f) of the Freddie Mac Act, the 

Secretary may pursue any or all of the 

following remedies in accordance with 

paragraph (e)(3), or applicable law, as 

appropriate: 

(A) A cease-and-desist order against 

the GSE for failing to submit the 

required data, information or report(s) 

in accordance with this section; 

(B) Civil money penalties against the 

GSE for failing to submit the required 

data, information or report(s) in 

accordance with this section; 

(C) Any other civil or administrative 

remedies or penalties against the GSE 

that may be available to the Secretary by 

virtue of the GSE’s failing to submit or 

certify the required data, information or 

report(s) in accordance with this 

section. 

(ii) Submissions required under 

subpart E of this part. After the 

Secretary makes a determination under 

paragraph (e)(1) of this section that any 

of the circumstances described in 

paragraphs (e)(1)(i) or (ii) has occurred 

with respect to data, information or 

report(s) required under subpart E of 

this part (but that are not required by 

sections 309(m) or (n) of the Fannie Mae 

Charter Act or by sections 307(e) or (f) 

of the Freddie Mac Act), the Secretary 

may pursue any civil or administrative 

remedies or penalties against the GSE 

that may be available to the Secretary. 

The Secretary shall pursue such 

remedies under applicable law. 

(3) Procedures. The Secretary shall 

comply with the procedures set forth in 

subpart G of this part in connection 

with any enforcement action that he or 

she may initiate against a GSE under 

paragraph (e) of this section.

Dated: October 22, 2004. 

John C. Weicher, 

Assistant Secretary for Housing—Federal 

Housing Commissioner.

Note: The Appendices will not appear in 

the Code of Federal Regulations.

Appendix A 

Departmental Considerations To Establish 

The Low- and Moderate-Income Housing 

Goal 

A. Introduction 

Sections 1 and 2 provide a basic 

description of the rule process. Section 3 

discusses HUD’s conclusions based on 

consideration of the factors. 

1. Establishment of Low- and ModerateIncome Housing Goal 

In establishing the Low- and ModerateIncome Housing Goals for the Federal 

National Mortgage Association (Fannie Mae) 

and the Federal Home Loan Mortgage 

Corporation (Freddie Mac), collectively 

referred to as the Government-Sponsored 

Enterprises (GSEs), section 1332 of the 

Federal Housing Enterprises Financial Safety 

and Soundness Act of 1992 (12 U.S.C. 4562) 

(FHEFSSA) requires the Secretary to 

consider: 

1. National housing needs; 

2. Economic, housing, and demographic 

conditions; 

3. The performance and effort of the 

enterprises toward achieving the Low- and 

Moderate-Income Housing Goal in previous 

years; 

4. The size of the conventional mortgage 

market serving low- and moderate-income 

families relative to the size of the overall 

conventional mortgage market; 

5. The ability of the enterprises to lead the 

industry in making mortgage credit available 

for low- and moderate-income families; and 

6. The need to maintain the sound 

financial condition of the enterprises. 

The Secretary also considered these factors 

in establishing a low- and moderate-income 

subgoal for home purchase loans on singlefamily-owner properties in metropolitan 

areas. 

2. Underlying Data 

In considering the statutory factors in 

establishing these goals, HUD relied on data 

from the 2001 American Housing Survey, the 

2000 Censuses of Population and Housing, 

the 2001 Residential Finance Survey (RFS), 

the 1995 Property Owners and Managers 

Survey (POMS), other government reports, 

reports submitted in accordance with the 

Home Mortgage Disclosure Act (HMDA), and 

the GSEs. In order to measure performance 

toward achieving the Low- and ModerateIncome Housing Goal in previous years, HUD 

analyzed the loan-level data on all mortgages 

purchased by the GSEs for 1993–2003 in 

accordance with the goal counting provisions 

established by the Department in the 

December 1995 and October 2000 rules (24 

CFR part 81). 

3. Conclusions Based on Consideration of the 

Factors 

The discussion of the first two factors 

covers a range of topics on housing needs 

and economic and demographic trends that 

are important for understanding mortgage 

markets. Information is provided which 

describes the market environment in which 

the GSEs must operate (for example, trends 

in refinancing activity). In addition, the 

severe housing problems faced by lowerVerDate jul<14>2003 18:20 Nov 01, 2004 Jkt 205001 PO 00000 Frm 00066 Fmt 4701 Sfmt 4700 C:\02NOR2.SGM 02NOR2
Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 63645

1Mortgage denial rates are based on 2002 HMDA 

data for home purchase loans; manufactured 

housing lenders are excluded from these 

comparisons.

2The ‘‘affordable lending performance’’ of Fannie 

Mae and Freddie Mae refers to the performance of 

the GSEs in funding loans for low-income and 

underserved borrowers through their purchase (or 

guarantee) of loans originated by primary lenders. 

It does not, of course, imply that the GSEs 

themselves are lenders originating loans in the 

primary market.

income families are discussed, as are the 

barriers that minorities face when attempting 

to become homeowners. This discussion 

serves to provide useful background 

information for the discussion of the 

Geographically Targeted and Special 

Affordable Housing Goals in Appendixes B 

and C, as well as for the Low- and ModerateIncome Housing Goal in this Appendix.

The third factor (past performance) and the 

fifth factor (ability of the GSEs to lead the 

industry) are also discussed in some detail in 

this Appendix. With respect to home 

purchase mortgages, the past performance of 

the GSEs and their ability to lead the 

industry are examined for all three housing 

goals; that analysis provides the basis for 

establishing the three subgoals for the GSEs’

acquisitions of home loans on single-familyowner properties. 

The fourth factor (size of the market) and 

the sixth factor (need to maintain the GSEs’

sound financial condition) are mentioned 

only briefly in this Appendix. Detailed 

analyses of the fourth factor and the sixth 

factor are contained in Appendix D and in 

the economic analysis of this rule, 

respectively. 

The factors are discussed in sections B 

through H of this appendix. Section I 

summarizes the findings and presents the 

Department’s conclusions concerning the 

Low- and Moderate-Income Housing Goal. 

Section I also gives the rationale for a lowand moderate-income subgoal for home 

purchase loans. 

The consideration of the factors in this 

Appendix has led the Secretary to the 

following conclusions: 

• Changing population demographics will 

result in a need for primary and secondary 

mortgage markets to meet nontraditional 

credit needs, respond to diverse housing 

preferences, and overcome information and 

other barriers that many immigrants and 

minorities face. Growing housing demand 

from immigrants (both those who are already 

here and those projected to come) and nontraditional homebuyers will help to offset 

declines in the demand for housing caused 

by the aging of the population. Immigrants 

and other minorities—who accounted for 

more than a third of household growth since 

the 1990s—will be responsible for almost 

two-thirds of the growth in the number of 

new households over the next ten years. As 

these demographic factors play out, the 

overall effect on housing demand will likely 

be sustained growth and an increasingly 

diverse household population from which to 

draw new renters and homeowners. 

• Despite the record national 

homeownership rate of 68.3 percent in 2003, 

much lower rates prevailed for minorities, 

especially for African-American households 

(48.4 percent) and Hispanics (47.4 percent), 

and these lower rates are only partly 

accounted for by differences in income, age, 

and other socioeconomic factors. 

• In addition to low incomes, barriers to 

homeownership that disproportionately 

affect minorities and immigrants include lack 

of capital for down payments and closing 

costs, poor credit history, lack of access to 

mainstream lenders, little understanding of 

the homebuying process, and continued 

discrimination in housing markets and 

mortgage lending. 

• A HUD-published study of 

discrimination in the rental and owner 

markets found that while differential 

treatment between minority and white home 

seekers had declined over the past ten years, 

it continued at an unacceptable level in the 

year 2000. In addition, disparities in 

mortgage lending continued across the nation 

in 2002, when the loan denial rate was 7.8 

percent for white mortgage applicants, but 

20.1 percent for African Americans and 15.5 

percent for Hispanics.1

• Americans with the lowest incomes face 

persistent housing problems. Recent HUD 

analysis reveals that in 2001, 5.1 million 

households had ‘‘worst case’’ housing needs, 

defined as housing costs greater than 50 

percent of household income or severely 

inadequate housing among unassisted verylow-income renter households. Among these 

households, 90 percent had a severe rent 

burden, 6 percent lived in severely 

inadequate housing, and 4 percent suffered 

from both problems. 

• Over the past ten years, there has been 

a ‘‘revolution in affordable lending’’ that has 

extended homeownership opportunities to 

historically underserved households. Fannie 

Mae and Freddie Mac have been a substantial 

part of this ‘‘revolution in affordable 

lending’’. During the mid-to-late 1990s, they 

added flexibility to their underwriting 

guidelines, introduced new low-downpayment products, and worked to expand the 

use of automated underwriting in evaluating 

the creditworthiness of loan applicants. 

HMDA data suggest that the industry and 

GSE initiatives are increasing the flow of 

credit to underserved borrowers. Between 

1993 and 2003, conventional loans to lowincome and minority families increased at 

much faster rates than loans to upper-income 

and non-minority families. 

• The Low- and Moderate-Income Goal 

was set at 50 percent beginning in 2001. 

Effective on January 1, 2001, several changes 

in counting requirements came into effect, 

including (1) ‘‘bonus points’’ (double credit) 

for purchases of mortgages on small (5–50 

unit) multifamily properties and, above a 

threshold level, mortgages on 2–4 unit 

owner-occupied properties; and (2) a 

‘‘temporary adjustment factor’’ (1.35 units 

credit) for Freddie Mac’s purchases of 

mortgages on large (>50 unit) multifamily 

properties. With these two counting rules, 

Fannie Mae’s performance was 51.5 percent 

in 2001, 51.8 percent in 2002 and 52.3 

percent in 2003, and Freddie Mac’s 

performance was 53.2 percent in 2001, 50.5 

percent in 2002, and 51.2 percent in 2003; 

thus, both GSEs surpassed this higher goal in 

all three years. 

• The bonuses and temporary adjustment 

factor expired at the end of 2003. Without 

these rules, Fannie Mae’s performance would 

have been 51.3 percent in 2000, 49.2 percent 

in 2001, 49.0 percent in 2002, and 48.7 

percent in 2003. Freddie Mac’s performance 

would have been 50.6 percent in 2000, 47.7 

percent in 2001, 46.1 percent in 2002, and 

45.0 percent in 2003. Thus, both Fannie Mae 

and Freddie Mac would have surpassed the 

50 percent goal in 2000 and fallen short in 

2001, 2002, and 2003.

• This Appendix includes a 

comprehensive analysis of each GSE’s 

performance in funding home purchase 

mortgages for borrowers and neighborhoods 

covered by the three housing goals—special 

affordable and low- and moderate-income 

borrowers and underserved. The GSEs’

performance in funding first-time home 

buyers is also examined. 

• While Freddie Mac has improved its 

affordable lending performance in recent 

years, it has consistently lagged the 

conventional conforming market in funding 

affordable home purchase loans for special 

affordable and low-moderate-income 

borrowers and underserved neighborhoods 

targeted by the housing goals.2 In 2003, its 

performance on the underserved areas goal 

was particularly low relative to both the 

performances of Fannie Mae and the market; 

in that year, underserved area loans 

accounted for only 24.0 percent of Freddie 

Mac’s purchases compared with 26.8 percent 

of Fannie Mae’s purchases and 27.6 percent 

of market originations. (These underserved 

area data are based on 1990 Census 

geography.)

• In general, Fannie Mae’s affordable 

lending performance has been better than 

Freddie Mac’s. But like Freddie Mac, Fannie 

Mae’s average performance during past 

periods (e.g., 1993–2003, 1996–2003, 1999–

2003) has been below market levels. 

However, it is encouraging that Fannie Mae 

markedly improved its affordable lending 

performance relative to the market during 

2001, 2002, and 2003, the first three years 

under the higher housing goal targets that 

HUD established in the GSE Final Rule dated 

October 2000. Over this three-year period, 

Fannie Mae led the primary market in 

funding special affordable and low-mod 

loans but lagged the market in funding 

underserved areas loans. In 2003, Fannie 

Mae’s increased performance placed it 

significantly above the special affordable 

market (a 17.1 percent share for Fannie Mae 

compared with a 15.9 percent share for the 

market) and the low-mod market (a 47.0 

percent share for Fannie Mae compared with 

a 44.6 percent share for the market). 

However, Fannie Mae continued to lag the 

underserved areas market in 2003 (a 26.8 

percent share for Fannie Mae compared with 

a 27.6 percent share for the market). In this 

case, which is referred to in the text as the 

‘‘purchase year’’ approach, Fannie Mae’s 

performance is based on comparing its 

purchases of all loans (both seasoned loans 

and newly-originated mortgages) during a 

particular year with loans originated in the 

market in that year. When Fannie Mae’s 

63646 Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 

3 Joint Center for Housing Studies of Harvard 

University, State of the Nation’s Housing 2004, p. 

35.

4 Joint Center for Housing Studies of Harvard 

University, State of the Nation’s Housing 2003, p. 

16.

5According to the National Association of 

Realtors, Housing Market Will Change in New 

Millennium as Population Shifts, November 7, 

1998. Forty-five percent of U.S. household wealth 

was in the form of home equity in 1998. Since 1968, 

home prices have increased each year, on average, 

at the rate of inflation plus two percentage points.

6Todd Buchholz, ‘‘Safe At Home: The New Role 

of Housing in the U.S. Economy,’’ a paper 

commissioned by the Homeownership Alliance, 

2002.

7Federal Reserve Board, ‘‘Recent Changes in U.S. 

Family Finances: Results from the 1998 and 2001 

Survey of Consumer Finances,’’ January 2003, p. 16.

8Mark Zandi, ‘‘Housing’s Rising Contribution,’’

June 2002, p. 5.

9 Joint Center for Housing Studies of Harvard 

University, State of the Nation’s Housing 1998.

10Lawrence Yun, ‘‘The Forecast,’’ National 

Association of Realtors Real Estate Outlook, 

February 2004, p. 4.

11 Joint Center for Housing Studies of Harvard 

University, State of the Nation’s Housing 2004, p. 

15.

performance is measured on an ‘‘origination 

year’’ basis (that is, allocating Fannie Mae’s 

purchases in a particular year to the year that 

the purchased loan was originated), Fannie 

Mae also led the 2003 market in funding 

special affordable and low- and moderateincome loans, and lagged the market in 

funding underserved area loans. 

• Both Fannie Mae and Freddie Mac lag 

the conventional conforming market in 

funding first-time homebuyers, and by a 

rather wide margin. Between 1999 and 2001, 

first-time homebuyers accounted for 27 

percent of each GSE’s purchases of home 

loans, compared with 38 percent for home 

loans originated in the conventional 

conforming market. 

• The GSEs have accounted for a 

significant share of the total (government as 

well as conventional) market for home 

purchase loans, but their market share for 

each of the affordable lending categories (e.g., 

low-income borrowers and census tracts) has 

been less than their share of the overall 

market. 

• The GSEs also account for a very small 

share of the market for important groups such 

as minority first-time homebuyers. 

Considering the total mortgage market (both 

government and conventional loans), it is 

estimated that the GSEs purchased only 14 

percent of loans originated between 1999 and 

2001 for African-American and Hispanic 

first-time homebuyers, or one-third of their 

share (42 percent) of all home purchase loans 

originated during that period. Considering 

the conventional conforming market and the 

same time period, it is estimated that the 

GSEs purchased only 31 percent of loans 

originated for African-American and 

Hispanic first-time homebuyers, or 

approximately one-half of their share (57 

percent) of all home purchase loans in that 

market. The GSEs’ small share of the firsttime homebuyer market could be due to the 

preponderance of high (over 20 percent) 

downpayment loans in their mortgage 

purchases. 

• This Appendix discusses the dynamic 

nature of the single-family mortgage market 

and the numerous changes that this market 

has undergone over the past few years. Some 

important trends that will likely factor into 

the GSEs’ performance in meeting the needs 

of underserved borrowers include the growth 

of the subprime market, the increasing use of 

automated underwriting systems, and the 

introduction of risk-based pricing into the 

market. 

• The long run outlook for the multifamily 

rental market is sustained, moderate growth, 

based on favorable demographics. The 

minority population, especially Hispanics, 

provides a growing source of demand for 

affordable rental housing. ‘‘Lifestyle renters’’

(older, middle-income households) are also a 

fast-growing segment of the rental 

population. Provision of affordable housing, 

however, will continue to challenge 

suppliers of multifamily rental housing and 

policy makers at all levels of government. 

Low incomes combined with high housing 

costs define a difficult situation for millions 

of renter households. Housing cost 

reductions are constrained by high land 

prices and construction costs in many 

markets. Government action—through land 

use regulation, building codes, and 

occupancy standards—are major contributors 

to those high costs. 

• The market for financing multifamily 

apartments has grown to record volumes. 

Fannie Mae and Freddie Mac have been 

among those boosting volumes and 

introducing new programs to serve the 

multifamily market. Fannie Mae’s 

multifamily purchases jumped from about 

$10 billion in 1999 and 2000 to $18.7 billion 

in 2001, $18.3 billion in 2002, and $33.3 

billion in 2003—the last three years were 

characterized by heavy refinancing activity. 

• Freddie Mac has re-entered the 

multifamily market, after withdrawing for a 

time in the early 1990s. Concerns regarding 

Freddie Mac’s multifamily capabilities no 

longer constrain its performance with regard 

to the housing goals. Freddie Mac’s 

multifamily purchases increased from a 

relatively low $3 billion in 1997 to 

approximately $7 billion during the next 

three years (1998 to 2000), before rising 

further to $11.9 billion in 2001, $13.3 billion 

in 2002, and $21.6 billion in 2003. 

• The overall presence of both GSEs in the 

rental mortgage market falls short of their 

involvement in the single-family owner 

market. Between 1999 and 2002, the GSEs’

purchases totaled for 61 percent of the owner 

market, but only 37 percent of the singlefamily rental and multifamily rental market. 

Certainly there is room for expansion of the 

GSEs in supporting the nation’s rental 

markets, and that expansion is needed if the 

GSEs are to make significant progress in 

closing the gaps between the affordability of 

their mortgage purchases and that of the 

overall conventional conforming market. 

• Considering both owner and rental 

properties, the GSEs’ presence in the goalsqualifying market has been significantly less 

than their presence in the overall 

conventional conforming mortgage market. 

Specifically, HUD estimates that the GSEs 

accounted for 55 percent of all owner and 

rental units financed in the primary market 

between 1999 and 2002, but only 48 percent 

of units qualifying for the low-mod goal, 48 

percent of units qualifying for the 

underserved areas goal, and 41 percent of 

units qualifying for special affordable goal. 

B. Factor 1: National Housing Needs

This section reviews the general housing 

needs of lower-income families that exist 

today and are expected to continue in the 

near future. Affordability problems that 

lower-income families face in both the rental 

and owner markets are examined. The 

section also describes racial disparities in 

homeownership and the causes of these 

disparities. It also notes some special 

problems, such as the need to rehabilitate our 

older urban housing stock, that are discussed 

throughout this appendix. 

1. Homeownership Gaps 

Despite recent record homeownership 

rates, many Americans, including 

disproportionate numbers of racial and 

ethnic minorities, are shut out of 

homeownership opportunities. Although the 

national homeownership rate for all 

Americans was 68.3 percent in 2003, the rate 

for minority households was lower—for 

example, just 48.4 percent of AfricanAmerican households and 47.4 percent of 

Hispanic households owned a home.3

Differences in income and age between 

minorities and whites do not fully explain 

these gaps. The Joint Center for Housing 

Studies estimated that if minorities owned 

homes at the same rates as whites of similar 

age and income, a homeownership gap of 10 

percentage points would still exist.4

a. Importance of Homeownership 

Homeownership is one of the most 

common forms of property ownership as well 

as savings.5 Historically, home equity has 

been the largest source of wealth for most 

Americans, and wealth gains in housing have 

been more widely distributed among the 

population than gains in the stock market.6

With stocks appreciating faster than home 

prices over the past decade, home equity as 

a share of all family assets fell from 38 

percent in 1989 to 33 percent in 1998 and 32 

percent in 2001.7 However, many of the gains 

in the stock market were erased after 1999 

and housing was once again a more 

significant asset in the household balance 

sheet than stocks in 2001.8 Even with a bull 

market through most of the 1990s, 59 percent 

of all homeowners in 1998 held more than 

half of their net wealth in the form of home 

equity.9 From 2001 to 2003, homes prices 

appreciated an average of 23 percent which 

meant $30,900 in housing equity 

accumulation for a typical homeowner.10

Moreover, unlike stock wealth, aggregate 

home equity has steadily increased over the 

past 40 years with only occasional small 

dips.11

Among low-income homeowners 

(household income less than $20,000), home 

equity accounted for about 72 percent of 

household wealth, and approximately 55 

percent for homeowners with incomes 

between $20,000 and $50,000. Median net 

wealth for low-income homeowners under 65 

Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 63647

12U.S. Department of Housing and Urban 

Development, ‘‘Economic Benefits of Increasing 

Minority Homeownership,’’ p. 7.

13http://www.whitehouse.gov/infocus/

homeownership/. Accessed July 28, 2004.

14Homeownership Alliance, ‘‘The Economic 

Contribution of the Mortgage Refinancing Boom,’’

December 2002, p. 2.

15Homeownership Alliance, ‘‘The Economic 

Contribution of the Mortgage Refinancing Boom,’’

December 2002, p. 4–5.

16Robert Dietz and Donald Haurin, ‘‘The Social 

and Private Consequences of Homeownership,’’

May 2001, p. 51.

17William M. Rohe, George McCarthy, and 

Shannon Van Zandt, ‘‘The Social Benefits and Costs 

of Homeownereship,’’ May 2000, p. 31.

18U.S. Deparmtent of Housing and Urban 

Development, ‘‘Economic Beneifts of Increasing 

Minority Homeownership,’’ p. 8–9.

19For a dicusssion of the causes of existing 

disparities in homeownership, see the various 

articles in Nicolas P. Retsinas and Eric S. Belsky 

(Eds), Low-Income Homeowernsip: Examining the 

Unexamined Goal, Washington, DC: Brookings 

Institution Press, 2002.

20 Joint Center for Housing Studies of Harvard 

University, State of the Nation’s Housing 2004, p. 

15.

21U.S. Census Bureau, Current Population 

Survey, March 2000.

22Fannie Mae, Fannie Mae National Housing 

Survey, 2002, p. 11.

23 Joseph Gyourko, Peter Linneman, and Susan 

Wachter. ‘‘Analyzing the Relationships among Race, 

Wealth, and Home Ownership in America,’’ Journal 

of Housing Economics 8 (2), p. 63–89, as discussed 

in Thomas P. Boehm and Alan M. Schlottmann. 

‘‘Housing and Wealth Accumulation: 

Intergenerational Impacts,’’ in Low-Income 

Homeownership: Examining the Unexamined Goal,

Brookings Institution Press (2002), p. 408.

24Fannie Mae, Fannie Mae National Housing 

Survey, 2002, p. 11.

25See Dan Immergluck, Stark Differences: The 

Explosion of the Subprime Industry and Racial 

Hypersegmentation in Home Equity Lending,

Woodstock Institute, October 2000; and Daniel 

Immergluck and Marti Wiles, Two Steps Back: The 

Dual Mortgage Market, Predatory Lending, and the 

Continued

was twelve times that of a similar renter.12

Thus a homeownership gap continues to 

translate directly into a wealth gap. For this 

reason, President Bush issued the 

‘‘Homeownership Challenge’’ in June 2002 to 

increase minority homeownership by 5.5 

million by the end of the decade. By 

December of 2003, the Census estimated that 

the number of minority homeowners had 

increased by 1.53 million. Meaning that in 

the fourth quarter of 2003, for the first time 

ever, the majority of minority households are 

homeowners.13

High rates of homeownership support 

economic stability within housing and 

related industries, sectors that contributed 

nearly one-third of the total gain in real GDP 

since the beginning of the decade.14 In 

addition, more than half of the refinancing 

mortgages in the first two years of the decade 

were cash-out, defined as refinancing 

procedures by which the mortgage balance is 

increased by more than five percent in order 

to tap into home equity. Cash-outs injected 

more than $300 billion into the economy 

between 2000 and 2002 and were responsible 

for one-fifth of real GDP growth since during 

that period.15 In addition to economic 

benefits such as jobs and residential 

investment, studies show that the better 

living environment associated with owning a 

home has positive impacts on children, in 

terms of lower rates of teenage pregnancy and 

higher reading other test scores. The current 

literature substantiates that the benefits of 

homeownership extend beyond individual 

homeowners and their families to society at 

large. Homeownership promotes social and 

community stability by increasing the 

number of stakeholders and reducing 

disparities in the distributions of wealth and 

income. The empirical literature is generally 

supportive of a relationship between 

homeownership and greater investment in 

property.16 Homeownership is also 

associated with neighborhood stability (lower 

mobility), greater participation in voluntary 

and political activities,17 and links to 

entrepreneurship.18

b. Barriers to Homeownership 19

Insufficient income, high debt burdens, 

and limited savings are obstacles to 

homeownership for younger families. As 

home prices skyrocketed during the late 

1970s and early 1980s, real incomes also 

stagnated, with earnings growth particularly 

slow for blue collar and less educated 

workers. Through most of the 1980s, the 

combination of slow income growth and 

increasing rents made saving for home 

purchase more difficult, and relatively high 

interest rates required large fractions of 

family income for home mortgage payments. 

Thus, during that period, fewer households 

had the financial resources to meet down 

payment requirements, closing costs, and 

monthly mortgage payments. 

Economic expansion and lower mortgage 

rates substantially improved homeownership 

affordability during the 1990s. Many young, 

low-income, and minority families who were 

closed out of the housing market during the 

1980s re-entered the housing market during 

the last decade. Even with an economic 

slowdown in 2000–2001 and climbing house 

appreciation in 2002–2003, after-tax 

mortgage payments fell in 2003 for buyers of 

median priced homes because of historically 

low interest rates.20 However, many 

households still lack the earning power to 

take advantage of today’s home buying 

opportunities. Several trends have 

contributed to the reduction in the real 

earnings of young adults without college 

education over the last 15 years, including 

technological changes that favor white-collar 

employment, losses of unionized 

manufacturing jobs, and wage pressures 

exerted by globalization. Over 42 percent of 

the nation’s population between the ages of 

25 and 34 had no advanced education in 

200021 and were therefore at risk of being 

unable to afford homeownership. African 

Americans and Hispanics, who have lower 

average levels of educational attainment than 

whites, are especially disadvantaged by the 

erosion in wages among less educated 

workers.

Immigrants and other minorities, who 

accounted for nearly 40 percent of the growth 

in the homeownership rate over the past five 

years, will be responsible for two-thirds of 

the growth in new households over the next 

ten years. These groups have unique housing 

needs and face numerous hurdles in 

becoming homeowners. In addition to low 

income, barriers to homeownership that 

disproportionately affect minorities and 

immigrants include: 

(1) Lack of capital for down payment and 

closing costs; 

(2) Poor credit history; 

(3) Lack of access to mainstream lenders; 

(4) Complexity and fear of the home buying 

process; and, 

(5) Continued discrimination in housing 

markets and mortgage lending. 

(i) Lack of Cash for Down Payment. In the 

2002 Fannie Mae National Housing Survey, 

40 percent of Hispanics reported not having 

enough money for a down payment as an 

obstacle to buying a home versus 32 percent 

of all Americans.22 A study by Gyourko, 

Linneman, and Wachter found significant 

racial differences in homeownership rates in 

‘‘wealth-constrained’’ households while 

finding no racial differences in 

homeownership rates among households 

with wealth sufficient to meet down payment 

and closing costs.23 Minorities and 

immigrants are much less likely to receive 

gifts and inheritances from their parents to 

assist them in becoming a homeowner.

(ii) Poor Credit History. Poor credit history 

also differentially affects minority 

households. In the same Fannie Mae survey, 

nearly a third of African-American 

respondents said their credit rating would be 

an obstacle to buying a home versus 23 

percent of all Americans.24 Because AfricanAmerican and Hispanic borrowers are more 

likely than others to have little traditional 

credit history or a poorer credit history, they 

face increased difficulties in being accepted 

for mortgage credit. This is because credit 

history scores (such as a FICO score) are a 

major component of the new automated 

mortgage scoring systems. These systems are 

more likely to refer minority borrowers for 

more intensive manual underwriting, rather 

than to automatically accept them for the less 

costly, expedited processing. In these 

situations, there is the additional concern 

that ‘‘referred’’ borrowers may not always 

receive a manual underwriting for the loan 

that they initially applied for, but rather be 

directed to a high-cost subprime loan 

product.

(iii) Lack of Access to Mainstream Lenders.

Minorities face heightened barriers in 

accessing credit because of their often limited 

access to mainstream lenders. Access to 

lenders becomes difficult when mainstream 

financial institutions are not located in 

neighborhoods where minorities live. The 

growth in subprime lending over the last 

several years has benefited credit-impaired 

borrowers—those who may have blemishes 

in their credit record, insufficient credit 

history, or non-traditional credit sources. 

Subprime lenders have allowed these 

borrowers to access credit that they could not 

otherwise obtain in the prime credit market. 

However, studies by HUD, The Woodstock 

Institute and others have shown that 

subprime lending is disproportionately 

concentrated in low-income and minority 

neighborhoods.25 While these studies 

63648 Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 

Undoing of Community Development, Woodstock 

Institute, Chicago, IL, November 1999. For a 

national analyses, see the HUD report Unequal 

Burden: Income and Racial Disparities in Subprime 

Lending in America, April 2000; and Randall M. 

Scheessele, Black and White Disparities in 

Subprime Mortgage Refinance Lending, Housing 

Finance Working Paper No. HF–114, Office of 

Policy Development and Research, U.S. Department 

of Housing and Urban Development, April 2002.

26Fannie Mae, Fannie Mae National Housing 

Survey, 2002, p. 9.

27See ‘‘Immigration Changes Won’t Hurt 

Housing,’’ in National Mortgage News, January 27, 

2003, p. 8.

28 Donald S. Bradley and Peter Zorn, ‘‘Fear of 

Homebuying: Why Financially Able Households 

May Avoid Ownership,’’ Secondary Mortgage 

Markets, 1996.

29Margery Austin Turner, Stephen L. Ross, 

George Galster, and John Yinger, ‘‘Discrimination in 

Metropolitan Housing Markets,’’ The Urban 

Institute Press, November 2002.

30Martin D. Abravanel and Mary K. Cunningham, 

How Much Do We Know? Public Awareness of the 

Nation’s Fair Housing Laws. A report prepared for 

HUD by the Urban Institute, Washington, DC, April 

2002.

31Margery Austin Turner, John Yinger, Stephen 

Ross, Kenneth Temkin, Diane Levy, David Levine, 

Robin Ross Smith, and Michelle deLair, What We 

Know About Mortgage Lending Discrimination, The 

Urban Institute, contract report for the Department 

of Housing and Urban Development, December 

1998.

32Margery Austin Turner, All Other Things Being 

Equal: A Paired Testing Study of Mortgage Lending 

Institutions, The Urban Institute Press, April 2002.

33Alicia H. Munnell, Geoffrey M.B. Tootell, Lynn 

E. Browne, and James McEneaney, ‘‘Mortgage 

Lending in Boston: Interpreting HMDA Data,’’

American Economic Review, 86, March 1996.

34See Charles W. Calomeris, Charles M. Kahn and 

Stanley D. Longhofer, ‘‘Housing Finance 

Intervention and Private Incentives: Helping 

Minorities and the Poor,’’ Journal of Money, Credit 

and Banking, 26, August 1994, pp. 634–74, for more 

discussion of this phenomenon, which is called 

‘‘statistical discrimination.’’

recognize that differences in credit behavior 

explain some of the disparities in subprime 

lending across neighborhoods, they argue 

that the absence of mainstream lenders has 

also contributed to the concentration of 

subprime lending in low-income and 

minority neighborhoods. More competition 

by prime lenders in inner city neighborhoods 

could lower the borrowing costs of families 

who currently have only the option of a highcost subprime loan. This issue of the lack of 

mainstream lenders in inner city 

neighborhoods is discussed further in 

subsection 2, below, in connection with 

disparities between neighborhoods.

(iv) Complexity and Fear of Homebuying 

Process. An additional barrier to 

homeownership is fear and a lack of 

understanding about the buying process and 

the risks of ownership. Many Americans 

could become homeowners if provided with 

information to correct myths, 

misinformation, and concerns about the 

mortgage process. Some potential 

homeowners, particularly minorities, are 

unaware that they may already qualify for a 

mortgage they can afford. The 2002 Fannie 

Mae survey revealed that 30 percent of 

Americans believe erroneously that they 

need to pay 20 percent of the cost of a home 

up-front. In addition, Fannie Mae reported 

that half of Americans are only ‘‘somewhat’’

or ‘‘not at all’’ comfortable with mortgage 

terms.26 Freddie Mac reports that six of 10 

Hispanics are uncomfortable with home 

buying terminology, and think they need 

‘‘perfect credit’’ to buy; and less than four in 

10 are aware that lenders are not required by 

law to give them the lowest interest rate 

possible.27 A study using focus groups with 

renters found that even among those whose 

financial status would make them capable of 

homeownership, many felt that the buying 

process was insurmountable because they 

feared rejection by the lender or being taken 

advantage of.28

(v) Discrimination in the Housing and 

Mortgage Markets. Finally, differential 

treatment of minorities in the sales and rental 

markets and in the mortgage lending market 

has been well documented. The continued 

discrimination in these markets is discussed 

in the next section. 

2. Disparities in Housing and Mortgage 

Markets 

Sales and Rental Markets. In 2002, HUD 

released its third Housing Discrimination 

Study (HDS) in the sale and rental of 

housing. The study, entitled Discrimination 

in Metropolitan Housing Markets: National 

Results from Phase I of The Housing 

Discrimination Study was conducted by the 

Urban Institute.29 This results of this HDS 

were based on 4,600 paired tests of minority 

and non-minority home seekers conducted 

during 2000 in 23 metropolitan areas 

nationwide. The report showed large 

decreases between 1989 and 2000 in the level 

of discrimination experienced by Hispanics 

and African Americans seeking to buy a 

home. There has also been a modest decrease 

in discrimination toward African Americans 

seeking to rent a unit. This downward trend, 

however, has not been seen for Hispanic 

renters, who now are more likely to 

experience discrimination in their housing 

search than do African-American renters. But 

while generally down since 1989, the report 

found that housing discrimination still exists 

at unacceptable levels. The greatest share of 

discrimination for Hispanic and AfricanAmerican home seekers can still be attributed 

to being told units are unavailable when they 

are available to non-Hispanic whites, and 

being shown and told about fewer units than 

comparable non-minority home seekers. 

Although discrimination is down on most 

areas for African-American and Hispanic 

homebuyers, there remain worrisome upward 

trends of discrimination in the areas of 

geographic steering for African Americans 

and, relative to non-Hispanic whites, the 

amount of help agents provide to Hispanics 

with obtaining financing. On the rental side, 

Hispanics were more likely in 2000 than in 

1989 to be quoted a higher rent than their 

white counterpart for the same unit.

Another HUD-sponsored study asked 

respondents to a nationwide survey if they 

‘‘thought’’ they had ever been discriminated 

against when trying to buy or rent a house 

or an apartment.30 While the responses were 

subjective, they are consistent with the 

findings of the HDS. African Americans and 

Hispanics were considerably more likely 

than whites to say they have suffered 

discrimination—24 percent of African 

Americans and 22 percent of Hispanics 

perceived discrimination, compared to only 

13 percent of whites.

Mortgage Lending Market. Research based 

on Home Mortgage Disclosure Act (HMDA) 

data suggests pervasive and widespread 

disparities in mortgage lending across the 

Nation. For 2001, the mortgage denial rate for 

white mortgage applicants was 23 percent, 

while 36 percent of African-American and 35 

percent of Hispanic applicants were denied. 

Two recent HUD-sponsored studies of 

paired-testing at the mortgage pre-application 

stage also points to discrimination by 

mortgage lenders. Based on its review of pair 

tests conducted by the National Fair Housing 

Alliance, the Urban Institute concluded that 

differential treatment discrimination at the 

pre-application level occurred at significant 

levels in at least some cities.31 Minorities 

were less likely to receive information about 

loan products, received less time and 

information from loan officers, and were 

quoted higher interest rates in most of the 

cities where tests were conducted. A second 

HUD-sponsored study by the Urban Institute 

used the paired testing methodology in Los 

Angeles and Chicago and found similar 

results. African Americans and Hispanics 

faced a significant risk of unequal treatment 

when they visited mainstream mortgage 

lending institutions to make pre-application 

inquiries.32

Several possible explanations for these 

lending disparities have been suggested. A 

study by the Boston Federal Reserve Bank 

found that racial disparities cannot be 

explained by reported differences in 

creditworthiness.33 In other words, 

minorities are more likely to be denied than 

whites with similar credit characteristics, 

which suggests lender discrimination. In 

addition, loan officers, who may believe that 

race is correlated with credit risk, may use 

race as a screening device to save time, rather 

than devote effort to distinguishing the 

creditworthiness of the individual 

applicant.34 This violates the Fair Housing 

Act.

Underwriting rigidities may fail to 

accommodate creditworthy low-income or 

minority applicants. For example, under 

traditional underwriting procedures, 

applicants who have conscientiously paid 

rent and utility bills on time but have never 

used consumer credit would be penalized for 

having no credit record. Applicants who 

have remained steadily employed, but have 

changed jobs frequently, would also be 

penalized. As discussed in Section C below, 

lenders, private mortgage insurers, and the 

GSEs have been adjusting their underwriting 

guidelines to take into account these special 

circumstances of lower-income families. 

Many of the changes recently undertaken by 

the industry focused on finding alternative 

underwriting guidelines to establish 

creditworthiness that do not disadvantage 

creditworthy minority or low-income 

applicants. However, because of the 

enhanced roles of credit scoring and 

automated underwriting in the mortgage 

origination process, it is unclear to what 

Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 63649

35Robert B. Avery, Patricia E. Beeson and Mark 

E. Sniderman, Understanding Mortgage Markets: 

Evidence from HMDA, Working Paper Series 94–21, 

Federal Reserve Bank of Cleveland, December 1994.

36HUD has published an update on ‘‘worst case 

housing needs,’’ which found that the number of 

such households rose from 4.86 million in 1999 to 

5.07 million in 2001. However, detailed tables for 

2001 have not been published.

37This does not constitute a significant difference 

from the 1999 figure of 4.9 million households. 

However, when the focus is narrowed to renters 

with incomes below 50 percent of AMI, a 

statistically significant change emerges; there were 

4 percent fewer units affordable to this group in 

2001 than there were in 1999.

38 Very-low-income households are defined as 

those whose income, adjusted for household size, 

does not exceed 50 percent of HUD-adjusted area 

median income. This differs from the definition 

adopted by Congress in the GSE Act of 1992, which 

uses a cutoff of 60 percent and which does not 

adjust income for family size for owner-occupied 

dwelling units.

39Edward N. Wolff, ‘‘Recent Trends in the Size 

Distribution of Household Wealth,’’ The Journal of 

Economic Perspectives, 12(3), (Summer 1998), p. 

137.

40 Joint Center for Housing Studies of Harvard 

University, State of the Nation’s Housing: 2000, p. 

24.

41 Joint Center for Housing Studies of Harvard 

University, State of the Nation’s Housing 2004. p. 

1–2, 4.

42Rent is measured in this report as gross rent, 

defined as contract rent plus the cost of any utilities 

that are not included in contract rent.

degree the reduced rigidity in industry 

standards will benefit borrowers who have 

been adversely impacted by the traditional 

guidelines as discussed in section C.7, some 

industry observers have expressed a concern 

that the greater flexibility in the industry’s 

written underwriting guidelines may not be 

reflected in the numerical credit and 

mortgage scores which play a major role in 

the automated underwriting systems that the 

GSEs and others have developed. 

Disparities Between Neighborhoods.

Mortgage credit also appears to be less 

accessible in low-income and high-minority 

neighborhoods. As discussed in Appendix B, 

2001 HMDA data show that mortgage denial 

rates are nearly twice as high in census tracts 

with low-income and/or high-minority 

composition, as in other tracts (16.8 percent 

versus 8.7 percent). Numerous studies have 

found that mortgage denial rates are higher 

in low-income census tracts, even accounting 

for other loan and borrower characteristics.35

These geographical disparities can be the 

result of cost factors, such as the difficulty of 

appraising houses in these areas because of 

the paucity of previous sales of comparable 

homes. Sales of comparable homes may also 

be difficult to find due to the diversity of 

central city neighborhoods. The small loans 

prevalent in low-income areas are less 

profitable to lenders because up-front fees to 

loan originators are frequently based on a 

percentage of the loan amount, although the 

costs incurred are relatively fixed. As noted 

above, racial disparities in mortgage access 

may be due to the fact that mainstream 

lenders are not doing business in certain 

inner city neighborhoods. There is evidence 

that mainstream lenders active in white and 

upper-income neighborhoods are much less 

active in low-income and minority 

neighborhoods—often leaving these 

neighborhoods to unregulated subprime 

lenders. Geographical disparities in mortgage 

lending are discussed further in Section C.8 

below (which examines subprime lending) 

and in Appendix B (which examines the 

Geographically Targeted Goal).

3. Affordability Problems and Worst Case 

Housing Needs 

The severe affordability problems faced by 

low-income homeowners and renters are 

documented in HUD’s ‘‘Worst Case Housing 

Needs’’ reports. These reports, which are 

prepared biennially for Congress, are based 

on the American Housing Survey (AHS), 

conducted every two years by the Census 

Bureau for HUD. The latest detailed report 

analyzes data from the 1999 AHS. Although 

it focuses on the housing problems faced by 

very-low-income renters, it also presents 

basic data on families and households in 

owner-occupied housing.36

The ‘‘Worst Case’’ report measures three 

types of problems faced by homeowners and 

renters: 

1. Cost or rent burdens where housing 

costs or rent exceed 50 percent of income (a 

‘‘severe burden’’) or range from 31 percent to 

50 percent of income (a ‘‘moderate burden’’); 

2. The presence of physical problems 

involving plumbing, heating, maintenance, 

hallway, or the electrical system, which may 

lead to a classification of a residence as 

‘‘severely inadequate’’ or ‘‘moderately 

inadequate;’’ and, 

3. Crowded housing, where there is more 

than one person per room in a residence. 

The study reveals that in 2001, 5.1 million 

very low income renter households had 

‘‘worst case’’ housing needs, defined as 

housing costs greater than 50 percent of 

household income or severely inadequate 

housing among unassisted very-low-income 

renter households.37 Among the 5.1 million 

worst case needs renters, 4.8 million (94 

percent) had a severe rent burden and 10 

percent of renters lived in housing that was 

severely inadequate.

a. Problems Faced by Owners 

Of the 68.8 million owner households in 

1999, 5.8 million (8 percent) confronted a 

severe cost burden and another 8.7 million 

(12.7 percent) faced a moderate cost burden. 

There were 870,000 households with severe 

physical problems, 2 million with moderate 

physical problems and 905,000 that were 

overcrowded. The report found that 25 

percent of American homeowners faced at 

least one severe or moderate problem.

Not surprisingly, problems were most 

common among very low-income owners.38

Almost a third of these households (31 

percent) faced a severe cost burden, and an 

additional 22 percent faced a moderate cost 

burden. And 8 percent of these families lived 

in severely or moderately inadequate 

housing, while 2 percent faced overcrowding. 

Only 42 percent of very-low-income owners 

reported no problems.

Over time the percentage of owners faced 

with severe or moderate physical problems 

has decreased, as has the portion living in 

overcrowded conditions. However, 

affordability problems have become more 

common—the shares facing severe 

(moderate) cost burdens were only 3 percent 

(5 percent) in 1978, but rose to 5 percent (11 

percent) in 1989 and 8 percent (13 percent) 

in 1999. The increase in affordability 

problems apparently reflects a rise in 

mortgage debt in the late 1980s and early 

1990s, from 21 percent of homeowners’

equity in 1983 to 36 percent in 1995.39 The 

Joint Center for Housing Studies also 

attributes this to the growing gap between 

housing costs and the incomes of the nation’s 

poorest households.40 As a result of the 

increased incidence of severe and moderate 

cost burdens, the share of owners reporting 

no problems fell from 84 percent in 1978 to 

78 percent in 1989 and 75 percent in 1999.

Between 1999 and 2001, the number of low 

income owners with severe cost burdens 

(meaning those with incomes below 120 

percent of AMI and spending more than half 

of their reported income on housing) shot up 

by one million. This increase proved to be 

the main cause of a highly significant nine 

percent jump in the overall number of low 

and moderate income owners and renters 

with critical housing needs. Part of this could 

be due to the heavy home equity borrowing 

that has characterized the housing market 

from the late 1990s to the present day, as 

well as the fact that increases in house prices 

have outpaced increases in household 

income. As a corollary, subprime lending, 

especially in minority communities, rose by 

about ten percentage points from the early 

1990s to 2001.41

b. Problems Faced by Renters 

Problems of all three types listed above are 

more common among renters than among 

homeowners. In 1999 there were 6.3 million 

renter households (19 percent of all renters) 

who paid more than 50 percent of their 

income for rent.42 Another 7.1 million faced 

a moderate rent burden. Thus in total 40 

percent of renters paid more than 30 percent 

of their income for rent.

Among very-low-income renters, 71 

percent faced an affordability problem, 

including 40 percent who paid more than 

half of their income in rent. Almost one-third 

(31 percent) of renters with incomes between 

51 percent and 80 percent of area median 

family income also paid more than 30 

percent of their income for rent. 

Affordability problems have increased over 

time among renters. The shares of renters 

with severe or moderate rent burdens rose 

from 32 percent in 1978 to 36 percent in 1989 

and 40 percent in 1999. 

The share of households living in 

inadequate housing in 1999 was higher for 

renters (11 percent) than for owners (4 

percent), as was the share living in 

overcrowded housing (5 percent for renters, 

but only 1 percent for owners). Crowding and 

inadequate housing were more common 

among lower-income renters, but among even 

the lowest income group, affordability was 

the dominant problem. The prevalence of 

inadequate and crowded rental housing 

diminished over time until 1995, while 

affordability problems grew. 

Other problems faced by renters discussed 

in the most recent detailed ‘‘Worst Case’’

report include a sharp decline (of 2.3 million, 

63650 Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 

43US Housing Market Conditions, 4th Quarter, 

2003. HUD Office of Policy Development and 

Research.

44Homeownership rates prior to 1993 are not 

strictly comparable with those beginning in 1993 

because of a change in weights from the 1980 

Census to the 1990 Census.

45National Association of Realtors, ‘‘Near Record 

Homes Sales Projected for 2003,’’ December 3, 2002.

46Fannie Mae, ‘‘Berson’s Economic and Mortgage 

Market Development Outlook,’’ December 2003. 

http://www.fanniemae.com/media/pdf/berson/

monthly/2003/121203.pdf.

47http://www.nahb.org.

or 14 percent) between 1991 and 1999 in the 

number of rental units affordable to very-lowincome families, and a worsening of the 

national shortage of units affordable and 

available to extremely-low-income families 

(those with incomes below 30 percent of area 

median income). In 2001, the shortage for 

extremely-low-income families was 

approximately 5 million units, not 

statistically different from the 1999 number. 

However, between 1999 and 2001, the 

number of units available to renters with 

incomes below 50 percent of AMI dropped 

from 78 units to 76 units per 100 renters, in 

part because more of the units affordable to 

this group of renters were occupied by 

higher-income renters. Shortages of units 

affordable and available to extremely-lowincome households were most pressing in the 

West and Northeast, especially in 

metropolitan areas in those regions. In 2001, 

the West was the only region to experience 

a significant decline in number of units 

affordable to renters with incomes below 50 

percent of AMI. This decline occurred even 

in the wake of an increase in affordable units 

in the West during the 1990s.

4. Rehabilitation and Other National Housing 

Needs 

In addition to the broad housing needs 

discussed above, there are additional needs 

confronting specific sectors of the housing 

and mortgage markets. One example of these 

specific needs concerns the rehabilitation of 

the nation’s older housing stock. A major 

problem facing lower-income households is 

that low-cost housing units continue to 

disappear from the existing housing stock. 

Older properties are in need of upgrading 

and rehabilitation. These aging properties are 

concentrated in central cities and older inner 

suburbs, and they include not only detached 

single-family homes, but also small 

multifamily properties that have begun to 

deteriorate. But obtaining the funds to fix up 

older properties can be difficult. The owners 

of small rental properties in need of 

rehabilitation may be unsophisticated in 

obtaining financing. The properties are often 

occupied, and this can complicate the 

rehabilitation process. Lenders may be 

reluctant to extend credit because of a 

sometimes-inaccurate perception of high 

credit risk involved in such loans. The GSEs 

and other market participants have recently 

begun to pay more attention to these needs 

for financing of affordable rental housing 

rehabilitation. However, extra effort is 

required, due to the complexities of 

rehabilitation financing, as there is still a 

need to do more. 

The rehabilitation of our aging housing 

stock is but one example of the housing and 

mortgage issues that need to be addressed. 

Several other examples will be provided 

throughout the following sections on the 

economic, housing, and demographic 

conditions in the single-family and 

multifamily markets, as well as in 

Appendices B–D. The discussion will cover 

a wide range of topics, such as subprime 

lending, predatory lending, automated 

underwriting systems, manufactured 

housing, the special needs of the singlefamily rental market, and challenges 

associated with producing affordable 

multifamily housing—just to name a few. 

C. Factor 2: Economic, Housing, and 

Demographic Conditions: Single-Family 

Mortgage Market 

This section discusses economic, housing, 

and demographic conditions that affect the 

single-family mortgage market. After a review 

of housing trends and underlying 

demographic conditions that influence 

homeownership, the discussion focuses on 

specific issues related to the single-family 

owner mortgage market. This subsection 

includes descriptions of recent market 

interest rate trends, refinance and home 

purchase activity, homebuyer characteristics, 

and the state of affordable lending. Other 

special topics examined include the growth 

in subprime lending, the increased use of 

automated underwriting, and the remaining 

homeownership potential among existing 

renters. Section D follows with a discussion 

of the economic, housing, and demographic 

conditions affecting the mortgage market for 

multifamily rental properties. 

1. Recent Trends in the Housing Market 

While most other sectors of the economy 

were weak or declining during 2001 and 

2002, the housing sector showed remarkable 

strength. Again in 2003, the housing market 

enjoyed an outstanding year. The numbers of 

single-family permits, starts, completions, 

new home sales, and existing home sales 

were record-breaking. Home ownership was 

also at an all-time high, and mortgage interest 

rates continued to stay under six percent on 

average. In addition, the prosperity of the 

market stimulated GDP, contributing 0.37 

percent to its overall growth rate of 3.1 

percent. Although the multifamily sector 

experienced high vacancies and low lease-up 

rates, the vitality of the single family market 

was strong enough to result in a spectacular 

peak in total permits and starts as well as 

builders’ attitudes and housing 

affordability.43

Single-Family Permits, Starts, and 

Completions. Builders took out 1,440,400 

single-family permits in 2003, up 6 percent 

from 2002. The 2003 level was the highest 

number of single-family permits ever 

reported in the 44-year history of this series. 

Single-family starts totaled 1,498,500 housing 

units, up 10 percent from 2002, a new singlefamily record. Construction was completed 

on 1,386,200 single-family housing units, up 

5 percent from 2002. 

Sales of New and Existing Homes. After 

leveling out in 2000, housing sales have 

boomed in the past three years, reaching 

record highs in 2001, 2002, and again in 

2003. New single family home sales, which 

increased an average 6.3 percent per year 

between 1992 and 2002, reached a record 

high of 1,085,000 units in 2003, an increase 

of 12 percent over 2002 sales. The market for 

new homes has been strong in the Mid 

Atlantic, Midwest and Great Plains. 

The National Association of Realtors 

reported that 6.1 million existing homes were 

sold in 2003, overturning the old record set 

in 2002 by almost 9 percent, and setting an 

all-time high in the 35-year history of the 

series. Combined new and existing home 

sales set a national record of 6.2 million in 

2002 and a record of almost 7.2 million in 

2003. 

One of the strongest sectors of the housing 

market in past years had been manufactured 

homes, but that sector has declined recently. 

Between 1991 and 1996, manufactured home 

shipments more than doubled, peaking in 

1998 at 373,000. However, shipments fell 

more than 20 percent in both 2000 and 2001. 

In 2002, the industry shipped 169,000 new 

manufactured homes, down 12.4 percent 

from 2001. This was the lowest number of 

manufactured home shipments since 1963. In 

2003, the number of new manufactured 

homes shipped plummeted to 131,000, down 

22.5 percent from 2002. Repossession has 

been cited as a cause for the sales drop-off, 

as has the popularity of conventional stickbuilt housing. 

Homeownership Rate. In 1980, 65.6 

percent of Americans owned their own 

home, but due to the unsettled economic 

conditions of the 1980s, this share fell to 63.8 

percent by 1989. But since 1994, gains in the 

homeownership rate have occurred in each 

year, with the rate reaching another record 

mark of 68.3 percent in 2003. 

Gains in homeownership have been 

widespread over the last eight years.44 As a 

result, the homeownership rate rose from:

• 42.0 percent in 1993 to 48.8 percent in 

2003 for African American households, 

• 39.4 percent in 1993 to 46.7 percent in 

2003 for Hispanic households, 

• 73.7 percent in 1993 to 79.1 percent in 

2003 for married couples with children, 

• 65.1 percent in 1993 to 68.4 percent in 

2003 for household heads aged 35–44, and 

• 48.9 percent in 1993 to 52.3 percent in 

2003 for central city residents.

However, as these figures demonstrate, 

sizable gaps in homeownership remain. 

Economy/Housing Market Prospects. Job 

growth has been less robust in the recent 

recovery than some previous recoveries. 

However, the economy grew at a rate of 2.2 

percent in 2002 and even faster in 2003.45

Although the Federal Reserve has recently 

begun raising short term interest rates, 

mortgage interest rates remain low, 

supporting housing affordability.

Fannie Mae expects existing home sales to 

reach 5.7 million in 2004 and 2005.46

Projected at 1.84 million in 2003, the 

National Association of Home Builders 

expects housing starts to decline to 1.77 

million in 2004 and 1.71 million in 2005.47

The Mortgage Bankers Association forecasts 

that 2004 housing starts will total 1.73 

million units and the 30-year fixed mortgage 

Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 63651

48Mortgage Bankers Association of America, 

Mortgage Finance Forecast, December 17, 2003. 

http://www.mbaa.org/marketdata/forecasts/

mffore1103.pdf.

49Fannie Mae, ‘‘Berson’s Economic and Mortgage 

Market Development Outlook,’’ December 2003.

50U.S. Census Bureau, Population Projections 

Table NP–T1.

51Martha Farnsworth Riche, ‘‘How Changes in 

the Nation’s Age and Household Structure Will 

Reshape Housing Demand in the 21st Century,’’ in 

Issue Papers on Demographic Trends Important to 

Housing, Urban Institute Final Report to the Office 

of Policy Development and Research, U.S. 

Department of Housing and Urban Development, 

September 2002, p. 5.

52 Joint Center for Housing Studies at Harvard 

University, State of the Nation’s Housing 2004, 

p.10–11.

53Barry Chiswick, Paul Miller, George Masnick, 

Zhu Xiao Di, and Martha Farnsworth Riche, Issue 

Papers on Demographic Trends Important to 

Housing. Urban Institute Final Report to the Office 

of Policy Development and Research, U.S. 

Department of Housing and Urban Development, 

September 2002.

54Martha Farnsworth Riche, ‘‘How Changes in 

the Nation’s Age and Household Structure Will 

Reshape Housing Demand in the 21st Century,’’ in 

Issue Papers on Demographic Trends Important to 

Housing. Urban Institute Final Report to the U.S. 

Department of Housing and Urban Development, 

September 2002, p. 4.

55 Ibid. p. 6.

56The National Association of Homebuilders 

estimates base housing demand will average 1.84 

million units but increases that estimate to 2.19 

million units with high immigration.

57 Joint Center for Housing Studies of Harvard 

University, State of the Nation’s Housing 1998, p. 

14.

58 Ibid. p. 15.

59Federation for American Immigration Reform, 

<http://www.fairus.org/html/042us604.htm#ins>, 

site visited December 13, 2002.

60 Joint Center for Housing Studies of Harvard 

University, State of the Nation’s Housing 2004, p. 

11–12.

61 Joint Center for Housing Studies of Harvard 

University, State of the Nation’s Housing 2002, pp. 

16–17.

62 George S. Masnick and Zhu Xiao Di, 

‘‘Projections of U.S. Households By Race/Hispanic 

Origin, Age, Family, Type, and Tenure to 2020: A 

Sensitivity Analysis,’’ in Issue Papers on 

Demographic Trends Important to Housing. Urban 

Institute Final Report to the U.S. Department of 

Housing and Urban Development, September 2002, 

p. 5.

63Fred Flick and Kate Anderson, ‘‘Future of 

Housing Demand: Special Markets,’’ Real Estate 

Outlook, 1998, p. 6.

rate will average 6.1 percent.48 After more 

than doubling from a relative trough in 2000 

to an estimated $2.6 trillion in 2002, Fannie 

Mae projected in December 2003 that 

mortgage originations will drop to $1.8 

trillion in 2004 and $1.5 trillion in 2005.49

2. Underlying Demographic Conditions 

Between 2000 and 2025, the U.S. 

population is expected to grow by an average 

of 2.5 million per year.50 This will likely 

result in at least 1.1 million new households 

per year.51 Recently revised increases in 

population projections by the Census Bureau 

push population figures higher with the Joint 

Center estimating new household growth at 

13.3 million from 2005 to 2015.52 This 

section discusses important demographic 

trends behind these overall household 

numbers that will likely affect housing 

demand in the future. These demographic 

forces include the baby-boom, baby-bust and 

echo baby-boom cycles; immigration trends; 

non-traditional and single households; 

‘‘trade-up buyers;’’ and the growing income 

inequality between people with different 

levels of education. HUD’s Office of Policy 

Development and Research funded a study, 

Issue Papers on Demographic Trends 

Important to Housing, which analyzes effects 

of demographic conditions on the housing 

market. The findings are presented 

throughout the sections that follow.53

As explained below, the role of traditional 

first-time homebuyers, 25-to-34-year-old 

married couples, in the housing market will 

be smaller in the current decade due to the 

aging of the population. For the first time in 

history, the population will have roughly 

equal numbers of people in every age group. 

Between 2000 and 2025, the Census Bureau 

projects that the largest growth in households 

will occur among householders 65 and 

over.54 Thus, an increasing percentage of the 

population will be past their home buying 

peak in the next two decades. However, 

because homeownership rates do not peak 

until population groups reach 65 to 74 years 

of age, this age cohort will continue to 

provide housing demand. According to 

Riche, the increasing presence of older 

households should increase the proportion of 

the population that owns, rather than rents 

housing.55

Growing housing demand from immigrants 

and non-traditional homebuyers will help to 

offset declines in the demand for housing 

caused by the aging of the population. 

Riche’s study estimates that minorities will 

account for two-thirds of the growth in U.S. 

households over the next 25 years,56 and by 

2025, non-family households will make up a 

third of all households. The ‘‘echo babyboom’’ (that is, children of the baby-boomers) 

will also add to housing demand in the 

current and next decades. Finally, the 

growing income inequality between people 

with and without a post-secondary education 

will continue to affect the housing market.

The Baby-Boom Effect. The demand for 

housing during the 1980s and 1990s was 

driven, in large part, by the coming of home 

buying age of the baby-boom generation, 

those born between 1945 and 1964. 

Homeownership rates for the oldest of the 

baby-boom generation, those born in the 

1940s, rival those of the generation born in 

the 1930s. Due to significant house price 

appreciation in the late-1970s and 1980s, 

older baby-boomers have seen significant 

gains in their home equity and subsequently 

have been able to afford larger, more 

expensive homes. Circumstances were not so 

favorable for the middle baby-boomers. 

Housing was not very affordable during the 

1980s, their peak home buying age period. As 

a result, the homeownership rate, as well as 

wealth accumulation, for the group of people 

born in the 1950s lags that of the generations 

before them.57

As the youngest of the baby-boomers (those 

born in the 1960s) reached their peak home 

buying years in the 1990s, housing became 

more affordable. While this cohort has 

achieved a homeownership rate equal to the 

middle baby-boomers, they live in larger, 

more expensive homes. As the baby-boom 

generation ages, demand for housing from 

this group is expected to wind down.58

The baby-boom generation was followed by 

the baby-bust generation, from 1965 through 

1977. Since this population cohort is smaller 

than that of the baby boom generation, it 

reduced housing demand in the preceding 

decade and is expected to do the same in the 

current decade, though, as discussed below, 

other factors kept the housing market very 

strong in the 1990s. However, the echo babyboom generation (the children of the babyboomers, who were born after 1977), while 

smaller than the baby-boom generation, will 

reach peak home buying age later in the first 

decade of the millennium. 

Immigrant Homebuyers. Past, present, and 

future immigration will also contribute to 

gains in the homeownership rate. During the 

1990s, 9.8 million legal immigrants entered 

the United States, as compared to 6.3 million 

entering in the 1980s and 4.2 million during 

the 1970s. Overall, the increase in the 

immigrant population directly accounted for 

35 percent of the nation’s rise in population 

in the 1990s.59 As a result, the foreign-born 

population of the United States more than 

tripled from 9.6 million in 1970 to 31.1 

million in 2000. Immigrants who become 

citizens buy homes at rates nearly as high as 

their same-aged native-born counterparts and 

for those aged 25 to 34, the gap is virtually 

nonexistent.60 Moreover, U.S.-born children 

of immigrants often have higher 

homeownership rates than the same-age 

children of native-born parents.61 However, 

there are concerns about the assimilation into 

homeownership of recent Hispanic 

immigrants who are less educated than 

earlier cohorts of immigrants. Many 

immigrants also locate in high-priced 

housing markets, which makes it more 

difficult for them to achieve homeownership.

Although net foreign immigration is 

projected to decline in the current decade 

after 2002, high levels of immigration in the 

late 1980s and throughout the 1990s will 

have lasting positive effects on housing 

demand. New immigration in the current and 

next decades is projected to create 6.9 

million net new households, but the majority 

of household growth in the period (16.9 

million) will come from people already 

resident in the U.S. including the foreignborn population.62 While immigrants tend to 

rent their first homes upon arriving in the 

United States, homeownership rates are 

substantial for those that have lived here for 

at least 6 years. In 1996, the homeownership 

rate for recent immigrants was 14.7 percent 

while it was 66.9 percent for foreign-born 

naturalized citizens after six years.63 Higherthan-average foreign-born fertility rates and 

high rates of homeownership for immigrants 

living in the country for several years and 

among the children of immigrants suggest 

that past immigration will continue to create 

housing demand.

Past and future immigration will lead to 

increasing racial and ethnic diversity, 

especially among the young adult 

63652 Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 

64Riche, 2002, p. 1.

65Average new-home price: U.S. Census Bureau, 

<http://www.census.gov/const/uspriceann.pdf>.

66Riche, 2002, p. 17.

67All data in this paragraph are from the U.S. 

Census Bureau’s Historical Income Table H2.

68 Jennifer Cheeseman Day and Eric C. 

Newburger, The Big Payoff: Educational Attainment 

and Synthetic Estimates of Work-Life Earnings, U.S. 

Bureau of the Census, Current Population Reports 

P23–210, July 2002, p. 3.

69U.S. Census Bureau, Historical Income Table 

H13.

70 ‘‘Mortgage Originations Hit Record-Busting 

$2.5 Trillion in 2002, IMF Numbers Reveal,’’ Inside 

Mortgage Finance, January 24, 2003, p. 3.

71Freddie Mac ‘‘Cash-Out Refi Report.’’

72 Interest rates in this section are effective rates 

paid on conventional home purchase mortgages on 

new homes, based on the Monthly Interest Rate 

Survey (MIRS) conducted by the Federal Housing 

Finance Board and published by the Council of 

Economic Advisers annually in the Economic 

Report of the President and monthly in Economic 

Indicators. These are average rates for all loan types, 

encompassing 30-year and 15-year fixed-rate 

mortgages and adjustable rate mortgages.

73U.S. Housing Market Conditions, 2nd Quarter 

2002, August 2002, Table 14.

74U.S. Housing Market Conditions, 4th Quarter 

2003, February 2004, pg.1.

75Mortgage Bankers Association website. MBA 

Weekly Survey of Mortgage Applications, Monthly 

Average Interest Rates On 30-Year Fixed-Rate 

Mortgages. http://www.mortgagebankers.org/

marketdata/index.html.

population. As immigrant minorities account 

for a growing share of first-time homebuyers 

in many markets, HUD and others will have 

to intensify their focus on removing 

discrimination from the housing and 

mortgage finance systems. The need to meet 

nontraditional credit needs, respond to 

diverse housing preferences, and overcome 

the information barriers that many 

immigrants face will take on added 

importance. In order to address these needs, 

the mortgage industry must offer innovative 

products and improve outreach efforts to 

attract minority homebuyers. 

Nontraditional and Single Homebuyers.

While overall growth in new households has 

slowed down, nontraditional households 

have become more important in the 

homebuyer market. As the population ages 

both relatively and absolutely, the nation’s 

households will become smaller and more 

diverse. Riche notes that in 2000, traditional 

family households represented fewer than 

one in four households and were surpassed 

by both single-person households and 

married couples without children. With later 

marriages and more divorces, single-parent 

and single-person households have increased 

rapidly. In fact, single-parent households 

grew from 4 percent of family households in 

1950 to 12 percent in 2000. Single-person 

households are now the nation’s second most 

numerous household type, accounting for 

over 25 percent of all households. In the 

future, longer life expectancies and the 

continuing preference for one or two children 

will make households without children even 

more numerous. Projected to compose 80 

percent of all households by 2025, 

nontraditional family households will play 

an increasingly important role in the home 

buying market.64

Trade-up Buyers. Due to weak house price 

appreciation, traditional ‘‘trade-up buyers’’

stayed out of the market during the early 

1990s. Their absence may explain, in part, 

the large representation of nontraditional 

homebuyers during that period. However, 

since 1995 home prices have increased more 

than 30 percent.65 The greater equity 

resulting from recent increases in home 

prices should lead to a larger role for ‘‘tradeup buyers’’ in the housing market during the 

next 10 to 15 years. In addition, the growing 

number of higher-income, mid-life 

households will increase households’

potential to ‘‘trade up’’ to more expensive 

housing.66

Growing Income Inequality. The Census 

Bureau reported that the top 5 percent of 

American households received 22.4 percent 

of aggregate household income in 2001, up 

from 21.4 percent in 1998 and up sharply 

from 16.1 percent in 1977. The share 

accruing to the lowest 80 percent of 

households fell from 56.5 percent in 1977 to 

50.8 percent in 1998 and again to 49.8 

percent in 2001. The share of aggregate 

income accruing to households between the 

80th and 95th percentiles of the income 

distribution was virtually unchanged from 

1977 to 2001.67

The increase in income inequality over 

past decades has been especially significant 

between those with and those without postsecondary education. The Census Bureau 

reports that by 1999, the annual earnings of 

workers with a bachelor’s degree were 1.8 

times the annual earnings of workers with a 

high school education.68 The inflationadjusted median earnings of high school 

graduates were at the same level in 2001 as 

in 1991 while the earnings of bachelor 

degree-holders rose nearly 9 percent over the 

same period.69

So, while homeownership is highly 

affordable, those without post-secondary 

education often lack the financial resources 

to take advantage of the opportunity. As 

discussed earlier, the days of the well-paying 

unionized factory job have passed. They have 

given way to technological change that favors 

white-collar jobs requiring college degrees, 

and wages in the manufacturing jobs that 

remain are experiencing downward pressures 

from economic globalization. The effect of 

this is that workers without the benefit of a 

post-secondary education find their demand 

for housing constrained. This is especially 

problematic for recent immigrants who are 

more likely to have limited educational 

attainment and English language proficiency. 

Summary. Over the next two-and-a-half 

decades, the number of U.S. households is 

projected to increase by nearly 27 million. Of 

these new households, non-Hispanic white 

and traditional households will contribute 

only one-third and one-tenth of the growth, 

respectively. As the baby-boomers aged out 

of their peak home buying stage and the 

baby-bust generation aged into their peak 

home buying stage in the late 1980s, demand 

for housing was dampened by demographic 

factors during the 1990s. (Of course, other 

factors such as low interest rates propelled 

the housing market to record levels during 

this period.) As the echo baby-boomers begin 

to enter their peak home buying age, housing 

demand should pick up again through the 

remainder of the current decade and into the 

next. As these demographic factors play out, 

the overall effect on housing demand will 

likely be sustained growth and an 

increasingly diverse household population 

from which to draw new homeowners. There 

are continuing concerns about the increasing 

income inequality of our population and 

those recent immigrants and other persons 

who have limited education. 

3. Basic Trends in the Single-Family 

Mortgage Market 

Mortgage lending in the nation is growing 

at unprecedented levels. Residential 

mortgage originations soared to $2.5 trillion 

in 2002, a 22 percent increase over the 

previous record of $2.06 trillion set in 

2001.70 Originations then jumped to $3.8 

trillion in 2003, with refinances accounting 

for 66 percent (or $2.5 trillion) of this total.

This boom in lending over the past three 

years can be attributed to low mortgage 

interest rates and a record number of 

refinances. Approximately 40 percent of 

mortgage debt outstanding, or $2.5 trillion, 

was refinanced during the 2001–02 refinance 

boom. Freddie Mac calculates total home 

equity cashed out in 2002 at 105.4 billion 

and estimates that number will increase to 

138.8 billion in 2003.71 This section focuses 

on recent interest rate trends, the refinance 

market, the home purchase market, and firsttime homebuyers. The section concludes by 

examining the GSEs’ acquisitions as a share 

of the primary single-family mortgage market, 

and provides mortgage market prospects.

a. Mortgage Characteristics 

Interest Rate Trends and Volatility.

Historically low mortgage interest rates in the 

late 1990s and 2001–2003 helped maintain 

consumer confidence in the housing sector as 

the economy emerged from its first recession 

in almost a decade. After high and 

fluctuating mortgage rates in the 1980s and 

early 1990s, recent years have seen a period 

of lower and more stable rates. The 1980s 

began with interest rates on mortgages for 

new homes above 12 percent but quickly rose 

to more than 15 percent.72 By 1987–88, rates 

dipped into single digits but were rising 

again by 1989–90. Rates declined in the early 

1990s, reaching a low of 6.8 percent in late 

1993. An upturn in rates in 1994 and 1995 

peaked at 8.3 percent in early 1995. By 1998, 

30-year fixed conventional mortgages 

averaged 6.95 percent, the lowest level since 

1968 but saw a rise in 1999 to 7.44 percent. 

Mortgage rates then continued to rise in 

2000, averaging 8.05 percent for the year, 

before falling to a low of 6.62 percent in 

October 2001 and averaging 6.97 percent for 

2001 as a whole.73 Rates averaged 5.83 

percent during 2003 74, reaching a low of 5.23 

in June. Rates in 2004 have averaged 5.83 

through August, reaching a low of 5.45 in 

March. 75

Other Loan Terms. When mortgage rates 

are low, most homebuyers prefer to lock in 

a fixed-rate mortgage (FRM). Adjustable-rate 

mortgages (ARMs) are more attractive when 

Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 63653

76http://www.fhfb.gov/mirs/mirs_t25.xls.

77http://www.fhfb.gov/mirs/mirstbl5.xls; data for 

2003 is average of May through December data.

78This is discussed in more detail in Paul 

Bennett, Richard Peach, and Stavros Peristani, 

Structural Change in the Mortgage Market and the 

Propensity to Refinance, Staff Report Number 45, 

Federal Reserve Bank of New York, September 

1998.

79http://www.fhfb.gov/mirs/mirs_t1.xls.

80Other sources of data on loan-to-value ratios 

such as the American Housing Survey and the 

Chicago Title and Trust Company indicate that 

high-LTV mortgages are somewhat more common in 

the primary market than the Finance Board’s 

survey. However, the Chicago Title survey does not 

separate FHA-insured loans from conventional 

mortgages. In addition, the statistics cited above 

pertain only to home purchase mortgages. 

Refinance mortgages generally have shorter terms 

and lower loan-to-value ratios than home purchase 

mortgages.

81The source for the refinance share and total 

mortgage originations is the Mortgage Bankers 

Association (http://www.mortgagebankers.org/

marketdata/forecasts/mffore1203.pdf, http://

www.mortgagebankers.org/marketdata/forecasts/

ffJUNE2004.pdf).

82Refinancing data is taken from Freddie Mac’s 

monthly Primary Mortgage Market Survey.

83There is some evidence that lower-income 

borrowers did not participate in the 1993 refinance 

boom as much as higher-income borrowers—see 

Paul B. Manchester, Characteristics of Mortgages 

Purchased by Fannie Mae and Freddie Mac: 1996–

97 Update, Housing Finance Working Paper No. 

HF–006, Office of Policy Development and 

Research, Department of Housing and Urban 

Development, August 1998, pp. 30–32.

84Economy.com, ‘‘The Economic Contribution of 

the Mortgage Refinancing Boom,’’ December 2002, 

p. 4.

85Fannie Mae, 2002 Fannie Mae National 

Housing Survey. <http://www.fanniemae.com/

global/pdf/media/survey/survey2002>, September 

4, 2002, p. 2.

86Economy.com, ‘‘The Economic Contribution of 

the Mortgage Refinancing Boom,’’ December 2002, 

p. 4.

87Mark M. Zandi, ‘‘Refinancing Boom,’’ Regional 

Finance Review, December 2002, p. 11.

88 Ibid. p. 14.

rates are high, because they carry lower rates 

than FRMs and because buyers may hope to 

refinance to an FRM when mortgage rates 

decline. The Federal Housing Finance Board 

(FHFB) reports that the ARM share of the 

market fell from 20 percent in 1993 to a 

record low of 12 percent in 1998, before 

rising back to 21 percent in 1999. The ARM 

share continued to rise to 24 percent in 2000, 

but then fell dramatically to a low of 12 

percent in 2001 as mortgage rates decreased. 

However, in 2002 and 2003, there was a 

rebound in the ARM share of the market. 

Though it still is nowhere near the size it was 

in the mid to late 1990s, the past two years 

have seen the share climb to 17 and 19 

percent, respectively.76

In 2003, the term-to-maturity was 30 years 

for 80 percent of conventional home 

purchase mortgages, continuing to decline 

after steadily climbing to a high of 90 percent 

in 2000. The other major term of maturity in 

2003 was 15 years (16 percent).77

Low- and no-point mortgages continue to 

be a popular option for mortgage purchases. 

FHFB reports that average initial fees and 

charges (‘‘points’’) have decreased from 2.5 

percent of loan balance in the mid-1980s to 

2 percent in the late-1980s, 1.5 percent in the 

early 1990s, and less than 1 percent in 1995–

97. The downward trend continued 

throughout the late 1990s with the average 

initial fees and charges reaching a low of onehalf percent in 2001, staying there in 2002, 

and dipping even further down in 2003. 

Coupled with declining interest rates, these 

lower transactions costs have increased the 

propensity of homeowners to refinance their 

mortgages.78

Another major change in the conventional 

home mortgage market has been the 

proliferation and then diminution of high 

loan-to-value ratio (LTV) mortgages. 

According to data from the Federal Housing 

Finance Board, loans with LTVs greater than 

90 percent (that is, down payments of less 

than 10 percent) made up less than 10 

percent of the market in 1989–91, but 25 

percent of the market in 1994–97, gradually 

decreasing to an average of 20 percent of the 

market in 2003. Loans with LTVs less than 

or equal to 80 percent fell from three-quarters 

of the market in 1989–91 to an average of 56 

percent of the market in 1994–97, but then 

rose to an average of 63 percent of mortgages 

originated in 1998–2001, and rose again to an 

average of 70 percent of mortgages originated 

in 2002–2003.79 As a result, the average LTV 

rose from 75 percent in 1989–91 to nearly 80 

percent in 1994–97, and then declined to 

76.2 percent in 2001, 75.1 percent in 2002, 

and 73.5 percent in 2003.80

b. Refinance Mortgages 

Over the past ten years, refinance booms 

occurred three times, during 1992–93, 1998, 

and 2001–03. Refinancing has fueled the 

growth in total mortgage originations, which 

were $638 billion in 1995 (a period of low 

refinance activity), but topped $2.5 trillion in 

2002 (a period of heavy refinance activity). 

The refinance share of total mortgage 

originations rose to 50 percent in 1998, then 

decreased to 19 percent in 2000 before 

jumping to 57 percent in 2001, and 59 

percent in 2002. During the 2001–02 

refinance boom, approximately 40 percent of 

the $2.5 trillion in mortgage debt outstanding 

was refinanced. In 2003, the refinance share 

of total mortgage originations hit 66 percent, 

though late 2003 saw a steep drop-off from 

a 68 percent share in the third quarter to a 

49 percent share in the fourth.81

In 1989–90 interest rates exceeded 10 

percent, and refinancings accounted for less 

than 25 percent of total mortgage 

originations.82 The subsequent sharp decline 

in mortgage rates drove the refinance share 

over 50 percent in 1992 and 1993 and 

propelled total single-family originations to 

more than $1 trillion in 1993—twice the level 

attained just three years earlier.

The refinance wave subsided after 1993, 

because most homeowners who found it 

beneficial to refinance had already done so 

and because mortgage rates rose once again.83

Total single-family mortgage originations 

bottomed out at $638 billion in 1995, when 

the refinance share was only 21 percent. 

Total originations, driven by the volume of 

refinancings, amounted to $1.507 trillion in 

1998, nearly 50 percent higher than the 

previous record level of $1.02 trillion 

attained in 1993.

The refinance wave from late 1997 through 

early 1999 reflected other factors besides 

interest rates, including greater borrower 

awareness of the benefits of refinancing, a 

highly competitive mortgage market, and the 

enhanced ability of the mortgage industry, 

utilizing automated underwriting and 

mortgage origination systems to handle an 

unprecedented volume of originations. The 

refinance share decreased to 19 percent in 

2000 before jumping to a record 57 percent 

in 2001. 

Historically low interest rates and 

declining mortgage transaction costs have 

driven the latest refinancing boom. Given 

these conditions, the after-tax cost saving on 

a new, lower-rate loan is much greater than 

the transaction costs of refinancing. In 

addition, the appreciation of housing prices 

has also contributed to the increase in 

refinancing. Over the past five years, the 

value of housing rose by approximately $5 

trillion, and the rise in value has enabled 

lenders to service refinancing homeowners 

because of greater confidence in the 

creditworthiness of borrowers.84

Over the past few years, homeowners have 

become more willing to draw on the rising 

equity in their homes. According to Fannie 

Mae’s 2002 National Housing Survey, 

homeowners that refinanced during 2001 

withdrew about $110 billion in accumulated 

home equity wealth.85 Freddie Mac estimates 

that more than one-half of all refinance 

mortgages in the past two years involved 

cash-out refinancing.86

The refinancing boom contributed to an 

estimated one-fifth of the national economy’s 

real GDP growth since late 2000.87 During 

2001 and 2002, roughly $270 billion was 

raised in cash-out refinancing. 

Approximately one-half of cash from cashout refinancing has enabled consumers to 

finance more spending for expenses such as 

home improvements, medical payments, 

education, and vehicles during a weakened 

economy. Roughly one-third of the cash from 

cash-out refinancing has allowed consumers 

to repay other debt.88 The remaining cash 

from cash-out refinancing has enabled 

consumers to invest in other assets. 

Refinancing households save approximately 

$10 billion in their annual interest payments 

on their mortgage and consumer installment 

liabilities.

The refinancing boom will have lingering 

effects. Mortgage borrowers that were able to 

secure low long-term interest rates through 

fixed rate mortgages will have more of their 

budgets to spend on other items. Meanwhile, 

cash-out borrowers, who are just receiving 

their money, will spend this year. It must be 

noted there is some concern regarding the 

potential for increased credit risk stemming 

from mortgage debt from cash out borrowers. 

According to a 2002 Regional Finance 

Review article, the mortgage liabilities of 

households have been growing at a rate more 

than double the growth in household 

incomes. However, this potential credit risk 

is moderated by the strong growth in housing 

values. The ratio of mortgage debt to housing 

63654 Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 

89Economy.com, ‘‘The Economic Contribution of 

the Mortgage Refinancing Boom,’’ December 2002, 

p. 9.

90Mortgage Bankers Association, ‘‘Mortgage 

Finance Forecast’’, September 17, 2004. http://

www.mortgagebankers.org/marketdata/forecasts/

mffore1203.pdf.

91Housing affordability varies markedly between 

regions, ranging in January 2004 from 194 in the 

Midwest to 107 in the West, with the South and 

Northeast falling in between.

92National Association of REALTORS. Housing 

Affordability Index, <http://www.realtor.org/

Research.nsf/Pages/HousingInx>, 2003.

93Fannie Mae, September 4, 2002, p.2.

94 Ibid.

95U.S. Department of Commerce, Bureau of the 

Census, Money Income of Households, Families, 

and Persons in the United States: 1992, Special 

Studies Series P–60, No. 184, Table B–25, October 

1993.

96Chicago Title and Trust Family of Insurers, 

Who’s Buying Homes in America, 1998.

97National Association of Realtors. ‘‘New NAR 

Survey of Home Buyers and Sellers Shows Growing 

Web Use in a Dynamic Housing Market.’’ http://

www.realtor.org.

98U.S. Housing Market Conditions, 3rd Quarter 

2001, November 2001, Table 4.

99National Association of Realtors. ‘‘New NAR 

Survey of Home Buyers and Sellers Shows Growing 

Web Use in a Dynamic Housing Market.’’ http://

www.realtor.org.

100 Joint Center for Housing Studies of Harvard 

University, State of the Nation’s Housing 2002, p. 

2.

values, the aggregate loan-to-value ratio, has 

remained fairly stable for a decade.89

c. Home Purchase Mortgages 

The volume of home purchase mortgages 

was $505 billion in 1995, rose to $848 billion 

in 1999, and remained in the $829–$873 

billion range between 1999–2001 before 

jumping to $1.02 trillion in 2002 and $1.30 

trillion in 2003. The Mortgage Bankers 

Association (MBA) forecasts that the home 

purchase volume will be $1.52 trillion in 

2004 as the home purchase share rises to 57 

percent of all originations.90 The home 

purchase share of total mortgage originations 

was 79 percent in 1995, declined to 50 

percent in 1998, rose to 81 in 2000, and 

sharply fell to 43 percent in 2001, 41 in 2002, 

and 34 percent in 2003, as refinance 

mortgage volume grew. This section 

discusses the important issue of housing 

affordability and then examines the value of 

homeownership as an investment.

The National Association of Realtors (NAR) 

has developed a housing affordability index, 

calculated as the ratio of median household 

income to the income needed to qualify for 

a median price home (the latter income is 

called the ‘‘qualifying income’’). In 1993, 

NAR’s affordability index was 133, which 

meant that the median family income of 

$37,000 was 33 percent higher than that 

income needed to qualify for the median 

priced home. Housing affordability remained 

at about 130 for 1994–97, with home price 

increases and somewhat higher mortgage 

rates being offset by gains in median family 

income.91 Falling interest rates and higher 

income led to an increase in affordability to 

143 in 1998, reflecting the most affordable 

housing in 25 years. Affordability remained 

high in 1999, despite the increase in 

mortgage rates. NAR’s affordability index 

declined from 140 in 1999 to 129 in 2000 as 

mortgage rates increased. The index turned 

upward to 136 in 2001 as mortgage rates fell 

and maintained this average in 2002, before 

rising further to 140 in 2003.92

Although the share of home purchase loans 

for lower-income households and/or 

households living in lower-income 

communities increased over the past decade, 

affordability still remains a challenge for 

many. The median sales price of existing 

single-family homes in the United States 

continues to rise, reaching $158,100 in 2002 

and $170,000 in 2003. The production of 

affordable housing and low interest rates 

could offset the negative impact of rising 

house prices, which undermine housing 

affordability for many Americans, 

particularly in several high-cost markets on 

the east and west coasts. 

As discussed earlier, barriers are 

preventing many potential homeowners from 

becoming homeowners, thus reducing the 

possible amount of home purchase loans. 

While the strong housing sector has provided 

financial security for many Americans, a 

2002 Fannie Mae survey found that 

‘‘information barriers still keep many 

financially qualified families-particularly 

minority Americans from becoming 

homeowners or obtaining the lowest-cost 

financing available to them.’’ 93

These homeownership barriers pose a 

serious problem for many Americans who 

view homeownership as a smart, safe, longterm investment, rating homeownership as a 

better investment than the stock market. 

Home equity is the single most important 

asset for approximately two-thirds of 

American households that are homeowners. 

Considering that half of all homeowners held 

at least 50 percent of their net wealth in 

home equity in 1998, increasing housing 

affordability is important for many 

Americans.94

First-time Homebuyers. First-time 

homebuyers are a driving force in the 

nation’s mortgage market. The recent low 

interest rates have made it an opportune time 

for first-time homebuyers, which are 

typically people in the 25–34 year-old age 

group that purchase modestly priced houses. 

As the post-World War II baby boom 

generation ages, the percentage of Americans 

in this age group decreased from 28.3 percent 

in 1980 to 25.4 percent in 1992.95 Even 

though this cohort is smaller, first-time 

homebuyers increased their share of home 

sales. According to Chicago Title data for 

major metropolitan areas, the first-time buyer 

share of the homebuyer market increased 

from roughly 40 percent in the beginning of 

the 1990s to 45–47 percent during the-mid 

and late 1990s.96 Since the late 1990s, 

industry survey data suggest that the firsttime homebuyer percentage has decreased 

slightly. In the first quarter of 2003, the share 

of all home purchases by first-time 

homebuyers was 40 percent compared to 42 

percent in 2001.97

In the 1990s, lenders developed special 

programs targeted to first-time homebuyers 

and revised their underwriting standards to 

enhance homeownership opportunities for 

low-income families with special 

circumstances. The disproportionate growth 

in the number of first-time homebuyers and 

minority homebuyers largely drove the rising 

trend in total home purchases. Analysis of 

the American Housing Survey (AHS) 

indicates there were 1.3 million new firsttime homebuyers during 1991, in comparison 

with over two million in each year between 

1996 and 2001. In addition, first-time 

homebuyers comprised approximately 60 

percent of all minority home purchases 

during the 1990s, compared with about 35 

percent of all home purchases by nonHispanic white families. 

In comparison to repeat homebuyers, firsttime homebuyers are more likely to be 

younger, have lower incomes, and purchase 

less expensive houses. According to the AHS, 

more than one-half or first-time homebuyers 

were below the age of 35, compared with less 

than one-quarter of repeat buyers in the 

1990s. Thirty-nine percent of first-time 

buyers had incomes below 80 percent of the 

median compared to 30 percent of repeat 

buyers. Fifty-four percent of first-time buyers 

purchased homes priced below $100,000, 

compared to 37 percent of repeat buyers. 

Minorities comprise a higher proportion of 

first-time buyers (32 percent) compared to 

repeat buyers (14 percent). Compared to 

repeat buyers, first-time homebuyers are 

more likely to purchase a home in the central 

city and more likely to be a female-headed 

household.98

The National Association of Realtors 

reports that the average first-time homebuyer 

in the first quarter of 2003 was 32 years old 

with a household income of $54,800, 

compared to an average age of 46 years and 

average household income of $74,600 for 

repeat buyers. The average first-time 

homebuyers made a downpayment of 6 

percent on a home that cost $136,000 while 

the average repeat buyer made a 

downpayment of 23 percent on a home 

costing $189,000. In the NAR survey, 37 

percent of first-time homebuyers were single 

compared to 28 percent of repeat buyers.99

Many African Americans and Hispanics 

are likely to purchase homes in the coming 

years, contributing to the number of first-time 

home-buyers fueling growth in the housing 

sector. The number of homeowners will rise 

by an average of 1.1 million annually over 

the next two decades. The sizeable rise in the 

foreign-born population since the 1970’s 

coupled with the increase in Latin American 

and Asian immigration will also contribute 

much to this growth.100

d. GSEs’ Acquisitions as a Share of the 

Primary Single-Family Mortgage Market 

Purchases by the GSEs of single-family 

mortgages amounted to $519 billion during 

the heavy refinancing year of 1993, stood at 

$215 billion in 1995, and were at $618 billion 

during the heavy refinancing year of 1998. 

Purchases then fell to $395 billion in 2000 

before reaching record levels during the 

heavy refinancing years of 2001 ($961 

billion) and 2002 ($1,090 billion). Purchases 

by Fannie Mae decreased from $316 billion 

in 1999 to $227 billion in 2000, before rising 

to $568 billion in 2001, $800 billion in 2002, 

and $1.3 trillion in 2003. Freddie Mac’s 

Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 63655

101Office of Federal Housing Enterprise Oversight 

(OFHEO), Report to Congress, 2004, Tables 1 and 

11.102Office of Federal Housing Enterprise 

Oversight. ‘‘Mortgage Markets and The Enterprises 

in 2001,’’ August 2002, p. 13. 103http://www.financialservicesfacts.org/

financial2/mortgage/mortgages/

?table_sort_734796=4. 104Mortgage market projections from the MBA’s 

MBA Mortgage Finance Forecast, December 17, 

2003. 2000 and 2001 numbers from the MBA’s MBA 

Mortgage Finance Forecast, January 10, 2002.

105See Charles, K. K. and E. Hurst (2002). ‘‘The 

Transition to Home Ownership and the Black-White 

Wealth Gap.’’ The Review of Economics and 

Statistics, 84(2): 281–297; Mayer, C. and G. 

Engelhardt (1996). ‘‘Gift Down Payments and 

Housing Affordability.’’ Journal of Housing 

Research, 7(1): 59–77; and Quercia, R. G., G. W. 

McCarthy, et al. (2003). ‘‘The Impacts of Affordable 

Lending Efforts on Homeownership Rates.’’ Journal 

of Housing Economics, 12(1): 29–59.

106Fannie Mae, 2003 Annual Housing Activities 

Report, 2004, pp. 8–9.

107Fannie Mae, ‘‘Fannie Mae’s Comments on 

HUD’s Proposed Housing Goals for Fannie Mae and 

Freddie Mac for the years 2005–2008 and 

Amendments to HUD’s Regulation of Fannie Mae 

and Freddie Mac,’’ July 16, 2004, p. I–58.

108Fannie Mae, 2003 Annual Housing Activities 

Report, 2004, p. 6.

single-family mortgage purchases followed a 

similar trend, falling from $233 billion in 

1999 to $168 billion in 2000, and then rising 

to $393 billion in 2001 and $475 billion in 

2002.101

The Office of Federal Housing Enterprise 

Oversight (OFHEO) estimates that the GSEs’

share of total originations in the conventional 

single-family mortgage market, measured in 

dollars, declined from 37 percent in 1996 to 

32 percent in 1997—well below the peak of 

51 percent attained in 1993. OFHEO 

attributes the 1997 downturn in the GSEs’

role to increased holdings of mortgages in 

portfolio by depository institutions and to 

increased competition with Fannie Mae and 

Freddie Mac by private label issuers. 

However, OFHEO estimates that the GSEs’

share of the conventional market rebounded 

sharply in 1998–99, to 43–42 percent. The 

GSEs’ share then decreased to approximately 

30 percent of the single-family conventional 

mortgages originated in 2000, and then 

increased sharply to 40 percent in 2001. 

Total GSE purchases, including loans 

originated in prior years, amounted to 46 

percent of conventional originations in 

2001102 and approximately 38 percent of 

family home mortgage originations in 

2002.103

e. Mortgage Market Prospects 

The Mortgage Bankers Association (MBA) 

reports that mortgage originations in 2001 

were $2.0 trillion, which is almost twice the 

volume of originations in 2000. Mortgage 

originations then increased to record levels of 

$2.5 trillion in 2002 and $3.8 trillion in 2003, 

with refinancings representing 66 percent of 

originations and the purchase volume 

amounting to $1.3 trillion. Estimates indicate 

that ARMs accounted for 19 percent of total 

mortgage originations in 2003.104 In its 

September 17, 2004 forecast, MBA predicts 

that single-family mortgage originations will 

amount to $2.7 trillion in 2004 and $1.8 

trillion in 2005, with refinancings 

representing 43 percent and 25 percent of 

originations respectively.

4. Affordable Lending in the Mortgage 

Market: New Products and Outreach 

Extending homeownership opportunities 

to historically underserved households has 

been a growing concern for conventional 

lenders, private mortgage insurers and the 

GSEs. The industry has responded in what 

some have called a ‘‘revolution in affordable 

lending’’. The industry has offered more 

customized mortgage products, more flexible 

underwriting, and expanded outreach so that 

the benefits of the mortgage market can be 

extended to those who have not been 

adequately served through traditional 

products, underwriting, and marketing. 

Fannie Mae and Freddie Mac have been a 

part of this ‘‘revolution in affordable 

lending’’. During the mid-to-late 1990s, they 

added flexibility to their purchase guidelines, 

they introduced new low-down-payment 

products, and they worked to expand the use 

of credit scores and automated underwriting 

in evaluating the creditworthiness of loan 

applicants. These major trends reflect 

changes in the GSEs’ underwriting that have 

impacted affordable lending. Through these 

trends, Fannie Mae and Freddie Mac have 

attempted to increase their capacity to serve 

low- and moderate-income homebuyers. 

This section summarizes recent initiatives 

undertaken by the GSEs and others in the 

industry to expand affordable housing. The 

end of this section will present evidence that 

these new industry initiatives are working, as 

increased mortgage credit has been flowing to 

low-income and minority families. The 

following section will continue the affordable 

lending theme by examining the performance 

of different market sectors (e.g., depositories, 

GSEs, etc.) in funding loans for low-income 

and minority families. That section will also 

discuss the important role that FHA plays in 

making affordable housing available to 

historically underserved groups as well as 

the continuing concern that participants in 

the conventional market could be doing even 

more to help underserved families. 

a. Lowering Down Payments and Up-Front 

Costs 

Numerous studies have concluded that 

saving enough cash for a down payment and 

for up-front closing costs is the greatest 

barrier that low-income and minority 

families face when considering 

homeownership.105 To assist in overcoming 

this barrier, the industry (including lenders, 

private mortgage insurers and the GSEs) 

began offering in 1994 mortgage products 

that required down payments of only 3 

percent, plus points and closing costs. Other 

industry efforts to reduce borrowers’ up-front 

costs included zero-point-interest-rate 

mortgages and monthly insurance premiums 

with no up front component. These new 

plans eliminated large up-front points and 

premiums normally required at closing.

During 1998, Fannie Mae introduced its 

‘‘Flexible 97’’ and Freddie Mac introduced its 

‘‘Alt 97’’ low down payment lending 

programs. Under these programs, borrowers 

were required to put down only 3 percent of 

the purchase price. The down payment, as 

well as closing costs, could be obtained from 

a variety of sources, including gifts, grants or 

loans from a family member, the government, 

a non-profit agency and loans secured by life 

insurance policies, retirement accounts or 

other assets. Fannie Mae continues to offer 

the ‘‘Flexible’’ line of products, and Freddie 

Mac continues to list ‘‘Alt 97.’’

In 2000, Fannie Mae launched the 

‘‘MyCommunityMortgage’’ suite of products, 

which provides high loan-to-value product 

options for low- and moderate-income 

borrowers. In 2003, Fannie Mae purchased or 

securitized more than $2.27 billion of 

MyCommunityMortgage products, which 

helped provide affordable housing solutions 

for 20,400 households. In addition, Fannie 

Mae enhanced the MyCommunityMortgage to 

help lenders further expand affordable 

financing to underserved families. Examples 

of these enhancements included adding 

MyCommunityMortgage to Desktop 

Underwriter in order to provide lenders 

easier access to customized CRA-targeted 

loan products, adding new credit and income 

flexibilities for borrowers purchasing single 

family homes, Community HomeChoice 

which offers more flexible requirements for 

persons with disabilities, Community 2–4 

FamilyTM to help make the purchase of 2–

4 unit homes more affordable for first time 

homebuyers, and Community RenovationTM 

1–4 Family Pilot to help borrowers with 

home improvement and housing preservation 

costs.106 Additionally, in 2003, Fannie Mae 

enhanced Community 2–4 Family and 

Community Renovation 1–4 Family pilots. 

This product provides lower down payments 

and flexible parameters for owner-occupants 

of 1–4 unit properties.107

Fannie Mae also expanded its ‘‘Flexible’’

product line with the ‘‘Flexible 100’’ product, 

which eliminates the requirement for a down 

payment by providing 100 percent loan-tovalue financing. The borrower is required to 

make either a minimum of 3% (of the lesser 

of the sales price or appraised value) from 

approved flexible sources or making a 

minimum contribution of $500 from their 

own funds. The 3% may come from a variety 

on sources such as gifts, grants, or unsecured 

loans from relatives, employers, public 

agencies, or nonprofits. In 2003, Fannie Mae 

purchased $13.7 billion in Flexible loans that 

benefited 100,866 households.108

Fannie Mae has also developed products 

specifically geared toward populations with 

unique needs such as seniors, Native 

Americans and families living near public 

transit routes. Examples of these targeted 

products include the Home Equity 

Conversion Mortgage (HECM) which allows 

seniors to convert the equity in their homes 

to receive cash. In 2003, Fannie Mae 

purchased 27,644 HECM’s for a total value of 

$1.87 billion. PaymentPowerTM allows 

borrowers with strong credit to skip their 

regularly scheduled monthly payment up to 

two times during a twelve-month period and 

up to ten times during the life of the loan. 

This pilot was launched in July 2002 and by 

year-end 2003, Fannie Mae purchased 963 

PaymentPowerTM mortgages totaling $126 

million. Navajo Community Guaranty 

Initiative allows Navajo families to contribute 

63656 Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 

109Fannie Mae, 2003 Annual Housing Activities 

Report, 2004, pp. 9–10.

110Freddie Mac, Opening Doors for America’s 

Families: Freddie Mac’s Annual Housing Activities 

Report for 2003, March 15, 2004, p. 62.

111Freddie Mac, Opening Doors for America’s 

Families: Freddie Mac’s Annual Housing Activities 

Report for 2003, March 15, 2004, p. 62.

112Freddie Mac, Opening Doors for America’s 

Families: Freddie Mac’s Annual Housing Activities 

Report for 2003, March 15, 2004, p. 62–64.

113Freddie Mac Public Comment Letter on HUD’s 

Proposed Goals, July 2004, p. 2.

114Freddie Mac, Opening Doors for America’s 

Families: Freddie Mac’s Annual Housing Activities 

Report for 2003, March 15, 2004, pp. 62–64.

115Fannie Mae, 2003 Annual Housing Activities 

Report, 2004, pp. 22–24.

116Fannie Mae, 2003 Annual Housing Activities 

Report, 2004, pp. 13–16.

117Fannie Mae, 2003 Annual Housing Activities 

Report, 2004, pp. 17–22.

118Fannie Mae, 2003 Annual Housing Activities 

Report, 2004, pp. 16.

119Fannie Mae, 2003 Annual Housing Activities 

Report, 2004, pp. 17–18.

120Fannie Mae, ‘‘Fannie Mae’s Comments on 

HUD’s Proposed Housing Goals for Fannie Mae and 

Freddie Mac for the years 2005–2008 and 

Amendments to HUD’s Regulation of Fannie Mae 

and Freddie Mac,’’ July 16, 2004, p. I–60.

a minimum of $500 or 1% of the purchase 

price, whichever is lower. This initiative, 

announced in 2003, will provide $3 million 

in home financing to help 60 families 

currently living on a reservation. The Smart 

CommuteTM Initiative, which targets 

borrowers purchasing homes near a public 

transit route, recognizes that homebuyers 

will save commuting expenses and therefore 

have more disposable income to pay housing 

expenses. In 2003 Fannie Mae purchased 

approximately $5 million in Smart 

CommuteTM Initiative loans.109

In 2000, Freddie Mac introduced its 

‘‘Freddie Mac 100’’ product, which is 

designed to assist borrowers who have good 

credit but lack the ability to provide a large 

down payment. ‘‘Freddie Mac 100’’ allows a 

100 percent loan-to-value ratio with the 

condition that the borrower has the funds for 

closing costs. In 2003, a refinance option was 

added to Freddie Mac 100 and the cost of the 

loan was reduced through lower mortgage 

insurance coverage and a lower fee for the 

product. These changes have made the 

Freddie Mac 100 available to borrowers who 

may not have been able to take advantage of 

the refinance boom as a result of low or no 

equity in their homes.110

Another Freddie Mac product, Affordable 

Gold 97 permits borrowers to make 3% 

down payments from personal cash and to 

use other sources to cover their closing costs, 

and offers flexible ratio and reserves 

guidelines. In 2003 this product was 

enhanced with a refinance option allowing 

more borrowers to take advantage of the low 

rates in the market. The Affordable Gold

100 provides 100 percent financing to lowand moderate-income borrowers for the 

purchase price of a home in California. 

Affordable Gold 100 combines mortgage 

insurance benefits provided by a state 

insurance fund, the secondary mortgage 

market, and a team of the nation’s leading 

mortgage lenders.111

Additional Freddie Mac products include 

the Alt 97SM for borrowers who have good 

credit but limited cash for a down payment. 

In 2003, this product was enhanced with a 

refinance option and reduced fees. The TwoFamily 95 Percent LTV Program offers low 

down payment loans to purchasers of twofamily properties when the borrowers occupy 

one of the units as their primary residence.112

Other initiatives include policies aimed at 

improving the homeownership rate among 

immigrant families and the Section 8 Rental 

to Homeownership program, which allows 

people currently receiving Section 8 rental 

subsidies to use them toward mortgage 

payments. 113 Freddie Mac purchases loans 

in which the borrower’s down payment 

consists of funds that have been matched 

through an Individual Development Account 

homebuyer savings program. And in 2003, 

Freddie Mac provided increased liquidity for 

affordable housing through a series of 

targeted investments in Mortgage Revenue 

Bonds containing state and local housing 

finance agency mortgages.114

b. Partnerships—Fannie Mae 

In addition to developing new affordable 

products, lenders and the GSEs have been 

entering into partnerships with local 

governments and nonprofit organizations to 

increase mortgage access to underserved 

borrowers. Fannie Mae operates 55 

partnership offices throughout the country, 

including the West Virginia Partnership 

Office, which opened in 2003. These offices 

coordinate Fannie Mae’s programs with local 

governments, lenders, public officials, 

housing organizations, community 

nonprofits, real estate professionals, and 

other local stakeholders.115

Fannie Mae continues to reach out to 

national groups and work with local affiliates 

to expand homeownership. Fannie Mae has 

established multi-year partnerships to 

increase affordable housing opportunities 

with organizations such as: The Enterprise 

Foundation, The Neighborhood 

Reinvestment Corporation, ACORN Housing 

Corporation, The National Council of La 

Raza, and many others engaged in promoting 

affordable housing. In 2003, Fannie Mae 

financed $1.3 billion of mortgages with these 

national partners and participating lenders, 

which resulted in 9,597 loans. For example, 

Fannie Mae maintains a partnership with the 

National Urban League (NUL) and the JP 

Morgan Chase Bank to increase NUL’s 

homeownership counseling capacity by 

providing the necessary technology and tools 

to support the effort, and to purchase $50 

million in mortgage products over five years 

that are specifically targeted to increase 

homeownership among minorities. In 2003, 

approximately $6 million in loans were 

originated through this initiative. Another 

example is Fannie Mae’s partnership with 

the AFL-CIO Housing Investment Trust (HIT) 

and Countrywide Home Loans, which 

launched ‘‘HIT HOME’’ in 2001. HIT HOME 

is an affordable home mortgage initiative that 

targets 13 million union members in 35 cities 

throughout the nation to provide union 

members with a variety of affordable 

mortgage choices that enable them to qualify 

for competitively priced loans with new repayment terms. In 2003, over $132 million 

worth of mortgages were originated through 

this partnership.116

In order to meet the needs of underserved 

and low- and moderate-income populations, 

Fannie Mae has targeted specific populations 

for initiatives. These include the Section 8 

Homeownership Initiative, which purchased 

81 Section 8 loans and funded an additional 

55 loans through a Community Development 

Financial Institution investment; the Native 

American Homeownership Initiative, which 

has committed to invest at least $350 million 

to support homeownership strategies for 

4,600 Native American families and to work 

with 100 tribes; the Minority- and WomenOwned Lenders Initiative, to reach 

underserved communities and to develop 

innovative solutions for increasing business 

opportunities for these lenders; The 

Employer-Assisted Housing Initiative, 

designed to assist employers in developing a 

company benefit that helps employees meet 

their housing needs; and the Initiative to 

Reduce Barriers to Affordable Housing, 

which has established local partnerships in 

seven new states and localities in 2003. 

Additionally, Fannie Mae conducts various 

underwriting experiments aimed at 

eliminating obstacles faced by prospective 

homebuyers across the country. In 2003, 

Fannie Mae approved $222 million worth of 

Housing and Community Development placebased commitments for a total of 55 

experiments.117

Fannie Mae’s American Dream 

Commitment is part of its National Minority 

Homeownership Initiative which has pledged 

to contribute at least $700 billion in private 

capital to serve 4.6 million families towards 

President George W. Bush’s goal of 

expanding homeownership to 5.5 million 

new minority Americans by the end of the 

decade. Towards this goal, in 2003, Fannie 

Mae executed 17 new Housing and 

Community Development lender 

partnerships which seek to provide $394 

billion in affordable housing lending to 

minority families.118

Under the American Dream Commitment, 

Fannie Mae has committed to establishing 

250 faith-based homeownership partnerships 

in communities across the country by the end 

of the current decade. The objective of this 

initiative is to build strong partnerships with 

national faith-based organizations in order to 

reach potential new homeowners, work with 

faith-based and nonprofit partners to help 

increase access to homeownership 

information and education, partner with 

lenders to increase access to mortgage 

financing, and provide faith-based 

organizations with the tools, training, and 

resources needed to advance their 

community development efforts. Fannie 

Mae’s work under the Faith-Based Initiative 

in 2003 resulted in $125 million in mortgage 

financing to underserved families across the 

country.119 Additionally, Fannie Mae 

attended more than 12 faith-based 

symposiums providing training and technical 

assistance to over 2,000 symposium 

attendees.120

Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 63657

121Freddie Mac, News Release, January 15, 1999.

122Freddie Mac Public Comment Letter on HUD’s 

Proposed Goals, July 2004, p. 3.

123Freddie Mac, Opening Doors for America’s 

Families: Freddie Mac’s Annual Housing Activities 

Report for 2003, March 15, 2004, pp. 67.

124Freddie Mac, Opening Doors for America’s 

Families: Freddie Mac’s Annual Housing Activities 

Report for 2003, March 15, 2004, pp. 66–67.

125Freddie Mac, Opening Doors for America’s 

Families: Freddie Mac’s Annual Housing Activities 

Report for 2003, March 15, 2004, pp. 37–38.

126Freddie Mac, Opening Doors for America’s 

Families: Freddie Mac’s Annual Housing Activities 

Report for 2003, March 15, 2004, pp. 38–39.

127Freddie Mac, Opening Doors for America’s 

Families: Freddie Mac’s Annual Housing Activities 

Report for 2003, March 15, 2004, pp. 39–40.

128Freddie Mac, Opening Doors for America’s 

Families: Freddie Mac’s Annual Housing Activities 

Report for 2003, March 15, 2004, pp. 42–43.

129Freddie Mac Public Comment Letter on HUD’s 

Proposed Goals, July 2004, p. 4.

130Freddie Mac, Opening Doors for America’s 

Families: Freddie Mac’s Annual Housing Activities 

Report for 2003, March 15, 2004, pp. 29–30.

131Freddie Mac, Opening Doors for America’s 

Families: Freddie Mac’s Annual Housing Activities 

Report for 2003, March 15, 2004, pp. 30–34.

132 Kenneth Temkin, Roberto Quercia, George 

Galster, and Sheila O’Leary, A Study of the GSEs’

Single Family Underwriting Guidelines: Final 

Report. Washington DC: U.S. Department of 

Housing and Urban Development, April 1999.

c. Partnerships—Freddie Mac 

Freddie Mac does not have a partnership 

office structure similar to Fannie Mae’s, but 

it has undertaken a number of initiatives in 

specific metropolitan areas.121 Freddie Mac 

works with affordable housing lenders to 

design creative solutions to meet 

homeownership needs of specific 

populations in targeted areas; explore 

efficient use of public subsidies to make 

homeownership more affordable and develop 

homebuyer education/counseling and debt 

management assistance programs.122 In 2001, 

Freddie Mac joined the Congressional Black 

Caucus to launch a new initiative, ‘‘With 

Ownership Wealth,’’ designed to increase 

African-American homeownership with one 

million new families by 2005.123 Freddie 

Mac has partnered with the National Council 

of La Raza (NCLR), 20 community based 

NCLR affiliated housing counseling 

organizations, the National Association of 

Hispanic Real Estate Professionals 

(NAHREP), EMT Applications and 

participating Freddie Mac Seller/Servicers 

including Bank of America, U.S. Bank and 

Wells Fargo Home Mortgage on the ‘‘En Su 

Casa’’ initiative. This $200 million 

homeownership initiative combines 

technology tools with flexible mortgage 

products to meet the needs of Hispanic 

borrowers. Mortgage products include low 

down payments, flexible credit underwriting 

and debt-to-income ratios, and streamlined 

processing for resident alien borrowers.124

In 2002, Freddie Mac joined with the City 

of Boston and the U.S. Conference of Mayors 

to make available the ‘‘Don’t Borrow 

Trouble’’ predatory lending educational 

campaign to approximately 1,100 cities. As of 

the end of 2003, the campaign has been 

launched in more than 30 localities. 

Additionally, in late 2003, Freddie Mac 

sponsored a national Don’t Borrow Trouble 

summit. Attorneys, community activists and 

local leaders from 23 cities convened to share 

campaign experiences and to learn about 

emerging predatory lending trends from some 

of the nation’s leading community lending 

experts.125

In addition, Freddie Mac joined with 

Rainbow/PUSH and the National Urban 

League to promote the CreditSmart

financial educational curriculum that helps 

consumers understand, obtain and maintain 

good credit, thereby preparing them for 

homeownership and other personal financial 

goals. Rainbow/PUSH has organized 

CreditSmart classes with more than 80 

churches across the nation, reaching more 

than 2,500 congregants. Bilingual curriculum 

was launched for this program in December 

2002, and during 2003 CreditSmart Espan ̃ ol 

conducted a total of 23 Train-the-Trainer 

workshops for their partners and their local 

partners resulting in 326 trainers who are 

authorized to teach the CreditSmart

Espan ̃ ol curriculum. Thus far 503 adults have 

been trained in the CreditSmart Espan ̃ ol 

financial literacy program.126 The 

CreditSmart/Homeownership Development 

Initiative with the National Urban League has 

nine affiliates located in Birmingham, AL; 

Charlotte, NC; Louisville, KY; Greenville, SC; 

Oklahoma City, OK; Springfield, IL; and 

Washington, DC; with Orlando, FL and 

Knoxville, TN added in 2003. Since the 

initiative’s launch in early 2002, 41 

CreditSmart financial literacy workshops 

have been presented to more than 600 

minority participants. Those participants are 

proceeding to the next steps to achieving 

homeownership, and in 2003 313 loans have 

closed as a direct result.127

In 2002 and 2003, Freddie Mac joined with 

the American Community Bankers, the Credit 

Union National Association, and the 

Independent Community Bankers of America 

in strategic alliances to better enable member 

banks and credit unions access to the 

secondary market.128

In June 2002, President George W. Bush 

challenged the nation’s housing industry to 

invest more than $1 trillion to make 

homeownership a reality for 5.5 million more 

minority households for the decade. Freddie 

Mac responded to the challenge with Catch 

the Dream which is a comprehensive set of 

25 high impact initiatives aimed at 

accelerating the growth in minority 

homeownership. The initiatives range from 

homebuyer education and outreach, to new 

technologies with innovative mortgage 

products. Freddie Mac has committed to 

purchase $400 billion in mortgages made to 

minority families by the end of the decade.129

Catch the Dream represents a collaborative 

effort with lenders, nonprofit housing and 

community-based organizations, and other 

industry participants to expand 

homeownership opportunities for America’s 

minorities.130 In 2003 initiatives were 

implemented in Birmingham, Charlotte, 

Atlanta, DeKalb County (GA), Lansing, and 

San Antonio. In 2003, single-family owner 

occupied mortgage purchases financed 

homes for almost 700,000 minority families, 

including mortgages for 133,000 AfricanAmerican and 250,000 Hispanic families 

(this comprised 16% of Freddie Mac’s singlefamily, owner-occupied mortgage purchases 

and 22.6% of their first-time homebuyer 

mortgage purchases).131

The programs mentioned above are 

examples of the partnership efforts 

undertaken by the GSEs. There are more 

partnership programs than can be adequately 

described here. Fuller descriptions of these 

programs are provided in their Annual 

Housing Activity Reports. 

d. Underwriting and GSE Purchase 

Guidelines 

Lenders, mortgage insurers, and the GSEs 

have also been modifying their mortgage 

underwriting standards to address the needs 

of families who have historically found it 

difficult to qualify under traditional 

guidelines. In addition to the changes in 

underwriting standards, the use of automated 

underwriting has dramatically transformed 

the mortgage application process. This 

section focuses on changes to traditional 

underwriting standards and recent GSE 

initiatives for credit-impaired borrowers. 

Subsequent sections will provide more 

details on the impact of automated 

underwriting. 

The GSEs modified their underwriting 

standards to address the needs of families 

who find qualifying under traditional 

guidelines difficult. The goal of these 

underwriting changes is not to loosen 

underwriting standards, but rather to identify 

creditworthiness by alternative means that 

more appropriately measures the unique 

circumstances of low-income, immigrant, 

and minority households. Examples of 

changes that the GSEs and others in the 

industry have made to their underwriting 

standards include the following:

• Using a stable income standard rather 

than a stable job standard (or a minimum 

period of employment). This particularly 

benefits low-skilled applicants who have 

successfully remained employed, even with 

frequent job changes. 

• Using an applicant’s history of rent and 

utility payments as a measure of 

creditworthiness. This measure benefits 

lower-income applicants who have not 

established a credit history. 

• Allowing pooling of funds for 

qualification purposes. This change benefits 

applicants with extended family members. 

Freddie Mac, for example, allows income 

from relatives who live together to pool their 

funds to cover downpayment and closing 

costs and to combine their incomes for use 

in calculating the borrower’s stable monthly 

income. 

These underwriting changes have been 

accompanied by homeownership counseling 

to ensure homeowners are ready for the 

responsibilities of homeownership. In 

addition, the industry has engaged in 

intensive loss mitigation to control risks. 

In 1999, HUD commissioned a study by the 

Urban Institute to examine the underwriting 

criteria that the GSEs use when purchasing 

mortgages from primary lenders.132

According to the study, while the GSEs had 

improved their ability to serve low- and 

moderate-income borrowers, it did not 

63658 Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 

133Temkin, et al. 1999, p. 28.

134Freddie Mac, Opening Doors for America’s 

Families: Freddie Mac’s Annual Housing Activities 

Report for 2003, March 15, 2004, pp. 36–37.

135Freddie Mac Public Comment Letter on HUD’s 

Proposed Goals, July 2004, p. 5.

136Freddie Mac, Opening Doors for America’s 

Families: Freddie Mac’s Annual Housing Activities 

Report for 2003, March 15, 2004, p. 19.

137Freddie Mac, 2002 Annual Housing Activities 

Report, 2003, p. 57.

138Fannie Mae, 2003 Annual Housing Activities 

Report, 2004, pp. 11–12.

139Fannie Mae, ‘‘Fannie Mae’s Comments on 

HUD’s Proposed Housing Goals for Fannie Mae and 

Freddie Mac for the years 2005–2008 and 

Amendments to HUD’s Regulation of Fannie Mae 

and Freddie Mac,’’ July 16, 2004, p. I–57.

appear at that time that they had gone as far 

as some primary lenders to serve these 

borrowers. From the Urban Institute’s 

discussion with lenders, it was found that 

primary lenders were originating mortgages 

to lower-income borrowers using 

underwriting guidelines that allow lower 

down payments, higher debt-to-income ratios 

and poorer credit histories than allowed by 

the GSEs’ guidelines.

From this and other evidence, the Urban 

Institute concluded that the GSEs were 

lagging the market in servicing low- and 

moderate-income and minority borrowers. 

Furthermore, the Urban Institute found ‘‘that 

the GSEs’ efforts to increase underwriting 

flexibility and outreach has been noticed and 

is applauded by lenders and community 

advocates. Despite the GSEs’ efforts in recent 

years to review and revise their underwriting 

criteria, however, they could do more to 

serve low- and moderate-income borrowers 

and to minimize disproportionate effects on 

minorities.’’ 133 Since the Urban Institute 

study, Freddie Mac and Fannie Mae have 

been playing a larger role in financing lowincome and minority borrowers. (See Section 

E.2.)

In addition to offering low-down-payment 

programs, the GSEs’ recent efforts have also 

centered around their automated 

underwriting systems and their treatment of 

borrowers with blemished credit, the latter 

being perhaps the most controversial 

underwriting issue over the past few years. 

Freddie Mac has a variety of products and 

initiatives aimed at providing borrowers with 

impaired credit more mortgage choices. 

These products include: CreditWorksSM

which helps borrowers with excessive debt 

and impaired credit to become eligible for a 

prime market rate mortgage faster than would 

otherwise be possible, Affordable Merit 

RateSM Mortgage which permits borrowers to 

qualify at an initial interest rate that in many 

cases is lower than the usual subprime rate, 

and LeasePurchase Plus Initiative, which 

provides closing cost and down payment 

assistance in addition to extensive 

counseling for borrowers who have had 

credit issues in the past or who have never 

established a credit history. During 2003, 

Freddie Mac entered into several new 

markets under the LeasePurchase Plus 

Initiative and purchased more than $16 

million in loans.134

According to Freddie Mac, its automated 

underwriting system, ‘‘Loan Prospector’’ has 

reduced costs, made approving mortgages 

easier and faster, and increased the 

consistency of the application of objective 

underwriting criteria. In addition, Freddie 

Mac states that ‘‘Loan Prospector’’ extends 

the benefits of the mortgage finance system 

to borrowers with less traditional credit 

profiles and limited savings by more 

accurately measuring risk. Since its 

introduction in 1995, Freddie Mac reports 

that they have doubled their share of 

mortgage purchases with loan-to-value 

rations of 95 percent or above.135 In 2003, 

lenders and brokers used Loan Prospector to 

evaluate 9.5 million loan applications and 

Loan Prospector has evaluated more than 35 

million mortgage applications since its 

introduction in 1995.136 Freddie Mac reports 

that its automated underwriting system, Loan 

Prospector, has resulted in higher approval 

rates for minority borrowers than under 

traditional manual underwriting because of 

improved predictive powers. As mentioned 

in Section C.7, the 2000 version of LP 

approved 87.1 percent of loans generated 

through affordable housing programs, 

compared to 51.6 percent approved by 

manual underwriting. The Freddie Mac study 

found automated mortgage scoring less 

discriminatory and more accurate in 

predicting risk. However, as noted below in 

the automated mortgage scoring section, 

there are concerns that the codification of 

certain underwriting guidelines could result 

in unintentional discrimination or disparate 

treatment across groups. In response to the 

potential disparate impact of automated 

underwriting, Freddie Mac have launched 

initiatives to make the mortgage process more 

transparent by disclosing both credit and 

non-credit factors that Loan Prospector 

consider when evaluating a loan application. 

In 2000, Freddie Mac has launched an 

initiative that published a list of all of the 

factors that Loan Prospector uses to analyze 

loans, and put the list on the Freddie Mac 

website.137

In 2003, Fannie Mae released two versions 

of its automated underwriting service, 

‘‘Desktop Underwriter’’ (DU), to expand its 

mortgage product offerings and to update 

underwriting guidelines. Desktop 

Underwriter 5.3 outlined new eligibility 

requirements for mortgages secured by 

manufactured homes. It also expanded the 

InterestFirstTM mortgage product line to offer 

borrowers greater purchasing power by 

allowing lower initial monthly payments 

than those available with traditional loan 

products. Desktop Underwriter 5.3.1 

enhanced the Flexible 100 mortgage to allow 

borrowers to contribute as little as $500 of 

their own funds to the transaction. The 

remainder of the funds can come from 

flexible sources of funds and interested party 

contributions subject to Fannie Mae’s 

standard contribution limit.138 In addition, 

Fannie Mae added MyCommunityMortgage 

to Desktop Underwriter in 2003, providing 

lenders easier access to customized CRAtargeted loan products.139 Automated 

mortgage scoring and the potential for 

disparate impacts on borrowers will be 

further discussed in a later section.

5. Affordable Single-family Lending: Data 

Trends 

a. 1993–2003 Lending Trends 

HMDA data suggest that the industry and 

GSE initiatives are increasing the flow of 

credit to underserved borrowers. Between 

1993 and 2003, conventional loans to lowincome and minority families increased at 

much faster rates than loans to higher income 

and non-minority families. As shown below, 

conventional home purchase originations to 

African Americans more than doubled 

between 1993 and 2003 and those to 

Hispanic borrowers more than tripled. Home 

loans to low-income borrowers and to lowincome and high-minority census tracts also 

more than doubled during this period.

1993–2003 

Growth rate: all 

home loans

(percent) 

1993–2003 

Growth rate: 

conventional 

home loans

(percent) 

African-American Borrowers ................................................................................................................................ 106 206 

Hispanic Borrowers .............................................................................................................................................. 235 357 

White Borrowers .................................................................................................................................................. 44 64 

Low-Income Borrower (Less than 80% of AMI) .................................................................................................. 101 150 

Upper-Income Borrower (More than 120% of AMI) ............................................................................................ 88 108 

Low-Income Census Tract (only 1993–2002) ..................................................................................................... 99 143 

Upper-Income Census Tract (only 1993–2002) .................................................................................................. 64 78 

High-Minority Tract (only 1993–2002) (50% or more minority) ........................................................................... 113 167 

Predominantly-White Tract (only 1993–2002) (Less than 10% minority) ........................................................... 53 64 

Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 63659

140Table A.3 also provides the same average 

(1999 to 2003) information as Tables A.1 and A.2 

but for total (both home purchase and refinance) 

loans. Thus, it provides a complete picture of 

overall mortgage activity.

141The ‘‘Total Market’’ is defined as all loans 

(including both government and conventional) 

below the conforming loan limit of $240,000 in 

1999, $252,700 in 2000, $275,000 in 2001, $300,700 

in 2002 and $322,700 in 2003.

GSE purchases showed similar trends, as 

indicated by the following 1993-to-2003 

percentage point increases for metropolitan 

areas: African-American borrowers (199 

percent), Hispanic borrowers (259 percent), 

and low-income borrowers (212 percent). 

While their annual purchases of all home 

loans increased by 60 percent between 1993 

and 2003, their purchases of mortgages that 

qualify for the three housing goals increased 

as follows: special affordable by 287 percent; 

low- and moderate-income by 156 percent; 

and underserved areas by 121 percent. 

While low interest rates and economic 

expansion certainly played an important role 

in the substantial increase in conventional 

affordable lending in recent years, most 

observers believe that the efforts of lenders, 

private mortgage insurers, and the GSEs were 

also important contributors. In addition, 

many observers believe that government 

initiatives such as the GSE housing goals and 

the Community Reinvestment Act have also 

played a role in the growth of affordable 

lending over the past 10 years. 

b. Affordable Lending Shares by Major 

Market Sector 

Section E below compares the GSEs’

performance with the performance of 

primary lenders in the conventional 

conforming market. To provide a useful 

context for that analysis, this section 

examines the role of the conventional 

conforming market in funding low-income 

and minority families and their 

neighborhoods. Information on the mortgage 

market’s funding of homes purchased by 

first-time homebuyers is also provided. In 

addition, this section compares the GSEs 

with other sectors of the mortgage market. 

The important role of FHA in the affordable 

lending market is highlighted and questions 

are raised about whether the conventional 

conforming market could be doing a better 

job helping low-income and minority 

borrowers obtain access to mortgage credit. 

Table A.1 reports borrower characteristics 

and Table A.2 reports neighborhood 

characteristics for home purchase mortgages 

insured by FHA, purchased by the GSEs, 

originated by depository institutions (mainly 

banks and thrift), and originated in the 

conventional conforming market and in the 

total market for owner-occupied properties in 

metropolitan areas.140 In this case, the ‘‘total’’

market consists of both the conventional 

conforming market and the government 

(mainly FHA and VA loans) market; ‘‘jumbo’’

loans above the conventional conforming 

loan limit are excluded from this analysis.141

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142The affordable market shares reported in Table 

A.1 for the ‘‘Conventional Conforming Market

W/O B&C’’ were derived by excluding the estimated 

number of B&C loans from the market data reported 

by HMDA. Because B&C lenders operate mainly in 

the refinance sector, excluding these loans from the 

conforming market has little impact on the home 

purchase percentages reported in Table A.1. It 

should be recognized that there exists some 

uncertainty regarding the number of B&C loans in 

the HMDA data. The adjustment assumes that the 

B&C loans represent one-half of the subprime 

market. The adjustment for home purchase loans is 

small because supbrime (B&C) loans are mainly 

refinance loans. The method for excluding B&C 

loans is explained in Section E below and 

Appendix D.

143Almost two-thirds of the borrowers with an 

FHA-insured home purchase loan make a 

downpayment less than five percent, and over 80 

percent are first-time home buyers. For discussions 

of the role of FHA in the mortgage market, see (a) 

Harold L. Bunce, Charles A. Capone, Sue G. Neal, 

William J. Reeder, Randall M. Scheessele, and 

Edward J. Szymanoski, An Analysis of FHA’s 

Single-Family Insurance Program, Office of Policy 

Development and Research, U.S. Department of 

Housing and Urban Development, 1995; and (b) 

Office of Policy Development and Research, ‘‘FHA’s 

Impact on Homeownership Opportunities for LowIncome and Minority Families During the 1990s’’

Issue Brief IV, U.S. Department of Housing and 

Urban Development, December 2000. For data on 

the credit characteristics of FHA borrowers, see 

Harold L. Bunce, William J. Reeder and Randall 

Scheessele, ‘‘Understanding Consumer Credit and 

Mortgage Scoring: A Work in Progress at HUD’’, 

U.S. Department of Housing and Urban 

Development, Unpublished Paper, 1999.

144FHA, which focuses on low downpayment 

loans and also accepts borrowers with credit 

blemishes, experiences higher mortgage defaults 

than conventional lenders and the GSEs. Still, the 

FHA system is actuarially sound because it charges 

an insurance premium that covers the higher 

default costs. For the results of FHA’s actuarial 

analysis, see Deloitte & Touche, Actuarial Review of 

MMI Fund as of FY 2000, report for the U.S. 

Department of Housing and Urban Development, 

January 2001.

145See Green and Associates, Fair Lending in 

Montgomery County: A Home Mortgage Lending 

Study, a report prepared for the Montgomery 

County Human Relations Commission, March 1998; 

and Calvin Bradford, Crisis in De ́ja` vu: A Profile of 

the Racial Patterns in Home Purchase Lending in 

the Baltimore Market. Report for The Public Justice 

Center, May 2000; and The Patterns of GSE 

Participation in Minority and Racially Changing 

Markets Reviewed from the Context of Levels of 

Distress Associated with High Levels of FHA 

Lending, GSE Study No. 11, U.S. Department of 

Housing and Urban Development, September 2000. 

For analysis suggesting some minorities receiving 

FHA loans could qualify for conventional loans, see 

Anthony Pennington-Cross, Anthony Yezer, and 

Joseph Nichols, Credit Risk and Mortgage Lending: 

Who Uses Subprime and Why? Working Paper No. 

00–03. Research Institute for Housing America, 

2000. Also see the series of recent studies 

concerning the lack of mainstream lenders in 

minority neighborhoods.

146For a comprehensive analysis of the GSEs’

purchases of minority loans through 1999, see 

Harold L. Bunce, An Analysis of GSE Purchases of 

Mortgages for African-American Borrowers and 

their Neighborhoods, Housing Finance Working 

Paper No. 11, Office of Policy Development and 

Research, HUD, December 2000.

HMDA is the source of the FHA, 

depository, and market data, while the GSEs 

provide their own data. Low-income, 

African-American, Hispanic, and minority 

borrowers are covered in Table A.1. Table 

A.2 provides information on four types of 

neighborhoods—low-income census tracts, 

tracts where minorities (or African 

Americans) account for more than 30 percent 

of the census tract population, and 

underserved areas as defined by HUD. The 

average data reported in Tables A.1 and A.2 

for the years 1999 to 2003 offer a good 

summary of recent lending to low-income 

and minority borrowers and their 

communities.142 Individual year data are also 

provided.

The focus of different market sectors on 

affordable lending is summarized by the 

percentages reported in Tables A.1 and A.2. 

These percentages show each sector’s 

‘‘distribution of business,’’ defined as the 

share of loans originated (or, for the GSEs, 

purchased) that had a particular borrower or 

neighborhood characteristic. The 

interpretation of the ‘‘distribution of 

business’’ percentages can be illustrated 

using the FHA percentage for low-income 

borrowers: Between 1999 and 2003, 51.2 

percent of all FHA-insured home purchase 

loans in metropolitan areas were originated 

for borrowers with an income less than 80 

percent of the local area median income. 

These percentages are to be contrasted with 

‘‘market share’’ percentages, which are 

presented below in Section E. A ‘‘market 

share’’ percentage is the share of loans with 

a particular borrower or neighborhood 

characteristic that was funded by a particular 

market sector (e.g., FHA-insured, GSEs, 

depositories). As will discussed below, 

FHA’s ‘‘market share’’ for low-income 

borrowers during the 1999-to-2003 period 

was estimated to be 24 percent which is 

interpreted as follows: Of all home purchase 

loans originated for low-income borrowers in 

metropolitan areas between 1999 and 2003, 

24 percent were FHA-insured loans. Thus, in 

this example, the ‘‘distribution of business’’

percentage measures the importance (or 

concentration) of low-income borrowers in 

FHA’s overall business while the ‘‘market 

share’’ percentage measures the importance 

of FHA to the market’s overall funding of 

loans for low-income borrowers. Both 

concepts are important for evaluating 

performance—for an industry sector such as 

FHA or the GSEs to have a significant impact 

on lending to a targeted group, that sector’s 

business must be concentrated on the 

targeted group and that sector must be of 

some size. The discussion below will focus 

on the degree to which different mortgage 

sectors concentrate on targeted groups, while 

Section E will also provide estimates of 

market shares. 

The main insights from the ‘‘distribution of 

business’’ percentages in Tables A.1 and A.2 

pertain to four topics. 

(i) FHA-Insured Loans. FHA has 

traditionally been the mechanism used by 

borrowers who face difficulty obtaining 

mortgage financing in the private 

conventional market. FHA has long been 

recognized as the major source of funding for 

first-time, low-income and minority 

homebuyers who are not often able to raise 

cash for large downpayments.143 Tables A.1 

and A.2 show that FHA places much more 

emphasis on affordable lending than the 

other market sectors. Between 1999 and 

2003, low-income borrowers accounted for 

51.2 percent of FHA-insured loans, compared 

with 27.8 percent of the home loans 

purchased by the GSEs, 29.1 percent of home 

loans originated by depositories, and 29.2 

percent of all originations in the 

conventional conforming market (see Table 

A.1). Likewise, 40.7 percent of FHA-insured 

loans were originated in underserved census 

tracts, while only 24.1 percent of the GSEpurchased loans, 25.9 percent of home loans 

originated by depositories, and 26.9 percent 

of conventional conforming loans were 

originated in these tracts (see Table A.2).144

As discussed in Section E, FHA’s share of the 

minority lending market is particularly high. 

While FHA insured only 16 percent of all 

home purchase mortgages originated below 

the conforming loan limit in metropolitan 

areas between 1999 and 2003, it is estimated 

that FHA insured 29 percent of all home 

loans originated for African-American and 

Hispanic borrowers.

(ii) Conventional and GSE Minority 

Lending. The affordable lending shares for 

the conventional conforming sector are low 

for minority borrowers, particularly AfricanAmerican and Hispanic borrowers. These 

borrowers accounted for only 15.2 percent of 

all conventional conforming loans originated 

between 1999 and 2003, compared with 34.4 

percent of FHA-insured loans and 19.2 

percent of all loans originated in the total 

(government and conventional conforming) 

market. Not surprisingly, the minority 

lending performance of conventional lenders 

has been subject to much criticism. Recent 

studies contend that primary lenders in the 

conventional market are not doing their fair 

share of minority lending which forces 

minorities, particularly African-American 

and Hispanic borrowers, to rely on more 

costly FHA and subprime loans.145 Thus, it 

appears that conventional lenders could be 

doing a better job helping minority borrowers 

obtain access to mortgage credit.

• The GSEs’ funding of minority loans can 

be compared with mortgages originated for 

minority borrowers in the conventional 

conforming market, although the latter may 

be a poor benchmark, as discussed above. 

Between 1999 and 2003, home purchase 

loans to African-American and Hispanic 

borrowers accounted for 10.4 percent of 

Freddie Mac’s purchases, 14.0 percent of 

Fannie Mae’s purchases, and 15.2 percent of 

loans originated in the conventional 

conforming market (or 14.3 percent if B&C 

loans are excluded from the market 

definition). Thus, since 1999, the AfricanAmerican and Hispanic share of the GSEs’

purchases has been lower than the 

corresponding share for the conventional 

conforming market.146

• As the above comparisons show, Fannie 

Mae has had a much better record than 

Freddie Mac in funding loans for minority 

families. And Fannie Mae significantly 

increased its purchases of loans for AfricanAmerican and Hispanic borrowers during 

2001, raising the share of its purchases to 

market levels—13.7 percent for both Fannie 

Mae and the conforming market (without 

B&C loans). In 2002, Fannie Mae surpassed 

63664 Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 

147Tables A.1, A.2, and A.3 include data for all 

home loans originated by depositories as well as for 

the subset of loans originated but not sold, the latter 

being a proxy for loans held in depository 

portfolios. (See the notes to Table A.1 for 

definitions of the depository data.)

148However, as shown in Table A.1 , depository 

institutions resemble other conventional lenders in 

their relatively low level of originating loans for 

African-American, Hispanic and minority 

borrowers. Within the conventional conforming 

market, Fannie Mae has done a better job than 

depositories in funding minority borrowers, 

particularly Hispanic borrowers and minority 

borrowers as a group. During the last three years, 

Fannie Mae has also funded African-American 

borrowers at a higher rate than have depository 

institutions.

149CRA loans are typically made to low-income 

borrowers earning less than 80 percent of area 

median income, and in moderate-income 

neighborhoods. For a comprehensive analysis of 

CRA and its impact on affordable lending, see 

Robert E. Litan, Nicolas P. Retsinas, Eric S. Belsky 

and Susan White Haag, The Community 

Reinvestment Act After Financial Modernization: A 

Baseline Report, U.S. Department of Treasury, 2000.

150 In this case, the market includes all 

government and conventional loans, including 

jumbo loans.

151For a comprehensive analysis of CRA and its 

impact on affordable lending, see Robert E. Litan, 

Nicolas P. Retsinas, Eric S. Belsky and Susan White 

Haag, The Community Reinvestment Act After 

Financial Modernization: A Baseline Report, U.S. 

Department of Treasury, 2000.

the conventional conforming market in 

funding African-American and Hispanic 

borrowers (a 15.8 percent share for Fannie 

Mae and a 15.0 share for the market), but in 

2003 fell slightly behind the market (a 16.6 

percent share for Fannie Mae and a 16.9 

percent share for the market). When all 

minority borrowers are considered, Fannie 

Mae has purchased mortgages for minority 

borrowers at a higher rate (years 2001, 2002 

and 2003) than these loans were originated 

by primary lenders in the conventional 

conforming market (without B&C loans). 

Freddie Mac, on the other hand, lagged 

behind both the market and Fannie Mae in 

funding loans for minority borrowers during 

2001–2003, as well as during the entire 1999-

to-2003 period. The share of Freddie Mac’s 

purchases for African-American and 

Hispanic borrowers declined from 10.9 

percent in both 2000 and 2001 to 10.1 

percent in 2002 before rising slightly to 10.7 

percent in 2003. 

• Considering the minority census tract 

data reported in Table A.2, Fannie Mae 

lagged behind the conforming market 

(without B&C loans) in high-minority 

neighborhoods and in high-African-American 

neighborhoods during the 1999-to-2003 

period. However, Fannie Mae improved its 

mortgage purchases in African-American 

neighborhoods after 2001 and essentially 

matched the market in 2001–2003. And 

during 2001, 2002 and 2003, Fannie Mae also 

purchased loans in high-minority census 

tracts at a higher rate than loans were 

originated by conventional lenders in these 

tracts. While Freddie Mac has generally 

lagged the primary market in funding 

minority neighborhoods, note in Table A.2 

that high African-American tracts increased 

from 3.9 percent of Freddie Mac’s purchases 

in 2001 to 5.3 percent in 2002, placing 

Freddie Mac above the conventional 

conforming market level (4.6 percent) in 

2002. However, in 2003, Freddie Mac fell 

behind the market. 

(iii) Low-Income Lending by the GSEs.

Information is also provided on the GSEs’

purchases of home loans for low-income 

borrowers (A.1) and for families living in 

low-income neighborhoods (A.2). 

Historically, the GSEs have lagged behind the 

conventional conforming market in funding 

affordable loans for these groups. During the 

1999-to-2003 period, low-income borrowers 

(census tracts) accounted for 27.4 (9.7) 

percent of Freddie Mac’s purchases, 28.1 

(10.1) percent of Fannie Mae’s purchases, 

29.1 (11.2) percent of loans originated by 

depositories, and 29.1 (11.1) percent of home 

loans originated by conventional conforming 

lenders (without B&C loans). By the end of 

this period, Fannie Mae had significantly 

improved its performance relative to the 

market. In 2003, low-income borrowers 

accounted for 31.0 percent of Fannie Mae’s 

purchases, compared with 29.2 percent for 

the conforming market. It is also interesting 

that even though Freddie Mac lagged the 

market in funding home loans for lowincome borrowers during 2002 (28.6 percent 

versus 29.1 percent), it surpassed the market 

in financing properties in low-income census 

tracts (11.3 percent versus 11.1 percent). 

During 2003, Freddie Mac’s performance was 

again below the market in low-income census 

tracts (a 10.3 share for Freddie Mac and a 

11.5 percent share for the market). A more 

complete analysis of the GSEs’ recent 

improvements in purchasing home loans that 

qualify for the housing goals is provided 

below in Section E. 

(iv) Depositories. Within the conventional 

conforming market, depository institutions 

(mainly banks and thrifts) are important 

providers of affordable lending for lowerincome families and their neighborhoods.147

Between 1999 and 2003, underserved areas 

accounted for 26.9 percent of loans held in 

depository portfolios, which compares 

favorably with the underserved areas 

percentage (26.2 percent) for the overall 

conventional conforming market.148

Depository lenders have extensive knowledge 

of their communities and direct interactions 

with their borrowers, which may enable them 

to introduce flexibility into their 

underwriting standards without unduly 

increasing their credit risk. The Community 

Reinvestment Act provides an incentive for 

banks and thrifts to initiate affordable 

lending programs with underwriting 

flexibility and to reach out to lower income 

families and their communities.149 Many of 

the CRA loans are held in portfolio by 

lenders, rather than sold to Fannie Mae or 

Freddie Mac.

(v) First-time Homebuyers. As explained in 

Section E, market information on first-time 

homebuyers is not as readily available as the 

HMDA data reported in Tables A.1 and A.2 

on the income and racial characteristics of 

borrowers and census tracts served by the 

mortgage market. However, the limited 

market data that are available from the 

American Housing Survey, combined with 

the first-time homebuyer data reported by 

FHA and the GSEs, indicate a rather large 

variation in the funding of first-time 

homebuyers across the different sectors of the 

mortgage market. Based on the American 

Housing Survey (AHS), it is estimated that 

first-time homebuyers accounted for 42.3 

percent of all home purchase loans originated 

throughout the market between 1999 and 

2001,150 and for 37.6 percent of home loans 

originated in the conventional conforming 

market. The AHS defines a first-time 

homebuyer as someone who has never 

owned a home. Using a more liberal 

definition of a first-time homebuyer 

(someone who has not owned a home in the 

past three years), FHA reports that first-time 

homebuyers accounted for 80.5 percent of all 

home loans that it insured between 1999 and 

2001 and the GSEs report that first-time 

homebuyers accounted for 26.5 percent of the 

home loans purchased by each GSE during 

that same period. Given FHA’s low 

downpayment requirements, it is not 

surprising that FHA focuses on first-time 

homebuyers. The GSEs, on the other hand, 

fall at the other end of the continuum, with 

their first-time homebuyer share (26.5 

percent) falling far short of the first-time 

homebuyer share (37.6 percent) of the 

conventional conforming market. Section E 

will include a more detailed comparison of 

the GSEs and the conventional conforming 

market in serving first-time homebuyers. In 

addition, Section E will conduct a market 

share analysis that examines the funding of 

minority first-time homebuyers. Consistent 

with the earlier discussion, that analysis 

suggests that conventional lenders and the 

GSEs have played a relatively small role in 

the market for minority first-time 

homebuyers. One analysis reported in 

Section E estimates that mortgage purchases 

by the GSEs between 1999 and 2001 totaled 

41.5 percent of all home loans originated, but 

they accounted for only 14.3 percent of home 

loans originated for first-time AfricanAmerican and Hispanic homebuyers.

c. Community Reinvestment Act 

The Community Reinvestment Act (CRA) 

requires depository institutions to help meet 

the credit needs of their communities.151

CRA loans are typically made to low-income 

borrowers earning less than 80 percent of 

area median income, and in moderateincome neighborhoods. CRA provides an 

incentive for lenders to initiate affordable 

lending programs with underwriting 

flexibility. CRA loans are usually smaller 

than typical conventional mortgages and also 

are more likely to have a higher LTV, higher 

debt-to-income ratios and no payment 

reserves, and may not be carrying private 

mortgage insurance (PMI). Generally, at the 

time CRA loans are originated, many do not 

meet the underwriting guidelines required in 

order for them to be purchased by one of the 

GSEs. Therefore, many of the CRA loans are 

held in portfolio by lenders, rather than sold 

to Fannie Mae or Freddie Mac. Evidence is 

growing that CRA-type lending to lowincome families can be profitable, 

particularly when combined with intensive 

loss mitigation efforts to control credit risk. 

In a recent survey conducted by the Federal 

Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 63665

152Board of Governors of the Federal Reserve 

System. The Performance and Profitability of CRARelated Lending. Washington, DC, 2000.

153This discussion of urban lending draws from 

Jeff Siegel, ‘‘Urban Lending Helps Increase Volume 

and Meet CRA Requirements,’’ Secondary 

Marketing Executive, February 2003, pp. 21–23.

154 Ibid.

155Fannie Mae, ‘‘Fannie Mae’s Comments on 

HUD’s Proposed Housing Goals for Fannie Mae and 

Freddie Mac for the years 2005–2008 and 

Amendments to HUD’s Regulation of Fannie Mae 

and Freddie Mac,’’ July 16, 2004, p. I–59.

156Fannie Mae, 2003 Annual Housing Activities 

Report, 2004, pp. 8–9.

157Freddie Mac, Opening Doors for America’s 

Families: Freddie Mac’s Annual Housing Activities 

Report for 2003, March 15, 2004, p. 64.

158U.S. Department of HUD, Office of Policy 

Development and Research, U.S. Housing Market 

Conditions, May 2004, p. 81.

159 Ibid.

160Fannie Mae, Fannie Mae National Housing 

Survey, 2002, p. 6.

161 Ibid. p. 8.

162 Joint Center for Housing Studies of Harvard 

University, State of the Nation’s Housing 2003, p. 

15.

Reserve, lenders reported that most CRA 

loans are profitable although not as profitable 

as the lenders’ standard products.152

Some anticipate that the big growth market 

over the next decade for CRA-type lending 

will be urban areas. There has been some 

movement of population back to cities, 

consisting of aging Baby Boomers (so-called 

‘‘empty nesters’’), the children of Baby 

Boomers (the Echo Boomers aged 18–25), and 

immigrants, particularly Hispanics but also 

Asians.153 The current low homeownership 

in inner cities (compared with the suburbs) 

also suggests that urban areas may be a 

potential growth market for lenders. Lenders 

are beginning to recognize that urban 

borrowers are different from suburban 

borrowers. A new or recent immigrant may 

have no credit history or, more likely, a loanworthy credit history that can’t be 

substantiated by the usual methods.154

Products for duplexes and four-plexes are not 

the same as a mortgage for a subdivision 

house in the suburbs. Programs are being 

implemented to meet the unique needs of 

urban borrowers. One program emphasizing 

urban areas was initiated by the American 

Community Bankers (ACB). Under the ACB 

program, which made $16.2 billion in loans 

in 2002, lenders originated a variety of 

affordable products for first-time homebuyers 

and non-traditional borrowers that are then 

sold to Fannie Mae, Freddie Mac, 

Countrywide, or other investors that are 

partnering with the ACB. It is reported that 

some lenders are making these nontraditional loans for the first time.

For banks and thrifts, selling their CRA 

loans will free up capital to make new CRA 

loans. As a result, the CRA market segment 

provides an opportunity for Fannie Mae and 

Freddie Mac to expand their affordable 

lending programs. Section E.3c below 

presents data showing that purchasing 

targeted seasoned loans has been one strategy 

that Fannie Mae has chosen to improve its 

goals performance. Fannie Mae has been 

offering CRA programs since mid-1997, when 

it launched a pilot program, ‘‘Community 

Reinvestment Act Portfolio Initiative,’’ for 

purchasing seasoned CRA loans in bulk 

transactions, taking into account track record 

as opposed to relying just on underwriting 

guidelines. Fannie Mae also started another 

pilot program in 1998, involving purchases of 

CRA loans on a flow basis, as they are 

originated. As part of the American Dream 

Commitment, Fannie Mae has committed to 

investing $20 billion in CRA-targeted 

business, and funding $530 billion in CRAeligible investments. One CRA-eligible 

product in 2003 included the 

MyCommunityMortgage TM suite, which 

provides flexible product options for low- to 

moderate-income families, including 

minorities, immigrants, first-time 

homebuyers, and underserved borrowers 

living in rural areas. MyCommunityMortgage 

is offered by over 300 lender partners 

nationwide, and marries targeted pricing 

with affordability features, such as 100 

percent loan-to-value ratios with only $500 

from the borrower’s own funds.155 In 2003, 

Fannie Mae purchased or securitized more 

than $2.27 billion of MyCommunityMortgage 

products, which helped provide affordable 

housing solutions for 20,400 households.156

In addition, Freddie Mac is also purchasing 

seasoned affordable mortgage portfolios 

originated by depositories to help meet their 

CRA objectives. In 2003, Freddie Mac 

developed credit enhancements that enable 

depositories to profitably sell their loans to 

Freddie Mac—these transactions facilitate 

targeted affordable lending activity by 

providing immediate liquidity. Freddie Mac 

also increased its ability to purchase smaller 

portfolios opening this option to many 

community banks that otherwise would not 

have an outlet for their portfolios.157 The 

billions of dollars worth of CRA loans that 

will be originated, as well as the CRA loans 

being held in bank and thrift portfolios, offer 

both GSEs an opportunity to improve their 

performance in the single-family area.

6. Potential Homebuyers 

While the growth in affordable lending and 

homeownership has been strong in recent 

years, attaining this Nation’s homeownership 

goals will not be possible without tapping 

into the vast pool of potential homebuyers. 

Due to record low interest rates, expanded 

homeownership outreach, and new flexible 

mortgage products, the homeownership rate 

reached an annual record of 67.9 percent in 

2002, reaching 68.6 percent in the fourth 

quarter of 2003.158 This section discusses the 

potential for further increases beyond those 

resulting from current demographic trends.

The potential homeowner population over 

the next decade will be highly diverse, as 

growing housing demand from immigrants 

(both those who are already here and those 

projected to come) and non-traditional 

homebuyers will help to offset declines in 

the demand for housing caused by the aging 

of the population. As noted in the above 

discussion of CRA, many of these potential 

homeowners will be located in urban areas. 

As noted in the above discussion of 

underlying demographic conditions (section 

C.2.), immigrants and other minorities—who 

accounted for nearly 40 percent of the growth 

in the nation’s homeownership rate over the 

past five years—will be responsible for 

almost two-thirds of the growth in the 

number of new households over the next ten 

years. This trend does not depend on the 

future inflow of new immigrants, as 

immigrants don’t enter the housing market 

until they have been in this country for 

eleven years. As noted by Fannie Mae staff, 

‘‘there are enough immigrants already in this 

country to keep housing strong for at least six 

and perhaps even 10 more years’’.159 As 

these demographic factors play out, the 

overall effect on housing demand will likely 

be sustained growth and an increasingly 

diverse household population from which to 

draw new homeowners.

Surveys indicate that these demographic 

trends will be reinforced by the fact that most 

Americans desire, and plan, to become 

homeowners. According to the 2002 Fannie 

Mae Foundation annual National Housing 

Survey, Americans rate homeownership as 

the best investment they can make, far ahead 

of 401Ks, retirement accounts, and stocks. 

The percentage of Americans who said it was 

a good time to buy a home was at its highest 

level since 1994 at 75 percent, a jump of 21 

percentage points since May 2001.160 In 

addition, the survey found that 27 percent of 

Americans report they are likely to buy in the 

next three years, and 23 percent of those have 

started to save or have saved enough money 

for a down payment.161

Further increases in the homeownership 

rate depend on whether or not recent gains 

in the home owning share(s) of specific 

groups are maintained. Minorities accounted 

for 17 percent of owner households in 2001, 

but the Joint Center for Housing Studies 

reports that minorities were responsible for 

more than 40 percent (a total of 5.2 million) 

of the net growth in homeowners between 

1993 and 2002.162 As reported by the Fannie 

Mae survey, 42 percent of African-American 

families reported that they were ‘‘very or 

fairly likely’’ to buy a home in the next three 

years, up from 38 percent in 1998 and 25 

percent in 1997. Among Hispanics and 

Hispanic immigrants, the numbers reached 

37 percent and 34 percent respectively. The 

2002 survey also reports that more than half 

of Hispanic renters cite homeownership as 

being ‘‘one of their top priorities’’. In 

addition, nearly a third (31 percent) of baby 

boomers said they are ‘‘very or fairly likely’’

to buy a home in the next three years.

In spite of these trends, potential minority 

and immigrant homebuyers see more 

obstacles to buying a home, compared with 

the general public. These barriers to 

homeownership are discussed in detail in 

section B.1.b above and include: lack of 

capital for down payment and closing costs; 

poor credit history; lack of access to 

mainstream lenders; complexity and fear of 

the homebuying process; and, continued 

discrimination in housing markets and 

mortgage lending. To address the needs of 

the new group of potential homeowners, the 

mortgage industry will have to address these 

needs on several fronts, such as expanding 

education and outreach efforts, introducing 

new products, and adjusting current 

underwriting standards to better reflect the 

63666 Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 

163 Joint Center for Housing Studies of Harvard 

University, State of the Nation’s Housing 1998, p.

20.

164 John W. Straka, ‘‘A Shift in the Mortgage 

Landscape: The 1990s Move to Automated Credit 

Evaluations,’’ Journal of Housing Research, 2000, 

(11)2: p. 207.

165Peter M. Zorn, Susan Gates, and Vanessa 

Perry, ‘‘Automated Underwriting and Lending 

Outcomes: The Effect of Improved Mortgage Risk 

Assessment on Under-Served Populations. Program 

on Housing and Urban Policy,’’ Conference Paper 

Series, Fisher Center for Real Estate and Urban 

Economics. University of California Berkeley, 2001, 

p. 5.

166Fannie Mae, 2003 Annual Housing Activities 

Report, 2004, p. 12.

167Fannie Mae, 2003 Annual Housing Activities 

Report, 2004, p. 36.

168Freddie Mac, Opening Doors for America’s 

Families: Freddie Mac’s Annual Housing Activities 

Report for 2003, March 15, 2004, p. 55.

169Freddie Mac, Opening Doors for America’s 

Families: Freddie Mac’s Annual Housing Activities 

Report for 2003, March 15, 2004, p. 54.

170Freddie Mac Public Comment Letter on HUD’s 

Proposed Goals, July 2004, p. 5.

171 Ibid. pp. 208–217.

172Robert B. Avery, Raphael W. Bostic, Paul S. 

Calem, and Glenn B. Canner, Credit Scoring: Issues 

and Evidence from Credit Bureau Files, mimeo, 

1998, p. 24.

special circumstances of these new 

households. 

Thus, the new group of potential 

homeowners will have unique needs. To tap 

this potential homeowner population, the 

mortgage industry will have to address these 

needs on several fronts, such as expanding 

education and outreach efforts, introducing 

new products, and adjusting current 

underwriting standards to better reflect the 

special circumstances of these new 

households. 

The Bush administration has outlined a 

plan to expand minority homeownership by 

5.5 million families by the end of the decade. 

The Joint Center for Housing Studies has 

stated that if favorable economic and housing 

market trends continue, and if additional 

efforts to target mortgage lending to lowincome and minority households are made, 

the overall homeownership rate could reach 

70 percent by 2010.163

7. Automated Underwriting Systems and 

Mortgage Scorecards 

This, and the following two sections, 

discuss special topics that have impacted the 

primary and secondary mortgage markets in 

recent years. They are automated mortgage 

scoring, subprime loans, and risk-based 

pricing. The GSEs’ use of automated 

underwriting and mortgage scoring systems 

was briefly discussed in the earlier section on 

underwriting standards. This section 

expands on issues related to automated 

underwriting, a process that has spread 

throughout the mortgage landscape over the 

past five years, due mainly to the efforts of 

Fannie Mae and Freddie Mac. 

Automated mortgage scoring was 

developed as a high-tech tool with the 

purpose of identifying credit risks in a more 

efficient manner. Automated mortgage 

scoring has grown as competition and 

decreased profit margins have created 

demands to reduce loan origination costs. As 

a result, automated mortgage scoring has 

become the predominant (around 60 to 70 

percent) mortgage underwriting method. 164

According to Freddie Mac economists, 

automated mortgage scoring has enabled 

lenders to expand homeownership 

opportunities, particularly for underserved 

populations.165 There is growing evidence 

that automated mortgage scoring is more 

accurate than manual underwriting in 

predicting borrower risks. Mortgage 

scorecards express the probability that an 

applicant will default as a function of several 

underwriting variables such as the level of 

down payment, monthly-payment-to-income 

ratios, cash reserves, and various indicators 

of an applicant’s creditworthiness or credit 

history. Mortgage scorecards are statistically 

estimated regression-type equations, based 

on historical relationships between mortgage 

foreclosures (or defaults) and the 

underwriting variables. The level of down 

payment and credit history indicators, such 

as a FICO score, are typically the most 

important predictors of default in mortgage 

scoring systems.

For example, HUD has developed FHA 

TOTAL Scorecard to evaluate the credit risk 

of FHA loans submitted to an automated 

underwriting system. The Scorecard works 

with Fannie Mae’s Desktop Underwriter to 

provide a recommended level of 

underwriting and documentation for FHA 

loans and to determine a loan’s eligibility for 

insurance with FHA. In 2003, Fannie Mae 

conducted a market test of the Scorecard 

with 18 FHA approved Desktop 

Underwriter lenders. Over 3,000 loans were 

submitted to the Total Scorecard through 

Desktop Underwriter during the market test 

period.166

This increased accuracy in risk assessment 

of mortgage scorecards has allowed risk 

managers to set more lenient risk standards, 

and thus originate more loans to marginal 

applicants. Applicants who would otherwise 

be rejected by manual underwriting are being 

qualified for mortgages with automated 

mortgage scoring in part because the 

scorecard allows an applicant’s weaker areas 

to be offset by stronger characteristics. 

Typically, applicants whose projected 

monthly debt payment (mortgage payment 

plus credit card payment plus automobile 

loan payment and so on) comprise a high 

percentage of their monthly income would be 

turned down by a traditional underwriting 

system that relied on fixed debt-to-income 

ratios (such as 36 percent). In a mortgage 

scoring system, these same applicants might 

be automatically accepted for a loan due to 

their stellar credit record or to their ability 

to raise more cash for a down payment. The 

entity funding or insuring the mortgage (i.e., 

a lender, private mortgage insurer, or a GSE) 

allows these positive characteristics to offset 

the negative characteristics because its 

confidence in the ability of the empiricallybased mortgage scorecard to accurately 

identify those applicants who are more likely 

or less likely to eventually default on their 

loan. The mortgage score is in essence a 

recommendation to the lender to accept the 

application, or to refer it for further review 

through manual underwriting. Accepted 

loans benefit from reduced document 

requirements and expedited processing. 

In 2003, Fannie Mae conducted a study of 

automated underwriting systems and 

concluded that the production cost per loan 

decreased significantly as lenders moved 

automated underwriting closer to the point of 

sale. Specifically, retail lenders using an 

integrated automated underwriting system at 

the point of sale reported originations savings 

of more than $1,000 over manual 

underwriting.167 Freddie Mac also reported 

that Loan Prospector reduces the average 

time lenders spend underwriting most loans 

and reduces origination costs by about an 

average of $650 or more per loan.168 In 

addition, Freddie Mac analyzed about 1,000 

loans originated in 1993 and 1994. Of the 

loans, manual underwriters rated 52 percent 

accept, compared to a Loan Prospector accept 

rate of 87 percent.169 In total, Freddie Mac 

reports that innovations in the originations 

process, including automated underwriting, 

have reduced mortgage transaction costs by 

more than 70 percent between 1990 and 2003 

from 1.87 points to 0.46 points—a decline of 

$1,410 per $100,000 borrowed.170

As explained above, automated mortgage 

scoring allows tradeoffs between risk factors 

to be quantified more precisely, providing 

the industry more confidence in ‘‘pushing 

the envelope’’ of acceptable expected default 

rates. The GSEs’ willingness to offer lowdown-payment programs was based on their 

belief that their scoring models could 

identify the more creditworthy of the cashconstrained applicants. The GSEs’ new 

‘‘timely reward’’ products for subprime 

borrowers (discussed later) are integrated 

with their mortgage scoring systems. 

Automated mortgage scoring presents the 

opportunity to remove discrimination from 

mortgage underwriting, to accept all 

applicants, and to bring fair, objective, 

statistically based competitive pricing, 

greatly reducing costs for all risk groups. 

Some institutions have sought to better 

model and automate marginal and higher-risk 

loans, which have tended to be more costly 

to underwrite and more difficult to 

automate.171

Along with the promise of benefits, 

however, automated mortgage scoring has 

raised concerns. These concerns are related 

to the possibility of disparate impact and the 

proprietary nature of the mortgage score 

inputs. The first concern is that low-income 

and minority homebuyers will not score well 

enough to be accepted by the automated 

underwriting system, resulting in their 

getting fewer loans. African-American and 

Hispanic borrowers, for example, tend to 

have a poorer credit history record than other 

borrowers, which means they are more likely 

to be referred (rather than automatically 

accepted) by automated mortgage scoring 

systems that rely heavily on credit history 

measures such as a FICO score. There is also 

a significant statistical relationship between 

credit history scores and the minority 

composition of an area, after controlling for 

other locational characteristics.172

The second concern relates to the ‘‘black 

box’’ nature of the scoring algorithm. The 

scoring algorithm is proprietary and therefore 

Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 63667

173Fannie Mae, September 4, 2002, p. 33.

174 Kenneth Temkin, Jennifer E.H. Johnson, and 

Diane Levy, Subprime Markets, The Role of GSEs, 

and Risk-Based Pricing, Washington: The Urban 

Institute. Report Prepared for the U.S. Department 

of Housing and Urban Development, 2002.

175Allen J. Fishbein, ‘‘Is Credit Scoring a Winner 

for Everyone?’’ Stone Soup, 2000, 14(3): pp. 14–15. 

See also Fitch IBCA, Inc., Residential Mortgage 

Credit Scoring, New York, 1995 and Jim Kunkel, 

‘‘The Risk of Mortgage Automation,’’ in Mortgage 

Banking, 1995, 57(8): pp. 69–76.

176Zorn et al., 2001, pp. 19–20.

177 Kenneth Temkin, Jennifer E.H. Johnson, and 

Diane Levy, Subprime Markets, The Role of GSEs, 

and Risk-Based Pricing, Washington: The Urban 

Institute. Report Prepared for the U.S. Department 

of Housing and Urban Development, 2002.

178Zorn, et al., 2001, pp. 14–15.

179 Ibid. p. 5. 180 Ibid. pp. 18–19.

it is difficult for applicants to know the 

reasons for their scores. However, it should 

be noted that the GSEs have taken steps to 

make their automated underwriting systems 

more transparent. Both Fannie Mae and 

Freddie Mac have published the factors used 

to make loan purchase decisions in Desktop 

Underwriter and Loan Prospector, 

respectively. In response to criticisms aimed 

at using FICO scores in mortgage 

underwriting, Fannie Mae’s new versions of 

Desktop Underwriter (DU) 5.3 and 5.3.1 [the 

newest versions are 5.3 and 5.3.1—they 

probably keep the following practices, but 

add no substantive underwriting practices, 

but rather lower downpayment options] 

replaces credit scores with specific credit 

characteristics and provides expanded 

approval product offerings for borrowers who 

have blemished credit. The specific credit 

characteristics include variables such as past 

delinquencies; credit records, foreclosures, 

and accounts in collection; credit card line 

and use; age of accounts; and number of 

credit inquiries.173

With automated mortgage scoring replacing 

traditional manual underwriting comes the 

fear that the loss of individual attention 

poses a problem for people who have 

inaccuracies on their credit report or for 

members of cultural groups or recent 

immigrants who do not use traditional credit 

and do not have a credit score. Some 

subprime lenders and underwriters have 

claimed that their manual underwriting of 

high-risk borrowers cannot be automated 

with mortgage scoring. Although automated 

mortgage scoring has greatly reduced the cost 

of many lower-risk loans that are easier to 

rate, the cost of manually underwriting grayarea and higher-risk applicants still remains 

high.174 There is also the fear that applicants 

who are referred by the automated system 

will not be given the full manual 

underwriting for the product that they 

initially applied for—rather they might be 

pushed off to higher priced products such as 

a subprime or FHA loan. In this case, the 

applicant may have had special 

circumstances that would have been clarified 

by the traditional manual underwriting, thus 

enabling the applicant to receive a prime 

loan consistent with his or her 

creditworthiness.

Banking regulators and legal analysts 

acknowledge the value of automated 

mortgage scoring, although some skeptics 

have noted concerns regarding fair lending, 

potential fraud, privacy issues, and the 

ability of models to withstand changing 

economic conditions.175 With the rise of 

automated mortgage scoring, the great 

difference in Internet usage known as the 

‘‘digital divide’’ could result in informational 

disadvantages for less educated and lowerincome consumers. In addition to the digital 

divide, the lack of financial literacy in the 

United States may also result in a disparate 

impact on low-income and minority 

borrowers.176

2002 Urban Institute Study. The Urban 

Institute submitted a report to HUD in 2002 

on subprime markets, the role of GSEs, and 

risk-based pricing.177 The study took a 

preliminary look at the use of automated 

underwriting systems for a small sample of 

lenders. After conducting interviews with 

both subprime and prime lenders, the report 

noted that all of the lenders in the study had 

implemented some type of automated 

underwriting system. These lenders stated 

that automated underwriting raised their 

business volume and streamlined their 

approval process. In addition, the lenders 

reported they were able to direct more 

underwriting resources to borderline 

applications despite an increase in business 

volume.

Even with the use of automated mortgage 

scoring, the lenders in the study continued 

to conduct at least a cursory review to 

validate the application material. The 

majority of the lenders still used manual 

underwriting to originate loans not 

recommended for approval with automated 

mortgage scoring. The lenders reported they 

formulated their policies and procedures to 

make certain that borrowers receive the best 

mortgage, according to product eligibility. 

This study will be further referenced in a 

following section regarding subprime 

markets. 

2001 Freddie Mac Study. According to a 

Freddie Mac study published by the Fisher 

Center for Real Estate and Urban Economics 

at University of California at Berkeley, 

underserved populations have benefited from 

automated mortgage scoring because of the 

increased ability to distinguish between a 

range of credit risks. In this paper, Freddie 

Mac economists compared the manual and 

automated mortgage scoring approval rates of 

a sample of minority loans originated in 

1993–94 and purchased by Freddie Mac. 

While manual underwriters rated 51 percent 

of the minority loans in the sample as accept, 

automated mortgage scoring would have 

rated 79 percent of the loans as accept. 178

In comparison to manual underwriting, 

this study found automated mortgage scoring 

not only less discriminatory but also more 

accurate in predicting risk. Two versions of 

Freddie Mac’s automated underwriting 

system, Loan Prospector (LP), were used to 

review three groups of mortgage loans 

purchased by Freddie Mac.179 The study 

found that LP was a highly accurate predictor 

of mortgage default. The resulting improved 

accuracy translates into benefits for 

borrowers, who would otherwise be rejected 

by manual underwriting to qualify for 

mortgages.

Analysis of the first group of loans showed 

that loans rated as ‘‘caution’’ were four times 

more likely to default than the average for all 

loans. Minority borrowers whose loans were 

rated as ‘‘caution’’ were five times more 

likely to default, and low-income borrowers 

whose loans were rated as ‘‘caution’’ were 

four times more likely to default than the 

average for all loans. The 2000 version of LP 

approved 87.1 percent of loans generated 

through affordable housing programs, 

compared to a 51.6 percent approval rate 

when the same loans were assessed using 

manual underwriting procedures. Further, 

the study found LP more accurate than 

manual underwriting at predicting default 

risk even with a higher approval rate. The 

study also demonstrated that Freddie Mac’s 

year 2000 version of LP was more accurate 

in predicting risk than its 1995 version. 

Concluding Observations. Automated 

underwriting has enabled lenders to reach 

new markets and expand homeownership 

opportunities, as illustrated by the 2001 

Freddie Mac study. Increased accuracy with 

automated mortgage scoring has led to the 

development of new mortgage products that 

would have been previously considered too 

risky. For example, Freddie Mac uses Loan 

Prospector to approve Alt A loans, which 

tend to have nontraditional documentation; 

A-minus loans, which pose a higher risk of 

default; and other higher-risk mortgages, like 

100 percent LTV loans. Both GSEs have and 

continue to add new products to develop 

their automated underwriting systems to 

reach more marginal borrowers. 

Despite the gains in automated mortgage 

scoring and other innovations, minorities are 

still less likely to be approved for a loan. The 

difference in minority and non-minority 

accept rates may reflect greater social 

inequities in financial capacity and credit, 

which are integral variables in both manual 

and automated underwriting. In the future, 

the accuracy of automated mortgage scoring 

will hinge on updating the models and 

making them more predictive while reducing 

the disparate impact on low-income and 

minority borrowers.180 The fairness of 

automated scoring systems will also depend 

importantly on whether referred applicants 

receive a traditional manual underwriting for 

the loan that they initially applied for, rather 

than being immediately offered a higher 

priced loan that does not recognize their true 

creditworthiness.

In addition to using automated 

underwriting systems as a tool to help 

determine whether a mortgage application 

should be approved, the GSEs’ automated 

underwriting systems are being further 

adapted to facilitate risk-based pricing. With 

risk-based pricing, mortgage lenders can offer 

each borrower an individual rate based on 

his or her risk. The division between the 

subprime and the prime mortgage market 

will begin to fade with the rise of risk-based 

pricing, which is discussed in the next 

section on the subprime market. 

63668 Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 

181Subprime origination data are from Inside 

Mortgage Finance. For the 2002 estimates, see 

‘‘Subprime Origination Market Shows Strong 

Growth in 2002,’’ Inside B&C Lending, published by 

Inside Mortgage Finance, February 3, 2003, page 1.

182Temkin et. al., 2002, p. 1.

183 Kenneth Temkin, Jennifer E.H. Johnson, Diane 

Levy, Subprime Markets, The Role of GSEs, and 

Risk Based Pricing, Washington: The Urban 

Institute. Report Prepared for the Department of 

Housing and Urban Development, 2002, p. 4.

184U.S. Department of Housing and Urban 

Development/U.S. Department of the Treasury, 

Curbing Predatory Lending Report, 2000, p.31.

185 ‘‘Wholesale Dominates Subprime Market 

Through 3rd Quarter ’02,’’ Inside B&C Lending,

published by Inside Mortgage Finance, December 

16, 2002, pp. 1–2.

186 Inside B&C Lending, November 16, 2002, p.2.

187Mortgage Information Corporation, The 

Market Pulse, Winter 2001, pp. 4–6.

188 Inside B&C Lending, published by Inside 

Mortgage Finance, February 17, 2003, page 13.

189 Daniel Immergluck, The Predatory Lending 

Crisis in Chicago: The Dual Mortgage Market and 

Local Policy, testimony before the Chicago City 

Council, April 5, 2000. Immergluck found that 

subprime lenders received 74 percent of refinance 

applications in predominantly black tracts 

compared to 21 percent in predominantly white 

tracts in 1998. According to Immergluck, these 

racial disparities provide evidence that the 

residential finance market in Chicago is 

hypersegmented, resulting in the increased 

likelihood that minorities receive mortgage credit 

from a subprime, rather than a prime, lender in 

Chicago. Also see Daniel Immergluck, Stark 

Differences: The Explosion of the Subprime 

Industry and Racial Hypersegmentation in Home 

Equity Lending, Woodstock Institute, October 2000

190See Randall M. Scheessele, Black and White 

Disparities in Subprime Mortgage Refinance 

Lending, Housing Finance Working Paper HF–014, 

Office of Policy Development and Research, U.S. 

Department of Housing and Urban Development, 

April 2002.

8. Subprime Lending 

The subprime mortgage market provides 

mortgage financing to credit-impaired 

borrowers—those who may have blemishes 

in their credit record, insufficient credit 

history, or non-traditional credit sources. 

This section examines several topics related 

to subprime lending including (a) the growth 

and characteristics of subprime loans, (b) the 

neighborhood concentration of subprime 

lending, (c) predatory lending, and (d) 

purchases of subprime mortgages by the 

GSEs. Section C.9 follows with a discussion 

of risk-based pricing.

a. The Growth and Characteristics of 

Subprime Loans 

The subprime market has grown rapidly 

over the past several years, increasing from 

an estimated $35 billion in 1994 to $160 

billion in 1999 and $173.3 billion in 2001, 

before rising to $213 billion in 2002. The 

subprime share of total market originations 

rose from 4.6 percent in 1994 to a high of 15 

percent in 1999, and then fell to 8.5 percent 

in both 2001 and 2002.181 Various factors 

have led to the rapid growth in the subprime 

market: Federal legislation preempting state 

restrictions on allowable rates and loan 

features, the tax reform act of 1986 which 

encouraged tax-exempt home equity 

financing of consumer debt, increased 

demand for and availability of consumer 

debt, a substantial increase in homeowner 

equity due to house price appreciation, and 

a ready supply of available funds through 

Wall Street securitization.182 It is important 

to note that subprime lending grew in the 

1990s mostly without the assistance of 

Fannie Mae and Freddie Mac.

Generally, there are three different types of 

products available for subprime borrowers. 

These include: Home purchase and refinance 

mortgages designed for borrowers with poor 

credit histories; ‘‘Alt A’’ mortgages that are 

usually originated for borrowers who are 

unable to document all of the underwriting 

information but who may have solid credit 

records; and high loan-to-value mortgages 

originated to borrowers with fairly good 

credit. Fannie Mae and Freddie Mac are more 

likely to serve the first two types of subprime 

borrowers.183

Borrowers use subprime loans for various 

purposes, which include debt consolidation, 

home improvements, and an alternative 

source of consumer credit. Between 1999 and 

2001, about two-thirds of subprime loans 

were refinance loans. It has been estimated 

that 59 percent of refinance loans were ‘‘cash 

out’’ loans.184 According to a joint HUDTreasury report, first liens accounted for 

more than three out of four loans in the 

subprime market.

The subprime market is divided into 

different risk categories, ranging from least 

risky to most risky: A-minus, B, C, and D. 

While there are no clear industry standards 

for defining the subprime risk categories, 

Inside Mortgage Finance defines them in 

terms of FICO scores—580–620 for A-minus, 

560–580 for B, 540–560 for C, and less than 

540 for D. The A-minus share of the 

subprime market rose from 61.6 percent in 

2000 to 70.7 percent in 2001.185 For the first 

nine months of 2002, the A-minus share 

accounted for 74 percent of the market, while 

the B share accounted for 11 percent, the C 

share accounted for 7.2 percent, and the D 

share accounted for 7.9 percent of the 

market.186

Delinquency rates by type of subprime loan 

are as follows: 3.36 percent for A-minus 

loans, 6.67 percent for B, 9.22 percent for C, 

and 21.03 percent for D, according to the 

Mortgage Information Corporation.187

Because of their higher risk of default, 

subprime loans typically carry much higher 

mortgage rates than prime mortgages. Recent 

quotes for a 30-year Fixed Rate Mortgage 

were 8.85 percent for A-minus (with an 85 

percent LTV), 9.10 percent for B credit (with 

an 80 percent LTV), and 10.35 percent for C 

credit (with a 75 percent LTV).188 As the low 

loan-to-value (LTV) ratios indicate, one loss 

mitigation technique used by subprime 

lenders is a high down payment requirement. 

Some housing advocates have expressed 

concern that the perceptions about the risk of 

subprime loans may not always be accurate, 

for example, creditworthy borrowers in inner 

city neighborhoods may be forced to use 

subprime lenders because mainstream 

lenders are not doing business in their 

neighborhoods (see below).

Subprime borrowers are much more likely 

to be low income and be a minority than 

other borrowers. Between 1999 and 2001, 

43.1 percent of subprime loans in the 

conventional conforming market went to 

low-income borrowers, compared with 29.5 

percent of conventional conforming loans. 

During that same period, 19.9 percent of 

subprime loans were for African-American 

borrowers, compared with 6.5 percent of all 

conventional conforming loans. However, 

what distinguishes subprime loans from 

other loans is their concentration in AfricanAmerican neighborhoods. 

b. The Neighborhood Concentration of 

Subprime Lending 

The growth in subprime lending over the 

last several years has benefited creditimpaired borrowers as well as those 

borrowers who choose to provide little 

documentation for underwriting. However, 

studies showing that subprime lending is 

disproportionately concentrated in lowincome and minority neighborhoods have 

raised concerns about whether mainstream 

lenders are adequately serving these 

neighborhoods. A study of subprime lending 

in Chicago by The Woodstock Institute 

concluded that a dual, hyper-segmented 

mortgage market existed in Chicago, as 

mainstream lenders active in white and 

upper-income neighborhoods were much less 

active in low-income and minority 

neighborhoods–effectively leaving these 

neighborhoods to unregulated subprime 

lenders.189 As part of the HUD-Treasury Task 

Force on Predatory Lending, HUD’s Office of 

Policy Development and Research released a 

national level study—titled Unequal Burden: 

Income and Racial Disparities in Subprime 

Lending in America—that showed families 

living in low-income and African-American 

neighborhoods in 1998 relied 

disproportionately on subprime refinance 

lending, even after controlling for 

neighborhood income. An update of that 

analysis for the year 2000 yields the 

following trends: 190

• In 2000, 36 percent of refinance 

mortgages in low-income neighborhoods 

were subprime, compared with only 16 

percent in upper-income neighborhoods.

• Subprime lending accounted for 50 

percent of refinance loans in majority African 

American neighborhoods—compared with 

only 21 percent in predominantly white areas 

(less than 30 percent of population is African 

American). 

• The most dramatic view of the disparity 

in subprime lending comes from comparing 

homeowners in upper-income AfricanAmerican and white neighborhoods. Among 

homeowners living in the upper-income 

white neighborhoods, only 16 percent turned 

to subprime lenders in 2000. But 42 percent 

of homeowners living in upper-income 

African-American neighborhoods relied upon 

subprime refinancing which is substantially 

more than the rate (30 percent) for 

homeowners living in low-income white 

neighborhoods. 

• Similar results are obtained when the 

analysis is conducted for borrowers instead 

of neighborhoods. Upper-income AfricanAmerican borrowers are twice as likely as 

low-income white borrowers to have 

subprime loans. Over one-half (54 percent) of 

Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 63669

191For an update to 2001, see The Association of 

Community Organizers for Reform Now (ACORN), 

Separate and Unequal Predatory Lending in 

America, 2002. In 2001, subprime lenders 

originated 27.8 percent of all conventional 

refinance loans for African-Americans, 13.6 percent 

for Hispanic homeowners, and just 6.3 percent for 

white homeowners. Overall, African-Americans 

were 4.4 times more likely to use a subprime lender 

than whites, and Hispanics were 2.2 times more 

likely to do so.

192Howard Lax, Michael Manti, Paul Raca, and 

Peter Zorn, ‘‘Subprime Lending: An Investigation of 

Economic Efficiency,’’ February 25, 2000.

193 It should also be noted that higher interest 

rates are only one component of the higher cost of 

subprime loans since borrowers also often face 

higher origination points. The Freddie Mac study 

did not find a large differential between prime and 

subprime loans in points paid, but the study notes 

that subprime loans often have points rolled into 

the loan principal, which cannot be identified with 

their data.

194Freddie Mac, We Open Doors for America’s 

Families, Freddie Mac’s Annual Housing Activities 

Report for 1997, March 16, 1998, p. 23.

195Rommy Fernandez, ‘‘Fannie Mae Eyes Half of 

the Subprime Market,’’ in The American Banker, 

March 1, 2002. Also see ‘‘Fannie Mae Vows More 

Minority Lending,’’ Washington Post, March 16, 

2000, p. EO1.

196For an overview of these studies, see Harold 

L. Bunce, Debbie Gruenstein, Christopher E. 

Herbert, Randall M. Scheessele, Subprime 

Foreclosures: The Smoking Gun of Predatory 

Lending, 2000. Also see Abt Associates Inc., 

Analyzing Trends in Subprime Originations and 

Foreclosures: A Case Study of the Atlanta Metro 

Area, February 2000 and Analyzing Trends in 

Subprime Originations and Foreclosures: A Case 

Study of the Boston Metro Area, September 2000; 

National Training and Information Center, Preying 

on Neighborhoods: Subprime Mortgage Lenders and 

Chicagoland Foreclosures, 2000; and the HUD 

study, Unequal Burden in Baltimore: Income and 

Racial Disparities in Subprime Lending, May 2000.

low-income African-American borrowers 

turn to subprime lenders, as does over onethird (35 percent) of upper-income AfricanAmerican borrowers. By comparison, only 24 

percent of low-income white borrowers and 

12 percent of upper-income white borrowers, 

rely upon subprime lenders for their 

refinance loans.191

It does not seem likely that these high 

market shares by subprime lenders in lowincome and African-American 

neighborhoods can be justified by a heavier 

concentration of households with poor credit 

in these neighborhoods. Rather, it appears 

that subprime lenders may have attained 

such high market shares by serving areas 

where prime lenders do not have a 

significant presence. The above finding that 

upper-income black borrowers rely more 

heavily on the subprime market than lowincome white borrowers suggests that a 

portion of subprime lending is occurring 

with borrowers whose credit would qualify 

them for lower cost conventional prime 

loans. A lack of competition from prime 

lenders in low-income and minority 

neighborhoods has increased the chances 

that borrowers in these communities are 

paying a high cost for credit. As explained 

next, there is also evidence that the higher 

interest rates charged by subprime lenders 

cannot be fully explained solely as a function 

of the additional risks they bear. Thus, a 

greater presence by mainstream lenders 

could possibly reduce the high up-front fees 

and interest rates being paid by residents of 

low-income and minority neighborhoods. 

The Freddie Mac study presented evidence 

that subprime loans bear interest rates that 

are higher than necessary to offset the higher 

credit risks of these loans.192 The study 

compared (a) the interest rate on subprime 

loans rated A-minus by the lenders 

originating these loans with (b) the interest 

rates on prime loans purchased by Freddie 

Mac and rated A-minus by a Freddie Mac 

underwriting model. Despite the fact that 

both loan groups were rated A-minus, on 

average the subprime loans bore interest rates 

that were 215 basis points higher. Even 

assuming that the credit risk of the subprime 

loans was in fact higher than the prime loans, 

the study could not account for such a large 

discrepancy in interest rates. Assuming that 

default rates might be three to four times 

higher for the subprime loans would account 

for a 90 basis point interest rate differential. 

Assuming that servicing the subprime loans 

would be more costly would justify an 

additional 25 basis point differential. But 

even after allowing for these possible 

differences, the Freddie Mac researchers 

concluded that the subprime loans had an 

unexplained interest rate premium of 100 

basis points on average.193

Banking regulators have recognized the 

link between the growth in subprime lending 

and the absence of mainstream lenders and 

have urged banks and thrifts that lending in 

these neighborhoods not only demonstrates 

responsible corporate citizenship but also 

profitable lending. Ellen Seidman, former 

Director of the Office of Thrift Supervision, 

stated that, ‘‘Many of those served by the 

subprime market are creditworthy borrowers 

who are simply stuck with subprime loans or 

subprime lenders because they live in 

neighborhoods that have too few credit or 

banking opportunities.’’

With respect to the question of whether 

borrowers in the subprime market are 

sufficiently creditworthy to qualify for more 

traditional loans, Freddie Mac has said that 

one of the promises of automated 

underwriting is that it might be better able to 

identify borrowers who are unnecessarily 

assigned to the high-cost subprime market. 

Freddie Mac has estimated that 10–30 

percent of borrowers who obtain mortgages 

in the subprime market could qualify for a 

conventional prime loan through Loan 

Prospector, Freddie Mac’s automated 

underwriting system.194 Fannie Mae has 

stated that half of all mortgage borrowers 

steered to the high-cost subprime market are 

in the A-minus category, and therefore are 

prime candidates for Fannie Mae.195

c. Predatory Lending 

Predatory lending has been a disturbing 

part of the growth in the subprime market. 

Although questions remain about its 

magnitude, predatory lending has turned 

homeownership into a nightmare for far too 

many households. The growing incidence of 

abusive practices has been stripping 

borrowers of their home equity, threatening 

families with foreclosure, and destabilizing 

neighborhoods. Also, in some cities, there are 

indications that unscrupulous realtors, 

mortgage brokers, appraisers, and lenders are 

duping some FHA borrowers into purchasing 

homes at an inflated price or with significant 

undisclosed repairs. The problems associated 

with home equity fraud and other mortgage 

abuses are not new ones, but the extent of 

this activity seems to be increasing. The 

expansion of predatory lending practices 

along with subprime lending is especially 

troubling since subprime lending is 

disproportionately concentrated in low- and 

very-low income neighborhoods, and in 

African-American neighborhoods. 

The term ‘‘predatory lending’’ is a short 

hand term that is used to encompass a wide 

range of abuses. While there is broad public 

agreement that predatory lending should 

have no place in the mortgage market, there 

are differing views about the magnitude of 

the problem, or even how to define practices 

that make a loan predatory. The joint HUDTreasury report, Curbing Predatory Home 

Mortgage Lending, concluded that a loan can 

be predatory when lenders or brokers: charge 

borrowers excessive, often hidden fees 

(called ‘‘packing fees’’); successively 

refinance loans at no benefit to the borrower 

(called ‘‘loan flipping’’); make loans without 

regard to a borrower’s ability to repay; and, 

engage in high-pressure sales tactics or 

outright fraud and deception. These practices 

are often combined with loan terms that, 

alone or in combination, are abusive or make 

the borrower more vulnerable to abusive 

practices. Vulnerable populations, including 

the elderly and low-income individuals, and 

low-income or minority neighborhoods, 

appeared to be especially targeted by 

unscrupulous lenders.

One consequence of predatory lending is 

that borrowers are stripped of the equity in 

their homes, which places them at an 

increased risk of foreclosure. In fact, high 

foreclosure rates for subprime loans provide 

the most concrete evidence that many 

subprime borrowers are entering into 

mortgage loans that they simply cannot 

afford. The high rate of foreclosures in the 

subprime market has been documented by 

HUD and others in recent research studies.196

These studies have found that foreclosures by 

subprime lenders grew rapidly during the 

1990s and now exceed the subprime lenders’

share of originations. In addition, the studies 

indicate that foreclosures of subprime loans 

occur much more quickly than foreclosures 

on prime loans, and that they are 

concentrated in low-income and AfricanAmerican neighborhoods. Of course, given 

the riskier nature of these loans, a higher 

foreclosure rate would be expected. With the 

information available it is not possible to 

evaluate whether the disparities in 

foreclosure rates are within the range of what 

would be expected for loans prudently 

originated within this risk class. But findings 

from these studies about the high rate of 

mortgage foreclosure associated with 

subprime lending reinforce the concern that 

predatory lending can potentially have 

devastating effects for individual families 

and their neighborhoods.

At this time, there are open questions 

about the effectiveness of the different 

approaches being proposed for eradicating 

63670 Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 

197 ‘‘OCC Cites Fannie, Freddie Predatory Lending 

Rules As Model,’’ Dow Jones Business News, 

February 25, 2003.

198Temkin et al., 2002, p. 1.

199 David A. Andrukonis, ‘‘Entering the Subprime 

Arena,’’ Mortgage Banking, May 2000, pp. 57–60.

200Subprime Lenders Mixed on Issue of GSE 

Mission Creep,’’ Inside B and C Lending, March 19, 

2001.

201Fannie Mae, ‘‘Fannie Mae’s Comments on 

HUD’s Proposed Housing Goals for Fannie Mae and 

Freddie Mac for the years 2005–2008 and 

Amendments to HUD’s Regulation of Fannie Mae 

and Freddie Mac,’’ July 16, 2004, p. I–59.

202Freddie Mac Public Comment Letter on HUD’s 

Proposed Goals, July 2004, p. 6.

203See Lederman, et al., Op cit.

204 Kenneth Temkin, Jennifer E. H. Johnson, and 

Diane K. Levy, ‘‘Subprime Markets, the Role of 

GSEs, and Risk-Based Pricing,’’ Urban Institute, 

August 2001, p. 1.

205 Inside Mortgage Finance’s, ‘‘Inside MBS & 

ABS,’’ December 15, 2000 and March 8, 2002.

206Statement by Mercy Jimenez of Fannie Mae in 

‘‘Fannie Mae: Forges Ahead in Subprime,’’

Secondary Marketing Executive, February 2003, 

p.15.

predatory lending and the appropriate roles 

of different governmental agencies—more 

legislation versus increased enforcement of 

existing laws, long-run financial education 

versus mortgage counseling, Federal versus 

state and local actions. In its recent issuance 

of predatory lending standards for national 

banks, the Office of the Comptroller of the 

Currency (OCC) cited the efforts of Fannie 

Mae and Freddie Mac in reducing predatory 

lending.197 The OCC advised banks against 

abusive practices, such as rolling singlepremium life insurance into a loan. The 

agency cited guidelines developed by Fannie 

Mae and Freddie Mac as a ‘‘useful reference’’

or starting point for national banks. 

Following publication of HUD’s proposed 

2000 Rule inviting comments on disallowing 

goals credit for high cost mortgage loans, 

Fannie Mae and Freddie Mac told lenders 

they would no longer purchase loans with 

certain abusive practices, such as excessive 

fees and failing to consider a borrower’s 

ability to repay the debt.

It is important to re-emphasize that 

predatory lending generally occurs in 

neighborhoods where borrowers have limited 

access to mainstream lenders. While 

predatory lending can occur in the prime 

market, it is ordinarily deterred in that 

market by competition among lenders, 

greater homogeneity in loan terms and 

greater financial information among 

borrowers. Thus, one solution to address this 

problem would be to encourage more 

mainstream lenders to do business in our 

inner city neighborhoods. 

Certain commenters urged the Department 

to adopt predatory lending safeguards in the 

final rule that would prohibit the GSEs from 

counting loans that included mandatory 

arbitration clauses or loans with prepayment 

penalties beyond three years towards the 

goals. In the 2000 rulemaking, the 

Department determined that the GSEs should 

not receive goals credit for purchasing high 

cost mortgages including mortgages with 

unacceptable features as explained in the 

preamble. The Department is aware that 

certain practices that were not enumerated in 

the regulations adopted in 2000, such as 

loans with prepayment penalties after three 

years and loans with mandatory arbitration 

clauses, often lock borrowers into 

disadvantageous loan products. The 

Department will rely on existing regulatory 

authorities to monitor the GSEs’ performance 

in this area. Should the Department later 

determine that there is a need to specifically 

enumerate additional prohibited predatory 

practices, it will address such practices in a 

future rulemaking. 

d. Purchases of Subprime Mortgages by the 

GSEs 

Fannie Mae and Freddie Mac have shown 

increasing interest in the subprime market 

since the latter half of the 1990s. The GSEs 

entered this market by purchasing securities 

backed by non-conforming loans. Freddie 

Mac, in particular, increased its subprime 

business through structured transactions, 

with Freddie Mac guaranteeing the senior 

classes of senior/subordinated securities. The 

two GSEs also purchase subprime loans on 

a flow basis. Fannie Mae began purchasing 

subprime loans through its Timely Payment 

Reward Mortgage program in June 1999, and 

Freddie Mac rolled out a similar product, 

Affordable Merit Rate, in May 2000 

(described below). In addition to purchasing 

subprime loans for borrowers with blemished 

credit, the GSEs also purchase another nonconforming loan called an Alternative-A or 

‘‘Alt-A’’ mortgage. These mortgages are made 

to prime borrowers who do not want to 

provide full documentation for loans. The 

GSEs’ interest in the subprime market has 

coincided with a maturation of their 

traditional market (the conforming 

conventional mortgage market), and their 

development of mortgage scoring systems, 

which they believe allows them to accurately 

model credit risk. Although the GSEs account 

for only a modest share of the subprime 

market today, some market analysts estimate 

that they could purchase as much as half of 

the overall subprime market in the next few 

years.198

Precise information on the GSEs’ purchases 

of subprime loans is not readily available. 

Data can be pieced together from various 

sources, but this can be a confusing exercise 

because of the different types of nonconforming loans (Alt-A and subprime) and 

the different channels through which the 

GSEs purchase these loans (through 

securitizations and through their ‘‘flowbased’’ product offerings). Freddie Mac, 

which has been the more aggressive GSE in 

the subprime market, purchased 

approximately $12 billion in subprime loans 

during 1999—$7 billion of A-minus and 

alternative-A loans through its standard flow 

programs and $5 billion through structured 

transactions.199 In 2000, Freddie Mac 

purchased $18.6 billion of subprime loans on 

a flow basis in addition to another $7.7 

billion of subprime loans through structured 

transactions.200 Freddie Mac securitized $9 

billion in subprime and Alt-A product in 

2001 and $11.1 billion in 2002.

Fannie Mae’s anti-predatory lending 

strategy includes eight major components. 

These components include: establishing 

business guidelines that ensure that liquidity 

is provided for only responsible lenders; 

expanding the application of conventional 

conforming mortgage practices to more 

borrowers; advancing the Mortgage 

Consumer Bill of Rights Agenda; offering a 

broad range of alternative responsible 

products; leveraging technology and the 

Internet to expand markets and reduce costs 

for consumers; working with partners to keep 

borrowers in their homes; supporting the 

home-buyer education industry to empower 

educators to reach more consumers; and 

supporting the Fannie Mae Foundation in 

consumer education and outreach.201

In recent years, Freddie Mac has instituted 

measures designed to protect consumers from 

predatory lending. For example, Freddie Mac 

has announced that, effective August 1, 2004, 

they will no longer invest in subprime 

mortgages originated after that date that 

contain mandatory arbitration clauses. Since 

2000, Freddie Mac has prohibited purchases 

of mortgages that impose a prepayment 

premium for a term of more than five years, 

and in March 2002, this prohibition was 

reduced to no more than three years. Freddie 

Mac does not purchase high-rate or high-fee 

loans that are covered by the Home 

Ownership and Equity Protection Act of 1994 

(HOEPA); and they do not purchase 

mortgages containing a prepaid singlepremium credit life, credit disability, credit 

unemployment or credit property insurance 

policy. Freddie Mac also requires all lenders 

servicing their loans to report monthly 

borrower mortgage payments to all four major 

credit repositories, and conducts onsite 

reviews of their customers and holds them 

accountable if their business practices do not 

meet Freddie Mac standards.202

Fannie Mae initiated its Timely Payments 

product in September 1999, under which 

borrowers with slightly damaged credit can 

qualify for a mortgage with a higher interest 

rate than prime borrowers. Under this 

product, a borrower’s interest rate will be 

reduced by 100 basis points if the borrower 

makes 24 consecutive monthly payments 

without a delinquency. Fannie Mae has 

revamped its automated underwriting system 

(Desktop Underwriter) so loans that were 

traditionally referred for manual 

underwriting are now given four risk 

classifications, three of which identify 

potential subprime (A-minus) loans.203

Fannie purchased about $600 million of 

subprime loans on a flow basis in 2000.204

Fannie Mae securitized around $0.6 billion 

in subprime mortgages in 2000, before 

increasing to $5.0 billion in 2001 and 7.3 

billion in 2002.205 In terms of total subprime 

activity (both flow and securitization 

activities), Fannie Mae purchased $9.2 

billion in 2001 and over $15 billion in 2002, 

the latter figure representing about 10 percent 

of the market, according to Fannie Mae 

staff.206

A greater GSE role in the subprime lending 

market will most likely have a significant 

impact on the subprime market. Currently, 

the majority of subprime loans are not 

purchased by GSEs, and the numbers of 

lenders originating subprime loans typically 

do not issue a large amount of prime loans. 

Partly in response to higher affordable 

Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 63671

207Temkin et al., 2002, p. 1.

208See Lax et al., 2000.

209Zorn, et al., 2001, p. 5.

210Fannie Mae, Remarks Prepared for Delivery by 

Franklin Raines, Chairman and CEO of Fannie Mae 

to the National Community Reinvestment Coalition. 

Washington, D.C. March 20, 2000.

211Annual Percentage Rate takes into account 

points, fees, and the periodic interest rate.

212Temkin et al., 2002, p.29.

213For example, see Radian’s product offerings at 

http://www.radiangroupinc.com.

214 Vanessa Bush, ‘‘Risk-Based Pricing Trend 

Could Make Mortgage Lending More Efficient,’’

America’s Community Banker, October 1, 1998.

housing goals set by HUD in its new rule set 

in 2000, the GSEs are increasing their 

business in the subprime market. In the 2000 

GSE Rule, HUD identified subprime 

borrowers as a market that can assist Fannie 

Mae and Freddie Mac in reaching their 

higher affordable housing goals while also 

helping establish more standardization in the 

subprime market. According to an Urban 

Institute study in 2002, many subprime 

lenders believe that successful companies 

serving high-risk borrowers need to have 

specialized expertise in outreach, servicing, 

and underwriting, which is lacking among 

most prime lenders.207 These lenders do not 

believe the more standardized approaches of 

prime lenders and the GSEs will work with 

subprime borrowers, who require the more 

customized and intensive origination and 

loan servicing processes currently offered by 

experienced subprime lenders.

As noted above, both Fannie Mae and 

Freddie Mac make the claim that the 

subprime market is inefficient, pointing to 

evidence indicating that subprime borrowers 

pay interest rates, points, and fees in excess 

of the increased costs associated with serving 

riskier borrowers in the subprime market.208

A recent Freddie Mac study found automated 

mortgage scoring less discriminatory and 

more accurate in predicting risk than manual 

systems such as those currently used by 

subprime lenders.209 According to Fannie 

Mae, although a high proportion of borrowers 

in the subprime market could qualify for less 

costly prime mortgages, it remains unclear 

why these borrowers end up in the subprime 

market.210 Fannie Mae and Freddie Mac 

believe they can bring more efficiency to the 

subprime market by creating standardized 

underwriting and pricing guidelines in the 

subprime market. An expanded GSE 

presence in the subprime market could be of 

significant benefit to lower-income and 

minority families if it attracted more 

mainstream lenders and competition to those 

inner-city neighborhoods that are currently 

served mainly by subprime lenders.

Several commenters indicated that to 

obtain the higher housing goals the GSEs 

would increase their purchasing of subprime 

loans. While some industry commenters 

welcome the entrance of the GSEs into the 

subprime market because their presence 

brings stability and standardizes business 

practices, they are concerned that 

unrealistically high goals could force the 

GSEs to jump into the market in a manner 

that negatively distorts underwriting and 

pricing. These commenters report that the 

GSEs can bring capital and standards but 

must gradually and carefully enter the 

subprime market in order to have a positive 

effect. 

In the past, Fannie Mae and Freddie Mac 

have voluntarily decided not to purchases 

subprime loans with features such as singlepremium life, HOEPA loans, and prepayment 

penalty terms that exceed three years. 

Freddie Mac indicated that the increased 

goals would limit its ability to influence 

subprime lending practices.

Several commenters suggest that if the 

GSEs are pushed to serve more of the 

subprime market, they will skim a significant 

portion of the lower-risk borrowers from that 

market. The resulting smaller subprime 

market would be comprised of the neediest 

borrowers. Concerned was raised by 

commenters that these higher risk borrowers 

would pay more based on three factors. First 

lower risk borrowers would not be present to 

subsidize them. Second, the market’s high 

fixed costs would be distributed across fewer 

borrowers. Finally, a significantly smaller 

subprime market for private lenders would 

drive some lenders out of business 

translating into less competition. 

9. Risk-Based Pricing 

The expanded use of automated 

underwriting and the initial uses of riskbased pricing are changing the mortgage 

lending environment, often blurring the 

distinctions between the prime and subprime 

market. Prime lenders are now using 

automated underwriting systems that are 

being adapted to facilitate risk-based pricing. 

For some time, the majority of prime 

mortgage borrowers have received loan rates 

based on average cost pricing. Generally, 

borrowers receive roughly the same Annual 

Percentage Rate 211 (APR), regardless of the 

risk of loss to the lender. The risk of all 

borrowers is averaged together, and the price 

is determined by the average risk.

In contrast, risk-based pricing enables 

mortgage lenders to offer each borrower an 

individualized interest rate based on his or 

her risk. Or, more broadly, to offer interest 

rates based on whether or not the borrower 

falls into a certain category of risk, such as 

specific loan-to-value and FICO score 

combination or specified mortgage score 

range. Lenders could also set the interest rate 

based on various factors including the 

probability of prepayment and characteristics 

of the underlying collateral, as well as the 

default risk of the borrower. Borrowers that 

pose a lower risk of loss to the lender would 

then be charged a comparatively lower rate 

than those borrowers with greater risk. Rather 

than lower risk borrowers cross-subsidizing 

higher risk borrowers like in average cost 

pricing, lower risk borrowers pay a relatively 

lower rate. 

In response to the expanded use of 

automated underwriting and pressures from 

the GSEs, other purchasers of loans, mortgage 

insurers, and rating firms, lenders are 

increasing their use of risk-based pricing.212

In today’s markets, some form of differential 

pricing exists for the various subprime 

categories, for new products targeted at 

credit-impaired borrowers (such as Fannie 

Mae’s Timely Payments product), and for 

private mortgage insurance across all credit 

ranges. For example, private mortgage 

insurers use FICO scores and ‘‘Accept’’

determinations from the GSEs’’ automated 

underwriting systems to make adjustments to 

insurance premiums.213 Rating agencies vary 

subordination requirements based on the 

credit qualify of the underlying collateral.

Many believe there is cross-subsidization 

within the crude risk categories used in 

today’s market. For example, some of the 

better quality subprime borrowers in the Aminus category may be inappropriately 

assigned to the subprime market. The GSEs 

and others are attempting to learn more about 

the subprime market, and their initial efforts 

suggest that there will be an increase in the 

use of risk-based pricing within this market, 

although it is recognized that the expansion 

of risk-based pricing depends importantly on 

these parties gaining a better understanding 

of the subprime borrower and the ability of 

their mortgage scoring systems to predict risk 

within this market. It must be noted that the 

power of the underlying algorithm in 

automated underwriting systems determines 

the ability of these systems to accurately 

predict risk and set prices. 

If prime lenders adopted risk-based 

pricing, many would be willing to lend to 

riskier subprime borrowers because their risk 

would now be offset with an increase in 

price. In theory, the mortgage market should 

expand because all mortgages will be 

approved at a price commensurate with risk, 

rather than setting a risk floor and approving 

no one beneath the floor. Risk-based pricing 

could also expand the prime lenders’ market 

by enabling them to reach a new group of 

underserved customers.214 Taking advantage 

of GSEs’ lower cost of capital, GSEs may be 

able to offer borrowers who could not afford 

a rate in the subprime market a rate they can 

afford resulting from risk-based pricing.

Risk-based pricing also poses challenges on 

the mortgage market because some of the 

more risky borrowers (who are currently 

cross-subsidized by less risky borrowers) may 

not be able to afford their higher, risk-based 

interest rate. Also, the adoption of an 

automated risk-based pricing system may 

have an uncertain effect on minority groups, 

who tend to have lower credit scores, as 

discussed earlier. On the other hand, if 

minorities are eligible for prime financing, 

the cost of financing minorities may fall as 

will the potential for subprime lenders to 

draw minorities to their higher-priced 

products. 

As the GSEs become more comfortable 

with subprime lending, the line between 

what today is considered a subprime loan 

versus a prime loan will likely deteriorate, 

making expansion by the GSEs look more 

like an increase in the prime market. This 

melding of markets could occur even if many 

of the underlying characteristics of subprime 

borrowers and the market’s evaluation of the 

risks posed by these borrowers remain 

unchanged. Increased involvement by the 

GSEs in the subprime market will result in 

more standardized underwriting guidelines 

and the increased participation of traditional 

lenders. In fact, there are indications that 

mainstream players are already increasing 

63672 Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 

215 ‘‘Improving Credit Quality, Maturing Business 

Stoke Confidence in Subprime MBS Market,’’ Inside 

MBS & ABS, published by Inside Mortgage Finance, 

February 21, 2003.

216 Ibid.

217U.S. Department of HUD, Office of Policy 

Development and Research, U.S. Housing Market 

Conditions: 4th Quarter 2003, February 2004, p. 3.

218U.S. Department of HUD, Office of Policy 

Development and Research, U.S. Housing Market 

Conditions: 4th Quarter 2003, February 2004, p. 84.

219See, for example, Marcus & Millichap 

Research Services, National Apartment Report,

January 2003.

220Marcus & Millichap Research Services, 

National Apartment Report, January 2004.

221U.S. Department of Labor, Bureau of Labor 

Statistics, ‘‘Bureau of Labor Statistics Data,’’

Accessed July 31, 2004, http://data.bls.gov/servlet/

SurveyOutputServlet?data_tool

=latest_numbers&series_id=LNS14000000.

222 ‘‘Apartment Landlords Gather to Dreary 

Outlook for Sector,’’ Wall Street Journal, January 15, 

2003, Section B.

223Mortgage Bankers Association of America, 

‘‘MBA News Link: Rental Market Demographics 

‘‘Favorable,’’ Report Says,’’ January 2003.

their activity in this market. According to 

staff from Moody’s Investors Service, the 

growing role of large mortgage aggregators in 

the subprime market has been a key factor in 

the improving credit qualify on deals issued 

in 2002.215 According to a representative 

from Washington Mutual, subprime credit 

qualify has also improved as lenders carve 

out new loan categories that fall somewhere 

between the large Alt A market and 

traditional subprime business.216 As the 

subprime market becomes more 

standardized, market efficiencies will reduce 

borrowing costs. Lending to credit-impaired 

borrowers will, in turn, increasingly make 

good business sense for the mortgage market.

D. Factor 2: Economic, Housing, and 

Demographic Conditions: Multifamily 

Mortgage Market 

1. Introduction 

At the time of the previous GSE 

rulemaking in 2000, the multifamily rental 

housing market was coming off several years 

of generally positive performance. Vacancies 

were low in most markets and rent increases 

were matching or exceeding economy-wide 

inflation. A key to this strong performance 

was the volume of new multifamily 

construction, which was at a level consistent 

with demand growth. Job growth and income 

gains helped many renters pay the higher 

rents without undue burden. As always, 

conditions varied from region to region, and 

across market segments, but the overall tone 

of the apartment market was quite healthy. 

Much has changed in the subsequent years. 

An economic slowdown reduced apartment 

demand, and with new multifamily 

construction about unchanged, vacancies 

rose and rents softened. Provision of decent 

housing affordable to households of moderate 

or low incomes is a challenge even in strong 

economic times, and with the unemployment 

rate rising above 6 percent before falling to 

about 5 and a half percent, affordability 

problems increased for many, despite the 

softness in rents. 

Despite the recent weakness in the 

apartment property market, the market for 

financing of apartments has grown to record 

volumes. The favorable long-term prospects 

for apartment investments, combined with 

record low interest rates, has kept investor 

demand for apartments strong and supported 

property prices. Refinancings too have 

grown, and credit quality has remained very 

high. Fannie Mae and Freddie Mac have been 

among those boosting volumes and 

introducing new programs to serve the 

multifamily market. 

This section will review these market 

developments, interpret the performance of 

Fannie and Freddie within that market 

context, and discuss future prospects for the 

multifamily rental market, its financing, and 

the GSE role. The intention here is only to 

update the discussion from 2000. For general 

background information on the multifamily 

mortgage market and the GSEs, see the 2000 

Rule and the HUD-sponsored research report, 

Study of Multifamily Underwriting and the 

GSEs’ Role in the Multifamily Market (Abt 

Associates, 2001).

2. The Multifamily Rental Housing Market: 

2000–2003 

The definition of ‘‘good’’ market conditions 

in multifamily rental housing depends on 

one’s perspective. Investors and lenders like 

low vacancies, steady rent increases, and 

rising property values. Developers like strong 

demand for new construction and favorable 

terms on construction financing. Consumers, 

in contrast, prefer low rents and a wide 

selection of available apartments. 

The mid- to late-1990s were among the 

most successful of recent history, in that 

apartment market conditions were generally 

good for all of these interest groups. 

Investment returns were favorable, 

construction volumes were steady at 

sustainable levels, and many consumers had 

income gains in excess of their rent increases. 

Market conditions for multifamily rental 

housing began to weaken toward the end of 

2000. Early warnings came from the publicly 

traded apartment companies, some of which 

reported easing in demand growth in the first 

months of 2001, coinciding with a slowdown 

in job growth to its lowest level since 1992. 

By 2003, rental units were experiencing 

record high vacancy rates and newly 

completed apartments faced record low 

absorption or ease-up rates. The rental sector 

vacancy rate averaged 9.8 percent in 2003, up 

0.8 percent from 2002, and the highest 

annual vacancy rate I the more than 40-year 

history of the measure.217

Apartments—especially those serving the 

top end of the rental market—appear to have 

performed worse than other rental housing in 

the past four years, after several years of rent 

growth and occupancies surpassing the rental 

market averages. The multifamily (5+ units in 

structure) vacancy rate has increased more 

than the overall rental market vacancy rate in 

each of the years 2000, 2001, 2002, and 2003. 

For example, the Census Bureau’s estimate of 

a 0.9 percentage point increase in vacancies 

for multi-family apartments in 2003 exceeds 

the overall rental vacancy rate of 0.6 

percent.218 Similarly, while rent growth has 

decelerated slightly for all rental housing 

according to the CPI, industry surveys of 

apartment rents show year-over-year declines 

in rents in many local markets.219 In 2003, 

asking rents remained flat nationally, as 

multifamily completions declined 5 

percent.220

a. Apartment Demand and Supply 

The primary reason for the softening in the 

multifamily rental market has been a 

reduction in the growth of consumer demand 

for apartment housing. The general 

slowdown in economic activity meant fewer 

apartment customers, with less money, than 

if the economy were vigorously expanding. 

Persistent low interest rates have also enticed 

renters into the home purchase market as 

evidenced by the U.S. homeownership rate, 

which grew to 68.4 percent in 2003, further 

contributing to a weakness in rental demand. 

The reduced demand is most evident in the 

national statistics on employment. Job 

growth began decelerating in late 2000 and 

throughout 2001, turning negative late that 

year. The largest year-over-year job loss of the 

economic downturn occurred in February 

2002, and year-over-year losses have 

continued through October 2003.221

Apartment demand seems particularly 

sensitive to labor market conditions, given 

the importance of rental housing to mobile 

individuals and families accepting new jobs 

or transfers. Reis, Inc., a real estate market 

research firm, estimates that the total number 

of occupied apartments (in properties with 

40+ units) actually declined in both 2001 and 

2002 in the large markets nationwide that are 

monitored by the company.222 Job numbers 

showed some rebound in the subsequent 

period.

Households, not jobs, fill apartments, and 

for this reason household formations are a 

preferable indicator of demand for 

apartments as well as other types of housing. 

The Census Bureau estimates that the total 

number of renter households nationwide has 

been essentially unchanged at approximately 

34.8 million since 1996. Yet during the late 

1990s apartment demand was expanding, 

and apartments were apparently picking up 

market share from other rental housing. The 

past two or three years may have seen a 

reversal of that trend in share. 

Long-term demographic trends are 

expected to be favorable for rental housing 

demand.223 The maturing of the ‘‘Baby Boom 

Echo’’ generation will increase the number of 

persons under age 25 who will seek rental 

housing, immigration is expected to continue 

to fuel demand for rental housing, and 

minority populations, while increasing their 

homeownership rates, are growing and will 

contribute to higher absolute demand for 

rental housing. Thus demographic trends 

support an improvement in the long-run 

demand for rental demand, which is likely to 

include higher multifamily rental demand.

Supply growth has been maintained, even 

though the current reduced multifamily 

demand warrants less new construction. 

Total multifamily starts (2+ units) have been 

running approximately 325-to-350 thousand 

annually for the past six years, according to 

Census Bureau statistics, adding about 1 

percent annually to the total multifamily 

stock. Most of these new units are built for 

rental use, with only about 20 percent in 

Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 63673

224U.S. Department of HUD, Office of Policy 

Development and Research, U.S. Housing Market 

Conditions: 4th Quarter 2003, February 2004, p. 70.

225Center for Housing Policy/National Housing 

Conference, ‘‘Housing America’s Working Families: 

A Further Exploration,’’ New Century Housing, Vol. 

3, No. 1, March 2002; Mark Obrinsky and Jill 

Meron, ‘‘Housing Affordability: The Apartment 

Universe,’’ National Multi Housing Council, 2002.

226 ‘‘Housing Affordability in the United States: 

Trends, Interpretations, and Outlook,’’ a report 

prepared for the Millennial Housing Commission by 

J. Goodman, November, 2001.

227 Joint Center for Housing Studies of Harvard 

University, State of the Nation’s Housing, 2002.

228Center for Housing Policy/National Housing 

Conference, ‘‘America’s Working Families and the 

Housing Landscape 1997–2001,’’ New Century 

Housing, Vol. 3, No. 2, November 2002

229Urban Land Institute, The ULI Forecast, 2002; 

Lendlease and PriceWaterhouseCoopers, Emerging 

Trends in Real Estate, 2003.

recent years reported as being built as for-sale 

condominium units. 

The reduced short-term demand has shown 

through in absorption speeds for new 

apartments. The percentage of newly 

completed unfurnished apartments rented 

within three months of completion fell from 

72 percent during 2000 to 63 percent during 

2001 and to 59 percent during 2002, the 

lowest level in the 33-year history of the data 

series, according to the Census Bureau. This 

percentage rose slightly to 60 percent in 

2003.224

b. Performance by Market Segments 

Some segments of the multifamily rental 

market have been more affected than others 

by the recent softening. As mentioned earlier, 

the top end of the apartment market seems 

especially hard hit, as measured by rising 

vacancies and reduced rent growth. This 

segment is particularly dependent on job 

growth and transfers for new customers, and 

is particularly vulnerable to losses of 

residents and prospective customers to home 

purchase. According to reports by apartment 

REITs and other investors, these top-end 

properties have not been getting the jobrelated in-movers, but have still been losing 

a lot of customers to home purchase. These 

properties generally have annual resident 

turnover rates of above 50 percent, and thus 

are particularly quickly influenced by 

changes in demand. Furthermore, this is the 

segment of the apartment market into which 

most of the new construction is built. 

Performance has varied geographically as 

well. Some of the coastal markets, especially 

in Northern California, saw the double-digit 

rent increases of the late 1990s replaced by 

double-digit declines, before stabilizing more 

recently. ‘‘Supply constrained markets’’ had 

been preferred by apartment investors during 

the 1990s, but recent market performance has 

reminded investors and analysts that all 

markets have their day. For example, 

Houston posted the biggest year-over-year 

rent increase of any major apartment market 

in 2001, despite a long-run history of 

moderate rent growth and few barriers to new 

apartment construction. Rent changes in the 

27 metro markets for which estimates are 

available from the CPI ranged from a low of 

¥0.3 percent to a high of 6.7 percent in the 

first half of 2003 relative to a year earlier. 

And across the 75 metro areas for which 

rental vacancy rates (apartments plus other 

rentals combined) are available, rates for the 

year 2002 ranged from 2.4 percent to 15.4 

percent, according to the Census Bureau. In 

a historical context, this variation is 

moderate, although up somewhat since the 

late 1990s. 

Conditions in the ‘‘affordable’’ segment of 

the apartment market are harder to track than 

in the high-end segment because of lesser 

investor interest and analyst coverage. Data 

for the late 1990s analyzed by the National 

Housing Conference saw affordability 

problems continuing, although a study of 

apartment renters by the National Multi 

Housing Council saw some improvement in 

affordability during the strong economic 

growth of 1997–1999.225 Other work noted 

that rent to income ratios for the lowest 

income quintile of renters rose during the 

late 1990s even as these ratios were stable or 

declining for other renters.226 Harvard’s State 

of the Nation’s Housing report for 2002 

highlighted the variability of the affordability 

problem from place to place.227

Little research is available on affordability 

trends since 1999. However, tabulations from 

the 2001 American Housing Survey indicate 

that income growth between 1999 and 2001 

in the lowest quintile of renter households 

continued to lag that of higher income 

renters, and fell short of the average rent 

increases during this period. Together, these 

statistics suggest that affordability has 

deteriorated early this decade among at least 

this group of very low-income renters. Other 

work using the AHS found that the number 

of low-to moderate-income working families 

with severe rental cost burdens increased 24 

percent between 1999 and 2001.228

The low-income housing tax credit 

(LIHTC) continues to finance much of the 

newly built multifamily rental housing that 

is affordable to households with moderate 

income. Restricted to households with 

incomes no greater than 60 percent of the 

local median, this program financed 

approximately 75,000 units in 2001, 

according to the National Council of State 

Housing Agencies, after running in the midto high-60 thousand range the previous three 

years. About 70 percent of these units are 

newly built, and the rest are renovations of 

existing units. 

Expenditures for improvements to existing 

rental apartments have grown in recent years. 

In 2001 the total of $11.3 billion was nearly 

twice the figure of three years earlier, 

according to the Census Bureau, and more 

than a third as large as construction spending 

for newly built multifamily structures, 

including owner-occupied condos. Many of 

these improvements are to older properties in 

high-demand neighborhoods. Improvements 

to the physical structures have external 

benefits. But often the renovations are in 

connection with re-positionings that move 

the apartments into a higher rent range and 

bring changes in the demographic 

composition of the resident base. 

In 2002, expenditures on total 

improvements to existing apartments 

declined to $9.8 billion, while new 

construction spending increased $2 billion. 

This shift further suggests a re-positioning to 

apartments with a higher rent range. 

Excluding units financed with tax credits or 

other subsidies, most of the multifamily 

rental construction in recent years has been 

targeted on the upper end of the market, 

often the only segment for which 

unsubsidized new construction is 

economically feasible. The median asking 

rent on new unfurnished apartments 

completed in 2001 was $877, up 11 percent 

over the previous two years. In 2002 median 

asking rent for these properties was $905. Of 

those units brought to market in 2002, 45 

percent were at rents at or above $950.

3. Multifamily Financing Trends 

In contrast to the softening observed in the 

demand/supply balance for multifamily, 

mortgage financing of these properties has 

been at a record pace in the past three years. 

a. Lending Volume 

Total multifamily mortgage debt 

outstanding increased 11 percent in 1999, 8.7 

percent in 2000, 11.2 percent in 2001, 9.6 

percent in 2002, and 11.2 percent in 2003 

according to the Federal Reserve’s flow of 

funds accounts. The dollar volume for 2003, 

$544.2 billion, is above those of any previous 

year. The pace seems to have slowed for 

2004, with the first quarter indicating an 

annualized growth of 4.9 percent. 

Furthermore, a 2003 survey by the Mortgage 

Bankers Association of America show that of 

48 member firms surveyed, representing all 

large mortgage banking firms an a cross 

section of smaller mortgage companies, 

multifamily origination volume increased 

21.5 percent in 2003—from $41 billion in 

2002 to $49.8 billion in 2003. 

The apparent inconsistency between 

current market fundamentals and financing 

can be explained by low interest rates. The 

same financial forces that lowered the 

mortgage rates for home purchasers to record 

lows by 2002 also reduced the financing 

costs of multifamily properties. The ten year 

Treasury yield, a common benchmark for 

multifamily loan pricing, fell to a 45-year low 

of 3.3 percent in June 2003 from 6.3 percent 

as recently as the end of 1999. 

Another feature boosting investor demand 

for apartment properties and the resulting 

demand for debt to finance those purchases 

has been the lack of attractive returns on 

many financial assets and other alternative 

investments. Despite the current weak 

performance of apartments, investors 

apparently are looking through to the longrun outlook for these assets, which is 

generally thought to be favorable, as 

indicated most recently by investor surveys 

fielded by the Urban Land Institute and by 

Lend Lease and PriceWaterhouseCoopers.229

The net change in mortgage debt 

outstanding is defined as loan originations 

less repayments and charge offs. As 

discussed in Appendix D, net change is a 

lower bound on originations. By all accounts, 

originations—for which no single source of 

estimates is available—are much higher than 

net change in most years. High levels of 

refinancings of existing multifamily 

mortgages in recent years has been a factor 

in originations exceeding the net change in 

debt outstanding. 

63674 Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 

230Merrill Lynch, A New Look at FHA 

Prepayments and Defaults, September 2002. 231Eight percent inflation adjusted.

Most mortgage lending is in the 

‘‘conventional’’ market. Multifamily loan 

programs of the Federal Housing 

Administration accounted about $7 billion in 

new insured mortgages in fiscal year 2003—

up from $6 billion in fiscal year 2002 and $5 

billion in fiscal 2001. Despite the recent 

increase in FHA originations, and the likely 

continued strong performance for FHA 

multifamily programs in the foreseeable 

future, 230 FHA remains but a small portion 

of the total multifamily mortgage market. 

Outstanding FHA-insured multifamily 

mortgage debt was $55 billion at the end of 

the first quarter of 2003—only about 11 

percent of all multifamily mortgage debt 

outstanding.

Multifamily lending has been spurred by 

new apartment construction, property sales, 

and refinancings. New multifamily 

construction was valued at $34.1 billion in 

2003, according to the Census Bureau, up 21 

percent from 2000.231 The number of new 

multifamily units completed over this period 

actually declined 12 percent, and the 

increased expenditures reflect higher costs 

per unit. The increase in asking rents 

described earlier suggests higher property 

values and greater debt carrying capacity.

b. Property Sales and Refinancings 

Sales of existing apartment properties tend 

to be procyclical. Increasing asset values 

bring buyers to the market and tempt sellers 

to realize their capital gains. In soft markets, 

in contrast, the bid-ask spread generally 

widens and the volume of sales declines, as 

sellers perceive current offers as beneath the 

property’s long run value and buyers are 

reluctant to pay for past performance or the 

hope of future gains. Sales tend to increase 

mortgage debt, because the loan originated to 

finance the purchaser’s acquisition is 

typically considerably larger than the 

mortgage retired by the seller. 

No source of apartment property sales 

statistics matches the comprehensive 

national coverage of the single-family market 

provided by the National Association of 

Realtors’ monthly estimates. But surveys by 

the National Multi Housing Council and 

other apartment industry reports indicate 

that transactions volume dipped during 2001 

but since then have grown appreciably in 

both number of sales and aggregate dollar 

value. 

Mortgage lending volumes have recently 

been boosted by shifts in property 

ownership. Publicly traded real estate 

investment trusts had been the big gainers 

during most of the 1990s, and by 1999 owned 

nearly 6 percent of all apartments nationwide 

and a considerably larger share of all big 

(100+ unit) properties. But beginning in 1999 

capital market developments made private 

buyers more competitive. Since then the 

number of apartments owned by large REITs 

has declined about 5 percent, with diverse 

private interests apparently picking up 

market share. 

Private investors are able to use more 

leverage—greater debt—in financing their 

transactions than the market permits the 

public REITs. As a result, the very low 

mortgage rates recently have given them an 

advantage in bidding on properties. In 

addition, equity funding costs of REITs rose 

as their stock prices flattened or moved down 

as part of the broader equity market 

correction. 

Refinancings have, by all accounts, also 

been strong. Despite the lockout provisions 

and yield maintenance agreements that 

constrain early refinancings of many 

multifamily loans, lenders reported very 

strong refinancing activity in 2001 and 

continuing into 2002. Although refinancing 

volume data for the entire market are not 

available, the trends in refinance volume for 

FHA and the GSEs show very strong 

increases in refinance activity during 2002 

and 2003. For example, FHA’s Section 

223(a)(7) program, which is limited to 

refinancing of FHA multifamily mortgages, 

experienced an increase in origination 

volume of 133 percent in Fiscal Year 2003 

and 181 percent in Fiscal Year 2002. ($1.73 

billion in FY 2003, $0.74 billion in FY 2002, 

and $0.26 billion in FY 2001). Similarly, the 

GSEs increased their combined volume of 

refinances by 83 percent from 1999–2000 to 

2001–2002, from $17.6 billion to $32.1 

billion. Refinancings, especially when 

motivated by a desire to lower interest 

expense rather than to extract equity, do not 

add as much to debt outstanding as do 

purchase loans, which often are much larger 

than the seller’s existing mortgage that is 

repaid at the time of sale. Nonetheless, 

refinancings represent a significant part of all 

multifamily mortgage lending.

c. Sources of Financing and Credit Quality 

The sources of funding of multifamily 

mortgages shifted somewhat in the past few 

years, judging from the Flow of Funds 

accounts. As shown in Table A.4, four 

categories of lenders have dominated 

multifamily mortgage lending since the mid1990s. Of those four, commercial banks have 

played a lesser, although still substantial, 

role in recent years, providing 20 percent of 

the $86 billion in net additional funding of 

multifamily mortgages during 2000 and 2001. 

The portfolio holdings of the GSEs, by 

contrast, have been much more important 

than during the last half of the 1990s. 

Mortgage backed securities, both from the 

GSEs and especially from other issuers, 

accounted for proportionally less of the 

growth in 2000–01 than in 1995–99, but 

between them still accounted for nearly half 

of all the net credit extensions. Some slight 

broadening of the base of multifamily lending 

in the past two years, as these four lender 

groups accounted for only 85 percent of the 

net credit extended in 2000 and 2001, 

compared to all of it in the previous five-year 

period.

Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 63675

232Freddie Mac Public Comment Letter on HUD’s 

Proposed Goals, July 2004, p.3.

The market values of apartment properties 

have generally held up well, although the 

most recent indicators suggest some 

flattening. Properties in the portfolios of 

pension funds continued to appreciate into 

the second half of 2002, according to the 

National Council of Real Estate Investment 

Fiduciaries, although at a reduced annual 

rate of less than 2 percent. And the sales 

price per square foot of ‘‘Class A’’ properties 

monitored by Global Real Analytics rose 

until turning down in early 2002, posting a 

1.6 percent year over year decline in the 

second quarter. 

The continuing value of collateral has 

helped keep loan quality high on multifamily 

mortgages. Delinquency rates from all major 

reporters are at or near record lows, and well 

below the rates reported for single-family 

mortgages and commercial properties. At 

commercial banks, the FDIC reports a 0.38 

non-current loan percentage in the second 

quarter of 2002. In life insurance company 

portfolios the only 0.05 percent of residential 

mortgages were overdue at the end of 2002, 

and as of the third quarter of 2002 the GSEs 

were both reporting similarly miniscule 

delinquency rates of below 0.1 percent; all of 

these rates are below those of a year earlier. 

Multifamily lenders have remained 

cautious in their underwriting and, together 

with their regulators; have avoided repeating 

the mistakes of the 1980s. Many of the senior 

loan officers surveyed quarterly by the 

Federal Reserve have reported tightening 

their terms on commercial mortgages, and 

that shift likely has occurred in their 

multifamily lending as well. Perhaps the best 

indicator of discipline in multifamily lending 

is the fact that, despite the strong apartment 

demand during the last half of the 1990s, 

construction never rose above its long-run 

sustainable level, unlike the rampant 

overbuilding that plagued the industry in the 

mid- and late-1980s. 

4. Recent GSE Involvement in Multifamily 

Finance

As the multifamily mortgage market has 

expanded since 1999, Fannie Mae and 

Freddie Mac have increased their lending, 

picked up market share, introduced new 

programs, and enhanced others. 

Beginning with their whole loans, the GSEs 

added 34 percent to their combined holdings 

of multifamily loans in 2001, and another 26 

percent in 2002 (see Table A.6 below). The 

growth in multifamily MBS volume was 

nearly as dramatic, increasing 26 percent in 

2001 and another 14 percent in 2002. The 

gains resulted in the GSEs increasing their 

share (whole loans and securities combined) 

of all multifamily debt outstanding to 22.8 

percent by the third quarter of 2003, up from 

19 percent at year-end 2001, 15 percent at 

year-end 1999 and 11 percent at the end of 

1995. By this combined measure of portfolio 

holdings and MBS outstanding, at year-end 

2002 Fannie Mae had nearly twice ($65 

billion versus $37 billion) the multifamily 

business of Freddie Mac, although Freddie 

was growing its multifamily business more 

rapidly (67 percent increase between 2000 

and 2002, compared to 46 percent increase 

for Fannie Mae). In 2003, Freddie Mac’s 

multifamily business activities totaled 

$21.587 billion ($14.894 billion of mortgage 

purchases and $6.693 billion in investment 

activities). These activities financed rental 

housing for 549,083 families. Nearly 92 

percent of these units were affordable to lowand moderate-income renters. Since 1993, 

Freddie Mac has purchased $75.5 billion in 

multifamily mortgages, financing housing for 

more than 2.2 million families.232

Measures that focus on new multifamily 

activity, specifically gross mortgage purchase 

volumes and new security issuance, vary 

across recent years and between the GSEs. 

For the GSEs combined, these measures of 

current business activity show sharp gains of 

over 70 percent in 2001, following small 

decreases in activity in 2000. In 2002, the 

GSEs combined posted small declines for 

both measures. Measures of multifamily gross 

mortgage purchases and new security 


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issuance diverged for the two GSEs in 2002. 

Fannie Mae experienced declines in these 

balance sheet and new business indicators in 

2002 while Freddie Mac experienced gains, 

particularly in new security issuance. As 

discussed earlier, the credit quality of GSE 

multifamily loans has remained very high 

even with the large gains in loan volume. 

Despite the substantial pickup in GSE 

multifamily activity, the position of these 

companies in the multifamily mortgage 

market remains well below their dominance 

in single-family mortgage finance. At the end 

of 2002, the GSEs’ market share of single 

family debt outstanding was 44 percent, 

twice the share of multifamily debt held or 

securitized by these two companies, 

according to Federal Reserve statistics. 

Furthermore, the multifamily share of all 

housing units financed by the GSEs 

combined has declined from its 1997 level 

(Table A.5), although the annual statistics are 

heavily influenced by the volume of 

refinancings in the single-family market, 

which spiked in 1998 and again in 2001 and 

2002 in response to the big decline in 

mortgage rates in those years. Because of 

lock-out agreements and other loan 

covenants, multifamily loans are not as prone 

to rate-induced refinancings as are singlefamily mortgages. 

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Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 63677

a. Contrasting Business Models 

While both Fannie Mae and Freddie Mac 

have significantly increased their multifamily 

activities in recent years, they have pursued 

distinct business models in achieving that 

growth. As shown in Table A.6, most of 

Fannie Mae’s multifamily growth has come 

in MBS products, whereas Freddie Mac has 

relied more on loans purchased and held in 

its portfolio. At the end of 2002, Fannie Mae 

had almost four dollars of outstanding MBSs 

for every dollar of portfolio holdings. Freddie 


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Mac, on the other hand, more than three times as much volume in portfolio as it had 

in MBS outstanding.


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Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 63679

233 ‘‘No Mistaking GSEs for Twins in 

Multifamily,’’ American Banker, October 2, 2002.

234This change was a percentage decrease but a 

volume increase.

The differing emphasis on portfolio 

holdings and securities issuance is related to 

the GSEs’ contrasting approaches to credit 

underwriting.233 Fannie Mae has long had 

risk-sharing arrangements with its 

multifamily loan originators, and currently 

has over 25 Delegated Underwriters and 

Servicers who are authorized to originate 

loans meeting Fannie Mae’s requirements for 

sale to the GSE without prior approval of 

individual transactions. These ‘‘DUS’’

lenders retain part of the credit risk on the 

loans sold to Fannie.

Freddie Mac has taken a different approach 

to credit underwriting. In the wake of large 

credit losses on its multifamily business in 

the late 1980s and 1990, Freddie Mac 

essentially withdrew from the market. When 

it re-entered in late 1993, the company 

elected to retain all underwriting in-house 

and not delegate this function to the loan 

originators participating in Freddie Mac’s 

Program Plus network. Because Freddie Mac 

assumes the entire credit risk on loans it 

purchases, some commercial banks and other 

financial institutions desiring to remove 

multifamily loans and all related liabilities 

from their books find Freddie Mac’s program 

preferable. 

b. Affordable Multifamily Lending 

Because most of the GSEs’ multifamily 

lending is on properties affordable to 

households with low-or moderate incomes, 

financing of affordable multifamily housing 

by the GSEs has increased almost as much as 

their total multifamily lending. 

Approximately 87 percent of Fannie Mae’s 

multifamily lending volume in 2003 

qualified as affordable to low-or moderate 

income households, according to Fannie 

Mae’s annual Housing Activity Report, as did 

92 percent of Freddie Mac’s multifamily 

units financed. For the entire multifamily 

rental market, HUD estimates that 90 percent 

of all housing units qualify as affordable to 

families at or below 100 percent of the area 

median income, the standard upon which the 

low- and moderate-income housing goal is 

defined. 

Owing to this high propensity to qualify as 

affordable lending, financing of multifamily 

rental housing is especially important for the 

GSEs attainment of their affordable housing 

goals. Less than 8 percent of the units 

financed by the GSEs in 2002 were 

multifamily rentals, as described above. Yet 

15 percent of the units qualifying as low- and 

moderate-income purchases were 

multifamily, according to Table 1 of the 

GSEs’ activity reports for 2002. 

The GSEs increased the volume of their 

affordable multifamily lending dramatically 

in 2001, the first year of the new, higher 

affordable housing goals set for the GSEs. As 

measured by number of units financed, the 

total affordable lending (shown in the ‘‘lowmod total’’ rows of Table A.7) more than 

doubled from a year earlier, especially after 

application of the upward adjustment factor 

authorized for Freddie Mac in the 2000 Rule. 

In 2003 the GSEs maintained a high volume 

of affordable multifamily lending.234

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235For background information on the Freddie 

Mac TAF, see pages 65054 and 65067–65068 of the 

2000 Rule.

236 Fannie Mae’s 2002 Annual Housing Activities 

Report, pages 24–27; Freddie Mac’s Annual 

Housing Activities Report for 2002, pages 41–47.

237Abt Associates Inc., An Assessment of the 

Availability and Cost of Financing for Small 

Multifamily Properties, a report prepared for the 

U.S. Department of Housing and Urban 

Development, Office of Policy Development and 

Research, August 2001.

238Fannie Mae, 2003 Annual Housing Activities 

Report, March 15, 2004, p. 26.

239Freddie Mac, Opening Doors for America’s 

Families: Freddie Mac’s Annual Housing Activities 

Report for 2003, March 15, 2004, p. 44.

240Fannie Mae, 2003 Annual Housing Activities 

Report, March 15, 2004, p. 27.

241Fannie Mae, 2003 Annual Housing Activities 

Report, March 15, 2004, p. 28.

242Fannie Mae, 2003 Annual Housing Activities 

Report, March 15, 2004, p. 29.

243Freddie Mac, Opening Doors for America’s 

Families: Freddie Mac’s Annual Housing Activities 

Report for 2003, March 15, 2004, p. 47.

244 ‘‘Fannie Courting Multifamily Sellers; Small 

Banks Balking,’’ American Banker, January 13, 

2003.

245Freddie Mac, Opening Doors for America’s 

Families: Freddie Mac’s Annual Housing Activities 

Repoert for 2003, March 15, 2004, p. 47 & 49.

246Freddie Mac, Opening Doors for America’s 

Families: Freddie Mac’s Annual Housing Activities 

Report for 2003, March 15, 2004, p. 50–52.

The figures in Table A.7 are exclusive of 

the ‘‘Temporary Adjustment Factor (TAF)’’

granted to Freddie Mac as part of the 2000 

Rule. The TAF was a response to Freddie 

Mac’s limited opportunities for refinancing 

business because of its minimal involvement 

in the multifamily market in the early and 

mid-1990s.235 The TAF, which expired at the 

end of 2003, provided a 20 percent upward 

adjustment to multifamily units in properties 

with 50 or more units, for purposes of the 

affordable housing goals.

Multifamily financing made major 

contributions not only to the GSEs’

attainment of the overall goal for affordable 

lending in 2002, but also to the ‘‘underserved 

areas’’ goal and ‘‘special affordable’’ goal. As 

shown in Table A.7, the 2001 increases in 

lending in each of these categories were 

substantial at both Fannie Mae and Freddie 

Mac, again leveling off for both in 2002. The 

GSEs also met the special multifamily 

affordable subgoal set in the 2000 Rule in 

both 2001 and 2002. 

c. Multifamily Initiatives of the GSEs 

Fannie Mae and Freddie Mac have taken a 

number of steps since 2000 to expand their 

multifamily lending and to respond 

specifically to the goals established in the 

2000 Rule. These initiatives are summarized 

in the annual activity reports filed by the 

GSEs.236

One focus of the 2000 Rule was on lending 

to small (5-to-50 units) multifamily 

properties, which the Rule identified as an 

underserved market. HUD-sponsored 

research has found that the supply of 

mortgage credit to small properties was 

impeded by the substantial fixed costs of 

multifamily loan originations, by owners’

insufficient documentation of property 

income and expense, and by the limited 

opportunities for fees for underwriting and 

servicing small loans.237 As a result, many 

multifamily lenders focus on larger 

properties, which were found to have more 

loan products available to them and to pay 

lower interest rates than did small properties.

In an attempt to promote the supply of 

credit to small properties, the 2000 Rule 

provided incentives for the GSEs to step up 

their involvement in this segment of the 

multifamily mortgage market. The incentives 

likely contributed to the huge increases in 

small property lending posted by both Fannie 

Mae and Freddie Mac in 2001 and continuing 

into 2002 (Table A.7). The combined total of 

these units financed in 2001 and 2002 was 

almost 8 times those financed in the previous 

two years. This lifted the percentage of all 

GSE multifamily lending that was on small 

properties to their highest levels ever. 

During 2003, multifamily business activity 

at Fannie Mae topped $33 billion which 

financed over 809,703 multifamily units. Of 

this total, over 87% were affordable to 

families at or below the median income of 

their communities.238 Freddie Mac 

multifamily business activities totaled a 

record $21.587 billion which financed rental 

housing for 549,083 families. Nearly 92 

percent of these apartment units were 

affordable to low- and moderate income 

renters.239

Programs introduced or enhanced by the 

GSEs in the past two years have contributed 

to these striking numerical results. Delegated 

Underwriting and Servicing (DUS) is Fannie 

Mae’s principle product line for purchasing 

individual multifamily loans. This product 

line is offered through 26 lenders with 

expertise in financing multifamily properties. 

In 2003, 91% of the DUS loan activity served 

affordable housing needs, 42% of DUS loans 

in underserved markets, and 52% addressed 

‘‘special affordable’’ needs.240 Believing that 

small multifamily properties are a vital part 

of the country’s affordable housing stock, 

Fannie Mae has focused efforts on providing 

financing for these projects through the 

development of the MFlex Loan Product, the 

3MaxExpress Streamlined Mortgage Loan 

Product and the Affordable Alliances Loan 

Product. The MFlex Loan Product was 

established in 2000 to target lending partners 

that serve small property borrowers and 

increase Fannie Mae’s participation in the 5–

50 unit property market. By 2003, Fannie 

Mae had seven MFlex lending partners and 

had purchased $1.6 billion of these loans. 

Fannie Mae markets its specialized 

3MaxExpress Streamlined Mortgage Loan 

Product line for loans worth less than or 

equal to $3 million. In 2003, Fannie Mae 

provided $1 billion in financing, which 

assisted over 34,000 families living in small 

multifamily properties. The Affordable 

Alliances Loan Product is responsible for 

debt investments in rental housing targeted 

to persons of low- and moderate-income and 

to rental markets that are underserved. 

During 2003, these financing initiatives 

provided affordable housing for 3,850 

families. 241 Fannie Mae additionally has 

federal Low-Income Housing Tax Credit 

(LIHTC) programs and special financing 

projects for special use properties such as 

Seniors Housing. In 2003, Fannie Mae 

committed over $1.6 billion in LIHTC equity 

properties to help make affordable rental 

housing possible for over 30,000 families.242

During 2003, Freddie Mac used innovative 

financing structures combined with prudent, 

flexible multifamily lending practices, which 

enabled them to reach a record level of 

multifamily mortgage purchases.243 The 

GSEs face strong competition in this market 

from small banks and other depository 

institutions that prefer to hold these loans in 

their own portfolios.244

In 2003, Freddie Mac continued to test 

initiatives through pilots, and implement 

enhancements to existing multifamily 

mortgage products which cover a broad array 

of eligible mortgage products. Freddie Mac’s 

tax-exempt bond credit enhancements with 

synthetic fixed-rate financing continued to be 

popular. Freddie Mac’s innovations to certain 

cash products including various 

combinations of fixed-rate, adjustable-rate 

and interest-only mortgages have been 

adopted by others in the industry. For 

example, the Fixed-to-Float execution 

provides borrowers with a reduced fixed 

interest rate and a one-year extension of the 

mortgage term at a floating rate. In 2003, 

borrowers used Fixed-to-Float option for $4.0 

billion in mortgages.245

In 2003, Freddie Mac purchased $6.6 

billion in mortgages to finance more than 

181,000 apartment units in 5-to 50-unit 

properties. Freddie Mac committed to invest 

$958 million to Low Income Housing Tax 

Credits (LIHTC). Altogether, the LIHTC 

investments made by Freddie Mac are 

approaching the $3.6 billion mark and have 

constructed or rehabbed more than 216,000 

rental units for very-low and low income 

families in close to 3,000 projects. In 2003, 

Freddie purchased $412 million in newly 

issued multifamily mortgage revenue bonds. 

These bonds, issued by state, county or city 

government agencies, finance the acquisition 

and rehabilitation of nonprofit borrowers or 

property owners who agree to keep rents at 

affordable levels. These multifamily bond 

purchases will finance 6,100 estimated units 

of affordable housing with an estimate that 

58 percent of those units will be affordable 

to very low income families. In 2003, Freddie 

issued a record $7.7 billion of securities 

backed by multifamily mortgages through 

negotiated transactions. More than 85 percent 

of these securities financed mortgages for 

affordable housing.246

The 2000 Rule discussed other ways in 

which the GSEs might help promote 

financing of affordable multifamily housing. 

Two of those were lending for property 

rehabilitation and leadership in establishing 

standards for affordable multifamily lending. 

Many affordable properties are old and in 

need of capital improvements if they are to 

remain in the housing stock. Rehabilitation 

lending is a specialized field, and one in 

which the GSEs for a variety of reasons have 

not been major players. Less than 1 percent 

of all GSE multifamily lending in 2002 was 

for property rehabilitation. In 2002, Fannie 

Mae hosted its first ever Preservation 

Advisory Meeting with leaders in the 

housing and real estate finance industry to 

identify best practices and formulate real 

63682 Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 

247Fannie Mae, 2002 Annual Housing Activities 

Report, 2003, p. 27.

248Abt Associates, ‘‘Study of Multifamily 

Underwriting and the GSEs’’ Role in the 

Multifamily Market,’’ Final Report to the U.S. 

Department of Housing and Urban Development, 

Office of Policy Development and Research, August 

2001.

249Federal Reserve, Survey of Professional 

Forecasters, November 2003.

250Board of Governors of the Federal Reserve 

System, Monetary Policy Report to the Congress, 

February 11, 2003, page 4.

251 Jack Goodman, ‘‘The Changing Demography of 

Multifamily Rental Housing,’’ Housing Policy 

Debate, Winter 1999.

252Remarks by Franklin D. Raines, Chairman and 

CEO, Fannie Mae, to the Executive Committee of 

the National Association of Home Builders, January 

18, 2003. See also Edward Glaeser and Joseph 

Gyourko, ‘‘The Impact of Zoning on Housing 

Affordability,’’ Working Paper 8835, National 

Bureau of Economic Research, March 2002.

253 ‘‘Capital Markets Outlook 2003,’’ Apartment 

Finance Today, Vol. 7, No. 1 (January/February 

2003).

world solutions to this critical policy 

issue.247

Setting standards for affordable 

multifamily lending was identified in the 

2000 Rule as another area where the GSEs 

could provide greater leadership. It was also 

noted, based on HUD-sponsored research 

underway at that time,248 that market 

participants believe the GSEs to be 

conservative in their approaches to affordable 

property lending and underwriting. Actions 

described in the GSEs’ annual activity reports 

for 2001, 2002 and 2003 indicate attempts by 

the GSEs to promote market standards that 

will reduce the transactions costs of 

multifamily lending while also providing 

programs that have the flexibility needed to 

deal with unique circumstances.

5. Future Prospects 

The outlook for the multifamily rental 

housing market is marked by near-term risks 

and longer-run optimism, according to most 

observers. The prospects for the next few 

quarters are dominated by the 

macroeconomy. In particular, job growth, 

with its implications for formations of 

households, will be a key for the resumption 

of growth in apartment demand. Many 

forecasters would ascribe to the Federal 

Reserve’s forecast of a slight increase in GDP 

growth to 4.3 percent in 2004 249, while also 

agreeing with the Fed’s warning that ‘‘An 

unusual degree of uncertainty attends the 

economic outlook at present, in large 

measure, but not exclusively, because of 

potential geopolitical developments.’’ 250

When consumer demand does pick up, 

recovery should be reasonably fast. While the 

recent production levels have outpaced 

demand, they have been near the middle of 

the long run historical range and very close 

to the average of the last half of the 1990s. 

Judging from the firm tone to rents and 

vacancies during that period, total 

multifamily completions production of 

275,000 to 350,000 units is a sustainable 

level of annual production—that is, the level 

consistent with long run demographic trends 

and replacement of units lost from the stock. 

Because new construction has remained 

moderate, there is no massive overhang of 

product that will need to be absorbed. With 

increased demand, vacancies should fall and 

rents firm reasonably promptly. A key 

assumption behind this forecast for vacancies 

and rents is that new apartment construction 

will not rise appreciably from its current 

level. 

Recovery in the apartment market may 

also, perversely, be promoted by the recent 

unprecedented strength of the single-family 

market. Typically, economic recoveries bring 

strong growth in single-family housing 

demand, some of that coming from apartment 

renters seeking more space. With singlefamily activity already near record highs, 

boosted by historically low mortgage 

interests rates and despite the recently soft 

economy, it is uncertain how much higher 

single-family demand—and the 

accompanying losses of apartment customers 

to homeownership—can go. 

A stronger economy will put the 

multifamily rental market back onto a longrun path that appears to promise sustained, 

moderate growth. As discussed in the 2000 

Rule, the demographic outlook is favorable 

for apartment demand. Even if the 

homeownership rate increases further and 

the total number of renter households grows 

only slowly, as described in the discussion 

of the single-family housing market earlier in 

this Rule, apartment demand can be expected 

to increase more rapidly than that for other 

rental housing, owing to the likely changes 

in age composition and reductions in average 

household size. One estimate projects the 

annual growth in apartment households to be 

one percent.251

a. The Outlook for Multifamily Housing 

Supply 

Regarding supply, one of the secrets of the 

success of the multifamily sector during the 

1990s was that production never rose above 

its long-run sustainable level. The discipline 

of developers, investors, and their lenders 

that brought that result needs to be continued 

if the apartment market is to maintain 

stability. 

Multifamily housing may benefit in the 

future from more favorable public attitudes 

and local land use regulation. Higher density 

housing is a potentially powerful tool for 

preserving open space, reducing sprawl, and 

promoting transportation alternatives to the 

automobile. The recently heightened 

attention to these issues may increase the 

acceptance of multifamily rental construction 

to both potential customers and their 

prospective neighbors. 

Provision of affordable housing will 

continue to challenge suppliers of 

multifamily rental housing and policy 

makers at all levels of governments. Low 

incomes combined with high housing costs 

define a difficult situation for millions of 

renter households. Housing cost reductions 

are constrained by high land prices and 

construction costs in many markets. 

Government action—through land use 

regulation, building codes, and occupancy 

standards—are major contributors to those 

high costs, as is widely recognized by market 

participants, including the leaders of the 

GSEs.252 Reflecting the preferences of the 

electorate, these regulated constraints are 

unlikely to change until voter attitudes 

change.

b. The Future Role of the GSEs 

Regarding the mortgage financing of 

multifamily rental apartments, it is hard to 

anticipate events that might disrupt the flow 

or alter the sources of mortgage credit to 

apartments. In the past, certain events have 

triggered such changes—notably the savings 

and loan debacle of the 1980s and Freddie 

Mac’s withdrawal from the market following 

large losses in the early 1990s—but these are, 

by definition, surprises. The current structure 

and performance of the multifamily mortgage 

market provide some comfort that the risks 

are slight. The lender base is not overly 

dependent on any one institution or lender 

type for either loan originations or funding. 

Lending discipline appears to have been 

maintained, given the low mortgage 

delinquency rates even during the weak 

economy of the past two years. The near term 

outlook of most market participants is for 

ample supply of mortgage financing at 

historically low interest rates.253 Yet 

complacency would be a mistake.

Responding to both market incentives and 

their public charters, Fannie Mae and 

Freddie Mac can be expected to build on 

their recent records of increased multifamily 

lending and continue to be leaders in 

financing volumes, in program innovations, 

and in standards setting. Certainly there is 

room for expansion of the GSEs’ share of the 

multifamily mortgage market, which, as 

mentioned earlier, is by the measure of dollar 

volume outstanding currently only about half 

the market share enjoyed by the GSEs in 

single-family lending. And from the 

perspective of units financed, the statistics 

from Table A.5 combined with data from the 

2001 American Housing Survey indicate that, 

while the GSEs financed 7.2 percent of all the 

nation’s year-round housing units that year, 

the percentage of multifamily rental units 

(that is renter-occupied units and vacant 

rental units in structures with at least five 

units) was only 5.7 percent. 

The sharp gains since 2000 in small 

property lending by Fannie Mae and Freddie 

Mac demonstrate that it is feasible for this 

important segment of the affordable housing 

market to be served by the GSEs. Building on 

the expertise and market contacts gained in 

the past three years, the GSEs should be able 

to make even greater in-roads in small 

property lending, although the challenges 

noted earlier will continue. 

The GSEs’ size and market position 

between loan originators and mortgage 

investors makes them the logical institutions 

to identify and promote needed innovations 

and to establish standards that will improve 

market efficiency. As their presence in the 

multifamily market continues to grow, the 

GSEs will have both the knowledge and the 

‘‘clout’’ to push simultaneously for market 

standardization and for programmatic 

flexibility to meet special needs and 

circumstances, with the ultimate goal of 

increasing the availability and reducing the 

Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 63683

254Performance for the 1993–95 period was 

discussed in the October 2000 rule.

255To separate out the effects of changes in 

counting rules that took effect in 2001, this section 

also compares performance in 2001 to estimated 

performance in 2000 if the 2001 counting rules had 

been in effect in that year.

cost of financing for affordable and other 

multifamily rental properties. 

E. Factor 3: Performance and Effort of the 

GSEs Toward Achieving the Low- and 

Moderate-Income Housing Goal in Previous 

Years 

This section first discusses each GSE’s 

performance under the Low- and ModerateIncome Housing Goal over the 1996–2003 

period.254 The data presented are ‘‘official 

results—i.e., they are based on HUD’s 

analysis of the loan-level data submitted to 

the Department by the GSEs and the counting 

provisions contained in HUD’s regulations in 

24 CFR part 81, subpart B. As explained 

below, in some cases these ‘‘official results’’

differ from goal performance reported by the 

GSEs in the Annual Housing Activities 

Reports (AHARs) that they submit to the 

Department.

The main finding of this section 

concerning the overall housing goals is that 

both Fannie Mae and Freddie Mac surpassed 

the Department’s Low- and Moderate-Income 

Housing Goals for each of the eight years 

during this period. Specifically: 

• The goal was set at 40 percent for 1996; 

Fannie Mae’s performance was 45.6 percent 

and Freddie Mac’s performance was 41.1 

percent. 

• The goal was set at 42 percent for 1997–

2000. Fannie Mae’s performance was 45.7 

percent in 1997, 44.1 percent in 1998, 45.9 

percent in 1999, and 49.5 percent in 2000; 

and Freddie Mac’s performance was 42.6 

percent in 1997, 42.9 percent in 1998, 46.1 

percent in 1999, and 49.9 percent in 2000. 

• In the October 2000 rule, the low- and 

moderate-income goal was set at 50 percent 

for 2001–03. As of January 1, 2001, several 

changes in counting provisions took effect for 

the low- and moderate-income goal, as 

follows: ‘‘bonus points’’ (double credit) for 

purchases of goal-qualifying mortgages on 

small (5–50 unit) multifamily properties and, 

above a threshold level, mortgages on 2–4 

unit owner-occupied properties; a 

‘‘temporary adjustment factor’’ (1.20 units 

credit, subsequently increased by Congress to 

1.35 units credit) for Freddie Mac’s 

purchases of goal-qualifying mortgages on 

large (more than 50 units) multifamily 

properties; changes in the treatment of 

missing data; a procedure for the use of 

imputed or proxy rents for determining goal 

credit for multifamily mortgages; and 

eligibility of purchases of certain qualifying 

government-backed loans to receive goal 

credit. These changes are explained below. 

Fannie Mae’s low-mod goal performance was 

51.5 percent in 2001, 51.8 percent in 2002, 

and 52.3 percent in 2003; Freddie Mac’s 

performance was 53.2 percent in 2001, 50.5 

percent in 2002, and 51.2 percent in 2003, 

thus both GSEs surpassed this higher goal in 

all three years. This section discusses the 

October 2000 counting rule changes in detail 

below, and provides data on what goal 

performance would have been in 2001–03 

without these changes.255

After the discussion of the overall housing 

goals in Sections E.1 to E.5, Sections E.6 to 

E.12 examine the role of the GSEs in funding 

home purchase loans for lower-income 

borrowers and for first-time homebuyers. A 

summary of the main findings from that 

analysis is given in Section E.6. Section E.13 

then summarizes some recent studies on the 

GSEs’ market role and section E.14 discusses 

the GSEs’ role in the financing of singlefamily rental properties. 

1. Performance on the Low- and ModerateIncome Housing Goal in 1996–2003 

HUD’s December 1995 rule specified that 

in 1996 at least 40 percent of the number of 

units financed by each of the GSEs that were 

eligible to count toward the Low- and 

Moderate-Income Goal should qualify as lowor moderate-income, and at least 42 percent 

of such units should qualify in 1997–2000. 

HUD’s October 2000 rule made various 

changes in the goal counting rules, as 

discussed below, and increased the Low- and 

Moderate-Income Goal to 50 percent for 

2001–03. 

Table A.8 shows low-mod goal 

performance over the 1996–2003 period, 

based on HUD’s analysis. The table shows 

that Fannie Mae surpassed the goals by 5.6 

percentage points and 3.7 percentage points 

in 1996 and 1997, respectively, while 

Freddie Mac surpassed the goals by narrower 

margins, 1.1 and 0.6 percentage points. 

During the heavy refinance year of 1998, 

Fannie Mae’s performance fell by 1.6 

percentage points, while Freddie Mac’s 

performance rose slightly, by 0.3 percentage 

point. Freddie Mac showed a gain in 

performance to 46.1 percent in 1999, 

exceeding its previous high by 3.2 percentage 

points. Fannie Mae’s performance in 1999 

was 45.9 percent, which, for the first time, 

slightly lagged Freddie Mac’s performance in 

that year. 

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256See Congressional Record, December 15, 2000, 

pp. H12295–96.

Both GSEs exhibited sharp gains in goal 

performance in 2000—Fannie Mae’s 

performance increased by 3.6 percentage 

points, to a record level of 49.5 percent, 

while Freddie Mac’s performance increased 

even more, by 3.8 percent percentage points, 

which also led to a record level of 49.9 

percent. Fannie Mae’s performance was 51.5 

percent in 2001, 51.8 percent in 2002, and 

52.3 percent in 2003; Freddie Mac’s 

performance was 53.2 percent in 2001, 50.5 

percent in 2002, and 51.2 percent in 2003. 

However, as discussed below, using 

consistent accounting rules for 2000–03, each 

GSE’s performance in 2001–03 was below its 

performance in 2000. 

The official figures for low-mod goal 

performance presented above differ from the 

corresponding figures presented by Fannie 

Mae and Freddie Mac in their Annual 

Housing Activity Reports to HUD by 0.2–0.3 

percentage point in both 1996 and 1997, 

reflecting minor differences in the 

application of counting rules. These 

differences also persisted for Freddie Mac for 

1998–2000, but the goal percentages shown 

above for Fannie Mae for these three years 

are the same as the results reported by Fannie 

Mae to the Department. Fannie Mae reported 

its performance in 2001 as 51.6 percent and 

Freddie Mac reported its performance as 53.6 

percent—both were slightly above the 

corresponding official figures of 51.5 percent 

and 53.4 percent, respectively. For 2002, 

Fannie Mae’s reported performance was the 

same as reported by HUD (51.8 percent), 

while Freddie Mac’s reported performance 

was 51.3 percent, slightly above HUD’s 

official figure of 50.5 percent. For 2003, 

Fannie Mae’s reported performance on this 

goal was 51.8 percent, somewhat below 

HUD’s official figure of 52.3 percent, while 

Freddie Mac’s reported performance (51.1 

percent) was essentially the same as HUD’s 

official figure of 51.2 percent. 

Fannie Mae’s performance on the Low- and 

Moderate-Income Goal was in the range 

between 44 percent and 46 percent between 

1996 and 1999, but jumped sharply in just 

one year, from 45.9 percent in 1999 to 49.5 

percent in 2000. Freddie Mac’s performance 

was in the range between 41 percent and 43 

percent between 1996 and 1998, and then 

rose to 46.1 percent in 1999 and 49.9 percent 

in 2000. As discussed above, official 

performance rose for both GSEs in 2001–02, 

but this was due to one-time changes in the 

counting rules—abstracting from counting 

rule changes, performance fell for both GSEs. 

Fannie Mae’s performance on the Low- and 

Moderate-Income Goal surpassed Freddie 

Mac’s in every year through 1998. This 

pattern was reversed in 1999, as Freddie Mac 

surpassed Fannie Mae in goal performance 

for the first time, though by only 0.2 

percentage point. This improved relative 

performance of Freddie Mac was due to its 

increased purchases of multifamily loans, as 

it re-entered that market, and to increases in 

the goal-qualifying shares of its single-family 

mortgage purchases. Freddie Mac’s 

performance also slightly exceeded Fannie 

Mae’s performance in 2000, 49.9 percent to 

49.5 percent. Freddie Mac’s official 

performance also exceeded Fannie Mae’s 

official performance in 2001, but this 

reflected a difference in the counting rules 

applicable to the two GSEs that was enacted 

by Congress; if the same counting rules were 

applied to both GSEs (that is, Freddie Mac 

did not receive the 1.35 Temporary 

Adjustment Factor), Fannie Mae’s 

performance would have exceeded Freddie 

Mac’s performance, by 51.5 percent to 50.5 

percent. 

In 2002, Freddie Mac’s performance on the 

low mod-goal (50.5 percent) fell short of 

Fannie Mae’s performance (51.8 percent), 

even though Freddie Mac had the advantage 

of the Temporary Adjustment Factor. The gap 

would have been wider without this factor, 

and in fact Freddie Mac’s performance would 

have been short of the goal, at 49.2 percent. 

This same pattern prevailed in 2003, when 

Freddie Mac’s performance on this goal (51.2 

percent) was significantly below Fannie 

Mae’s performance (52.3 percent), even 

though Fannie Mae did not have the 

advantage of the Temporary Adjustment 

Factor. The gap in performance between the 

GSEs would have been much wider without 

this factor, as Freddie Mac’s performance 

would again have fallen short of the goal, at 

48.4 percent. 

2. Changes in the Goal Counting Rules for 

2001–03

A number of changes in the counting rules 

underlying the calculation of low- and 

moderate-income goal performance took 

effect beginning in 2001, as follows: 

• Bonus points for multifamily and singlefamily rental properties. During the 2001–03 

period the Department awarded ‘‘bonus 

points’’ (double credit in the numerator) for 

goal-qualifying units in small (5–50 unit) 

multifamily properties and, above a 

threshold, 2–4 unit owner-occupied 

properties whose loans were purchased by 

the GSEs. By letters dated December 24, 

2003, the Department notified the GSEs that 

these bonus points would not be in effect 

after December 31, 2003. 

• Freddie Mac’s Temporary Adjustment 

Factor. As part of the Consolidated 

Appropriations Act of 2000, Congress 

required the Department to award 1.35 units 

of credit for each unit financed in ‘‘large’’

multifamily properties (i.e., those with 51 or 

more units) in the numerator in calculating 

performance on the housing goals for Freddie 

Mac for 2001–03.256 This ‘‘temporary 

adjustment factor’’ (TAF) did not apply to 

goal performance for Fannie Mae during this 

period. By letters dated December 24, 2003, 

the Department notified Freddie Mac that 

this factor would not be in effect after 

December 31, 2003.

• Missing data for single-family properties.

In the past, if a GSE lacked data on rent for 

rental units or on borrower income for 

owner-occupied units in single-family 

properties whose mortgages it purchased, 

such units were included in the 

denominator, but not in the numerator, in 

calculating goal performance. Since some of 

these units likely would have qualified for 

one or more of the housing goals, this rule 

lowered goal performance. Under the new 

counting rules for the low- and moderateincome goal and the special affordable goal 

that took effect in 2001, the GSEs are allowed 

to exclude loans with missing borrower 

income from the denominator if the property 

is located in a below-median income census 

tract. This exclusion is subject to a ceiling of 

1 percent of total owner-occupied units 

financed. The enterprises are also allowed to 

exclude single-family rental units with 

missing rental information from the 

denominator in calculating performance for 

these two goals; there is no ceiling or 

restriction to properties located in belowmedian income census tracts for this 

exclusion of single-family rental units. No 

single-family loans can be excluded from the 

denominator in calculating performance on 

the underserved areas goal—that is, if a GSE 

does not have sufficient information to 

determine whether or not a property is 

located in an underserved area, all units in 

such a property are included in the 

denominator, but not in the numerator, in 

calculating performance on this goal.

• Missing data and proxy rents for 

multifamily properties. In the past, if a GSE 

lacked data on rent for rental units in 

multifamily properties whose mortgages it 

purchased, such units were included in the 

denominator, but not in the numerator, in 

calculating goal performance. Since some of 

these units likely would have qualified for 

one or more of the housing goals, this rule 

lowered goal performance. Under the new 

counting rules that took effect in 2001, if rent 

is missing for multifamily units, a GSE may 

estimate ‘‘proxy rents,’’ and, up to a ceiling 

of 5 percent of total multifamily units 

financed, may apply these proxy rents in 

determining whether such units qualify for 

the low- and moderate income goal and 

special affordable goal. If such proxy rents 

cannot be estimated, these multifamily units 

are excluded from the denominator in 

calculating performance under these goals. 

No multifamily loans can be excluded from 

the denominator in calculating performance 

on the underserved areas goal—that is, if a 

GSE does not have sufficient information to 

determine whether or not a property is 

located in an underserved area, all units in 

such a property are included in the 

denominator, but not in the numerator, in 

calculating performance on this goal. 

• Purchases of certain government-backed 

loans. Prior to 2001, purchases of 

government-backed loans were not taken into 

account in determining performance on the 

GSEs’ low- and moderate-income and 

underserved area housing goals. That is, all 

such loans were excluded from both the 

numerator and the denominator in 

calculating goal performance on these two 

goals, and in accordance with Section 

1333(b)(1)(A) of the Federal Housing 

Enterprises Financial Safety and Soundness 

Act of 1992, purchases of only certain 

government-backed loans were included in 

determining performance on the GSEs’

special affordable goals. In October 2000 the 

Department took steps to encourage the 

enterprises to play more of a role in the 

secondary market for several types of 

government-backed loans where it appeared 

that greater GSE involvement could increase 

the liquidity of such mortgages. Home equity 

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257Prior to the October 2000 rule, purchases of 

these government-backed mortgages were only 

eligible for credit under the special affordable goal.

conversion mortgages (HECMs) were 

developed in the late-1980s by the Federal 

Housing Administration (FHA); these 

mortgages allow senior citizens to draw on 

the equity in their homes to obtain monthly 

payments to supplement their incomes. Thus 

purchases of FHA-insured HECMs now count 

toward the low- and moderate-income 

housing goals if the mortgagor’s income is 

less than median income for the area. 

Similarly, purchases of mortgages on 

properties on tribal lands insured under 

FHA’s Section 248 program or HUD’s Section 

184 program may qualify for the GSEs’

housing goals. And purchases of mortgages 

under the Rural Housing Service’s Single 

Family Housing Guaranteed Loan Program 

may also count toward all of the housing 

goals.257

3. Effects of Changes in the Counting Rules 

on Goal Performance in 2001–03

Because of the changes in the low- and 

moderate-income goal counting rules that 

took effect in 2001, direct comparisons 

between official goal performance in 2000 

and 2001–03 are somewhat of an ‘‘apples-tooranges comparison.’’ For this reason, the 

Department has calculated what performance 

would have been in 2000 under the 2001–03 

rules; this may be compared with official 

performance in 2001–03—an ‘‘apples-toapples comparison.’’ HUD has also calculated 

what performance would have been in 2001–

03 under the 1996–2000 rules; this may be 

compared with official performance in 

2000—an ‘‘oranges-to-oranges comparison.’’

These comparisons are presented in Table 

A.9. 

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258Exclusion of loans with missing information 

had a greater impact on Fannie Mae’s goal 

performance than on Freddie Mac’s goal 

performance.

259Federal Register, October 31, 2000, Footnote 

145, p. 65141.

Specifically, Table A.9 shows performance 

under the low- and moderate-income goal in 

three ways. Baseline A represents 

performance under the counting rules in 

effect in 1996–2000. Baseline B incorporates 

the technical changes in counting rules—

changes in the treatment of missing data 

(including use of proxy rents), and eligibility 

for the goals of certain government-backed 

loans. Baseline C incorporates in addition to 

the technical changes the bonus points and, 

for Freddie Mac, the temporary adjustment 

factor. Baseline B corresponds to the 

counting approach proposed in this rule to 

take effect in 2005. Boldface figures under 

Baseline A for 1999–2000 and under Baseline 

C for 2001–03 indicate official goal 

performance, based on the counting rules in 

effect in those years—e.g., for Fannie Mae, 

45.9 percent in 1999, 49.5 percent in 2000, 

51.5 percent in 2001, 51.8 percent in 2002, 

and 52.3 percent in 2003. 

• Performance on the Low- and ModerateIncome Goal under 1996–2000 Counting 

Rules Plus Technical Changes. If the 

‘‘Baseline B’’ counting approach had been in 

effect in 2000–03 and the GSEs’ had 

purchased the same mortgages that they 

actually did purchase in those years, both 

Fannie Mae and Freddie Mac would have 

surpassed the low- and moderate-income 

goal in 2000 and fallen short in 2001, 2002, 

and 2003. Specifically, Fannie Mae’s 

performance would have been 51.3 percent 

in 2000, 49.2 percent in 2001, 49.0 percent 

in 2002, and 48.7 percent in 2003. Freddie 

Mac’s performance would have been 50.6 

percent in 2000, 47.7 percent in 2001, 46.1 

percent in 2002, and 45.0 percent in 2003. 

• Performance on the Low- and ModerateIncome Goal under 2001–2003 Counting 

Rules. If the 2001–03 counting rules had also 

been in effect in 2000 and the GSEs’ had 

purchased the same mortgages that they 

actually did purchase in those years (i.e., 

abstracting from any behavioral effects of 

‘‘bonus points,’’ for example), both GSEs 

would have substantially surpassed the lowand moderate-income goal in all four years, 

but both GSEs’ performance figures would 

have deteriorated somewhat from 2000 to 

2001, and, for Freddie Mac, from 2001 to 

2002 and 2003. Specifically, Fannie Mae’s 

‘‘Baseline C’’ performance would have been 

52.5 percent in 2000, 51.5 percent in 2001, 

51.8 percent in 2002, and 52.3 percent in 

2003. Freddie Mac’s performance would have 

been 55.1 percent in 2000, surpassing its 

official performance level of 53.2 percent in 

2001, 50.5 percent in 2002, and 51.2 percent 

in 2003. Measured on this consistent basis, 

then, Fannie Mae’s performance fell by 1.0 

percentage point in 2001, and Freddie Mac’s 

by 1.9 percentage points in 2001 and an 

additional 2.0 percentage points in 2002–03. 

These reductions were primarily due to 

2001–03 being years of heavy refinance 

activity. 

Details of Effects of Changes in Counting 

Rules on Goal Performance in 2001–03. As 

discussed above, counting rule changes that 

took effect in 2001 had significant positive 

impacts on the performance of both GSEs on 

the low- and moderate-income goal in that 

year—3.8 percentage points for Fannie Mae, 

and 6.0 percentage points for Freddie Mac. 

This section breaks down the effects of these 

changes on goal performance for both GSEs; 

results are shown in Table A.9. 

• Freddie Mac. The largest impact of the 

counting rule changes on Freddie Mac’s goal 

performance was due to the application of 

the temporary adjustment factor for 

purchases of mortgages on large multifamily 

properties, as enacted by Congress; this 

added 2.7 percentage points to goal 

performance in 2001, as shown in Table A.9. 

Bonus points for purchases of mortgages on 

small multifamily properties added 1.5 

percentage points to performance, and bonus 

points for purchase of mortgages on owneroccupied 2–4 unit rental properties added 1.4 

percentage points to performance. The 

remaining impact (0.5 percentage point) was 

due to technical changes in counting rules—

primarily, the exclusion of single-family 

units with missing information from the 

denominator in calculating goal performance. 

Credit for purchases of qualifying 

government-backed loans played a minor role 

in determining Freddie Mac’s goal 

performance. These same patterns also 

appeared in 2002. But in 2003, bonus points 

for purchases of low-mod mortgages on 

single-family rental properties had a larger 

impact on Freddie Mae’s low-mod goal 

performance than Freddie Mac’s temporary 

adjustment factor. 

• Fannie Mae. The temporary adjustment 

factor applies to Freddie Mac’s goal 

performance, but not to Fannie Mae’s 

performance, thus counting rule changes had 

less impact on its performance than on 

Freddie Mac’s performance in 2001. The 

largest impact of the counting rule changes 

on Fannie Mae’s goal performance was due 

to the application of bonus points for 

purchases of mortgages on owner-occupied 

2–4 unit rental properties, which added 1.6 

percentage points to performance, and for 

purchases of mortgages on small multifamily 

properties, which added 0.7 percentage point 

to performance. The remaining impact (1.3 

percentage points) was due to technical 

changes—primarily, the exclusion of singlefamily units with missing information from 

the denominator in calculating goal 

performance.258 Credit for purchases of 

qualifying government-backed loans and the 

use of proxy rent for multifamily properties 

played a minor role in determining Fannie 

Mae’s goal performance. These same patterns 

also appeared in 2002 for Fannie Mae, but for 

2003 bonus points for purchases of low-mod 

mortgages on small multifamily properties 

had more impact on performance than bonus 

points for single-family rental properties.

4. Bonus Points for the Low- and ModerateIncome Goal 

As discussed above, the Department 

established ‘‘bonus points’’ to encourage the 

GSEs to step up their activity in 2001–03 in 

two segments of the mortgage market—the 

small (5–50 unit) multifamily mortgage 

market, and the market for mortgages on 2–

4 unit properties where 1 unit is owneroccupied and 1–3 units are occupied by 

renters. Bonus points did not apply to 

purchases of mortgages for owner-occupied 

1-unit properties, for investor-owned 1–4 

unit properties, and for large (more than 50 

units) multifamily properties, although as 

also discussed above, a ‘‘temporary 

adjustment factor’’ applied to Freddie Mac’s 

purchases of qualifying mortgages on large 

multifamily properties. 

Bonus points for small multifamily 

properties. Each unit financed in a small 

multifamily property that qualified for any of 

the housing goals was counted as two units 

in the numerator (and one unit in the 

denominator) in calculating goal performance 

for that goal. For example, if a GSE financed 

a mortgage on a 40-unit property in which 10 

of the units qualified for the low- and 

moderate-income goal, 20 units would be 

entered in the numerator and 40 units in the 

denominator for this property in calculating 

goal performance. 

Small multifamily bonus points thus 

encouraged the GSEs to play a larger role in 

this market, and also to purchase mortgages 

on such properties in which large shares of 

the units qualified for the housing goals. 

Some evidence may be gleaned from the data 

provided to HUD by the GSEs for 2001–03. 

Fannie Mae financed 37,403 units in small 

multifamily properties in 2001 that were 

eligible for the low- and moderate-income 

goal, 58,277 such units in 2002, and 214,619 

such units in 2003, as compared with only 

7,196 such units financed in 2000. Small 

multifamily properties also accounted for a 

greater share of Fannie Mae’s multifamily 

business in 2001–03—7.4 percent of total 

multifamily units financed in 2001, 13.2 

percent in 2002, and 28.6 percent in 2003, up 

from 2.5 percent in 2000. However, HUD’s 

2000 rule reported information from the 1991 

Residential Finance Survey that small 

multifamily properties accounted for 37 

percent of all multifamily units, thus Fannie 

Mae was still less active in this market than 

in the market for large multifamily 

properties.259

Within the small multifamily market, there 

was no evidence that Fannie Mae targeted 

affordable properties to a greater extent in 

2001–03 than in 2000. That is, 87 percent of 

Fannie Mae’s small multifamily units 

qualified for the low- and moderate-income 

goal in 2000; this fell to 75 percent in 2001, 

rose to 89 percent in 2002, and then declined 

to 82 percent in 2003.

Freddie Mac financed 50,299 units in small 

multifamily properties in 2001 that were 

eligible for the low- and moderate-income 

goal, 22,255 such units in 2002, and 177,561 

such units in 2003, as compared with only 

such units financed in 2000. Small 

multifamily properties also accounted for a 

significantly greater share of Freddie Mac’s 

multifamily business in 2001–2003—16.1 

percent of total multifamily units financed in 

2001, 7.5 percent in 2002, and 25.4 percent 

in 2003, up from 1.8 percent in 2000. 

Within the small multifamily market, there 

was some evidence that Freddie Mac targeted 

affordable properties to a greater extent in 

2001–2002 than in 2000. That is, 87 percent 

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260 ‘‘Fannie Courting Multifamily Sellers; Small 

Banks Balking,’’ American Banker, January 13, 

2003, p. 1.

261 In New England, MSAs were defined through 

mid-2003 in terms of Towns rather than Counties, 

and the portion of a New England county outside 

of any MSA was regarded as equivalent to a county 

in establishing the metropolitan or nonmetropolitan location of a property. The MSA 

definitions established by the Office of Management 

and Budget (OMB) in June, 2003 defined MSAs in 

New England in terms of counties.

262The procedure is explained in detail in annual 

releases entitled ‘‘HUD Methodology for Estimating 

FY [year] Median Family Incomes’’ for years 1993 

through 2002, issued by the Economic and Market 

Analysis Division, Office of Economic Affairs, 

PD&R, U.S. Department of Housing and Urban 

Development.

263The procedure applicable to the decennial 

census data used to generate estimated rents is 

explained in connection with data used to define 

Underserved Areas in Appendix B.

264Transition from the 2002 methodology to the 

2005 methodology is occurring in stages in 2003 

and 2004. To generate the area median income 

estimates used to score GSE loans in 2003, data 

from the 2000 census on 1999 area median incomes 

were adjusted to 2001 using Bureau of Labor 

Statistics survey data on rates of change in average 

incomes for MSAs and counties between 1999 and 

2000, data on rates of change in median incomes 

for the United States and individual States between 

1999 and 2001 from Census Bureau’s Current 

Population Survey and American Communities 

Survey, and an assumed 3.5 percent per year 

inflation factor between 2001 and 2003. (See ‘‘HUD 

Methodology for Estimating FY 2003 Median 

Family Incomes,’’ issued by the Economic and 

Market Analysis Division, op cit.) A similar 

procedure has been used to generate area median 

income estimates for scoring GSE loans in 2004.

of Freddie Mac’s small multifamily units 

qualified for the low- and moderate-income 

goal in 2000; this rose to 96 percent in 2001, 

but declined back to 87 percent in 2002 and 

2003. 

In summary, then, there is strong evidence 

that bonus points for small multifamily 

properties had an impact on Fannie Mae’s 

role in this market in 2001–2003 and an even 

larger impact on Freddie Mac’s role in this 

market. In addition, Fannie Mae has 

announced a program to increase its role in 

this market further in future years.260

Bonus points for single-family rental 

properties. Above a threshold, each unit 

financed in a 2–4 unit property with at least 

one owner-occupied unit (referred to as 

‘‘OO24s’’ below) that qualified for any of the 

housing goals was counted as two units in 

the numerator (and one unit in the 

denominator) in calculating goal performance 

for that goal in 2001–2003. The threshold 

was equal to 60 percent of the average 

number of such qualifying units over the 

previous five years. For example, Fannie Mae 

financed an average of 50,030 low- and 

moderate-income units in these types of 

properties between 1996 and 2000, and 

101,423 such units in 2001. Thus Fannie Mae 

received 71,405 bonus points in this area in 

2001—that is, 101,423 minus 60 percent of 

50,030. So 172,828 units were entered in the 

numerator for these properties in calculating 

low- and moderate-income goal performance. 

Single-family rental bonus points thus 

encouraged the GSEs to play a larger role in 

this market, and also to purchase mortgages 

on such properties in which large shares of 

the units qualified for the housing goals. As 

for small multifamily bonus points, again 

some evidence may be gleaned from the data 

provided to HUD by the GSEs for 2001–03. 

Fannie Mae financed 175,103 units in 

OO24s in 2001 that were eligible for the lowand moderate-income goal, 229,632 such 

units in 2002, and 355,994 such units in 

2003, well above the 77,930 units financed in 

2000. However, with the refinance boom, 

Fannie Mae’s total single-family business 

increased at approximately the same rate as 

its OO24 business in 2001–03, thus the share 

of its business accounted for by OO24s was 

the same in 2001–03 as in 2000—4 percent. 

Within the OO24 market, there was no 

evidence that Fannie Mae targeted affordable 

properties to a greater extent in 2001–03 than 

in 2000. That is, approximately 55–60 

percent of Fannie Mae’s OO24 units qualified 

for the low- and moderate-income goal in 

each of these three years. 

Freddie Mac financed 96,050 units in 

OO24s in 2001 that were eligible for the lowand moderate-income goal, 146,222 such 

units in 2002, and 154,535 such units in 

2003, as compared with the 49,993 units 

financed in 2000. However, Freddie Mac’s 

total single-family business increased at 

approximately the same rate as its OO24 

business in 2001–02, thus the share of its 

business accounted for by OO24s was the 

same in 2002 as in 2000—4 percent. And its 

total single-family business increased at a 

faster rate than its OO24 business in 2003, 

thus the share of its business accounted for 

by OO24s declined to 3 percent last year. 

As for Fannie Mae, within the OO24 

market there was no evidence that Freddie 

Mac targeted affordable properties to a 

greater extent in 2001–03 than in 2000. That 

is, 68–69 percent of Fannie Mae’s OO24 units 

qualified for the low- and moderate-income 

goal in each year from 2000 through 2002; 

this decreased to 64 percent in 2003. 

5. Effects of 2000 Census on Scoring of Loans 

Toward the Low- and Moderate-Income 

Housing Goal 

Background. Scoring of housing units 

under the Low- and Moderate-Income 

Housing Goal is based on data for mortgagors’

incomes for owner-occupied units, rents for 

rental units, and area median incomes, as 

follows: 

For single-family owner-occupied units: 

The mortgagors’ income at the time of 

mortgage origination. 

The median income of an area specified as 

follows: (i) For properties located in 

Metropolitan Statistical Areas (MSAs), the 

area is the MSA; and (ii) for properties 

located outside of MSAs, the area is the 

county or the non-metropolitan portion of the 

State in which the property is located, 

whichever has the larger median income, as 

of the year of mortgage origination (which 

may be for the current year or a prior year). 

For rental units in single-family properties 

with rent data are available (assuming no 

income data available for actual or 

prospective tenants): 

The unit rent (or average rent for units of 

the same type) at the time of mortgage 

origination. 

The area median income as specified for 

single-family owner-occupied units. 

For rental units in multifamily properties 

where rent data are available: 

The unit rent (or the average rent for units 

of the same type) at the time of mortgage 

acquisition by the GSE. 

The area median income as specified for 

single-family owner-occupied units, but as of 

the year the GSE acquired the mortgage. 

For rental units in multifamily properties 

where rent data are not available, the GSE 

may apply HUD-estimated rents which are 

based on the following area data: 

The median rent in the census tract where 

the property is located, as of the most recent 

decennial census. 

The area median income as specified for 

single-family owner-occupied units, but as of 

the most recent decennial census. 

Thus, scoring loans under the Low- and 

Moderate-Income Goal requires a data series 

showing annual median incomes for MSAs, 

non-metropolitan counties, and the nonmetropolitan portions of states; and 

decennial census data on median incomes for 

census tracts.261

For scoring loans purchased by the GSEs 

year-by-year from 1993 through 2002, area 

median income estimates produced by HUD’s 

Economic and Market Analysis Division were 

used. An example will illustrate the 

estimation procedure. To generate the area 

median income estimates that were used to 

score GSE loans in 2002, data from the 1990 

census on 1989 area median incomes were 

adjusted to 2002 using Bureau of Labor 

Statistics survey data on rates of cha nge in 

average incomes for MSAs and counties 

between 1989 and 1999, data from the 

Census Bureau’s Current Population Survey 

on rates of change in median family incomes 

for the nine Census Divisions between 1989 

and 2000, and an assumed 4.0 percent per 

year inflation factor between 2000 and 

2002.262 263

2005 Procedure. Relative to the above 

procedure, scoring of loans purchased by the 

GSEs in and after 2005 will be affected by 

two factors. First, the Economic and Market 

Analysis Division has begun to incorporate 

data from the 2000 census into its procedure 

for estimating annual area median incomes 

and American Community Survey data are 

becoming available at increasingly finer 

levels of geographical detail for use in annual 

updating. Beginning in 2005 Bureau of Labor 

Statistics data on rates of inflation in average 

wages will not be used. For 2005, the 

procedure for estimating area median 

incomes will be to adjust 2000 census data 

on 1999 area median incomes to 2003 using 

data from the Census Bureau’s American 

Community Survey (ACS) on rates of change 

in average incomes for States between 1999 

and 2003, with a further adjustment to 2005 

based on an appropriate annual inflation 

factor.264 Increasingly more detailed ACS 

data will be available and will be used in 

subsequent years, as ACS estimates for 

metropolitan and micropolitan areas and 

counties become available.

The second factor is the Office of 

Management and Budget’s June, 2003, reVerDate jul<14>2003 18:20 Nov 01, 2004 Jkt 205001 PO 00000 Frm 00111 Fmt 4701 Sfmt 4700 C:\02NOR2.SGM 02NOR2
63690 Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 

265HUD has deferred application of the 2003 

MSA specification to 2005, pending completion of 

the present rulemaking process.

specification of MSA boundaries based on 

analysis of 2000 census data.265

Analysis. For purposes of specifying the 

level of the Low- and Moderate-Income 

Housing Goal, HUD developed a 

methodology for scoring loans purchased by 

the GSEs in past years through 2002 as 

though the re-benchmarking of area median 

income estimates to the 2000 census and the 

2003 re-designation of MSAs had been in 

effect and HUD had been using an ACS-based 

estimation procedure at the time the 

estimates for these years were prepared. For 

this purpose, HUD created a series of annual 

estimates of median incomes for MSAs, nonmetropolitan counties, and the nonmetropolitan portions of states. For 2000, the 

estimates were 1999 census medians trended 

by three-fourths of the 4.0 percent annual 

trending factor (to adjust the figures from 

mid-1999 to April 1, 2000). For 2001, the 

estimates were based on one-and-threefourths years of trending, since no data 

would have been available to use for 

updating. The 2002 estimates would have 

used one year of data and 1.75 years of 

trending. The 2003 estimates would have 

used two years of data plus 1.75 years of 

trending. Area median incomes from 1989 to 

1999 were estimated based on trend-lines 

between 1989 and 1999 census data. The 

2003 OMB MSA designations were applied. 

The resulting estimates of area median 

incomes for MSAs, non-metropolitan 

counties, and the non-metropolitan parts of 

States, were used to re-score loans purchased 

by the GSEs between 1999 and 2002, and 

were used further in estimating the share of 

loans originated in metropolitan areas that 

would be eligible to score toward the Lowand Moderate-Income Housing Goal, from 

HMDA data. The results of the retrospective 

GSE analysis are provided in Table A.10. The 

results of the GSE-HMDA comparative 

analysis are presented in the next section. 

Table A.10 shows three sets of estimates 

for each GSE, based respectively on the 

counting rules in place in 2001–2002 (but 

disregarding the bonus points and Temporary 

Adjustment Factor), on the addition of 2000 

census re-benchmarking, and finally on the 

addition of both 2000 census rebenchmarking and 2003 MSA specification. 

Re-benchmarking occurred to adjust for some 

differences between Census 1990 and Census 

2000 tracts. 

BILLING CODE 4210–27–P

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BILLING CODE 4210–27–C


ER02NO04.019</GPH>
63692 Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 

266The ‘‘affordable lending performance’’ of 

Fannie Mae and Freddie Mac refers to the 

performance of the GSEs in funding loans for lowincome and underserved borrowers through their 

purchase (or guarantee) of loans originated by 

primary lenders. It does not, of course, imply that 

the GSEs themselves are lenders originating loans 

in the primary market.

267Throughout this analysis, the terms ‘‘home 

loan’’ and ‘‘home mortgage’’ will refer to a ‘‘home 

purchase loan,’’ as opposed to a ‘‘refinance loan.’’

As noted earlier, the mortgage data reported in this 

paper are for metropolitan areas, unless stated 

otherwise. Restricting the GSE data to metropolitan 

areas is necessary to make it comparable with the 

HMDA-reported conventional primary market data, 

which is more reliable for metropolitan areas. The 

analysis of first-time homebuyers in Sections E.9 

and E.12 cover both metropolitan and nonmetropolitan areas.

6. GSEs Compared With the Primary 

Conventional Conforming Mortgage Market 

This section and the next five sections 

(Sections E.7 to E.12) provide a detailed 

analysis of the extent to which the GSEs’ loan 

purchases mirror or depart from the patterns 

found in the primary mortgage market. As in 

Section C.5, the GSEs’ affordable lending 

performance is also compared with the 

performance of depository lenders such as 

commercial banks and thrift institutions. 

Dimensions of lending considered include 

the three ‘‘goals-qualifying’’ categories—

special affordable borrowers, less-thanmedian income borrowers, and underserved 

areas. The special affordable category 

consists mainly of very-low-income 

borrowers, or borrowers who have an annual 

income less than 60 percent of area median 

income. Because this category is more 

targeted than the broadly-defined less-thanmedian-income (or low-mod) category, the 

discussion below will often focus on the 

special affordable category as well as the 

underserved areas category which adds a 

neighborhood dimension (low-income and 

high-minority census tracts) to the analysis. 

This section will also compare the 

performance of Fannie Mae and Freddie Mac 

in funding first-time homebuyers with that of 

primary lenders in the conventional 

conforming market. 

The remainder of this introductory section 

E.6 provides a list of the major and specific 

findings which are presented in detail in the 

following Sections E.7 through 12. Sections 

7 and 8 define the primary mortgage market 

and discuss some technical issues related to 

the use of the GSE and HMDA data. Sections 

8 and 9 compare the GSEs’ performance with 

market performance for home purchase and 

first-time homebuyer loans, while Section 10 

does the same for total single family loans 

(that is, refinance loans and home purchase 

loans). Section 11 examines GSE purchases 

in individual metropolitan areas. Following 

these analyses, Section 12 examines the 

overall market share of the GSEs in important 

submarkets such as first-time homebuyers. 

a. Main Findings on GSEs’ Performance in 

the Single-family Market 

There are six main findings from this 

analysis concerning the GSEs’ purchases of 

single-family-owner mortgages: 

1. While Freddie Mac has improved its 

affordable lending performance in recent 

years, it has consistently lagged the 

conventional conforming market in funding 

affordable home purchase loans for special 

affordable and low-moderate-income 

borrowers and underserved neighborhoods 

targeted by the housing goals.266 In 2003, its 

performance on the underserved areas goal 

was particularly low relative to both the 

performances of Fannie Mae and the market; 

in that year, underserved area loans 

accounted for only 24.0 percent of Freddie 

Mac’s purchases compared with 26.8 percent 

of Fannie Mae’s purchases and 27.6 percent 

of market originations.

2. In general, Fannie Mae’s affordable 

lending performance has been better than 

Freddie Mac’s. But like Freddie Mac, Fannie 

Mae’s average performance during past 

periods (e.g., 1993–2003, 1996–2003, 1999–

2003) has been below market levels. 

However, it is encouraging that Fannie Mae 

markedly improved its affordable lending 

performance relative to the market during 

2001, 2002, and 2003, the first three years 

under the higher housing goal targets that 

HUD established in the GSE Final Rule dated 

October 2000. 

Over this three-year period, Fannie Mae 

led the primary market in funding special 

affordable and low-mod loans but lagged the 

market in funding underserved areas loans. 

In 2003, Fannie Mae’s increased performance 

placed it significantly above the special 

affordable market (a 17.1 percent share for 

Fannie Mae compared with a 15.9 percent 

share for the market) and the low-mod 

market (a 47.0 percent share for Fannie Mae 

compared with a 44.6 percent share for the 

market). However, Fannie Mae continued to 

lag the underserved areas market in 2003 (a 

26.8 percent share for Fannie Mae compared 

with a 27.6 percent share for the market). In 

this case, which is referred to in the text as 

the ‘‘purchase year’’ approach, Fannie Mae’s 

performance is based on comparing its 

purchases of all loans (both seasoned loans 

and newly-originated mortgages) during a 

particular year with loans originated in the 

market in that year. When Fannie Mae’s 

performance is measured on an ‘‘origination 

year’’ basis (that is, allocating Fannie Mae’s 

purchases in a particular year to the year that 

the purchased loan was originated), Fannie 

Mae also led the 2003 market in funding 

special affordable and low- and moderateincome loans, and lagged the market in 

funding underserved area loans. 

3. Both Fannie Mae and Freddie lag the 

conventional conforming market in funding 

first-time homebuyers, and by a rather wide 

margin. Between 1999 and 2001, first-time 

homebuyers accounted for 27 percent of each 

GSE’s purchases of home loans, compared 

with 38 percent for home loans originated in 

the conventional conforming market. 

4. The GSEs have accounted for a 

significant share of the total (government as 

well as conventional) market for home 

purchase loans, but their market share for 

each of the affordable lending categories (e.g., 

low-income borrowers and census tracts) has 

been less than their share of the overall 

market. 

5. The GSEs also account for a very small 

share of the market for important groups such 

as minority first-time homebuyers. 

Considering the total mortgage market (both 

government and conventional loans), it is 

estimated that the GSEs purchased only 14 

percent of loans originated between 1999 and 

2001 for African-American and Hispanic 

first-time homebuyers, or less than half of 

their share (42 percent) of all home purchase 

loans originated during that period. 

Considering the conventional conforming 

market and the same time period, it is 

estimated that the GSEs purchased only 31 

percent of loans originated for AfricanAmerican and Hispanic first-time 

homebuyers, or about one-half of their share 

(57 percent) of all home purchase loans in 

that market. 

6. The GSEs’ small share of the first-time 

homebuyer market could be due to the 

preponderance of high (over 20 percent) 

downpayment loans in their mortgage 

purchases. 

b. Specific Findings on GSE Performance in 

the Single-family Market 

This section presents 17 specific findings 

from the analyses reported in Sections E.7 

through 12; they are grouped under the 

following five topic-headings: 

(b.1) Longer-term Performance of the GSEs; 

(b.2) Performance of the GSEs During 

Recent Years;

(b.3) The GSEs’ Funding First-time 

Homebuyer Loans; 

(b.4) Performance of the GSEs Based on 

Total (Home Purchase and Refinance) Loans; 

(b.5) GSE Market Shares; and, 

(b.6) Additional Findings. 

(b.1) Longer-Term Performance of the GSEs 

The longer-run performance of the GSEs is 

examined between 1993 and 2003 (which 

covers the period since the housing goals 

were put into effect) and between 1996 and 

2003 (which covers the period under the 

current definitions of the housing goals). Of 

the two borrower-income goals, the analysis 

below will typically focus on the special 

affordable category, which is a more targeted 

category than the rather broadly defined lowand moderate-income category. 

(1) Since the early nineties, the mortgage 

industry has introduced new affordable 

lending programs and has allowed greater 

flexibility in underwriting lower-income 

loans. There is evidence that these programs 

are paying off in terms of more mortgages for 

low-income and minority borrowers. As 

noted earlier, Fannie Mae and Freddie Mac 

have played an active role in this upsurge of 

affordable lending, as indicated by the high 

growth rates of their goals-qualifying 

business. 

• Between 1993 and 2003, the GSEs’

purchases of home loans in metropolitan 

areas increased by 60 percent.267 Their 

purchases of home loans for the three 

housing goals increased at much higher 

rates—287 percent for special affordable 

loans, 156 percent for low- and moderateincome loans, and 121 percent for loans in 

underserved census tracts.

(2) Both Fannie Mae and Freddie Mac have 

improved their purchases of affordable loans 

since the housing goals were put in place, as 

indicated by the increasing share of their 

business going to the three goals-qualifying 

categories. (See Table A.15 in Section E.9.) 

Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 63693

268Unless otherwise noted, the conventional 

conforming market data reported in this section 

exclude an estimate of B&C loans; the less-risky Aminus portion of the subprime market is included 

in the market definition. See Section E.7 and 

Appendix D for a discussion of primary market 

definitions and the uncertainty surrounding 

estimates of the number of B&C loans in HMDA 

data. As noted there, B&C loans are much more 

likely to be refinance loans rather than home 

purchase loans.

269Fannie Mae had a particularly poor year 

during 1999. Therefore, the text also reports 

averages for 2000–2003, dropping the year 1999 (see 

Table A.13 in Section E.9).

• Between 1992 and 2003, the special 

affordable share of Fannie Mae’s business 

almost tripled, rising from 6.3 percent to 17.1 

percent, while the underserved areas share 

increased more modestly, from 18.3 percent 

to 26.8 percent. The figures for Freddie Mac 

are similar. The special affordable share of 

Freddie Mac’s business rose from 6.5 percent 

to 15.6 percent, while the underserved areas 

share also increased but more modestly, from 

18.6 percent to 24.0 percent. 

(3) While both GSEs improved their 

performance, they have lagged the primary 

market in providing affordable loans to lowincome borrowers and underserved 

neighborhoods. Freddie Mac’s average 

performance, in particular, fell far short of 

market performance during the 1990s. Fannie 

Mae’s average performance was better than 

Freddie Mac’s during the 1993–2003 period 

as well as during the 1996–2003 period, 

which covers the period under HUD’s 

currently-defined housing goals. 

• Between 1993 and 2003, 12.2 percent of 

Freddie Mac’s mortgage purchases were for 

special affordable borrowers, compared with 

13.3 percent of Fannie Mae’s purchases, 15.4 

percent of loans originated by depositories, 

and 15.5 percent of loans originated in the 

conventional conforming market (without 

estimated B&C loans).268

• Considering the underserved areas 

category for the 1996–2003 period, 22.0 

percent of Freddie Mac’s purchases financed 

properties in underserved neighborhoods, 

compared with 24.0 percent of Fannie Mae’s 

purchases, 25.1 percent of loans originated 

by depositories, and 25.7 percent of loans 

originated in the conventional conforming 

market. 

(b.2) Performance of the GSEs During Recent 

Years 

The recent performance of the GSEs is 

examined for the four-year period between 

1999 and 2003 and then for 2001, 2002 and 

2003, which were the first three years that 

the GSEs operated under the higher goal 

targets established by HUD in the 2000 Rule. 

As explained below, the most interesting 

recent trend concerned Fannie Mae, which 

improved its performance during 2001–2003, 

at a time when the conventional conforming 

market was showing little change in 

affordable lending. 

(4) During the recent 1999-to-2003 period, 

both Fannie Mae and Freddie Mac fell 

significantly below the market in funding 

affordable loans.

• Between 1999 and 2003, special 

affordable loans accounted for 15.1 percent of 

Fannie Mae’s purchases, 14.7 percent of 

Freddie Mac’s purchases, and 16.2 percent of 

loans originated in the market; thus, the 

‘‘Fannie-Mae-to-market’’ ratio was 0.93 and 

the ‘‘Freddie-Mac-to-market’’ ratio was also 

0.91. 

• During the same period, underserved 

area loans accounted for 24.7 percent of 

Fannie Mae’s purchases, 23.1 percent of 

Freddie Mac’s purchases, and 26.2 percent of 

loans originated in the market; the ‘‘FannieMae-to-market’’ ratio was 0.94 and the 

‘‘Freddie-Mac-to-market’’ ratio was only 

0.88.269

(5) After experiencing declines from 1997 

to 1999, Fannie Mae’s affordable lending 

performance improved between 2000 and 

2003. 

• After declining from 23.0 percent in 

1997 to 20.4 percent in 1999, the share of 

Fannie Mae’s purchases financing properties 

in underserved areas jumped by three 

percentage points to 23.4 percent in 2000, 

and then increased further to 26.7 percent in 

2002 and 26.8 percent in 2003. 

• After declining from 13.2 percent in 

1998 to 12.5 percent in 1999, the share of 

Fannie Mae’s purchases going to special 

affordable loans rebounded to 13.3 percent in 

2000, 14.9 percent in 2001, 16.3 percent in 

2002 and 17.1 percent in 2003. 

(6) Freddie Mac’s performance on the two 

borrower-income categories improved 

between 2000 and 2002, but not as much as 

Fannie Mae’s performance. Freddie Mac’s 

performance on the underserved areas 

category increased substantially between 

2001 and 2002, but then declined between 

2002 and 2003. 

• The share of Freddie Mac’s singlefamily-owner business going to special 

affordable home loans increased from 9.2 in 

1997 to 14.7 percent in 2000 before falling to 

14.4 percent in 2001 and rising to 15.8 

percent in 2002 and 15.6 percent in 2003. 

• Freddie Mac’s purchases of underserved 

area loans increased at a modest rate from 

19.7 percent in 1997 to 22.3 percent in 2001, 

before jumping to 25.8 percent in 2002 and 

then dropping to 24.0 percent in 2003. 

(7) The long-standing pattern of Fannie 

Mae outperforming Freddie Mac was 

reversed during 1999 and 2000. But that 

pattern returned in 2001–2003 when Fannie 

Mae outperformed Freddie Mac on all three 

goals-qualifying categories. 

• Fannie Mae and Freddie Mac had 

practically the same performance in 1992 on 

the three housing goal categories—special 

affordable loans accounted for 6.3 percent of 

Fannie Mae’s purchases and 6.5 percent of 

Freddie Mac’s purchases, for a ‘‘Fannie-Maeto-Freddie-Mac’’ ratio of 0.97. The 1992 ratio 

for underserved areas was also 0.98 and that 

for low-mod, 1.02. Reflecting Fannie Mae’s 

much better performance, the special 

affordable ‘‘Fannie-Mae-to-Freddie-Mac’’

ratio had risen to 1.27 by 1997, the 

underserved area ratio to 1.17, and the lowmod ratio to 1.10. 

• However, in 1999, the ‘‘Fannie-Mae-toFreddie-Mac’’ ratio for each of the three 

goals-qualifying categories fell to slightly 

below one. 1999 was the first year since 1992 

that Freddie Mac had outperformed Fannie 

Mae in purchasing affordable home loans 

(although only by a very slight margin). 

• In 2000, Freddie Mac’s sharper increases 

in special affordable and low-mod purchases 

further reduced the ‘‘Fannie-Mae-to-FreddieMac’’ ratios for these two categories to 0.90 

and 0.96, respectively. Fannie Mae’s sharper 

increase in underserved areas funding 

resulted in the ‘‘Fannie-Mae-to-Freddie-Mac’’

ratio rising from slightly below one (0.98) in 

1999 to 1.06 in 2000. 

• Fannie Mae’s stronger performance 

during 2001–2003 returned the ‘‘Fannie-Maeto-Freddie-Mac’’ ratios for special affordable 

and low-mod loans to above one (1.10 and 

1.09 respectively), indicating better 

performance for Fannie Mae in 2003. The 

‘‘Fannie-Mae-to-Freddie-Mac’’ ratio for the 

underserved area category increased to 1.12 

by 2003. 

(8) While Freddie Mac has consistently 

improved its performance relative to the 

market, it continued to lag the market in 

funding affordable home loans during 2001–

2003. 

• Unlike Fannie Mae, Freddie Mac had not 

made any progress through 1997 in closing 

its gap with the market. The ‘‘Freddie Macto-market’’ ratio for the special affordable 

category actually declined from 0.63 in 1992 

to 0.59 in 1996. But Freddie Mac’s sharp 

improvement in special affordable purchases 

resulted in the ‘‘Freddie-Mac-to-market’’ ratio 

rising to 0.89 by 2000. After declining from 

0.84 in 1992 to 0.79 in 1997, the ‘‘FreddieMac-to-market’’ ratio for underserved areas 

had risen only modestly to 0.84 by the year 

2000. Thus, Freddie Mac’s improvements 

prior to 2001 allowed it to close its gap with 

the market, mainly for the special affordable 

category where its gap had been the widest. 

• During 2001, 2002 and 2003, Freddie 

Mac continued to close its gap with the 

market on the special affordable and lowmod categories. By 2003, these ‘‘FreddieMac-to-market’’ ratios were higher than in 

2000, although they both continued to fall 

below one: at 0.98 for both categories. 

Between 2002 and 2003, Freddie Mac’s 

market ratio for underserved areas fell from 

0.98 to 0.87 (24.0 percent for Freddie Mac 

and 27.6 percent for Fannie Mae). Thus, 

during 2003, Freddie Mac lagged the market 

on all three goals-qualifying categories. 

(9) Through 1998, Fannie Mae had 

significantly improved its performance 

relative to the market. But as a result of shifts 

in its purchases of affordable loans, Fannie 

Mae lagged the market even further in 2000 

than it had in some earlier years. During 

2001–2003, Fannie Mae again improved its 

performance relative to the market and, in 

2003, Fannie Mae led the special affordable 

and low-mod markets but lagged the 

underserved areas market. 

• The above analysis and the data reported 

under this specific finding (9) are based on 

the ‘‘purchase year’’ approach for measuring 

GSE activity. The purchase year approach 

assigns GSE purchases of both prior-year 

(seasoned) and newly-originated mortgages to 

the calendar year in which they were 

purchased by the GSE; this results in an 

inconsistency with the HMDA-reported 

market data, which covers only newlyoriginated mortgages. Sections E.9 and E.10 

63694 Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 

270As explained in Section E.9, deducting B&C 

loans from the market totals has more impact on the 

market percentages for total (both home purchase 

and refinance) loans than for only home purchase 

loans. The effects of excluding B&C loans from the 

total market can be seen by comparing the third and 

sixth columns of data in Table A.19 in Section E.10.

271See Glenn B. Canner, Wayne Passmore, and 

Brian J. Surette, ‘‘Distribution of Credit Risk Among 

Providers of Mortgages to Lower-Income and 

Minority Homebuyers’ in Federal Reserve Bulletin,

82(12): 1077–1102, December, 1996.

also report the results of an alternative 

‘‘origination year’’ approach that assigns GSE 

purchases to their year of origination, placing 

them on a more consistent basis with the 

HMDA-reported market data. The findings 

from the origination-year approach are 

discussed under specific finding (10).

• Fannie Mae’s decline in performance 

during 1999 resulted in the ‘‘Fannie-Mae-tomarket’’ ratio falling sharply to 0.74 for 

special affordable, to 0.81 for underserved 

areas and to 0.89 for low-mod. In 2000, 

Fannie Mae improved and reversed its 

declining trend, as the ‘‘Fannie-Mae-tomarket’’ ratios increased to 0.80 for special 

affordable purchases, to 0.89 for underserved 

area purchases, and to 0.93 for low-mod 

purchases. 

• During 2001, Fannie Mae increased its 

special affordable percentage by 1.6 

percentage points to 14.9 percent, which was 

only 0.7 percentage point below the market’s 

performance of 15.6 percent. Fannie Mae 

increased its low-mod percentage from 40.8 

percent to 42.9 percent at the same time that 

the low-mod share of the primary market was 

falling from 43.9 percent to 42.9 percent, 

placing Fannie Mae at the market’s 

performance. Similarly, Fannie Mae 

increased its underserved area percentage 

from 23.4 percent in 2000 to 24.4 percent in 

2001 while the underserved area share of the 

primary market was falling from 26.2 percent 

to 25.2 percent, placing Fannie Mae at 0.8 

percentage point from the market’s 

performance. 

• During 2002, Fannie Mae continued to 

improve its performance on all three goals 

categories. Using the purchase-year approach 

to measure GSE performance, Fannie Mae 

slightly led the market on the special 

affordable category (16.3 percent for Fannie 

Mae and 16.1 percent for the market), led the 

market on the low-mod category (45.3 

percent for Fannie Mae compared with 44.6 

percent for the market), and led the market 

on the underserved area category (26.7 

percent for Fannie Mae versus 26.3 percent 

for the market). 

• During 2003, Fannie Mae’s further 

improvement resulted in Fannie Mae leading 

the special affordable market (17.1 percent 

for Fannie Mae compared with 15.9 percent 

for the market) and continuing to lead the 

low-mod market (47.0 percent for Fannie 

Mae compared with 44.6 percent for the 

market). During 2003, Fannie Mae lagged 

behind the underserved areas market (26.8 

percent for Fannie Mae compared with 27.6 

percent for the market). 

(10) This analysis addresses several 

technical issues involved in measuring GSE 

performance. The above analysis was based 

on the ‘‘purchase year’’ approach, as defined 

in (9) above. An alternative ‘‘origination 

year’’ approach has also been utilized, which 

assigns GSE purchases to their year of 

origination, placing them on a more 

consistent basis with the HMDA-reported 

market data. While the average results (e.g., 

1999–2003 GSE performance) are similar 

under the two reporting approaches, GSE 

performance in any particular year can be 

affected, depending on the extent to which 

the GSE has purchased goals-qualifying 

seasoned loans in that particular year. 

• The choice of which approach to follow 

particularly affected conclusions about 

Fannie Mae’s performance relative to the 

market in 2002 (but not in 2001). Under the 

origination-year approach, Fannie Mae 

lagged the market on all three housing goal 

categories during 2001 and on the 

underserved area category during 2002. In 

2002, Fannie Mae matched the market on the 

special affordable category and led the 

market on the low-mod category (45.5 

percent for Fannie Mae compared with 44.6 

percent of the market). 

• During 2003, the origination year 

approach gives the similar results as the 

purchase year approach—Fannie Mae led the 

special affordable and low-mod markets and 

lagged the underserved areas market. 

(b.3) The GSEs’ Funding of First-time 

Homebuyer Loans 

(11) The GSEs’ funding of first-time 

homebuyers has been compared to that of 

primary lenders in the conventional 

conforming market. Both Fannie Mae and 

Freddie lag the market in funding first-time 

homebuyers, and by a rather wide margin. 

• First-time homebuyers account for 27 

percent of each GSE’s purchases of home 

loans, compared with 38 percent for home 

loans originated in the conventional 

conforming market. 

(b.4) Performance of the GSEs Based on Total 

(Home Purchase and Refinance) Loans 

(12) The GSEs’ acquisitions of total loans 

(including refinance loans as well as home 

purchase loans) were also examined. The 

main results indicate (a) Freddie Mac has 

improved its performance but has 

consistently lagged the market in funding 

loans (home purchase and refinance) that 

qualify for the housing goals; and (b) Fannie 

Mae has not only improved its performance 

but matched the low-mod market in 2001 and 

2002 and led both the special affordable and 

low-mod markets in 2003. Fannie Mae, 

however, lagged the primary market in 

funding underserved areas during 2003. (See

Table A.20 of Section E.10, which is based 

on the purchase-year approach for measuring 

GSE activity.)

• 1999–2003. During the recent 1999-to2003 period, both Fannie Mae and Freddie 

Mac fell significantly below the market in 

funding affordable total (home purchase and 

refinance) loans. Between 1999 and 2003, 

special affordable loans accounted for 14.0 

percent of Fannie Mae’s purchases, 13.2 

percent of Freddie Mac’s purchases, and 15.6 

percent of loans originated in the market; 

thus, the ‘‘Fannie-Mae-to-market’’ ratio was 

0.93 and the ‘‘Freddie-Mac-to-market’’ ratio 

was 0.88 during this period. 

• During the same period, underserved 

area loans accounted for 23.8 percent of 

Fannie Mae’s purchases, 22.1 percent of 

Freddie Mac’s purchases, and 25.2 percent of 

loans originated in the market; thus, the 

‘‘Fannie-Mae-to-market’’ ratio was 0.94 and 

the ‘‘Freddie-Mac-to-market’’ ratio was 

0.88.270

• 2002 and 2003. During 2002, the first of 

these two years of heavy refinancing, Fannie 

Mae’s performance was slightly above the 

market on the low-mod category and slightly 

below market performance on the special 

affordable and underserved areas categories; 

essentially, Fannie Mae matched the market 

on all three categories in 2002. In 2003, 

Fannie Mae led the market on the special 

affordable and low-mod categories and 

lagged the market on the underserved areas 

category. The 2003 ‘‘Fannie-Mae-to-market’’

ratios were 1.02 for special affordable loans, 

1.03 for low-mod loans, and 0.97 for 

underserved area loans. In 2003, the 

‘‘Freddie-Mac-to-market’’ ratios were much 

lower: 0.86 for special affordable loans, 0.90 

for low-mod loans, and 0.82 for underserved 

area loans. 

(b.5) GSE Market Shares 

This analysis includes an expanded 

‘‘market share’’ analysis that documents the 

GSEs’’ contribution to important segments of 

the home purchase and first-time homebuyer 

markets. 

(13) The GSEs account for a significant 

share of the total (government as well as 

conventional conforming) market for home 

purchase loans. However, the GSEs’ market 

share for each of the affordable lending 

categories is much less than their share of the 

overall market. 

• The GSEs’ purchases were estimated to 

be 46 percent of all home loans originated in 

metropolitan areas between 1999 and 2003 

but only 30 percent of loans originated for 

African-American and Hispanic borrowers, 

38 percent of loans originated for low-income 

borrowers, and 37 percent for properties in 

underserved areas. The GSEs’ market share 

for the various affordable lending categories 

increased during 2001–2003, but the abovementioned pattern remained. 

• A study by staff from the Federal Reserve 

Board suggests that the GSEs have a much 

more limited role in the affordable lending 

market than is suggested by the data 

presented above.271 The Fed study, which 

combined market share, downpayment, and 

default data, concluded that the GSEs play a 

very minimal role in providing credit support 

and assuming credit risk for low-income and 

minority borrowers; for example, the study 

concluded that in 1995 the GSEs provided 

only four percent of the credit support going 

to African-Americans and Hispanic 

borrowers.

• Section V of this study begins to 

reconcile these different results by examining 

the role of the GSEs in the first-time 

homebuyer market and the downpayment 

characteristics of mortgages purchased by the 

GSEs. 

(14) The market role of the GSEs appears 

to be particularly low in important market 

segments such as minority first-time 

homebuyers. 

Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 63695

272 In this comparison, a higher special affordable 

percentage for HMDA-reported mortgage 

originations that lenders report as also being sold 

to the GSEs—as compared with the special 

affordable percentage for newly-originated 

mortgages that the GSEs report as being actually 

purchased by them—would suggest that HMDA 

market data are biased; that is, in this situation, the 

special affordable percentage for all mortgage 

originations reported in HMDA would likely be 

larger than the special affordable percentage for all 

new mortgage originations, including those not 

reported in HMDA as well as those reported in 

HMDA.

273The market definition in this section is 

narrower than the ‘‘Total Market’’ data presented 

earlier in Tables A.1 and A.2, which included all 

home loans below the conforming loan limit, that 

is, government loans as well as conventional 

conforming loans. The market share analysis 

reported in Section E.12 also examine the GSEs’

role in the overall market.

274And there is some evidence that many 

subprime loans are not even reported to HMDA, 

although there is nothing conclusive on this issue. 

See Fair Lending/CRA Compass, June 1999, p. 3.

275The list of subprime lenders as well as 

Scheessele’s list of manufactured housing lenders 

are available at http://www.huduser.org/

publications/hsgfin.html.

276The one-half estimate is conservative as some 

observers estimate that B&C loans account for only 

30–40 percent of the subprime market. However, 

varying the B&C share from 50 percent to 30 percent 

does not significantly change the following analysis 

of home purchase loans because subprime loans are 

mainly for refinance purposes. Overstating the 

share of B&C loans in this manner also allows for 

any differences in HMDA reporting of different 

Continued

• Recent analysis has estimated that the 

GSEs’ share of the market for first-time 

African-American and Hispanic homebuyers 

was only 14.3 percent between 1999 and 

2001, or about one-third of their share (41.5 

percent) of all home purchases during that 

period. This analysis includes the total 

market, including government and 

conventional loans. 

• A similar market share analysis was 

conducted for the conventional conforming 

market. Between 1999 and 2001, the GSEs’

purchases accounted for 56.6 percent of all 

home loans originated in the conventional 

conforming market of both metropolitan 

areas and non-metropolitan areas. Their 

purchases of first-time homebuyer loans, on 

the other hand, accounted for only 39.8 

percent of all first-time homebuyer loans 

originated in that market. 

• The GSEs have funded an even lower 

share of the minority first-time homebuyer 

market in the conventional conforming 

market. Between 1999 and 2001, the GSEs 

purchases of African-American and Hispanic 

first-time homebuyer loans represented 30.9 

percent of the conventional conforming 

market for these loans. Thus, while the GSEs 

have accounted for 56.6 percent of all home 

loans in the conventional conforming market, 

they have accounted for only 30.9 percent of 

loans originated in that market for AfricanAmerican and Hispanic first-time 

homebuyers. 

(15) A noticeable pattern among the lowerincome-borrower loans purchased by the 

GSEs is the predominance of loans with high 

downpayments. This pattern of purchasing 

mainly high downpayment loans is one 

factor explaining why the Fed study found 

such a small market role for the GSEs. It may 

be the explanation for the small role of 

Fannie Mae and Freddie Mac in the first-time 

homebuyer market. Further study of this 

issue is needed. 

• During 2001 and 2002, approximately 50 

percent of Fannie Mae’s special affordable, 

low-mod, and underserved areas loans had 

downpayments of at least 20 percent, a 

percentage only slightly smaller that the 

corresponding percentage (53 percent) for all 

Fannie Mae’s home loan purchases. Similar 

patterns of above-20-percent downpayments 

on goals-qualifying loans were evident in 

Freddie Mac’s 2001, 2002, and 2003 

purchases, as well as in prior years for both 

GSEs. During 2003, Fannie Mae’s high 

downpayment share of their special 

affordable purchases dropped to 45 percent 

while the patterns for Fannie Mae’s low-mod 

and underserved area purchases did not 

change, remaining about 50 percent. 

(b.6) Additional Findings 

This analysis examines two additional 

topics related to minority first-time 

homebuyers and the use of HMDA data for 

measuring the characteristics of loans 

originated in the conventional conforming 

market.

(16) The share of the GSEs’ purchases for 

minority first-time homebuyers was much 

less than the share of newly-originated 

mortgages in the conventional conforming 

market for those homebuyers. 

• Between 1999 and 2001, minority firsttime homebuyers accounted for 6.6 percent 

of Fannie Mae’s purchases of home loans, 5.8 

percent of Freddie Mac’s purchases, and 10.6 

percent of home loans originated in the 

conventional conforming market. For this 

subgroup, Fannie Mae’s performance is 62 

percent of market performance, while 

Freddie Mac’s performance is 55 percent of 

market performance. 

(17) Some studies have concluded that 

HMDA data overstate the share of market 

loans going to low-income borrowers and 

underserved areas. This analysis does not 

support that conclusion. 

• This compares the low-income and 

underserved areas characteristics of the 

GSEs’ purchases of newly-originated 

(‘‘current-year’’) loans as reported both by the 

GSEs’’ own data and by HMDA data.272 For 

recent years, HMDA data on loans sold to the 

GSEs do not always have higher percentages 

of low-income and underserved areas loans 

than the GSEs’ own data on their purchases 

of newly-originated mortgages. For example, 

from 1996–2003, both HMDA and Fannie 

Mae reported that special affordable loans 

accounted for about 13 percent of Fannie 

Mae’s purchases of newly-originated loans. 

HMDA reported a 22.6 underserved areas 

percentage for Fannie Mae, which was rather 

similar to the underserved areas percentage 

(23.1 percent) reported by Fannie Mae itself. 

Given that similar patterns were observed for 

Freddie Mac’s mortgage purchases, it appears 

that there is no upward bias in the HMDAbased market benchmarks used in this study.

7. Definition of Primary Market 

Conventional Conforming Market. The 

market analysis section is based mainly on 

HMDA data for mortgages originated in the 

conventional conforming market of 

metropolitan areas during the years 1992 to 

2003. Only conventional loans with a 

principal balance less than or equal to the 

conforming loan limit are included; the 

conforming loan limit was $322,700 in 

2003—these are called ‘‘conventional 

conforming loans.’’ The GSEs’ purchases of 

FHA-insured, VA-guaranteed, and Rural 

Housing Service loans are excluded from this 

analysis. The conventional conforming 

market is used as the benchmark against 

which to evaluate the GSEs because that is 

the market definition Congress requires that 

HUD consider when setting the affordable 

housing goals. However, as discussed in 

Section II, some have questioned whether 

lenders in the conventional market are doing 

an adequate job meeting the credit needs of 

minority borrowers, which suggests that this 

market provides a low benchmark.273

Manufactured Housing Loans. Both GSEs 

have raised questions about whether loans on 

manufactured housing should be excluded 

when comparing the primary market with the 

GSEs. The GSEs purchase these loans, but 

they have not played a significant role in the 

manufactured housing loan market. As 

emphasized by HUD in its 2000 GSE Rule, 

manufactured housing is an important source 

of home financing for low-income families 

and for that reason, should be included in 

any analysis of affordable lending. However, 

for comparison purposes, data are also 

presented for the primary market defined 

without manufactured housing loans. 

Because this analysis focuses on 

metropolitan areas, it does not include the 

substantial number of manufactured housing 

loans originated in non-metropolitan areas. 

Subprime Loans. Both GSEs also raised 

questions about whether subprime loans 

should be excluded when comparing the 

primary market with their performance. In its 

final 2000 GSE Rule, HUD argued that 

borrowers in the A-minus portion of the 

subprime market could benefit from the 

standardization and lower interest rates that 

typically accompany an active secondary 

market effort by the GSEs. A-minus loans are 

not nearly as risky as B&C loans and the 

GSEs have already started purchasing Aminus loans (and likely the lower ‘‘B’’ grade 

subprime loans as well). The GSEs 

themselves have mentioned that a large 

portion of borrowers in the subprime market 

could qualify as ‘‘A credit.’’ This analysis 

includes the A-minus portion of the 

subprime market, or conversely, excludes the 

B&C portion of that market. 

Unfortunately, HMDA does not identify 

subprime loans, much less separate them into 

their A-minus and B&C components.274

Randall M. Scheessele at HUD has identified 

approximately 200 HMDA reporters that 

primarily originate subprime loans and 

account for about 60–70 percent of the 

subprime market.275 To adjust HMDA data 

for B&C loans, this analysis follows HUD’s 

2000 Rule which assumed that the B&C 

portion of the subprime market accounted for 

one-half of the loans originated by the 

subprime lenders included in Scheessele’s 

list.276 As shown below, the effects of 

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types of loans—for example, if B&C loans account 

for 35 percent of all subprime loans, then assuming 

that they account for 50 percent is equivalent to 

assuming that B&C loans are reported in HMDA at 

70 percent of the rate of other loans.

277The reductions in the market shares are more 

significant for total loans, which include refinance 

as well as home purchase loans; for data on total 

loans, see Table A.19 in Section 10. Subprime 

lenders have been focusing more on home purchase 

loans recently. The home purchase share of loans 

originated by the subprime lenders in Scheessele’s 

list increased from 26 percent in 1999 to 36 percent 

in 2000 before dropping to about 30 percent during 

the heavy refinancing years of 2001 and 2002.

278 In 2001 (2002), lenders reported in HMDA that 

they purchased 851,735 (906,684) conventional 

conforming, home purchase loans in metropolitan 

areas; this compares with 2,763,230 (2,929,197) 

loans that these same lenders reported that they 

originated in metropolitan areas.

279See Randall M. Scheessele, HMDA Coverage of 

the Mortgage Market, Housing Finance Working 

Paper No. HF–007. Office of Policy Development 

and Research, U.S. Department of Housing and 

Urban Development, July, 1998.

280 In this example, HMDA-reported purchased 

loans insured by FHA have been reduced from 

411,930 to 100,251 by a procedure that accounts for 

missing data and overlapping purchased and 

originated loans. See Harold L. Bunce, The GSEs’

Funding of Affordable Loans: A 2000 Update,

Working Paper HF–013, Office of Policy 

Development and Research, HUD, April 2002, for 

an alternative analysis showing that a market 

estimate based on adding HMDA-reported 

purchased loans to HMDA-reported originations 

would substantially overstate the volume of FHA 

mortgage originations in metropolitan areas.

281See Chapter III, ‘‘Reporting of Brokered and 

Correspondent Loans under HMDA’’, in Exploratory 

Study of the Accuracy of HMDA Data, by Abt 

Associates Inc. under contract for the Office of 

Policy Development and Research, HUD, February 

12, 1999, page 18.

282The percentage shares for purchased loans are 

obtained after eliminating purchased loans without 

data and purchased loans that overlap with 

originated loans. The calculations included 138,536 

purchased loans for 2001 and 182,290 purchased 

loans for 2002.

283Readers not interested in these technical 

issues may want to proceed to Section E.9, which 

compares GSE performance to the primary market.

284See Jim Berkovec and Peter Zorn, ‘‘How 

Complete is HMDA? HMDA Coverage of Freddie 

Mac Purchases,’’ The Journal of Real Estate 

Research, Vol. II, No. 1, Nov. 1, 1996.

adjusting the various market percentages for 

B&C loans are minor mostly because the 

analysis in this section focuses on home 

purchase loans, which historically have 

accounted for less than one quarter of the 

mortgages originated by subprime lenders—

the subprime market is mainly a refinance 

market.277

Lender-Purchased Loans in HMDA. When 

analyzing HMDA data, Fannie Mae includes 

in its market totals those HMDA loans 

identified as having been purchased by the 

reporting lender, above and beyond loans 

that were originated by the reporting 

lender.278 Fannie Mae contends that there are 

a subset of loans originated by brokers and 

subsequently purchased by wholesale 

lenders that are neither reported by the 

brokers nor the wholesale lenders as 

originations but are reported by the 

wholesale lenders as purchased loans. 

According to Fannie Mae, these HMDAreported purchased loans should be added to 

HMDA-reported originated loans to arrive at 

an estimate of total mortgage originations.

This rule’s market definition includes only 

HMDA-reported originations; purchased 

loans are excluded from the market 

definition. While some purchased loans may 

not be reported as originations in HMDA (the 

Fannie Mae argument), there are several 

reasons for assuming that most HMDAreported purchased loans are also reported in 

HMDA as market originations. First, Fed staff 

have told HUD that including purchased 

loans would result in double counting 

mortgage originations.279 Second,

comparisons of HMDA-reported FHA data 

with data reported by FHA supports the 

Fed’s conclusion. For instance, FHA’s own 

data indicate that during 2001 FHA insured 

752,319 home purchase loans in 

metropolitan areas; the sum of HMDAreported purchased home loans and HMDAreported originated home loans in 

metropolitan areas alone yields a much 

higher figure of 845,176 FHA-insured loans 

during 2001.280 While these calculations are 

for the FHA market (rather than the 

conventional market), they suggest that 

including HMDA-reported purchased loans 

in the market definition would overstate 

mortgage origination totals. Third, Abt 

Associates surveyed nine wholesale lenders 

and questioned them concerning their 

guidelines for reporting in HMDA loans 

purchased from brokers. Most of these 

lenders said brokered loans were reported as 

originations if they [the wholesale lender] 

make the credit decision; this policy is 

consistent with the Fed’s guidelines for 

HMDA reporting. Abt Associates concluded 

that ‘‘brokered loans do seem more likely to 

be reported as originations * * *.’’ 281

Finally, it should be noted that including 

purchased loans in the market definition 

does not significantly change the goalsqualifying shares of the market, mostly 

because borrower income data are missing for 

the majority of purchased loans. In addition, 

the low-income and underserved area shares 

for purchased and originated loans are rather 

similar. In 2001, the following differences in 

shares for the conventional conforming home 

purchase market were obtained for purchased 

and originated loans: Low-income (25.8 

percent for purchased loans, 28.3 percent for 

market originations), low-mod income (41.3 

percent, 43.2 percent), and underserved areas 

(24.2 percent, 25.8 percent). The comparisons 

were also similar for 2002.282

8. Technical Issues: Using HMDA Data To 

Measure the Characteristics of GSE Purchases 

and Mortgage Market Originations 283

This section discusses important technical 

issues concerning the use of HMDA data for 

measuring the GSEs’ performance relative to 

the characteristics of mortgages originated in 

the primary market. The first issue concerns 

the reliability of HMDA data for measuring 

the borrower income and census tract 

characteristics of loans sold to the GSEs. 

Fannie Mae, in particular, has contended that 

HMDA data understates the percentages of its 

business that qualify for the three housing 

goals. In its comments on the proposed 2000 

Rule, Fannie Mae questioned HUD’s reliance 

on HMDA data for measuring its 

performance. As discussed below, HMDA 

data on loans sold to the GSEs do not include 

prior-year (seasoned) loans that are sold to 

the GSEs. Since about one-fourth of GSE 

purchases in any particular year involve 

loans originated in prior years, HMDA data 

will not provide an accurate measure of the 

goals-qualifying characteristics of the GSEs’

total purchases when the characteristics of 

prior-year loans differ from those of newlyoriginated, current-year loans. 

A related issue concerns the appropriate 

definition of the GSE data when making 

annual comparisons of GSE performance 

with the market. On the one hand, the GSE 

annual data can be expressed on a purchaseyear basis, which means that all GSE 

purchases in a particular year would be 

assigned to that particular year. 

Alternatively, the GSE annual data can be 

expressed on an origination-year basis, which 

means that GSE purchases in a particular 

year would be assigned to the calendar year 

that the GSE-purchased mortgage was 

originated; for example, a GSE’s purchase 

during 2001 of a loan originated in 1999 

would be assigned to 1999, the year the loan 

was originated. These two approaches are 

discussed further below. 

A final technical issue concerns the 

reliability of HMDA for measuring the 

percentage of goals-qualifying loans in the 

primary market. Both GSEs refer to findings 

from a study by Jim Berkovec and Peter Zorn 

concerning potential bias in HMDA data.284

Based on a comparison of the borrower and 

census tract characteristics between FreddieMac-purchased loans (from Freddie Mac’s 

own data) and loans identified in 1993 

HMDA data as sold to Freddie Mac, Berkovec 

and Zorn conclude that HMDA data overstate 

the percentage of conventional conforming 

loans originated for lower-income borrowers 

and for properties located in underserved 

census tracts. If HMDA data overstate the 

percentage of goals-qualifying loans, then 

HUD’s market benchmarks (which are based 

on HMDA data) will also be overstated. The 

analysis below does not support the Berkovec 

and Zorn findings—it appears that HMDA 

data do not overstate the share of goalsqualifying loans in the market. The 

discussion below of the GSEs’ purchases of 

prior-year and current-year loans also 

highlights the strategy of purchasing 

seasoned loans that qualify for the housing 

goals. The implications of this strategy for 

understanding recent shifts in the relative 

performance of Fannie Mae and Freddie Mac 

are discussed below in Section E.9.

a. GSEs’ Purchases of ‘‘Prior-Year’’ and 

‘‘Current-Year’’ Mortgages 

There are two sources of loan-level 

information about the characteristics of 

mortgages purchased by the GSEs—the GSEs 

themselves and HMDA data. The GSEs 

provide detailed data on their mortgage 

purchases to HUD on an annual basis. As 

part of their annual HMDA reporting 

responsibilities, lenders are required to 

indicate whether their new mortgage 

originations or the loans that they purchase 

(from affiliates and other institutions) are 

sold to Fannie Mae, Freddie Mac or some 

Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 63697

285For another discussion of this issue, see 

Randall M. Scheessele, HMDA Coverage of the 

Mortgage Market, Housing Finance Working Paper 

HF–007, Office of Policy Development and 

Research, Department of Housing and Urban 

Development, July 1998. Scheessele reports that 

HMDA data covered 81.6 percent of the loans 

acquired by Fannie Mae and Freddie Mac in 1996. 

The main reason for the under-reporting of GSE 

acquisitions is a few large lenders failed to report 

the sale of a significant portion of their loan 

originations to the GSEs. Also see the analysis of 

HMDA coverage by Jim Berkovec and Peter Zorn. 

‘‘Measuring the Market: Easier Said than Done,’’

Secondary Mortgage Markets. McLean VA: Freddie 

Mac, Winter 1996, pp. 18–21; as well as the 

Berkovec and Zorn study cited in the above 

footnote.

286Between 1993 and 1996, the GSEs’ purchases 

of prior-year loans were not as targeted as they were 

after 1996; thus, during this period, HMDA 

provided reasonable estimates of the goalsqualifying percentages of the GSEs’ purchases of all 

(both current-year and prior-year) loans, with a few 

exceptions (see Table A.11).

287The ‘‘prior-year’’ share dropped to 16 percent 

during the heavy refinancing year of 2003. During 

the 1990s, the GSEs increased their purchases of 

seasoned loans; see Paul B. Manchester, Goal 

Performance and Characteristics of Mortgages 

Purchased by Fannie Mae and Freddie Mac, 1998–

2000, Housing Finance Working Paper No. HF–015, 

Office of Policy Development and Research, HUD, 

May 2001.

other entity. There have been numerous 

studies by HUD staff and other researchers 

that use HMDA data to compare the borrower 

and neighborhood characteristics of loans 

sold to the GSEs with the characteristics of 

all loans originated in the market. One 

question is whether HMDA data, which is 

widely available to the public, provides an 

accurate measure of GSE performance, as 

compared with the GSEs’ own data.285

Fannie Mae has argued that HMDA data 

understate its past performance, where 

performance is defined as the percentage of 

Fannie Mae’s mortgage purchases accounted 

for by one of the goal-qualifying categories. 

As explained below, over the past six years, 

HMDA has provided rather reliable nationallevel information on the goals-qualifying 

percentages for the GSEs’ purchases of 

‘‘current-year’’ (i.e., newly-originated) loans, 

but not for their purchases of ‘‘prior-year’’

loans.286

In any given calendar year, the GSEs can 

purchase mortgages originated in that 

calendar year or mortgages originated in a 

prior calendar year. In 2001 and 2002, for 

example, purchases of prior-year mortgages 

accounted for approximately 20 percent of 

the home loans purchased by each GSE.287

HMDA data provide information mainly on 

newly-originated mortgages that are sold to 

the GSEs—that is, HMDA data on loans sold 

to the GSEs will not include many of their 

purchases of prior-year loans. The 

implications of this for measuring GSE 

performance can be seen in Table A.11, 

which provides annual data on the borrower 

and census tract characteristics of GSE 

purchases, as measured by HMDA data and 

by the GSEs’ own data. Table A.11 divides 

each of the GSEs’ goals-qualifying 

percentages for a particular acquisition year 

into two components, the percentage for 

‘‘prior-year’’ loans and the percentage for 

‘‘current-year’’ loans.

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ER02NO04.020</GPH><FNP>
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288Freddie Mac’s underserved area figures for 

2002 and 2003 showed particularly large 

discrepancies. As shown in Table A.11, Freddie 

Mac reported that 25.0 (23.4) percent of the currentyear loans it purchased during 2002 (2003) financed 

properties in underserved areas, a figure much 

higher than the 21.4 (20.3) percent that HMDA 

reported as underserved area loans sold to Freddie 

Mac during 2002. These discrepancies are the 

largest in Table A.11, and it is not clear what 

explains them. This downward bias for HMDA data, 

is the opposite of that suggested by Berkovec and 

Zorn, who argued that affordability percentages 

from HMDA data are biased upward.

Consider Fannie Mae’s special affordable 

purchases in 2002. According to Fannie 

Mae’s own data, 16.3 percent of its purchases 

during 2002 were special affordable loans. 

According to HMDA data, only 15.5 percent 

of loans sold to Fannie Mae fell into the 

special affordable category. In this case, 

HMDA data underestimate the special 

affordable share of Fannie Mae’s purchases 

during 2002. What explains these different 

patterns in the GSE and HMDA data? The 

reason that HMDA data underestimate the 

special affordable percentage of Fannie Mae’s 

2002 purchases can be seen by disaggregating 

Fannie Mae’s purchases during 2002 into 

their prior-year and current-year 

components. Table A.11 shows that the 

overall figure of 16.3 percent for special 

affordable purchases is a weighted average of 

18.8 percent for Fannie Mae’s purchases 

during 2002 of prior-year mortgages and 15.8 

percent for its purchases of current-year 

purchases. The HMDA-reported figure of 15.5 

percent is based mainly on newly-mortgaged 

(current-year) loans that lenders reported as 

being sold to Fannie Mae during 2002. The 

HMDA figure is similar in concept to the 

current-year percentage from the GSEs’ own 

data. And the HMDA figure and the GSE 

current-year figure are practically the same in 

this case (15.5 versus 15.8 percent). Thus, the 

relatively large share of special affordable 

mortgages in Fannie Mae’s purchases of 

prior-year mortgages explains why Fannie 

Mae’s own data show an overall (both prioryear and current-year) percentage of special 

affordable loans that is higher than that 

reported for Fannie Mae in HMDA data. 

b. Reliability of HMDA Data 

With the above explanation of the basic 

differences between GSE-reported and 

HMDA-reported loan information, issues 

related to the reliability of HMDA data can 

now be discussed. Table A.12 presents the 

same information as Table A.11, except that 

the data are aggregated for the years 1993–5, 

1996–2003, and 1999–2003. Comparing 

HMDA-reported data on GSE purchases with 

GSE-reported current-year data suggests that, 

on average, HMDA data have provided 

reasonable estimates of the goals-qualifying 

percentages for the GSEs’ current-year 

purchases (with the exception of Freddie 

Mac’s underserved area loans, as discussed 

below). For example, Fannie Mae reported 

that 13.7 percent of the current-year loans it 

purchased between 1996 and 2003 were for 

special affordable borrowers. In their HMDA 

submissions, lenders reported a nearly 

identical figure of 13.4 percent for the special 

affordable share of loans that they sold to 

Fannie Mae. The corresponding numbers for 

Freddie Mac were 12.8 percent reported by 

them and 12.1 percent reported by HMDA. 

During the same period, both Fannie Mae 

and HMDA reported that approximately 23 

percent of current-year loans purchased by 

Fannie Mae financed properties in 

underserved areas. However, Freddie Mac 

reported that 21.3 percent of the current-year 

loans it purchased between 1996 and 2003 

financed properties in underserved areas, a 

figure somewhat higher than the 19.6 percent 

that HMDA reported as underserved area 

loans sold to Freddie Mac during that 

period.288

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289The data in Table A.12 that support Berkovec 

and Zorn are the 1993–95 special affordable and 

low-mod data (particularly for Freddie Mac) that 

show HMDA over reporting percentages by more 

than a half percentage point. Otherwise, the data in 

Table A.12, as well as Table A.11, do not present 

a picture of HMDA’s having an upward bias in 

reporting targeted loans. In fact, the recent years’

data suggest a downward bias in HMDA’s reporting 

of targeted loans.

290Of course, on an individual year basis, the 

GSEs’ current-year data can differ significantly from 

the HMDA-reported data on GSE purchases. The 

other annual data reported in Table A.11 show a 

mixture of results—in some cases the HMDA 

percentage is larger than the GSE ‘‘current year’’

percentage (e.g., Fannie Mae’s special affordable 

purchases in 2000) while in other cases the HMDA 

percentage is smaller than the GSE current year 

percentage (e.g., Freddie Mac’s special affordable 

purchases in recent years). As noted in the text, the 

differential is typically in the opposite direction to 

that predicted by Berkovec and Zorn, particularly 

on the underserved areas category.

291Table A.12 also includes aggregates for the 

more recent period, 1999–2003. The ratios of 

HMDA-reported-to-GSE-reported averages for this 

sub-period are similar to those reported for 1996–

2003.

292Under the origination-year approach, GSE 

performance for any specific origination year (say 

year 2000) at the end of a particular GSE purchase 

year (say year 2003) is subject to change in the 

future years. Table A.16 (in Section E.9 below) 

reports that 13.7 percent of year-2000 mortgage 

originations that Fannie Mae purchased through 

year 2002 qualify as special affordable; the special 

affordable share for the market was 16.6 percent in 

2000, which indicates that, to date, Fannie Mae has 

lagged the primary market in funding special 

affordable mortgages originated during 2000. 

However, Fannie Mae’s special affordable 

performance could change in the future as Fannie 

Mae continues to purchase year-2000 originations 

during 2004 and the following years. Of course, 

whether Fannie Mae’s future purchases result in it 

ever leading the 2000-year market is not known at 

this time.

The facts that the Fannie Mae and HMDA 

figures for special affordable, low-mod and 

underserved area loans are similar, and that 

the Freddie Mac discrepancies are the result 

of Freddie Mac reporting higher percentages 

than HMDA, suggest that the Berkovec and 

Zorn conclusions about HMDA being upward 

biased are wrong.289 For the 1996-to-2003 

period, the discrepancies reported in Table 

A.11 as well as Table A.12 are mostly 

consistent with HMDA being biased in a 

downward direction, not an upward 

direction as Berkovec and Zorn contend.290

In particular, the Freddie-Mac-reported 

underserved area percentage (as well as its 

special affordable percentage) being larger 

than the HMDA-reported underserved area 

percentage suggests a downward bias in 

HMDA. The more recent and complete 

(Fannie Mae data as well as Freddie Mac 

data) analysis does not support the Berkovec 

and Zorn finding that HMDA overstates the 

goals-qualifying percentages of the market.291

c. Purchase-Year Versus Origination-Year 

Reporting of GSE Data 

In comparing the GSEs’ performance to the 

primary market, HUD has typically expressed 

the GSEs’ annual performance on a purchaseyear basis. That is, all mortgages (including 

both current-year mortgages and prior-year 

mortgages) purchased by a GSE in a 

particular year are assigned to the year of 

GSE purchase. The approach of including a 

GSE’s purchases of both ‘‘current-year’’ and 

‘‘prior-year’’ mortgages gives the GSE full 

credit for their purchase activity in the year 

that the purchase actually takes place; this 

approach is also consistent with the statutory 

requirement for measuring GSE performance 

under the housing goals. However, this 

approach results in an obvious ‘‘apples to 

oranges’’ problem with respect to the HMDAbased market data, which include only 

newly-originated mortgages (i.e., current-year 

mortgages). To place the GSE and market 

data on an ‘‘apples to apples’’ basis, HUD has 

also used an alternative approach that 

expresses the GSE annual data on an 

origination-year basis. In this case, all 

purchases by a GSE in any particular year 

would be fully reported but they would be 

allocated to the year that they were 

originated, rather than to the year they were 

purchased. Under this approach, a GSE’s data 

for the year 2000 would not only include that 

GSE’s purchases during 2000 of newlyoriginated mortgages but also any year-2000-

originations purchased in later years (i.e., 

during 2001, 2002 and 2003 in this analysis). 

This approach places the GSE and the market 

data on a consistent, current-year basis. In 

the above example, the market data would 

present the income and underserved area 

characteristics of mortgages originated in 

2000, and the GSE data would present the 

same characteristics of all year-2000-

mortgages that the GSE has purchased to date 

(i.e., through year 2003).292

Below, results will be presented for both 

the purchase-year and origination-year 

approaches. Following past HUD studies that 

have compared GSE performance with the 

primary market, most of the analysis in this 

section reports the GSE data on a purchaseyear basis; however, the main results are 

repeated with the GSE data reported on an 

origination-year basis. This allows the reader 

to compare any differences in findings about 

how well the GSEs have been doing relative 

to the market. 

9. Affordable Lending by the GSEs: Home 

Purchase Loans 

This section compares the GSEs’ affordable 

lending performance with the primary 

market for the years 1993–2003. The analysis 

in this section begins by presenting the GSE 

data on a purchase-year basis. As discussed 

above, the GSE data that are reported to HUD 

include their purchases of mortgages 

originated in prior years as well as their 

purchases of mortgages originated during the 

current year. The market data reported by 

HMDA include only mortgages originated in 

the current year. This means that the GSEversus-market comparisons are defined 

somewhat inconsistently for any particular 

calendar year. Each year, the GSEs have 

newly-originated loans available for 

purchase, but they can also purchase loans 

from a large stock of seasoned (prior-year) 

loans currently being held in the portfolios 

of depository lenders. One method for 

making the purchase-year data more 

consistent is to aggregate the data over 

several years, instead of focusing on annual 

data. This provides a clearer picture of the 

types of loans that have been originated and 

are available for purchase by the GSEs. This 

approach is taken in Tables A.14 and A.15, 

which are discussed below. Another method 

for making the GSE and market data 

consistent is to express the GSE data on an 

origination-year basis; that approach is taken 

in Table A.16, which is discussed after 

presenting the annual results on a purchaseyear basis. 

a. Longer-Term Performance, 1993–2003 and 

1996–2003 

Table A.13 summarizes the funding of 

goals-qualifying mortgages by the GSEs, 

depositories and the conforming market for 

the ten-year period between 1993 and 2003. 

Data are also presented for two important 

sub-periods: 1993–95 (for showing how 

much the GSEs have improved their 

performance since the early-to-mid 1990s); 

and 1996–2003 (for analyzing their 

performance since the current definitions of 

the housing goals were put into effect). Given 

the importance of the GSEs for expanding 

homeownership, this section focuses on 

home purchase mortgages, and the next 

section will examine first-time homebuyer 

loans. Section IV below will briefly discuss 

the GSEs’ overall performance, including 

refinance and home purchase loans. Several 

points stand out concerning the affordable 

lending performance of Freddie Mac and 

Fannie Mae over the two longer-term periods, 

1993–2003 and 1996–2003. 

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293As shown in Table A.13, the depository 

percentage is higher (16.8 percent) if the analysis 

is restricted to those newly-originated loans that 

depositories do not sell (the latter being a proxy for 

loans held in depositories’ portfolios). Note that 

during the recent, 1999-to-2003 period (also 

reported in Table A.13), there is less difference 

between the two depository figures.

294Unless stated otherwise, the market in this 

section is defined as the conventional conforming 

market without estimated B&C loans.

Freddie Mac lagged both Fannie Mae and 

the primary market in funding affordable 

home loans in metropolitan areas between 

1993 and 2003. During that period, 12.2 

percent of Freddie Mac’s mortgage purchases 

were for special affordable (mainly very-lowincome) borrowers, compared with 13.3 

percent of Fannie Mae’s purchases, 15.4 

percent of loans originated by 

depositories,293 and 15.5 percent of loans 

originated in the conforming market without 

B&C loans.294

Although Freddie Mac consistently 

improved its performance during the 1990s, 

a similar pattern characterized the 1996–2003 

period. During that period, 40.3 percent of 

Freddie Mac’s purchases were for low- and 

moderate-income borrowers, compared with 

42.2 percent of Fannie Mae’s purchases, 43.1 

percent of loans originated by depositories, 

and 43.6 percent of loans originated in the 

conventional conforming market. Over the 

same period, 22.0 percent of Freddie Mac’s 

purchases financed properties in 

underserved neighborhoods, compared with 

24.0 percent of Fannie Mae’s purchases, 25.1 

percent of depository originations, and 25.7 

percent of loans originated in the primary 

market. 

Fannie Mae’s affordable lending 

performance was better than Freddie Mac’s 

over the 1993 to 2003 period as well as 

during the 1996 to 2003 period. However, 

Fannie Mae lagged behind depositories and 

the overall market in funding affordable 

loans during both of these periods (see above 

paragraph). Between 1996 and 2003, the 

‘‘Fannie-Mae-to-market’’ ratio was only 0.89 

on the special affordable category, obtained 

by dividing Fannie Mae’s performance of 

14.1 percent by the market’s performance of 

15.9 percent. Fannie Mae’s market ratio was 

0.97 on the low-mod category and 0.93 on the 

underserved area category. The ‘‘FreddieMac-to-market’’ ratios for 1996–2003 were 

lower—0.83 for special affordable, 0.92 for 

low-mod, and 0.86 for underserved areas. 

The above analysis has defined the market 

to exclude B&C loans, which HUD believes 

is the appropriate market definition. 

However, to gauge the sensitivity of the 

results to how the market is defined, Table 

A.14 shows the effects on the market 

percentages for different definitions of the 

conventional conforming market, such as 

excluding manufactured housing loans, small 

loans, and all subprime loans (i.e., the Aminus portion of the subprime market as well 

as the B&C portion). For example, the average 

special affordable (underserved area) market 

percentage for 1996–2003 would fall by about 

1.6 (1.2) percentage points if both small loans 

(less than $15,000) and manufactured loans 

in metropolitan areas were also dropped from 

the market definition (see right-hand-side 

column in Table A.14). Except for Fannie 

Mae’s relative performance on the low-mod 

category, the above findings with respect to 

the GSEs’ longer-term performance are not 

much affected by the choice of market 

definition.

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b. Recent Performance, 1999–2003 

This and the next subsection focus on the 

average data for 1999–2003 in Table A.13 and 

the annual data reported in Table A.14. As 

explained below, the annual data are useful 

for showing shifts in the relative positions of 

Fannie Mae and Freddie Mac that began in 

1999, and for highlighting the improvements 

made by Fannie Mae during 2001–2003 

(which were the first three years under 

HUD’s higher goal levels) and by Freddie 

Mac during 2002. Between 1993 and 1998, 

Freddie Mac’s performance fell below Fannie 

Mae’s, but a sharp improvement in Freddie 

Mac’s performance during 1999 pushed it 

pass Fannie Mae on all three goals-qualifying 

categories. In 2000, Fannie Mae improved its 

underserved areas performance enough to 

surpass Freddie Mac on that category, while 

Freddie Mac continued to out-perform 

Fannie Mae on the borrower-income 

categories (special affordable and low-mod). 

By 2002, Fannie Mae had improved its 

performance enough to surpass Freddie Mac 

on all three goals-qualifying categories and to 

lead the special affordable and low-mod 

markets, while lagging the underserved areas 

market. 

Consider first the average data for 1999–

2003 reported in Table A.13. During this 

recent period, Freddie Mac’s average 

performance was similar to Fannie Mae’s 

performance for the special affordable 

category. Between 1999 and 2003, 14.7 

percent of Freddie Mac’s purchases and 15.1 

percent Fannie Mae’s mortgage purchases 

consisted of special affordable loans, 

compared with a market average of 16.2 

percent. During this period, Freddie Mac 

purchased low-mod loans lower than the rate 

of Fannie Mae—42.6 percent for Freddie 

Mac, 43.6 percent for Fannie Mae, and 44.1 

percent for the market. Freddie Mac (23.1 

percent) also purchased underserved area 

loans at a lower rate than Fannie Mae (24.7 

percent) and the primary market (26.2 

percent). As these figures indicate, both 

Fannie Mae and Freddie Mac continued to 

lag the market during this recent four-year 

period. The GSEs’ market ratios were 0.91–

0.93 for special affordable loans and 0.97–

0.99 for low-mod loans. Although less than 

one (where one indicates equal performance 

with the market), the ‘‘Fannie-Mae-tomarket’’ ratio (0.94) for the underserved area 

category was much higher than the ‘‘FreddieMac-to-market’’ ratio (0.88). 

Fannie Mae’s performance in 1999 was 

significantly below its long run trend. Thus, 

averages for 2000–2003 are also presented in 

Table A.13, dropping 1999. These data show 

an increase in Fannie Mae’s performance 

relative to the market. Between 2000 and 

2003, special affordable (underserved area) 

loans accounted for 15.6 percent (25.5 

percent) of Fannie Mae’s purchases, 

compared with 16.0 percent (26.4 percent) 

for the market. During this 2000–2003 period, 

Fannie Mae slightly led the low-mod market 

(44.4 percent for Fannie Mae and 44.1 

percent for the primary market). 

Table A.14 shows the effects on the market 

percentages for 1999–2003 (as well as 2000–

2003) of different definitions of the 

conventional conforming market. Excluding 

both small loans and manufactured housing 

loans (as well as B&C loans) in metropolitan 

areas would reduce the 1999–2003 market 

percentage for special affordable loans from 

16.2 percent to 14.9 percent, which would 

place Fannie Mae slightly above the market 

and Freddie Mac close to the market. 

Similarly, excluding these loans would 

reduce the 1999–2003 market percentage for 

underserved areas from 26.2 percent to 25.2 

percent, which would raise Fannie Mae’s 

market ratio from 0.94 to 0.98 and Freddie 

Mac’s, from 0.88 to 0.92. As shown in Table 

A.14, Fannie Mae is even closer to the market 

averages if the year 1999 is dropped—over 

the 2000–2003 period, Fannie Mae’s 

performance on the underserved area 

category is practically at market levels under 

the above alternative definition of the market, 

and its performance on the special affordable 

and low-mod categories is above market 

levels. 

Finally, Tables A.13 and A.14 report GSE 

and market data for the even more recent 

period, 2001–2003, which represents the first 

three years under the current housing goal 

targets (put in place by HUD in its Final Rule 

dated October 30, 2000). These data show 

that Freddie Mac’s average performance 

during this period was below the market on 

each of the three housing goals (with market 

ratios of 0.96 for special affordable, 0.98 for 

low-mod, and 0.91 for underserved areas and 

that Fannie Mae’s average performance was 

above the market on the special affordable 

and low-mod categories (with a market ratio 

of 1.02 on each category) but below the 

market on the underserved areas category 

(with a market ratio of 0.98). 

c. GSEs’ Performance—Annual Data 

Freddie Mac’s Annual Performance. As 

shown by the annual data reported in Table 

A.15, Freddie Mac significantly improved its 

purchases of goals-qualifying loans during 

the 1990s. Its purchases of loans for special 

affordable borrowers increased from 6.5 

percent of its business in 1992 to 9.2 percent 

in 1997, and then jumped to 14.7 percent in 

2000 before falling slightly to 14.4 percent in 

2001 and rising again to almost 16 percent in 

2002 and 2003. The underserved areas share 

of Freddie Mac’s purchases increased at a 

more modest rate, rising from 18.6 percent in 

1992 to 22.3 percent by 2001; it then jumped 

to 25.8 percent in 2002 but fell to 24.0 

percent in 2003.

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295Table A.14 reports annual market percentages 

that exclude the effects of manufactured housing, 

small loans, and subprime loans. Freddie Mac’s 

performance is closer to the market average under 

the alternative market definitions, particularly 

during 2001 and 2002.

296Prior to 2002, Freddie Mac’s performance on 

the underserved areas category had not approached 

the market even under the alternative market 

definitions reported in Table A.14.

297Freddie Mac, on the other hand, fell further 

behind the market during this period. In 1992, 

Freddie Mac had a slightly higher underserved 

areas percentage (18.6 percent) than Fannie Mae 

(18.3 percent). However, Freddie Mac’s 

underserved areas percentage had only increased to 

19.8 percent by 1998 (versus 22.7 percent for 

Fannie Mae). Thus, the ‘‘Freddie Mac-to-market’’

ratio fell from 0.84 in 1992 to 0.82 in 1998.

With its improved performance, Freddie 

Mac closed its gap with the market in 

funding goals-qualifying loans. In 2003, 

special affordable loans accounted for 15.6 

percent of Freddie Mac’s purchases and 15.9 

percent of loans originated in the 

conventional conforming market, which 

produces a ‘‘Freddie-Mac-to-market’’ ratio of 

0.98 (15.6 divided by 15.9). Table A.15 shows 

the trend in the ‘‘Freddie-Mac-to-market’’

ratio from 1992 to 2003 for each of the goalsqualifying categories. For the special 

affordable and low-mod categories, Freddie 

Mac’s performance relative to the market 

remained flat (at approximately 0.60 and 

0.80, respectively) through 1997; by 2003, the 

‘‘Freddie-Mac-to-market’’ ratios had risen to 

0.98 for both the special affordable and lowmod categories. 

Surprisingly, Freddie Mac did not make 

much progress during the 1990s closing its 

gap with the market on the underserved areas 

category. The ‘‘Freddie-Mac-to-market’’ ratio 

for underserved areas was the same in 2000 

(0.84) as it was in 1992 (0.84). While it rose 

to 0.88 in 2001, that was due more to a 

decline in the market level than to an 

improvement in Freddie Mac’s performance. 

However, due to a substantial increase in 

Freddie Mac’s underserved area percentage 

from 22.3 percent in 2001 to 25.8 percent in 

2002, Freddie Mac’s performance approached 

market performance (26.3 percent) during 

2002. 295 In the ten years under the housing 

goals, the year 2002 represented the first time 

that Freddie Mac’s performance in 

purchasing home loans in underserved areas 

had ever been within two percentage points 

of the market’s performance.296 But, as noted 

above, Freddie Mac’s performance on the 

underserved areas goal fell to 24.0 percent in 

2003, leaving it with a ‘‘Freddie Mac-toMarket’’ ratio of 0.87.

Fannie Mae’s Annual Performance. With 

respect to purchasing affordable loans, 

Fannie Mae followed a different path than 

Freddie Mac. Fannie Mae improved its 

performance between 1992 and 1998 and 

made much more progress than Freddie Mac 

in closing its gap with the market. In fact, by 

1998, Fannie Mae’s performance was close to 

that of the primary market for some 

important components of affordable lending. 

In 1992, special affordable loans accounted 

for 6.3 percent of Fannie Mae’s purchases 

and 10.4 percent of all loans originated in the 

conforming market, giving a ‘‘Fannie Mae-tomarket’’ ratio of 0.61. By 1998, this ratio had 

risen to 0.86, as special affordable loans had 

increased to 13.2 percent of Fannie Mae’s 

purchases and to 15.4 percent of market 

originations. A similar trend in market ratios 

can be observed for Fannie Mae on the 

underserved areas category. In 1992, 

underserved areas accounted for 18.3 percent 

of Fannie Mae’s purchases and 22.2 percent 

of market originations, for a ‘‘Fannie Mae-tomarket’’ ratio of 0.82. By 1998, underserved 

areas accounted for 22.7 percent of Fannie 

Mae’s purchases and 24.2 percent of market 

originations, for a higher ‘‘Fannie Mae-tomarket’’ ratio of 0.94.297

The year 1999 saw a shift in the above 

patterns, with Fannie Mae declining in 

overall performance while the share of goalsqualifying loans in the market increased. 

Between 1998 and 1999, the special 

affordable share of Fannie Mae’s business 

declined from 13.2 percent to 12.5 percent 

while this type of lending in the market 

increased from 15.4 percent to 17.0 percent. 

For this reason, the ‘‘Fannie-Mae-to-market’’

ratio for special affordable loans declined 

sharply from 0.86 in 1998 to 0.74 in 1999. 

The share of Fannie Mae’s purchases in 

underserved areas also declined, from 22.7 

percent in 1998 to 20.4 percent in 1999, 

which lowered the ‘‘Fannie-Mae-to-market’’

ratio from 0.94 to 0.81. 

After declining in 1999, Fannie Mae’s 

performance rebounded in 2000, particularly 

on the underserved areas category. Fannie 

Mae’s underserved areas percentage jumped 

by three percentage points from 20.4 percent 

in 1999 to 23.4 percent in 2000. The 2000 

figure was similar to its level in 1997 but 

below Fannie Mae’s peak performances of 

24–25 percent during 1994 and 1995. 

Between 1999 and 2000, the ‘‘Fannie-Mae-tomarket’’ ratio for underserved areas increased 

from 0.81 to 0.89. Fannie Mae improved its 

performance on the special affordable goal at 

a more modest rate. Fannie Mae’s special 

affordable percentage increased by 0.8 

percentage points from 12.5 percent in 1999 

to 13.3 percent in 2000. The 2000 figure was 

similar to its previous peak level (13.2 

percent) in 1998. The ‘‘Fannie-Mae-tomarket’’ ratio for special affordable loans 

increased from 0.74 in 1999 to 0.80 in 2000, 

with the latter figure remaining below Fannie 

Mae’s peak market ratio (0.86) in 1998. 

Fannie Mae continued its improvement in 

purchasing targeted home loans during 2001, 

at a time when the conventional conforming 

market was experiencing a decline in 

affordable lending; and again in 2002, at a 

time when the conventional conforming 

market was increasing enough to return 

approximately to its year-2000 level. Thus, 

during the 2000-to-2003 period, Fannie Mae 

significantly improved its targeted 

purchasing performance while the primary 

market originated targeted home loans at 

about the same rate in 2002 as it did in 2000. 

As a result, Fannie Mae’s performance during 

2001 approached the market on the special 

affordable and underserved area categories 

and matched the market on the low-mod 

category. In 2002, Fannie Mae outperformed 

the market on all three areas categories.

As shown in Table A.15, Fannie Mae 

increased its special affordable percentage by 

1.6 percentage points, from 13.3 percent in 

2000 to 14.9 percent in 2001, and then 

increased it further to 16.3 percent in 2002, 

the latter being slightly above the market’s 

performance of 16.1 percent. The ‘‘FannieMae-to-market’’ ratio for special affordable 

loans jumped from 0.80 in 2000 to 1.01 in 

2002. In 2003, Fannie Mae’s special 

affordable performance jumped to 17.1 

percent while the market declined slightly to 

15.9 percent, increasing Fannie Mae’s market 

ratio to 1.08. 

Between 2000 and 2001, Fannie Mae 

increased its low-mod percentage from 40.8 

percent to 42.9 percent at the same time that 

the low-mod share of the primary market was 

falling from 43.9 percent to 42.9 percent, 

placing Fannie Mae at the market’s 

performance in 2001. During 2002, the lowmod share of Fannie Mae’s purchases of 

home loans increased further to 45.3 percent, 

placing Fannie Mae 0.7 percentage points 

above the market performance of 44.6 

percent. Between 2002 and 2003 Fannie 

Mae’s performance jumped to 47.0 percent, 

while the primary market remained at 44.6 

percent, giving Fannie Mae a market ratio of 

1.05 in 2003. 

Fannie Mae increased its underserved area 

percentage from 23.4 percent in 2000 to 24.2 

percent in 2001 while the underserved area 

share of the primary market was falling from 

26.4 percent to 25.2 percent, placing Fannie 

Mae at less than one percentage point from 

the market’s performance. The ‘‘Fannie-Maeto-market’’ ratio for underserved area loans 

was 0.97 in 2001. During 2002, the 

underserved area share of Fannie Mae’s 

purchases of home loans increased further to 

26.7 percent, placing Fannie Mae slightly 

ahead of market performance (26.3 percent). 

However, between 2002 and 2003, Fannie 

Mae showed little improvement (rising to 

26.8 percent) while the market increased to 

27.6 percent, leaving Fannie Mae with a 

market ratio of 0.97. 

As noted earlier, Tables A.13 and A.14 

summarize Fannie Mae’s average 

performance over the 2001–2003 period. 

During these first three years under the 

current housing goal targets, Fannie Mae led 

the special affordable market (average 

performance of 16.2 percent versus 15.9 

percent for the market) and the low-mod 

market (average performance of 45.2 percent 

versus 44.1 percent for the market) but lagged 

the underserved areas market (average 

performance of 26.0 percent versus 26.4 

percent for the market). Table A.14 also 

reports Fannie Mae’s 2001–2003 performance 

under alternative definitions of the primary 

market. As shown there, the above findings 

of Fannie Mae’s improvement relative to the 

market during 2001–2003 are further 

reinforced when lower market percentages 

are used. For example, Fannie Mae 

essentially matches the underserved areas 

market if manufactured housing loans in 

metropolitan areas (in addition to B&C loans) 

are excluded from the market definition (a 

Fannie Mae share of 26.0 percent and a 

market share of 26.1 percent). 

Changes in the ‘‘Fannie-Mae-to-FreddieMac’’ Performance Ratio. The above 

discussion documents shifts in the relative 

performance of Fannie Mae and Freddie Mac 

63708 Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 

over the past few years. To highlight these 

changing patterns, Table A.15 reports the 

ratio of Fannie Mae’s performance to Freddie 

Mac’s performance for each goals category for 

the years 1992 to 2003. As shown there, the 

‘‘Fannie-Mae-to-Freddie-Mac’’ ratio for the 

special affordable category increased from 

approximately one in 1992 (indicating equal 

performance) to over 1.3 during the 1994–97 

period, indicating that Fannie Mae clearly 

out-performed Freddie Mac during this 

period. Between 1997 and 2000, Freddie Mac 

substantially increased its special affordable 

share (from 9.2 percent to 14.7 percent), 

causing the ‘‘Fannie-Mae-to-Freddie-Mac’’

ratio to fall from 1.27 in 1997 to 0.90 in 2000 

(indicating Freddie Mac surpassed Fannie 

Mae). But Fannie Mae’s stronger performance 

during 2001–2003 returned the ratio to above 

one (1.03 in 2001 and 2002 and 1.10 in 2003), 

indicating better performance for Fannie Mae 

(e.g., 17.1 percent in 2002 versus 15.6 percent 

for Freddie Mac). The ‘‘Fannie-Mae-toFreddie-Mac’’ performance ratio for low-mod 

loans followed a similar pattern, standing at 

1.07 in 2003 (47.0 percent for Fannie Mae 

versus 43.8 percent for Freddie Mac). 

Prior to 2000, the ‘‘Fannie-Mae-to-FreddieMac’’ ratio for underserved areas had also 

followed a pattern similar to that outlined 

above for special affordable loans, but at a 

lower overall level—rising from about one in 

1992 (indicating equal performance) to 

approximately 1.2 during the 1994–97 

period, before dropping to slightly below one 

(0.98) in 1999. However, Fannie Mae 

increased its underserved areas percentage 

from 20.4 percent in 1999 to 24.4 percent in 

2001 while Freddie Mac only increased its 

percentage from 20.9 percent to 22.3 percent. 

This resulted in the ‘‘Fannie-Mae-to-FreddieMac’’ ratio rising from 0.98 in 1999 to 1.09 

in 2001. But during 2002, Freddie Mac’s 

underserved area percentage jumped by 3.5 

percentage points to 25.8 percent, while 

Fannie Mae’s increased at a more modest rate 

(by 2.3 percentage points) to 26.7 percent, 

with the result being that the ‘‘Fannie-Maeto-Freddie-Mac’’ ratio for underserved area 

loans fell from 1.09 in 2001 to 1.03 in 2002. 

During 2003, Fannie Mae essentially 

maintained its performance (26.8 percent), 

while Freddie Mac reduced its performance 

by 1.8 percentage points to 24.0 percent. This 

increased the 2003 ‘‘Fannie Mae-to-Freddie 

Mac’’ ratio for underserved areas to 1.12. 

To conclude, while Freddie Mac ended the 

1990s on a more encouraging note than 

Fannie Mae, the past four years (2000, 2001, 

2002 and 2003) have seen a substantial 

improvement in Fannie Mae’s performance 

on all three goals-qualifying categories. 

Fannie Mae ended the 1990s with a decline 

in affordable lending performance at the 

same time that Freddie Mac was improving 

and the share of goals-qualifying loans was 

increasing in the market. Both GSEs’

performance during 2000 was encouraging—

Freddie Mac continued to improve, 

particularly with respect to the borrowerincome categories, while Fannie Mae 

reversed its declining performance, 

particularly with respect to underserved 

areas. During 2000, Freddie Mac 

outperformed Fannie Mae on the special 

affordable and low-mod categories, while 

Fannie Mae purchased a higher percentage of 

loans in underserved areas. During 2001, 

Fannie Mae continued to improve its 

performance while Freddie Mac’s 

performance remained about the same and 

the market’s originations of affordable loans 

declined somewhat. The result was that 

during 2001 Fannie Mae outperformed 

Freddie Mac on all three goals-qualifying 

categories, and even matched the market on 

the low-mod category. During 2002, both 

Fannie Mae and Freddie Mac again improved 

their performance; Fannie Mae continued to 

outperform Freddie Mac and outperformed 

the market on all three goals-qualifying 

categories. While Freddie Mac lagged the 

market on all three goals-qualifying 

categories during 2002, it had significantly 

closed its gap by the end of 2002, particularly 

on the underserved area category. During 

2003, Fannie Mae made significant 

improvement in the special affordable and 

low-mod categories, allowing it to lead the 

primary market. Freddie Mac, on the other 

hand, simply maintained its 2002 

performance in these two categories, which 

meant it lagged further behind Fannie Mae. 

On the underserved area category, Fannie 

Mae maintained its 2002 performance during 

2003 while Freddie Mac significantly 

reduced its performance, leaving both GSEs, 

but particularly Freddie Mac, behind the 

primary market on this category. 

GSE Purchases of Seasoned Loans. When 

the GSE data are expressed on a purchaseyear basis (as in the above analysis), one 

factor which affects each GSE’s performance 

concerns their purchases of seasoned (prioryear) loans. As shown in Table A.11, Fannie 

Mae followed a strategy of purchasing 

targeted seasoned loans between 1996 and 

1998, and again during 2000–2002—all years 

when Fannie Mae improved its overall 

affordable lending performance. For example, 

consider Fannie Mae’s underserved area 

performance of 24.4 percent during 2001, 

which was helped by its purchases of 

seasoned mortgages on properties located in 

underserved neighborhoods. The 

underserved area percentage for Fannie 

Mae’s purchases of newly-originated 

(current-year) mortgages was only 23.3 

percent in 2001, or 1.9 percentage points 

below the market average of 25.2 percent. 

Fannie Mae obtained its higher overall 

percentage (24.4 percent) by purchasing 

seasoned loans with a particularly high 

concentration (28.3 percent) in underserved 

areas. Similarly, during 2001, the special 

affordable share of Fannie Mae’s purchases of 

newly-originated mortgages was only 14.2 

percent, or 1.4 percentage points below the 

market average of 15.6 percent. Again, Fannie 

Mae improved its overall performance by 

purchasing seasoned loans with a high 

percentage (18.1 percent) of special 

affordable loans, enabling Fannie Mae to 

reduce its gap with the market to 0.7 

percentage points—14.9 percent versus 15.6 

percent. 

As shown in Table A.11, Freddie Mac also 

followed a strategy of purchasing seasoned 

special affordable loans mainly after 1999. 

Prior to 2000, Freddie Mac had not pursued 

such a strategy, or at least not to the same 

degree as Fannie Mae. During the 1997–99 

period, Freddie Mac’s purchases of prior-year 

mortgages and newly-originated mortgages 

had similar percentages of special affordable 

(and low-mod) borrowers. Over time, there 

have been small differentials between 

Freddie Mac’s prior-year and newlyoriginated mortgages for the underserved 

areas category but they have been smaller 

than the differentials for Fannie Mae (see

Table A.11). 

d. GSEs’ Annual Purchases of Home Loans—

Origination-Year Basis 

Table A.16 reports GSE purchase data for 

1996 to 2003 on an origination-year basis. 

Recall that in this case, mortgages purchased 

by a GSE in any particular calendar year are 

allocated to the year that the mortgage was 

originated, rather than to the year that the 

mortgage was purchased (as in the above). 

This approach places the GSE and the market 

data on a consistent, current-year basis, as 

explained earlier. 

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298These figures include estimates of first-time 

homebuyer loans for those home purchase loans 

with a missing first-time homebuyer indicator; the 

estimates were obtained by multiplying the GSE’s 

first-time homebuyer share (based only on data with 

a first-time homebuyer indicator) by the number of 

loans with a missing first-time homebuyer 

indicator. 299The first-time homebuyer share for Fannie 

Mae was almost 35 percent between 1996 and 1998; 

it then dropped to 30 percent in 1998 and to 26 

percent in 1999. The first-time homebuyer share for 

Freddie Mac was approximately 29 percent in 1996 

and 1997 before dropping to about 25 percent in 

1998 and 1999.

300See Harold L. Bunce and John L. Gardner, 

‘‘First-time Homebuyers in the Conventional 

Conforming Market: The Role of the GSEs’’

(unpublished paper), January 2004. An update of 

this work to include data for 2002 and 2003 shows 

similar patterns as those reported in the text for 

1999–2001. See Harold L. Bunce and John L. 

Gardner, ‘‘First-time Homebuyers in the 

Conventional Conforming Market: The Role of 

GSEs: An Update’’ (October, 2004).

In general, the comparisons of Freddie 

Mac’s and the market’s performance are 

similar to those discussed in Sections E.9.ac above, except for some differences on the 

special affordable category. The ‘‘Freddie 

Mac to market’’ ratios in Table A.16 show 

that Freddie Mac has improved its 

performance but has also consistently lagged 

the primary market in funding mortgages 

covered by the housing goals. 

The ‘‘Fannie Mae to market’’ ratios in 

Table A.16 show that Fannie Mae has 

improved its performance, has generally 

outperformed Freddie Mac, and led the 

market during 2003 on both the special 

affordable and low-mod goals. Under the 

origination-year approach, Fannie Mae 

lagged the market on all three housing goal 

categories during 2001 and on the 

underserved area category during 2002. 

Fannie Mae matched the market in funding 

special affordable loans during 2002 and led 

the market in funding low-mod loans. During 

2003, Fannie Mae led the primary market on 

both the special affordable and low-mod 

categories but lagged the market on the 

underserved area category. For instance in 

2003, low- and moderate-income loans 

accounted for 47.0 percent of Fannie Mae’s 

purchases and 44.6 percent of the market 

originations, placing Fannie Mae 2.4 

percentage points above the market. On the 

other hand, underserved areas accounted for 

26.3 percent of Fannie Mae’s purchases 

during 2003, which was 1.4 percentage 

points below market performance. 

e. GSEs’ Purchases of First-Time Homebuyer 

Mortgages—1999 to 2001 

While not a specific housing goal category, 

mortgages for first-time homebuyers are an 

important component of the overall home 

loan market. Making financing available for 

first-time homebuyers is one approach for 

helping young families enter the 

homeownership market. Therefore, this 

section briefly compares the GSEs’ funding of 

first-time homebuyer loans with that of 

primary lenders in the conventional 

conforming market. 

During the past few years, the GSEs have 

increased their purchases of first-time 

homebuyer loans. For example, Fannie Mae’s 

annual purchases of first-time homebuyer 

loans increased from approximately 287,000 

in 1999 to 423,485 in 2003.298 However, 

since 1999, the first-time homebuyer share of 

the GSEs’ purchases of home loans has 

remained relatively flat, varying within the 

25–28 percent range.299

Table A.17a compares the first-time 

homebuyer share of GSE purchases with 

corresponding share of home loans originated 

in the conventional conforming market. 

Readers are referred to recent work by Bunce 

and Gardner 300 for the derivation of the 

estimates of first-time homebuyer market 

shares reported in Table A.17a. Between 

1999 and 2001, first-time homebuyers 

accounted for 26.5 percent of Fannie Mae’s 

purchases of home loans, 26.5 percent of 

Freddie Mac’s, and 37.6 percent of home 

loans originated in the conventional 

conforming market. Thus, both Fannie Mae 

and Freddie Mac fell substantially short of 

the primary market in financing first-time 

homebuyers during this time period. The 

GSEs’ performance was only 70.5 percent of 

market performance (26.5 percent divided by 

37.6 percent).

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Table A.17a also reports first-time 

homebuyer shares for African Americans and 

Hispanics and for all minorities. Between 

1999 and 2001, African-American and 

Hispanic first-time homebuyers accounted 

for 4.0 percent of Fannie Mae’s purchases of 

home loans, 3.4 percent of Freddie Mac’s 

purchases, and 6.9 percent of home loans 

originated in the conventional conforming 

market. for this subgroup, Fannie Mae’s 

performance is 58 percent of market 

performance, while Freddie Mac’s 

performance is 49 percent of market 

performance. The group of all minority firsttime homebuyers accounted for 6.6 percent 

of Fannie Mae’s purchases of home loans, 5.8 

percent of Freddie Mac’s purchases, and 10.6 

percent of home loans originated in the 

conventional conforming market. In this case, 

Fannie Mae’s performance is 62 percent of 

market performance, while Freddie Mac’s 

performance is 55 percent of market 

performance. 

Section E.12 below will continue this 

examination of first-time homebuyers by 

presenting market share analysis that 

estimates the GSEs’ overall importance in the 

funding of first-time homebuyers. 

f. Low- and Moderate-Income Subgoal for 

Home Purchase Loans 

The Department is proposing to 

establishing a subgoal of 45 percent for each 

GSE’s purchases of home purchase loans for 

low- and moderate-income families in the 

single-family-owner market of metropolitan 

areas for 2005, with the subgoal rising to 46 

percent for 2006 and 47 percent for 2007 and 

2008. If the GSEs meet this subgoal, they will 

be leading the primary market by 

approximately one percentage point in 2005 

and by three percentage points in 2007–08, 

based on historical data (see below). This 

home purchase subgoal will encourage the 

GSEs to expand homeownership 

opportunities for lower-income homebuyers 

who are expected to enter the housing market 

over the next few years. As detailed in 

Section I, there are four specific reasons for 

establishing this subgoal: (1) The GSEs have 

the expertise, resources, and ability to lead 

the single-family-owner market, which is 

their ‘‘bread and butter’’ business; (2) except 

for the recent performance of Fannie Mae, the 

GSEs have historically lagged the primary 

market for low- and moderate-income loans, 

not led it; (3) the GSEs can improve their 

funding of first-time homebuyers and help 

reduce troublesome disparities in 

homeownership and access to mortgage 

credit; and (4) there are ample opportunities 

for the GSEs to expand their purchases in 

important and growing market segments such 

as the market for minority first-time 

homebuyers. Sections E.9 and G of this 

appendix provide additional information on 

opportunities for an enhanced GSE role in 

the home purchase market and on the ability 

of the GSEs to lead that market. 

As shown in Tables A.13 and A.15, lowand moderate-income families accounted for 

an average of 44.1 percent of home purchase 

loans originated in the conventional 

conforming market of metropolitan areas 

between 1999 and 2003; the figure is 43.6 

percent if the average is computed for the 

years between 1996 and 2003 or 44.1 percent 

if the average is computed for the more 

recent 2001–2003 period. Loans in the B&C 

portion of the subprime market are excluded 

from these market averages. To reach the 45-

percent subgoal for 2005, Freddie Mac would 

have to improve its performance by one 

percentage point over its approximately 44 

percent low-mod performance during 2002 

and 2003, while Fannie Mae would have to 

maintain its performance of 45–47 percent 

over these two years. To reach the 47 percent 

subgoal in 2007–08, Freddie Mac would have 

to improve by three percentage points over 

its 2002–3 performance while Fannie Mae 

would have to maintain its 2003 performance 

of 47 percent. 

As explained earlier, HUD will be rebenchmarking its median incomes for 

metropolitan areas and non-metropolitan 

counties based on 2000 Census median 

incomes, and will be incorporating the effects 

of the new OMB metropolitan area 

definitions. As shown in Table 17b, HUD 

projected the effects of these two changes on 

the low- and moderate-income shares of the 

single-family-owner market for the years 

1999–2003. These estimates will be referred 

to as ‘‘projected data’’ while the 1990-based 

data reported in the various tables will be 

referred to as ‘‘historical data.’’ With the 

historical data, the average low-mod share of 

the conventional conforming market (without 

B&C loans) was 44.2 percent for home 

purchase loans (weighted average of 1999–

2003 percentages in Table A.13); the 

corresponding average with the projected 

data was 43.5 percent, a differential of 0.7 

percentage points. However, note that in 

2003, the projected data for both GSEs and 

the market exhibit higher low-mod shares 

than the corresponding historical data. For 

2003, the low-mod shares for the projected 

and historical data are as follows: Fannie 

Mae (47.5 percent for the projected data 

versus 47.0 percent for the historical data), 

Freddie Mac (44.2 percent versus 43.8 

percent), and the market (45.6 percent versus 

44.6 percent). Thus, based on 2003 

experience, it appears that the low-mod share 

for single-family-owners in the conventional 

conforming market actually increase based 

on the re-benchmarking of area median 

incomes and the new OMB definitions of 

metropolitan areas. Thus, based on 2003 

data, the 47-percent subgoal for 2007 is 2.4 

percentage points above the 2003 market.

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301The GSE total (home purchase and refinance) 

data in Tables A.18–A.20 are presented on a 

purchase-year basis; Table A.21 presents similar 

data on an origination-year basis.

In terms of projected data, Fannie Mae 

could meet both the 2005 and 2007 subgoals 

by maintaining its projected 2003 low-mod 

performance of 47.5 percent. Freddie Mac’s 

projected low-mod performance for 2003 was 

44.2 percent, about 0.4 percentage points 

above its 2003 performance of 43.8 percent 

based on historical data. Thus, to reach the 

45-percent subgoal for 2005, Freddie Mac 

would have to increase its 2003 projected 

performance by 0.8 percentage point, and to 

reach the 47-percent 2007 subgoal, Freddie 

Mac would have to increase its performance 

by 2.8 percentage points over its projected 

performance of 44.2 percent for 2003. 

The subgoal applies only to the GSEs’

purchases in metropolitan areas because the 

HMDA-based market benchmark is only 

available for metropolitan areas. HMDA data 

for non-metropolitan areas are not reliable 

enough to serve as a market benchmark. The 

Department is also setting home purchase 

subgoals for the other two goals-qualifying 

categories, as explained in Appendices B and 

C. 

It should be noted that the findings in subsections 9.a–e above concerning the 

performance of the GSEs relative to the home 

purchase market do not change when 

projected, rather than historical data, are 

used. 

10. GSEs Purchases of Total (Home Purchase 

and Refinance) Loans 

Section E.9 examined the GSEs’

acquisitions of home purchase loans, which 

is appropriate given the importance of the 

GSEs for expanding homeownership 

opportunities. To provide a complete picture 

of the GSEs’ mortgage purchases in 

metropolitan areas, Tables A.18, A.19, A.20, 

and A.21 report the GSEs’ purchases of all 

single-family-owner mortgages, including 

both home purchase loans and refinance 

loans.301

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Table A.18 provides a long-run perspective 

on the GSEs’ overall performance. Between 

1993 and 2003, as well as during the 1996–

2003 period, the GSEs’ performance was 81–

91 percent of market performance for the 

special affordable category, 91–97 percent of 

market performance for the low-mod 

category, and 87–93 percent of market 

performance for the underserved areas 

category. For example, between 1996 and 

2003, underserved areas accounted for 23.4 

percent of Fannie Mae’s purchases and 21.8 

percent of Freddie Mac’s purchases, 

compared with 25.2 percent for the 

conventional conforming market (without 

B&C loans). Similarly, for special affordable 

loans, both GSEs lagged the market during 

the 1996–2003 period—Fannie Mae and 

Freddie Mac averaged approximately 13.0 

percent while the market was over two 

percentage points higher at 14.8 percent. 

Similar to the patterns discussed for home 

purchase loans, Fannie Mae has tended to 

outperform Freddie Mac. This can be seen by 

examining the various ‘‘Fannie-Mae-toFreddie-Mac’’ ratios in Table A.18, which are 

all equal to or greater than one. Over the 

recent 1999–2003 period, Fannie Mae and 

Freddie Mac continued to lag the overall 

market on all three goals-qualifying 

categories. Special affordable (underserved 

area) loans averaged 14.0 (23.8) percent of 

Fannie Mae’s purchases, 13.2 (22.1) percent 

of Freddie Mac’s purchases, and 15.0 (25.2) 

percent of market originations. For Fannie 

Mae, the market ratio was 0.93 for special 

affordable loans, 0.98 for low-mod loans, and 

0.94 for underserved area loans. As with 

home purchase loans, dropping the year 1999 

and characterizing recent performance by the 

2000–2003 period improves the performance 

of both GSEs relative to the market, but 

particularly Fannie Mae. Over the 2000–2003 

period, the ‘‘Fannie-Mae-to-market’’ ratio was 

0.97 for special affordable loans, 1.00 for lowmod loans, and 0.96 for underserved area 

loans. Over the last three years (2001–2003), 

the ‘‘Fannie-Mae-to-market’’ ratios are even 

higher—1.00 for special affordable loans, 

1.01 for low-mod loans, and 0.98 for 

underserved area loans. In other words, 

during the first three years under the current 

housing goal targets, Fannie Mae matched the 

special affordable market, led the low-mod 

market, and lagged the underserved areas 

market. 

The above analysis has defined the market 

to exclude B&C loans. Table A.19 shows the 

effects on the market percentages of different 

definitions of the conventional conforming 

market. For example, the average 1999–2003 

market share for special affordable 

(underserved areas) loans would fall to 14.4 

(24.8) percent if small loans and 

manufactured housing loans in metropolitan 

areas were excluded from the market 

definition along with B&C loans. In this case, 

the market ratio for Fannie Mae (Freddie 

Mac) would be was 0.97 (0.92) for special 

affordable loans, 1.00 (0.95) for low-mod 

loans, and 0.96 (0.89) for underserved area 

loans. 

Shifts in performance occurred during 

2001–2003, the first three years under HUD’s 

higher housing goal targets. Table A.20 

shows that both GSEs improved their overall 

performance between 1999 and 2000, but 

they each fell back a little during the heavy 

refinancing year of 2001. But the primary 

market (without B&C loans) experienced a 

much larger decline in affordable lending 

during the refinancing wave than did either 

of the GSEs. Fannie Mae stood out in 2001 

because of its particularly small decline in 

affordable lending. Between 2000 and 2001, 

Fannie Mae’s special affordable lending fell 

by only 0.6 percentage points while Freddie 

Mac’s fell by 2.8 percentage points and the 

market’s fell by 3.6 percentage points. The 

corresponding percentage point declines for 

the underserved areas category were 1.0 for 

Fannie Mae, 1.9 for Freddie Mac, and 3.8 for 

the market. By the end of 2001, Fannie Mae 

led Freddie Mac in all three goals-qualifying 

categories, and had erased its gap with the 

low-mod market, but continued to lag the 

market on the special affordable and 

underserved areas categories. 

During the refinancing wave of 2002, 

Fannie Mae improved slightly on the special 

affordable and low-mod categories and 

declined slightly on the underserved area 

category. Freddie Mac showed slight 

improvement on the special affordable and 

underserved area categories and remained 

about the same on the low-mod category. The 

result of these changes can be seen by 

considering the market ratios in Table A.20. 

In 2002, special affordable loans accounted 

for 14.3 percent of Fannie Mae’s purchases 

and 14.4 percent of loans originated in the 

non-B&C portion of the conventional 

conforming market, yielding a ‘‘Fannie-Maeto-market’’ ratio of 0.99. Since Fannie Mae’s 

market ratio for the special affordable 

category stood at 0.80 in 2000, Fannie Mae 

substantially closed its gap with the market 

during 2001 and 2002. During this period, 

Fannie Mae also mostly eliminated its market 

gap for the other two goals-qualifying 

categories. In 2002, underserved area loans 

accounted for 24.0 percent of Fannie Mae’s 

purchases and 24.2 percent of loans 

originated in the non-B&C portion of the 

conventional conforming market, yielding a 

‘‘Fannie-Mae-to-market’’ ratio of 0.99, or 

approximately one. During 2002, low-mod 

loans accounted for 42.2 percent of Fannie 

Mae’s purchases and 42.0 percent of loans 

originated in the market, yielding a ‘‘FannieMae-to-market’’ ratio of 1.00 (also note that 

Fannie Mae slightly outperformed the lowmod market during 2001). Thus, during 2002, 

Fannie Mae essentially matched the market 

on each of the three goals-qualifying 

categories.

In 2003, Fannie Mae’s continued to 

improve its performance on the special 

affordable and low-mod categories. In 2003, 

special affordable loans accounted for 14.3 

percent of Fannie Mae’s purchases and 14.0 

percent of loans originated in the market, 

yielding a ‘‘Fannie-Mae-to-market’’ ratio of 

1.02. During that year, low-mod loans 

accounted for 42.3 percent of Fannie Mae’s 

purchases and 41.2 percent of total (home 

purchase and refinance) loans originated in 

the market, yielding a ‘‘Fannie-Mae-tomarket’’ ratio of 1.03. On the underserved 

areas category, Fannie Mae continued to lag 

behind the market (a 23.7 percent share for 

Fannie Mae and a 24.5 percent share for the 

market). 

Freddie Mac significantly lagged the 

single-family (home purchase and refinance 

loans combined) market during 2001–2003. 

In 2003, the ‘‘Freddie-Mac-to-market’’ ratios 

were 0.86 for special affordable loans, 0.98 

for low-mod loans, and 0.82 for underserved 

area loans. 

Subprime Loans. Table A.14 in Section E.9 

showed that the goals-qualifying shares of the 

home purchase market did not change much 

when originations by subprime lenders are 

excluded from the analysis; the reason is that 

subprime lenders operate primarily in the 

refinance market. Therefore, in this section’s 

analysis of the total market (including 

refinance loans), one would expect the 

treatment of subprime lenders to significantly 

affect the market estimates and, indeed, this 

is the case. For the year 2001, excluding 

subprime loans reduced the goal-qualifying 

shares of the total market as follows: special 

affordable, from 15.0 to 13.9 percent; lowmod, from 42.3 to 40.9 percent; and 

underserved areas, from 25.7 to 23.9 percent. 

(See Table A.19.) Similar declines take place 

in 2002 and 2003. 

As explained earlier, the comparisons in 

this appendix have defined the market to 

exclude the B&C portion of the subprime 

market. Industry observers estimate that Aminus loans account for about two-thirds of 

all subprime loans while the more risky B&C 

loans account for the remaining one-third. As 

explained earlier, this analysis reduces the 

goal-qualifying percentages from the HMDA 

data by half the differentials between (a) the 

market (unadjusted) and (b) the market 

without the specialized subprime lenders 

identified by Scheessele. As shown in Table 

A.19, accounting for B&C loans in this 

manner reduces the year 2001 HMDAreported goal-qualifying shares of the total 

(home purchase and refinance) conforming 

market as follows: special affordable, from 

15.0 to 14.5 percent; low-mod, from 42.3 to 

41.6 percent; and underserved areas, from 

25.7 to 24.9 percent. Obviously, the GSEs’

performance relative to the market will 

depend on which market definition is used 

(much as it did with the earlier examples of 

excluding manufactured housing loans in 

metropolitan areas from the market 

definition). For example, defining the 

conventional conforming market to exclude 

subprime loans, rather than only B&C loans, 

would increase Fannie Mae’s 2001 special 

affordable (underserved area) market ratio 

from 0.96 to 1.00 (0.97 to 1.01). Similarly, it 

would increase Freddie Mac’s special 

affordable (underserved area) market ratio 

from 0.92 to 0.96 (0.90 to 0.94). For the 

broader-defined low-mod category, 

redefining the 2001 market to exclude 

subprime loans, rather than only B&C loans, 

would increase Fannie Mae’s (Freddie Mac’s) 

market ratio from 1.00 to 1.02 (0.97 to 0.98). 

Table A.21 reports GSE purchase data for 

total (home purchase and refinance) loans on 

an origination-year basis. The ‘‘Freddie Macto-market’’ ratios in Table A.21 show that 

Freddie Mac has lagged the primary market 

in funding mortgages covered by the housing 

goals. The ‘‘Fannie Mae-to-market’’ ratios in 

Table A.21 show that Fannie Mae has always 

lagged the primary market in funding home 

purchase and refinance mortgages for 

63720 Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 

properties in underserved areas but, in 2002 

and 2003, led the low-mod market, and in 

2003 led the special affordable market. 

11. GSE Mortgage Purchases in Individual 

Metropolitan Areas 

While the above analyses, as well as earlier 

studies, concentrate on national-level data, it 

is also instructive to compare the GSEs’

purchases of mortgages in individual 

metropolitan areas (MSAs). In this section, 

the GSEs’ purchases of single-family owneroccupied home purchase loans are compared 

to the market in individual MSAs. There are 

three steps. First, goals-qualifying 

percentages for conventional conforming 

mortgage originations (without B&C loans) 

are computed for each year and for each 

MSA, based on HMDA data. Second,

corresponding goals-qualifying percentages 

are computed for each GSE’s purchases for 

each year and for each MSA. These two sets 

of percentages are the same as those used in 

the aggregate analysis discussed in the above 

sections. Third, the ‘‘GSE-to-market’’ ratio is 

then calculated by dividing each GSE 

percentage by the corresponding market 

percentage. For example, if it is calculated 

that one of the GSEs’ purchases of low- and 

moderate-income loans in a particular MSA 

is 40 percent of their overall purchases in 

that MSA, while 44 percent of all home loans 

(excluding B&C loans) in that MSA qualify as 

low-mod, then the GSE-to-market ratio is 40/

44 (or 0.91). The goals-qualifying ratios for 

Fannie Mae and Freddie Mac can be 

compared for each MSA in a similar manner. 

Tables A.22, A.23, and A.24 summarize the 

performance of the GSEs within MSAs for 

2001, 2002 and 2003 originations of home 

purchase loans. A GSE’s performance is 

determined to be lagging the market if the 

ratio of the GSE housing goal loan purchases 

to their overall purchases is less than 99 

percent of that same ratio for the market. 

(The analysis was conducted where the ‘‘lag’’

determination is made at 98 percent instead 

of 99 percent and the results showed little 

change.) In the example given in the above 

paragraph, that GSE would be considered 

lagging the market. Tables A.22 (2001), A.23 

(2002) and A.24 (2003) report the number of 

MSAs in which each GSE under-performs the 

market with respect to each of the three 

housing goal categories. The following points 

can be made from this data: 

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302Following the purchase-year approach used in 

Sections E.9 and E.10, the GSE purchase data 

include their acquisitions of ‘‘prior-year’’ as well as 

‘‘current-year’’ mortgages, while the market data 

include only newly-originated (or ‘‘current year’’) 

mortgages.

Fannie Mae’s improvement between 2001 

and 2003 shows up in these tables. In 2001, 

Fannie Mae lagged the market in 264 (80 

percent) of the 331 MSAs in the purchase of 

underserved area loans; this number 

decreased to 236 (71 percent) MSAs in 2002 

and to 243 (73 percent) MSAs in 2003. 

Fannie Mae’s improvement was even greater 

for special affordable and low-mod loans; in 

the latter case, Fannie Mae lagged the market 

in 51 (15 percent) MSAs in 2003, compared 

with 194 (59 percent) MSAs in 2001. 

Freddie Mac’s improvement between 2001 

and 2003 was greater for underserved area 

loans. In 2001, Freddie Mac lagged the 

market in 261 (79 percent) of the 331 MSAs 

in the purchase of underserved area loans; 

this number decreased to 168 (51 percent) 

MSAs in 2002 before rising to 222 (67 

percent) MSAs in 2003. Freddie Mac’s made 

less improvement on the special affordable 

and low-mod categories; in the former case, 

Freddie Mac lagged the market in 234 (71 

percent) MSAs in 2003, compared with 279 

(84 percent) MSAs in 2001. 

12. GSE Market Shares: Home Purchase and 

First-Time Homebuyer Loans 

This section examines the role that the 

GSEs have played in the overall affordable 

lending market for home loans. There are two 

differences from the above analyses in 

Sections E.9 and E.10. The first difference is 

that this section focuses on ‘‘market share’’

percentages rather than ‘‘distribution of 

business’’ percentages. A ‘‘market share’’

percentage measures the share of loans with 

a particular borrower or neighborhood 

characteristic that is funded by a particular 

market sector (such as FHA or the GSEs). In 

other words, a ‘‘market share’’ percentage 

measures a sector’s share of all home loans 

originated for a particular targeted group. The 

‘‘market share’’ of a sector depends not only 

on the degree to which that sector 

concentrates its business on a targeted group 

(i.e., its ‘‘distribution of business’’

percentage) but also on the size, or overall 

mortgage volume, of the sector. If an industry 

sector has a large ‘‘market share’’ for a 

targeted group, then that sector is making an 

important contribution to meeting the credit 

needs of the group. Both ‘‘distribution of 

business’’ and ‘‘market share’’ data are 

important for evaluating the GSEs’

performance. In fact, given the large size of 

the GSEs, one would expect that a ‘‘market 

share’’ analysis would highlight their 

importance to the affordable lending market. 

The second difference is that this section 

also examines the role of the GSEs in the 

total market for home loans, as well as in the 

conventional conforming market. Such an 

approach provides a useful context for 

commenting on the contribution of Fannie 

Mae and Freddie Mac to overall affordable 

lending, particularly given evidence that 

conventional lenders have done a relatively 

poor job providing credit access to 

disadvantaged families, which renders the 

conventional market a poor benchmark for 

evaluating GSE performance. The analysis of 

first-time homebuyers conducts the market 

share analysis in terms of both the total 

market (Section E.12.b) and the conventional 

conforming market (Section E.12.c). 

While the GSEs have accounted for a large 

share of the overall market for home 

purchase loans, they have accounted for a 

very small share of the market for important 

groups such as minority first-time 

homebuyers. But as this section documents, 

the GSEs have been increasing their share of 

the low-income and minority market, which 

provides an optimistic note on which to go 

forward. 

Section E.12.a uses HMDA and GSE data 

to estimate the GSEs’ share of home loans 

originated for low-income and minority 

borrowers and their neighborhoods. Sections 

E.12.b and E.12.c summarize recent research 

on the role of the GSEs in the first-time 

homebuyer market. Section E.12.d examines 

the downpayment characteristics of home 

loans purchased by the GSEs, a potentially 

important determinant of the GSEs’ ability to 

reach first-time homebuyers. 

a. GSEs’ Share of Home Purchase Lending 

Table A.25 reports market share estimates 

derived by combining HMDA market data 

with GSE and FHA loan-level data. To 

understand these estimates, consider the GSE 

market share percentage of 46 percent for 

‘‘All Home Purchase Loans’’ at the bottom of 

the first column in the table. That market 

share percentage is interpreted as follows: 

It is estimated that home loans acquired by 

Fannie Mae and Freddie Mac during the 

years, 1999 to 2003, totaled 46 percent of all 

home loans originated in metropolitan areas 

during that period. 

It should be noted that ‘‘all home loans’’

refers to all government (FHA and VA) loans 

plus all conventional loans less than the 

conforming loan limit; in other words, only 

‘‘jumbo loans’’ are excluded from this 

analysis.302 The analysis is restricted to 

metropolitan areas because HMDA data (the 

source of the market estimates) are reliable 

only for metropolitan areas. B&C originations 

are included in the market data, since the 

purpose here is to gauge the GSEs’ role in the 

overall mortgage market. As discussed in 

Section E.9, excluding B&C loans, or even all 

subprime loans, would not materially affect 

this analysis of the home loan market since 

subprime loans are mainly for refinance 

purposes. The analysis below frequently 

combines purchases by Fannie Mae and 

Freddie Mac since previous sections have 

already compared their performance relative 

to each other.

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303As explained in Section E.7, the GSEs’

affordable lending performance is evaluated relative 

to the conventional conforming market, as required 

by Congress in the 1992 GSE Act that established 

the housing goals. However, it is insightful to 

examine their overall role in the mortgage market 

and to contrast them with other major sectors of the 

market such as FHA. There is no intention here to 

imply that the GSEs should purchase the same 

types of loans that FHA insures.

304As explained in the notes to Table A.25, 

HMDA data are the source of the market figures. It 

is assumed that HMDA data cover 85 percent of all 

mortgage originations in metropolitan areas. If 

HMDA data covered higher (lower) percentages of 

market loans, then the market shares for both the 

GSEs and FHA would be lower (higher).

305See Harold L. Bunce, The GSEs’ Funding of 

Affordable Loans: A 2000 Update, Housing Finance 

Working Paper No. HF–013, Office of Policy 

Development and Research, HUD, April 2002.

306Bunce explains numerous assumptions and 

caveats related to combining American Housing 

Survey data on homebuyers with FHA and GSE 

data on mortgages. For example, the American 

Housing Survey (AHS) data used by Bunce 

included both financed home purchases and homes 

purchased with cash. If only financed home 

purchases were used, the market shares of both 

FHA and the GSEs would have been slightly higher 

(although the various patterns would have 

remained the same). The AHS defines first-time 

homebuyers as buyers who have never owned a 

home, while FHA and the GSEs define a first-time 

homebuyer more expansively as buyers who have 

not owned a home in the past three years. If it were 

possible to re-define the FHA and GSE data to be 

consistent with the AHS data, the FHA and GSE 

first-time homebuyer shares would be lower (to an 

unknown degree). For additional caveats with the 

AHS data, also see David A. Vandenbroucke, Sue 

G. Neal, and Harold L. Bunce, ‘‘First-Time 

Homebuyers: Trends from the American Housing 

Survey’’, November 2001, U.S. Housing Market 

Condition, a quarterly publication of the Office of 

Policy Development and Research at HUD. In some 

years, home purchases as measured by the AHS 

declined while home purchases as measured by 

other data sources (e.g., HMDA) increased. In 

addition, the AHS home purchase data for separate 

minority groups (e.g., African-Americans, 

Hispanics) sometimes exhibited shifts inconsistent 

with other sources.

307BNV’s methodology for estimating first-time 

borrowers consists of three steps: (1) estimate the 

total number of home purchase loans originated 

during a particular year using a mortgage market 

model that they develop; (2) disaggregate the home 

purchase loans in step (1) into racial and ethnic 

groups using HMDA data for metropolitan areas; 

and (3) for each racial and ethnic group in step (2), 

estimate the number of first-time homebuyers using 

mortgage and first-time homebuyer information 

from the American Housing Survey.

The GSE market share percentage for 

‘‘Low-Income Borrowers’’ at the top of the 

first column of Table A.25 has a similar 

interpretation: 

It is estimated that home loans for lowincome borrowers acquired by Fannie Mae 

and Freddie Mac between 1999 and 2003 

totaled 38 percent of all home loans 

originated for low-income borrowers in 

metropolitan areas. 

According to the data in Table A.25, the 

GSEs account for a major portion of the 

market for targeted groups. For example, 

purchases by Fannie Mae and Freddie Mac 

represented 38 percent of the low-incomeborrower market and 36–38 percent of the 

markets in low-income, high-minority, and 

underserved census tracts. Thus, access to 

credit in these historically underserved 

markets depends importantly on the 

purchase activities of Fannie Mae and 

Freddie Mac. However, the data in Table 

A.25 show that the GSEs’ role in low-income 

and minority markets is significantly less 

than their role in the overall home loan 

market. Fannie Mae and Freddie Mac 

accounted for 46 percent of all home loans 

but only 37 percent of the loans financing 

properties in underserved neighborhoods. 

Their market share was even lower for loans 

to African-American and Hispanic 

borrowers—30 percent, or 16 percentage 

points less than the GSEs’ overall market 

share of 46 percent. 

An encouraging finding is that the GSEs 

have increased their presence in the 

affordable lending market during 2002 and 

2003, when they accounted for 40–44 percent 

of the loans financing properties in lowincome, high-minority, and underserved 

neighborhoods and for 34 percent of loans for 

African-American and Hispanic borrowers. 

These market share figures for the GSEs are 

generally higher than their performance 

during the two earlier years, 2000 and 2001. 

To provide additional perspective, Table 

A.25 also reports market share estimates for 

FHA.303 During the 1999–2003 period, FHA’s 

overall market share was less than half of the 

GSEs’ market share, as FHA insured only 16 

percent of all home mortgages originated in 

metropolitan areas. However, FHA’s shares of 

the underserved segments of the market were 

much higher than its overall market share. 

For instance, between 1999 and 2003, FHA 

insured 24 percent of all mortgages 

originated in low-income census tracts, even 

though it insured only 16 percent of all home 

loans. FHA’s share of the market was 

particularly high for African-American and 

Hispanic borrowers, as FHA insured 29 

percent of all home loans originated for these 

borrowers between 1999 and 2003—a figure 

only one percentage point higher than the 

GSEs’ share of 30 percent.304 Thus, during 

the 1999–2003 period, FHA’s overall market 

share (16.0 percent) was about one-third of 

the GSEs’ market share (45.6 percent), but its 

share of the market for loans to AfricanAmericans and Hispanics was almost equal 

to the GSEs’ share of that market.

The data for the two recent years (2002 and 

2003) indicate a larger market role for Fannie 

Mae and Freddie Mac relative to FHA. While 

the GSEs continued to have a much larger 

share of the overall market than FHA (47–49 

percent for the GSEs versus 11–14 percent for 

FHA), their share of home loans for AfricanAmericans and Hispanics jumped to 34 

percent during 2002 and 2003, which was 

higher than the percentage share for FHA 

(17–25 percent). The differentials in market 

share between FHA and the GSEs on the 

other affordable lending categories listed in 

Table A.25 were also higher in 2002 and 

2003 than in earlier years. 

b. The GSEs’ Share of the Total First-Time 

Homebuyer Market 

This section summarizes two recent 

analyses of mortgage lending to first-time 

homebuyers; these two studies examine the 

total mortgage market, including both 

government and conventional loans 

originated throughout the U.S. (i.e., in both 

metropolitan areas and non-metropolitan 

areas). Section E.12.c will summarize a third 

study of first-time homebuyers that focuses 

on the conventional conforming market. All 

three studies are market share studies that 

examine the GSEs’ role in the first-time 

homebuyer market. 

First, a study by Bunce concluded that the 

GSEs have played a particularly small role in 

funding minority first-time homebuyers.305

Because HMDA does not require lenders to 

report information on first-time homebuyers, 

Bunce used data from the American Housing 

Survey to estimate the number of first-time 

homebuyers in the market. Using American 

Housing Survey data on home purchases 

from 1997 to 1999, Bunce estimated that the 

GSEs’ share of the market for first-time 

African-American and Hispanic homebuyers 

was only 10–11 percent, or less than onethird of their share (36 percent) of all home 

purchases during that period. FHA’s share of 

this market was 36 percent, or twice its share 

(18 percent) of all home purchases.306 These 

data highlight the small role that the GSEs 

have played in the important market for 

minority first-time homebuyers.

Bunce, Neal and Vandenbroucke (BNV) 

recently updated through 2001 the study by 

Bunce. In addition, BNV developed an 

improved methodology that combined 

industry, HMDA and AHS data to estimate 

the number of first-time homebuyers (by race 

and ethnicity) in the mortgage market during 

the years 1996 to 2001.307 BNV’s analysis 

includes the total mortgage market, that is, 

the government, conventional conforming, 

and jumbo sectors of the mortgage market.

Table A.26 presents the key market shares 

estimated by BNV for the GSEs and FHA. The 

first figure (40.7) in Table A.26 is interpreted 

as follows: purchases of home loans by 

Fannie Mae and Freddie Mac totaled 40.7 

percent of all home loans financed between 

1996 and 2001. Going down the first column 

shows that the GSEs’ share of the first-time 

homebuyer market was 24.5 percent during 

the 1996-to-2001—a market share 

significantly lower than their overall market 

share of 40.7 percent. 

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308See Bunce, Neal, and Vandenbroucke, op. cit.,

for comparisons of various estimates of the market 

shares for FHA and the GSEs using different data 

bases and estimation methods. One can compare (a) 

the 1999–2001 market shares for FHA and the 

conventional conforming market in metropolitan 

areas calculated using the same methodology as 

Table A.25 with (b) the 1999–2001 market share 

estimates reported in Table A.25 for the entire 

mortgage market (including jumbo loans and 

covering non-metropolitan areas as well as 

metropolitan areas). The results are strikingly 

consistent. For the 1999-to-2001 period, the FHA 

share of the overall (African American and 

Hispanic) home loan market is estimated to be 19.0 

percent (35.8 percent) under (a) versus 16.4 percent 

(31.2 percent) under (b). Lower percentage shares 

are expected for (b) because (b) includes jumbo 

loans. For the same period, the GSE share of the 

overall (African American and Hispanic) home loan 

market is estimated to be 46.0 percent (25–28 

percent) under (a) versus 41.5 percent (24.3 percent) 

under (b).

309For other analyses of the GSEs’ market role, 

see the following study by economists at the 

Federal Reserve Board: Glenn B. Canner, Wayne 

Passmore, and Brian J. Surette, ‘‘Distribution of 

Credit Risk among Providers of Mortgages to LowerIncome and Minority Homebuyers’’ in Federal 

Reserve Bulletin, 82(12): 1077–1102, December, 

1996. This study considered several characteristics 

of the GSEs’ loan purchases (such as amount of 

downpayment) and concluded that the GSEs have 

played a minimal role in providing credit support 

for underserved borrowers.

FHA’s greater focus on first-time 

homebuyers is also reflected in the market 

share data reported in Table A.26. While 

FHA insured only 16.6 percent of all home 

loans originated between 1996 and 2001, it 

insured 30.9 percent of all first-timehomebuyer loans during that period. The 

GSEs, on the other hand, accounted for a 

larger share (40.7 percent) of the overall 

home purchase market but a smaller share 

(24.5 percent) of the first-time homebuyer 

market. 

Table A.26 also reports home purchase and 

first-time homebuyer information for 

minorities. During the more recent 1999-to2001 period, the GSEs’ loan purchases 

represented 41.5 percent of all home 

mortgages but only 24.3 percent of home 

loans for African-American and Hispanic 

families, and just 14.3 percent of home loans 

for African-American and Hispanic first-time 

homebuyers. During this period, the GSEs’

role in the market for first-time AfricanAmerican and Hispanic homebuyers was 

only one-third of their role in the overall 

home loan market (14.3 percent versus 41.5 

percent). 

FHA, on the other hand, accounted for a 

much larger share of the minority first-time 

homebuyer market than it did of the overall 

homebuyer market. Between 1999 and 2001, 

FHA insured 46.5 percent of all loans for 

African-American and Hispanic first-time 

homebuyers—a market share that was almost 

three times its overall market share of 16.4 

percent.308 While FHA’s market share was 

two-fifths of the GSEs’ share of the overall 

home purchase market (16.4 percent versus 

41.5 percent), FHA’s market share was over 

three times the GSEs’ share of the market for 

first-time African-American and Hispanic 

homebuyers (46.5 percent versus 14.3 

percent). This finding that the GSEs have 

played a relatively minor role in the firsttime minority market is similar to the 

conclusion reached by the Fed researchers 

(see below) and Bunce (2002) that the GSEs 

have provided little credit support to this 

underserved borrower group.

The results reported in Table A.26 for the 

year 2001 suggest some optimism concerning 

the GSEs’ role in the first-time homebuyer 

market. As explained in earlier sections, both 

GSEs, but particularly Fannie Mae, improved 

their affordable lending performance during 

2001, at a time when the overall market’s 

performance was slightly declining. This 

improvement is reflected in the higher firsttime market shares for the GSEs during the 

year 2001, compared with the two previous 

years, 1999 and 2000 (not reported). The 

GSEs’ share of the market for first-time 

African-American and Hispanic homebuyers 

jumped from about 11–12 percent during 

1999 and 2000 to 19.7 percent in 2001. 

Fannie Mae’s share of this market almost 

doubled during this period, rising from 7.0 

percent in 1999 to 12.6 percent in 2001. 

Thus, while the GSEs continue to play a 

relatively small role in the minority first-time 

homebuyer market, during 2001 they 

improved their performance in this area.309

c. The GSEs’ Share of the Conventional 

Conforming, First-time Homebuyer Market 

Bunce and Gardner (2004) recently 

conducted an analysis of first-time 

homebuyers for the conventional conforming 

market. The Bunce and Gardner analysis 

used a similar methodology to the study by 

Bunce, Neal, and Vandenbroucke of first-time 

homebuyers in the total mortgage market. 

Bunce and Gardner restricted their analysis 

to the funding of first-time homebuyers in the 

conventional conforming market, which is 

the market where Fannie Mae and Freddie 

Mac operate. Their market share results are 

summarized in Table A.27.

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Between 1999 and 2001, the GSEs’

purchases accounted for 56.6 percent of all 

home loans originated in the conventional 

conforming market of both metropolitan 

areas and non-metropolitan areas. In other 

words, Fannie Mae and Freddie Mac funded 

almost three out of every five homebuyers 

entering the conventional conforming market 

between 1999 and 2001. Their purchases of 

first-time homebuyer loans, on the other 

hand, accounted for only 39.8 percent of all 

first-time homebuyer loans originated in that 

market. Thus, while the GSEs funded 

approximately two out of every five first-time 

homebuyers entering the conventional 

conforming market, their market share (39.8 

percent) for first-time homebuyers was only 

70 percent of their market share (56.6 

percent) for all homebuyers. 

As shown in Table A.27, the GSEs have 

funded an even lower share of the minority 

first-time homebuyer market. Between 1999 

and 2001, the GSEs purchases of AfricanAmerican and Hispanic first-time homebuyer 

loans represented 30.9 percent of the 

conventional conforming market for these 

loans. Thus, while the GSEs have accounted 

for 56.6 percent of all home loans in the 

conventional conforming market, they have 

accounted for only 30.9 percent of loans 

originated in that market for AfricanAmerican and Hispanic first-time 

homebuyers. 

The market share data in Table A.27 show 

some slight differences between the Freddie 

Mac and Fannie Mae in serving minority 

first-time homebuyers. During the 1999-to2001 period, Freddie Mac’s share (11.9 

percent) of the African-American and 

Hispanic first-time homebuyer market was 

only one-half of its share (24.0 percent) of the 

home loan market. On the other hand, Fannie 

Mae’s share (19.0 percent) of the AfricanAmerican and Hispanic first-time homebuyer 

market was almost 60 percent of its share 

(32.5 percent) of the home loan market. Thus, 

while Fannie Mae performance in serving 

minority first-time homebuyers has been 

poor, it has been better than Freddie Mac’s. 

This difference in performance between 

Fannie Mae and Freddie Mac was also seen 

in the portfolio percentages reported earlier 

in Table A.17a. Loans for African-American 

and Hispanic first-time homebuyers 

accounted for 6.9 percent of Fannie Mae’s 

purchases of home loans between 1999 and 

2001, a figure higher than Freddie Mac 

percentage of 5.3 percent. Loans for AfricanAmerican and Hispanic first-time 

homebuyers accounted for 10.2 percent of all 

home loans originated in the conventional 

conforming market. 

d. Downpayments on Loans Purchased by the 

GSEs 

The level of downpayment can be an 

important obstacle to young families seeking 

their first homes. Examining the 

downpayment characteristics of the 

mortgages purchased by the GSEs might help 

explain why they have played a rather 

limited role in the first-time homebuyer 

market 

Table A.28 reports the loan-to-value (LTV) 

distribution of home purchase mortgages 

acquired by the GSEs between 1997 and 

2003. In Table A.29, LTV data are provided 

for the GSEs’ purchases of home loans that 

qualify for the three housing goals—special 

affordable, low-mod, and underserved areas. 

Three points stand out.

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310Canner, et al., op. cit.

311The Impact of Secondary Mortgage Market and 

GSE Purchases on Underserved Neighborhood 

Housing Markets: Final Report to HUD. July 2002.

312 GSE Service to Rural Areas, 2002.

313An Analysis of the Effects of the GSE 

Affordable Goals on Low- and Moderate-Income 

Families, 2001.

314 Van Order, Robert. 1996. ‘‘Discrimination and 

the Secondary Mortgage Market.’’ In John Goering 

and Ronald Wienk, eds. Mortgage Discrimination, 

Race, and Federal Policy. The Urban Institute Press, 

Washington, DC: 335–363.

First, the GSEs (and particularly Fannie 

Mae) have recently increased their purchases 

of home loans with low downpayments. 

After remaining about 4 percent of Fannie 

Mae’s purchases between 1997 and 2000, 

over-95-percent-LTV loans (or less-than-fivepercent downpayment loans) jumped to 7.1 

percent during 2001, 7.7 percent in 2002 and 

11.5 percent in 2003. It is interesting that this 

jump in less-than-five-percent downpayment 

loans occurred in the same years that Fannie 

Mae improved its purchases of loans for lowincome homebuyers, as discussed in earlier 

sections. As a share of Freddie Mac’s 

purchases, over-95-percent-LTV loans 

increased from 1.1 percent in 1997 to 5.9 

percent in 2000, before falling to 4.3 percent 

in 2001, 4.8 percent in 2002 and 4.7 percent 

in 2003. If the low-downpayment definition 

is expanded to ten percent (i.e., over-90-

percent-LTV loans), Freddie Mac had about 

the same percentage (25 percent) of lowdownpayment loans during 2001 as Fannie 

Mae. In fact, under the more expansive 

definition, Freddie Mac had the same share 

of over-90-percent-LTV loans in 2001 as it 

did in 1997 (about 25 percent), while Fannie 

Mae exhibited only a modest increase in the 

share of its purchases with low 

downpayments (from 23.2 percent in 1997 to 

25.4 percent in 2001). The share of over-90-

percent-LTV loans in Freddie Mac’s 

purchases fell sharply from 25.0 percent in 

2001 to 21.9 percent in 2002 and 19.9 percent 

in 2003, while the share in Fannie Mae’s 

purchases fell more modestly from 25.4 

percent in 2001 to 24.2 percent in 2002 

before rebounding to 25.3 percent in 2003. 

Second, loans that qualify for the housing 

goals have lower downpayments than nonqualifying loans. In 2001 and 2002, over-95-

percent-LTV loans accounted for about 15 

percent of Fannie Mae’s purchases of special 

affordable loans, 13 percent of low-mod 

loans, and 12 percent of underserved area 

loans, compared with about 7.5 percent of 

Fannie Mae’s purchases of all home loans. 

(See Table A.29.) In 2003 these percentages 

increased to 23, 19 and 19 percent for special 

affordable, low-mod and underserved areas 

respectively. These low-downpayment shares 

for 2001, 2002 and 2003 were double those 

for 2000 when over-95-percent-LTV loans 

accounted for 8.4 percent of Fannie Mae’s 

purchases of special affordable loans and 

about 7 percent of its purchases of low-mod 

and underserved area loans. Fannie Mae’s 

low-downpayment shares during 2001 were 

higher than Freddie Mac’s shares of 12.3 

percent for special affordable loans and about 

9 percent for low-mod and underserved area 

loans. Between 2001 and 2003, Freddie Mac’s 

over-95-percent-LTV shares fell sharply to 3–

4 percent for the three housing goal 

categories, while Fannie Mae’s shares 

increased to the 13–23 percent range. Under 

the more expansive, over-90-percent-LTV 

definition, almost one-third of Fannie Mae’s 

goals-qualifying purchases during 2001 

would be considered low downpayment, as 

would a slightly smaller percentage of 

Freddie Mac’s purchases. However, during 

2003, Freddie Mac’s over-90-percent-LTV 

shares for the goals-qualifying loans fell to 

20–22 percent. 

Third, a noticeable pattern among goalsqualifying loans purchased by the GSEs is the 

predominance of loans with high 

downpayments. For example, 54.3 percent of 

special affordable home loans purchased by 

Freddie Mac during 2003 had a 

downpayment of at least 20 percent, a 

percentage not much lower than the highdownpayment share (59.5 percent) of all 

Freddie Mac’s home loan purchases. 

Similarly, 49.8 percent of the home loans 

purchased by Fannie Mae in underserved 

areas during 2003 had a twenty percent or 

higher downpayment, compared with 54.6 

percent of all home loans purchased by 

Fannie Mae. 

Thus, the data in Tables A.28 and A.29 

show a preponderance of high downpayment 

loans, even among lower-income borrowers 

who qualify for the housing goals. The past 

focus of the GSEs on high-downpayment 

loans provides some insight into a study by 

staff at the Federal Reserve Board who found 

that the GSEs have offered little credit 

support to the lower end of the mortgage 

market.310 The fact that approximately half of 

the goals-qualifying loans purchased by the 

GSEs have a downpayment of over twenty 

percent is also consistent with findings 

reported earlier concerning the GSEs’

minimal service to first-time homebuyers, 

who experience the most problems raising 

cash for a downpayment. On the other hand, 

the recent experience of Fannie Mae suggests 

that purchasing low-downpayment loans 

may be one technique for reaching out and 

funding low-income and minority families 

who are seeking to buy their first home.

13. Other Studies of the GSEs’ Performance 

Relative to the Market 

This section summarizes briefly the main 

findings from other studies of the GSEs’

affordable housing performance. These 

include studies by the HUD and the GSEs as 

well as studies by academics and research 

organizations. 

Freeman and Galster Study.311 A recent 

study by Lance Freeman and George Galster 

uses econometric analysis to test whether the 

Government-Sponsored Enterprises (GSEs) 

Fannie Mae and Freddie Mac purchases of 

home mortgages in neighborhoods 

traditionally underserved by financial 

institutions stimulate housing market activity 

in those neighborhoods. Specifically, this 

study analyzes data of single-family home 

sales volumes and prices of mortgages 

originated from 1993–1999 in Cleveland, OH.

The study concludes that aggressive 

secondary market purchasing behavior by 

non-GSE entities stimulated sales volumes 

and prices of homes in low-income and 

predominantly minority-occupied 

neighborhoods of Cleveland. The study 

results also showed a positive relationship 

between home transaction activity and the 

actions of the secondary mortgage market, 

and concludes that the secondary mortgage 

market (and the non-GSE sector in particular) 

purchases of mortgages had a positive effect 

on the number of sales transactions one year 

later. However, the study also concludes that 

although non-GSE purchases of non-home 

purchase mortgages appeared to boost prices 

one and two years later, no consistent 

impacts of purchasing rates on sales prices 

could be observed. In addition, there was no 

robust evidence that GSE purchasing rates 

were positively associated with single-family 

home transactions volumes or sales prices 

during any periods. 

Urban Institute Rural Markets Study.312 A 

study by Jeanette Bradley, Noah Sawyer, and 

Kenneth Temkin uses both quantitative and 

qualitative data to explore the issue of GSE 

service to rural areas. The study first 

summarizes the existing research on rural 

lending and GSE service to rural areas. It 

then reviews the current underwriting 

guidelines of Fannie Mae, Freddie Mac, the 

USDA Rural Housing Service, and Farmer 

Mac, focusing on issues relevant to rural 

underwriting. The GSE public-use database is 

used to analyze GSE non-metro loan 

purchasing patterns from 1993–2000. Finally, 

the study presents the results of a series of 

discussions conducted with key national 

industry and lender experts and local experts 

in three rural sites in south-central Indiana, 

southwestern New Mexico and southern New 

Hampshire chosen for the diversity of their 

region, population, economic structures, and 

housing markets.

The authors of the study conclude that 

while Fannie Mae and Freddie Mac have 

increased their lending to rural areas since 

1993, their non-metro loan purchases still lag 

behind their role in metro loan purchases, 

particularly in regard to the percentage of 

affordable loans. From the discussions with 

experts, the authors of the study make the 

following policy recommendations: 

Underserved populations and rural areas 

should be specifically targeted at the censustract level; HUD should set manufactured 

housing goals; HUD should consider 

implementing a survey of small rural lenders 

or setting up an advisory group of small rural 

lenders in order to determine their 

suggestions for creating stronger 

relationships between the GSEs and rural 

lenders with the goal of increasing GSE nonmetro purchase rates. 

Urban Institute GSE Impacts Study.313 A 

report by Thomas Thibodeau, Brent 

Ambrose, and Kenneth Temkin analyzes the 

extent to which the GSEs’ responses to the 

Federal Housing Enterprises Financial Safety 

and Soundness Act’s (FHEFSSA) affordable 

housing goals have had their intended effect 

of making low- and moderate-income 

families better off. Specifically the report 

examines several methodologies determining 

that the conceptual model created by Van 

Order in 1996 314 provided the most complete 

description of how the primary and 

secondary markets interact. This model was 

then applied in a narrow scope to capital 

market outcomes which included GSE 

market shares and effective borrowing costs, 

63734 Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 

315Are the GSEs Leading, and if So Do They Have 

Any Followers? An Analysis of the GSEs’ Impact on 

Home Purchase Lending to Underserved Markets 

During the 1990s. University of Notre Dame 

Working Paper and Technical Series Number 2003–

2. 2002.

316A detailed discussion of the GSEs’ activities in 

this area is contained in Theresa R. Diventi, The 

GSEs’ Purchases of Single-Family Rental Property 

Mortgages, Housing Finance Working Paper No. 

HF–004, Office of Policy Development and 

Research, Department of Housing and Urban 

Development, (March 1998).

and housing market outcomes that include 

low- and moderate-income homeownership 

rates. Finally, metropolitan American 

Housing Survey (AHS) data for eight cities 

were used to conduct empirical analyses of 

the two categories of outcomes. These cities 

included areas surveyed in 1992, the year 

before HUD adopted the affordable housing 

goals, to provide the baseline for the analysis. 

Four metropolitan areas were surveyed in 

1992 and again in 1996: Cleveland, 

Indianapolis, Memphis and Oklahoma City. 

Four cities were surveyed in 1992 and again 

in 1998: Birmingham, Norfolk, Providence 

and Salt Lake City.

The study’s empirical analysis suggests 

that the GSE affordable goals have helped to 

make homeownership more attainable for 

target families. The assessment of the effects 

of the affordable goals on capital markets 

showed that the GSE share of the 

conventional conforming market has 

increased, especially for lower income 

borrowers and neighborhoods. The study also 

concludes that the affordable housing goals 

have an impact on the purchase decisions of 

Fannie Mae and Freddie Mac. The study also 

finds that interest rates are lower in markets 

in which Fannie Mae and Freddie Mac 

purchase a higher proportion of conventional 

loans. Finally, the study’s analysis shows 

that overall lending volume in a metropolitan 

area increases when the GSEs purchase 

seasoned loans. 

Specifically, that homeownership rates 

increased at a faster rate for low-income 

families when compared to all families, and 

that in a subset of MSAs, minority 

homeownership rates also grew faster when 

compared to overall homeownership changes 

in those MSAs. 

Finally, the affordable housing goal effects 

were examined for 80 MSAs in relation to the 

homeownership rate changes between 1991 

and 1997. The study found that the GSEs, by 

purchasing loans originated to low-income 

families, helped to reduce the disparity 

between homeownership rates for lower and 

higher income families, suggesting that the 

liquidity created when the GSEs purchase 

loans originated to low-income families is 

recycled into more lending targeted to lower 

income homebuyers. 

The authors of the study qualify their 

results by stating that they are based on 

available data that does not provide the level 

of detail necessary to conduct a fully 

controlled national assessment. 

Williams and Bond Study.315 Richard 

Williams and Carolyn Bond examine GSE 

leadership of the mortgage finance industry 

in making credit available for low- and 

moderate-income families. Specifically, it 

asks if the GSEs are doing relatively more of 

their business with underserved markets than 

other financial institutions, and whether the 

GSEs’ leadership helps to narrow the gap in 

home mortgage lending that exists between 

served and underserved markets. The study 

uses HMDA data for metropolitan areas and 

the Public Use Data Base at HUD for 

compilations of GSE data sets for the entire 

nation (GSE PUDB File B) to conduct 

descriptive and multivariate analyses of 

nationwide lending between 1993 and 2000. 

Additionally, separate analyses are 

conducted that include and exclude loans 

from subprime and manufactured housing 

lenders.

The study concludes that the GSEs are not 

leading: They do not purchase relatively 

more underserved market loans than the 

primary market makes nor do they purchase 

as many of these loans as their secondary 

market competitors. Additionally, the study 

concludes that the disparities between the 

GSEs and the primary market are even greater 

once the growing role of subprime and 

manufactured housing is considered. The 

authors admit that there have been signs of 

progress, particularly in 1999 and 2000 when 

primary market lending to underserved 

markets increased and GSE purchases of 

underserved market loans increased even 

faster. Regardless, the study concludes that 

there continues to be significant racial, 

economic, and geographic disparities in the 

way that the benefits of GSE activities are 

distributed and that the benefits of GSE 

activities still go disproportionately to 

members of served rather than underserved 

markets. 

14. The GSEs’ Support of the Mortgage 

Market for Single-Family Rental Properties

The 1996 Property Owners and Managers 

Survey reported that 49 percent of rental 

units are found in the ‘‘mom and pop shops’’

of the rental market—‘‘single-family’’ rental 

properties, containing 1–4 units. These small 

properties are largely individually-owned 

and managed, and in many cases the ownermanagers live in one of the units in the 

property. They include many properties in 

older cities, in need of financing for 

rehabilitation. Single-family rental units play 

an especially important role in lower-income 

housing, over half of such units are 

affordable to very low-income families. 

There is not, however, a strong secondary 

market for single-family rental mortgages. 

While single-family rental properties 

comprise a large segment of the rental stock 

for lower-income families, they make up a 

small portion of the GSEs’ business. Between 

1999 and 2002, single-family rental 

properties accounted for only 7.6 percent of 

total (both single-family and multifamily) 

units financed by the GSEs during this 

period. It follows that since single-family 

rentals make up such a small part of the GSEs 

business, they have not penetrated the singlefamily rental market to the same degree that 

they have penetrated the owner-occupant 

market. Table A.30 below shows that 

between 1999 and 2002, the GSEs financed 

61 percent of owner-occupied dwelling units 

in the conventional conforming market, but 

only 40 percent of single-family rental units. 

There are a number of factors that have 

limited the development of the secondary 

market for single-family rental property 

mortgages thus explaining the lack of 

penetration by the GSEs. Little is collectively 

known about these properties as a result of 

the wide spatial dispersion of properties and 

owners, as well as a wide diversity of 

characteristics across properties and 

individuality of owners. This makes it 

difficult for lenders to properly evaluate the 

probability of default and severity of loss for 

these properties. 

Single-family rental properties could be 

important for the GSEs housing goals, 

especially for meeting the needs of lowerincome families. Between 1999 and 2002, 87 

percent of the GSEs’ single-family rental 

units qualified for the Low- and ModerateIncome Goal, compared with 40 percent of 

one-family owner-occupied properties. (See 

Table A.30.) This heavy focus on lowerincome families meant that single-family 

rental properties accounted for 14 percent of 

the units qualifying for the Low- and 

Moderate-Income Goal, even though they 

accounted for 7.6 percent of the total units 

(single-family and multifamily) financed by 

the GSEs. 

Given the large size of this market, the high 

percentage of these units which qualify for 

the GSEs’ housing goals, and the weakness of 

the secondary market for mortgages on these 

properties, an enhanced presence by Fannie 

Mae and Freddie Mac in the single-family 

rental mortgage market would seem 

warranted.316 Single-family rental housing is 

an important part of the housing stock 

because it is an important source of housing 

for lower-income households.

Despite the size and importance of singlefamily rental properties for low-income 

people, HUD received several comments 

advocating exclusion of single-family rentals 

from goals consideration. These commenters 

pointed out that single-family owneroccupiers often maintain their properties 

more effectively than single-family absentee 

landlords or their tenants. HUD was asked to 

exclude single-family investor owned 

properties to reduce these neighborhood 

effects. 

Community associations raise an important 

issue for neighborhood development. 

However, they do not address the question of 

effective goals promotion for all segments of 

the housing market. They compare 

maintenance by owner-occupiers to 

maintenance by investors in the single-family 

market. This does not address the housing 

outcomes for tenants with access to singlefamily rental compared to tenants in 

multifamily rental. With nearly half of rental 

units in older cities composed of smaller 

single-family units, denial of goals eligibility 

for single-family investors would exclude a 

substantial proportion of housing units 

available to low income people. 

Furthermore, single-family investors 

provide additional market benefits to the 

housing system. The whole structure of the 

GSEs provides liquidity to the housing 

market by allowing investors additional 

channels to fund mortgages. The question is 

not always between single-family investors 

and single-family owner-occupiers. 

Sometimes, the question is between a singleVerDate jul<14>2003 18:20 Nov 01, 2004 Jkt 205001 PO 00000 Frm 00156 Fmt 4701 Sfmt 4700 C:\02NOR2.SGM 02NOR2
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317Senate Report 102–282, May 15, 1992, p. 35.

318Tables A.30 and A.31 examine GSE purchases 

on a ‘‘going forward basis by origination year’’. 

Specifically, it considers GSE purchases of: (a) 2000 

mortgage originations during 2000, 2001, 2002 and 

2003; (b) 2001 originations during 2001, 2002 and 

2003; and (c) 2002 originations during 2002 and 

2003. In other words, this analysis looks at the 

GSEs’ purchases of a particular origination year 

cohort through 2003. This approach contrasts with 

the approach that examines GSE purchases on a 

‘‘backward looking basis by purchase year’’, for 

example, GSE purchases during 2000 of both new 

2000 originations and originations during previous 

years (the latter called ‘‘prior-year’’ or seasoned 

loans). Either approach is a valid method for 

examining GSE purchases; in fact, when analyzing 

aggregated data such as the combined 1999–2002 

data in Table A.30, the two approaches yield 

somewhat similar results. HUD’s methodology for 

deriving the market estimates is explained in 

Appendix D. B&C loans have been excluded from 

the market estimates in Tables A.30 and A.31.

family investor and a property unable to be 

sold or even abandoned. Although the goals 

strongly support home ownership for lowincome neighborhoods, investors in singlefamily properties also play an important role. 

F. Factor 4: Size of the Conventional 

Conforming Mortgage Market Serving Lowand Moderate-Income Families Relative to 

the Overall Conventional Conforming Market 

The Department estimates that dwelling 

units serving low- and moderate-income 

families will account for 51–56 percent of 

total units financed in the overall 

conventional conforming mortgage market 

during 2005–2008, the period for which the 

Low- and Moderate-Income Housing Goal 

will be effective. The market estimates 

exclude B&C loans and allow for much more 

adverse economic and market affordability 

conditions than have existed recently. The 

detailed analyses underlying these estimates 

are presented in Appendix D. 

G. Factor 5: GSEs’ Ability to Lead the 

Industry 

FHEFSSA requires the Secretary, in 

determining the Low- and Moderate-Income 

Housing Goal, to consider the GSEs’ ability 

to ‘‘lead the industry in making mortgage 

credit available for low- and moderateincome families.’’ Congress indicated that 

this goal should ‘‘steer the enterprises toward 

the development of an increased capacity 

and commitment to serve this segment of the 

housing market’’ and that it ‘‘fully expect[ed] 

[that] the enterprises will need to stretch 

their efforts to achieve [these goals].’’317

The Department and independent 

researchers have published numerous studies 

examining whether or not the GSEs have 

been leading the single-family market in 

terms of their affordable lending 

performance. This research, which is 

summarized in Section E, concludes that the 

GSEs have generally lagged behind primary 

lenders in funding first-time homebuyers, 

lower-income borrowers and underserved 

communities, although Fannie Mae’s recent 

performance has placed it ahead of the 

special affordable and low-mod markets for 

single-family-owner loans. As required by 

FHEFSSA, the Department has produced 

estimates of the portion of the total (singlefamily and multifamily) mortgage market that 

qualifies for each of the three housing goals 

(see Appendix D). Congress intended that the 

Department use these market estimates as 

one factor in setting the percentage target for 

each of the housing goals. The Department’s 

estimate for the size of the Low- and 

Moderate-Income market is 51–56 percent, 

which is higher than the GSEs’ performance 

on that goal.

This section provides another perspective 

on the GSEs’ performance by examining the 

share of the total conventional conforming 

mortgage market and the share of the goalqualifying markets (low-mod, special 

affordable, and underserved areas) accounted 

for by the GSEs’ purchases. This analysis, 

which is conducted by product type (singlefamily owner, single-family rental, and 

multifamily), shows the relative importance 

of the GSEs in each of the goal-qualifying 

markets. 

1. GSEs’ Role in Major Sectors of the 

Mortgage Market 

Tables A.30 and A.31a compare GSE 

mortgage purchases with HUD’s estimates of 

the numbers of units financed in the 

conventional conforming market. Table A.30 

presents aggregate data for 1999–2002 while 

Table A.31a presents more summary market 

share data for individual years 2000, 2001 

and 2002.318 (As explained below, Tables 

A.31b and A.31c repeat this information but 

for lower multifamily shares of the mortgage 

market.) HUD estimates that there were 

47,551,039 owner and rental units financed 

by new conventional conforming mortgages 

between 1999 and 2002. Fannie Mae’s and 

Freddie Mac’s mortgage purchases financed 

26,118,927 of these dwelling units, or 55 

percent of all dwelling units financed. As 

shown in Table A.30, the GSEs have played 

a smaller role in the goals-qualifying markets 

than they have played in the overall market. 

Between 1999 and 2002, new mortgages were 

originated for 26,051,771 dwelling units that 

qualified for the Low- and Moderate-Income 

Goal; the GSEs low-mod purchases financed 

12,608,215 dwelling units, or 48 percent of 

the low-mod market. Similarly, the GSEs’

purchases accounted for 48 percent of the 

underserved areas market, but only 41 

percent of the special affordable market. 

Obviously, the GSEs did not lead the 

industry during this period in financing units 

that qualify for the three housing goals. They 

need to improve their performance and it 

appears that there is ample room in the nonGSE portions of the goals-qualifying markets 

for them to do so. For instance, the GSEs 

were not involved in three-fifths of the 

special affordable market during the 1999-to2002 period.

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319Based on Table A.30, multifamily properties 

represented 14.8 percent of total units financed 

between 1999 and 2002 (obtained by dividing 

7,018,044 multifamily units by 47,551,039 ‘‘Total 

Market’’ units). Increasing the single-family-owner 

number in Table A.30 by 2,648,757 to account for 

excluded B&C mortgages increases the ‘‘Total 

Market’’ number to 50,199,796, which produces a 

multifamily share of 14.0 percent. See Appendix D 

for discussion of the B&C market.

320Abt Associates, op. cit. (August 2002).

321The problem of secondary market ‘‘adverse 

selection’’ is described in James R. Follain and 

Edward J. Szymanoski. ‘‘A Framework for 

Evaluating Government’s Evolving Role in 

Multifamily Mortgage Markets,’’ Cityscape: A 

Journal of Policy Development and Research 1(2), 

1995.

While the GSEs are free to meet the 

Department’s goals in any manner that they 

deem appropriate, it is useful to consider 

their performance relative to the industry by 

property type. The GSEs accounted for 61 

percent of the single-family owner market but 

only 35 percent of the multifamily market 

and 40 percent of the single-family rental 

market (or a combined 37 percent share of 

the rental market). 

Single-Family Owner Market. As stated in 

the 2000 Rule, the single-family-owner 

market is the bread-and-butter of the GSEs’

business, and based on the financial and 

other factors discussed below, the GSEs 

clearly have the ability to lead the primary 

market in providing credit for low- and 

moderate-income owners of single-family 

properties. However, the GSEs have 

historically lagged behind the market in 

funding single-family-owner loans that 

qualify for the housing goals and, as 

discussed in Section E, they have played a 

rather small role in funding minority firsttime homebuyers. The market share data 

reported in Table A.30 for the single-familyowner market tell the same story. The GSEs’

purchases of single-family-owner loans 

represented 61 percent of all single-familyowner loans originated between 1999 and 

2002, compared with 57 percent of the lowmod loans that were originated, 55 percent of 

underserved area loans, and 52 percent of the 

special affordable loans. 

The data in Table A.31a indicate the GSEs’

growing market share during the heavy 

refinance years of 2001 and 2002. For 

example, the GSEs accounted for 74 percent 

of the overall single-family-owner market in 

2002, and 67–69 percent of the markets 

covered by the three housing goal categories. 

While this improvement is an encouraging 

trend, there are ample opportunities for the 

GSEs to continue their improvement. Almost 

one-third of the goals-qualifying loans 

originated during 2002 remained available to 

the GSEs to purchase; there are clearly 

affordable loans being originated that the 

GSEs can purchase. Furthermore, the GSEs’

purchases under the housing goals are not 

limited to new mortgages that are originated 

in the current calendar year. The GSEs can 

purchase loans from the substantial, existing 

stock of affordable loans held in lenders’

portfolios, after these loans have seasoned 

and the GSEs have had the opportunity to 

observe their payment performance. In fact, 

based on Fannie Mae’s recent experience, the 

purchase of seasoned loans appears to be one 

effective strategy for purchasing goalsqualifying loans. 

The data in Table A.31a show a strong 

upward trend from 2000 and 2001 to 2002 in 

the GSE share of the single-family-owner 

market. Their share of 2000 financed units in 

the conventional conforming market totaled 

48 percent. This increased to 55 percent in 

2001 then to 74 percent in 2002. The large 

increase in 2002 can be attributed to the 

relatively low interest rates and heavy 

refinancing activity in 2003. During such a 

period, the share of fixed rate mortgage 

originations increases relative to adjustable 

rate mortgages. Due to the higher risk 

associated with fixed rate mortgages, less 

thrift institutions are willing to hold them, 

and, thus, more are sold to the GSEs. As a 

result, during low interest rate periods, the 

GSE share of mortgages increases. 

Single-Family Rental Market. Single-family 

rental housing is a major source of lowincome housing. As discussed in Appendix 

D, data on the size of the primary market for 

mortgages on these properties is limited, but 

available information indicate that the GSEs 

are much less active in this market than in 

the single-family owner market. HUD 

estimates that GSE purchases between 1999 

and 2002 totaled only 40 percent of all 

newly-mortgaged single-family rental units 

that were affordable to low- and moderateincome families. 

As explained in the 2000 Rule, many of 

these properties are ‘‘mom-and-pop’’

operations, which may not follow financing 

procedures consistent with the GSEs’

guidelines. Much of the financing needed in 

this area is for rehabilitation loans on 2–4 

unit properties in older areas, a market in 

which the GSEs’ have not played a major 

role. However, this sector could certainly 

benefit from an enhanced role by the GSEs, 

and the data in Table A.30 indicate that there 

is room for such an enhanced role, as 

approximately two-thirds of this market 

remains for the GSEs to enter. 

Once again, Table A.31a shows a large 

increase in the GSE share of newlymortgaged units financed in 2002 compared 

to those financed in 2000 and 2001. As 

described above for the single-family owner 

market, this large increase is due to the large 

share of fixed-rate mortgages, compared to 

adjustable rate mortgages, originated during 

2002. 

Multifamily Market. Fannie Mae is the 

largest single source of multifamily finance 

in the United States, and Freddie Mac has 

made a solid reentry into this market over the 

last nine years. However, there are a number 

of measures by which the GSEs lag the 

multifamily market. For example, the share 

of GSE resources committed to the 

multifamily purchases falls short of the 

multifamily proportion prevailing in the 

overall mortgage market. HUD estimates that 

newly-mortgaged units in multifamily 

properties represented almost 15 percent of 

all (single-family and multifamily) dwelling 

units financed between 1999 and 2002.319 As 

shown in Table A.30, multifamily 

acquisitions represented 9.5 percent of 

dwelling units financed by the GSEs between 

1999 and 2002.

The GSEs’ role in the multifamily market 

is significantly smaller than in single-family. 

As shown in Table A.30, GSE purchases have 

accounted for 35 percent of newly financed 

multifamily units between 1999 and 2002—

a market share much lower than their 61 

percent share of the single-family-owner 

market. Stated in terms of portfolio shares, 

single-family-owner loans accounted for 83 

percent of all dwelling units financed by the 

GSEs during this period, versus 75 percent of 

all units financed in the conventional 

conforming market. 

While it is recognized that the GSEs have 

been increasing their multifamily purchases, 

a further enlargement of their role in the 

multifamily market seems feasible and 

appropriate, particularly in the affordable 

(lower rent) end of the market. As noted in 

Section D.3, market participants believe that 

the GSEs have been conservative in their 

approaches to affordable multifamily lending 

and underwriting.320 Certainly the GSEs face 

a number of challenges in better meeting the 

needs of the affordable multifamily market. 

For example, thrifts and other depository 

institutions may sometimes retain their best 

loans in portfolio, and the resulting 

information asymmetries may act as an 

impediment to expanded secondary market 

transaction volume.321 However, the GSEs 

have demonstrated that they have the depth 

of expertise and the financial resources to 

devise innovative solutions to problems in 

the multifamily market. The GSEs can build 

on their recent records of increased 

multifamily lending and innovative products 

to make further in-roads into the affordable 

market. As explained in Section D.3, the 

GSEs have the expertise and market presence 

to push simultaneously for market 

standardization and for programmatic 

flexibility to meet the special needs and 

circumstances of the lower-income portion of 

the multifamily market.

As discussed in Appendix D, the GSEs 

questioned HUD’s historical estimates of the 

multifamily market as too high. Section C of 

Appendix D discusses these comments and 

responds. As indicated in Table A.30, 

multifamily loans accounted for 14.8 percent 

of all financed units in the market, excluding 

B&C loans. As reported in Appendix D, HUD 

also conducted sensitivity analyses that 

reduced its 1999–2002 multifamily shares for 

the market by approximately two percentage 

points. The results for these lower 

multifamily market shares are reported in 

Table A.31b (1999–2002 aggregate results) 

and Table A.31c (2000–2002 individual year 

results). In this case, 1999–2002 multifamily 

units decreased from 7,018,044 units to 

5,991,036 units (reducing the multifamily 

share from 14.8 percent to 12.9 percent). 

With these reduced multifamily market 

numbers, the GSEs’ share of the multifamily 

market increased from 35 percent to 41 

percent. The GSEs also accounted for higher 

shares of the goals-qualifying multifamily

market: 42 percent for low-mod units, 34 

percent for underserved area units, and 37 

percent for special affordable units. In this 

case, the GSEs’ shares of the overall goalsqualifying markets increased as follows: lowmod—from 48 percent (see right column of 

Table A.30) to 50 percent (see right column 

Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 63741

322This section is based heavily on ‘‘DU and LP 

Usage Continues to Rise,’’ in Inside Mortgage 

Technology published by Inside Mortgage Finance, 

January 27, 2003, page 1–2.

323Fannie Mae, 2002 Annual Housing Activities 

Report, 2003, pp. 10–11.

324Freddie Mac, Opening Doors for America’s 

Families: Freddie Mac’s Annual Housing Activities 

Report for 2003, March 15, 2004, p. 14.

325Freddie Mac, 2002 Annual Housing Activities 

Report, 2003, p. 52.

326 Inside Mortgage Finance, ‘‘Online Volume 

Comprises One-Fourth of Total Originations in First 

Half ’02,’’ September 20, 2002, p. 8.

of Table A.31b); underserved areas—from 48 

percent to 49 percent; and special 

affordable—from 41 percent to 43 percent.

Conclusions. While HUD recognizes that 

some segments of the market may be more 

challenging for the GSEs than others, the data 

reported in Table A.30 and Tables A.31a–c 

show that the GSEs have ample opportunities 

to purchase goals-qualifying mortgages. 

Furthermore, if a GSE makes a business 

decision to not pursue certain types of goalsqualifying loans in one segment of the 

market, they are free to pursue goalsqualifying owner and rental property 

mortgages in other segments of the market. 

As market leaders, the GSEs should be 

looking for innovative ways to pursue this 

business. Furthermore, there is evidence that 

the GSEs can earn reasonable returns on their 

goals business. The Regulatory Analysis that 

accompanies this final rule provides 

evidence that the GSEs can earn financial 

returns on their purchases of goals-qualifying 

loans that are only slightly below their return 

on equity from their normal business. 

2. Qualitative Dimensions of the GSEs’

Ability to Lead the Industry 

This section discusses several qualitative 

factors that are indicators of the GSEs’ ability 

to lead the industry in affordable lending. It 

discusses the GSEs’ role in the mortgage 

market; their ability, through their 

underwriting standards, new programs, and 

innovative products, to influence the types of 

loans made by private lenders; their 

development and utilization of state-of-theart technology; the competence, expertise 

and training of their staffs; and their financial 

resources. 

a. Role in the Mortgage Market 

The GSEs have played a dominant role in 

the single-family mortgage market. As 

reported in Section C.3, mortgage purchases 

by the GSEs reached extraordinary levels in 

2001–2003. Purchases by Fannie Mae stood 

at $568 billion in 2001 and $848 billion in 

2002. Freddie Mac’s single-family mortgage 

purchases were $393 billion in 2001 and 

$475 billion in 2002. The Office of Federal 

Housing Enterprise Oversight (OFHEO) 

estimates that the GSEs purchased 40 percent 

of newly-originated conventional mortgages 

in 2001. Total GSE purchases, including 

loans originated in prior years, amounted to 

46 percent of conventional originations in 

2001. 

The dominant position of the GSEs in the 

mortgage market is reinforced by their 

relationships with other market institutions. 

Commercial banks, mutual savings banks, 

and savings and loans are their competitors 

as well as their customers—they compete to 

the extent they hold mortgages in portfolio, 

but at the same time they sell mortgages to 

the GSEs. They also buy mortgage-backed 

securities, as well as the debt securities used 

to finance the GSEs’ portfolios. Mortgage 

bankers sell virtually all of their prime 

conventional conforming loans to the GSEs. 

Private mortgage insurers are closely linked 

to the GSEs, because mortgages purchased by 

the enterprises that have loan-to-value ratios 

in excess of 80 percent are normally required 

to be covered by private mortgage insurance, 

in accordance with the GSEs’ charter acts. 

b. Underwriting Standards for the Primary 

Mortgage Market 

The GSEs’ underwriting guidelines are 

followed by virtually all originators of prime 

mortgages, including lenders who do not sell 

many of their mortgages to Fannie Mae or 

Freddie Mac. The guidelines are also 

commonly followed in underwriting 

‘‘jumbo’’ mortgages, which exceed the 

maximum principal amount which can be 

purchased by the GSEs (the conforming loan 

limit)—such mortgages eventually might be 

sold to the GSEs, as the principal balance is 

amortized or when the conforming loan limit 

is otherwise increased. Changes that the 

GSEs have made to their underwriting 

standards in order to address the unique 

needs of low-income families were discussed 

in Section C.4 of this Appendix. The GSEs’

market influence is one reason these new, 

more flexible underwriting standards have 

spread throughout the market. Because the 

GSEs’ guidelines set the credit standards 

against which the mortgage applications of 

lower-income families are judged, the 

enterprises have a profound influence on the 

rate at which mortgage funds flow to lowand moderate-income borrowers and 

underserved neighborhoods. 

As discussed below, the GSEs’ new 

automated underwriting systems are widely 

used to originate mortgages in today’s 

market. As discussed in Sections C.7 and C.8, 

the GSEs have started adapting their 

underwriting systems for subprime loans and 

other loans that have not met their traditional 

underwriting standards. 

c. State-of-the-Art Technology 

Both GSEs are in the forefront of new 

developments in mortgage industry 

technology. Automated underwriting and 

online mortgage processing are a couple of 

the new technologies that have impacted the 

mortgage market, expanding homeownership 

opportunities. This section provides an 

overview of these new technologies and the 

extent of their use. 

Each enterprise released an automated 

underwriting system in 1995—Freddie Mac’s 

‘‘Loan Prospector’’ (LP) and Fannie Mae’s 

‘‘Desktop Underwriter’’ (DU). During 2001 

and 2002, roughly 60 percent of all newlyoriginated mortgages the GSEs purchased 

were processed through these systems. 

Lenders and brokers used LP to evaluate 7.3 

million loan applications in 2001, 8.2 million 

in 2002,322 and 9.5 million in 2003. 

Similarly, DU was used to evaluate 8 million 

loans in 2001, over 10 million in 2002, and 

14.8 million loans in 2003. The GSEs’

systems have also been adapted for FHA and 

jumbo loans. Automated underwriting 

systems are being further adapted to facilitate 

risk-based pricing, which enables mortgage 

lenders to offer each borrower an individual 

rate based on his or her risk. As discussed 

earlier, concerns about the use of automated 

underwriting and risk-based pricing include 

the disparate impact on minorities and lowincome borrowers and the ‘‘black box’’ nature 

of the score algorithm.

The GSEs are using their state-of-the-art 

technology in certain ways to help expand 

homeownership opportunities. For example, 

Fannie Mae has developed Fannie Mae 

Property GeoCoder a computerized mapping 

service offered to lenders, nonprofit 

organizations, and state and local 

governments to help them determine whether 

a property is located in an area that qualifies 

for Fannie Mae’s community lending 

products designed to increase 

homeownership and revitalization in 

traditionally underserved areas. In addition, 

eFannieMae.com is Fannie Mae’s businessto-business Web site where lenders can 

access product information and important 

technology tools, view upcoming events, and 

receive news about training opportunities. 

This site receives on average 80,000 visitors 

per week.323 Freddie Mac has introduced in 

recent years Internet-based debt auctions, 

debt repurchase operations, and debt 

exchanges. These mechanisms benefit 

investors by providing more uniform pricing, 

greater transparency and faster price 

discovery—all of which makes Freddie Mac 

debt more attractive to investors and reduces 

the cost of funding mortgages.324 In addition, 

Freddie Mac has provided automated tools 

for lenders to identify and work with 

borrowers most likely to encounter problems 

making their mortgage payments. 

EarlyIndicator has become the industry 

standard for default management technology. 

It can reduce the consequences of mortgage 

delinquency for borrowers, servicers and 

investors.325

The GSEs are also expanding 

homeownership opportunities through the 

use of the Internet in processing mortgage 

originations. New online mortgage 

originations reached $267.6 billion in the 

first half of 2002, compared with $97 billion 

for the first six months of 2001. The 2002 sixmonth volume comprised 26.5 percent of the 

estimated $1.01 trillion in total mortgage 

originations for the same time period.326

Freddie Mac made Loan Prospector on the 

Internet service available to lenders for their 

retail operations. Freddie Mac also adopted 

the mortgage industry’s XML (extensible 

markup language) data standard, which is 

integral to streamlining and simplifying 

Internet-based transactions. In addition, 

Congress enacted legislation that allows the 

use of electronic signature in contracts in 

2001, making a completely electronic 

mortgage transaction possible. With the use 

of electronic signatures, electronic mortgages 

are expected to improve the mortgage 

process, further reducing origination and 

servicing costs. In October 2000, Freddie Mac 

63742 Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 

327Freddie Mac, Opening Doors for America’s 

Families: Freddie Mac’s Annual Housing Activities 

Report for 2003, March 15, 2004, p. 38.

328Fannie Mae, 2002 Annual Housing Activities 

Report, 2003, p. 12.

329Fannie Mae, 2003 Annual Housing Activities 

Report, 2004, p. 13.

330Fannie Mae, 2003 Annual Housing Activities 

Report, 2004, p. 13.

331Fannie Mae, 2002 Annual Housing Activities 

Report, 2003, p. 10.

332The 22% decrease in Fannie Mae’s 2002 net 

income resulted primarily from a $4.508 billion 

increase in purchased options expense, which 

occurred due to an increase in the notional amount 

of purchased options outstanding and the declining 

interest rate environment. Recorded purchased 

options expense for 2001 was only $37 million by 

comparison. Fannie Mae 2002 Annual Report, 2003, 

p. 23.

333Fannie Mae, 2003 Annual Report, ‘‘Financial 

Highlights.’’

334Fannie Mae, 2003 Annual Report, ‘‘Financial 

Highlights.’’

335Fannie Mae, 2003 Annual Report, ‘‘Financial 

Highlights’’ and United States Securities and 

Exchange Commission form 10–K, p. 108.

336Fannie Mae, 2003 Annual Report to 

Shareholders, Financial Highlights and Financial 

Information.

337Freddie Mac, Consolidated Statements of 

Income 2003 and Freddie Mac Core Tables 2003.

338 ‘‘The Standard and Poor’s Five Hundred: 

Performance Ranking S&P 500’’, Business Week,

April 5, 2004, p. 127.

339 ‘‘Fortune 500 Largest U.S. Corporations,’’

Fortune, April 5, 2004, p. F–1.

340 ‘‘Fortune 500 Largest U.S. Corporations,’’

Fortune, July 26, 2004, p. 159.

purchased its first electronic mortgage under 

the new law.

The GSEs also offer a variety of other 

online tools and applications that have the 

potential to make the mortgage loan process 

more cost effective and efficient for lenders. 

Freddie Mac, for example, has launched 

dontborrowtrouble.com, which contains 

information on local anti-predatory lending 

campaigns, consumer tips on avoiding 

predatory lending, and information on how 

to start a local campaign and obtain 

additional resources.327 Fannie Mae offers 

‘‘HomeBuyer Funds Finder,’’ a one-stop 

online resource designed for lenders and 

other housing professionals, enables users to 

access a database of local housing subsidy 

programs available for low- and moderateincome borrowers. In 2002, the HomeBuyer 

Funds Finder Web site received over 24,500 

hits.328 ‘‘Home Counselor Online’’ provides 

homeownership counselors with the 

necessary tools to help consumers financially 

prepare to purchase a home. In 2003, 641 

counselors representing over 2,000 

organizations used Home Counselor 

Online.329 ‘‘True Cost Calculator 2.0’’ is 

designed to help homebuyers make informed 

home purchase decisions by helping them 

compare loan products and prices. Over 60 

Fannie Mae partners officer the True Cost 

Calculator through their Web sites and a 

Spanish version is also available on 

Univision.com.330 A more complete list of 

Fannie Mae’s online tool and applications 

can be found in its Annual Housing 

Activities Report. In 2002, Fannie Mae’s total 

eBusiness volume was $1.1 trillion, up from 

$800 billion in 2000.331

d. Staff Resources 

Both Fannie Mae and Freddie Mac are 

well-known throughout the mortgage 

industry for the expertise of their staffs in 

carrying out their current programs, 

conducting basic and applied research 

regarding mortgage markets, developing 

innovative new programs, and undertaking 

sophisticated analyses that may lead to new 

programs in the future. The leaders of these 

corporations frequently testify before 

Congressional committees on a wide range of 

housing issues, and both GSEs have 

developed extensive working relationships 

with a broad spectrum of mortgage market 

participants, including various nonprofit 

groups, academics, and government housing 

authorities. Federal agencies and foreign 

governments and businesses seek them out 

for advice and consultation because of their 

expertise. The role that the GSEs have played 

in spreading the use of technology 

throughout the mortgage market reflects the 

enormous expertise of their staff. 

e. Financial Strength 

Fannie Mae. The benefits that accrue to the 

GSEs because of their GSE status, as well as 

their solid management, have made them two 

of the nation’s most profitable businesses. 

Fannie Mae’s net income was $3.9 billion in 

1999, $4.4 billion in 2000, $5.9 billion in 

2001, $4.6 billion in 2002,332 and $7.9 billion 

in 2003.333 Fannie Mae’s return on equity 

averaged 24.0 percent over the 1995–99 

period—far above the rates achieved by most 

financial corporations. Fannie Mae’s return 

on equity was 26.0 percent in 2003, while 

this represented no change from 2002, it was 

an increase of 3 percent over 2001.334 In 

2003, Fannie Mae’s total stockholders’ equity 

increased by 37% to $22.373 million, core 

business earnings grew by 14 percent ($7.3 

billion), credit losses increased by $42 

million to $111 million with the resulting 

credit loss ratio at .006% (represents credit 

losses divided by average single family 

mortgage credit book of business) and taxable 

equivalent revenues grew by 24 percent.335

Fannie Mae’s basic net earnings per 

common share increased from $3.75 in 1999 

to $7.93 in 2003, dividends per common 

share have increased from $.96 in 1998 to 

$1.68 in 2003, a 27% increase over 2002, and 

operating earnings per diluted common share 

increased from 2002 to 2003 by 71% to 

$7.72.336

Freddie Mac. Freddie Mac has shown 

similar trends. Freddie Mac’s net income was 

$3.158 billion in 2001, $10.090 billion in 

2002, and $4.891 billion in 2003, and total 

stockholder’s equity increased by 10% over 

2002 to $31.562 billion. Freddie Mac’s return 

on equity averaged 23.4 percent over the 

1995–1999 period, also well above the rates 

achieved by most financial corporations. 

Credit losses increased by $8 million to $82 

million with the resulting credit loss ratio at 

0.7 (represents annualized credit losses 

divided by average total mortgage portfolio). 

Basic earnings per common share (after 

cumulative effect of change in accounting 

principles, net of taxes) was $4.25 in 2001, 

$14.23 in 2002 and $6.80 in 2003. Dividends 

per common share have increased from 0.80 

in 2001 to $1.04 in 2003, an 18% increase 

over 2002, and operating earnings per diluted 

common share (after cumulative effect of 

change in accounting principles, net of taxes) 

decreased from 2002 to 2003 by $7.39 to 

$6.79.337

Other Indicators. Additional indicators of 

the strength of the GSEs are provided by 

various rankings of American corporations. 

Business Week has reported that among 

Standard & Poor’s performance ranking of 

500 companies in 2004, Fannie Mae was 

ranked 117, down from 91 in 2003 and 

Freddie Mac was listed as ‘‘INC’’ for 2004 

and 16th for 2003. Additionally, Fannie Mae 

was ranked as 29th in overall market value, 

17th in sales and 9th in profits, and Freddie 

Mac was ranked 59th in market value and 

‘‘NR’’ in sales and profits.338 According to 

Fortune’s annual listing of the 500 largest 

U.S. Corporations, Fannie Mae was ranked 

20th in 2003, down from 16th in 2002, and 

Freddie Mac was ‘‘displaced’’ from the 

ranking in 2003, but ranked 32nd in 2002. 

Additionally, Fannie Mae ranked 11th for 

most profitable companies, 3rd for revenues 

per employee, and in the ‘‘Diversified 

Financials’’ category, they ranked 2nd out of 

12 companies.339 And, according to Fortune’s 

Global 500 listing of the world’s largest 

corporations, Fannie Mae ranked 56th in 

2003, (ranking 17th in highest profits) down 

from 45th in 2002, and Freddie Mac ranked 

104th in 2003, down from 90th in 2002.340

f. Conclusion About Leading the Industry 

In light of these considerations, the 

Secretary has determined that the GSEs have 

the ability to lead the industry in making 

mortgage credit available for low- and 

moderate-income families. 

H. Factor 6: The Need to Maintain the Sound 

Financial Condition of the GSEs 

HUD has undertaken a separate, detailed 

economic analysis of this final rule, which 

includes consideration of (a) the financial 

returns that the GSEs earn on low- and 

moderate-income loans and (b) the financial 

safety and soundness implications of the 

housing goals. Based on this economic 

analysis and the Office of Federal Housing 

Enterprise Oversight review, HUD concludes 

that the goals raise minimal, if any, safety 

and soundness concerns. 

I. Determination of the Low- and ModerateIncome Housing Goals 

The annual goal for each GSE’s purchases 

of mortgages financing housing for low- and 

moderate-income families is being 

established at 52 percent of eligible units 

financed in each of calendar years 2005, 53 

percent in 2006, 55 percent in 2007, and 56 

percent in 2008. This goal will remain in 

effect thereafter, unless changed by the 

Secretary prior to that time. In addition, a 

low- and moderate-income subgoal of 45 

percent in 2005, 46 percent in 2006, and 47 

percent in both 2007 and 2008 is being 

established for the GSEs’ acquisitions of 

single-family-owner home purchase loans in 

metropolitan areas. This subgoal is designed 

to encourage the GSEs to lead the primary 

market in offering homeownership 

Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 63743

341National Association of Home Builder, 2004 

Spring Construction Forecast Conference, April 21, 

2004.

opportunities to low- and moderate-income 

families. The Secretary’s consideration of the 

six statutory factors that led to the choice of 

these goals is summarized in this section. 

1. Housing Needs and Demographic 

Conditions 

Affordability Problems. Data from the 2000 

Census and the American Housing Surveys 

demonstrate that there are substantial 

housing needs among low- and moderateincome families. Many of these households 

are burdened by high homeownership costs 

or rent payments and will likely continue to 

face serious housing problems. There is 

evidence of persistent housing problems for 

Americans with the lowest incomes. Since 

1977, the percentage of U.S. households with 

worst case needs has hovered around five 

percent, with the worst year being 1983 (6.03 

percent) and the best year being 1999 (4.72 

percent). The proportion in 2001 was 4.77 

percent, which is not significantly different 

from the 1999 figure. HUD’s analysis of 

American Housing Survey data reveals that, 

in 2001, 5.1 million unassisted very-low 

income renter households had ‘‘worst-case’’

housing needs, defined as housing costs 

greater than 50 percent of household income 

or severely inadequate housing. Among these 

households, 90 percent had a severe rent 

burden, 6 percent lived in severely 

inadequate housing, and 4 percent suffered 

from both problems. Among the 34 million 

renters in all income categories, 6.3 million 

(19 percent) had a severe rent burden and 

over one million renters (3 percent) lived in 

housing that was severely inadequate. 

Demographic Trends. Changing population 

demographics will result in a need for the 

primary and secondary mortgage markets to 

meet nontraditional credit needs, respond to 

diverse housing preferences and overcome 

information and other barriers that many 

immigrants and minorities face. It is 

projected that there will be 1.2 million new 

households each year over the next decade. 

The aging of the baby-boom generation and 

the entry of the baby-bust generation into 

prime home buying age will have a 

dampening effect on housing demand. 

However, the continued influx of immigrants 

will increase the demand for rental housing, 

while those who immigrated during the 

1980s and 1990s will be in the market for 

owner-occupied housing. Immigrants and 

other minorities—who accounted for nearly 

40 percent of the growth in the nation’s 

homeownership rate over the past five 

years—will be responsible for almost twothirds of the growth in the number of new 

households over the next ten years. Nontraditional households have become more 

important, as overall household formation 

rates have slowed. With later marriages, 

divorce, and non-traditional living 

arrangements, the fastest growing household 

groups have been single-parent and singleperson households. As these demographic 

factors play out, the overall effect on housing 

demand will likely be sustained growth and 

an increasingly diverse household 

population from which to draw new renters 

and homeowners. According to the National 

Association of Homebuilders, annual housing 

demand will average from 1.84 to 2.19 

million units over the next decade.341

Growth in Single-Family Affordable 

Lending. Many younger, minority and lowerincome families did not become homeowners 

during the 1980s due to the slow growth of 

earnings, high real interest rates, and 

continued house price increases. Over the 

past ten years, economic expansion, 

accompanied by low interest rates and 

increased outreach on the part of the 

mortgage industry, has improved 

affordability conditions for these families. As 

this appendix has explained, there has been 

a ‘‘revolution in affordable lending’’ that has 

extended homeownership opportunities to 

historically underserved households. The 

mortgage industry has offered more 

customized mortgage products, more flexible 

underwriting, and expanded outreach to lowincome and minority borrowers. Fannie Mae 

and Freddie Mac have been a big part of this 

‘‘revolution in affordable lending’’. HMDA 

data suggest that the industry and GSE 

initiatives are increasing the flow of credit to 

underserved borrowers. Between 1993 and 

2003, conventional loans to low-income and 

minority families increased at much faster 

rates than loans to upper-income and nonminority families. Thus, the 1990s and the 

early part of the current decade have seen the 

development of a strong affordable lending 

market. 

Disparities in Housing and Mortgage 

Markets. Despite this strong growth in 

affordable lending, serious disparities in the 

nation’s housing and mortgage markets 

remain. The homeownership rate for AfricanAmerican and Hispanic households is about 

25 percentage points below that of white 

households. In addition to low income, 

barriers to homeownership that 

disproportionately affect minorities and 

immigrants include: lack of capital for down 

payment and closing costs; poor credit 

history; lack of access to mainstream lenders; 

little understanding of the homebuying 

process; and continued discrimination in 

housing markets and mortgage lending. With 

respect to the latter, a recent HUD-sponsored 

study of discrimination in the rental and 

owner markets found that while differential 

treatment between minority and white home 

seekers had declined over the past ten years, 

it continued at an unacceptable level in the 

year 2000. In addition, disparities in 

mortgage lending continued across the nation 

in 2003, when the loan denial rate for 

African-American applicants was almost 

three times that for white applicants, even 

after controlling for income of the applicant. 

HUD studies also show that AfricanAmericans and Hispanics are subject to 

discriminatory treatment during the prequalification process of applying for a 

mortgage.

Single-Family Mortgage Market. Heavy 

refinancing due to low interest rates 

increased single-family mortgage originations 

to record levels during 2001–2003. 

Demographic forces, industry outreach, and 

low interest rates also kept lending for home 

purchase at record levels as well. As noted 

above, the potential homeowner population 

over the next decade will be highly diverse, 

as growing demand from immigrants and 

minorities are expected to sustain the home 

purchase market, as our population ages. 

Single-family housing starts are expected to 

continue in the 1.65–1.70 million range over 

the next few years. Refinancing of existing 

mortgages, which accounted for about 60 

percent of originations during 2001–2003 is 

expected to return to more normal levels. As 

this Appendix has explained, the GSEs will 

continue to play a dominant role in the 

single-family market and will both impact 

and be affected by major market 

developments such as the growth in 

subprime lending and the increasing use 

automated underwriting. 

Multifamily Mortgage Market. The market 

for financing of multifamily apartments has 

grown to record volumes. The favorable longterm prospects for apartments, combined 

with record low interest rates, have kept 

investor demand for apartments strong and 

supported property prices. As explained 

above, Fannie Mae and Freddie Mac have 

been among those boosting volumes and 

introducing new programs to serve the 

multifamily market. The long run outlook for 

the multifamily rental market is sustained, 

moderate growth, based on favorable 

demographics. The minority population, 

especially Hispanics, provides a growing 

source of demand for affordable rental 

housing. ‘‘Lifestyle renters’’ (older, middleincome households) are also a fast growing 

segment of the rental population. However, 

provision of affordable housing will continue 

to challenge suppliers of multifamily rental 

housing and policy makers at all levels of 

government. Low incomes combined with 

high housing costs define a difficult situation 

for millions of renter households. Housing 

cost reductions are constrained by high land 

prices and construction costs in many 

markets. Government action—through land 

use regulation, building codes, and 

occupancy standards—are major contributors 

to those high costs. In addition to fewer 

regulatory barriers and costs, multifamily 

housing would benefit from more favorable 

public attitudes. Higher density housing is a 

potentially powerful tool for preserving open 

space, reducing sprawl, and promoting 

transportation alternatives to the automobile. 

The recently heightened attention to these 

issues may increase the acceptance of 

multifamily rental construction to both 

potential customers and their prospective 

neighbors. 

2. Past Performance of the GSEs 

This section reviews the low- and 

moderate-income performance of Fannie Mae 

and Freddie Mac. It first reviews the GSEs’

performance on the Low- and ModerateIncome Goal, then reviews findings from 

Section E.2 regarding the GSEs’ purchases of 

home loans for historically underserved 

families and their communities. Finally, it 

reviews findings from Section G concerning 

the GSEs’ presence in owner and rental 

markets. 

a. Housing Goals Performance 

In the October 2000 rule, the low- and 

moderate-income goal was set at 50 percent 

63744 Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 

for 2001–03. Effective on January 1, 2001, 

several changes in counting requirements 

came into effect for the low- and moderateincome goal, as follows: (a) ‘‘Bonus points’’

(double credit) for purchases of mortgages on 

small (5–50 unit) multifamily properties and, 

above a threshold level, mortgages on 2–4 

unit owner-occupied properties; (b) a 

‘‘temporary adjustment factor’’ (1.35 units 

credit) for Freddie Mac’s purchases of 

mortgages on large (more than 50 units) 

multifamily properties; (c) changes in the 

treatment of missing data; and (d) a 

procedure for the use of imputed or proxy 

rents for determining goal credit for 

multifamily mortgages. Fannie Mae’s 

performance was 51.5 percent in 2001, 51.8 

percent in 2002, and 52.3 percent in 2003; 

Freddie Mac’s performance was 53.2 percent 

in 2001, 50.5 percent in 2002, and 51.2 

percent in 2003—thus both GSEs surpassed 

this higher goal in all three years. 

Counting requirements (a) and (b) expired 

at the end of 2003, while (c) and (d) will 

remain in effect after that. If this counting 

approach—without the bonus points and the 

‘‘temporary adjustment factor’’—had been in 

effect in 2000 and 2001, and the GSEs had 

purchased the same mortgages that they 

actually did purchase in both years, then 

Fannie Mae’s performance would have been 

51.3 percent in 2000, 49.2 percent in 2001, 

49.0 percent in 2002, and 48.7 percent in 

2003. Freddie Mac’s performance would have 

been 50.6 percent in 2000, 47.7 percent in 

2001, 46.1 percent in 2002, and 44.6 percent 

in 2003. Thus, both Fannie Mae and Freddie 

Mac would have surpassed the low- and 

moderate-income goal of 50 percent in 2000 

and fallen short in 2001 through 2003. (See

Figure A.1.) 

BILLING CODE 4210–27–P

Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 63745


ER02NO04.044</GPH><FNP>
63746 Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 

b. Single-Family Affordable Lending Market 

The GSEs have played a major role in the 

single-family mortgage market over the past 

ten years. Their purchases of single-familyowner mortgages accounted for 61 percent of 

all mortgages originated in the single-family 

conventional conforming market between 

1999 and 2002. Their underwriting and 

purchase guidelines are market standards, 

used in all segments of the mortgage market. 

The GSEs have worked to improve their 

affordable lending record—they have 

introduced new low-downpayment products 

targeted at lower-income families; they have 

customized their underwriting standards to 

recognize the unique needs of immigrant and 

minority families; and, they have entered 

into numerous partnerships with lenders and 

non-profit groups to reach out to underserved 

populations. The enterprises’ role in the 

mortgage market is also reflected in their use 

of cutting edge technology, such as the 

development of Loan Prospector and Desktop 

Underwriter, the automated underwriting 

systems developed by Freddie Mac and 

Fannie Mae, respectively. Both GSEs are also 

entering new and challenging fields of 

mortgage finance, such as purchasing 

subprime mortgages. 

Despite these efforts and the overall gains 

in goal performance, the Department remains 

concerned about the GSEs’ support of home 

lending for the lower-income end of the 

market and for first-time homebuyers. The 

shares of the GSEs’ purchases are too low, 

particularly for underserved areas and groups 

such as minority first-time homebuyers. 

This appendix included a comprehensive 

analysis of the GSEs’ performance in funding 

home purchase mortgages for families and 

communities that historically have not been 

well served by the mortgage market. The 

following findings are offered with respect to 

the GSEs’ acquisitions of home purchase 

loans that qualify for the three housing goals 

(special affordable and underserved areas as 

well as low- and moderate-income) and their 

acquisitions of first-time homebuyer loans: 

• Fannie Mae and Freddie Mac have both 

improved their support for the single-family 

affordable lending market over the past 

eleven years, but historically over past 

periods, such as 1993–2003, 1996–2003, and 

1999–2003, they have lagged the overall 

conventional conforming market in providing 

affordable loans to lower-income borrowers 

and underserved areas. This finding is based 

on HUD’s analysis of GSE and HMDA data 

and on numerous studies by academics and 

research organizations. 

• The GSEs have shown different patterns 

of mortgage purchases. Except for two years 

(1999 and 2000), Fannie Mae has performed 

better than Freddie Mac since 1993 on all 

three goals-qualifying categories—low-mod, 

special affordable, and underserved areas. As 

a result, the percentage of Freddie Mac’s 

purchases benefiting historically underserved 

families and their neighborhoods has been 

less than the corresponding shares of total 

market originations, while Fannie Mae’s 

purchases have been somewhat closer to the 

patterns of originations in the primary 

market. 

• The above patterns can be seen by the 

following percentage shares of home 

purchase loans that qualified for the three 

housing goals between 1996 and 2003:

Special

affordable 

(percent) 

Low-Mod 

(percent) 

Underserved 

areas

(percent) 

Freddie Mac ............................................................................................................................................. 13.2 40.3 22.0 

Fannie Mae .............................................................................................................................................. 14.1 42.2 24.0 

Market (w/o B&C) .................................................................................................................................... 15.9 43.6 25.7 

• During 2001–2003, Fannie Mae 

improved its performance enough to lead the 

special affordable and low-moderate income 

markets, although it continued to lag the 

underserved areas market. During 2001–

2003, Freddie Mac lagged the conventional 

conforming market on all three goalsqualifying categories; see Figure A.2 for the 

low- and moderate-income shares for Fannie 

Mae, Freddie Mac and the market.

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• Both Fannie Mae and Freddie lag the 

conventional conforming market in funding 

first-time homebuyers, and by a rather wide 

margin. Between 1999 and 2001, first-time 

homebuyers accounted for 27 percent of each 

GSE’s purchases of home loans, compared 

with 38 percent for home loans originated in 

the conventional conforming market. 

• The GSEs also account for a very small 

share of the market for important groups such 

as minority first-time homebuyers. 

Considering the total mortgage market (both 

government and conventional loans), it is 

estimated that the GSEs purchased only 14 

percent of loans originated between 1999 and 

2001 for African-American and Hispanic 

first-time homebuyers, or one-third of their 

share (42 percent) of all home purchase loans 

originated during that period. Considering 

the conventional conforming market and the 

same time period, it is estimated that the 

GSEs purchased only 31 percent of loans 

originated for African-American and 

Hispanic first-time homebuyers, or 

approximately one-half of their share (57 

percent) of all home purchase loans in that 

market. 

To summarize, the Department’s analysis 

suggests that, except for Fannie Mae’s recent 

performance on the special affordable and 

low-moderate categories, the GSEs have not 

been leading the single-family-owner market 

in purchasing goals-qualifying and first-time 

homebuyer loans. Freddie Mac, in 

participation, continues to lag the market on 

all categories considered. There is room for 

Freddie Mac, as well as Fannie Mae, to 

further improve their performance in 

purchasing affordable loans in the 

underserved portion of the market, 

particularly in the minority first-time 

homebuyer market. Evidence suggests that 

there is a significant population of potential 

homebuyers who might respond well to 

aggressive outreach by the GSEs—immigrants 

and minorities, in particular, are expected to 

be a major source of future homebuyers. 

Furthermore, studies indicate the existence 

of a large untapped pool of potential 

homeowners among the rental population. 

Indeed, the GSEs’ recent experience with 

new outreach and affordable housing 

initiatives is important confirmation of this 

potential. To move the GSEs into a 

leadership position, the Department is 

establishing three subgoals for home 

purchase loans that qualify for the three 

housing goals. The low- and moderateincome subgoal is discussed in Section I.3 

below. 

c. Overall Market Shares 

This appendix also included an analysis of 

the GSEs’ role in the overall (owner and 

rental) conventional conforming mortgage 

market. While GSE mortgage purchases 

represented 55 percent of total dwelling units 

financed between 1999 and 2002, they 

represented smaller shares of the three goalsqualifying markets: 48 percent of housing 

units financed for both low- and moderateincome families and properties located in 

underserved areas; and 41 percent of units 

financed for the very-low-income and other 

families that qualify as special affordable. 

(See Figure A.3.) In other words, the GSEs 

accounted for approximately 50 percent or 

less of the single-family and multifamily 

units financed in the goals-qualifying 

markets. This market share analysis suggests 

that there is room for the GSEs to increase 

their purchases in these goals-qualifying 

markets. 

BILLING CODE 4210–27–C

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342As shown in Table A.31b, the GSEs’ share of 

the rental market increases to 41 percent when a 

lower multifamily share is assumed in the market 

analyses.

343Senate Report 102–282, May 15, 1992, p. 36.

The market analysis also examined the 

GSEs’ presence in the owner-occupied home 

purchase mortgage and rental property 

sectors of the mortgage market: single-family 

owner (a 61 percent share for the GSEs 

between 1999 and 2002) and single-family 

rental and multifamily rental (a combined 

rental share of 37 percent). The GSEs, and 

particularly Freddie Mac, have historically 

played a smaller role in the market financing 

rental properties, as compared with their role 

in the owner market. Fannie Mae and 

Freddie Mac have recently increased their 

purchases of these mortgages, but their 

purchases totaled only 37 percent of the 

rental units that received financing between 

1999 and 2002.342 A further increased 

presence by Fannie Mae and Freddie Mac 

would bring lower interest rates and liquidity 

to this market, as well as improve their 

housing goals performance.

d. The GSEs’ Purchases of Multifamily 

Mortgages 

Fannie Mae and, especially, Freddie Mac 

have rapidly expanded their presence in the 

multifamily mortgage market in the period 

since the passage of FHEFSSA. The Senate 

report on this legislation in 1992 referred to 

the GSEs’ activities in the multifamily arena 

as ‘‘troubling,’’ citing Freddie Mac’s 

September 1990 suspension of its purchases 

of new multifamily mortgages and criticism 

of Fannie Mae for ‘‘creaming’’ the market.343

Freddie Mac has successfully rebuilt its 

multifamily acquisition program, as shown 

by the increase in its purchases of 

multifamily mortgages: from $27 million in 

1992 to $3 billion in 1997 and then to 

approximately $7 billion during the next 

three years (1998 to 2000), before rising 

further to $11.9 billion in 2001, $13.3 billion 

in 2002, and $21.6 billion in 2003. 

Multifamily properties accounted for 10.3 

percent of all dwelling units (both owner and 

rental) financed by Freddie Mac during 2003. 

Concerns regarding Freddie Mac’s 

multifamily capabilities no longer constrain 

their performance with regard to low- and 

moderate-income families. 

Fannie Mae never withdrew from the 

multifamily market, but it has also stepped 

up its activities in this area substantially, 

with multifamily purchases rising from $3.0 

billion in 1992 to $9.4 billion in 1999, $18.7 

billion in 2001, $18.3 billion in 2002, and 

$33.3 billion in 2003. Multifamily units as a 

share of all dwelling units (both owner and 

rental) financed by Fannie Mae varied in the 

10–13 percent range between 1999 and 2001, 

before falling to 7.3 percent during heavy 

refinancing year of 2002 and 8 percent in 

2003. 

The increased purchases of multifamily 

mortgages by Fannie Mae and Freddie Mac 

have major implications for the Low- and 

Moderate-Income Housing Goal, since a very 

high percentage of multifamily units have 

rents which are affordable to low- and 

moderate-income families. However, the 

potential of the GSEs to lead the multifamily 

mortgage industry has not been fully 

developed. As reported earlier in Tables A.30 

and A.31b, the GSEs’ purchases between 

1999 and 2002 accounted for 35–41 percent 

of the multifamily units that received 

financing during this period. Certainly there 

are ample opportunities and room for 

expansion of the GSEs’ share of the 

multifamily mortgage market. The GSEs’ size 

and market position between loan originators 

and mortgage investors makes them the 

logical institutions to identify and promote 

needed innovations and to establish 

standards that will improve market 

efficiency. As their role in the multifamily 

market continues to grow, the GSEs will have 

the knowledge and market presence to push 

simultaneously for standardization and for 

programmatic flexibility to meet special 

needs and circumstances, with the ultimate 

goal of increasing the availability and 

reducing the cost of financing for affordable 

and other multifamily rental properties. 

3. Ability to Lead the Single-Family-Owner 

Market: A Low- and Moderate-Income 

Subgoal 

As discussed in Section E, the Department 

is proposing to establish a subgoal of 45 

percent for each GSE’s purchases of home 

purchase loans for low- and moderateincome families in the single-family-owner 

market of metropolitan areas for 2005, with 

the subgoal rising to 46 percent in 2006 and 

47 percent in 2007 and 2008. The purpose of 

this subgoal is to encourage the GSEs to 

improve their acquisitions of home purchase

loans for lower-income families and firsttime homebuyers who are expected to enter 

the homeownership market over the next few 

years. If the GSEs meet this goal, they will 

be leading the primary market by 

approximately one percentage point in 2005 

and by three percentage points in 2007 and 

2008, based on the income characteristics of 

home purchase loans reported in HMDA. 

Between 2002 and 2003, HMDA data show 

that low- and moderate-income families 

accounted for an (unweighted) average of 

44.1 percent of single-family-owner loans 

originated in the conventional conforming 

market of metropolitan areas. (The market 

and GSE data reported in this paragraph are 

based on ‘‘projected’’ data that account for 

new Census geography and the new OMB 

metropolitan area definitions; see Table 

A.17b.) Loans in the B&C portion of the 

subprime market are not included in these 

averages. To reach the 45-percent (47-

percent) subgoal, Freddie Mac would have to 

improve its performance by 0.8 (2.8) 

percentage points over its 2003 performance. 

Fannie Mae would have to keep up its high 

level (47.5 percent) of performance during 

2003. The approach taken is for the GSEs to 

obtain their leadership position by staged 

increases in the low-mod subgoal; this will 

enable the GSEs to take new initiatives in a 

correspondingly staged manner to achieve 

the new subgoal each year. Thus, the 

increases in the low-mod subgoal are 

sequenced so that the GSEs can gain 

experience as they improve and move toward 

the new higher subgoal targets. 

As explained in Section E.9, the subgoal 

applies only to the GSEs’ purchases in 

metropolitan areas because reliable market 

data for non-metropolitan areas are not 

available from HMDA. The Department is 

also setting home purchase subgoals for the 

other two goals-qualifying categories, as 

follows: 17–18 percent for special affordable 

loans and 32–34 percent for underserved area 

loans (also called Geographically Targeted 

loans). 

The Department considered the following 

factors when setting the subgoal for low- and 

moderate-income loans. 

(a) The GSEs have the ability to lead the 

market. The GSEs have the ability to lead the 

primary market for single-family-owner 

loans, which is the ‘‘bread-and-butter’’ of 

their business. They both have substantial 

experience in this market, which means there 

are no issues as whether or not the GSEs have 

yet penetrated the market, as there are with 

the single-family rental and multifamily 

markets. Both GSEs have not only been 

operating in the owner market for years, they 

have been the dominant players in that 

market, funding 61 percent of the singlefamily-owner mortgages financed between 

1999 and 2002. As discussed in Section G, 

their underwriting guidelines are industry 

standards and their automated mortgage 

systems are widely used throughout the 

mortgage industry. Through their new 

downpayment and subprime products, and 

their various partnership initiatives, the GSEs 

have shown that they have the capacity to 

reach out to lower-income families seeking to 

buy a home. Both Fannie Mae and Freddie 

Mac have the staff expertise and financial 

resources to make the extra effort to lead the 

primary market in funding single-familyowner mortgages for low- and moderateincome mortgages, as well for special 

affordable and undeserved area mortgages. 

(b) GSEs’ Performance Relative to the 

Market. Even though the GSEs have had the 

ability to lead the home purchase market, 

their past average performance (1993–2003, 

1996–2003, and 1999–2003) has been below 

market levels. During 2002 and 2003, Fannie 

Mae improved its performance enough to 

lead the low-mod market for home purchase 

loans, but Freddie Mac, although it also 

improved its performance during this recent 

period, continues to lag behind the primary 

market. The subgoals will ensure that Fannie 

Mae maintains and further improves its 

above-market performance and that Freddie 

Mac not only erases its current gap with the 

market but also takes a leadership position as 

well. With respect to the GSEs’ historical 

performance, low- and moderate-income 

mortgages accounted for 40.3 (42.6) percent 

of Freddie Mac’s purchases during 1996–

2003 (1999–2003), for 42.2 (43.6) percent of 

Fannie Mae’s purchases, and for 43.6 (44.1) 

percent of primary market originations 

(excluding B&C loans). The type of 

improvement needed for Freddie Mac to 

meet this new low-mod subgoal was 

demonstrated by Fannie Mae during 2001–

2003, as Fannie Mae increased its low-mod 

purchases from 40.8 percent of its singlefamily-owner business in 2000 to 45.3 

percent in 2002 and47.0 percent in 2003. (As 

noted above, Fannie Mae’s 2003 performance 

was slightly higher at 47.5 percent when 

measured based on the new 2000 Census 

geography and new OMB definitions.)

Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 63751

(c) Disparities in Homeownership and 

Credit Access Remain. There remain 

troublesome disparities in our housing and 

mortgage markets, even after the ‘‘revolution 

in affordable lending’’ and the growth in 

homeownership that has taken place since 

the mid-1990s. The homeownership rate for 

African-American and Hispanic households 

remains 25 percentage points below that of 

white households. Minority families face 

many barriers in the mortgage market, such 

as lack of capital for down payment and lack 

of access to mainstream lenders (see above). 

Immigrants and minorities are projected to 

account for almost two-thirds of the growth 

in the number of new households over the 

next ten years. As emphasized throughout 

this Appendix, changing population 

demographics will result in a need for the 

primary and secondary mortgage markets to 

meet nontraditional credit needs, respond to 

diverse housing preferences and overcome 

information and other barriers that many 

immigrants and minorities face. The GSEs 

have to increase their efforts in helping these 

families because so far they have played a 

surprisingly small role in serving minority 

first-time homebuyers. It is estimated that the 

GSEs accounted for 46.5 percent of all (both 

government and conventional) home loans 

originated between 1999 and 2001; however, 

they accounted for only 14.3 percent of home 

loans originated for African-American and 

Hispanic first-time homebuyers. Within the 

conventional conforming market, it is 

estimated that the GSEs purchased only 20 

percent of loans originated for AfricanAmerican and Hispanic first-time 

homebuyers, even though they accounted for 

57 percent of all home purchase loans in that 

market. A subgoal for home purchase loans 

should increase the GSEs’ efforts in 

important sub-markets such as the one for 

minority first-time homebuyers. 

(d) There are ample opportunities for the 

GSEs to improve their performance. Lowand moderate-income loans are available for 

the GSEs to purchase, which means they can 

improve their performance and lead the 

primary market in purchasing loans for 

borrowers with less-than-median income. 

Three indicators of this have already been 

discussed. First, Sections B and C of this 

appendix and Appendix D explain that the 

affordable lending market has shown an 

underlying strength over the past few years 

that are unlikely to vanish (without a 

significant increase in interest rates or a 

decline in the economy). The low-mod share 

of the home purchase market has averaged 

43.6 percent since 1996 and annually has 

ranged from 42.1 percent to 44.8 percent. 

Second, the market share data reported in 

Table A.30 of Section G demonstrate that 

there are newly-originated low- and 

moderate-income loans available each year 

for the GSEs to purchase. As noted above, the 

GSEs have only a minimal presence in 

special sub-markets such the minority firsttime homebuyer market, which suggests 

there are ample opportunities available for 

the GSEs to increase their purchases of loans 

for low- and moderate-income families. 

Finally, the GSEs’ purchases under the 

subgoal are not limited to new mortgages that 

are originated in the current calendar year. 

The GSEs can purchase loans from the 

substantial, existing stock of affordable loans 

held in lenders’ portfolios, after these loans 

have seasoned and the GSEs have had the 

opportunity to observe their payment 

performance. In fact, based on Fannie Mae’s 

recent experience, the purchase of seasoned 

loans appears to be one useful strategy for 

purchasing goals-qualifying loans. 

For the reasons given above, the Secretary 

believes that the GSEs can do more to raise 

the low- and moderate-income shares of their 

mortgages on these properties. This can be 

accomplished by building on various 

programs that the enterprises have already 

started, including (1) their partnership and 

outreach efforts, (2) their incorporation of 

greater flexibility into their underwriting 

guidelines, (3) their purchases of CRA loans, 

and (4) their targeting of important markets 

where they have had only a limited presence 

in the past, such as the market for minority 

first-time homebuyers. A wide variety of 

quantitative and qualitative indicators 

indicate that the GSEs’ have the resources 

and financial strength to improve their 

affordable lending performance enough to 

lead the market for low- and moderateincome families. The recent experience of 

Fannie Mae indicates that the GSEs can lead 

the low- and moderate-income market. 

4. Size of the Mortgage Market for Low- and 

Moderate-Income Families 

As detailed in Appendix D, the low- and 

moderate-income mortgage market accounts 

for 51 to 56 percent of dwelling units 

financed by conventional conforming 

mortgages. In estimating the size of the 

market, HUD excluded the effects of the B&C 

market. HUD also used alternative 

assumptions about future economic and 

market affordability conditions that were less 

favorable than those that existed over the last 

five years. HUD is well aware of the volatility 

of mortgage markets and the possible impacts 

of changes in economic conditions on the 

GSEs’ ability to meet the housing goals. 

Should conditions change such that the goals 

are no longer reasonable or feasible, the 

Department has the authority to revise the 

goals. 

5. The Low- and Moderate-Income Housing 

Goal for 2005–2008. 

The Low- and Moderate-Income Housing 

Goal is 52 percent of eligible units for 2005, 

53 percent for 2006, 55 percent for 2007, and 

56 percent for 2008. The market for the Lowand Moderate-Income Goal is estimated to be 

51–56 percent. Under the new counting rules 

(i.e., 2000-Census income re-benchmarking 

and the new OMB metropolitan area 

definitions), Fannie Mae’s low- and 

moderate-income performance is estimated to 

have been 46.3 percent in 1999, 51.2 percent 

in 2000, 48.7 percent in 2001, 47.9 percent 

in 2002, and 49.5 percent in 2003—for 2005, 

Fannie Mae would have to increase its 

performance by 3.3 percentage points over its 

average (unweighted) performance of 48.7 

percent over these last five years, or by 0.8 

percentage point over its previous peak 

performance (51.2 percent in 2000). By 2008, 

Fannie Mae’s performance would have to 

increase by 6.3 percentage points over 

average 1999–2003 performance, and by 5.8 

percentage points over its previous peak 

performance in 2000. Freddie Mac’s 

performance is estimated to have been 46.0 

percent in 1999, 50.2 percent in 2000, 47.0 

percent in 2001, 44.6 percent in 2002, and 

45.3 percent in 2003—for 2005, Freddie Mac 

would have to increase its performance by 

5.3 percentage points over its average 

(unweighted) performance of 46.7 percent 

over these last five years, or by 1.8 percentage 

points over its previous peak performance 

(50.2 percent in 2000). By 2008, Freddie 

Mac’s performance would have to increase by 

9.3 percentage points over average 1999–

2003 performance, and by 5.8 percentage 

points over its previous peak performance. 

However, the low- and moderate-income 

market is estimated to be 51–56 percent. 

Thus, the GSEs should be able to improve 

their performance enough to meet these goals 

of 52–56 percent. 

The objective of the Low- and ModerateIncome Goal is to bring the GSEs’

performance to the upper end of HUD’s 

market range estimate for this goal (51–56 

percent), consistent with the statutory 

criterion that HUD should consider the GSEs’

ability to lead the market for each Goal. To 

enable the GSEs to achieve this leadership, 

the Department is proposing modest 

increases in the Low- and Moderate-Income 

Goal for 2005 which will increase further, 

year-by-year through 2008, to achieve the 

ultimate objective for the GSEs to lead the 

market under a range of foreseeable economic 

circumstances by 2008. Such a program of 

staged increases is consistent with the 

statutory requirement that HUD consider the 

past performance of the GSEs in setting the 

Goals. Staged annual increases in the Lowand Moderate-Income Goal will provide the 

enterprises with opportunity to adjust their 

business models and prudently try out 

business strategies, so as to meet the required 

2008 level without compromising other 

business objectives and requirements. 

Figure A.3 summarizes many of the points 

made in this section regarding opportunities 

for Fannie Mae and Freddie Mac to improve 

their overall performance on the Low- and 

Moderate-Income Goal. The GSEs’ purchases 

provided financing for 26,118,927 (or 55 

percent) of the 47,551,039 single-family and 

multifamily units that were financed in the 

conventional conforming market between 

1999 and 2002. However, in the low- and 

moderate-income part of the market, the 

12,608,215 units that were financed by GSE 

purchases represented only 48 percent of the 

26,051,771 dwelling units that were financed 

in the market. Thus, there appears to be 

ample room for the GSEs to increase their 

purchases of loans that qualify for the Lowand Moderate-Income Goal. Examples of 

specific market segments that would 

particularly benefit from a more active 

secondary market have been provided 

throughout this appendix. 

6. Conclusions 

Having considered the projected mortgage 

market serving low- and moderate-income 

families, economic, housing and 

demographic conditions for 2005–08, and the 

GSEs’ recent performance in purchasing 

mortgages for low- and moderate-income 

families, the Secretary has determined that 

63752 Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 

1This analysis excludes Puerto Rico. In addition, 

tracts are excluded if median income is suppressed 

in the underlying census data. There are 379 such 

tracts. When reporting analysis of mortgage loan 

denial, origination, and application rates later in 

this appendix, tracts are excluded if there are no 

purchase or refinance applications. Tracts are also 

excluded if: (1) Group quarters constitute more than 

50 percent of housing units or (2) there are less than 

15 home purchase applications in the tract and the 

tract denial rates equal 0 or 100 percent. Excluded 

tracts account for a small percentage of mortgage 

loan applications (1.4 percent). These tracts are not 

excluded from HUD’s underserved areas if they 

meet the income and minority thresholds. Rather, 

the tracts are excluded to remove the effects of 

outliers from the analysis.

the goals of 52 percent of eligible units 

financed in 2005, 53 percent in 2006, 55 

percent in 2007, and 56 percent in 2008 are 

feasible. The Secretary is also establishing a 

subgoal of 45 percent for the GSEs’ purchases 

of single-family-owner home purchase 

mortgages in metropolitan areas in 2005, 

increasing to 46 percent in 2006 and 47 

percent in 2007 and 2008. The Secretary has 

considered the GSEs’ ability to lead the 

industry as well as the GSEs’ financial 

condition. The Secretary has determined that 

the proposed goals and the proposed 

subgoals are necessary and appropriate.

Appendix B—Departmental 

Considerations To Establish the Central 

Cities, Rural Areas, and Other 

Underserved Areas Goal 

A. Introduction 

1. Establishment of Goal 

The Federal Housing Enterprises Financial 

Safety and Soundness Act of 1992 

(FHEFSSA) requires the Secretary to 

establish an annual goal for the purchase of 

mortgages on housing located in central 

cities, rural areas, and other underserved 

areas (the ‘‘Underserved Areas Housing 

Goal’’). 

In establishing this annual housing goal, 

Section 1334 of FHEFSSA requires the 

Secretary to consider: 

1. Urban and rural housing needs and the 

housing needs of underserved areas; 

2. Economic, housing, and demographic 

conditions; 

3. The performance and effort of the 

enterprises toward achieving the 

Underserved Areas Housing Goal in previous 

years; 

4. The size of the conventional mortgage 

market for central cities, rural areas, and 

other underserved areas relative to the size of 

the overall conventional mortgage market; 

5. The ability of the enterprises to lead the 

industry in making mortgage credit available 

throughout the United States, including 

central cities, rural areas, and other 

underserved areas; and 

6. The need to maintain the sound 

financial condition of the enterprises. 

Organization of Appendix. The remainder 

of Section A first defines the Underserved 

Areas Housing Goal for both metropolitan 

areas and nonmetropolitan areas. Sections B 

and C address the first two factors listed 

above, focusing on findings from the 

literature on access to mortgage credit in 

metropolitan areas (Section B) and in 

nonmetropolitan areas (Section C). Separate 

discussions are provided for metropolitan 

and nonmetropolitan (rural) areas because of 

differences in the underlying markets and the 

data available to measure them. Section D 

discusses the past performance of the GSEs 

on the Underserved Areas Housing Goal (the 

third factor) and Sections E-G report the 

Secretary’s findings for the remaining factors. 

Section H presents the Department’s rules 

relating to the definition of underserved areas 

in nonmetropolitan areas. Section I 

summarizes the Secretary’s rationale for 

establishing a subgoal for single-familyowner home purchase mortgages and for 

setting the level for the Underserved Areas 

Housing Goal. 

2. HUD’s Underserved Areas Housing Goal 

HUD’s definition of the geographic areas 

targeted by this goal is basically the same as 

that used during 1996–2003. It is divided 

into a metropolitan component and a 

nonmetropolitan component. However, as 

explained below, switching to 2000 Census 

geography increases the number of census 

tracts defined as underserved, and this 

necessitates an adjustment of the goal level. 

Metropolitan Areas. This rule provides that 

within metropolitan areas, mortgage 

purchases will count toward the goal when 

those mortgages finance properties that are 

located in census tracts where (1) median 

income of families in the tract does not 

exceed 90 percent of area (MSA) median 

income or (2) minorities comprise 30 percent 

or more of the residents and median income 

of families in the tract does not exceed 120 

percent of area median income. 

In this Rule, the underserved census tracts 

are defined in terms of the 2000 Census 

rather than the 1990 Census. As shown in 

Table B.1a, switching to 2000 Census data 

and re-specified MSA boundaries as of June 

2003, increases the proportions of 

underserved census tracts, population, 

owner-occupied housing units, and 

population below the poverty line in 

metropolitan areas. The definition now 

covers 26,959 (51.3 percent) of the 52,585 

census tracts in metropolitan areas, which 

include 48.7 percent of the population and 

38.0 percent of the owner-occupied housing 

units in metropolitan areas.1 The 1990-based 

definition covered 21,587 (47.5 percent) of 

the 45,406 census tracts in metropolitan 

areas, which included 44.3 percent of the 

population and 33.7 percent of the owneroccupied units in metropolitan areas.

The census tracts included in HUD’s 

definition of underserved areas exhibit low 

rates of mortgage access and distressed 

socioeconomic conditions. Between 1999 and 

2002, the unweighted average mortgage 

denial rate in these tracts was 17.5 percent, 

almost double the average denial rate (9.3 

percent) in excluded tracts. The underserved 

tracts include 75.3 percent of the number of 

persons below the poverty line in 

metropolitan areas. 

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2 Kalawao County, Hawaii, which has a very 

small population, is excluded from the analysis for 

1990 but included for 2000.

HUD’s establishment of this definition is 

based on a substantial number of studies of 

mortgage lending and mortgage credit flows 

conducted by academic researchers, 

community groups, the GSEs, HUD and other 

government agencies. As explained in the 

2000 Rule, one finding stands out from the 

existing research literature on mortgage 

access for different types of neighborhoods: 

High-minority and low-income 

neighborhoods continue to have higher 

mortgage denial rates and lower mortgage 

origination rates than other neighborhoods.

A neighborhood’s minority composition and 

its level of income are highly correlated with 

access to mortgage credit. 

Nonmetropolitan Areas. In 

nonmetropolitan areas, mortgage purchases 

count toward the Underserved Areas Housing 

Goal for properties which are located in 

counties where (1) median income of families 

in the county does not exceed 95 percent of 

the greater of (a) state nonmetropolitan 

median income or (b) nationwide 

nonmetropolitan median income, or (2) 

minorities comprise 30 percent or more of 

the residents and median income of families 

in the county does not exceed 120 percent of 

the greater of (a) state nonmetropolitan 

median income or (b) nationwide 

nonmetropolitan median income. 

In 1995, two important factors influenced 

HUD’s definition of nonmetropolitan 

underserved areas—lack of available data for 

measuring mortgage availability in rural areas 

and lenders’ difficulty in operating mortgage 

programs at the census tract level in rural 

areas. Because of these factors, the 1995 Rule 

(as well as the 2000 Rule) used a more 

inclusive, county-based approach to 

designating underserved portions of rural 

areas. As discussed in a later section, HUD 

is now replacing the county-based definition 

with a tract-based definition. 

As shown in Table B.1b, switching from 

1990 to 2000 Census data and incorporating 

the June, 2003 specification of metropolitan 

areas causes a slight decrease in underserved 

proportions of counties, population, owneroccupied housing units, and poverty 

population in non-metropolitan areas. In 

terms of the 2000 Census geography and June 

2003 metropolitan area specification, the 

definition covers 1,260 (61.4 percent) of the 

2,052 counties in nonmetropolitan areas, 

which include 51.0 percent of the 

population, 50.7 percent of the owneroccupied housing units, and 64.3 percent of 

the population below the poverty level in 

non-metropolitan areas. The 1990-based 

definition covered 1,514 (65.5 percent) of the 

2,311 counties in non-metropolitan areas, 

which included 54.6 percent of the 

population, 53.4 percent of the owneroccupied units, and 67.9 percent of the poor 

in non-metropolitan areas.2

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Data comparable to that in Table B.1b is 

presented in Table B.1c based on census 

tracts, rather than counties, in 

nonmetropolitan areas. As indicated, the 

tract-based definition includes 6,782 (54.9 

percent) of the 12,359 nonmetropolitan 

census tracts in the country. These tracts 

contain 52.5 percent of the nonmetropolitan 

population (comparable to the 51.0 percent 

using a county-based definition) and 50.4 

percent of owner-occupied housing units 

(close to the corresponding figure of 50.7 

percent under the county-based approach). 

But the tract-based approach better targets 

families most in need, as shown, for example, 

by the fact that it includes 68.9 percent of the 

population in poverty, exceeding the 

corresponding figure of 64.3 percent under 

the county-based definition of 

nonmetropolitan underserved areas.

GSE Performance. Table B.1d shows the 

increases in the GSEs’ overall goals 

performance under the more expansive 

geography of the 2000 Census. During 2000, 

Fannie Mae’s performance would have been 

an estimated 37.5 percent if underserved 

areas were defined in terms of 2000 Census 

geography, compared with 31.0 percent 

under 1990 Census geography. These results 

for Fannie Mae (adjusted to be comparable 

with the 2000 figures) are 35.7 percent and 

30.4 percent for 2001; 35.0 percent and 30.2 

percent for 2002; and 34.1 percent and 29.2 

percent for 2003. The corresponding figures 

for Freddie Mac are 34.1 percent and 29.2 

percent for 2000 performance; 32.5 percent 

and 28.2 percent for 2001 performance; 32.4 

percent and 28.0 percent for 2002 

performance; and 31.6 percent and 27.7 

percent for 2003 performance. (The 2001–03 

housing goals percentages in the table are 

adjusted to exclude the effects of the bonus 

points and Freddie Mac’s Temporary 

Adjustment Factor, which became applicable 

in 2001 for scoring of loans toward the 

housing goals.)


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3 In this appendix, the term ‘‘central city’’ is used 

to mean ‘‘OMB-designated central city.’’

4The actual denial rates were as follows: 23.6 

percent for low-income (80% AMI or less) African 

Americans, 15.5 percent for upper-income (120% 

AMI or more) African Americans. 11.4 percent for 

low-income Whites, and 5.6 percent for upperincome Whites. The overall denial rate in the 

conventional conforming home purchase market 

was 9.7 percent in 2002. The data exclude 

applications to lenders that specialize in 

manufactured home lending.

5Alicia H. Munnell, Lynn E. Browne, James 

McEneaney, and Geoffrey M.B. Tootell, ‘‘Mortgage 

Lending in Boston: interpreting HMDA Data,’’

American Economic Review, March 1996.

6William C. Hunter, ‘‘The Cultural Affinity 

Hypothesis and Mortgage Lending Decisions,’’ WP–

95–8, Federal Reserve Bank of Chicago, 1995. 

Hunter confirmed that race was a factor in denial 

rates of marginal applicants. While denial rates 

were comparable for borrowers of all races with 

‘‘good’’ credit ratings, among those with ‘‘bad’’

credit ratings or high debt ratios, minorities were 

significantly more likely to be denied than 

similarly-situated whites. The study concluded that 

the racial differences in denial rates were consistent 

with a cultural gap between white loan officers and 

minority applicants, and conversely, a cultural 

affinity with white applicants.

7For a reassessment of the Boston Fed study, see

Stephen Ross and John Yinger, The Color of Credit,

MIT Press 2002, and other studies cited there.

Goal and Subgoal Levels. The Department 

establishes the Underserved Areas Housing 

Goal as 37 percent of eligible units financed 

for 2005, 38 percent for 2006 and 2007, and 

39 percent for 2008. 

HUD is establishing a subgoal of 32 percent 

for the share of each GSE’s total singlefamily-owner mortgage purchases that 

finance single-family-owner properties 

located in underserved census tracts of 

metropolitan areas for 2005, with this 

subgoal rising to 33 percent for 2006 and 

2007 and 34 percent in 2008. In this case, 

subgoal performance for a particular calendar 

year would be calculated for each GSE by 

dividing (a) the number of mortgages 

purchased by the GSE that finance singlefamily-owner properties located in 

underserved areas (i.e., census tracts) of 

metropolitan areas by (b) the number of 

mortgages purchased by the GSE that finance 

single-family-owner properties located in 

metropolitan areas. As explained in Section 

H, the purpose of this subgoal is to encourage 

the GSEs to lead the primary market in 

funding mortgages in underserved census 

tracts. 

B. Consideration of Factors 1 and 2 in 

Metropolitan Areas: The Housing Needs of 

Underserved Urban Areas and Housing, 

Economic, and Demographic Conditions in 

Underserved Urban Areas 

This section discusses differential access to 

mortgage funding in urban areas and 

summarizes available evidence on 

identifying those neighborhoods that have 

historically experienced problems gaining 

access to credit. Section B.1 provides an 

overview of the problem of unequal access to 

mortgage funding, focusing on discrimination 

and other housing problems faced by 

minority families and the communities 

where they live. Section B.2 examines 

mortgage access at the neighborhood level 

and discusses in some detail the rationale for 

the Underserved Areas Housing Goal in 

metropolitan areas. The most thorough 

studies available provide strong evidence 

that low-income and high-minority census 

tracts are underserved by the mortgage 

market. Section B.3 presents recent statistics 

on the credit characteristics and 

socioeconomic characteristics of underserved 

areas under HUD’s definition. Readers are 

referred to the expansive literature on this 

issue, which is reviewed in some detail in 

Appendix B of HUD’s 2000 Rule. This 

section focuses on some of the main studies 

and their findings. 

Three main points are made in this section: 

• Both borrowers and neighborhoods can 

be identified as currently being underserved 

by the nation’s housing and mortgage 

markets. Appendix A provided evidence of 

racial disparities in the sale and rental of 

housing and in the provision of mortgage 

credit. Partly as a result of this, the 

homeownership rate for minorities is 

substantially below that for whites. 

• The existence of substantial 

neighborhood disparities in mortgage credit 

is well documented for metropolitan areas. 

Research has demonstrated that census tracts 

with lower incomes and higher shares of 

minority population consistently have poorer 

access to mortgage credit, with higher 

mortgage denial rates and lower origination 

rates for mortgages. Thus, the income and 

minority composition of an area is a good 

measure of whether that area is being 

underserved by the mortgage market. 

• Research supports a targeted 

neighborhood-based definition of 

underservice. Studies conclude that 

characteristics of mortgage loan applicants 

and the neighborhood where the property is 

located are the major determinants of 

mortgage denial rates and origination rates.

Once these characteristics are accounted 

for, other influences, such as location in a 

central city, play only a minor role in 

explaining disparities in mortgage lending.3

1. Discrimination in the Mortgage and 

Housing Markets—An Overview 

The nation’s housing and mortgage markets 

are highly efficient systems, where most 

homebuyers can put down relatively small 

amounts of cash and obtain long-term 

funding at relatively small spreads above the 

lender’s borrowing costs, even though 

transactions costs are still too high and too 

bundled. Unfortunately, this highly efficient 

financing system does not work everywhere 

or for everyone. Studies have shown that 

access to credit often depends on improper 

evaluation of characteristics of the mortgage 

applicant and the neighborhood in which the 

applicant wishes to buy. In addition, though 

racial discrimination has become less blatant 

in the home purchase market, studies have 

shown that it is still widespread in more 

subtle forms. Partly as a result of these 

factors, the homeownership rate for 

minorities is substantially below that of 

whites. Appendix A provided an overview of 

the homeownership gaps and lending 

disparities faced by minorities. This section 

briefly reviews evidence on lending 

discrimination as well as a recent HUDsponsored study of discrimination in the 

housing market. 

Mortgage Denial Rates. A quick look at 

mortgage denial rates reported by Home 

Mortgage Disclosure Act (HMDA) data 

reveals that in 2002 minority denial rates 

were higher than those for white loan 

applicants. For lower-income borrowers, the 

denial rate for African Americans applying 

for conventional loans was 2.1 times the 

denial rate for white borrowers, while for 

higher-income borrowers, the denial rate for 

African Americans was 2.7 times the rate for 

white borrowers.4

Differentials in denial rates, such as those 

reported above, are frequently used to 

demonstrate the problems that minorities 

face obtaining access to mortgage credit. 

However, an important question is the degree 

to which variations in denial rates reflect 

lender bias against certain kinds of borrowers 

relative to the degree to which they reflect 

the credit quality of potential borrowers (as 

indicated by applicants’ available assets, 

credit rating, employment history, etc.). 

Without fully accounting for the 

creditworthiness of the borrower, racial 

differences in denial rates cannot be 

attributed to lender bias. Some studies of 

credit disparities have attempted to control 

for credit risk factors that might influence a 

lender’s decision to approve a loan. 

Boston Fed Study. The best example of 

accounting for credit risk is the study of 

mortgage denial rates by researchers at the 

Federal Reserve Bank of Boston.5 This 

landmark study found that racial differentials 

in mortgage denial rates cannot be fully 

explained by differences in credit risk. To 

control for credit risk, the Boston Fed 

researchers included 38 borrower and loan 

variables indicated by lenders to be critical 

to loan decisions. For example, the Boston 

Fed study included a measure of the 

borrower’s credit history, which is a variable 

not included in other studies. The Boston 

Fed study found that minorities’ higher 

denial rates could not be explained fully by 

income and credit risk factors. The denial 

rate for African Americans and Hispanics 

was 17 percent, compared with 11 percent 

for Whites with similar characteristics. That 

is, African Americans and Hispanics were 

about 60 percent more likely to be denied 

credit than Whites, even after controlling for 

credit risk characteristics such as credit 

history, employment stability, liquid assets, 

self-employment, age, and family status and 

composition. Although almost all highlyqualified applicants were approved, 

differential treatment was observed among 

borrowers with more marginal qualifications. 

That is, highly-qualified borrowers of all 

races seemed to be treated equally, but in 

cases where there was some flaw in the 

application, white applicants seemed to be 

given the benefit of the doubt more 

frequently than minority applicants. A 

subsequent refinement of the data used by 

the Federal Reserve Bank of Boston 

confirmed the findings of that study.6

The Boston Fed study, as well as 

reassessments of that study by other 

researchers, concluded that the effect of 

borrower race on mortgage rejections persists 

even after controlling for legitimate 

determinants of lenders’ credit decisions.7

Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 63759

8Since upfront loan fees are frequently 

determined as a percentage of the loan amount, 

lenders are discouraged from making smaller loans 

in older neighborhoods, because such loans 

generate lower revenue and are less profitable to 

lenders.

9Traditional underwriting practices may have 

excluded some lower income families that are, in 

fact, creditworthy. Such families tend to pay cash, 

leaving them without a credit history. In addition, 

the usual front-end and back-end ratios applied to 

applicants’ housing expenditures and other ongoing costs may be too stringent for lower income 

households, who typically pay larger shares of their 

income for housing (including rent and utilities) 

than higher income households.

10Margery A. Turner and Felicity Skidmore, eds., 

Mortgage Lending Discrimination: A Review of 

Existing Evidence. The Urban Institute: Washington, 

DC, June 1999.

11Margery Austin Turner, All Other Things Being 

Equal: A Paired Testing Study of Mortgage Lending 

Institutions, The Urban Institute Press, April 2002.

12Margery Austin Turner, Stephen L. Ross, 

George Galster, and John Yinger, Discrimination in 

Metropolitan Housing Markets, The Urban Institute 

Press, November 2002.

13How Much Do We Know? Public Awareness of 

the Nation’s Fair Housing Laws, prepared for HUD 

by Martin D. Abravanel and Mary K. Cunningham 

of the Urban Institute, April 2002.

14U.S. Bureau of the Census, August 2002. The 

co-authors of the study were John Iceland and 

Daniel H. Wienberg. For a summary of the study, 

see ‘‘Residential Segregation Still Prevalent’’, 

National Mortgage News, January 6, 2003, page 1.

15See Randall M. Scheessele, Black and White 

Disparities in Subprime Mortgage Refinance 

Lending, Housing Finance Working Paper No. HF–

14, Office of Policy Development and Research, 

U.S. Department of Housing and Urban 

Development, April 2002.

Thus, these studies imply that variations in 

mortgage denial rates, such as those reported 

above, are not determined entirely by 

borrower risk, but reflect discrimination in 

the housing finance system. However, the 

independent race effect identified in these 

studies is still difficult to interpret. In 

addition to lender bias, access to credit can 

be limited by loan characteristics that reduce 

profitability 8 and by underwriting standards 

that have disparate effects on minority and 

lower-income borrowers and their 

neighborhoods.9

Paired-Testing Studies. As discussed in 

Appendix A, paired testing studies of the 

pre-qualification process have supported the 

findings of the Boston Fed study. Based on 

a review of paired tests conducted by the 

National Fair Housing Alliance, The Urban 

Institute concluded that differential 

treatment discrimination at the preapplication level occurred at significant 

levels in at least some cities. Minorities were 

less likely to receive information about loan 

products, received less time and information 

from loan officers, and were quoted higher 

interest rates in most of the cities where tests 

were conducted.10 Another Urban Institute 

study used the paired testing methodology to 

examine the pre-application process in Los 

Angeles and Chicago. African Americans and 

Hispanics faced a significant risk of unequal 

treatment when they visited mainstream 

mortgage lending institutions to make preapplication inquiries.11

Sales and Rental Markets. In 2002, HUD 

released its third Housing Discrimination 

Study (HDS) in the sale and rental of 

housing. The study, entitled Discrimination 

in Metropolitan Housing Markets: National 

Results from Phase I of the Housing 

Discrimination Study (HDS), was conducted 

by the Urban Institute.12 The results of this 

HDS were based on 4,600 paired tests of 

minority and non-minority home seekers 

conducted during 2000 in 23 metropolitan 

areas nationwide. The report showed large 

decreases between 1989 and 2000 in the level 

of discrimination experienced by Hispanics 

and African Americans seeking to buy a 

home. There has also been a modest decrease 

in discrimination toward African Americans 

seeking to rent a unit. This downward trend, 

however, has not been seen for Hispanic 

renters, who now are more likely to 

experience discrimination in their housing 

search than are African American renters. 

But while generally down since 1989, the 

report found that housing discrimination still 

exists at unacceptable levels. The greatest 

share of discrimination for Hispanic and 

African American home seekers can still be 

attributed to being told units are unavailable 

when they are available to non-Hispanic 

whites and being shown and told about fewer 

units than a comparable non-minority. 

Although discrimination is down on most 

areas for African American and Hispanic 

homebuyers, there remain worrisome upward 

trends of discrimination in the areas of 

geographic steering for African Americans 

and, relative to non-Hispanic whites, the 

amount of help agents provide to Hispanics 

with obtaining financing. On the rental side, 

Hispanics are more likely in 2000 than in 

1989 to be quoted a higher rent than their 

white counterpart for the same unit.

Another HUD-sponsored study asked 

respondents to a nationwide survey if they 

‘‘thought’’ they had ever been discriminated 

against when trying to buy or rent a house 

or an apartment.13 While the responses were 

subjective, they are consistent with the 

findings of the HDS. African Americans and 

Hispanics were considerably more likely 

than whites to say they have suffered 

discrimination—24 percent of African 

Americans and 22 percent of Hispanics 

perceived discrimination, compared to only 

13 percent of whites.

Segregation in Urban Areas.

Discrimination, while not the only cause, 

contributes to the pervasive level of 

segregation that persists between African 

Americans and Whites in our urban areas. 

The Census Bureau recently released one of 

the most exhaustive studies of residential 

segregation ever undertaken, entitled Racial 

and Ethnic Residential Segregation in the 

United States: 1980–2000.14 The Census 

Bureau found that the United States was still 

very much racially divided. While African 

Americans have made modest strides, they 

remain the most highly segregated racial 

group. The authors said that residential 

segregation likely results from a variety of 

factors, including choices people make about 

where they want to live, restrictions on their 

choices, or lack of information. The fact that 

many mainstream lenders do not operate in 

segregated areas makes it even more difficult 

for minorities to obtain access to reasonablepriced mortgage credit.15 Section C.8 of 

Appendix A cited several studies showing 

that these inner city neighborhoods are often 

served mainly by subprime lenders. In 

addition, there is evidence that denial rates 

are higher in minority neighborhoods 

regardless of the race of the applicant. The 

next section explores the issue of credit 

availability in neighborhoods in more detail.

2. Evidence About Access to Credit in Urban 

Neighborhoods—An Overview 

HUD’s Underserved Areas Housing Goal 

focuses on low-income and high-minority 

neighborhoods that are characterized by high 

loan application denial rates and low loan 

origination rates. As explained in Section B.3 

below, the mortgage denial rate during 2001 

in census tracts defined as underserved by 

HUD was twice the denial rate in excluded 

(or ‘‘served’’) tracts. In addition to such 

simple denial rate comparisons, there is a 

substantial economics literature justifying the 

targeted neighborhood definition that HUD 

has used to define underserved areas. 

Appendix B of the 1995 and 2000 GSE Rules 

reviewed that literature in some detail; thus, 

this section simply provides an overview of 

the main studies supporting the need to 

improve credit access to low-income and 

high-minority neighborhoods. Readers not 

interested in this overview may want to 

proceed to Section B.3, which examines the 

credit and socioeconomic characterizes of the 

census tracts included in HUD’s underserved 

area definition. 

As explained in HUD’s 2000 Rule, the 

viability of neighborhoods—whether urban, 

rural, or suburban—depends on the access of 

their residents to mortgage capital to 

purchase and improve their homes. While 

neighborhood problems are caused by a wide 

range of factors, including substantial 

inequalities in the distribution of the nation’s 

income and wealth, there is increasing 

agreement that imperfections in the nation’s 

housing and mortgage markets are hastening 

the decline of distressed neighborhoods. 

Disparate denial of credit based on 

geographic criteria can lead to disinvestment 

and neighborhood decline. Discrimination 

and other factors, such as inflexible and 

restrictive underwriting guidelines, limit 

access to mortgage credit and leave potential 

borrowers in certain areas underserved. 

Data on mortgage credit flows are far from 

perfect, and issues regarding the 

identification of areas with inadequate access 

to credit are both complex and controversial. 

For this reason, it is essential to define 

‘‘underserved areas’’ as accurately as possible 

based on existing data and evidence. There 

are three sets of studies that provide the 

rationale for the Department’s definition of 

underserved areas: (1) Studies examining 

racial discrimination against individual 

mortgage applicants; (2) studies that test 

whether mortgage redlining exists at the 

neighborhood level; and (3) studies that 

support HUD’s targeted approach to 

measuring areas that are underserved by the 

mortgage market. In combination, these 

studies provide strong support for the 

definition of underserved areas chosen by 

HUD. The main studies of discrimination 

against individuals have already been 

summarized in Section B.1 above. Thus, this 

section focuses on the neighborhood-based 

63760 Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 

16These studies, which were conducted at the 

census tract level, typically involved regressing the 

number of mortgage originations (relative to the 

number of properties in the census tract) on 

characteristics of the census tract including its 

minority composition. A negative coefficient 

estimate for the minority composition variable was 

often interpreted as suggesting redlining. For a 

discussion of these models, see Eugene Perle, 

Kathryn Lynch, and Jeffrey Horner, ‘‘Model 

Specification and Local Mortgage Market 

Behavior,’’ Journal of Housing Research, Volume 4, 

Issue 2, 1993, pp. 225–243.

17For critiques of the early HMDA studies, see 

Andrew Holmes and Paul Horvitz, ‘‘Mortgage 

Redlining: Race, Risk, and Demand,’’ The Journal of 

Finance, Volume 49, No. 1, March 1994, pp. 81–99; 

and Michael H. Schill and Susan M. Wachter, ‘‘A 

Tale of Two cities: Racial and Ethnic Geographic 

Disparities in Home Mortgage Lending in Boston 

and Philadelphia,’’ Journal of Housing Research,

Volume 4, Issue 2, 1993, pp. 245–276.

18Like early HMDA studies, an analysis of deed 

transfer data in Boston found lower rates of 

mortgage activity in minority neihborhoods. The 

discrepancies held even after controlling for 

income, house values and other economic and nonracial factors that might explain differences 

differences in demand and housing market activity. 

The study concluded that ‘‘the housing market and 

the credit market together are functioning in a way 

that has hurt African American neighborhoods in 

the city of Boston.’’ Katherine L. Bradbury, Karl E. 

Case, and Constance R. Dunham, ‘‘Geographic 

Patterns of Mortgage Lending in Boston, 1982–

1987,’’ New England Economic Review, September/

October 1989, pp. 3–30.

19Using an analytical approach similar to that of 

Bradbury, Case, and Dunham, Anne Shlay found 

evidence of fewer mortgage loans originated in 

black census tracts in Chicago and Baltimore. See 

Anne Shlay, ‘‘Not in That Neighborhood: The 

Effects of Population and Housing on the 

Distribution of Mortgage Finance within the 

Chicago SMSA,’’ Social Sciene Research, Volume 

17, No. 2, 1988, pp. 137–163; and ‘‘Financing 

Community: Methods for Assessing Residential 

Credit Disparities, Market Barriers, and Institutional 

Reinvestment Performance in the Metropolis,’’

Journal of Urban Affiars, Volume 11, No. 3, 1989, 

pp. 201–223.

20Holmes and Horvitz, op. cit.

21Schill and Wachter, op. cit.

22Schill and Wachter, page 271. Munnell, et al.

reached similar conclusions in their study of 

Boston. They found that the race of the individual 

mattered, but that once individual characteristics 

were controlled, racial composition of the 

neighborhood was insignificant.

23Fred J. Phillips-Patrick and Clifford V. Rossi, 

‘‘Statistical Evidence of Mortgage Redlining? A 

Cautionary Tale’’, The Journal of Real Estate 

Research, Volume 11, Number 1, 1996, pp.13–23.

24Samuel L. Myers, Jr. and Tsze Chan, ‘‘Racial 

Discrimination in Housing Markets: Accounting for 

Credit Risk’’, Social Science Quarterly, Volume 76, 

Number 3, September 1995, pp. 543–561.

25For another study that uses HMDA data on 

reasons for denial to construct a proxy for bad 

credit, see Steven R. Holloway, ‘‘Exploring the 

Neighborhood Contingency of Race Discrimination 

in Mortgage Lending in Columbus, Ohio’’, Annals 

of the Association of American Geographers, 

Volume 88, Number 2, 1998, pp. 252–276. 

Holloway finds that mortgage denial rates are 

higher for black applicants (particularly those who 

are making large loan requests) in all-white 

neighborhoods than in minority neighborhoods, 

while the reverse is true for white applicants 

making small loan requests.

studies in (2) and (3). As noted above, this 

brief overview of these studies draws from 

Appendix B of the 1995 GSE Rule; readers 

are referred there for a more detailed 

treatment of earlier studies of the issues 

discussed below. 

a. Controlling for Neighborhood Risk and 

Tests of the Redlining Hypothesis 

In its deliberations leading up to 

FHEFSSA, Congress was concerned about 

geographic redlining—the refusal of lenders 

to make loans in certain neighborhoods 

regardless of the creditworthiness of 

individual applicants. During the 1980s and 

early 1990s, a number of studies using 

HMDA data (such as that reported in Tables 

B.2 and B.3, below) attempted to test for the 

existence of mortgage redlining. Consistent 

with the redlining hypothesis, these studies 

found lower volumes of loans going to lowincome and high-minority neighborhoods.16

However, such analyses were criticized 

because they did not distinguish between 

demand, risk, and supply effects 17—that is, 

they did not determine whether loan volume 

was low because families in high-minority 

and low-income areas were unable to afford 

homeownership and therefore were not 

applying for mortgage loans, or because 

borrowers in these areas were more likely to 

default on their mortgage obligations, or 

because lenders refused to make loans to 

creditworthy borrowers in these areas.18, 19

More Comprehensive Tests of the Redlining 

Hypothesis. Recent statistical studies have 

sought to test the redlining hypothesis by 

more completely controlling for differences 

in neighborhood risk and demand. In these 

studies, the explanatory power of 

neighborhood race is reduced to the extent 

that the effects of neighborhood risk and 

demand are accounted for; thus, they do not 

support claims of racially induced mortgage 

redlining. Many of these studies find that the 

race of the individual borrower is more 

important than the racial composition of the 

neighborhood. However, these studies cannot 

reach definitive conclusions about redlining 

because segregation in inner cities makes it 

difficult to distinguish the impacts of 

geographic redlining from the effects of 

individual discrimination. The following are 

two good examples of these studies. 

Holmes and Horvitz examined variations 

in conventional mortgage originations across 

census tracts in Houston.20 Their model 

explaining census-tract variations in 

mortgage originations included the following 

types of explanatory variables: (a) The 

economic viability of the loan, (b) 

characteristics of properties in and residents 

of the tract (e.g., house value, income, age 

distribution and education level), (c) 

measures of demand (e.g., recent movers into 

the tract and change in owner-occupied units 

between 1980 and 1990), (d) measures of 

credit risk (defaults on government-insured 

loans and change in tract house values 

between 1980 and 1990), and (e) the racial 

composition of the tract, as a test for the 

existence of racial redlining. Most of the 

neighborhood risk and demand variables 

were significant determinants of the flow of 

conventional loans in Houston. The 

coefficients of the racial composition 

variables were insignificant, which led 

Holmes and Horvitz to conclude that 

allegations of redlining in the Houston 

market could not be supported.

Schill and Wachter include several 

individual borrower and neighborhood 

characteristics to explain mortgage 

acceptance rates in Philadelphia and 

Boston.21 They found that the applicant race 

variables—whether the applicant was African 

American or Hispanic—showed significant 

negative effects on the probability that a loan 

would be accepted. Schill and Wachter stated 

that this finding does not provide evidence 

of individual race discrimination because 

applicant race is most likely serving as a 

proxy for credit risk variables omitted from 

their model (e.g., credit history, wealth and 

liquid assets). Schill and Wachter find that 

when their neighborhood risk proxies are 

included in the model along with the 

individual loan variables, the percentage of 

the census tract that was African American 

became insignificant. Thus, similarly to 

Holmes and Horvitz, Schill and Wachter 

stated that ‘‘once the set of independent 

variables is expanded to include measures 

that act as proxies for neighborhood risk, the 

results do not reveal a pattern of redlining.’’22

Other Redlining Studies. To highlight the 

methodological problems of single-equation 

studies of mortgage redlining, Fred PhillipsPatrick and Clifford Rossi developed a 

simultaneous equation model of the demand 

and supply of mortgages, which they 

estimated for the Washington, DC 

metropolitan area.23 Phillips-Patrick and 

Rossi found that the supply of mortgages is 

negatively associated with the racial 

composition of the neighborhood, which led 

them to conclude that the results of singleequation models (such as the one estimated 

by Holmes and Horvitz) are not reliable 

indicators of redlining or its absence. 

However, Phillips-Patrick and Rossi noted 

that even their simultaneous equations model 

does not provide definitive evidence of 

redlining because important underwriting 

variables (such as credit history), which are 

omitted from their model, may be correlated 

with neighborhood race.

A few studies of neighborhood redlining 

have attempted to control for the credit 

history of the borrower, which is the main 

omitted variable in the redlining studies 

reviewed so far. Samuel Myers, Jr. and Tsze 

Chan, who studied mortgage rejections in the 

state of New Jersey in 1990, developed a 

proxy for bad credit based on the reasons that 

lenders give in their HMDA reports for 

denying a loan.24 They found that 70 percent 

of the gap in rejection rates could not be 

explained by differences in Black and white 

borrower characteristics, loan characteristics, 

neighborhoods or bad credit. Myers and Chan 

concluded that the unexplained Black-white 

gap in rejection rates is a result of 

discrimination. With respect to the racial 

composition of the census tract, they found 

that Blacks are more likely to be denied loans 

in racially integrated or predominantly-white 

neighborhoods than in predominantly-Black 

neighborhoods. They concluded that middleclass Blacks seeking to move out of the inner 

city would face problems of discrimination 

in the suburbs.25

Geoffrey Tootell has authored two papers 

on neighborhood redlining based on the 

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26See Geoffrey M. B. Tootell, ‘‘Redlining in 

Boston: Do Mortgage Lenders Discriminate Against 

Neighborhoods?’’, Quarterly Journal of Economics, 

111, November, 1996, pp. 1049–1079; and 

‘‘Discrimination, Redlining, and Private Mortgage 

Insurance’’, unpublished manuscript, October 1995.

27Tootell notes that both omitted variables and 

the strong correlation between borrower race and 

neighborhood racial composition in segregated 

cities have made it difficult for previous studies to 

distinguish the impacts of geographic redlining 

from the effects of individual borrower 

discrimination. He can unravel these effects 

because he includes a direct measure of credit 

history and because over half of minority applicants 

in the Boston Fed data base applied for mortgages 

in predominately white areas.

28Stephen L. Ross and Geoffrey M. B. Tootell, 

‘‘Redlining, the Community Reinvestment Act, and 

Private Mortgage Insurance’’, unpublished 

manuscript, March 1999.

29William W. Lang and Leonard I. Nakamura, ‘‘A 

Model of Redlining,’’ Journal of Urban Economics, 

Volume 33, 1993, pp. 223–234.

30Paul S. Calem, ‘‘Mortgage Credit Availability in 

Low- and Moderate-Income Minority 

Neighborhoods: Are Information Externalities 

Critical?’’ Journal of Real Estate Finance and 

Economics, Volume 13, 1996, pp. 71–89.

31 David C. Ling and Susan M. Wachter, 

‘‘Information Externalities and Home Mortgage 

Underwriting,’’ Journal of Urban Economics, 

Volume 44, 1998, pp. 317–332.

32Robert B. Avery, Patricia E. Beeson, and Mark 

S. Sniderman, ‘‘Neighborhood Information and 

Home Mortgage Lending,’’ Journal of Urban 

Economics, Volume 45, 1999, pp. 287–310.

33William Shear, James Berkovec, Ann 

Dougherty, and Frank Nothaft, ‘‘Unmet Housing 

Needs: The Role of Mortgage Markets,’’ Journal of 

Housing Economics, Volume 4 , 1996, pp. 291–306. 

These researchers regressed the number of mortgage 

originations per 100 properties in the census tract 

on several independent variables that were 

intended to account for some of the demand and 

supply (i.e., credit risk) influences at the census 

tract level. See also Susan Wharton Gates, ‘‘Defining 

the Underserved,’’ Secondary Mortgage Markets, 

Continued

mortgage rejection data from the Boston Fed 

study.26 Tootell’s studies are important 

because they include a direct measure of 

borrower credit history, as well as the other 

underwriting, borrower, and neighborhood 

characteristics that are included in the 

Boston Fed data base; thus, his work does not 

have the problem of omitted variables to the 

same extent as previous redlining studies.27

Tootell found that lenders in the Boston area 

did not appear to be redlining neighborhoods 

based on the racial composition of the census 

tract or the average income in the tract. 

Consistent with the Boston Fed and Schill 

and Wachter studies, Tootell found that it is 

the race of the applicant that mostly affects 

the mortgage lending decision; the location of 

the applicant’s property appears to be far less 

relevant. However, he did find that the 

decision to require private mortgage 

insurance (PMI) depends on the racial 

composition of the neighborhood. Tootell 

suggested that, rather than redline 

themselves, mortgage lenders may rely on 

private mortgage insurers to screen 

applications from minority neighborhoods. 

Tootell also noted that this indirect form of 

redlining would increase the price paid by 

applicants from minority areas that are 

approved by private mortgage insurers.

In a 1999 paper, Stephen Ross and Geoffrey 

Tootell used the Boston Fed data base to take 

a closer look at both lender redlining and the 

role of private mortgage insurance (PMI) in 

neighborhood lending.28 They had two main 

findings. First, mortgage applications for 

properties in low-income neighborhoods 

were more likely to be denied if the applicant 

did not apply for PMI. Ross and Tootell 

concluded that their study provides the first 

direct evidence based on complete 

underwriting data that some mortgage 

applications may have been denied based on 

neighborhood characteristics that legally 

should not be considered in the underwriting 

process. Second, mortgage applicants were 

often forced to apply for PMI when the 

housing units were in low-income 

neighborhoods. Ross and Tootell concluded 

that lenders appeared to be responding to 

CRA by favoring low-income tracts once PMI 

has been received, and this effect counteracts 

the high denial rates for applications without 

PMI in low-income tracts.

Studies of Information Externalities.

Another group of studies related to redlining 

and the credit problems facing low-income 

and minority neighborhoods focus on the 

‘‘thin’’ mortgage markets in these 

neighborhoods and the implications of 

lenders not having enough information about 

the collateral and other characteristics of 

these neighborhoods. The low numbers of 

house sales and mortgages originated in lowincome and high-minority neighborhoods 

result in individual lenders perceiving these 

neighborhoods to be more risky. It is argued 

that lenders do not have enough historical 

information to project the expected default 

performance of loans in low-income and 

high-minority neighborhoods, which 

increases their uncertainty about investing in 

these areas. 

This recent group of studies that focus on 

economies of scale in the collection of 

information about neighborhood 

characteristics has implications for the 

identification of underserved areas and 

understanding the problems of mortgage 

access in low-income and minority 

neighborhoods. William Lang and Leonard 

Nakamura argue that individual home sale 

transactions generate information which 

reduce lenders’ uncertainty about property 

values, resulting in greater availability of 

mortgage financing.29 Conversely, appraisals 

in neighborhoods where transactions occur 

infrequently will tend to be more imprecise, 

resulting in greater uncertainty to lenders 

regarding collateral quality, and more 

reluctance by them in approving mortgage 

loans in neighborhoods with thin markets. As 

a consequence, ‘‘prejudicial practices of the 

past may lead to continued differentials in 

lending behavior.’’

If low-income or minority tracts have 

experienced relatively few recent 

transactions, the resulting lack of information 

available to lenders will result in higher 

denial rates and more difficulty in obtaining 

mortgage financing, independently of the 

level of credit risk in these neighborhoods. A 

number of empirical studies have found 

evidence consistent with the notion that 

mortgage credit is more difficult to obtain in 

areas with relatively few recent sales 

transactions. Some of these studies have also 

found that low transactions volume may 

contribute to disparities in the availability of 

mortgage credit by neighborhood income and 

minority composition. Paul Calem found 

that, in low-minority tracts, higher mortgage 

loan approval rates were associated with 

recent sales transactions volume, consistent 

with the Lang and Nakamura hypothesis.30

While this effect was not found in highminority tracts, he concludes that 

‘‘informational returns to scale’’ contribute to 

disparities in the availability of mortgage 

credit between low-minority and highminority areas. Empirical research by David 

Ling and Susan Wachter found that recent 

tract-level sales transaction volume does 

significantly contribute to mortgage loan 

acceptance rates in Dade County, Florida, 

also consistent with the Lang and Nakamura 

hypothesis.31

Robert Avery, Patricia Beeson, and Mark 

Sniderman found significant evidence of 

economies associated with the scale of 

operation of individual lenders in a 

neighborhood.32 They concluded that ‘‘The 

inability to exploit these economies of scale 

is found to explain a substantial portion of 

the higher denial rates observed in lowincome and minority neighborhoods, where 

the markets are generally thin.’’ Low-income 

and minority neighborhoods often suffer 

from low transactions volume, and low 

transactions volume represents a barrier to 

the availability of mortgage credit by making 

mortgage lenders more reluctant to approve 

and originate mortgage loans in these areas.

b. Geographic Dimensions of Underserved 

Areas—Targeted Versus Broad Approaches 

HUD’s definition of metropolitan 

underserved areas is a targeted neighborhood 

definition, rather than a broad definition that 

would encompass entire cities. It also focuses 

on those neighborhoods experiencing the 

most severe credit problems, rather than 

neighborhoods experiencing only moderate 

difficulty obtaining credit. During the 

regulatory process leading to the 1995 rule, 

some argued that underserved areas under 

this goal should be defined to include all 

parts of all central cities, as defined by OMB. 

HUD concluded that such broad definitions 

were not a good proxy for mortgage credit 

problems—to use them would allow the 

GSEs to focus on wealthier parts of cities, 

rather than on neighborhoods experiencing 

credit problems. Appendix B of the 1995 and 

2000 Rules reviewed findings from academic 

researchers that support defining 

underserved areas in terms of the minority 

and/or income characteristics of census 

tracts, rather than in terms of a broad 

definition such as all parts of all central 

cities. This section briefly reviews two of the 

studies. The targeted nature of HUD’s 

definition is also examined in Section B.3 

below, which describes the credit and 

socioeconomic characteristics of underserved 

census tracts. 

Shear, Berkovec, Dougherty, and Nothaft

conducted an analysis of mortgage flows and 

application acceptance rates in 32 

metropolitan areas that supports a targeted 

definition of underserved areas.33 They 

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1994 Mortgage Market Review Issue, 1995, pp. 34–

48.

34See Avery, et al.

35Methodological and econometric challenges 

that researchers will have to deal with are discussed 

in Mitchell Rachlis and Anthony Yezer, ‘‘Serious 

Flaws in Statistical Tests for Discrimination in 

Mortgage Markets,’’ Journal of Housing Research,

Volume 4, 1993, pp. 315–336.

found: (a) Low-income census tracts and 

tracts with high concentrations of African 

American and Hispanic families had lower 

rates of mortgage applications, originations, 

and acceptance rates; and (b) once census 

tract influences were accounted for, central 

city location had only a minimal effect on 

credit flows. These authors recognized that it 

is difficult to interpret their estimated 

minority effects—the effects may indicate 

lender discrimination, supply and demand 

effects not included in their model but 

correlated with minority status, or some 

combination of these factors. Still, they 

conclude that income and minority status are 

better indicators of areas with special needs 

than central city location.

Avery, Beeson, and Sniderman of the 

Federal Reserve Bank of Cleveland 

specifically addressed the issue of 

underserved areas in the context of the GSE 

legislation.34 Their study examined 

variations in application rates and denial 

rates for all individuals and census tracts 

included in the 1990 and 1991 HMDA data 

base. These authors found that the individual 

applicant’s race exerts a strong influence on 

mortgage application and denial rates. 

African American applicants, in particular, 

had unexplainably high denial rates. Once 

individual applicant and other neighborhood 

characteristics were controlled for, overall 

denial rates for purchase and refinance loans 

were only slightly higher in minority census 

tracts than non-minority census tracts. For 

white applicants, on the other hand, denial 

rates were significantly higher in minority 

tracts. That is, minorities had higher denial 

rates wherever they attempted to borrow, but 

whites faced higher denials when they 

attempt to borrow in minority 

neighborhoods. In addition, Avery et al.

found that home improvement loans had 

significantly higher denial rates in minority 

neighborhoods. Given the very strong effect 

of the individual applicant’s race on denial 

rates, the authors noted that since minorities 

tend to live in segregated communities, a 

policy of targeting minority neighborhoods 

may be warranted. They also found that the 

median income of the census tract had strong 

effects on both application and denial rates 

for purchase and refinance loans, even after 

other variables were accounted for. Avery, 

Beeson and Sniderman concluded that a 

tract-level definition is a more effective way 

to define underserved areas than using the 

list of OMB-designated central cities as a 

proxy.

c. Conclusions from the Economics Literature 

about Urban Underserved Areas 

The implications of studies by HUD and 

others for defining underserved areas can be 

summarized briefly. First, the existence of 

large geographic disparities in mortgage 

credit is well documented. Low-income and 

high-minority neighborhoods receive 

substantially less credit than other 

neighborhoods and fit the definition of being 

underserved by the nation’s credit markets. 

Second, researchers are testing models that 

more fully account for the various risk, 

demand, and supply factors that determine 

the flow of credit to urban neighborhoods. 

The studies by Holmes and Horvitz, Schill 

and Wachter, and Tootell are examples of 

this research. Their attempts to test the 

redlining hypothesis show the analytical 

insights that can be gained by more rigorous 

modeling of this issue. However, the fact that 

urban areas are highly segregated means that 

the various loan, applicant, and 

neighborhood characteristics currently being 

used to explain credit flows are often highly 

correlated with each other, which makes it 

difficult to reach definitive conclusions about 

the relative importance of any single variable 

such as neighborhood racial composition. 

Thus, their results are inconclusive, and the 

need continues for further research on the 

underlying determinants of geographic 

disparities in mortgage lending.35

Finally, much research strongly supports a 

targeted definition of underserved areas. 

Studies by Shear, et al. and Avery, Beeson, 

and Sniderman conclude that characteristics 

of both the applicant and the neighborhood 

where the property is located are the major 

determinants of mortgage denials and 

origination rates—once these characteristics 

are controlled for, other influences such as 

central city location play only a minor role 

in explaining disparities in mortgage lending. 

HUD recognizes that the mortgage 

origination and denial rates forming the basis 

for the research mentioned in the preceding 

paragraph, as well as for HUD’s definition of 

underserved areas, are the result of the 

interaction of individual risk, demand and 

supply factors that analysts have yet to fully 

disentangle and interpret. The need 

continues for further research addressing this 

problem. 

3. Characteristics of HUD’s Underserved 

Areas 

a. Credit Characteristics 

HMDA data provide information on the 

disposition of mortgage loan applications 

(originated, approved but not accepted by the 

borrower, denied, withdrawn, or not 

completed) in metropolitan areas. HMDA 

data include the census tract location of the 

property being financed and the race and 

income of the loan applicant(s). Therefore, 

this is a rich data base for analyzing mortgage 

activity in urban neighborhoods. HUD’s 

analysis using HMDA data for 2003 shows 

that high-minority and low-income census 

tracts have both relatively high loan 

application denial rates and relatively low 

loan origination rates. 

Table B.2 presents mortgage denial and 

origination rates by the minority composition 

and median income of census tracts in 

metropolitan areas. Two patterns are clear: 

• Census tracts with higher percentages of 

minority residents have higher mortgage 

denial rates and lower mortgage origination 

rates than all-white or substantially-white 

tracts. For example, in 2003 the denial rate 

for census tracts that are over 90 percent 

minority (20.6 percent) was 2.3 times that for 

census tracts with less than 10 percent 

minority (9.0 percent). 

• Census tracts with lower incomes have 

higher denial rates and lower origination 

rates than higher income tracts. For example, 

in 2003 mortgage denial rates declined from 

23.2 percent to 7.2 percent as tract income 

increased from less than 40 percent of area 

median income to more than 150 percent of 

area median income. 

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• Table B.3 illustrates the interaction 

between tract minority composition and tract 

income by aggregating the data in Table B.2 

into nine minority and income combinations. 

The low-minority (less than 30 percent 

minority), high-income (over 120 percent of 

area median) group had a denial rate of 7.2 

percent and an origination rate of 32.4 loans 

per 100 owner occupants in 2003. The highminority (over 50 percent), low-income 

(under 90 percent of area median) group had 

a denial rate of 19.3 percent and an 

origination rate of only 17.8 loans per 100 

owner occupants. The other groupings fall 

between these two extremes.


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The advantages of HUD’s underserved area 

definition can be seen by examining the 

minority-income combinations highlighted in 

Table B.3. The sharp differences in denial 

rates and origination rates between the 

underserved and remaining served categories 

illustrate that HUD’s definition delineates 

areas that have significantly less success in 

receiving mortgage credit. In 2003 

underserved areas had over one and a threefourths times the average denial rate of 

served areas (15.9 percent versus 8.9 percent) 

and two-thirds the average origination rate 

per 100 owner occupants (20.1 versus 29.1). 

HUD’s definition does not include highincome (over 120 percent of area median) 

census tracts even if they meet the minority 

threshold. The average denial rate (10.3 

percent) for high-income tracts with a 

minority share of population over 30 percent 

is much less than the denial rate (15.9 

percent) in underserved areas as defined by 

HUD. 

Figure B.1 compares underserved and 

served areas within central cities and 

suburbs. First, Figure B.1 shows that HUD’s 

definition targets central city neighborhoods 

that are experiencing problems obtaining 

mortgage credit. The 16.8 percent denial rate 

in these neighborhoods in 2003 was almost 

twice the 8.9 percent denial rate in the 

remaining areas of central cities. A broad, 

inclusive definition of ‘‘central city’’ that 

includes all areas of all central cities would 

include these ‘‘remaining’’ portions of cities. 

Figure B.1 shows that these areas, which 

account for approximately 36 percent of the 

population in central cities, appear to be well 

served by the mortgage market. As a whole, 

they are not experiencing problems obtaining 

mortgage credit.

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Second, Figure B.1 shows that HUD’s 

definition also targets underserved census 

tracts in the suburbs as well as in central 

cities. The average denial rate in underserved 

suburban areas (14.8 percent) is 1.7 times 

that in the remaining served areas of the 

suburbs (8.7 percent), and is almost as large 

as the average denial rate (16.8 percent) in 

underserved central city tracts. Low-income 

and high-minority suburban tracts appear to 

have credit problems similar to their central 

city counterparts. These suburban tracts, 

which account for 34 percent of the suburban 

population, are included in HUD’s definition 

of other underserved areas. 

b. Socioeconomic Characteristics 

The targeted nature of HUD’s definition 

can be seen from the data presented in Table 

B.4, which show that families living in tracts 

within metropolitan areas that are 

underserved based on HUD’s definition 

experience much more economic and social 

distress than families living in served areas. 

For example, the poverty rate in underserved 

census tracts is 18.5 percent, or over three 

times the poverty rate (5.7 percent) in served 

census tracts. The unemployment rate and 

the high-school dropout rate are also higher 

in underserved areas. In addition, there are 

nearly three times more female-headed 

households with children in underserved 

areas (30.0 percent) than in served areas (13.2 

percent). Three-fourths of units in served 

areas are owner-occupied, while only onehalf of units in underserved areas are owneroccupied.

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C. Consideration of Factors 1 and 2 in 

Nonmetropolitan Areas: The Housing Needs 

of Underserved Rural Areas and the Housing, 

Economic, and Demographic Conditions in 

Underserved Rural Areas 

Based on discussions with rural lenders in 

1995, the definition of underserved rural 

areas was established at the county level, 

since such lenders usually do not make 

distinctions on a census tract basis. A 

nonmetropolitan county is classified as an 

underserved area if median income of 

families in the county does not exceed 95 

percent of the greater of state 

nonmetropolitan or national nonmetropolitan 

median income, or minorities comprise 30 

percent or more of the residents and the 

median income of families in the county does 

not exceed 120 percent of the greater of state 

nonmetropolitan or national nonmetropolitan 

median income. For nonmetropolitan areas 

the median income component of the 

underserved definition is broader than that 

used for metropolitan areas. While tract 

income is compared with area income for 

metropolitan areas, in rural counties income 

is compared with the greater of state 

nonmetropolitan income and national 

nonmetropolitan income. This is based on 

HUD’s analysis of 1990 census data, which 

indicated that comparing county 

nonmetropolitan income only to state 

nonmetropolitan income would lead to the 

exclusion of many lower-income lowminority counties from the definition, 

especially in Appalachia. Based on 1990 

census geography, underserved counties 

account for 57 percent (8,091 of 14,419) of 

the census tracts and 54 percent of the 

population in rural areas. By comparison, the 

definition of metropolitan underserved areas 

encompassed 47 percent of metropolitan 

census tracts and 44 percent of metropolitan 

residents. 

The purchasing of loans from underserved 

areas by the GSEs is intended to induce 

greater homeownership among moderate, 

low, very low income, and poor families and 

minorities. For various reasons, including 

creditworthiness and lending discrimination, 

these groups experience greater difficulty in 

securing loans under fair and reasonable 

terms and in buying decent and affordable 

housing, and it is for them that the 

geographic goals were designed. The 

geographic goals, then, are meant to target 

places where these ‘‘underserved’’

populations live in order to stimulate local 

mortgage lending and, it is hoped, the 

availability of credit to those families who 

reside there who, otherwise, will have 

difficulty securing credit. This section 

addresses the basic question of whether and 

the extent to which HUD’s definition of 

underservice in nonmetropolitan areas 

effectively targets areas that encompass large 

populations of socially and economically 

disadvantaged families. 

Table B.5 shows data on demographic and 

socioeconomic conditions of underserved 

and served nonmetropolitan areas based on 

HUD’s definition applied at the county level 

using Census 2000 data. (A later section 

considers the effects of applying the 

definition of the census tract level.) Several 

variables are used to describe area 

demographic and socioeconomic conditions.

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36The purchase affordability index assesses the 

extent to which a family with the median income 

of a given area would be able to afford a housing 

unit that carries the median purchase price of that 

area. For example, a purchase affordability index 

number less than 100 means that a family with the 

median income would not qualify for a mortgage on 

a unit with the median value; a purchase 

affordability index equal to 100 means that a family 

with the median income has exactly the level of 

income needed to qualify for a mortgage on a unit 

with the median value; and an index number 

greater than 100 means that a family with the 

median income has 20 percent more than the level 

of income needed to qualify for a mortgage on a unit 

with the median value. The rental affordability 

index is similarly constructed.

37 J.J. Mikesell, ‘‘Housing Problems across Types 

of Rural Households’’, Rural Conditions and 

Trends, Volume 9, Number 2, pp. 97–101, 1999. 38Performance for the 1993–95 period was 

discussed in the October 2000 rule.

39To separate out the effects of changes in 

counting rules that took effect in 2001, this section 

also compares performance in 2001 to estimated 

performance in 2000 if the 2001 counting rules had 

been in effect in that year.

On the national level, a few key results 

show that the 1995 definition of underservice 

captures a potentially disadvantaged segment 

of the population. In examining the minority 

composition, one can see that the percentage 

of African Americans, Hispanics/Latinos, and 

total minority population is higher in 

underserved nonmetropolitan areas as 

compared to served nonmetropolitan areas. 

Overall, the minority population of 

underserved areas is 25.8 percent as 

compared with 9.3 percent in served areas. 

Other supporting results include median 

family income, poverty rate, unemployment 

rate, school dropout rate, and in-migration 

rate. Specifically we find: 

• Median income is approximately 

$10,000 less in underserved areas than in 

served areas. This represents an average gap 

of 25 percent. 

• Poverty in underserved areas is twice the 

rate in served areas (14.5 vs. 7.5 percent). 

• Unemployment is 7.3 percent in 

underserved areas and 5.2 percent in served 

areas. 

• The school dropout rate is 28.1 percent 

in underserved areas and 18.7 percent in 

served areas. 

• Migration into underserved areas is 

somewhat lower than in served areas: 7.4 vs. 

8.0 percent. 

Table B.5 also includes data on 

homeownership rates, housing affordability, 

housing quality, and overcrowding. On 

several of these dimensions, housing 

conditions and needs in underserved areas 

are not substantially worse than in served 

areas. Although housing quality and 

crowding appear to be marginally worse in 

underserved areas, homeownership in the 

two areas is about the same and owning a 

home actually appears to be more affordable 

in underserved areas than in served areas. 

Specific findings include the following: 

• Homeownership is slightly higher in 

underserved than in served nonmetropolitan 

counties: 74.3 percent vs. 73.7 percent. 

Removing manufactured homes lowers 

ownership rates slightly, because ownership 

of such homes is relatively high, but this 

does not affect the basic result. 

• Owner-occupied and rental vacancy 

rates are both somewhat higher in 

underserved areas. 

• Median housing unit values are 

significantly lower in underserved areas: 

$67,358 vs. $88,099. 

• The value of a housing affordability 

index for owner-occupied housing is slightly 

higher in underserved areas.36 On average, 

median income is 1.83 times higher than 

income required to qualify to buy a home of 

median value in underserved areas. The 

comparable factor for served areas is 1.78.

• Rental affordability is approximately the 

same in underserved and served areas. 

• While nearly all housing in served and 

underserved areas have complete plumbing 

and kitchens, the percentage of units with 

incomplete facilities in underserved is twice 

the percentage in served areas. 

• Crowded units are a small share of all 

housing in nonmetropolitan areas, but the 

rate is higher for underserved areas: 4.3 vs. 

2.3 percent.

Mikesell 37 found using the 1995 American 

Housing Survey that while the rate of 

homeownership in nonmetropolitan areas is 

higher than metropolitan areas, the quality of 

housing is lower as compared to 

metropolitan areas. Results based on the 2000 

Census show that the homeownership rate 

for nonmetropolitan areas was 74 percent (73 

percent without manufactured homes), and 

for metropolitan areas it was 64 percent, but 

both metropolitan and nonmetropolitan areas 

had approximately 97.5 percent of units with 

complete plumbing and 99 percent with 

complete kitchens.

D. Factor 3: Previous Performance and Effort 

of the GSEs in Connection With the Central 

Cities, Rural Areas and Other Underserved 

Areas Goal 

Section D.1 reports the past performance of 

each GSE with regard to the Underserved 

Areas Housing Goal. Section D.2 then 

examines the role that the GSEs are playing 

in funding single-family mortgages in 

underserved urban neighborhoods based on 

HUD’s analysis of GSE and HMDA data. That 

section also discusses an underserved area 

subgoal for home purchase loans. Section D.3 

concludes this section with an analysis of the 

GSEs’ purchases in rural (nonmetropolitan) 

areas. 

The increased coverage of the Underserved 

Areas Housing goal due to switching to 2000 

census geography is discussed throughout 

this section. 

1. Past Performance of the GSEs 

This section discusses each GSE’s 

performance under the Underserved Areas 

Housing Goal over the 1996–2003 period.38

As explained in Appendix A, the data 

presented are ‘‘official HUD results’’ which, 

in some cases, differ from goal performance 

reported by the GSEs in the Annual Housing 

Activities Reports (AHARs) that they submit 

to the Department.

The main finding of this section is that 

Fannie Mae surpassed the Department’s 

Underserved Areas Housing Goals for each of 

the seven years during this period. Freddie 

Mac surpassed the goal in six of the seven 

years, falling slightly short in 2002. 

Specifically: 

• The goal was set at 21 percent for 1996; 

Fannie Mae’s performance was 28.1 percent 

and Freddie Mac’s performance was 25.0 

percent. 

• The goal was set at 24 percent for 1997–

2000. Fannie Mae’s performance was 28.8 

percent in 1997, 27.0 percent in 1998, 26.8 

percent in 1999, and 31.0 percent in 2000; 

and Freddie Mac’s performance was 26.3 

percent in 1997, 26.1 percent in 1998, 27.5 

percent in 1999, and 29.2 percent in 2000. 

• In the October 2000 rule, the 

underserved areas goal was set at 31 percent 

for 2001–03. As of January 1, 2001, several 

changes in counting requirements came into 

effect for the undeserved areas goal, as 

follows: ‘‘bonus points’’ (double credit) for 

purchases of goal-qualifying mortgages on 

small (5–50 unit) multifamily properties and, 

above a threshold level, mortgages on 2–4 

unit owner-occupied properties; a 

‘‘temporary adjustment factor’’ (1.20 units 

credit, subsequently increased by Congress to 

1.35 units credit) for Freddie Mac’s 

purchases of goal-qualifying mortgages on 

large (more than 50-unit) multifamily 

properties; and eligibility for purchases of 

certain qualifying government-backed loans 

to receive goal credit. These changes are 

explained below. Fannie Mae’s performance 

was 32.6 percent in 2001, 32.4 percent in 

2002, and 32.1 percent in 2003; and Freddie 

Mac’s performance was 31.7 percent in 2001, 

slightly less than 31 percent in 2002, and 

32.7 percent in 2003, thus Fannie Mae 

surpassed this higher goal in all three years 

and Freddie Mac surpassed the goal in 2001 

and 2003, but fell slightly short in 2002. This 

section discusses the October 2000 counting 

rule changes in detail below, and provides 

data on what goal performance would have 

been in 2001–03 without these changes.39

a. Performance on the Underserved Areas 

Housing Goal in 1996–2003 

HUD’s December 1995 rule specified that 

in 1996 at least 21 percent of the number of 

units financed by each of the GSEs that were 

eligible to count toward the Underserved 

Areas Goal should qualify as units in 

properties located in underserved areas, and 

at least 24 percent should qualify in 1997–

2000. HUD’s October 2000 rule made various 

changes in the goal counting rules, as 

discussed below, and increased the 

Underserved Areas Goal to 31 percent for 

2001–03. 

Table B.6 shows performance on the 

underserved areas goal over the 1996–2003 

period, based on HUD’s analysis. The table 

shows that Fannie Mae surpassed the goals 

by 7.1 percentage points and 4.8 percentage 

points in 1996 and 1997, respectively, while 

Freddie Mac surpassed the goals by narrower 

margins, 4.0 and 2.3 percentage points. In 

1998 Fannie Mae’s performance fell by 1.8 

percentage points, while Freddie Mac’s 

performance fell only slightly, by 0.2 

percentage point. Freddie Mac showed a gain 

in performance to 27.5 percent in 1999, 

exceeding its previous high by 1.2 percentage 

points. Fannie Mae’s performance in 1999 

was 26.8 percent, which, for the first time, 

slightly lagged Freddie Mac’s performance in 

that year. 

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40Unlike the low- and moderate-income and 

special affordable goals, there is no exclusion of 

units from the denominator for units with missing 

information about the area in which a property is 

located. That is, such units are counted in the 

denominator, but not in the numerator, in 

determining underserved areas goal performance.

41See Congressional Record, December 15, 2000, 

pp. H12295–96.

Both GSEs exhibited sharp gains in goal 

performance in 2000—Fannie Mae’s 

performance increased by 4.2 percentage 

points, to a record level of 31.0 percent, 

while Freddie Mac’s performance increased 

somewhat less, by 1.7 percentage points, 

which also led to a record level of 29.2 

percent. Fannie Mae’s performance was 32.6 

percent in 2001, 32.4 percent in 2002, and 

32.1 percent in 2003; Freddie Mac’s 

performance was 31.7 percent in 2001, 

slightly less than 31 percent in 2002, and 

32.7 percent in 2003. However, as discussed 

below, using consistent accounting rules for 

2000–03, under one method each GSE’s 

performance in 2001–03 was below its 

performance in 2000. 

Fannie Mae’s performance on the 

underserved areas goal surpassed Freddie 

Mac’s in every year through 1998. This 

pattern was reversed in 1999, as Freddie Mac 

surpassed Fannie Mae in goal performance 

for the first time, though by only 0.7 

percentage point. This improved relative 

performance of Freddie Mac was due to its 

increased purchases of multifamily loans, as 

it re-entered that market, and to increases in 

the goal-qualifying shares of its single-family 

mortgage purchases. However, Fannie Mae’s 

performance once again exceeded Freddie 

Mac’s performance in 2000, 31.0 percent to 

29.2 percent. Fannie Mae’s official 

performance also exceeded Freddie Mac’s 

official performance in 2001–02, despite the 

fact that Freddie Mac benefited from a 

difference in the counting rules applicable to 

the two GSEs as enacted by Congress; if the 

same counting rules were applied to both 

GSEs, Fannie Mae’s performance would have 

exceeded Freddie Mac’s performance. In fact, 

Freddie Mac would have just attained the 

goal, at 31.4 percent in 2003, and fallen short 

of the goal in 2001 and 2002. 

b. Changes in the Goal Counting Rules for 

2001–03 

Several changes in the counting rules 

underlying the calculation of underserved 

areas goal performance took effect beginning 

in 2001. These also applied to the low- and 

moderate-income goal and are discussed in 

Appendix A; only brief summaries of those 

changes are given here:40 Bonus points for 

multifamily and single-family rental 

properties. Each qualifying unit in a small 

multifamily property counted as two units in 

the numerator in calculating performance on 

all of the goals for 2001–03. And, above a 

threshold equal to 60 percent of the average 

number of qualifying rental units financed in 

owner-occupied properties over the 

preceding five years, each unit in a 2–4 unit 

owner-occupied property also counted as two 

units in the numerator in calculating goal 

performance.

Freddie Mac’s Temporary Adjustment 

Factor. Freddie Mac received a ‘‘Temporary 

Adjustment Factor’’ of 1.35 units of credit for 

each qualifying unit financed in ‘‘large’’

multifamily properties (i.e., those with 51 or 

more units) in the numerator in calculating 

its performance on the housing goals for 

2001–03.41 This factor did not apply to units 

in large multifamily properties in 

underserved areas whose mortgages were 

financed by Fannie Mae during this period.

Purchases of certain government-backed 

loans. Prior to 2001, purchases of 

government-backed loans were not taken into 

account in determining performance on the 

GSEs’ low- and moderate-income and 

underserved area housing goals. As discussed 

in Appendix A, the 2000 rule established 

eligibility for FHA-insured home equity 

conversion mortgages (HECMs) for 

mortgagors in underserved areas, purchases 

of mortgages on properties on tribal lands 

insured under FHA’s Section 248 program or 

HUD’s Section 184 program, and purchases 

of mortgages under the Rural Housing 

Service’s Single Family Housing Guaranteed 

Loan Program to count toward the 

underserved area goal. 

c. Effects of Changes in the Counting Rules 

on Goal Performance 

Because of the changes in the underserved 

areas goal counting rules that took effect in 

2001, direct comparisons between official 

goal performance in 2000 and 2001–03 are 

somewhat of an ‘‘apples-to-oranges 

comparison.’’ For this reason, the Department 

has calculated what performance would have 

been in 2000 under the 2001–03 rules; this 

may compared with official performance in 

2001–03—an ‘‘apples-to-apples comparison.’’

HUD has also calculated what performance 

would have been in 2001–03 under the 1996–

2000 rules; this may be compared with 

official performance in 2000—an ‘‘oranges-tooranges comparison.’’ These comparisons are 

presented in Table B.7a.

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42 65 FR 65141 & n. 145 (2000).

Specifically, Table B.7a shows 

performance under the underserved areas 

goal in three ways. Baseline A represents the 

counting rules in effect in 1996–2000. 

Baseline B incorporates the one minor 

technical change in counting rules pertaining 

to the underserved areas goal—eligibility of 

certain government-backed loans for goals 

credit. Baseline C incorporates in addition to 

that technical change the bonus points and, 

for Freddie Mac, the temporary adjustment 

factor. Boldface figures under Baseline A for 

1999–2000 and under Baseline C for 2001–

02 indicate official goal percentages based on 

the counting rules in effect in those years—

e.g., for Freddie Mac, 27.5 percent in 1999, 

29.2 percent in 2000, 31.7 percent in 2001, 

slightly less than 31 percent in 2002, and 

32.7 percent in 2003. 

Performance on the Underserved Areas 

Goal under 1996–2000 Counting Rules Plus 

Technical Changes. If the ‘‘Baseline B’’

counting approach had been in effect in 

2000–03 and the GSEs’’ had purchased the 

same mortgages that they actually did 

purchase in those years, Fannie Mae would 

have just matched the underserved areas goal 

in 2000 and fallen short in 2001–03, while 

Freddie Mac would have fallen short of the 

goal in all four years, 2000–03. Specifically, 

Fannie Mae’s performance would have been 

31.0 percent in 2000, 30.4 percent in 2001, 

30.2 percent in 2002, and 29.2 percent in 

2003. Freddie Mac’s performance would have 

been 29.2 percent in 2000, 28.2 percent in 

2001, 28.0 percent in 2002, and 27.7 percent 

in 2003. 

Performance on the Underserved Areas 

Goal under 2001–2003 Counting Rules. If the 

2001–03 counting rules had been in effect in 

2000–02 and the GSEs had purchased the 

same mortgages that they actually did 

purchase in those years (i.e., abstracting from 

any behavioral effects of ‘‘bonus points,’’ for 

example), both GSEs would have surpassed 

the underserved areas goal in all four years, 

and both GSEs’ performance figures would 

have increased from 2000 to 2002. 

Specifically, Fannie Mae’s ‘‘Baseline C’’

performance would have been 32.3 percent 

in 2000, 32.6 percent in 2001, 32.4 percent 

in 2002, and 32.1 percent in 2003. Freddie 

Mac’s performance would have been 31.4 

percent in 2000, 31.7 percent in 2001, 

slightly less than 31.0 percent in 2002, and 

32.7 percent in 2003. Measured on this 

consistent basis, then, Fannie Mae’s 

performance increased by 0.3 percentage 

point in 2001, fell by 0.7 percentage points 

in 2002, and increased by 1.3 percentage 

points in 2003. These increases were the 

effect of increased purchases of mortgages 

eligible to receive bonus points between 2000 

and 2001–03. 

Details of Effects of Changes in Counting 

Rules on Goal Performance in 2001. As 

discussed above, counting rule changes that 

took effect in 2001 had significant impacts on 

the performance of both GSEs on the 

underserved areas goal in that year—2.2 

percentage points for Fannie Mae, and 3.5 

percentage points for Freddie Mac. This 

section breaks down the effects of these 

changes on goal performance for both GSEs; 

results are shown in Table B.7a along with 

figures for other years. 

Freddie Mac. The largest impact of the 

counting rule changes on Freddie Mac’s goal 

performance was due to bonus points for 

purchases of mortgages on small multifamily 

properties; this added 1.3 percentage points 

to goal performance in 2001, 0.5 percentage 

point in 2002, and 2.9 percentage points in 

2003, as shown in Table B.7a. The 

application of the temporary adjustment 

factor for purchases of mortgages on large 

multifamily properties enacted by Congress 

added 0.9 percentage points to goal 

performance in 2002 and 1.3 percentage 

points in 2003. Bonus points for purchase of 

mortgages on owner-occupied 2–4 unit rental 

properties also added 1.1 percentage points 

to performance in 2001, 1.6 percentage points 

in 2002, and 0.9 percentage point in 2003. 

Credit for purchases of qualifying 

government-backed loans played a minor role 

in determining Freddie Mac’s goal 

performance. 

Fannie Mae. The temporary adjustment 

factor which applied to Freddie Mac’s goal 

performance did not apply to Fannie Mae, 

thus overall counting rule changes had less 

impact on its performance than on Freddie 

Mac’s performance in 2001–03. The largest 

impact of the counting rule changes on 

Fannie Mae’s goal performance was due to 

the application of bonus points for purchases 

of mortgages on owner-occupied 2–4 unit 

rental properties, which added 1.7 

percentage points to performance in 2001, 1.8 

percentage points in 2002, and 1.7 percentage 

points in 2003, and for purchases of 

mortgages on small multifamily properties, 

which added 0.5 percentage point to 

performance in 2001, 0.8 percentage point in 

2002, and 1.2 percentage points in 2003. 

Credit for purchases of qualifying 

government-backed loans also played a 

minor role in determining Fannie Mae’s goal 

performance. 

d. Bonus Point Incentives for the GSEs’

Purchases in Underserved Areas 

The Department established ‘‘bonus 

points’’ for 2001–03 to encourage the GSEs to 

step up their activity in two segments of the 

mortgage market—the small (5–50 unit) 

multifamily mortgage market, and the market 

for mortgages on 2–4 unit properties where 

1 unit is owner-occupied and 1–3 units are 

occupied by renters. 

Bonus points for small multifamily 

properties. Each unit financed in a small 

multifamily property that qualified for any of 

the housing goals was counted as two units 

in the denominator (and one unit in the 

numerator) in calculating goal performance 

for that goal. 

Fannie Mae financed 37,389 units in small 

multifamily properties in 2001 that were 

eligible for the underserved areas goal, an 

increase of more than 400 percent from the 

7,196 units financed in 2000. Further 

increases were recorded in 2002, to 77,382 

units, and in 2003, to 230,290 units. As 

explained in Appendix A, small multifamily 

properties also accounted for a greater share 

of Fannie Mae’s multifamily business in 

2001—7.4 percent of total multifamily units 

financed, up from 2.5 percent in 2000, with 

this share rising to 16.8 percent in 2002 and 

28.9 percent in 2003. However, HUD’s 

Housing Goals 2000 Final Rule cited a 

Residential Finance Survey finding that 

small multifamily properties account for 37 

percent of total units in multifamily 

mortgaged properties, thus Fannie Mae is 

still somewhat less active in this market than 

in the market for large multifamily 

properties.42

Within the small multifamily market, there 

was some evidence that Fannie Mae targeted 

properties in underserved areas to a greater 

extent in 2001 than in 2000. That is, 56 

percent of Fannie Mae’s small multifamily 

units qualified for the underserved areas goal 

in 2000, but this rose to 64 percent in 2001. 

The share of Fannie Mae’s small multifamily 

units that qualified for the underserved areas 

goal was 65 percent in 2002 and 50 percent 

in 2003. 

Freddie Mac financed 50,211 units in small 

multifamily properties in 2001 that were 

eligible for the underserved areas goal, an 

increase of more than 1500 percent from the 

small base of 2,985 units financed in 2000. 

Financing of such units actually fell in 2002, 

to 22,195 units, but rebounded to 181,126 

units in 2003. Small multifamily properties 

also accounted for a significantly greater 

share of Freddie Mac’s multifamily business 

in 2001—16.1 percent of total multifamily 

units financed, up from 1.8 percent in 2000, 

with this share amounting to 7.1 percent in 

2002 and 30.5 percent in 2003.

Within the small multifamily market, there 

was some evidence that Freddie Mac targeted 

properties in underserved areas to a greater 

extent in 2001 than in 2000. That is, 61 

percent of Freddie Mac’s small multifamily 

units qualified for the underserved areas goal 

in 2000; this rose to 86 percent in 2001. The 

share of Freddie Mac’s small multifamily 

units that qualified for the underserved areas 

goal was 88 percent in 2002 and 87 percent 

in 2003. 

Bonus points for single-family rental 

properties. Above a threshold, each unit 

financed in a 2–4 unit property with at least 

one owner-occupied unit (referred to as 

‘‘OO24s’’ below) that qualified for any of the 

housing goals was counted as two units in 

the denominator (and one unit in the 

numerator) in calculating goal performance 

for that goal in 2001–03. The threshold was 

equal to 60 percent of the average number of 

such qualifying units over the previous five 

years. For example, Fannie Mae financed an 

average of 47,100 underserved area units in 

these types of properties between 1996 and 

2000, and 105,946 such units in 2001. Thus 

in 2001 Fannie Mae received 77,688 bonus 

points in this area in 2001—that is, 105,946 

minus 60 percent of 47,100. So 183,629 units 

were entered in the numerator for these 

properties in calculating underserved area 

goal performance. 

Single-family rental bonus points thus 

encouraged the GSEs to play a larger role in 

this market, and also to purchase mortgages 

on such properties in which large shares of 

the units qualify for the housing goals. As for 

small multifamily bonus points, some 

evidence on the effects of such bonus points 

on the GSEs’ operations may be gleaned from 

the data provided to HUD by the GSEs for 

2001–2003. 

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43 In New England, MSAs were defined through 

mid-2003 in terms of Towns rather than Counties, 

and the portion of a New England county outside 

of any MSA is regarded as equivalent to a county 

in establishing metropolitan or non-metropolitan 

location of a property. The MSA definitions 

established by the Office of Management and 

Budget (OMB) in June, 2003 defined MSAs in New 

England in terms of counties.

44The procedure used to generate estimated rents 

in connection with the Low- and Moderate Income 

and Special Affordable Housing Goals, as 

mentioned in Appendixes A and C, uses similar 

data series.

45HUD has deferred application of the 2000 

census data and 2003 MSA designations to 2005, 

pending completion of the present rulemaking 

process.

46 8,717 tracts included both served and 

underserved area, out of a total of 61,493 tracts that 

could be classified as served or underserved or 

assigned an underservice factor.

Fannie Mae financed 177,872 units in 

OO24s in 2001 that were eligible for the 

underserved areas goal, an increase of 116 

percent from the 82,464 units financed in 

2000. Further increases were recorded in 

2002, to 231,581 units, and in 2003, to 

353,916 units. However, as a result of the 

refinance boom Fannie Mae’s total singlefamily business increased at approximately 

the same rate as its OO24 business in 2001–

03, thus the share of its business accounted 

for by OO24s was the same in 2001 as in 

2000—4 percent, with this share also 

amounting to 4 percent in 2002 and 2003. 

Within the OO24 market, there was no 

evidence that Fannie Mae targeted affordable 

properties to a greater extent in 2001 than in 

2000. That is, approximately 60 percent of 

Fannie Mae’s OO24 units qualified for the 

underserved area goal in both 2000 and 2001. 

The share of Fannie Mae’s OO24 units that 

qualified for the underserved areas goal was 

62 percent in 2002 and 60 percent in 2003. 

Freddie Mac financed 96,983 units in 

OO24s in 2001 that were eligible for the 

underserved areas goal, an increase of 91 

percent from the 50,868 units financed in 

2000. Further increases were recorded in 

2002, to 146,502 units, and in 2003, to 

154,924 units. However, with the refinance 

boom, Freddie Mac’s total single-family 

business increased at approximately the same 

rate as its OO24 business in 2001–03, thus 

the share of its business accounted for by 

OO24s was the same in 2001 as in 2000—3 

percent, with this share amounting to 3.7 

percent in 2002 and 3.1 percent in 2003. 

As for Fannie Mae, within the OO24 

market there was no evidence that Freddie 

Mac targeted affordable properties to a 

greater extent in 2001 than in 2000. That is, 

60 percent of Fannie Mae’s OO24 units 

qualified for the underserved areas goal in 

both 2000 and 2001. The share of Freddie 

Mac’s OO24 units that qualified for the 

underserved areas goal was 61 percent in 

2002 and 50 percent in 2003. 

e. Effects of 2000 Census on Scoring of Loans 

Toward the Underserved Areas Housing Goal 

Background. Scoring of housing units 

under the Underserved Areas Housing Goal 

is based on decennial census data used to 

identify underserved areas, as follows: For 

properties in MSAs scoring is based on the 

median income of the census tract where the 

property is located, the median income of the 

MSA, and the percentage minority 

population in the census tract where the 

property is located. For properties located 

outside of MSAs scoring is based on the 

median income of the county, the median 

income of the non-metropolitan portion of 

the State in which the property is located or 

of the non-metropolitan portion of the United 

States, whichever has the larger median 

income, and the percentage minority 

population in the county where the property 

is located. Thus, scoring loans under the 

Underserved Areas Housing Goal requires 

decennial census data on median incomes for 

metropolitan census tracts, MSAs, nonmetropolitan counties, the non-metropolitan 

portions of States, and the non-metropolitan 

portion of the United States. The 

determination has been based on 1990 census 

data through 2004, and beginning in 2005 

will be based on 2000 census data.43, 44 Under 

this rule, the basis for the determination 

outside of MSAs will change from counties 

to census tracts beginning in 2005.

2005 Procedure. Relative to the above 

procedure, Underserved Areas Housing Goals 

performance percentages for loans purchased 

by the GSEs in and after 2005 will be affected 

by three factors. First, 2000 census data on 

median incomes and minority populations 

replace 1990 census data. Second, the Office 

of Management and Budget in June, 2003, 

respecified MSA boundaries based on 

analysis of 2000 census data. Third, the 

Department’s re-specification of the 

Underserved Areas goal in terms of census 

tracts rather than counties in nonmetropolitan areas will come into effect.45

Thus, for properties located outside of MSAs 

the basis of determination for nonmetropolitan areas will be changed for 

properties located outside of MSAs to: The 

median income of the census tract where the 

property is located; the median income of the 

non-metropolitan portion of the State in 

which the property is located or of the nonmetropolitan portion of the United States, 

whichever is larger; and the percentage 

minority population in the census tract 

where the property is located.

Analysis. HUD used 2000 census data to 

generate underserved area designations for 

census tracts as defined for the 2000 census 

with 2003 MSA designations. Because Fannie 

Mae and Freddie Mac geocoded the 

mortgages they purchased prior to 2003 

based on census tract boundaries as 

established for the 1990 census, GSE 

mortgages purchased prior to 2003 can be 

directly identified as being from a served or 

underserved area only where the property is 

located in a 1990-defined census tract whose 

area consists entirely of whole 2000-defined 

census tracts, or portions of such tracts, 

which are all designated either as served or 

as underserved. In the situation where the 

area of a 1990-defined census tract includes 

whole 2000-defined census tracts, or portions 

of such tracts, some of which are served and 

some underserved, HUD calculated an 

‘‘underservice factor’’ defined as the 

underserved percentage of the 1990-defined 

tract’s population, based on population data 

from the 2000 census.46 These factors were 

used in estimating underservice percentages 

for aggregated GSE purchases in and before 

2003 based on the 2000 census.

The resulting underserved areas file was 

used to re-score loans purchased by the GSEs 

between 1999 and 2003, and was used 

further in estimating the share of loans 

originated in metropolitan areas that would 

be eligible to score toward the Underserved 

Areas Housing Goal, from HMDA data. The 

results of the retrospective GSE analysis are 

provided in Table B.7b The results of the 

GSE–HMDA comparative analysis are 

presented in the next section. 

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Table B.7b shows four sets of estimates for 

each GSE, based respectively on the counting 

rules in place in 2001–2003 (but disregarding 

the bonus points and Temporary Adjustment 

Factor), on shifting from 1990 to 2000 census 

data on median incomes and minority 

concentrations, on the further addition 2003 

MSA specification, and finally on shifting 

from counties to tracts as the basis for scoring 

loans in non-metropolitan areas. 

2. GSEs’ Mortgage Purchases in Metropolitan 

Neighborhoods 

Metropolitan areas accounted for about 85 

percent of total GSE purchases under the 

Underserved Areas Housing Goal in 2001 and 

2002. This section uses HMDA and GSE data 

for metropolitan areas to examine the 

neighborhood characteristics of the GSEs’

mortgage purchases. In subsection 2.a, the 

GSEs’ performance in underserved 

neighborhoods is compared with the overall 

market. This section therefore expands on the 

discussion in Appendix A, which compared 

the GSEs’ funding of affordable loans with 

the overall conventional conforming market. 

A subgoal that the Department is establishing 

for each GSE’s acquisitions of home purchase 

loans financing properties in the underserved 

census tracts of metropolitan areas is also 

discussed subsection 2a. In subsection 2.b., 

the characteristics of the GSEs’ purchases 

within underserved areas are compared with 

those for their purchases in served areas. 

a. Comparisons With the Primary Market 

Market Comparisons Based on 1990 

Census Geography. Section E.8–10 in 

Appendix A provided detailed information 

on the GSEs’ funding of mortgages for 

properties located in underserved 

neighborhoods for the years 1993 to 2003. To 

take advantage of historical data going back 

to 1993, these comparisons were first made 

using 1990 Census tract geography. The 

findings with respect to the GSEs’ funding of 

underserved neighborhoods are similar to 

those reported in Appendix A regarding the 

GSEs’ overall affordable lending performance 

in the single-family-owner market. While 

both GSEs improved their performance, they 

historically lagged the conventional 

conforming market in providing affordable 

loans to underserved neighborhoods. The 

two GSEs themselves engaged in very 

different patterns of funding—Freddie Mac 

was less likely than Fannie Mae to fund 

home loans in underserved neighborhoods, 

as the following percentage shares for home 

purchase loans indicate:

Year Freddie Mac

(percent) 

Fannie Mae

(percent) 

Market

(w/o B&C)

(percent) 

1996–2003 ................................................................................................................................... 22.0 24.0 25.7 

1999–2003 ................................................................................................................................... 23.1 24.7 26.2 

2001–2003 ................................................................................................................................... 24.1 26.0 26.4 

Between 1996 and 2003, 22.0 percent of 

Freddie Mac’s purchases financed properties 

in underserved neighborhoods, compared 

with 24.0 percent of Fannie Mae’s purchases 

and 25.7 percent of home purchase loans 

originated in the conventional conforming 

market (excluding B&C loans). Thus, Freddie 

Mac performed at only 86 percent of the 

market (22.0 divided by 25.7), while Fannie 

Mae performed at 93 percent of the market. 

Freddie Mac’s recent performance has been 

slightly closer to the market. Over the past 

three years (2001 to 2003), Freddie Mac 

performed at 91 percent of the market (24.1 

percent for Freddie Mac compared at 26.4 

percent for the market). (See Tables A.13 to 

A.16 in Appendix A for complete data going 

back to 1993.) 

Fannie Mae has funded underserved areas 

at a higher level than Freddie Mac, as 

indicated above. And during 2001 and 2003, 

Fannie Mae average performance was only 

slightly below the market. In 2003, the share 

of Fannie Mae’s purchases going to 

underserved areas was 26.8 percent, 

compared with a market level of 27.6 

percent. Like Freddie Mac, Fannie Mae’s 

longer-term performance (since 1993 or 1996) 

as well as its recent average performance 

(1999 to 2003) has consistently been below 

market levels. Still, it is encouraging that 

Fannie Mae significantly improved its 2001–

2003 performance and closed its gap with the 

market during the first three years of HUD’s 

higher housing goal levels. 

Market Comparisons Based on 2000 

Census Geography. As explained in Section 

A.2 of this appendix, HUD will be defining 

underserved areas based on 2000 Census data 

beginning in 2005. The number of census 

tracts in metropolitan areas covered by 

HUD’s definition will increase from 21,587 

tracts (based on 1990 Census) to 26,959 tracts 

(based on 2000 Census and new OMB 

metropolitan area specifications). The 

increase in the number of tracts defined as 

underserved means that both GSE 

performance and the market estimates will be 

higher than reported above. This section 

provides an analysis of the performance of 

the GSEs in the single-family-owner market 

based on 2000 census tract geography. For 

the years 1999, 2000, 2001, and 2002, HUD 

used the apportionment technique to reallocate 1990-based GSE and HMDA data 

into census tracts as defined by the 2000 

Census. GSE and HMDA data for 2003 were 

already expressed in terms of 2000 Census 

geography. 

The main results are provided in Table B.8, 

which compares the GSEs to the market 

using both the 1990 Census geography and 

the 2000 Census geography. Switching to the 

2000-based tracts increases the underserved 

area share of market originations by about 

five percentage points. Between 1999 and 

2003, 31.4 percent of home purchase 

mortgages (without B&C loans) were 

originated in underserved tracts based on 

2000 geography, compared with 26.2 percent 

based on 1990 geography—a differential of 

5.2 percentage points. As also shown in 

Table B.8, the underserved areas share of 

Fannie Mae’s purchases rises by 5.3 

percentage points, and the underserved areas 

share of Freddie Mac’s purchases rises by 5.2 

percentage points. Thus, the conclusions 

reported above and in Appendix A about the 

GSEs’ performance relative to the market 

about remain the same when the analysis is 

conducted based on 2000 Census geography.

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It is interesting to repeat the earlier 1990-

based analysis of home purchase loans but 

this time based on the 2000 Census 

geography. The following results are obtained 

for home purchase loans from Table B.8:

Year Freddie Mac

(percent) 

Fannie Mae

(percent) 

Market

(w/o B&C)

(percent) 

1999 ............................................................................................................................................. 25.6 25.3 30.2 

2000 ............................................................................................................................................. 27.3 29.0 31.7 

2001 ............................................................................................................................................. 27.3 29.8 30.7 

2002 ............................................................................................................................................. 31.7 32.3 31.8 

2003 ............................................................................................................................................. 29.0 32.0 32.5 

1996–2003 (estimate) .................................................................................................................. 27.2 29.3 30.9 

1999–2003 (average) .................................................................................................................. 28.3 30.0 31.4 

2001–2003 (average) .................................................................................................................. 29.4 31.4 31.7 

Between 1999 and 2003, 28.3 percent of 

Freddie Mac’s purchases and 30.0 percent of 

Fannie Mae’s purchases financed properties 

in underserved neighborhoods, compared 

with 31.4 percent home purchase loans 

originated in the conventional conforming 

market (excluding B&C loans). Thus, Freddie 

Mac performed at 90 percent of the market 

level, while Fannie Mae performed at 96 

percent of the market level—both results 

similar to those reported above for 

underserved areas based on 1990 Census 

geography. The 2000 Census data show that 

the Fannie Mae has been much closer to the 

market during the recent 2001–2003 period. 

The share of Fannie Mae’s purchases going 

to underserved areas was 31.4 during 2001–

2003, which placed it close to the market 

level of 31.7 percent. However, the 2000-

based results show that, like Freddie Mac, 

Fannie Mae’s longer-term performance (since 

1996) as well as its recent average 

performance (1999 to 2003) have consistently 

been below market levels. (Note that the 

1996–2003 averages reported above are 

estimated by adding the following 2000-

Census versus 1990-Census differentials 

calculated for 1999–2003: 5.2 percentage 

points for Freddie Mac, 5.3 for Fannie Mae, 

and 5.2 for the market.) 

Underserved Area Subgoal for Home 

Purchase Loans. The Department is 

establishing a subgoal of 32 percent for each 

GSE’s acquisitions of home purchase loans 

financing single-family-owner properties 

located in the underserved census tracts of 

metropolitan areas for 2005, with this 

subgoal rising to 33 percent for 2006 and 

2007, and to 34 percent in 2008. If the GSEs 

meet the 2008 subgoal, they will be leading 

the primary market by over two percentage 

points, based on historical data. This home 

purchase subgoal will encourage the GSEs to 

provide additional credit and capital to urban 

neighborhoods that historically have not 

been adequately served by the mortgage 

industry—but in the future may be the very 

neighborhoods where the growing population 

of immigrants and minorities choose to live. 

As detailed in Section I.5 of this appendix, 

there are four specific reasons for 

establishing this subgoal: (1) The GSEs have 

the expertise, resources, and ability to lead 

the single-family-owner market, which is 

their ‘‘bread and butter’’ business; (2) the 

GSEs have been lagging the primary market 

in underserved areas, not leading it; (3) the 

GSEs can help reduce troublesome 

neighborhood disparities in access to 

mortgage credit; and (4) there are ample 

opportunities for the GSEs to expand their 

purchases in low-income and high-minority 

neighborhoods. Sections E.9 and G of 

Appendix A provide additional information 

on the opportunities for an enhanced GSE 

role in underserved area segment of the home 

purchase market and on the ability of the 

GSEs to lead that market. 

As discussed above, underserved areas 

accounted for an average of approximately 

31.5 percent of home purchase loans 

originated in the conventional conforming 

market of metropolitan areas (computed over 

1999–2003 or over 2001–2003). To reach the 

34-percent subgoal for 2008, both GSEs will 

have to improve over their earlier peak 

performances—Freddie Mac by 2.3 

percentage points over its previous peak 

performance of 31.7 percent in 2002, and 

Fannie Mae by 1.7 percentage points over its 

previous peak performance of 32.3 percent in 

2003. To meet the 2008 subgoal, Freddie Mac 

will have to improve by 2.6 percentage points 

over its 2002–2003 average (unweighted) 

performance of 30.4 percent, while Fannie 

Mae will have to improve by 1.8 percentage 

points over its 2002–2003 average 

performance of 32.2 percent. 

The subgoal applies only to the GSEs’

purchases in metropolitan areas because the 

HMDA-based market benchmark is only 

available for metropolitan areas. HMDA data 

for non-metropolitan counties are not reliable 

enough to serve as a market benchmark. The 

Department is also setting home purchase 

subgoals for the other two goals-qualifying 

categories, as explained in Appendices A and 

C. 

b. Characteristics of GSEs’ Purchases of 

Mortgages on Properties in Metropolitan 

Underserved Areas 

Several characteristics of loans purchased 

in 2003 by the GSEs in metropolitan 

underserved areas are presented in Table B.9. 

As shown, borrowers in underserved areas 

are more likely than borrowers in served 

areas to be first-time homebuyers, all female, 

all male and younger than 40. And, as 

expected, borrowers in underserved areas are 

more likely to have below-median income 

and to be members of minority groups. For 

example, first-time homebuyers make up 6.7 

percent of the GSEs’ mortgage purchases in 

underserved areas and 4.2 percent of their 

business in served areas. In underserved 

areas, 53.7 percent of borrowers had incomes 

below the area median, compared with 36.4 

percent of borrowers in served areas. 

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47Heather MacDonald, ‘‘Fannie Mae and Freddie 

Mac in Nonmetropolitan Housing Markets: Does 

Space Matter?’’ Cityscape: A Journal of Policy 

Development and Research, Volume 5, 2001, pp. 

219–264. 48 Jeanette Bradley, Noah Sawyer and Kenneth 

Temkin, Factors Influencing GSE Service to Rural 

Areas, The Urban Institute, prepared for U.S. 

Department of Housing and Urban Development, 

2002. 49Affordable loans are defined as borrowers 

earning less than 80 percent the Area Median 

Income.

Minorities’ share of the GSEs’ mortgage 

purchases in underserved areas (33.2 

percent) was greater than two times their 

share in served areas (13.9 percent). And the 

pattern was even more pronounced for 

African Americans and Hispanics, who 

accounted for 23.1 percent of the GSEs’

business in underserved areas, but only 7.0 

percent of their purchases in served areas. 

Other similarities in Fannie Mae and 

Freddie Mac purchases in served and 

underserved areas include the following. The 

GSEs are slightly more likely to purchase 

refinance loans in served areas than in 

underserved areas; mortgage purchases with 

loan-to-value ratios below 80 percent are 

more likely to be in underserved than in 

served areas; and seasoned mortgage 

purchases are more likely to be in 

underserved than in served areas. 

3. GSE Mortgage Purchases in 

Nonmetropolitan Areas 

There are numerous studies that have 

evaluated the impact of the GSEs’ purchases 

on metropolitan areas, but few address the 

impact on nonmetropolitan areas; therefore, 

our understanding of the GSEs and the 

nonmetropolitan markets is very limited. 

A study of the GSE market share in 

underserved counties 47 found that location 

has a role in the accessibility of credit for 

some people in nonmetropolitan areas (low 

income, minority, and first-time 

homebuyers). West North Central counties 

(Minnesota, Missouri, South Dakota, Iowa, 

Kansas, Nebraska, and North Dakota) have 

much lower GSE activity than all other 

geographic regions, suggesting that the 1995 

definition of underservice does not capture 

the specific characteristics of this region, 

leading to limited GSE activity.

Additionally, The Urban Institute prepared 

a report for HUD that investigated the factors 

influencing GSE activity in nonmetropolitan 

areas.48 The authors found that Fannie Mae 

and Freddie Mac have increased their 

lending to nonmetropolitan areas since 1993; 

however, there are still weak areas in terms 

of the percentage of affordable loans being 

offered.49 They also established that GSE 

underwriting criteria was not a major barrier 

in nonmetropolitan areas.

In nonmetropolitan areas, the financial 

market is often made up of locally owned 

community banks, manufactured home 

lenders, and subprime lenders. Industry 

representatives contacted by the Urban 

Institute researchers assessed that the barriers 

nonmetropolitan lenders faced were in the 

areas of availability of sales comparables, 

technology, and the type and number of 

lenders in the area. They also believed that 

for the GSEs’ market share to improve in 

underserved nonmetropolitan areas, the GSEs 

would have to begin to build relationships 

with the community lenders and provide 

education/training on how to sell loans 

directly to the GSEs rather than using 

intermediaries. 

a. Effects of 2000 Census Geography 

In order to compare served and 

underserved areas, either in terms of GSE 

performance or socioeconomic 

characteristics, it is first necessary to update 

current geographic (county) designations, 

which reflect 1990 census median income 

and minority population data, to reflect 

newly available 2000 census data. Table B.10 

shows the impact on 2000, 2001, and 2002 

GSE purchases. These are reported for total 

GSE purchases and separately for Fannie Mae 

and Freddie Mac. As above, the results also 

are shown separately for counties that change 

classification and those that do not. This 

analysis is limited to nonmetropolitan areas 

based on both the pre- and post-June, 2003 

OMB metropolitan area designations. 

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50Underserved areas make up about 56 percent of 

the census tracts in nonmetropolitan areas and 47 

percent of the census tracts in metropolitan areas. 

This is one reason why underserved areas comprise 

a larger portion of the GSEs’ single-family 

mortgages in nonmetropolitan areas (39 percent) 

than in metropolitan areas (23 percent).

Applying 2000 census median income and 

minority population data results in a slight 

drop in the proportion of counties that are 

classified as underserved. Out of a total of 

2,493 counties, 1,514 (65.5 percent) are 

underserved based on 1990 data, and 1,260 

(61.4 percent) based on 2000 data. This small 

net change disguises a somewhat larger shift 

of counties, as about 11.2 percent of currently 

underserved counties are reclassified as 

served counties and 4.6 percent of currently 

served counties are reclassified as 

underserved. 

Comparing underserved and served 

nonmetropolitan areas in Table B.10, it is 

apparent that underserved nonmetropolitan 

areas make up a larger percentage of 

nonmetropolitan areas as a whole than do 

served nonmetropolitan areas, as shown by 

the number of counties (1,260 for 

underserved (61.4%); 792 for served 

(38.6%)). These relationships hold true also 

for the number of households (9.5 million for 

underserved (50.5%); 9.3 million for served 

(49.5%)), and the population (24.9 million 

for underserved (51%); 23.9 million for 

served (49%)) as shown in Table B.5. 

Table B.10 shows that Fannie Mae’s 

performance in 2002 (40.2 percent) was 

somewhat higher than Freddie Mac’s (36.3 

percent). This gap widens slightly (1.8 

percent) in applying 2000 census income and 

minority data and 2003 metropolitan area 

definitions. 

b. Characteristics of GSEs’ Purchases of 

Mortgages on Properties in Nonmetropolitan 

Underserved Areas 

Nonmetropolitan mortgage purchases made 

up 12.6 percent of the GSEs’ total mortgage 

purchases in 2003. Mortgages in underserved 

counties made up 38.6 percent of the GSEs’

business in nonmetropolitan areas.50

Unlike the underserved areas definition for 

metropolitan areas, which is based on census 

tracts, the rural underserved areas definition 

is based on counties. Rural lenders argued 

that they identified mortgages by the counties 

in which they were located rather than the 

census tracts; and therefore, census tracts 

were not an operational concept in rural 

areas. Market data on trends in mortgage 

lending for metropolitan areas are provided 

by HMDA; however, no comparable data 

source exists for rural mortgage markets. The 

absence of rural market data is a constraint 

for evaluating credit gaps in rural mortgage 

lending and for defining underserved areas. 

One concern is whether the broad 

definition overlooks differences in borrower 

characteristics in served and underserved 

counties that should be included. Table B.11 

compares borrower and loan characteristics 

for the GSEs’ mortgage purchases in served 

and underserved areas. 

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51 60 FR 61,925–58 (1995) (Appendix B).

Fannie Mae is slightly more likely and 

Freddie Mac is less likely to purchase loans 

for first-time homebuyers in underserved 

areas than in served areas. Mortgages to firsttime homebuyers accounted for 4.3 percent 

of Fannie Mae’s mortgage purchases in 

served counties, compared with 4.6 percent 

of its purchases in underserved counties. For 

Freddie Mac the corresponding figures are 

3.4 percent in served counties and 3.3 

percent in underserved counties. 

The GSEs are more likely to purchase 

mortgages for high-income borrowers in 

underserved than in served counties. 

Surprisingly, borrowers in served counties 

were more likely to have incomes below the 

median than in underserved counties (39.6 

percent compared to 35.4 percent). These 

findings lend some support to the claim that, 

in rural underserved counties, the GSEs 

purchase mortgages for borrowers that 

probably encounter few obstacles in 

obtaining mortgage credit. 

The following similarities in Fannie Mae 

and Freddie Mac purchases in served and 

underserved counties in nonmetropolitan 

areas mirror those found for the GSEs in 

served and underserved census tracts in 

metropolitan areas. The GSEs are slightly 

more likely to purchase refinance loans in 

served than in underserved counties; 

mortgage purchases with loan-to-value ratios 

below 80 percent are more likely to be in 

underserved than in served counties; and 

seasoned mortgage purchases are more likely 

to be in underserved than in served counties. 

E. Factor 4: Size of the Conventional 

Conforming Mortgage Market for 

Underserved Areas 

HUD estimates that underserved areas 

account for 35–39 percent of the 

conventional conforming mortgage market. 

The analysis underlying this estimate is 

detailed in Appendix D. 

F. Factor 5: Ability to Lead the Industry 

This factor is the same as the fifth factor 

considered under the goal for mortgage 

purchases on housing for low- and moderateincome families. Accordingly, see Section G 

of Appendix A for a discussion of this factor, 

as well as Section I.5 of this Appendix, 

which describes the home purchase subgoal 

which is designed to place the GSEs in a 

leadership role in the underserved market. 

G. Factor 6: Need to Maintain the Sound 

Financial Condition of the Enterprises 

HUD has undertaken a separate, detailed 

economic analysis of this rule, which 

includes consideration of (a) the financial 

returns that the GSEs earn on loans in 

underserved areas and (b) the financial safety 

and soundness implications of the housing 

goals. Based on this economic analysis and 

reviewed by the Office of Federal Housing 

Enterprise Oversight, HUD concludes that the 

goals raise minimal, if any, safety and 

soundness concerns. 

H. Defining Nonmetropolitan Underserved 

Areas 

1. Whether to Adopt a Tract-Based Definition 

of Underserved Areas 

The current county-based definition for 

targeting GSE purchases to underserved 

nonmetropolitan areas was adopted in 1995 

over alternative narrower definitions, such as 

census tracts, despite the use of census tracts 

in metropolitan areas. In the 1995 Final Rule, 

HUD found the merits of a county-based 

system of targeting outweighed a tract-based 

system. Now, with seven years of experience 

under a county-based system, the release of 

Census 2000 data, and improvements in 

information technology and systems, HUD 

can reexamine whether to switch to census 

tracts for defining underserved 

nonmetropolitan areas. This section 

compares impacts of the potential shift in 

definition for both served and underserved 

populations as determined by tract-based and 

county-based definitions using a number of 

common industry variables as focal points for 

analysis. 

The rationale for choosing counties in 1995 

rested primarily on perceived shortcomings 

of census tracts.51 In particular, rural lenders 

did not perceive their market areas in terms 

of census tracts, but rather, in terms of 

counties. Another concern was a perceived 

lack of reliability in geocoding 1990 census 

tracts. At the same time, HUD found merit in 

using a tract-based geography for 

nonmetropolitan areas. Because tracts 

encompass more homogeneous populations 

than counties, they permit more precise 

targeting of underserved populations. In 

other words, more homogeneous geographic 

areas increase the potential for targeting the 

GSE mortgage purchases into areas where 

borrowers are more likely to face obstacles 

and other challenges in securing mortgage 

credit.

The criteria used for this analysis include 

the following: 

7. Do tracts provide a sharper delineation 

of served and underserved areas? 

Specifically, are underserved 

nonmetropolitan populations more clearly 

differentiated by adopting tracts vs. counties? 

Could service to the underserved 

nonmetropolitan populations be more 

comprehensive under tract-based definitions? 

8. What is the impact on GSE purchasing 

patterns if underserved areas are defined by 

tract? 

9. Applying the current criteria for 

identifying underserved areas to tracts would 

result in reclassifying approximately 23 

percent of all tracts, with 28 percent of tracts 

in served counties being redesignated as 

underserved and 19 percent of tracts in 

underserved counties being redesignated as 

served. Overall, roughly the same percentage 

of families (and population) would be 

reclassified. However, because underserved 

tracts are somewhat less densely populated 

than served tracts, the corresponding 

proportions of families that shift from served 

and underserved counties are closer: 25 vs. 

21 percent. 

a. Do Census Tracts Allow a Sharper 

Delineation of Served and Underserved 

Areas? 

This section compares the differences in 

housing need and economic, demographic, 

and housing conditions in served and 

underserved nonmetropolitan areas classified 

on, respectively, counties and tracts. 

Additionally, the ‘‘efficiency’’ with which 

counties and tracts cover the target 

populations is compared. That is, does tractbased targeting do a better job of capturing 

lower income households and excluding 

higher income households than county-based 

targeting? 

Table B.12 presents several indicators of 

socioeconomic and housing condition in 

served and underserved areas under both a 

tract-based and a county-based definition. In 

addition, served and underserved counties 

are subdivided into their served and 

underserved tract components. This allows a 

closer examination of the population and 

housing characteristics of the tracts that are 

reclassified (i.e., served to underserved or 

visa versa) under tract-based targeting. Thus, 

area characteristics of housing need and 

housing, economic, and demographic 

conditions can be compared, for the 

following four groups of tracts: (1) Tracts in 

served counties that would remain ‘‘served’’

classified as tracts; (2) tracts that remain 

‘‘underserved’’; (3) tracts that shift from 

served to underserved; and (4) tracts that 

shift from underserved to served. In addition, 

we provide counts of tracts falling into each 

of these groups. If a tract-based classification 

of underserved areas improves geographic 

targeting, the regrouping of tracts would be 

more similar to one another than to the other 

tracts in their respective counties: e.g., 

formerly underserved areas that become 

served should be more similar to tracts that 

were and remain served than to underserved 

(unchanged). 

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52 In areas with 30 percent or greater minority 

population, all families with income in excess of 

120 percent of the greater of State or national 

median income are counted as qualifying as ‘‘in 

need’’ for these computations. Similarly, in areas 

with less than 30 percent minority, those minority 

(headed) families with income between 95 and 120 

percent of the applicable median income are not 

classified as ‘‘in need.’’

Socioeconomic and Demographic 

Conditions. Table B.12 shows that in 

important socioeconomic and demographic 

characteristics, tract-based targeting would 

more effectively distinguish underserved 

populations. Median family income, poverty, 

unemployment, school dropout rates, and 

minority population all exhibit greater 

differences between served and underserved 

areas using tracts. For example, the 

difference in median income between served 

and underserved counties is $9,579, or 

alternatively, between served and 

underserved tracts, the difference is $12,744. 

Similarly, there is a 7-percentage point gap 

in poverty rates (7.5 vs. 14.5 percent poverty) 

using counties, which widens to 8.6 

percentage points (6.6 vs. 15.3 percent) using 

tracts. Minority population also is captured 

somewhat better with tracts, with the served/

underserved gap increasing from 16.5 to 17.3 

percentage points. In all cases, the levels of 

the indicators for underserved areas move in 

a direction consistent with targeting lower 

income households and areas with higher 

minority populations. 

The 4-way breakdown of served and 

underserved counties reveals some 

significant differences between the two 

component groups. In most respects, 

‘‘underserved tracts’’ (i.e., those meeting the 

underserved criteria), whether located in an 

underserved or served county, are more alike 

than they are like served tracts. Using median 

income again to illustrate, the effect of 

reclassifying areas by tract characteristics is 

to put together two groups of underserved 

tracts: Tracts that were in previously 

underserved counties and are not reclassified 

and tracts that were in served counties but 

meet the underserved criteria. A new group 

of served tracts is similarly formed. In both 

cases, the difference in median incomes of 

the constituent groups is about $3,500. In 

contrast, the served and underserved 

counties now encompass ‘‘served’’ and 

‘‘underserved’’ groups of tracts whose 

respective median incomes differ by almost 

$11,000. Combined with the fact that a fairly 

large number of tracts are affected overall 

(i.e., switch), these results support an 

assessment that counties are relatively crude 

for targeting underserved populations. 

Housing Needs and Conditions. Table B.12 

shows that tract-based targeting would 

produce modest gains in focusing GSE 

purchases on areas with relatively greater 

housing needs and conditions as measured 

by low owner-occupancy, higher vacancy 

rates, and crowding. For each of these 

indicators, measured need increases in 

underserved areas and the gap between 

served and underserved areas widens when 

tracts are used to classify areas. Most notably, 

the percent of owner-occupied housing units 

switches from being higher in underserved 

than served counties to being significantly 

lower among underserved tracts. With a shift 

to tracts overall ownership drops in 

underserved areas, from 74 to 72 percent, and 

increases in served areas from 74 to 77 

percent. In contrast, the homeownership rate 

for tracts located in served counties that 

would be deemed underserved if judged 

separately is only 65 percent. In fact, this rate 

is much lower even than underserved tracts 

in underserved counties. Shifting these tracts 

from served to underserved largely accounts 

for the switching of homeownership rates. 

Results for other indicators of housing 

need and conditions are less clear-cut. No 

definitive patterns are apparent for two, 

admittedly weak, measures of housing 

quality—units with complete plumbing and 

units with complete kitchen facilities, as well 

as for crowding. Purchase affordability, as 

measured by the ratio of median housing 

value to the income necessary to qualify for 

a loan for the median valued unit, is higher 

in underserved areas than in served areas. 

However, the measure of purchase 

affordability presented here is influenced by 

many market and other economic factors, 

some of which do not relate to housing need. 

For example, a low affordability ratio may 

reflect abundant supply, but it may also 

reflect low demand stemming from, e.g., 

limited availability of credit or high interest 

rates. 

Coverage Efficiency. The coverage 

efficiency index measures the effect of 

adopting tract-based targeting. This index can 

be used to indicate how well underserved 

areas encompass populations deemed to be 

underserved (‘‘sensitivity’’) and to exclude 

populations that are deemed to be served 

(‘‘specificity’’). The index is computed for 

median income as the difference in two 

percentages: (1) The proportion of all families 

in nonmetropolitan areas that meet the 

applicable income threshold who live in 

underserved tracts minus (2) the proportion 

of all families in nonmetropolitan areas that 

do not meet the applicable underserved 

income threshold who live in underserved 

areas. This difference can range from 1 

(perfect) to ¥1 (bad; perverse). For example, 

a coverage efficiency index equal to 1 implies 

that every family in need is living in an 

underserved area while there are no families 

who are not in need living in an underserved 

area; a coverage efficiency index equal to ¥1 

implies that none of the families in need live 

in an underserved area, or equivalently, all 

families in underserved areas are not in need. 

Comparing coverage efficiency for counties 

and tracts indicates that tracts do a better job; 

capturing a higher percentage of 

nonmetropolitan families whose income falls 

below the applicable income threshold and 

excluding more families whose income 

exceeds the threshold.52 Overall, the 

efficiency index rises from 0.22 to 0.274.

Given income thresholds that are not far 

away from median income in most places 

and the degree of income variation even with 

census tract boundaries, it should not come 

as a great surprise that neither the levels of 

coverage efficiency (0.22–0.27) nor 

improvement produced in applying tracts (5 

percentage points) are not more dramatic. 

Nevertheless, tracts do produce better 

tracking of lower income, very low income, 

and minority families. 

b. Does GSE Performance Vary Between 

Served and Underserved Tracts Within 

Underserved Counties? 

A similar analytical approach is used to 

examine how a shift to tracts would impact 

GSE purchases. Having applied income and 

minority thresholds from the 2000 census 

and updating census tract geography, Table 

B.13 compares, respectively, 2000, 2001, and 

2002 GSE purchases for served and 

underserved counties and tracts and also for 

the served and underserved tracts within 

county boundaries. On net there would be 

somewhat more tracts classified as 

underserved under a tract-based system than 

currently: 6,782 vs. 6,414. As noted above, 

however, 23.1 percent of all tracts are 

reclassified. Moving to tracts also would have 

a significant effect on the relative 

performance of the GSEs. In 2002, Fannie 

Mae’s performance would drop 2.1 

percentage points to 35.4 percent, while 

Freddie Mac’s performance would increase 

by 0.9 percent to 32.7 percent. 

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53A more comprehensive presentation of this 

analysis may be found in Economic Systems, Inc., 

Indicators of Mortgage Market Underservice in NonMetropolitan Areas, Interim Report to HUD, March 

2003, Chapter 6.

Differences between qualifying purchases 

of single-family and multifamily loans are 

further increased when assessed at the tract 

level. Performance for single-family loans 

drops 0.7 percentage points to 35.2, but for 

multifamily increases by 2.5 percentage 

points to 46.8. These changes dramatically 

compound the results observed in updating 

to 2000 census data, resulting in a widening 

of the single- and multifamily performance 

difference from the current level of 7.0 

percentage points to 11.6 percentage points. 

2. Alternative Definitions of Underservice 

The current definition of underservice in 

nonmetropolitan areas was established in 

1995 to be relatively broad, encompassing 

nearly twice as many underserved as served 

counties and somewhat more than half of the 

total nonmetropolitan population. This was 

done primarily to ensure that certain areas 

with low incomes and/or high minority 

populations, which might not be considered 

underserved in comparison to the rest of 

their State, would nevertheless be identified 

as underserved from a national perspective. 

This section summarizes a new analysis, 

based on 2000 census data, to evaluate the 

extent to which the current definition focuses 

GSE purchasing activity toward stimulating 

mortgage lending in areas with populations 

having greatest housing need. Alternative 

definitions of underservice are considered as 

follows: (1) Variations of the current 

thresholds; (2) applying only the State 

median income level for qualifying 

underserved counties and tracts; and (3) 

establishing different thresholds in 

micropolitan and ‘‘outside of core’’

nonmetropolitan areas. In each case the 

objective is to assess how redesignating 

served and underserved areas would affect 

relative conditions and needs and GSE 

purchasing performance. In distinguishing 

micropolitan and ‘‘outside of core’’ areas, it 

is of interest to determine whether it would 

be appropriate to establish different 

thresholds for underservice. The overarching 

criterion for evaluating and comparing 

definitions is their ability to serve very lowincome, low-income and moderate-income 

households, households in poverty, first-time 

homebuyers, minorities, and households in 

remote locations.53

In the current definition, areas are 

classified as underserved if either the 

minority population share is greater than 30 

percent and median income is less than 120 

percent of the greater of State 

nonmetropolitan or national nonmetropolitan 

median income; or area median income is 

less than or equal to 95 percent of the greater 

of State nonmetropolitan or national 

nonmetropolitan median income. The greater 

of State nonmetropolitan or national median 

income is termed the ‘‘reference income.’’

Denoting the current thresholds as ‘‘30/120/

95,’’ the following set of alternative 

thresholds are evaluated: 

• 30/120/95 vs. 30/120/90 vs. 30/120/80—

to examine the effect of lowering the general 

income threshold from 95 percent to 90 

percent to 80 percent. 

• 30/120/95 vs. 30/110/95 vs. 30/110/80—

to examine the effect of lowering both the 

minority (from 120% to 110%) and general 

income (from 95% to 80%) thresholds; and 

• 30/120/95 vs. 50/120/95—to examine the 

effect of increasing the minority population 

threshold that must be attained before 

applying the minority income threshold. 

For each alternative, indicators of 

socioeconomic and housing conditions are 

calculated for served and underserved areas 

for each alternative and compare the results 

to the current definition. Of particular 

interest is whether certain thresholds of 

minority population and median income 

capture the differences in housing needs and 

conditions between served and underserved 

areas better than others. The ‘‘coverage 

efficiency’’ of each alternative relative to 

households below the poverty line, below 50, 

70, and 95 percent of area reference income, 

and below the alternative income level(s) 

used to define underservice, is also 

presented. GSE purchasing activity is also 

examined for each alternative definition, 

specifically, the percentage of eligible loans 

that qualify towards the goal for underserved 

areas defined by different thresholds. Each 

analysis is conducted both with counties and 

tracts as the geographic unit. 

County Results. The main effect of 

lowering the general income threshold from 

95 to 90 to 80 percent of the reference income 

is to roughly halve the number of counties 

and population residing in underserved 

areas. Under the current definition, 11.6 

million people reside in underserved areas as 

opposed to fewer than 10 million in served 

areas. With a general income threshold of 80 

percent, 5.7 million would be left in 

underserved areas. A 90 percent threshold 

would produce a shift of approximately half 

this amount. 

In terms of social, economic, demographic, 

and housing characteristics, lowering the 

income threshold from 95 to 80 percent 

would have the following notable 

consequences: 

• Minority population in underserved 

areas would increase from 12.4 to 20.8 

percent with no significant change in served 

areas. 

• Median income would fall in both served 

and underserved areas with the difference 

remaining nearly constant at $10,000. 

• Poverty, unemployment, school drop out 

rates all would be higher in both served and 

underserved areas. The gap would increase 

for each of these characteristics. 

• Migration into underserved areas (from 

other States) would be relatively lower than 

into served areas with an 80 percent income 

threshold.

• Indicators of homeownership would 

decline somewhat in underserved areas 

relative to served areas. For all units, for 

example, ownership would decline from 74.3 

to 72.9 percent in underserved areas and 

increase from 73.5 to 74.3 percent in served 

areas. 

• Median housing values would fall in 

both served and underserved areas with a 

significant narrowing in the gap from 

approximately $25,000 to $19,000 at an 80 

percent median income threshold. 

• Housing affordability would decline in 

underserved areas, becoming nearly equal 

with affordability in served areas at 80 

percent. 

• Crowding would be higher in 

underserved areas, absolutely and relative to 

served areas. Thus, more narrowly defined 

underserved areas would more strongly 

manifest conditions and needs associated 

with underservice: lower income, higher 

poverty, higher minority populations, lower 

homeownership, lower affordability, more 

crowding, etc. However, served areas would 

expand to encompass significant numbers of 

these same underserved and target 

populations. 

Use of the coverage efficiency index 

highlights one of the tradeoffs between using 

a low median income threshold versus a high 

median income threshold in redefining 

underservice. Coverage efficiency based on 

all variables examined, including 

‘‘underserved,’’ poor, very low income, low 

income and even moderate income families, 

declines sharply as the income threshold is 

lowered from 95 to 80 percent, becoming 

negative for most groups. Coverage for the 

‘‘underserved’’ cohort declines from 22.0 to 

¥1.0 percent, and for families with up to 95 

percent of reference income, it declines from 

17.2 to ¥10.0 percent. These changes result 

from losing almost half of the families in 

target income ranges without any appreciable 

gain in specificity, i.e., shrinking the 

proportion of people living in underserved 

counties with incomes above the respective 

target levels. Similar patterns are observed 

for families with below 70 percent of 

reference income, below 50 percent of 

reference income, and families in poverty. 

The second set of comparisons builds on 

the first set by lowering the income threshold 

applicable to areas with relatively high 

minority populations (30 percent) from 120 

to 110 percent in addition to the general 

threshold. This change further shrinks, albeit, 

only marginally, the size and population of 

underserved areas. Minority underserved 

populations would be smaller and 

socioeconomic and housing conditions 

would be worse. Not surprisingly, coverage 

efficiencies and GSE purchase performance 

levels also would decline across the board, 

although the marginal effects of reducing the 

minority income threshold are quite small. 

The 30/110/80 alternative is the narrowest 

definition examined and produces the biggest 

losses in efficiency and GSE performance. 

The third variation of the current 

definition is an increase in the minority 

population threshold from 30 to 50 percent. 

Thus, if an area does not qualify as 

underserved against the general income 

threshold of 95 percent it could still qualify 

if its population is 50 percent minority and 

median income is less than or equal to 120 

percent of the reference income level. 

Relatively few counties qualify solely 

under the current minority thresholds. 

Raising the population threshold would trim 

this number by an additional 73 counties 

(457 tracts). Not surprisingly, the percent 

minority in underserved areas would 

decrease. However, the areas being 

redesignated as served are apparently 

somewhat above average in terms of 

Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 63791

54Note that, unlike the other panels in tables 6.3 

and 6.8, ‘‘underserved population’’ is defined 

according to the applicable definition. Thus, 

eliminating the national median income test, 

narrows the defined cohort of underserved families. 

Despite this, coverage falls.

55 Denial rates are computed for mortgage 

applications without manufactured housing loans. 

Origination rates equal home purchase and 

refinance mortgages (without subprime loans) per 

100 owner occupants in a census tract.

56The differentials in denial rates are due, in part, 

to differing risk characteristics of the prospective 

borrowers in different areas. However, use of denial 

rates is supported by the findings in the Boston Fed 

study which found that denial rate differentials 

persist, even after controlling for risk of the 

borrower. See Section B for a review of that study.

socioeconomic and housing conditions in 

underserved areas and below-average in 

terms of conditions in served areas. Coverage 

efficiencies for all cohorts would be lower 

than for the current definition of 

underservice and GSE performance overall 

would be approximately 90 percent of the 

current level.

Using the State median income, alone, as 

the general reference income would reduce 

the number underserved counties relative to 

the current definition, and, although there 

would still be more underserved counties 

(1,274 vs. 1,064), the underserved population 

actually would become smaller than the 

served population. The effect of this 

alternative on differences in housing 

conditions and needs between served and 

underserved areas is generally small and 

ambiguous, but overall, results in less 

contrast. Consistent with the results for other 

alternatives, applying a State median income 

standard, alone, would result in lower 

coverage efficiency across all target groups. 

Census Tract Results. As discussed above, 

the adoption of a tract-based system would 

result in greater coverage efficiency of 

underserved populations and sharper 

distinctions in the socioeconomic, 

demographic and housing characteristics of 

served and underserved areas. That is, tracts 

more effectively carve out areas that exhibit 

characteristics that are associated with 

underservice, such as low income, large 

minority populations and low 

homeownership. The converse is true for 

served areas. In analysis at the tract level, 

these patterns tend to be maintained quite 

consistently. A tract-based system would 

improve the power to differentiate 

underserved and served populations. 

According to virtually every indicator of 

socioeconomic, demographic, and housing 

conditions, applying State median income, 

alone, with a tract-based geography would 

produce superior differentiation to the 

current county-based definition. In terms of 

coverage efficiency, we again see 

improvement with tracts, but not enough to 

offset the loss of eliminating the national 

median income threshold. For the 

underserved population, for example, 

coverage efficiency would be 16.9 percent 

with tracts, still below 22 percent under the 

current definition.54

I. Determination of the Underserved Areas 

Housing Goal 

The annual goal for each GSE’s purchases 

of mortgages financing housing for properties 

located in geographically targeted areas 

(central cities, rural areas, and other 

underserved areas) is 37 percent of eligible 

units financed in 2005, 38 percent in 2006 

and 2007, and 39 percent in 2008. The 2008 

goal will remain in effect in subsequent 

years, unless changed by the Secretary prior 

to that time. The goal of 37 percent for 2005 

is larger than the goal of 31 percent for 2001–

03 mainly because, compared with the 1990 

Census, the 2000 Census includes a larger 

number of census tracts that meet HUD’s 

definition of underserved area. The new 37 

percent-39 percent goals are commensurate 

with recent market share estimates of 37–39 

percent for 1999–2002, presented in 

Appendix D. 

In addition, an Underserved Areas Housing 

Subgoal of 32 percent is established for the 

GSEs’ acquisitions of single-family-owner 

home purchase loans in metropolitan areas in 

2005, with the subgoal rising to 33 percent 

in 2006 and in 2007 and 34 percent in 2008. 

The subgoal is designed to encourage the 

GSEs to lead the primary market in providing 

mortgage credit in underserved areas. 

This section summarizes the Secretary’s 

consideration of the six statutory factors that 

led to the Underserved Area Housing Goal 

and the subgoal for home purchase loans in 

metropolitan areas. This section discusses 

the Secretary’s rationale for defining 

underserved areas and it compares the 

characteristics of such areas and untargeted 

areas. The section draws heavily from earlier 

sections which have reported findings from 

HUD’s analyses of mortgage credit needs as 

well as findings from other research studies 

investigating access to mortgage credit. 

1. Housing and Credit Disparities in 

Metropolitan Areas 

To identify areas underserved by the 

mortgage market, HUD focused on two 

traditional measures used in a number of 

studies based on HMDA data: Application 

denial rates and mortgage origination rates 

per 100 owner-occupied units. Tables B.2 

and B.3 in Section B of this Appendix 

presented detailed data on denial and 

origination rates by the racial composition 

and median income of census tracts for 

metropolitan areas. Aggregating this data is 

useful in order to examine denial and 

origination rates for broader groupings of 

census tracts: 55

Minority composition

(percent) 

Denial 

rate

(percent) 

Orig. 

rate 

0–30 ................................ 9.6 26.7 

30–50 .............................. 12.4 26.9 

50–100 ............................ 17.2 20.8 

Tract income 

Denial 

rate

(percent) 

Orig. 

rate 

Less than 90% of AMI .... 16.9 18.1 

90–120% ......................... 11.3 25.4 

Greater than 120% ......... 7.8 32.7 

Two points stand out. First, high-minority 

census tracts have higher denial rates and 

lower origination rates than low-minority 

tracts. Specifically, tracts that are over 50 

percent minority have nearly twice the denial 

rate and two-thirds the origination rate of 

tracts that are under 30 percent minority.56

Second, census tracts with lower incomes 

have higher denial rates and lower 

origination rates than higher income tracts. 

Tracts with income less than 90 percent of 

area median income have over twice the 

denial rate and almost half of the origination 

rate of tracts with income over 120 percent 

of area median income.

In both the 1995 and the 2000 GSE Rules, 

HUD’s research determined that 

‘‘underserved areas’’ could best be 

characterized in metropolitan areas as census 

tracts where: (1) Median income of families 

in the tract does not exceed 90 percent of 

area (MSA) median income or (2) minorities 

comprise 30 percent or more of the residents 

and median income of families in the tract 

does not exceed 120 percent of area median 

income. The earlier analysis was based on 

1990 Census data. HUD has now conducted 

the same analysis using 2000 Census data 

and has determined that the above definition 

continues to be a good proxy for underserved 

areas in metropolitan areas. The income and 

minority cutoffs produce sharp differentials 

in denial and origination rates between 

underserved areas and adequately served 

areas. For example, in 2003 the mortgage 

denial rate in underserved areas (15.9 

percent) was over one and three-fourths 

times that in adequately served areas (8.9 

percent). 

These minority population and income 

thresholds apply in the suburbs as well as in 

central cities. The average denial rate in 

underserved suburban areas (14.8 percent) is 

1.7 times that in the remaining served areas 

of the suburbs (8.7 percent), and is almost as 

large as the average denial rate (16.8 percent) 

in underserved central city tracts. Lowincome and high-minority suburban tracts 

appear to have credit problems similar to 

their central city counterparts. Thus HUD 

uses the same definition of underserved areas 

throughout metropolitan areas—there is no 

need to define such areas differently in 

central cities and in the suburbs. 

This definition of metropolitan 

underserved areas based on 2000 Census 

geography includes 26,316 of the 51,040 

census tracts in metropolitan areas, covering 

49.2 percent of the metropolitan population 

in 2000. (By contrast, the 1990-based 

definition included 21,587 of the 45,406 

census tracts in metropolitan areas, covering 

44.3 percent of the metropolitan population 

in 1990.) The 2000-based definition includes 

75.7 percent of the population living in 

poverty in metropolitan areas. The 

unemployment rate in underserved areas is 

more than twice that in served areas, and 

owner units comprise only 51.6 percent of 

total dwelling units in underserved tracts, 

versus 75.9 percent of total units in served 

tracts. As shown in Table B.14, this 

definition covers most of the population in 

several distressed central cities including 

Bridgeport (100 percent), Newark (99

63792 Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 

percent), and Detroit (93 percent). The 

nation’s five largest cities also contain large 

concentrations of their population in 

underserved areas: New York (68 percent), 

Los Angeles (72 percent), Chicago (75 

percent), Houston (73 percent), and Phoenix 

(50 percent). 

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2. Identifying Underserved Portions of 

Nonmetropolitan Areas 

Based on an exploration of alternative 

numerical criteria for identifying 

underserved nonmetropolitan areas using 

2000 census data, HUD has concluded that 

the current definition of underservice is 

broad but efficacious and that any narrower 

definition of underservice would not serve 

congressional intent under FHEFSSA. 

Narrowing the definition of underservice 

potentially could promote more intense 

purchasing in needier communities, but this 

seems unlikely. On the contrary, the greatest 

marginal impact of GSE purchasing could be 

in the very areas that would be excluded 

under the alternatives. 

Research comparing a tract-based system 

for defining underserved areas with the 

current county-based system, using 2000 

census data, indicates that a tract-based 

system would result in more effective 

geographic targeting of GSE purchases. 

Although the total number of tracts 

designated as served and underserved areas 

would change very little, 23 percent of all 

tracts would be reclassified, reassigning 

approximately equal numbers of families 

from served to underserved and from 

underserved to served. 

The main effect of the reclassification is to 

align tracts into more homogeneous and 

distinct groups as measured by differences in 

key socioeconomic and demographic 

characteristics such as median family 

income, poverty, unemployment, school 

dropouts, and minority population. As a 

result of reclassification, underserved areas 

stand out more as areas of lower income and 

economic activity and somewhat larger 

minority populations. 

Tract-based targeting would potentially 

focus GSE purchases in areas with relatively 

greater housing needs and conditions as 

measured by owner-occupancy, vacancy 

rates, and crowding. For each of these 

indicators, measured need increases in 

underserved areas and the gap between 

served and underserved areas widens when 

tracts are used to classify areas. Most notably, 

homeownership would be significantly lower 

in underserved areas relative to served areas 

under a tract-based system. Currently, and 

contrary to expectations, homeownership 

actually is slightly greater in underserved 

areas. Driving this reversal is the fact that 

tracts in served counties that would be 

reclassified as underserved tracts have an 

ownership rate of just 65 percent, which is 

much lower even than in the underserved 

tracts in underserved counties, where 

ownership is 73 percent. Meanwhile, the 

served tracts in served and underserved 

counties have the same ownership rate of 77 

percent, which is significantly higher than in 

underserved areas. 

Two groups of measures of housing 

conditions—housing quality and 

affordability—exhibit less clear-cut results 

from applying tracts. However, we conclude 

that these results are consistent with the 

ambiguous patterns discussed in chapter 4 

above and do not undermine the overall 

conclusion that basing geographic targeting 

on tracts would more sharply define areas 

with greater housing need and adverse 

housing conditions. 

Not surprisingly, the results from analyzing 

housing, socioeconomic, and demographic 

characteristics are further reinforced in 

finding that a tract-based system would better 

capture underserved populations and 

exclude served populations from geographic 

targeting. Defining underserved families as 

those in any area whose income was less 

than 95 percent of the reference income (or 

in areas with a minority population of 30 

percent or more, families with incomes 

below 120 percent of the reference income) 

the use of more refined tract geography 

results in a 5 percentage point increase in the 

coverage efficiency index, from 22 to 27 

percent. This reflects two improvements 

under a tract system: Underserved areas 

would capture more of the nonmetropolitan 

‘‘underserved’’ families (62 vs. 65 percent) 

and fewer ‘‘served’’ families (decreasing from 

40 to 37 percent of families in underserved 

areas). 

3. Past Performance of the GSEs 

Goals Performance. In the October 2000 

rule, the underserved areas goal was set at 31 

percent for 2001–03. Effective on January 1, 

2001, several changes in counting 

requirements came into effect for the 

undeserved areas goal, as follows: (a) ‘‘Bonus 

points’’ (double credit) for purchases of 

mortgages on small (5–50 unit) multifamily 

properties and, above a threshold level, 

mortgages on 2–4 unit owner-occupied 

properties; (b) a ‘‘temporary adjustment 

factor’’ (1.35 units credit) for Freddie Mac’s 

purchases of mortgages on large (more than 

50 unit) multifamily properties; and (c) 

eligibility for purchases of certain qualifying 

government-backed loans to receive goal 

credit. Under these counting rules, as shown 

in Table B.7a and Figure B.2, Fannie Mae’s 

performance was 32.6 percent in 2001, 32.4 

percent in 2002, and 32.1 percent in 2003, 

while Freddie Mac’s performance was 31.7 

percent in 2001, slightly less than 31 percent 

in 2002, and 32.7 percent in 2003; thus 

Fannie Mae surpassed the goal of 31 percent 

in all thee years, while Freddie Mac fell 

slightly short of the goal in 2002.

63794 Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 

BILLING CODE 4210–27–C

Counting requirements (a) and (b) expired 

at the end of 2003, while (c) will remain in 

effect. If this counting approach—without the 

bonus points and the ‘‘temporary adjustment 

factor’’—had been in effect in 2000–03, and 

the GSEs’ had purchased the same mortgages 

that they actually did purchase in both years, 

then Fannie Mae’s performance would have 

been 31.0 percent in 2000, 30.4 percent in 

2001, 30.1 percent in 2002, and 29.2 percent 

in 2003. Freddie Mac’s performance would 


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have been 29.2 percent in 2000, 28.2 percent 

in 2001, 28.0 percent in 2002, and 27.7 

percent in 2003. Therefore, Fannie Mae 

would have just matched the underserved 

areas goal of 30 percent in 2000 and fallen 

short in 2001–03, while Freddie Mac would 

have fallen short of the goal in all four years, 

2000–2003. 

The above performance figures are for 

underserved areas (census tracts in 

metropolitan areas and counties in nonmetropolitan areas) defined in terms of 1990 

Census geography. Switching to 2000 Census 

data increases the coverage of underserved 

areas, which increases the share of the GSEs’

purchases in underserved areas by 

approximately 5 percentage points. Based on 

2000 Census geography, and excluding 

counting requirements (a) and (b) then 

Fannie Mae’s performance would have been 

38.1 percent in 2000, 36.6 percent in 2001, 

35.9 percent in 2002, and 34.1 percent in 

2003, as shown in Table B.7b. Freddie Mac’s 

performance would have been 35.1 percent 

in 2000, 33.5 percent in 2001, 33.3 percent 

in 2002, and 31.6 percent in 2003. 

Single-Family-Owner Home Purchase 

Mortgages. Sections E.9 of Appendix A and 

D.2 of this appendix compared the GSEs’

funding of home purchase loans in 

underserved areas with originations by 

lenders in primary market. To take advantage 

of HMDA and GSE data going back to 1993, 

the analysis was conducted using 1990 

Census tract geography. While both GSEs 

have improved their performance since 1993, 

they have both lagged the conventional 

conforming market in providing affordable 

loans to underserved areas. The 1990-based 

analysis shows that the two GSEs have 

engaged in very different patterns of 

funding—Freddie Mac has been much less 

likely than Fannie Mae to fund home loans 

in underserved neighborhoods. HUD will 

begin defining underserved areas based on 

2000 Census geography and new OMB 

definitions of metropolitan areas in 2005, the 

first year of the rule. As noted above, the 

2000-based definition of underserved areas 

includes 5,372 more census tracts in 

metropolitan areas than the 1990-based 

definition, which means the GSE-market 

comparisons need to be updated to 

incorporate tract designations from the 2000 

Census. Therefore, for the years 1999, 2000, 

2001, and 2002, HUD used various 

apportionment techniques to re-allocate 

1990-based GSE and HMDA data into census 

tracts as defined by the 2000 Census. The 

GSE and HMDA data for 2003 were already 

based on 2000 geography, so no 

apportionment was needed for that year. 

Switching to the 2000-based tracts increases 

the underserved area share of market 

originations by 5.7 percentage points. 

Between 1999 and 2002, 31.4 percent of 

mortgage originations (without B&C loans) 

were originated in underserved tracts based 

on 2000 geography, compared with 26.2 

percent based on 1990 geography. As shown 

in Table B.8 of Section D.2, the underserved 

areas share of each GSE’s purchases also rises 

by approximately five percentage points. 

Thus, conclusions about the GSEs’

performance relative to the market are similar 

whether the analysis is conducted in terms 

of 2000 Census geography or 1990 Census 

geography. 

The analysis for home purchase loans 

based on 2000 Census geography will be 

summarized here (see Section D.2 of this 

appendix for a similar analysis using 1990-

based geography):

Year Freddie Mac

(percent) 

Fannie Mae

(percent) 

Market

(w/o B&C)

(percent) 

1999 ................................................................................................................................. 25.6 25.3 30.2 

2000 ................................................................................................................................. 27.3 29.0 31.7 

2001 ................................................................................................................................. 27.3 29.8 30.7 

2002 ................................................................................................................................. 31.7 32.3 31.8 

2003 ................................................................................................................................. 29.0 32.0 32.5 

1996–2003 (estimate) ...................................................................................................... 27.2 29.3 30.9 

1999–2003 (average) ...................................................................................................... 28.3 30.0 31.4 

2001–2003 (average) ...................................................................................................... 29.4 31.4 31.7 

Between 1999 and 2003, 28.3 percent of 

Freddie Mac’s purchases and 30.0 percent of 

Fannie Mae’s purchases financed properties 

in underserved neighborhoods, compared 

with 31.4 percent home purchase loans 

originated in the conventional conforming 

market (excluding B&C loans). Thus, Freddie 

Mac performed at 90 percent of the market 

level, while Fannie Mae performed at 96 

percent of the market level—both results 

similar to those reported above for 

underserved areas based on 1990 Census 

geography. The 2000 Census data show that 

Fannie Mae has been much closer to the 

market during the recent 2001–2003 period. 

The share of Fannie Mae’s purchases going 

to underserved areas was 31.4 percent during 

2001–2003, which placed it closer to the 

market level of 31.7 percent. However, the 

2000-based results show that, like Freddie 

Mac, Fannie Mae’s longer-term performance 

(since 1996) as well as its recent average 

performance (1999 to 2003) have consistently 

been below market levels. But, it is 

encouraging that Fannie Mae significantly 

improved its performance relative to the 

market during the first two years of HUD’s 

higher housing goal levels. (See Section D.2 

for the method of estimating the 1996–2003 

average results.) 

4. Ability To Lead the Single-Family-Owner 

Market: A Subgoal for Underserved Areas 

The Secretary believes the GSEs can play 

a leadership role in underserved markets. 

Thus, as discussed in Section D.2, the 

Department is establishing a subgoal of 32 

percent for each GSE’s acquisitions of home 

purchase loans for single-family-owner 

properties located in the underserved census 

tracts of metropolitan areas in 2005, rising to 

33 percent in 2006 and 2007 and to 34 

percent in 2008. If the GSEs meet the 2008 

subgoal, they will be leading the primary 

market by over two percentage points. As 

discussed above, underserved areas 

accounted for an average of approximately 

31.5 percent of home purchase loans 

originated in the conventional conforming 

market of metropolitan areas (computed over 

1999–2003 or over 2001–2003). To reach the 

subgoal for 2008, both GSEs will have to 

improve over their earlier peak 

performances—Freddie Mac by 2.3 

percentage points over its previous peak 

performance of 31.7 percent in 2002, and 

Fannie Mae by 1.7 percentage points over its 

previous peak performance of 32.3 percent in 

2003. To meet the 2008 subgoal, Freddie Mac 

will have to improve by 2.6 percentage points 

over its 2002–2003 average (unweighted) 

performance of 30.4 percent, while Fannie 

Mae will have to improve by 1.8 percentage 

points over its 2002–2003 average 

performance of 32.2 percent. 

The subgoal applies only to the GSEs’

purchases in metropolitan areas because the 

HMDA-based market benchmark is only 

available for metropolitan areas. HMDA data 

for nonmetropolitan counties are not reliable 

enough to serve as a market benchmark. The 

Department is also setting home purchase 

subgoals for the other two goals-qualifying 

categories, as explained in Appendices A and 

C.

The approach taken is for the GSEs to 

obtain their leadership position by staged 

increases in the underserved areas subgoal; 

this will enable the GSEs to take new 

initiatives in a correspondingly staged 

manner to achieve the new subgoal each 

year. Thus, the increases in the underserved 

areas subgoal are sequenced so that the GSEs 

can gain experience as they improve and 

move toward the new higher subgoal targets. 

Appendix A discusses in some detail the 

factors that the Department considered when 

setting the subgoal for low- and moderateincome loans. Several of the considerations 

were general in nature—for example, related 

to the GSEs’ overall ability to lead the singlefamily-owner market—while others were 

specific to the low-mod subgoal. Because the 

reader can refer to Appendix A, this 

appendix provides a briefer discussion of the 

more general factors. The specific 

63796 Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 

57See Dan Immergluck, Star Differences: The 

Explosion of the Subprime Industry and Racial 

Hypersegmentation in Home Equity Lending,

Woodstock Institute, October 2000; and Daniel 

Immergluck and Marti Wiles, Two Steps Back: The 

Dual Mortgage Market, Predatory Lending, and the 

Undoing of Community Development, Woodstock 

Institute, Chicago, IL, November 1999. For a 

national analyses, see the HUD report Unequal 

Burden: Income and Racial Disparities in Subprime 

Lending in America, April 2000; and Randall M. 

Scheessele, Black and White Disparities in 

Subprime Mortgage Refinance Lending, Housing 

Finance Working Paper No. HF–114, Office of 

Policy Development and Research, U.S. Department 

of Housing and Urban Development, April 2002.

considerations that led to the subgoal for 

underserved areas can be organized around 

the following four topics: 

(1) The GSEs have the ability to lead the 

market. As discussed in Appendix A, the 

GSEs have the ability to lead the primary 

market for single-family-owner loans, which 

is their core business. Both GSEs have been 

dominant players in the home purchase 

market for years, funding 57 percent of the 

single-family-owner mortgages financed 

between 1999 and 2002. Through their many 

new product offerings and their various 

partnership initiatives, the GSEs have shown 

that they have the capacity to operate in 

underserved neighborhoods. They also have 

the staff expertise and financial resources to 

make the extra effort to lead the primary 

market in funding single-family-owner 

mortgages in underserved areas. 

(2) The GSEs have lagged the market. Even 

though they have the ability to lead the 

market, they have not done so, as discussed 

above. Fannie Mae demonstrated the type of 

improvement needed to meet this new 

underserved area subgoal during 2001 and 

2002. During 2001, underserved area loans 

declined as a percentage of primary market 

originations (from 31.7 to 30.7 percent), but 

they increased as a percentage of Fannie 

Mae’s purchases (from 29.0 to 29.8 percent); 

and during 2002, they increased further as a 

percentage of Fannie Mae’s purchases (from 

29.8 to 32.3 percent), placing Fannie Mae at 

the market level. 

(3) There are disparities among 

neighborhoods in access to mortgage credit.

There remain troublesome neighborhood 

disparities in our mortgage markets, even 

after the substantial growth in conventional 

lending to low-income and minority 

neighborhoods that accompanied the socalled ‘‘revolution in affordable lending’’. 

There is growing evidence that inner city 

neighborhoods are not being adequately 

served by mainstream lenders. Some have 

concluded that a dual mortgage market has 

developed in our nation’s financing system, 

with conventional mainstream lenders 

serving white families living in the suburbs 

and FHA and subprime lenders serving 

minority families concentrated in inner city 

neighborhoods.57 In addition to the 

unavailability of mainstream lenders, 

families living in these often highlysegregated neighborhoods face many 

additional hurdles, such as lack of cash for 

a down payment, credit problems, and 

discrimination. Immigrants and minorities, 

who disproportionately live in underserved 

areas, are projected to account for almost 

two-thirds of the growth in the number of 

new households over the next ten years. To 

meet the diverse and unique needs of these 

families, the GSEs must continue adjusting 

their underwriting guidelines and offering 

new products so that they can better serve 

these areas and hopefully attract more 

mainstream lenders into our inner city 

neighborhoods.

(4) There are ample opportunities for the 

GSEs to improve their performance.

Mortgages are available for the GSEs to 

purchase in underserved areas. They can 

improve their performance and lead the 

primary market in purchasing loans in these 

low-income and high-minority 

neighborhoods. The underserved areas share 

of the home purchase market has consistently 

been around 31 percent since 1995 (and 32 

percent in the last two years), which suggests 

a degree of underlying strength in the market. 

According to the market share data reported 

in Table A.30 of Appendix A, the GSEs have 

been purchasing 48 percent of new 

originations in underserved areas, which 

means there are plenty of purchase 

opportunities left for them in the non-GSE 

portion of that market. In addition, the GSEs’

purchases under the subgoal are not limited 

to new mortgages that are originated in the 

current calendar year. The GSEs can 

purchase loans from the substantial, existing 

stock of affordable loans held in lenders’

portfolios, after these loans have seasoned 

and the GSEs have had the opportunity to 

observe their track record. In fact, both GSEs 

have often purchased seasoned loans that 

were used to finance properties in 

underserved areas (see Table A.11 in 

Appendix A). 

To summarize, although single-familyowner mortgages comprise the ‘‘bread-andbutter’’ of their business, the GSEs have 

lagged behind the primary market in 

financing properties in underserved areas. 

For the reasons given above, the Secretary 

believes that the GSEs can do more to raise 

the share of their home loan purchases in 

underserved areas. This can be accomplished 

by building on efforts that the enterprises 

have already started, including their new 

affordable lending products, their many 

partnership efforts, their outreach to inner 

city neighborhoods, their incorporation of 

greater flexibility into their underwriting 

guidelines, and their purchases of CRA loans. 

A wide variety of quantitative and qualitative 

indicators indicate that the GSEs have the 

resources and financial strength to improve 

their affordable lending performance enough 

to lead the market in underserved areas. 

5. Size of the Mortgage Market for 

Underserved Areas 

As detailed in Appendix D, the market for 

mortgages in underserved areas is projected 

to account for 35–39 percent of dwelling 

units financed by conventional conforming 

mortgages; in estimating the size of the 

market, HUD used alternative assumptions 

about future economic and market conditions 

that were less favorable than those that 

existed over the last five years. HUD is well 

aware of the volatility of mortgage markets 

and the possible impacts on the GSEs’ ability 

to meet the housing goals. Should conditions 

change such that the goals are no longer 

reasonable or feasible, the Secretary has the 

authority to revise the goals. 

6. The Underserved Areas Housing Goal for 

2005–2008 

The Underserved Areas Housing Goal for 

2005 is 37 percent of eligible purchases, 

rising to 38 percent in 2006 and 2007 and 39 

percent in 2008. Five percent of the six 

percentage point increase in 2005 simply 

reflects the expanded coverage of HUD’s 

definition in the 2000 Census tract data. The 

bonus points for small multifamily properties 

and owner-occupied 2–4 units, as well as 

Freddie Mac’s Temporary Adjustment Factor, 

will no longer be in effect for goal counting 

purposes. It is recognized that neither GSE 

would have met the 37 percent target for 

2005 in the past three years, and only Fannie 

Mae would have met this goal in 2000. 

Specifically, Fannie Mae’s performance is 

projected to have been 37.5 percent in 2000, 

35.7 percent in 2001, 35.0 percent in 2002, 

and 34.1 percent in 2003 under a 2000-based 

underserved area goal. On this basis, Freddie 

Mac’s performance is projected to have been 

34.1 percent in 2000, 32.5 percent in 2001, 

32.4 percent in 2002, and 31.7 percent in 

2003. However, GSE goal performance in 

2001–03 was reduced by the heavy refinance 

wave of this period.

The objective of HUD’s Underserved Areas 

Housing Goal is to bring the GSEs’

performance to the upper end of HUD’s 

market range estimate for this goal (35–39 

percent), consistent with the statutory 

criterion that HUD should consider the GSEs’

ability to lead the market for each Goal. To 

enable the GSEs to achieve this leadership, 

the Department is modestly increasing the 

Underserved Areas Housing Goal for 2005 

which will increase further through 2008, to 

achieve the ultimate objective for the GSEs to 

lead the market under a range of foreseeable 

economic circumstances by 2008. Such a 

program of staged increases is consistent 

with the statutory requirement that HUD 

consider the past performance of the GSEs in 

setting the Goals. Staged increases in the 

Underserved Areas Housing Goal will 

provide the enterprises with opportunity to 

adjust their business models and prudently 

try out business strategies, so as to meet the 

required 2008 level without compromising 

other business objectives and requirements. 

The analysis of this section implies that 

there are many opportunities for Fannie Mae 

and Freddie Mac to improve their overall 

performance on the Underserved Areas 

Housing Goal. The GSEs provided financing 

for 55 percent of the single-family and 

multifamily units that were financed in the 

conventional conforming market between 

1999 and 2002. However, in the underserved 

areas portion of the market, the GSE 

purchases represented only 48 percent of the 

dwelling units that were financed in the 

market. Thus, there appears to be ample 

room for the GSEs to increase their purchases 

of loans that qualify for the Underserved 

Areas Housing Goal. In addition, there are 

several market segments that would benefit 

from a greater secondary market role by the 

GSEs, and many of these market segments are 

concentrated in underserved areas. 

Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 63797

1Performance for the 1993–95 period was 

discussed in HUD’s Housing Goals 2000 Finale 

Rule.

2To separate out the effects of changes in 

counting rules that took effect in 2001, this section 

also compares performance in 2001 to estimated 

performance in 2000 if the 2001 counting rules had 

been in effect in that year. Freddie Mac’s goal 

performance in 2002 has been revised due to coding 

errors that were discovered in HUD’s review of 

2002 data, as discussed in HUD’s press release No. 

04–105, October 15, 2004.

7. Conclusions 

Having considered the projected mortgage 

market serving low- and moderate-income 

families, economic, housing and 

demographic conditions for 2005–08, and the 

GSEs’ recent performance in purchasing 

mortgages in underserved areas the Secretary 

has determined that the annual goal of 37 

percent of eligible units financed in 2005, 38 

percent in 2006 and 2007, and 39 percent in 

2008 is feasible. The Secretary has also 

established a subgoal of 32 percent for the 

GSEs’ purchases of single-family-owner 

mortgages in metropolitan areas for 2005, 

rising to 33 percent in 2006 and 2007 and 34 

percent in 2008. The Secretary has 

considered the GSEs’ ability to lead the 

industry as well as the GSEs’ financial 

condition. The Secretary has determined that 

the goals and subgoals are necessary and 

appropriate.

Appendix C—Departmental 

Considerations To Establish the Special 

Affordable Housing Goal 

A. Introduction 

1. Establishment of the Goal 

The Federal Housing Enterprises Financial 

Safety and Soundness Act of 1992 

(FHEFSSA) requires the Secretary to 

establish a special annual goal designed to 

adjust the purchase by each GSE of mortgages 

on rental and owner-occupied housing to 

meet the unaddressed needs of, and 

affordable to, low-income families in lowincome areas and very-low-income families 

(the Special Affordable Housing Goal). 

In establishing the Special Affordable 

Housing Goal, FHEFSSA requires the 

Secretary to consider: 

1. Data submitted to the Secretary in 

connection with the Special Affordable 

Housing Goal for previous years; 

2. The performance and efforts of the GSEs 

toward achieving the Special Affordable 

Housing Goal in previous years; 

3. National housing needs of targeted 

families; 

4. The ability of the GSEs to lead the 

industry in making mortgage credit available 

for low-income and very-low-income 

families; and 

5. The need to maintain the sound 

financial condition of the enterprises. 

2. The Goal and Subgoals

Special Affordable Housing Goal. The rule 

provides that the Special Affordable Housing 

Goal will be 22 percent in 2005, 23 percent 

in 2006, 25 percent in 2007, and 27 percent 

in 2008. 

Units That Count Toward the Goal. Units 

that count toward the Special Affordable 

Housing Goal include units occupied by lowincome owners and renters in low-income 

areas, and very low-income owners and 

renters. Other low-income rental units in 

multifamily properties count toward the goal 

where at least 20 percent of the units in the 

property are affordable to families whose 

incomes are 50 percent of area median 

income or less, or where at least 40 percent 

of the units are affordable to families whose 

incomes are 60 percent of area median 

income or less. 

Multifamily Subgoal. HUD has established 

a special affordable subgoal for GSE 

purchases of multifamily mortgages. This 

subgoal is expressed in terms of a minimum 

annual dollar volume of multifamily 

mortgage purchases for units qualifying for 

the goal, rather than as a percentage of total 

units financed, as for the three housing goals. 

Both GSEs have consistently surpassed the 

multifamily subgoal since its establishment 

in 1996. The rule increases the subgoal such 

that, of the total Special Affordable mortgage 

purchases each year, each GSE must 

purchase special affordable multifamily 

mortgages in dollar amount equal to at least 

1 percent of its combined (i.e., single-family 

and multifamily) annual average mortgage 

purchases over the 2000–2002 period. The 

level of this subgoal is $5.49 billion per year 

for Fannie Mae and $3.92 billion per year for 

Freddie Mac. 

Single-Family-Owner Home Purchase 

Subgoal. The Department is establishing a 

subgoal of 17 percent for the share of each 

GSE’s purchases of single-family-owner 

home purchase mortgages that qualify as 

special affordable and are originated in 

metropolitan areas in 2005 and 2006, with 

the subgoal rising to 18 percent in 2007 and 

2008. 

B. Consideration of the Factors 

In considering the factors under FHEFSSA 

to establish the Special Affordable Housing 

Goal, HUD relied upon data gathered from 

the American Housing Survey through 2001, 

the Census Bureau’s 1991 and 2001 

Residential Finance Surveys, the 1990 and 

2000 Censuses of Population and Housing, 

Home Mortgage Disclosure Act (HMDA) data 

for 1992 through 2003, and annual loan-level 

data from the GSEs on their mortgage 

purchases through 2003. Appendix D 

discusses in detail how these data resources 

were used and how the size of the 

conventional conforming market for this goal 

was estimated. 

The remainder of Section C discusses the 

factors listed above, and Section D provides 

the Secretary’s rationale for establishing the 

Special Affordable Housing Goal. 

Factors 1 and 2. Data submitted to the 

Secretary in connection with the Special 

Affordable Housing Goal for previous years, 

and the performance and efforts of the 

enterprises toward achieving the Special 

Affordable Housing Goal in previous years. 

The discussions of these two factors have 

been combined because they overlap to a 

significant degree. 

This section discusses each GSE’s 

performance under the Special Affordable 

Housing Goal over the 1996–2003 period.1 As 

explained in Appendix A, the data presented 

are ‘‘official HUD results’’ which, in some 

cases, differ from goal performance reported 

by the GSEs in the Annual Housing Activities 

Reports (AHARs) that they submit to the 

Department.

The main finding of this section is that 

both Fannie Mae and Freddie Mac surpassed 

the Department’s Special Affordable Housing 

Goals for each of the seven years during this 

period. Specifically: 

• The goal was set at 12 percent for 1996; 

Fannie Mae’s performance was 15.4 percent 

and Freddie Mac’s performance was 14.0 

percent. 

• The goal was set at 14 percent for 1997–

2000. Freddie Mac’s performance was 15.2 

percent in 1997, 15.9 percent in 1998, 17.2 

percent in 1999, and 20.7 percent in 2000; 

and Fannie Mae’s performance was 17.0 

percent in 1997, 14.3 percent in 1998, 17.6 

percent in 1999, and 19.2 percent in 2000. 

• In HUD’s Housing Goals 2000 Final Rule, 

the special affordable goal was set at 20 

percent for 2001–03. As of January 1, 2001, 

several changes in counting requirements 

took effect for the special affordable goal, as 

follows: ‘‘bonus points’’ (double credit) for 

purchases of goal-qualifying mortgages on 

small (5–50 unit) multifamily properties and, 

above a threshold level, mortgages on 2–4 

unit owner-occupied properties; a 

‘‘temporary adjustment factor’’ (1.20 units 

credit, subsequently increased by Congress to 

1.35 units credit) for Freddie Mac’s 

purchases of goal-qualifying mortgages on 

large (more than 50-unit) multifamily 

properties; changes in the treatment of 

missing data; a procedure for the use of 

imputed or proxy rents for determining goal 

credit for multifamily mortgages; and 

changes regarding the ‘‘recycling’’ of funds 

by loan originators. These changes are 

explained below. Fannie Mae’s performance 

was 21.6 percent in 2001, 21.4 percent in 

2002, and 21.2 percent in 2003. Freddie 

Mac’s performance was 22.6 percent in 2001, 

20.4 percent in 2002, and 21.4 percent in 

2003. Both GSEs surpassed this higher goal 

in all years. This section discusses the 

October 2000 counting rule changes in detail 

and provides data on what goal performance 

would have been in 2001–03 without these 

changes.2

In addition, HUD has established a special 

affordable subgoal for GSE purchases of 

multifamily mortgages. This subgoal is 

expressed in terms of a minimum annual 

dollar volume of multifamily mortgage 

purchases for units qualifying for the goal, 

rather than as a percentage of total units 

financed, as for the three housing goals. As 

discussed below, both GSEs surpassed the 

multifamily subgoal in each of these years. 

a. Performance on the Special Affordable 

Housing Goal in 1996–2003 

HUD’s Housing Goals 1995 Final Rule 

specified that in 1996 at least 12 percent of 

the number of units financed by each of the 

GSEs that were eligible to count toward the 

Special Affordable Housing Goal should 

qualify for the goal (that is, be for very lowincome families or low-income families in 

low-income areas), and at least 14 percent 

should qualify in 1997–2000. HUD’s October 

63798 Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 

2000 rule made various changes in the goal 

counting rules, as discussed below, and 

increased the Special Affordable Housing 

Goal to 20 percent for 2001–03. 

In the December 1995 rule, the minimum 

special affordable multifamily subgoals for 

1996–2000 were set at 0.8 percent of the total 

dollar volume of each GSE’s mortgage 

purchases in 1994, or $1.29 billion annually 

for Fannie Mae and $0.99 billion annually for 

Freddie Mac. These subgoals were increased 

for 2001–03 in the October 2000 rule, to 

$2.85 billion annually for Fannie Mae and 

$2.11 billion annually for Freddie Mac, or 1.0 

percent of the average dollar volume of each 

GSE’s mortgage purchases over the 1997–99 

period. 

Table C.1 and Figure C.1 show 

performance on the special affordable goal 

and the special affordable multifamily 

subgoal over the 1996–2003 period, based on 

HUD’s analysis. The table shows that Fannie 

Mae surpassed the goals by 3.4 percentage 

points and 3.0 percentage points in 1996 and 

1997, respectively, while Freddie Mac 

surpassed the goals by narrower margins, 2.0 

and 1.2 percentage points. In 1998 Fannie 

Mae’s performance fell by 2.7 percentage 

points, while Freddie Mac’s performance 

continued to rise, by 0.7 percentage point, 

thus for the first time Freddie Mac 

outperformed Fannie Mae on this goal. 

Freddie Mac showed a gain in performance 

to 17.2 percent in 1999, while Fannie Mae 

exhibited an even greater gain, to 17.6 

percent 

BILLING CODE 4210–27–P

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ER02NO04.067</GPH>
63800 Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 

BILLING CODE 4210–27–C


ER02NO04.068</GPH>
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3 During 1996–2000 Freddie Mac took steps to 

acquire representations and warranties from lenders 

to attest that they were ‘‘recycling’’ the proceeds 

from the sales of qualifying loans. Fannie Mae did 

not take such steps; rather, Fannie Mae excluded 

such loans from the denominator in making its own 

calculations of its special affordable goal 

performance. In 1996–2000 HUD counted all 

eligible loans in the denominator, and, in the 

absence of measures to verify ‘‘recycling’’ by Fannie 

Mae, did not award credit in the numerator of the 

special affordable goal for most of Fannie Mae’s 

seasoned mortgage purchases.

4See Congressional Record, December 15, 2000, 

pp. H12295–96.

Both GSEs exhibited sharp gains in goal 

performance in 2000—Fannie Mae’s 

performance increased by 1.6 percentage 

points, to a record level of 19.2 percent, 

while Freddie Mac’s performance increased 

even more, by 3.5 percentage points, which 

also led to a record level of 20.7 percent. 

Fannie Mae’s performance was 21.6 percent 

in 2001, 21.4 percent in 2002 and 21.3 

percent in 2003; Freddie Mac’s performance 

was 22.6 percent in 2001, 20.4 percent in 

2002, and 21.4 percent in 2003. However, as 

discussed below, using consistent accounting 

rules for 2000–03, each GSE’s Special 

Affordable Housing Goal performance fell in 

every year from 2001 through 2003—in total, 

by 2 percentage points for Fannie Mae and 

3.2 percentage points for Freddie Mac. 

With regard to the special affordable 

multifamily subgoal, Fannie Mae’s purchases 

have exceeded the subgoal by wide margins 

in all years, with performance ranging from 

184 percent of the goal in 1996 to 315 percent 

of the goal in 1999. Fannie Mae’s subgoal was 

more than doubled in the October 2000 rule, 

to a minimum of $2.85 billion in each year 

from 2001 through 2003, but its qualifying 

purchases amounted to $7.36 billion, or 258 

percent of the goal, in 2001, and $7.57 

billion, or 260 percent of the goal, in 2002; 

and $12.10 billion, or 425 percent of the 

subgoal, in 2003. 

Freddie Mac has also exceeded its special 

affordable multifamily subgoals in every 

year, albeit by smaller margins than Fannie 

Mae. In 1996 Freddie Mac’s special 

affordable multifamily mortgage purchases 

amounted to $1.06 billion, or 107 percent of 

the goal. This ratio rose to 122 percent in 

1997, and exceeded 200 percent for each year 

from 1998 through 2000. Freddie Mac’s 

subgoal was more than doubled in the 

October 2000 rule, to a minimum of $2.11 in 

each year from 2001 through 2003, but its 

qualifying purchases amounted to $4.65 

billion, or 220 percent of the goal, in 2001; 

$5.22 billion, or 247 percent of the goal, in 

2002; and $8.79 billion, or 417 percent of the 

subgoal, in 2003. 

The official figures for Freddie Mac’s 

special affordable goal performance 

presented above differ from the 

corresponding figures presented by Freddie 

Mac in its Annual Housing Activity Reports 

to HUD by 0.1–0.2 percentage point for 1996–

2000, reflecting minor differences in the 

application of counting rules. The official 

figures for special affordable goal 

performance by both GSEs are the same as 

those submitted by the enterprises for both 

GSEs for 2001, and for Fannie Mae for 2002. 

However, for 1996–2000, HUD’s official 

special affordable goal performance figures 

for Fannie Mae were approximately 1–3 

percentage points lower than the 

corresponding figures reported by the 

enterprise. This was due to differences 

between HUD and Fannie Mae in the 

application of counting requirements 

applicable to purchases of portfolios of 

seasoned loans, based on a statutory 

requirement that the proceeds of such GSE 

purchases by the loan sellers should be 

‘‘recycled’’ in order for the GSE to receive 

Special Affordable goal credit.3 This 

discrepancy did not persist in 2001–02 

because of a change in counting 

requirements, described below. And for 2002, 

HUD’s official goal performance figure was 

20.4 percent, somewhat below the figure of 

20.6 percent submitted to the Department by 

Freddie Mac. For 2003, official performance 

on this goal for both GSEs was somewhat 

greater than that reported by the GSEs—

official performance was 21.2 percent for 

Fannie Mae (as compared with 20.9 percent 

reported by Fannie Mae to the Department) 

and 21.4 percent for Freddie Mac (as 

compared with 20.3 percent reported by 

Freddie Mac to the Department).

Fannie Mae’s performance on the Special 

Affordable Housing Goal surpassed Freddie 

Mac’s in 1996–97. This pattern was reversed 

in 1998, as Freddie Mac surpassed Fannie 

Mae in goal performance for the first time, 

though by only 0.2 percentage point. This 

improved relative performance of Freddie 

Mac was due to its increased purchases of 

multifamily loans, as it re-entered that 

market, and to increases in the goalqualifying shares of its single-family 

mortgage purchases. However, Fannie Mae 

again surpassed Freddie Mac in special 

affordable goal performance in 1999, 17.6 

percent to 17.2 percent; Freddie Mac 

regained the lead in 2000, 20.7 percent to 

19.2 percent. Freddie Mac’s official 

performance also exceeded Fannie Mae’s 

official performance in 2001, but this 

reflected a difference in the counting rules 

applicable to the two GSEs that was enacted 

by Congress; if the same counting rules were 

applied to both GSEs, Fannie Mae’s 

performance would have exceeded Freddie 

Mac’s performance, by 21.6 percent to 21.1 

percent. 

In 2002, Freddie Mac’s performance on the 

special affordable goal was below Fannie 

Mae’s performance (21.4 percent), even 

though Freddie Mac had the advantage of the 

Temporary Adjustment Factor (TAF), which 

did not apply to performance by Fannie Mae. 

Freddie Mac’s performance would have 

trailed Fannie Mae’s without this factor, and 

in fact Freddie Mac would have fallen short 

of the goal, at 19.3 percent. In 2003, Freddie 

Mac’s performance (21.4 percent) slightly 

exceeded Fannie Mae’s performance (21.2 

percent), but this resulted from application of 

the TAF to Freddie Mac’s performance—

without this, Freddie Mac’s performance 

would have been 20.2 percent, barely in 

excess of the 20 percent goal. 

b. Changes in the Goal Counting Rules for 

2001–03

Several changes in the counting rules 

underlying the calculation of special 

affordable goal performance took effect 

beginning in 2001. Most of these also applied 

to the low- and moderate-income goal and 

are discussed in Appendix A; only brief 

summaries of those changes are given here: 

• Bonus points for multifamily and singlefamily rental properties. Each qualifying unit 

in a small multifamily property counted as 

two units in the numerator in calculating 

special affordable goal performance on all of 

the goals for 2001–03. And, above a threshold 

equal to 60 percent of the average number of 

qualifying rental units financed in owneroccupied properties over the preceding five 

years, each qualifying unit in a 2–4 unit 

owner-occupied property also counted as two 

units in the numerator in calculating goal 

performance. 

• Freddie Mac’s Temporary Adjustment 

Factor. Freddie Mac received a ‘‘Temporary 

Adjustment Factor’’ of 1.35 units of credit for 

each qualifying unit financed in ‘‘large’’

multifamily properties (i.e., those with 51 or 

more units) in the numerator in calculating 

special affordable goal performance for 2001–

03.4 This factor did not apply to special 

affordable units in large multifamily 

properties whose mortgages were financed by 

Fannie Mae during this period.

• Missing data for single-family properties.

The GSEs may exclude loans with missing 

borrower income from the denominator if the 

property is located in a below-median 

income census tract, subject to a ceiling of 1 

percent of total owner-occupied units 

financed. The enterprises are also allowed to 

exclude single-family rental units with 

missing rental information from the 

denominator in calculating performance for 

the special affordable goal. 

• Missing data and proxy rents for 

multifamily properties. If rent is missing for 

multifamily units, the GSEs may apply 

‘‘proxy rents,’’ up to a ceiling of 5 percent of 

total multifamily units financed, in 

determining whether such units qualify for 

the special affordable goal. If such proxy 

rents cannot be estimated, these multifamily 

units are excluded from the denominator in 

calculating performance under these goals. 

• Change in ‘‘recycling’’ requirements.

Under Section 1333(b)(1)(B) of FHEFSSA, if 

a GSE acquires a portfolio of mortgages 

originated in a previous year (that is, 

seasoned mortgages) that qualify under the 

Special Affordable Housing goal, the seller 

must be ‘‘engaged in a specific program to 

use the proceeds of such sales to originate 

additional loans that meet such goal’’ and 

such purchases or refinancings must 

‘‘support additional lending for housing that 

otherwise qualifies under such goal’’ in order 

to receive credit toward the goal. This has 

been referred to as the ‘‘recycling 

requirement.’’ The 2000 rule both clarified 

the conditions under which HUD would 

regard these statutory conditions to be 

satisfied and established certain categories of 

lenders that would be presumed to meet the 

recycling requirements. These included BIFinsured and SAIF-insured depository 

institutions that are regularly in the business 

of mortgage lending and which are subject to, 

63802 Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 

5The revised requirements are codified at 24 CFR 

81.14(e)(4). The changes are discussed in detail in 

the rule preamble, 68 FR 65074–76 (October 31, 

2000).

and have received at least a satisfactory 

Community Reinvestment Act performance 

evaluation rating under specified 

conditions.5

c. Effects of Changes in the Counting Rules 

on Goal Performance 

Because of the changes in special 

affordable goal counting rules that took effect 

in 2001, direct comparisons between official 

goal performance in 2000 and 2001–03 are 

somewhat of an ‘‘apples-to-oranges 

comparison.’’ For this reason, the Department 

has calculated what performance would have 

been in 2000 under the 2001–03 rules; this 

may be compared with official performance 

in 2001–03—an ‘‘apples-to-apples 

comparison.’’ HUD has also calculated what 

performance would have been in 2001–03 

under the 1996–2000 rules; this may be 

compared with official performance in 

2000—an ‘‘oranges-to-oranges comparison.’’

These comparisons are presented in Table 

C.2. 

Specifically, Table C.2 shows performance 

under the special affordable goal in three 

ways. Baseline A presents performance under 

the counting rules in effect for 1996–2000. 

Baseline B incorporates the technical changes 

in counting rules—changes in the treatment 

of missing data (including use of proxy 

rents), and changes in procedures related to 

the ‘‘recycling’’ requirement. Baseline C

incorporates in addition to the technical 

changes the bonus points and, for Freddie 

Mac, the temporary adjustment factor. 

Baseline B corresponds to the counting 

approach used in this rule to take effect in 

2005. Boldface figures under Baseline A for 

1999–2000 and under Baseline C for 2001–

03 indicate official goal performance based 

on the counting rules in effect in those 

years—e.g., for Freddie Mac, 17.2 percent in 

1999, 20.7 percent in 2000, 22.6 percent in 

2001, 20.4 percent in 2002 and 21.4 percent 

in 2003. 

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6Exclusion of loans with missing information had 

a greater impact on Fannie Mae’s goal performance 

than on Freddie Mac’s goal performance.

7 ‘‘Fannie Courting Multifamily Sellers; Small 

Banks Balking.’’ American Banker, January 13, 

2003, p.1.

• Performance on the Special Affordable 

Housing Goal under 1996–2000 Counting 

Rules Plus Technical Changes. If the 

‘‘Baseline B’’ counting approach had been in 

effect in 2000–03 and the GSEs’’ had 

purchased the same mortgages that they 

actually did purchase in those years, Fannie 

Mae would have surpassed the special 

affordable goal in both 2000 and 2001, but 

not in 2002 or 2003, while Freddie Mac 

would have surpassed the goal in 2000 but 

fallen short in 2001–2003. Specifically, 

Fannie Mae’s performance would have been 

21.4 percent in 2000, 20.2 percent in 2001, 

19.9 percent in 2002, and 19.3 percent in 

2003. Freddie Mac’s performance would have 

been 21.0 percent in 2000, 19.3 percent in 

2001, 18.1 percent in 2002, and 17.8 percent 

in 2003. 

• Performance on the Special Affordable 

Housing Goal under 2001–2003 Counting 

Rules. If the 2001–03 counting rules had been 

in effect in 2000–03 and the GSEs’ had 

purchased the same mortgages that they 

actually did purchase in that year (i.e., 

abstracting from any behavioral effects of 

‘‘bonus points,’’ for example), both GSEs 

would have substantially surpassed the 

special affordable goal in all four years, but 

both GSEs’ performance figures would have 

deteriorated somewhat between 2000 and 

2003. Specifically, Fannie Mae’s ‘‘Baseline 

C’’ performance would have been 22.2 

percent in 2000, 21.6 percent in 2001, 21.4 

percent in 2002, and 21.2 percent in 2003. 

Freddie Mac’s performance would have been 

23.4 percent in 2000, 22.6 percent in 2001, 

20.4 percent in 2002 and 21.4 percent in 

2003. Measured on this consistent basis, 

then, Fannie Mae’s performance fell by 0.9 

percentage point between 2000 and 2003. 

Freddie Mac’s ‘‘Baseline C’’ performance fell 

by 2.0 percentage points between 2000 and 

2003. These reductions were primarily due to 

2001–03 being years of heavy refinance 

activity. 

Details of Effects of Changes in Counting 

Rules on Goal Performance in 2001–03. As 

discussed above, counting rule changes that 

took effect in 2001 had significant impacts on 

the performance of both GSEs on the special 

affordable goal in 2001—3.0 percentage 

points for Fannie Mae and 3.5 percentage 

points for Freddie Mac. This section breaks 

down the effects of these changes on goal 

performance for both GSEs; results are shown 

in Table C.2. 

• Freddie Mac. The largest impact of the 

counting rule changes on Freddie Mac’s goal 

performance was due to the application of 

the temporary adjustment factor for 

purchases of mortgages on large multifamily 

properties, as enacted by Congress; this 

added 1.4 percentage points to goal 

performance in 2001, as shown in Table C.2. 

Bonus points for purchases of mortgages on 

small multifamily properties added 1.1 

percentage points to performance, and bonus 

points for purchase of mortgages on owneroccupied 2–4 unit rental properties added 0.7 

percentage point to performance. The 

remaining impact (0.2 percentage point) was 

due to technical changes in counting rules—

primarily, the exclusion of single-family 

units with missing information from the 

denominator in calculating goal performance. 

Changes in the Department’s counting rules 

related to ‘‘recycling’’ did not play a role in 

Freddie Mac’s performance on the special 

affordable goal. These same patterns also 

generally appeared in 2002. But in 2003 

bonus points for financing special affordable 

unit in small multifamily properties had a 

greater impact on performance that the 

temporary adjustment factor. 

• Fannie Mae. The temporary adjustment 

factor applied to Freddie Mac’s goal 

performance, but not to Fannie Mae’s 

performance, thus counting rule changes had 

less impact on its performance than on 

Freddie Mac’s performance in 2001–03. The 

largest impacts of the counting rule changes 

on Fannie Mae’s goal performance in 2001 

were due to the application of bonus points 

for purchases of mortgages on owneroccupied 2–4 unit rental properties, which 

added 0.9 percentage point to performance; 

bonus points for purchases of mortgages on 

small multifamily properties, which added 

0.4 percentage point to performance; and 

technical changes, which added 1.6 

percentage points to performance—the latter 

included the change in the Department’s 

rules regarding ‘‘recycling’’ and the exclusion 

of single-family units with missing 

information from the denominator in 

calculating goal performance.6 The use of 

proxy rents for multifamily properties played 

a minor role in determining Fannie Mae’s 

special affordable goal performance. These 

same patterns also generally appeared in 

2002 and 2003.

d. Bonus Points for the Special Affordable 

Housing Goal

As discussed above and in Appendix A, 

the Department established ‘‘bonus points’’

to encourage the GSEs to step up their 

activity in 2001–03 in two segments of the 

mortgage market—the small (5–50 unit) 

multifamily mortgage market, and the market 

for mortgages on 2–4 unit properties where 

1 unit is owner-occupied and 1–3 units are 

occupied by renters. Bonus points did not 

apply to purchases of mortgages for owneroccupied 1-unit properties, for investorowned 1–4 unit properties, and for large

(> 50-unit) properties, although as also 

discussed above, a ‘‘temporary adjustment 

factor’’ applied to Freddie Mac’s purchases of 

goal-qualifying mortgages on large 

multifamily properties. 

Bonus points for small multifamily 

properties. Each unit financed in a small 

multifamily property that qualified for any of 

the housing goals was counted as two units 

in the numerator (and one unit in the 

denominator) in calculating goal performance 

for that goal. For example, if a GSE financed 

a mortgage on a 40-unit property in which 10 

of the units qualified for the special 

affordable goal, 20 units would be entered in 

the numerator and 40 units in the 

denominator for this property in calculating 

goal performance. 

Fannie Mae financed 37,449 units in small 

multifamily properties in 2001 that were 

eligible for the special affordable goal, 58,277 

such units in 2002, and 214,619 such units 

in 2003—this compares with only 7,196 such 

units financed in 2000. Small multifamily 

properties also accounted for a greater share 

of Fannie Mae’s multifamily business in 

2001–03—7.4 percent of total multifamily 

units financed in 2001, 13.2 percent in 2002, 

and 28.4 percent in 2003, up from 2.5 percent 

in 2000. However, HUD’s 2000 rule reported 

information from the 1991 Residential 

Finance Survey that small multifamily 

properties accounted for 37 percent of all 

multifamily units, thus Fannie Mae was still 

less active in this market than in the market 

for large multifamily properties. Within the 

small multifamily market, there was no 

evidence that Fannie Mae targeted affordable 

properties to a greater extent in 2001–03 than 

in 2000. That is, 61 percent of Fannie Mae’s 

small multifamily units qualified for the 

special affordable goal in 2000; this fell to 46 

percent in 2001, 52 percent in 2002, and 42 

percent in 2003.

Freddie Mac financed 50,299 units in small 

multifamily properties in 2001 that were 

eligible for the special affordable goal, 22,255 

such units in 2002, and 177,561 such units 

in 2003, as compared with only 2,996 such 

units financed in 2000. Small multifamily 

properties also accounted for a significantly 

greater share of Freddie Mac’s multifamily 

business in 2001–03—16.0 percent of total 

multifamily units financed in 2001, 7.5 

percent in 2002, and 30.0 percent in 2003, up 

from 1.8 percent in 2000. 

Within the small multifamily market, there 

was some evidence that Freddie Mac targeted 

affordable properties to a greater extent in 

2001 than in 2000. That is, 55 percent of 

Freddie Mac’s small multifamily units 

qualified for the special affordable goal in 

2000; this rose to 73 percent in 2001, but 

declined to 60 percent in 2002 and 54 

percent in 2003. 

In summary, then, there is evidence that 

bonus points for small multifamily properties 

had an impact on Fannie Mae’s role in this 

market in 2001–03 and an even larger impact 

on Freddie Mac’s role in this market. In 

addition, Fannie Mae has announced a 

program to increase its role in this market 

further in future years.7

Bonus points for single-family rental 

properties. Above a threshold, each unit 

financed in a 2–4 unit property with at least 

one owner-occupied unit (referred to as 

‘‘OO24s’’ below) that qualified for any of the 

housing goals was counted as two units in 

the numerator (and one unit in the 

denominator) in calculating goal performance 

for that goal in 2001–03. The threshold was 

equal to 60 percent of the average number of 

such qualifying units over the previous five 

years. For example, Fannie Mae financed an 

average of 24,780 special affordable units in 

these types of properties between 1996 and 

2000, and 55,118 such units in 2001. Thus 

Fannie Mae received 40,250 bonus points in 

this area in 2001—that is, 55,118 minus 60 

percent of 24,780. So 95,368 units were 

entered in the numerator for these properties 

in calculating special affordable goal 

performance. 

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8 In New England, MSAs were defined through 

mid-2003 in terms of Towns rather than Counties, 

and the portion of a New England county outside 

of any MSA was regarded as equivalent to a county 

in establishing the metropolitan or nonmetropolitan location of a property. The MSA 

definitions established by the Office of Management 

and Budget (OMB) in June 2003 defined MSAs in 

New England in terms of counties.

9HUD has deferred application of the 2003 MSA 

specification to 2005, pending completion of the 

present rulemaking process.

Fannie Mae financed 176,369 units in 

OO24s that were eligible for the special 

affordable goal in 2001, 229,827 such units 

in 2002, and 355,994 such units in 2003, as 

compared with 77,985 such units financed in 

2000. However, Fannie Mae’s total singlefamily business increased at approximately 

the same rate as its OO24 business over the 

2001–03 period, thus the share of this 

business accounted for by OO24s was the 

same in 2001–03 as in 2000—4 percent. 

Within the OO24 market, there was no 

evidence that Fannie Mae targeted special 

affordable properties to a greater extent in 

2001–03 than in 2000. That is, approximately 

30 percent of Fannie Mae’s OO24 units 

qualified for the special affordable goal in 

each of these years. 

Freddie Mac financed 96,204 units in 

OO24s that were eligible for the special 

affordable goal in 2001, 146,242 such units 

in 2002, and 154,535 such units in 2003, as 

compared with 49,993 such units financed in 

2000. However, Freddie Mac’s total singlefamily business increased at approximately 

the same rate as its OO24 business between 

2000 and 2002, thus the share of this 

business accounted for by OO24s was the 

same in 2002 as in 2000—4 percent. And its 

overall single-family business increased more 

rapidly than its OO24 business in 2003, thus 

OO24 units accounted for 3 percent of all 

single-family units last year. 

As for Fannie Mae, within the OO24 

market there was no evidence that Freddie 

Mac targeted special affordable properties to 

a greater extent in 2001–03 than in 2000. 

That is, approximately 32–36 percent of 

Freddie Mac’s OO24 units qualified for the 

special affordable goal in each of these four 

years. 

e. Effects of 2000 Census on Scoring of Loans 

Toward the Special Affordable Housing Goal 

Background. Scoring of housing units 

under the Special Affordable Housing Goal is 

based on data for mortgagors’ incomes for 

owner-occupied units, rents for rental units, 

area median incomes, and, for units that are 

in the low-income but not the very lowincome range, decennial census data used to 

determine whether the median income for 

the area where the property is located is in 

the low-income range. Specifically, for 

single-family owner-occupied units scoring is 

based on. 

• The mortgagors’ income at the time of 

mortgage origination 

• The median income of an area specified 

in the same way as for the Low- and 

Moderate-Income Housing Goal, that is: (i) 

For properties located in Metropolitan 

Statistical Areas (MSAs) the area is the MSA; 

and (ii) for properties located outside of 

MSAs, the area is the county or the nonmetropolitan portion of the State in which 

the property is located, whichever has the 

larger median income, as of the year of 

mortgage origination (which may be for the 

current year or a prior year). 

• Also, if the property is located in a 

Metropolitan Statistical Area (MSA), the 

determination for purposes of the Special 

Affordable Housing Goal involves data on 

median income of the MSA; or if the property 

is located elsewhere, the median income of 

the county or the non-metropolitan portion of 

the State in which the property is located, 

whichever is larger, as of the most recent 

decennial census.

Analogous specifications to those detailed in 

Appendix A for the Low- and ModerateIncome Housing Goal are applied in the case 

of the Special Affordable Housing Goal for 

rental units in single-family properties with 

rent data available (assuming no income data 

available for actual or prospective tenants), 

for rental units in multifamily properties 

where rent data are available, and for rental 

units in multifamily properties where rent 

data are not available. 

Thus, scoring loans under the Special 

Affordable Housing Goal requires a data 

series showing annual median incomes for 

MSAs, non-metropolitan counties, and the 

non-metropolitan portions of states; 

decennial census data on median incomes for 

census tracts; and decennial census data on 

median incomes for MSAs, non-metropolitan 

counties, and the non-metropolitan portions 

of States.8

For scoring loans purchased by the GSEs 

year-by-year from 1993 through 2003, area 

median income estimates produced by HUD’s 

Economic and Market Analysis Division were 

used. The same median income data series 

described in Appendix A for the Low- and 

Moderate-Income Goal was used. The 

determination of low-income areas was based 

on 1990 census data. 

2005 Procedure. Relative to the above 

procedure, scoring of loans purchased by the 

GSEs in and after 2005 will be affected by 

two factors—first, re-benchmarking of area 

median incomes to the 2000 census as 

described in Appendix A, with a shift from 

1990 to 2000 census data for identifying lowincome areas, and second, the Office of 

Management and Budget’s June, 2003, respecification of MSA boundaries based on 

analysis of 2000 census data.9

Analysis. For purposes of specifying the 

level of the Special Affordable Housing Goal, 

the HUD estimates of area median incomes 

for MSAs, non-metropolitan counties, and 

the non-metropolitan parts of States, as 

described in Appendix A, were used in 

conjunction with the data identifying lowincome areas based on the 2000 census, to rescore loans purchased by the GSEs between 

1999 and 2003. The same data series were 

used further in estimating the share of loans 

originated in metropolitan areas that would 

be eligible to score toward the Special 

Affordable Housing Goal, from HMDA data. 

The results of the retrospective GSE analysis 

are provided in Table C.3. The results of the 

GSE–HMDA comparative analysis are 

presented in the next section. 

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Table C.3 shows three sets of estimates for 

each GSE, based respectively on the counting 

rules in place in 2001–2003 (but disregarding 

the bonus points and Temporary Adjustment 

Factor), on the addition of 2000 census rebenchmarking and low-income areas, and 

finally on the further addition of 2003 MSA 

specification. 

f. The GSEs’ Multifamily Special Affordable 

Purchases 

Since 1996 each GSE has been subject to 

an annual dollar-based subgoal for Special 

Affordable multifamily mortgage purchases, 

as discussed above. This subgoal was 

established for 1996–2000 as 0.8 percent of 

the total dollar volume of single-family and 

multifamily mortgages purchased by the 

respective GSE in 1994. Thus Fannie Mae’s 

subgoal was $1.29 billion per year and 

Freddie Mac’s subgoal was $988 million per 

year during that period. Fannie Mae 

surpassed the subgoal by $1.08 billion, $1.90 

billion, $2.24 billion, $2.77 billion, and $2.50 

billion in 1996, 1997, 1998, 1999, and 2000 

respectively, while Freddie Mac exceeded 

the subgoal by $18 million, $220 million, 

$1.70 billion, $1.27 billion, and $1.41 billion. 

The subgoal was established for 2001–03 as 

1.0 percent of the average annual volume of 

each GSE’s total mortgage purchases over the 

1997–99 period. Thus Fannie Mae’s subgoal 

was established as $2.85 billion per year and 

Freddie Mac’s as $2.11 billion per year. In 

2001 Fannie Mae exceeded its subgoal by 

$4.51 billion and Freddie Mac exceeded its 

subgoal by $2.54 billion. In 2002, Fannie Mae 

exceeded its subgoal by $4.72 billion and 

Freddie Mac exceeded its subgoal by $3.11 

billion. Both GSEs exceeded their subgoals in 

2003 by wide margins—Fannie Mae, with 

special affordable multifamily purchases of 

$12.11 billion (goal of $2.85 billion), and 

Freddie Mac, with purchases of $8.79 billion 

(goal of $2.11 billion.) Those subgoals are 

also in effect for 2004. Table C.1 includes 

figures on subgoal performance, and they are 

depicted graphically in Figure C.2.

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g. Characteristics of the GSEs’ Special 

Affordable Purchases 

The following analysis presents 

information on the composition of the GSEs’

Special Affordable purchases according to 

area income, unit affordability, tenure of unit 

and property type (single-or multifamily). 

Tables C.4 and C.5 show that each GSE’s 

reliance on multifamily housing units to 

meet the special affordable goal has been 

variable from year to year since 1996. Fannie 

Mae’s multifamily purchases were at 37.7 

percent in 1996,28.8 percent in 2001, and 

20.0 percent in 2002, with a high of 44.0 

percent in 1997 and a low of 19.6 percent in 

2003. Freddie Mac’s multifamily purchases 

represented 29.4 percent of all purchases 

qualifying for the goal in 1996, 27.0 percent 

in 2001, and 20.4 percent in 2002, with a 

high of 31.5 percent in 1997 and a low of 

20.4 percent in 2002. The two GSEs’

purchase percentages for single-family owner 

properties exhibited a similar variability 

through this entire period, as did their 

purchases of mortgages financing singlefamily rental units from 1996 through 2003. 

Both GSEs’ high points for mortgages 

financing single-family rental units occurred 

in 2002: Fannie Mae’s purchase percentage 

was 20.0 percent while Freddie Mac’s was 

18.1 percent.

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10Tabulations of the 2001 American Housing 

Survey by HUD’s Office of Policy Development and 

Research. The results in the table categorize renters reporting housing assistance as having no housing 

problems.

Tables C.4 and C.5 also show the allocation 

of units qualifying for the goal as related to 

the family income and area median income 

criteria in the goal definition. Very-lowincome families (shown in the two leftmost 

columns in the tables) accounted for 83.4 

percent of Fannie Mae’s units qualifying 

under the goal in 1997, rising to 85.2 percent 

in 1999. For Freddie Mac, very-low-income 

families accounted for 81.9 percent of units 

qualifying under the goal in 1997, rising to 

84.9 percent in 1999. In contrast, mortgage 

purchases from low-income areas (shown in 

the first and third columns in the tables) 

accounted for 33.7 percent of Fannie Mae’s 

units qualifying under the goal in 1997, 

compared to 35.5 percent in 2001. The 

corresponding percentages for Freddie Mac 

were 38.3 percent in 1997 and 35.5 percent 

in 2001. Thus given the definition of special 

affordable housing in terms of household and 

area income characteristics, both GSEs have 

consistently relied substantially more on 

low-income characteristics of households 

than low-income characteristics of census 

tracts to meet this goal. 

h. The GSEs’ Performance Relative to the 

Market 

Section E.9 in Appendix A uses HMDA 

data and GSE loan-level data for home 

purchase mortgages on single-family-owner 

properties in metropolitan areas to compare 

the GSEs’ performance in special affordable 

lending to the performance of depositories 

and other lenders in the conventional 

conforming market. (See Tables A.13 to A.16 

in Appendix A.). There were two main 

findings with respect to the special affordable 

category. First, Freddie Mac and Fannie Mae 

have historically lagged depositories and the 

overall market in providing mortgage funds 

for special affordable borrowers over periods, 

such as 1993–2003, 1996–2003 and 1999–

2003. Between 1993 and 2003, 12.2 percent 

of Freddie Mac’s mortgage purchases were 

for special affordable borrowers, 13.3 percent 

of Fannie Mae’s purchases, 15.4 percent of 

loans originated by depositories, and 15.5 

percent of loans originated in the 

conventional conforming market (without 

estimated B&C loans). For the recent years, 

the GSE-market comparisons are as follows:

Year

(in percent) 

Freddie Mac

(in percent) 

Fannie Mae

(in percent) 

Market

(w/o B&C)

(in percent) 

1999 ............................................................................................................................................. 12.8 12.5 17.0 

2000 ............................................................................................................................................. 14.7 13.3 16.6 

2001 ............................................................................................................................................. 14.4 14.9 15.6 

2002 ............................................................................................................................................. 15.8 16.3 16.1 

2003 ............................................................................................................................................. 15.6 17.1 15.9 

1996–2003 ................................................................................................................................... 13.2 14.1 15.9 

1999–2003 ................................................................................................................................... 14.7 15.1 16.2 

2001–2003 ................................................................................................................................... 15.2 16.2 15.9 

During the period between 1999 and 2003, 

the GSEs’ performance was slightly over 90 

percent of the market—special affordable 

loans accounted for 15.1 percent of Fannie 

Mae’s purchases, 14.7 percent of Freddie 

Mac’s purchases, and 16.2 percent of loans 

originated in the conforming market. 

Second, while both GSEs have improved 

their performance over the past few years, 

Fannie Mae has been made more progress 

than Freddie Mac in erasing its gap with the 

market. During the first three years (2001, 

2002, and 2003) of HUD’s new housing goal 

targets, the average share of Fannie Mae’s 

purchases going to special affordable loans 

was 16.2 percent, which was above the 

market average of 15.9 percent. The share of 

Freddie Mac’s purchases going to special 

affordable loans was 15.2 percent during this 

period. 

Section G in Appendix A discusses the role 

of the GSEs both in the overall special 

affordable market and in the different 

segments (single-family owner, single-family 

rental, and multifamily rental) of the special 

affordable market. The GSEs’ special 

affordable purchases accounted for 41 

percent of all special affordable owner and 

rental units that were financed in the 

conventional conforming market between 

1999 and 2002. The GSEs’ 41-percent share 

of the special affordable market was threefourths of their 55-percent share of the 

overall market. Even in the owner market, 

where the GSEs account for 61 percent of the 

market, their share of the special affordable 

market was only 52 percent during this 

period. While the GSEs improved their 

market shares during 2001–2003, this 

analysis shows that there is room and ample 

opportunities for the GSEs, and particularly 

Freddie Mac, to improve their performance 

in purchasing affordable loans at the lowerincome end of the market. Section C.3 of this 

appendix discusses a home purchase subgoal 

designed to place the GSEs in such a 

leadership position in the special affordable 

single-family-owner market. 

Factor 3. National Housing Needs of LowIncome Families in Low-Income Areas and 

Very-Low-Income Families 

This discussion concentrates on very-lowincome families with the greatest needs. It 

complements Section C of Appendix A, 

which presents detailed analyses of housing 

problems and demographic trends for lowerincome families which are relevant to the 

issue addressed in this part of Appendix C. 

Data from the American Housing Survey 

demonstrate that housing problems and 

needs for affordable housing continue to be 

more pressing in the lowest-income 

categories than among moderate-income 

families, as established in HUD’s analysis for 

the 1995 and 2000 Final Rules. Table C.6 

displays figures on several types of housing 

problems—high housing costs relative to 

income, physical housing defects, and 

crowding–for both owners and renters. 

Figures are presented for households 

experiencing multiple (two or more) of these 

problems as well as households experiencing 

a severe degree of either cost burden or 

physical problems. Housing problems in 

2001 continued to be much more frequent for 

the lowest-income groups.10 Incidence of 

problems is shown for households in the 

income range covered by the special 

affordable goal, as well as for higher income 

households.

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This analysis shows that priority problems 

of severe cost burden or severely inadequate 

housing are noticeably concentrated among 

renters and owners with incomes below 60 

percent of area median income: 30.5 percent 

of renter households and 34.9 percent of 

owner households had priority problems. In 

contrast, in the next higher income range, up 

to 80 percent of area median income, 2.5 

percent of renter households and 7.3 percent 

of owner households had priority problems. 

The table demonstrates the significance of 

affordability problems: Sixty-five percent of 

very-low-income renter families had rent 

burden over 30 percent of income; 35 percent 

had rent burden over 50 percent of income. 

Thirteen percent had moderately or severely 

inadequate housing; 6 percent lived in 

crowded conditions, defined as more than 

one person per room. 

Factor 4. The Ability of the Enterprises To 

Lead the Industry in Making Mortgage Credit 

Available for Low-Income and Very-LowIncome Families 

The discussion of the ability of Fannie Mae 

and Freddie Mac to lead the industry in 

Section G of Appendix A is relevant to this 

factor—the GSEs’ roles in the owner and 

rental markets, their role in establishing 

widely-applied underwriting standards, their 

role in the development of new technology 

for mortgage origination, their strong staff 

resources, and their financial strength. 

Additional analyses of the potential ability of 

the enterprises to lead the industry in the 

low- and very-low-income market appears 

below in Section D, which explains the 

Department’s rationale for the home purchase 

subgoal for Special Affordable loans. 

Factor 5. The Need to Maintain the Sound 

Financial Condition of the GSEs 

HUD has undertaken a separate, detailed 

economic analysis of this final rule, which 

includes consideration of (a) the financial 

returns that the GSEs earn on special 

affordable loans and (b) the financial safety 

and soundness implications of the housing 

goals. Based on this economic analysis, HUD 

concludes that the housing goals in this final 

rule raise minimal, if any, safety and 

soundness concerns. 

C. Determination of the Special Affordable 

Housing Goal 

Several considerations, many of which are 

reviewed in Appendixes A and B and in 

previous sections of this Appendix, led to the 

determination of the Special Affordable 

Housing Goal, the multifamily special 

affordable subgoal, and the special affordable 

subgoal for home purchase loans on singlefamily-owner properties in metropolitan 

areas. 

1. Severe Housing Problems 

The data presented in Section C.3 

demonstrate that housing problems and 

needs for affordable housing are much more 

pressing in the lowest-income categories than 

among moderate-income families. The high 

incidence of severe problems among the 

lowest-income renters reflects severe 

shortages of units affordable to those renters. 

At incomes below 60 percent of area median, 

34.7 percent of renters and 21.6 percent of 

owners paid more than 50 percent of their 

income for housing. In this same income 

range, 65.6 percent of renters and 42.4 

percent of owners paid more than 30 percent 

of their income for housing. In addition, 31.5 

percent of renters and 23.8 percent of owners 

exhibited ‘‘priority problems’’, meaning 

housing costs over 50 percent of income or 

severely inadequate housing. 

Homeownership gaps and other disparities in 

the housing and mortgage markets discussed 

in Section H of Appendix A also apply to 

Special Affordable housing and mortgages. 

2. GSE Performance and the Market 

a. The GSEs’ Special Affordable Housing 

Goals Performance 

In the October 2000 rule, the special 

affordable goal was set at 20 percent for 

2001–03. Effective on January 1, 2001, 

several changes in counting requirements 

came into effect for the special affordable 

goal, as follows: (a)‘‘bonus points’’ (double 

credit) for purchases of mortgages on small 

(5–50 unit) multifamily properties and, above 

a threshold level, mortgages on 2–4 unit 

owner-occupied properties; (b) a ‘‘temporary 

adjustment factor’’ (1.35 unit credit) for 

Freddie Mac’s purchases of mortgages on 

large (more than 50 unit) multifamily 

properties; (c) changes in the treatment of 

missing data; (d) a procedure for the use of 

imputed or proxy rents for determining goal 

credit for multifamily mortgages; and (e) 

changes regarding the ‘‘recycling’’ of funds 

by loan originators. 

Counting requirements (a) and (b) expired 

at the end of 2003 while (c)–(e) will remain 

in effect after that. If this counting 

approach—without the bonus points and the 

‘‘temporary adjustment factor’’—had been in 

effect in 2000–2003, and the GSEs’ had 

purchased the same mortgages that they 

actually did purchase in both years, then 

Fannie Mae’s performance would have been 

21.4 percent in 2000, 20.2 percent in 2001, 

19.9 percent in 2002, and 19.4 percent in 

2003. Freddie Mac’s performance would have 

been 21.0 percent in 2000, 19.3 percent in 

2001, 18.1 percent in 2002, and 17.8 percent 

in 2003. Fannie Mae would have surpassed 

the special affordable goal in both 2000 and 

2001, but not in 2002 or 2003. Freddie Mac 

would have surpassed the goal in 2000 but 

fallen short in 2001–03. 

The above performance figures are for the 

special affordable goal defined in terms of 

1990 Census geography. Switching to 2000 

Census data slightly increases the coverage of 

special affordable goal, which increases the 

special affordable share of the GSEs’

purchases by up to one percentage point. 

Based on 2000 Census geography and adding 

2003 MSAs, and excluding counting 

requirements (a) and (b), then Fannie Mae’s 

performance would have been 21.7 percent 

in 2000, 20.1 percent in 2001, 19.4 percent 

in 2002, and 20.8 percent in 2003. Freddie 

Mac’s performance would have been 20.8 

percent in 2000, 19.1 percent in 2001, 17.3 

percent in 2002 and 19.0 percent in 2003. See 

Table C.3. 

b. Single-Family Market Comparisons in 

Metropolitan Areas 

The Special Affordable Housing Goal is 

designed, in part, to ensure that the GSEs 

maintain a consistent focus on serving the 

very low-income portion of the housing 

market where housing needs are greatest. 

Section C compared the GSEs’ performance 

in special affordable lending to the 

performance of depositories and other 

lenders in the conventional conforming 

market for single-family home loans. The 

analysis showed that while both GSEs have 

been improved their performance, their past 

average performance (1993–2003, 1996–2003, 

and 1999–2003) has been below market 

levels. During 2002 and 2003, Fannie Mae 

improved its performance enough to lead the 

special affordable market for home purchase 

loans, but Freddie Mac, although it also 

improved its performance during this recent 

period, continues to lag behind the primary 

market. Between 1999 and 2003, special 

affordable borrowers accounted for 15.1 

percent of the home loans purchased by 

Fannie Mae, 14.7 percent of Freddie Mac’s 

purchases, 16.2 percent of home loans 

originated by depositories, and 16.2 percent 

of all home loans originated in the 

conventional conforming market (without 

B&C loans). As noted above, while both GSEs 

have improved their performance over the 

past few years, Fannie Mae has made more 

progress than Freddie Mac in closing its gap 

with the market. During 2003, the share of 

Fannie Mae’s purchases going to special 

affordable loans was 17.1 percent, which was 

1.2 percentage points above the market 

average of 15.9 percent. The share of Freddie 

Mac’s purchases going to special affordable 

loans had improved to 15.6 percent by 2003. 

(See Figure C.3.)

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63816 Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 

3. Ability To Lead the Single-Family Owner 

Market: A Special Affordable Subgoal 

The Secretary believes the GSEs can play 

a leadership role in the special affordable 

market. Thus, the Department is establishing 

a subgoal of 17 percent for each GSE’s 

purchases of home purchase loans for special 

affordable families in the single-family-owner 

market of metropolitan areas for 2005 and 

2006, rising to 18 percent during 2007 and 

2008. The purpose of this subgoal is to 

encourage the GSEs to improve their 

purchases of mortgages for very-low-income 

and minority first-time homebuyers who are 

expected to enter the housing market over the 

next few years. If the GSEs meet the 18-

percent subgoal, they will be leading the 

primary market by approximately two 

percentage points, based on the income 

characteristics of home purchase loans 

reported in HMDA. HMDA data show that 

special affordable families accounted for an 

average of 16.2 (15.9) percent of singlefamily-owner loans originated in the 

conventional conforming market of 

metropolitan areas between 1999 and 2003 

(2001 and 2003). Loans in the B&C portion 

of the subprime market are not included in 

these averages. As explained in Appendix D, 

HUD also projected special affordable shares 

for the market for 1999 to 2002 using the new 

2000 Census geography and the new OMB 

specifications. For special affordable loans, 

the 2000-based Census data resulted in 

special affordable shares for the market and 

the GSEs that were similar to the 1990-based 

special affordable shares reported in Section 

C of this appendix. 

To reach the 18-percent subgoal for 2008, 

Freddie Mac would have to improve its 

performance by 2.4 percentage points over its 

special affordable share of 15.6 percent in 

2003. Fannie Mae would have to improve its 

performance by 0.9 percentage point over its 

market-leading special affordable share of 

17.1 percent in 2003. The approach taken is 

for the GSEs to obtain their leadership 

position by staged increases in the special 

affordable subgoal; this will enable the GSEs 

to take new initiatives in a correspondingly 

staged manner to achieve the new subgoal 

each year. Thus, the increases in the special 

affordable subgoal are sequenced so that the 

GSEs can gain experience as they improve 

and move toward the new higher subgoal 

targets. 

The subgoal applies only to the GSEs’

purchases in metropolitan areas because the 

HMDA-based market benchmark is only 

available for metropolitan areas. HMDA data 

for non-metropolitan counties are not reliable 

enough to serve as a market benchmark. The 

Department is also setting home purchase 

subgoals for the other two goals-qualifying 

categories, as explained in Appendices A and 

B. Sections E.9 and G of Appendix A provide 

additional information on the opportunities 

for an enhanced GSE role in the special 

affordable segment of the home purchase 

market and on the ability of the GSEs to lead 

that market. 

The preamble and Appendix A discuss in 

some detail the factors that the Department 

considered when setting the subgoal for lowand moderate-income loans. Several of the 

considerations were general in nature—for 

example, related to the GSEs’ overall ability 

to lead the single-family-owner market—

while others were specific to the low-mod 

subgoal. Because the reader can refer to 

Appendix A, this appendix provides a briefer 

discussion of the more general factors. The 

specific considerations that led to the subgoal 

for special affordable loans can be organized 

around the following four topics: 

(1) The GSEs have the ability to lead the 

market. As discussed in Appendix A, the 

GSEs have the ability to lead the primary 

market for single-family-owner loans, which 

is their ‘‘bread-and-butter’’ business. Both 

GSEs have been dominant players in the 

home purchase market for years, funding 61 

percent of the single-family-owner mortgages 

financed between 1999 and 2002. Through 

their many new product offerings and their 

various partnership initiatives, the GSEs have 

shown that they have the capacity to reach 

out to very-low-income and other special 

affordable borrowers. They also have the staff 

expertise and financial resources to make the 

extra effort to lead the primary market in 

funding single-family-owner mortgages for 

special affordable borrowers. 

(b) GSEs’ Performance Relative to the 

Market. Even though the GSEs have had the 

ability to lead the home purchase market, 

their past average performance (1993–2003, 

1996–2003, and 1999–2003) has been below 

market levels. During 2003, Fannie Mae 

improved its performance enough to lead the 

special affordable market for home purchase 

loans, but Freddie Mac, although it also has 

improved its performance, continues to lag 

behind the primary market. The subgoals will 

ensure that Fannie Mae maintains and 

further improves its above-market 

performance and that Freddie Mac not only 

erases its current gap with the market but 

also takes a leadership position as well. With 

respect to the GSEs’ historical performance, 

special affordable mortgages accounted for 

13.2 (14.7) percent of Freddie Mac’s 

purchases during 1996–2003 (1999–2003), for 

14.1 (15.1) percent of Fannie Mae’s 

purchases, and for 15.9 (16.2) percent of 

primary market originations (excluding B&C 

loans). The type of improvement needed for 

Freddie Mac to meet this new special 

affordable subgoal was demonstrated by 

Fannie Mae during 2001–2003, as Fannie 

Mae increased its special affordable 

performance from 14.9 percent of its singlefamily-owner business in 2001 to 16.3 

percent in 2002 to 17.1 percent in 2003. 

(3) Disparities in Homeownership and 

Credit Access Remain. There remain 

troublesome disparities in our housing and 

mortgage markets, even after the ‘‘revolution 

in affordable lending’’ and the growth in 

homeownership that has taken place since 

the mid-1990s. The homeownership rate for 

African-American and Hispanic households 

remains 25 percentage points below that of 

white households. Minority families face 

many barriers in the mortgage market, such 

as lack of capital for down payment and lack 

of access to mainstream lenders (see above). 

Immigrants and minorities—many of whose 

very-low-income levels will qualify them as 

special affordable—are projected to account 

for almost two-thirds of the growth in the 

number of new households over the next ten 

years. As emphasized in Appendix A, 

changing population demographics will 

result in a need for the primary and 

secondary mortgage markets to meet 

nontraditional credit needs, respond to 

diverse housing preferences, and overcome 

information and other barriers that many 

immigrants and minorities face. The GSEs 

have to increase their efforts in helping 

special affordable families—but so far they 

have played a surprisingly small role in 

serving minority first-time homebuyers. It is 

estimated that the GSEs accounted for 46.5 

percent of all (both government and 

conventional) home loans originated between 

1999 and 2001; however, they accounted for 

only 14.3 percent of home loans originated 

for African-American and Hispanic first-time 

homebuyers. A subgoal for special affordable 

home purchase loans should increase the 

GSEs’ efforts in important sub-markets such 

as the one for minority first-time 

homebuyers. 

(4) There are ample opportunities for the 

GSEs to improve their performance. Special 

affordable mortgages are available for the 

GSEs to purchase, which means they can 

improve their performance and lead the 

primary market in purchasing loans for these 

very-low-income borrowers. Sections B, C, 

and I of Appendix A and Section H of 

Appendix D explain that the special 

affordable lending market has shown an 

underlying strength over the past few years 

that is unlikely to vanish (without a 

significant increase in interest rates or a 

decline in the economy). The special 

affordable share of the home purchase market 

has averaged approximately 16 percent since 

1996 and annually has been in the 15–17 

percent range. Second, the market share data 

reported in Table A.30 of Appendix A 

demonstrate that there are newly originated 

loans available each year for the GSEs to 

purchase. The GSEs’ purchases of singlefamily owner loans represented 61 percent of 

all single-family-owner loans originated 

between 1999 and 2002, compared with 52 

percent of the special affordable loans that 

were originated during this period. Thus, half 

of the special affordable conforming market 

is not touched by the GSEs. As noted above, 

the situation is even more extreme for special 

sub-markets such the minority first-time 

homebuyer market where the GSEs have only 

a minimal presence. Between 1999 and 2001, 

the GSEs purchased only 33 percent of 

conventional conforming loans originated for 

minority first-time homebuyers, even though 

they purchased 57 percent of all home loans 

originated in the conventional conforming 

market during that period. But also 

important, the GSEs’ purchases under the 

subgoal are not limited to new mortgages that 

are originated in the current calendar year. 

The GSEs can purchase loans from the 

substantial, existing stock of special 

affordable loans held in lenders’ portfolios, 

after these loans have seasoned and the GSEs 

have had the opportunity to observe their 

payment performance. In fact, based on 

Fannie Mae’s recent experience, the purchase 

of seasoned loans appears to be one useful 

strategy for purchasing goals-qualifying 

loans. 

For the reasons given above, the Secretary 

believes that the GSEs can do more to raise 

Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 63817

the special affordable shares of the home 

loans they purchase on single-family-owner 

properties. This can be accomplished by 

building on efforts that the enterprises have 

already started, including their new 

affordable lending products aimed at special 

groups such as first-time homebuyers, their 

many partnership efforts, their outreach to 

inner city neighborhoods, their incorporation 

of greater flexibility into their underwriting 

guidelines, and their purchases of seasoned 

CRA loans. A wide variety of quantitative 

and qualitative indicators indicate that the 

GSEs’ have the resources and financial 

strength to improve their special affordable 

performance enough to lead the market.

4. Size of the Overall Special Affordable 

Mortgage Market 

As detailed in Appendix D, single-family 

and multifamily special affordable mortgages 

are estimated to account for 23–27 percent of 

the dwelling units financed by conventional 

conforming mortgages; in estimating the size 

of the market, HUD used alternative 

assumptions about future economic and 

market affordability conditions that were less 

favorable than those that existed over the 

past several years. HUD is well aware of the 

volatility of mortgage markets and the 

possible impacts on the GSEs’ ability to meet 

the housing goals. Should conditions change 

such that the goals are no longer reasonable 

or feasible, the Secretary has the authority to 

revise the goals. 

5. The Special Affordable Housing Goal for 

2005–2008 

The Special Affordable Housing Goal for 

2005 is 22 percent of eligible purchases, a 

two percentage point increase over the 

current goal of 20 percent, with the goal 

rising to 23 percent in 2006, 25 percent in 

2007, and 27 percent in 2008. The bonus 

points for small multifamily properties and 

owner-occupied 2–4 unit properties, as well 

as Freddie Mac’s Temporary Adjustment 

Factor, will no longer be in effect for goal 

counting purposes. It is recognized that 

neither GSE would have met the 22-percent 

target for 2005 in the past three years. Under 

the new counting rules, Fannie Mae’s special 

affordable performance is estimated to have 

been 18.6 percent in 1999, 21.7 percent in 

2000, 20.1 percent in 2001, 19.4 percent in 

2002, and 20.8 percent in 2003. Fannie Mae 

would have to increase its performance in 

2005 by 1.9 percentage points over its 

average (unweighted) performance of 20.1 

percent over these last five years. By 2008 

this increase relative to average 1999–2003 

performance would be 6.9 percentage points. 

Freddie Mac’s performance is projected to 

have been 17.4 percent in 1999, 20.8 percent 

in 2000, 19.1 percent in 2001, 17.3 percent 

in 2002, and 19.0 percent in 2003. Freddie 

Mac would have to increase its performance 

in 2005 by 3.3 percentage points over its 

average (unweighted) performance of 18.7 

percent over these last five years. By 2008 

this increase relative to average 1999–2002 

performance would be 8.3 percentage points. 

However, GSE goal performance in 2001–03 

was reduced by the heavy refinance wave of 

this period. 

The objective of HUD’s Special Affordable 

Goal is to bring the GSEs’ performance to the 

upper end of HUD’s market range estimate 

for this goal (23–27 percent), consistent with 

the statutory criterion that HUD should 

consider the GSEs’ ability to lead the market 

for each Goal. To enable the GSEs to achieve 

this leadership, the Department is 

establishing modest increases in the Special 

Affordable Goal for 2005, which will increase 

year-by-year through 2008, to achieve the 

ultimate objective for the GSEs to lead the 

market under a range of foreseeable economic 

circumstances by 2008. Such a program of 

staged increases is consistent with the 

statutory requirement that HUD consider the 

past performance of the GSEs in setting the 

Goals. Staged annual increases in the Special 

Affordable Goal will provide the enterprises 

with opportunity to adjust their business 

models and prudently try out business 

strategies, so as to meet the required 2008 

level without compromising other business 

objectives and requirements. 

Section C compared the GSEs’ role in the 

overall market with their role in the special 

affordable market. The GSEs’ purchases 

provided financing for 26,118,927 dwelling 

units, which represented 55 percent of the 

47,551,039 single-family and multifamily 

units that were financed in the conventional 

conforming market between 1999 and 2002. 

However, in the special affordable part of the 

market, the 5,103,186 units that were 

financed by GSE purchases represented only 

41 percent of the 12,413,759 dwelling units 

that were financed in the market. Thus, there 

appears to be ample room for the GSEs to 

improve their performance in the special 

affordable market. In addition, there are 

several market segments (e.g., first-time 

homebuyers) that would benefit from a 

greater secondary market role by the GSEs, 

and special affordable borrowers are 

concentrated in these markets. 

6. Multifamily Special Affordable Subgoals 

Based on the GSEs’ past performance on 

the special affordable multifamily subgoals, 

and on the outlook for the multifamily 

mortgage market, HUD is establishing that 

these subgoals be retained and increased for 

the 2005–2008 period. Unlike the overall 

goals, which are expressed in terms of 

minimum goal-qualifying percentages of total 

units financed, these subgoals for 2001–03 

and in prior years have been expressed in 

terms of minimum dollar volumes of goalqualifying multifamily mortgage purchases. 

Specifically, each GSE’s special affordable 

multifamily subgoal is currently equal to 1.0 

percent of its average total (single-family plus 

multifamily) mortgage volume over the 1997–

99 period. Under this formulation, in October 

2000 the subgoals were set at $2.85 billion 

per year for Fannie Mae and $2.11 billion per 

year for Freddie Mac, in each of calendar 

years 2001 through 2003. These represented 

increases from the goals for 1996–2000, 

which were $1.29 billion annually for Fannie 

Mae and $0.99 billion annually for Freddie 

Mac. These subgoals are also in effect for 

2004. 

HUD’s Determination. The multifamily 

mortgage market and both GSEs’ multifamily 

transactions volume grew significantly over 

the 1993–2003 period, indicating that both 

enterprises have provided increasing support 

for the multifamily market, and that they 

have the ability to continue to provide 

further support for the market.

Specifically, Fannie Mae’s total eligible 

multifamily mortgage purchase volume 

increased from $4.6 billion in 1993 to $12.5 

billion in 1998, and then jumped sharply to 

$18.7 billion in 2001 and $18.3 billion in 

2002, and $33.3 billion in 2003. Its special 

affordable multifamily mortgage purchases 

followed a similar path, rising from $1.7 

billion in 1993 to $3.5 billion in 1998 and 

$4.1 billion in 1999, and also jumping 

sharply to $7.4 billion in 2001 and $7.6 

billion in 2002 and $12.2 billion in 2003. As 

a result of its strong performance, Fannie 

Mae’s purchases have been at least twice its 

minimum subgoal in every year since 1997—

247 percent of the subgoal in that year, 274 

percent in 1998, 315 percent in 1999, 294 

percent in 2000, and, under the new higher 

subgoal level, 258 percent in 2001, 266 

percent in 2002, and 426 percent in 2003. 

Freddie Mac’s total eligible multifamily 

mortgage purchase volume increased even 

more sharply, from $0.2 billion in 1993 to 

$6.6 billion in 1998, and then jumped 

sharply in 2001 to $11.8 billion and $13.3 

billion in 2002, and $21.5 billion in 2003. Its 

special affordable multifamily mortgage 

purchases followed a similar path, rising 

from $0.1 billion in 1993 to $2.7 billion in 

1998, and also jumping sharply to $4.6 

billion in 2001 and $5.2 billion in 2002, and 

$8.8 billion in 2003. As a result of its strong 

performance, Freddie Mac’s purchases have 

also been at least twice its minimum subgoal 

in every year since 1998—272 percent of the 

subgoal in that year, 229 percent in 1999, 243 

percent in 2000, and, under the new higher 

subgoal level, 220 percent in 2001, 247 

percent in 2002, and 417 percent in 2003. 

The Special Affordable Housing 

Multifamily Subgoals set forth in this rule are 

reasonable and appropriate based on the 

Department’s analysis of this market. The 

Department’s decision to retain the 

multifamily subgoal is based on the fact that 

HUD’s analysis indicates that multifamily 

housing still serves the housing needs of 

lower-income families and families in lowincome areas to a greater extent than singlefamily housing. By retaining the multifamily 

subgoal, the Department ensures that the 

GSEs continue their activity in this market, 

and that they achieve at least a minimum 

level of special affordable multifamily 

mortgage purchases that are affordable to 

lower-income families. The Department 

establishes each GSE’s special affordable 

multifamily subgoal as 1.0 percent of its 

average annual dollar volume of total (singlefamily and multifamily) mortgage purchases 

over the 2000–2002 period. In dollar terms, 

the Department’s subgoal is $5.49 billion per 

year in special affordable multifamily 

mortgage purchases for Fannie Mae, and 

$3.92 billion per year in special affordable 

multifamily mortgage purchases for Freddie 

Mac. These subgoals would be less than 

actual special affordable multifamily 

mortgage purchase volume in 2001–2003 for 

both GSEs; thus the Department believes that 

they would be feasible for the 2005–2008 

period. 

Some commenters advocated increasing 

the special affordable multifamily subgoals 

63818 Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 

1 Dixie M. Blackley and James R. Follain, ‘‘A 

Critique of the Methodology Used to Determine 

Affordable Housing Goals for the Government 

Sponsored Housing Enterprises,’’ unpublished 

report prepared for Office of Policy Development 

and Research, Department of Housing and Urban 

Development, October 1995; and ‘‘HUD’s Market 

Share Methodology and its Housing Goals for the 

Government Sponsored Enterprises,’’ unpublished 

paper, March 1996. 2See Freddie Mac, ‘‘Comments on Estimating the 

Size of the Conventional Conforming Market for 

Each Housing Goal: Appendix III to the Comments 

of the Federal Home Loan Mortgage Corporation on 

HUD’s Regulation of the Federal National Mortgage 

Association (Fannie Mae) and the Federal Home 

Loan Mortgage Corporation (Freddie Mac)’’, May 8, 

2000, page 1.

3See Fannie Mae, ‘‘Fannie Mae’s Comments on 

HUD’s Regulation of the Federal National Mortgage 

Association (Fannie Mae) and the Federal Home 

Loan Mortgage Corporation (Freddie Mac)’’, May 8, 

2000, page 53.

4See Freddie Mac, ‘‘Comments of the Federal 

Home Loan Mortgage Corporation on HUD’s 

Proposed Housing Goals for the Federal National 

Mortgage Association (Fannie Mae) and the Federal 

Home Loan Mortgage Corporation (Freddie Mac) for 

the Years 2005–2008 and Amendments to HUD’s 

Regulation of Fannie Mae and Freddie Mac,’’ July 

16, 2004; and Fannie Mae, ‘‘Fannie Mae’s 

Comments on HUD’s Proposed Housing Goals for 

the Federal National Mortgage Association (Fannie 

Mae) and the Federal Home Loan Mortgage 

Corporation (Freddie Mac) for the Years 2005–2008 

and Amendments to HUD’s Regulation of Fannie 

Mae and Freddie Mac,’’ July 16, 2004.

5Readers not interested in this overview may 

want to proceed to Section C, which begins the 

market analysis by examining the size of the 

multifamily market.

6Sections 1332(b)(4), 1333(a)(2), and 1334(b)(4).

from the levels in the rule. In light of the high 

levels of such purchases by both GSEs in 

2003, HUD considered raising these subgoals, 

but decided not to do so because HUD 

believes that the overall special affordable 

goals established in this final rule will 

provide sufficient incentives for the GSEs to 

play a major role in the special affordable 

multifamily mortgage market, and that in all 

likelihood they will continue to exceed these 

subgoals by significant margins for 2005–08. 

7. Conclusion 

HUD has determined that the Special 

Affordable Housing Goal in this rule 

addresses national housing needs within the 

income categories specified for this goal, 

while accounting for the GSEs’ past 

performance in purchasing mortgages 

meeting the needs of very-low-income 

families and low-income families in lowincome areas. HUD has also considered the 

size of the conventional mortgage market 

serving very-low-income families and lowincome families in low-income areas. 

Moreover, HUD has considered the GSEs’

ability to lead the industry as well as their 

financial condition. HUD has determined 

that a Special Affordable Housing Goal of 22 

percent in 2005, 23 percent in 2006, 25 

percent in 2007, and 27 percent in 2008 is 

both necessary and achievable. HUD has also 

determined that a multifamily special 

affordable subgoal for 2005–2008 set at 1.0 

percent of the average of each GSE’s 

respective dollar volume of combined 

(single-family and multifamily) 1999–2002 

mortgage purchases in is both necessary and 

achievable. Finally, HUD is establishing a 

subgoal of 17 percent for the GSEs’ purchases 

of single-family-owner mortgages that qualify 

for the special affordable goal and are 

originated in metropolitan areas, for 2005, 

with this subgoal remaining at 17 percent in 

2006, then rising to 18 percent in both 2007 

and 2008. The Secretary has considered the 

GSEs’ ability to lead the industry as well as 

the GSEs’ financial condition. The Secretary 

has determined that the goals, the 

multifamily subgoals, and the single-familyowner subgoals are necessary and 

appropriate.

Appendix D—Estimating the Size of the 

Conventional Conforming Market for 

Each Housing Goal 

A. Introduction 

In establishing the three housing goals, the 

Secretary is required to assess, among a 

number of factors, the size of the 

conventional market for each goal. This 

appendix explains HUD’s methodology for 

estimating the size of the conventional 

market for each of the three housing goals. 

Following this overview, Section B 

summarizes the main components of HUD’s 

market-share model and identifies those 

parameters that have a large effect on the 

relative market shares. Sections C and D 

discuss two particularly important market 

parameters, the size of the multifamily 

market and the share of the single-family 

mortgage market accounted for by singlefamily rental properties. Section E provides 

a more systematic presentation of the model’s 

equations and main assumptions. Sections F, 

G, and H report HUD’s estimates for the Lowand Moderate-Income Goal, the Underserved 

Areas Goal, and the Special Affordable 

Housing Goal, respectively. 

HUD received numerous comments on the 

proposed rule relating to its market 

methodology and the size of its market ranges 

for each of the three goals. These comments, 

and HUD’s responses to them, are discussed 

throughout this appendix. 

In developing this final rule, HUD has 

followed the same basic approach that it 

followed in the last two GSE final rules and 

the recent GSE proposed rule. HUD has 

carefully reviewed existing information on 

mortgage activity in order to understand the 

weakness of various data sources and has 

conducted sensitivity analyses to show the 

effects of alternative parameter assumptions. 

HUD is well aware of uncertainties with 

some of the data and much of this appendix 

is spent discussing the effects of alternative 

assumptions about data parameters and 

presenting the results of an extensive set of 

sensitivity analyses, many of the latter being 

directly related to comments received on the 

proposed rule. 

In an earlier critique of HUD’s market share 

model, Blackley and Follain (1995, 1996) 

concluded that conceptually HUD had 

chosen a reasonable approach to determining 

the size of the mortgage market that qualifies 

for each of the three housing goals.1 Blackley 

and Follain correctly note that the challenge 

lies in getting accurate estimates of the 

model’s parameters. In their comments on 

the 2000 Proposed GSE Rule, both Fannie 

Mae and Freddie Mac stated that HUD’s 

market share model (outlined in Section B 

below) was a reasonable approach for 

estimating the goals-qualifying (low-mod, 

special affordable, and underserved areas) 

shares of the mortgage market. Freddie Mac 

stated:

We believe the Department takes the correct 

approach in the Final rule by examining 

several different data sets, using alternative 

methodologies, and conducting sensitivity 

analysis. We applaud the Department’s 

general approach for addressing the 

empirical challenges.2

***

Similarly, Fannie Mae stated that ‘‘HUD has 

developed a reasonable model for assessing 

the size of the affordable housing market.’’ 3

However, in their comments on the 

proposed rule, both GSEs criticized HUD’s 

implementation of its market methodology.4

As noted above, their major criticisms and 

HUD’s responses to their criticisms can be 

found throughout this appendix. HUD 

recognizes that there is no single, perfect data 

set for estimating the size of the affordable 

lending market and that available data bases 

on different sectors of the market must be 

combined in order to implement its market 

share model (as outlined in Section B below). 

As this appendix will show, HUD has 

carefully combined various mortgage market 

data bases in a manner which draws on the 

strength of each in order to implement its 

market methodology and to arrive at a 

reasonable range of estimates for the three 

goals-qualifying shares of the mortgage 

market. In this appendix, HUD demonstrates 

the robustness of its market estimates by 

reporting the results of numerous sensitivity 

analyses that examine a range of assumptions 

about the relative importance of the rental 

and owner markets and the goals-qualifying 

shares of the owner portion of the mortgage 

market.

This appendix reviews in some detail 

HUD’s efforts to combine information from 

several mortgage market databases to obtain 

reasonable values for the model’s parameters. 

The next section provides an overview of 

HUD’s market share model.

B. Overview of HUD’s Market Share 

Methodology 5

1. Definition of Market Share 

The size of the market for each housing 

goal is one of the factors that the Secretary 

is required to consider when setting the level 

of each housing goal.6 Using the Low- and 

Moderate-Income Housing Goal as an 

example, the market share in a particular 

year is defined as follows:

Low- and Moderate-Income Share of 

Market: The number of dwelling units 

financed by the primary mortgage market in 

a particular calendar year that are occupied 

by (or affordable to, in the case of rental 

units) families with incomes equal to or less 

than the area median income divided by the 

total number of dwelling units financed in 

the conforming conventional primary 

mortgage market. 

There are three important aspects to this 

definition. First, the market is defined in 

terms of ‘‘dwelling units’’ rather than, for 

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7So-called ‘‘jumbo’’ mortgages, greater than 

$333,700 in 2004 for 1-unit properties, are excluded 

in defining the conforming market. There is some 

overlap of loans eligible for purchase by the GSEs 

with loans insured by the FHA and guaranteed by 

the Veterans Administration.

8The owner of the SF 2–4 property is counted in 

(a).

9Property types (b), (c), and (d) consist of rental 

units. Property types (b) and (c) must sometimes be 

combined due to data limitations; in this case, they 

are referred to as ‘‘single-family rental units’’ (SF–

R units).

10The property shares and low-mod percentages 

reported here are based on one set of model 

assumptions; other sets of assumptions are 

discussed in Section E.

example, ‘‘value of mortgages’’ or ‘‘number of 

properties.’’ Second, the units are ‘‘financed’’

units rather than the entire stock of all 

mortgaged dwelling units; that is, the marketshare concept is based on the mortgage flow 

in a particular year, which will be smaller 

than total outstanding mortgage debt. Third, 

the low- and moderate-income market is 

expressed relative to the overall conforming 

conventional market, which is the relevant 

market for the GSEs.7 The low- and 

moderate-income market is defined as a 

percentage of the conforming market; this 

percentage approach maintains consistency 

with the method for computing each GSE’s 

performance under the Low- and ModerateIncome Goal (that is, the number of low- and 

moderate-income dwelling units financed by 

GSE mortgage purchases relative to the 

overall number of dwelling units financed by 

GSE mortgage purchases).

2. Three-Step Procedure 

Ideally, computing the low- and moderateincome market share would be 

straightforward, consisting of three steps: 

Step 1: Projecting the market shares of the 

four major property types included in the 

conventional conforming mortgage market, 

i.e.—

(a) Single-family owner-occupied dwelling 

units (SF–O units); 

(b) Rental units in 2–4 unit properties 

where the owner occupies one unit (SF 2–4 

units); 8

(c) Rental units in one-to-four unit 

investor-owned properties (SF Investor 

units); and, 

(d) Rental units in multifamily (5 or more 

units) properties (MF units).9

Step 2: Projecting the ‘‘goal percentage’’ for 

each of the above four property types (for 

example, the ‘‘Low- and Moderate-Income 

Goal percentage for single-family owneroccupied properties’’ is the percentage of 

those dwelling units financed by mortgages 

in a particular year that are occupied by 

households with incomes below the area 

median). 

Step 3: Multiplying the four percentages in 

(2) by their corresponding market shares in 

(1), and summing the results to arrive at an 

estimate of the overall share of dwelling units 

financed by mortgages that are occupied by 

low- and moderate-income families. 

The four property types are analyzed 

separately because of their differences in 

low- and moderate-income occupancy. 

Rental properties have substantially higher 

percentages of low- and moderate-income 

occupants than owner-occupied properties. 

This can be seen in the top portion of Table 

D.1, which illustrates Step 3’s basic formula 

for calculating the size of the low- and 

moderate-income market.10 In this example, 

low- and moderate-income dwelling units are 

estimated to account for 53.9 percent of the 

total number of dwelling units financed in 

the conforming mortgage market.

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11This goal will be referred to as the 

‘‘Underserved Areas Goal’’.

12The example in Table D.1 is based on 1990 

Census tract geography. As explained in Section G, 

switching to 2000 Census tract geography 

(scheduled for 2005) increases the underserved 

areas market share by approximately five 

percentage points.

To examine the other housing goals, the 

‘‘goal percentages’’ in Step 2 would be 

changed and the new ‘‘goal percentages’’

would be multiplied by Step 1’s property 

distribution, which remains constant. For 

example, the Underserved Areas Goal 11

would be derived as illustrated in the bottom 

portion of Table D.1. In this example, units 

eligible under the Underserved Areas Goal 

are estimated to account for 31.4 percent of 

the total number of dwelling units financed 

in the conforming mortgage market.12

3. Data Issues 

Unfortunately, complete and consistent 

mortgage data are not readily available for 

carrying out the above three steps. A single 

data set for calculating either the property 

shares or the housing goal percentages does 

not exist. However, there are several major 

data bases that provide a wealth of useful 

information on the mortgage market. HUD 

combined information from the following 

sources: the Home Mortgage Disclosure Act 

(HMDA) reports, the American Housing 

Survey (AHS), HUD’s Survey of Mortgage 

Lending Activity (SMLA), the Census 

Bureau’s AHS-based Property Owners and 

Managers Survey (POMS), and the Census 

Bureau’s recent 2001 Residential Finance 

Survey (RFS). In addition, information on the 

mortgage market was obtained from the 

Mortgage Bankers Association, Fannie Mae, 

Freddie Mac and other organizations. 

Property Shares. To derive the property 

shares, HUD started with forecasts of singlefamily mortgage originations (expressed in 

dollars). These forecasts, which are available 

from the GSEs and industry groups such as 

the Mortgage Bankers Association, do not 

provide information on conforming 

mortgages, on owner versus renter mortgages, 

or on the number of units financed. Thus, to 

estimate the number of single-family units 

financed in the conforming conventional 

market, HUD had to project certain market 

parameters based on its judgment about the 

reliability of different data sources. Sections 

D and E report HUD’s findings related to the 

single-family market. 

Total market originations are obtained by 

adding multifamily originations to the singlefamily estimate. Because of the wide range of 

estimates available, the size of the 

multifamily mortgage market turned out to be 

one of the most controversial issues raised 

during the initial rule-making process during 

1995; this was also an issue that the GSEs 

focused on in their comments on the 2000 

final rule and their comments on the 2004 

proposed GSE rule. Because most renters 

qualify under the Low- and Moderate-Income 

Goal, the chosen market size for multifamily 

can have a substantial effect on the overall 

estimate of the low- and moderate-income 

market (as well as on the estimate of the 

special affordable market). Thus, it is 

important to consider estimates of the size of 

the multifamily market in some detail, as 


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13This section is based on analysis by Jack 

Goodman under contract with the Urban Institute.

Section C does. In addition, given the 

uncertainty surrounding estimates of the 

multifamily mortgage market, it is important 

to consider a range of market estimates, as 

Sections F–H do. 

Goal Percentages. To derive the goal 

percentages for each property type, HUD 

relied heavily on HMDA, AHS, POMS and 

RFS data. For single-family-owner 

originations, HMDA provides comprehensive 

information on borrower incomes and census 

tract locations for metropolitan areas. 

Unfortunately, it provides no information on 

the incomes of renters living in mortgaged 

properties (either single-family or 

multifamily) or on the rents (and therefore 

the affordability) of rental units in mortgaged 

properties. The AHS, however, does provide 

a wealth of information on rents and the 

affordability of the outstanding stock of 

single-family and multifamily rental 

properties. An important issue here concerns 

whether rent data for the stock of rental 

properties can serve as a proxy for rents on 

newly-mortgaged rental properties. During 

the 2000 rule-making process, POMS data 

were used to examine the rents of newlymortgaged rental properties; thus, the POMS 

data supplements the AHS data. The recently 

released RFS provides information on 

property shares (e.g., the relative importance 

of rental versus owner properties) and several 

other important parameters in HUD’s market 

model. The data base issues as well as other 

technical issues related to the goal 

percentages (such as the need to consider a 

range of mortgage market environments) are 

discussed in Sections F, G, and H, which 

present the market share estimates for the 

Low- and Moderate-Income Goal, the 

Underserved Areas Goal, and the Special 

Affordable Goal, respectively. 

4. Conclusions 

HUD is using the same basic methodology 

for estimating market shares that it used in 

its 1995 and 2000 final rules and its 2004 

proposed rule. As demonstrated in the 

remainder of this appendix, HUD has 

attempted to reduce the range of uncertainty 

around its market estimates by carefully 

reviewing all known major mortgage data 

sources, by considering comments on the 

2004 proposed rule, and by conducting 

numerous sensitivity analyses to show the 

effects of alternative assumptions. Sections C, 

D, and E report findings related to the 

property share distributions called for in Step 

1, while Sections F, G, and H report findings 

related to the goal-specific market parameters 

called for in Step 2. These latter sections also 

report the overall market estimates for each 

housing goal calculated in Step 3.

In considering the levels of the goals, HUD 

carefully examined comments by the GSEs 

and others on the methodology used to 

establish the market share for each of the 

goals. Based on that thorough evaluation, as 

well as HUD’s additional analysis for this 

final rule, HUD concludes that its basic 

methodology is a reasonable and valid 

approach to estimating market shares. As in 

the past, HUD recognizes the uncertainty 

regarding some of these estimates, which has 

led the Department to undertake a number of 

sensitivity and other analyses to reduce this 

uncertainty and also to provide a range of 

market estimates (rather than precise point 

estimates) for each of the housing goals. 

C. Size of the Conventional Multifamily 

Mortgage Market 13

This Section C differs from the version 

published in the May 3, 2004, Proposed Rule 

in the following ways: The estimates from the 

‘‘HUD New’’ and ‘‘Flow of Funds’’ methods 

discussed below in parts 2 and 3 have been 

updated through 2003, and responses to 

comments received on those methods have 

been added to those sections. The part titled 

‘‘Most Likely Range’’ has been revised in 

light of the 2003 estimates and comments 

received. The discussion of ‘‘Loan Amount 

per Unit,’’ part 5, has been revised in 

response to comments and to newly available 

data from the GSEs and the 2003 American 

Housing Survey. The multifamily mix 

discussion, part 6, has been revised in 

accordance with other changes. Section C.7 

has been added on the multifamily mix as 

estimated from the newly released 2001 

Residential Finance Survey (RFS). Lastly, 

Section C.8 discusses the multifamily mixes 

that will be examined in HUD’s projection 

model for 2005–2008. Other than these 

changes and minor editorial corrections, the 

text in this section is identical to that in the 

Proposed Rule published May 3, 2004. 

Changes to Tables D.2 through D.5 are noted 

in the text and table notes. The old Table D.5 

is now D.5a and Tables D.5b and D.5c have 

been added.

This section provides estimates of (a) the 

annual dollar volume of conventional 

multifamily mortgage originations and (b) the 

annual average loan amount per unit 

financed. The estimates build on research 

reported in the Final Rule on HUD’s 

Regulation of Fannie Mae and Freddie Mac 

as published in the Federal Register on 

October 31, 2000, especially in Appendix D. 

That material from the 2000 Rule will not be 

repeated here but will be referenced or 

summarized where appropriate. 

This section uses the information on dollar 

volume of multifamily originations and 

average loan amounts to estimate the number 

of multifamily units financed each year as a 

percentage share of the total (both singlefamily and multifamily) number of dwelling 

units financed each year. This percentage 

share, called the ‘‘multifamily mix’’, is an 

important parameter in HUD’s projection 

model of the mortgage market for 2005–08 

(see Section C.8 below) 

Estimating this ‘‘multifamily mix’’ is 

important because relative to its share of the 

overall housing market, the multifamily 

rental sector has disproportionate importance 

for the housing goals established for Fannie 

Mae and Freddie Mac. This is because most 

multifamily rental units are occupied by 

households with low or moderate incomes. 

Between 1999 and 2002, for example, the 

GSEs purchased mortgages on approximately 

26.1 million housing units, of which only 9.5 

percent were multifamily rental units. 

However, of the GSEs’ purchases qualifying 

as mortgages on low- and moderate-income 

housing during this period, 18 percent of the 

units financed were multifamily rental units. 

Of the GSEs’ purchases qualifying as special 

affordable mortgages during this period, 25 

percent of the units financed were 

multifamily rental units. 

The methods used in the 2000 Rule for 

estimating the size of the multifamily 

mortgage market and related variables were 

the product of extensive research by HUD 

and review by interested parties. The 

approach here is first to extend those 

estimates through 2002 using the same 

methods as in the 2000 Rule, and then to 

present alternative methods, along with 

commentary. 

1. Data Sources 

The data sources available for estimating 

the size of the multifamily mortgage market 

are more limited in scope and timeliness 

than was the case for the 2000 Rule. Among 

the key sources described in detail in the 

2000 Rule, the following are now less useful: 

Survey of Mortgage Lending Activity. This 

survey has been discontinued; estimates are 

available only through 1997. 

Residential Finance Survey: The 1991 

Residential Finance Survey (RFS) is now 13 

years out of date. (See Section C.7 for results 

from the 2001 RFS.) 

Urban Institute Statistical Model: This 

model, developed in 1995 and calibrated 

using data from 1975–1990, is now even 

further removed from its calibration period 

and probably captures current market 

conditions less accurately. 

Estimates from the GSEs: As part of their 

comments on the proposed 2000 Rule, 

Fannie Mae and Freddie Mac shared with 

HUD their own estimates of the size of the 

multifamily mortgage market.

Fortunately, several key sources are available 

with the timeliness and quality comparable 

to the sources used during development of 

the 2000 Rule. These sources are: The Home 

Mortgage Disclosure Act (HMDA); activity 

reports submitted to HUD and the Office of 

Federal Enterprise Oversight (OFHEO) by 

Fannie Mae and Freddie Mac; non-GSE 

mortgage-backed security issuance from the 

Commercial Mortgage Alert database; and 

multifamily mortgage activity by life 

insurance companies, as estimated by the 

American Council of Life Insurers (ACLI). 

For background information on each of these 

sources, readers are referred to Appendix D 

of the 2000 Rule. 

2. Estimates Based on ‘‘HUD New’’

Methodology 

In the 2000 Rule, HUD developed a new 

methodology for estimating aggregate 

multifamily conventional loan originations. 

The method, here labeled ‘‘HUD New’’, was 

developed to make full use of the available 

data, and in particular the four sources listed 

above, which encompass most of the 

multifamily mortgage market. 

The advantages of HUD New are that it 

provides reasonably complete coverage of the 

market, produces those estimates within nine 

months of the end of the year, generally 

includes only current originations and avoids 

double counting. The main disadvantage of 

HUD New is that it produces a lower bound 

estimate. Some loan originators are missed, 

including pension funds, government entities 

63822 Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 

at the federal, state, and local levels, real 

estate investment trusts, and some mortgage 

bankers. Also, excluded are loans made by 

private individuals and partnerships. In 

addition to these exclusions, estimates from 

the covered lenders require some judgmental 

adjustments to conform to the definitions and 

time intervals of HUD New. 

Despite these limitations, HUD New is one 

sound way to estimate the size of the 

multifamily conventional mortgage market. 

Although the method requires unavoidable 

judgment calls on which analysts may differ, 

sensitivity analysis can be performed to show 

the effects of different multifamily 

origination volumes on the goals qualifying 

market estimates (see Sections F–H). Due to 

the reasonableness of the HUD New 

approach, the value of maintaining 

continuity in estimation methods, and the 

fact that no data has become available in the 

past few years that would argue for 

modifying HUD New, it is used here for the 

baseline estimate of the size of the 

conventional multifamily mortgage market in 

2000, 2001, 2002 and 2003. 

The estimates from HUD New are 

presented in Table D.2. This table is the 

counterpart of Table D.5 in the 2000 Rule. 

The historical years have two columns each, 

one for the estimates presented in the 2000 

Rule and one for estimates independently 

produced as part of this research. Footnotes 

to the table provide more complete 

descriptions of the components. Additional 

background on the calculations is provided 

in the 2000 Rule (Appendix D, Section C). 

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The revisions to the historical estimates 

(i.e., those in the 2000 Final Rule) result from 

both revisions to some of the input data and 

recalculations. For the years 1995 through 

1998, the revisions are small for the estimates 

of total originations. The only one of note is 

a 5 percent upward revision to the estimate 

for 1995, prompted by a recalculation of the 

entry for life insurance companies. The 

revision to 1999 is larger, and results mostly 

from the substitution of the actual HMDA 

results for that year for the projected value 

used in the 2000 Rule. Surprisingly, the 

revised estimate for 2000 based on complete 

data for that year only varies slightly from the 

projection made at the time of the 2000 Rule. 

Most of the historical estimates produced in 

2000 can be replicated or closely 

approximated, including those for Fannie 

and Freddie, CMBS, HMDA, and life 

insurance companies. The replicability of the 

CMBS figures is especially important, in light 

of all the selection criteria and hand 

calculations required to generate those 

estimates from the CMBS database. (In the 

2000 Rule, the estimates for Freddie Mac and 

CMBS originations in 1997 appear to have 

been switched, and the revised estimates 

make this correction.) 

The revised figures for 1999 and 2000 

indicate that total conventional originations 

dropped 8 percent in 1999 from 1998’s very 

strong level and another 13 percent in 2000. 

However, the HUD New estimate indicates 

that total conventional originations then 

jumped 40 percent in 2001 and further 

increased 15 percent in 2002. Judging from 

Survey of Mortgage Lending Activity 

estimates since 1970, the 2002 number is a 

new record high. For 2002, most of the 

increased volume is due to increases by 

HMDA lenders and life insurance companies. 

One possible concern is that the significant 

increase in the HMDA number in 2002 was 

caused by the FFIEC relaxing its eligibility 

requirements between 2001 and 2002. This 

concern turns out to be unfounded. The 

FFIEC actually raised its eligibility 

requirements. The level of assets required by 

FFIEC to be reported to HMDA increased 

from $31 million in 2001 to $32 million in 

2002. In addition, the number of HMDA 

reporters decreased from 7,771 in 2001 to 

7,638 in 2002. 

Compared with the version of Table D.2 in 

the Proposed Rule of May 3, 2004, the 

version here updates the estimates through 

2003 and revises the 2001 and 2002 estimates 

slightly in response to newly available data. 

The data for 2003 point to a large, broadbased increase in the volume of multifamily 

lending. Total conventional originations, 

estimated at $89 billion, are up 32 percent 

from 2002, easily reaching a new record high. 

A large increase was observed in each of the 

five market segments listed in Table D.2. 

Several organizations commented on the 

HUD New method. Fannie Mae says it 

involves double counting of originations. 

However, the one example they offer—

between life insurance company data and 

CMBS data—should not be subject to double 

counting because securitizations by life 

insurance companies are deleted from the 

CMBS totals, as noted in Table D.2 and in 

documentation included in the 2000 Rule. 

Freddie Mac, through its contractor, uses an 

approach similar to HUD New but uses 

different data sources. Inadequate details are 

provided on the tabulations and judgments 

applied to evaluate the method. Lastly, MBA 

expresses a preference for the estimates 

provided by HUD New and says, without 

providing detail, that estimates developed by 

their consultants are similar to those 

presented in HUD New. 

The comments received fail to note the 

point made repeatedly in the proposed rule 

text that the HUD New estimates are lower 

bounds on the volumes of originations. While 

HUD New is characterized in the proposed 

rule as providing ‘‘* * * the baseline 

estimate of the size of the conventional 

multifamily mortgage market * * *’’, other 

language in the rule makes clear that 

‘‘baseline’’ is used in the sense of ‘‘starting 

point.’’ For example, the proposed rule also 

states that ‘‘* * * unavoidable gaps in 

coverage make the resulting HUD New 

figures lower-bound estimates of actual 

originations rather than best ‘point’

estimates’’ (p. 24450). 

3. An Alternative Method 

The HUD New method makes use of all the 

available sources of data on individual 

origination sources in attempting to estimate 

total conventional mortgage originations. 

However, as discussed in the 2000 Rule and 

summarized above, unavoidable gaps in 

coverage make the resulting HUD New 

figures lower-bound estimates of actual 

originations rather than best ‘‘point’’

estimates. In addition, even for those loans 

that are available, certain assumptions must 

be made to convert the available data into 

estimates corresponding to the desired 

definition and time periods. An alternative to 

the bottom-up approach of HUD New avoids 

some of the data problems. The Federal 

Reserve’s Flow of Funds accounts provide 

the most complete and timely set of estimates 

of multifamily mortgage credit. The Flow of 

Funds statistics refer to net changes in credit 

outstanding rather than gross originations. 

Specifically, balance sheet estimates of 

mortgage assets of lenders are used to 

produce estimated changes in holdings of 

mortgages over time. An alternative label for 

the resulting time series is ‘‘net change in 

mortgage debt outstanding.’’

The historical relationship between gross 

originations and net change can be used to 

estimate recent origination volume. Separate 

information on FHA multifamily activity can 

be used to convert the total originations to 

estimates of only conventional originations. 

The Flow of Funds method that is described 

in this section will be called ‘‘FoF-based.’’

Flow of Funds estimates of mortgage debt 

outstanding are based on data from sources 

of varying accuracy and timeliness. Bank and 

thrift institution holdings, taken from 

regulatory filings, are by all accounts highly 

accurate, as are those from the government 

sponsored agencies and direct Federal 

government holdings. The private MBS data 

and the life insurance company figures, both 

taken from Wall Street sources, are also 

thought to be reasonably accurate. Less 

accurate are the estimates of loans made by 

private individuals and certain institutions, 

for which comprehensive data on loans 

outstanding is provided only once every ten 

years, through the Residential Finance 

Survey. Fortunately, the depository 

institutions, GSEs, and mortgage-backed 

securities account for the bulk of all holdings 

of mortgage debt (approximately 72 percent, 

according to the Flow of Funds estimates for 

year-end 2001). Thus, most of the Flow of 

Funds data are from highly accurate sources. 

The net change in mortgage debt 

outstanding in any year is the lower bound 

on originations. This is because the net 

change is defined as originations less the sum 

of principal repayments and charge offs. 

Historically loan originations have exceeded 

the net change by a considerable margin in 

both the multifamily and single-family 

markets. There are several reasons why the 

relationship of originations to net change 

differs between the multifamily and singlefamily sectors, but the basic principles apply 

to both sectors. 

Table D.3 presents the annual estimates 

from the Flow of Funds. Also shown are the 

estimates of multifamily conventional 

originations as published in Table D.10 from 

the 2000 rule, and FHA originations from 

HUD administrative records. 

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The ratio of mortgage originations to net 

change should be positively correlated with 

the proportion of total originations that are 

refinancings, for which the net change in 

mortgage debt would be expected to be low 

relative to that on loans taken out in 

connection with a property acquisition. (This 

is the pattern observed in the single-family 

mortgage market.) Refinancings, in turn, 

would be expected to be prevalent relative to 

purchase loans at times when interest rates 

are low relative to their recent past. 

The historical evidence generally supports 

this expectation regarding the relationship of 

originations to net lending. As shown in 

Table D.3, total originations have been 

highest relative to net change when interest 

rates have been low relative to their recent 

past. [Note: Columns A1, B1, C1, and D1 are 

the figures appearing in the Proposed Rule 

version of this table. Owing to extensive 

revisions to the input data, new columns 

with the revised inputs and calculated values 

have been added to facilitate comparisons. 

These revised figures appear in Columns A2, 

B2, C2, and D2.] The ten-year Treasury yield, 

a common benchmark for pricing multifamily 

mortgages, has generally trended down since 

1990. The early 1990s were all marked by 

high originations relative to net change, and 

these were also years in which interest rates 

were particularly low relative to their trailing 

five-year averages. In 1996 and 1997, by 

contrast, originations were less high relative 

to net change, and these were years in which 

interest rates were only slightly lower than 

their five-year trailing averages. In estimating 

conventional originations for 1999–2002, the 

1998 experience is a useful benchmark. That 

year, total originations exceeded the net 

change by about 80 percent, as shown in 

Table D.3. There was also a big drop in 

interest rates in 1998 relative to the recent 

past, providing an incentive for refinancings. 

As shown in the table, interest rates rose 

slightly in 1999 and again in 2000, 

presumably diminishing the incentive to 

refinance. Nonetheless, the net change in 

mortgage debt was higher in 1999 and 2000 

than it had been in 1998. 

Putting all this together, it seems that the 

appropriate ratio of total originations to net 

change to apply to 1999 and 2000 would be 

below that of 1998 and of most other years 

of the 1990s. Applying a ratio of 1.5 to the 

net change estimates in 1999 and 2000 

results in a total originations estimate of 

approximately $56 billion. Subtracting the $4 

billion in FHA originations results in 

estimates of $52 billion for conventional 

originations in each year. A subjective 

confidence band around this point estimate 

is at least +/¥$2 billion. 

Turning to the estimate for 2001, the first 

thing to note is that net change in mortgage 

debt jumped to $48 billion from $37 billion 

of the previous two years. The second thing 

to note is that interest rates fell by nearly a 

percentage point in 2001 relative to their past 

average. For both of these reasons, total 

originations in 2001 would be expected to 

have been higher than in 1999 or 2000. How 

much higher is a subjective judgment, but 1.5 

would seem an appropriate multiple to apply 

to the net change number in 2001. This is the 

same multiple as in 1999 and 2000, despite 

the added refinancing incentive in 2001. By 

the beginning of 2001, there were relatively 

few properties ‘‘at risk’’ of refinancing. Many 

presumably had refinanced in one of the 

preceding years, and lock-out provisions, 

yield maintenance agreements, and other 

loan conditions may have kept these 

properties from coming in for refinancings. 

Also, there may have been some short-run 

capacity problems in the multifamily loan 

origination industry in 2001 that further 

curtailed volume. 

Applying the 1.5 multiple to 2001’s net 

change of $48 billion yields a total 

originations estimate of $71 billion. 

Subtracting FHA business results in a 

conventional originations estimate of $67 

billion, to which a subjective confidence 

band of at least ±$2 billion appears 

warranted. 

As seen in Table D.3, the Flow of Funds 

methodology indicates that total 

conventional originations decreased 6.5% 

between 2001 and 2002. In 2002, the net 

change in mortgage debt decreased slightly to 

$44 billion. Using the 1.5 multiple for 2002’s 

net change of $44.2 billion yields a total 

originations estimate of $67 billion. 

Subtracting $4.5 billion of FHA business 

results in a conventional originations 

estimate of $62 billion. 

This Flow of Funds estimate is over $5 

billion less than the estimate from HUD New. 

This is surprising given that the HUD New 

method is supposed to serve as a lower 

boundary on the size of the multifamily 

market, while the Flow of Funds method is 

designed to produce a higher ‘‘point’’

estimate of the actual size of the market. 

Like the estimates for HUD New, those for 

the Flow of Funds method have been revised 

and updated through 2003 to incorporate 

new data. As with HUD New, the Flow of 

Funds method suggests a large increase in 

conventional mortgage lending in 2003. The 

estimate for conventional originations in 

2003 is $75 billion, up 29 percent from the 

revised estimate for 2002. In percentage 

terms, the increase in 2003 almost matches 

that of the HUD New method’s estimates of 

Table D.2. 

The originations estimates for earlier years, 

and especially 2000–2002, have been revised 

downward in response to revisions by the 

Federal Reserve to the Flow of Funds 

accounts and by an update to HUD’s FHA 

estimate for 2002. The downward revision 

was largest for 2000, for which year the new 

figure of $44 billion of conventional 

originations is $8 billion less than the earlier 

estimate.

The big increase in estimated originations 

in 2003 is largely the result of the Federal 

Reserve’s estimate of a large increase that 

year in net change in mortgage debt 

outstanding, shown in column A2 of Table 

D.3 The increase in 2003 in the Flow of 

Funds accounts is likely to be fairly accurate, 

because almost all of it is attributable to 

holder types for which the Fed has reliable 

statistics, specifically depository institutions 

and GSE mortgage securities. As in 1999–

2002, in 2003 the net change was converted 

into total originations by applying a 

multiplier of 1.5, under the assumption that 

the continued decline in interest rates 

provided even stronger incentives for 

refinancing. As shown in the last columns of 

Table D.3, ten-year Treasury yields in 2003 

averaged about 60 basis points below those 

of 2002, and approximately 130 basis points 

below the average of the previous five years. 

Comments on the Flow of Funds method 

for estimating multifamily originations 

focused on the approach to converting net 

change into loan originations. Fannie Mae 

argued that it was preferable to convert by 

applying a liquidation rate to the stock of 

mortgage debt and deriving originations as 

net change plus estimated liquidations. A 

trade organization noted the historical 

instability of the ratio of originations to net 

change and argued that the ‘‘HUD New’’

approach to estimating originations was 

superior. Freddie Mac and its consultant, 

while not commenting directly on the Flow 

of Funds method, expressed a preference for 

a modified version of HUD New, as described 

in the previous part of this section. 

The most recent data suggest that 

originations may in fact have been higher 

than estimated in the Flow of Funds 

approach and that the 1.5 multiplier used to 

convert net change into originations is too 

low. The reason is that in both 2002 and 

2003, the 1.5 multiplier results in estimated 

conventional originations that are less than 

those produced by the HUD New method. As 

discussed earlier, HUD New provides a lower 

bound estimate. Fannie Mae’s lower 

estimates of originations in recent years, 

relative to those in the proposed rule, result 

from the liquidation rate used in the 

calculation, which is that from Fannie Mae’s 

own portfolio. But Fannie Mae’s liquidation 

rate would be expected to fall below the 

market wide average, because Fannie Mae’s 

multifamily business has been growing more 

rapidly than the market overall, and as a 

result its loans presumably on average are 

‘‘younger’’ and consequently less likely to 

prepay or be retired than are the loans in the 

market as a whole. Lastly, regarding the 

historical instability of the ratio of 

originations to net change noted by a trade 

organization, Table D.3 of the proposed rule 

also presented the annual difference between 

originations and net change, which is 

considerably more stable. The differences 

corresponding to the 1.5 multiplier for the 

past several years are, as shown in D.3, below 

the historical averages. This is additional 

evidence that the 1.5 multiplier is perhaps 

too low. 

4. Most Likely Range 

In the 2000 Rule, estimates of conventional 

multifamily loan originations from various 

sources and methods were evaluated in 

determining the most likely range of annual 

originations. Those estimates were 

summarized in Table D.10 in the 2000 Rule. 

Some of the estimates from that table are 

reproduced below, in Table D.4, along with 

updates and estimates from the Flow of 

Funds method. 

Both HUD New (column #4 in Table D.4) 

and FoF-based (column #9) indicate a surge 

in lending activity in 2001. Some 

corroboration of this jump is provided by 

other indicators, flawed though they may be. 

HMDA has well-documented coverage 

problems with multifamily loans, but it is 

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noteworthy that HMDA-estimated 

conventional originations stayed in the same 

general range ($26 to $31 billion) in 1998–

2000 before jumping to $36 billion in 2001. 

The composite of 1.25 times HMDA 

originations plus life insurance 

commitments, described in the 2000 Rule 

and updated here in column #5, also follows 

this basic path. Similarly, aggregate GSE 

multifamily purchases and securitizations 

stayed in the same general level in 1998–

2000, before jumping in 2001, although this 

trend reflects changes in both market size 

and GSE market share. FHA originations (not 

shown) also rose substantially in 2001, but 

this too may indicate more than just market 

size trends. 

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Column #11 of Table D.4 gives the likely 

ranges of originations for each of the years. 

These are based on the estimates from all 

sources and interpretations of their strengths 

and weaknesses. In 1999, the $4 billion 

upward revision to the HUD New estimate 

from the preliminary figure reported in the 

2000 Rule, together with the higher estimate 

produced by the FoF-based method, justify 

an upward revision to the $45–$48 range 

estimated in the 2000 Rule. The revised range 

is set at $50–54 billion. In 2000, HUD New 

(revised and extended version) suggests that 

originations were somewhat lower than in 

1999, but FoF-based has originations holding 

at $52 billion. Balancing these conflicting 

indicators, a range of $48–$52 billion is 

selected for 2000. Finally, all indicators point 

to a substantial pickup in 2001, and the range 

that seems to fit best with those indicators is 

$65–$69 billion. 

In 2002, the various methods of estimation 

give a mixed picture. HUD New indicates a 

surge in lending activity in 2002, while the 

flow of funds method shows a decrease in 

lending activity. Other methods also show 

divergent trends. The composite of 1.25 times 

HMDA originations plus life insurance 

commitments also shows a significant 

increase between 2001 and 2002. On the 

other hand, aggregate GSE multifamily 

purchases and securitizations showed a 

slight decrease between 2001 and 2002. FHA 

originations (not shown) also decreased 

slightly in 2002. 

While this is a subjective judgment, 1.5 

may not be the appropriate multiple to apply 

to net mortgage debt outstanding in the flow 

of funds model in 2002. The difference 

between the flow of funds estimate and the 

HUD estimate cannot be reconciled without 

adjusting the FoF multiple. Given the low 

interest rates in 2002, and a refinancing boom 

in the single-family mortgage market, it could 

be that the multifamily market also had a 

significant amount of refinancing activity. In 

such a case, there could be an increase in the 

size of the multifamily market without a 

corresponding increase in net mortgage debt 

outstanding. A higher multiple would need 

to be applied to the Flow of Funds model to 

compensate for the increase in multifamily 

refinancings. 

Due to data limitations, the above remains 

a speculation. The largest increase in 

multifamily volume came from HMDA 

reporting lenders. The HMDA data do not 

allow for the separation of multifamily 

purchase originations from refinancings. 

Other data sources need to be explored to 

determine if an adjustment to the FoF-based 

model is appropriate. 

Both HUD New and the FoF-based method 

indicate a large increase in conventional 

multifamily loan originations in 2003. But 

the FoF estimates for each of the previous 

four years have been revised downward in 

light of revised input data. According to 

these updated and revised estimates, 

conventional multifamily originations by 

HUD New have exceeded the estimates of 

FoF in two of the past five years, and in the 

other three years FoF exceeded HUD New by 

only narrow margins. Because HUD New 

produces lower bound estimates of 

originations, whereas FoF is intended to 

provide best point estimates, the Department 

concludes that the 1.5 multiplier applied in 

the FoF method is too low, and as a result 

the FoF estimates understate originations in 

the past several years. In light of this 

probable underestimate of the multiplier, and 

after consideration of comments received, the 

Department believes that the likely ranges of 

conventional originations for 2002 and 

earlier years as published in the May, 2004, 

Proposed Rule continue to be reasonable 

estimates, although likely on the conservative 

side. As for 2003, the estimates from HUD 

New and FoF indicate a substantially higher 

likely range, which the Department has set at 

$85 billion to $100 billion. As explained in 

Section C.6 below, HUD will conduct 

sensitivity analyses in Sections F–H showing 

the effects of different multifamily mixes on 

the historical estimates of the goalsqualifying shares of the mortgage market. 

5. Loan Amount per Unit 

In determining the size of the conventional 

multifamily mortgage market for purposes of 

the GSE rules, the measure of market size is 

the annual number of conventionally 

financed multifamily rental housing units. 

The number of units is derived by dividing 

the aggregate annual originations by an 

estimate of the average loan amount per 

housing unit financed. For this reason, 

accuracy in the estimate of loan amount per 

unit is as important as accuracy in the dollar 

estimate of aggregate conventional 

originations. A 10 percent error in either will 

result in a 10 percent error in the estimate 

of market size. 

The 2000 Rule used estimates of loan 

amount per unit drawn from various sources. 

As summarized in Table D.9 of the 2000 Rule 

and the accompanying text, the estimates for 

1993–1998 were taken from the GSEs and for 

1999 from CMBS data. ‘‘Unpaid Principal 

Balance’’ or UPB—a balance sheet measure 

which for current year loan originations will 

differ little from the initial loan amount—is 

used to calculate aggregate originations of 

loans bought or securitized by the GSEs or 

pooled into non-GSE mortgage-backed 

securities. The figures from Table D.9 of the 

2000 Rule are reproduced below in Table 

D.5a, along with updated estimates from all 

three sources for 2000, 2001 and 2002. The 

estimates that are new since the 2000 Rule 

appear in italics. 

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14 1990 is excluded from this calculation because 

of the unusually high multifamily mix that year. 

Also, the estimated multifamily mix from the HUD 

New Method is also provided for 2002 since it was 

greater than the estimate from the Flow of Funds 

method.

Several options are available for 

developing estimates for 2000, 2001 and 

2002. The first is to use the UPB (unpaid 

principal balance) per unit estimates from the 

GSEs. These estimates, taken from the Fannie 

Mae and Freddie Mac annual activity reports 

to HUD, are as follows, computed as in the 

2000 Rule as a unit-weighted average of the 

unpaid principal balance (UPB) per 

multifamily unit in Fannie Mae’s and 

Freddie Mac’s portfolios:

1997 ............................................ $27,266 

1998 ............................................ 31,041 

1999 ............................................ 35,038 

2000 ............................................ 37,208 

2001 ............................................ 37,258 

2002 ............................................ 39,787 

The figure for 2002 is approximately 46 

percent higher than in 1997. Both Fannie 

Mae and Freddie Mac’s portfolios generate 

estimates of between $39,000 and $40,000 for 

2002. 

Several alternative approaches to 

estimating loan amount per unit are 

available. The first is to base the estimate on 

CMBS data, as was done for 1999 in the 2000 

Rulemaking. As shown in the last column of 

Table D.5, the estimates of UPB/unit from 

this source are somewhat below those of the 

GSEs and indicate less increase since the late 

1990s. 

In the first 10 months of 2002, CMBS 

properties showed a UPB/unit of $37,038, a 

nearly 14 percent jump over the previous 

year. Although slightly below the UPB/unit 

for the GSEs, the CMBS numbers are closer 

to the GSE calculations than in previous 

years. 

Another approach is to move the 1999 

estimate of UPB/unit forward by some 

justifiable index. The 2001 estimates use the 

change in average rent on multifamily rental 

units from the American Housing Survey. 

Because AHS data are not available for 2002, 

the 2002 estimate uses the consumer price 

index for rent of primary residence. Both 

AHS and CPI rent estimates are listed below:

Year Median Mean CPI 

1999 ........................................................................................................................................................................... $550 $592 177.5 

2001 ........................................................................................................................................................................... 590 647 192.1 

2002 ........................................................................................................................................................................... N/A N/A 199.7 

There is some variation between the two 

measures. In the AHS, median rent rose 7.3 

percent over this two-year period, and mean 

rent increased 9.3 percent. Meanwhile, the 

CPI showed an increase of 8.2 percent. In 

2001, using the AHS produces an estimate of 

$34,000. The CPI yields a smaller estimate for 

2001; applying the 8.2 percent increase from 

the CPI results in a 2001 estimate of $33,200. 

Since the AHS data are unavailable in 2002, 

the CPI provides a 2002 estimate of 

approximately $35,000. 

In 2001, the rent-adjusted 1999 estimate 

was in between the estimates from the CMBS 

and GSE data, and was a fair estimate of the 

actual size of the market. In 2002, however, 

the rent-adjusted number is below both the 

CMBS and GSE calculations. The rentadjusted number could be underestimating 

the 2002 UPB/unit. Either the CMBS or GSE 

calculations, or an average of the various 

methods could be used. Sections F–H will 

report the results of sensitivity analyses 

showing the effects of the different 

multifamily mortgage estimates and different 

per unit amounts on the goals-qualifying 

shares for the year 2002. Under the various 

estimates, the multifamily mix (defined 

below) for 2002 varies from 9.5 percent–11 

percent. 

Since the proposed rule was issued by the 

Department, data for 2003 have become 

available that permit updates of some of the 

sources of UPB/unit estimates. The GSEs’

experience, shown in the bottom row of 

Table D.5a, was mixed. Fannie Mae’s UPB/

unit increased about 4 percent from 2002, but 

Freddie Mac’s dropped 9 percent. The 

volume-weighted average UPB/unit for the 

GSEs in 2003 was $39,082, off about 2 

percent from the 2002 average of $39,787 

shown in the text table above. 

The most recent rent estimates from the 

American Housing Survey also suggest 

limited or negative recent growth in UPB/

unit. The median and mean rents for 2003 

that correspond to those in the table above 

are $609 and $671. Given the logic of this 

method as described in the proposed rule, it 

seemed most appropriate to use the percent 

increase in AHS rents from 1999 to 2003 to 

update the 1999 UPB/unit ($30,719) to a 2003 

figure. Using the 13.3 percent increase in 

mean rent between 1999 and 2003 (the 

increase in median was only 10.7 percent) 

and moving the baseline UPB/unit from 1999 

forward to 2003 by this proportion brings the 

2003 UPB/unit to $34,805. That is the 

number appearing in Table D.5a. For 

comparison, the CPI rent index rose 15.8 

percent between 1999 and 2003. 

In commenting on HUD’s UPB/unit 

estimates for 2000–2002, as published in the 

May 2004 Proposed Rule, both Fannie Mae 

and Freddie Mac expressed the view that the 

estimates were too low. They cited both their 

own experience and other evidence and 

argued that HUD’s reliance on CMBS and 

rent data, and switching of benchmark years, 

resulted in UPB/unit estimates that were 

substantially below the actual market 

averages. 

In reviewing the comments and in light of 

these new data HUD has concluded that the 

estimates in the proposed rule likely were too 

low. The more difficult determination is 

where to set the estimates. The Department 

has not revised its estimate of UPB/unit for 

2002 and earlier years, because of this 

uncertainty. The situation is similar to that 

discussed in the previous part of this section 

in discussing the likely range of conventional 

multifamily originations, where the new data 

lead the Department to think the Flow of 

Funds estimates may be too low, but no 

adjustments were made to the likely range as 

reported in Table D.4. If adjustments were 

made to the historical estimates of 

originations and UPB per unit, the revisions 

would be at least partially offsetting, with 

little net effect on the historical estimates of 

number of multifamily units financed. As for 

2003, weighing all available information, the 

Department has set the UPB/unit at $39,082, 

the weighted average of the GSEs’ actual 

UPB/unit for that year. As explained in the 

next section, goals-qualifying estimates for 

1995–2002 are reported in Sections F–H that 

include multifamily mixes approximately 

two-three percentage points lower that the 

multifamily mixes suggested by the most 

likely range of multifamily dollar estimates 

and the UPB/unit estimates. 

6. Multifamily Mix During the 1990s 

This section uses the information on dollar 

volume of multifamily originations (Table 

D.4) and average loan amounts (Table D.5a) 

to estimate the number of multifamily units 

financed each year as a percentage share of 

the total (both single-family and multifamily) 

number of dwelling units financed each year. 

Because of the high goals-qualifying shares of 

multifamily housing, the multifamily mix is 

an important parameter in HUD’s projection 

model for the overall market; other things 

equal, a higher multifamily mix (or 

conversely, a lower share of single-family 

loans) leads to a higher estimate of goalsqualifying loans in the overall mortgage 

market. This percentage share, or 

‘‘multifamily mix’’, is reported in the last two 

columns of Table D.4 for the years 1991 to 

2002.14 The ‘‘minimum’’ (‘‘maximum’’) 

multifamily mix figure reflects the low 

(upper) end of the ‘‘likely range’’ of 

multifamily dollar originations, also reported 

in Table D.4. Because they will be compared 

with other estimates of the MF mix, these 

‘‘likely range’’ data are reproduced in the first 

two columns of Table D.5b. 

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15The projection model for 2002 showed the 

following multifamily mixes for 2002: 11.1 percent 

for the HUD New multifamily estimate ($67.4 

billion); 10.5 percent for the top end ($64 billion) 

of the Flow of Funds multifamily range ($60–64 

billion), 10.3 percent for the mid-point ($62 billion), 

and 9.9 percent for the low end ($60 billion). In 

Sections F–H, HUD will consider multifamily mixes 

as low as 9.5 percent for 2002.

16For purposes of sensitivity analysis, the lower 

MF mixes were derived as follows: three percentage 

points were subtracted from the 1995–1997 midpoint MF mixes, which were in the high 18-to-21-

percent range; two percentage points were 

subtracted from the 1998–2000 mid-point MF 

mixes, which were in the 14-to-17-percent range; 

and 1.5 percentage points were subtracted from the 

2001–2002 mid-point MF mixes, which were less 

than 13 percent.

17HUD estimated ICF’s MF mixes by including 

subprime loans in the data that ICF reported on 

pages 58–60 of its Appendix (for the Best Estimate) 

and on pages 63–65 of its Appendix (for the Lower 

Bound Estimate). To the extent that ICF also 

excluded other single-family loans (in addition to 

subprime SF loans), the estimates reported in the 

text overstate ICF’s initial MF mixes.

Table D.5b includes several averages of the 

MF mix for different time periods between 

1991 and 2002. Based on the ‘‘likely range’’

of annual conventional multifamily 

origination volume, multifamily units have 

represented 15.3 percent (the average of the 

‘‘minimum’’ figures) to 16.6 percent (the 

average of the ‘‘maximum’’ figures) of units 

financed each year between 1991 and 2002. 

Considering the mid-points of the ‘‘likely 

range’’, the multifamily mix averaged 15.9 

percent during this period. Notice that the 

multifamily mix is lower during years of 

heavy refinancing when single-family 

originations dominate the mortgage market; 

the multifamily mix was only 13–14 percent 

during 1993, 1998, and 2001, and 11 percent 

(or less) during 2002.15 As discussed in 

Sections F–H, record single-family 

originations ($3.8 trillion) during 2003 likely 

resulted in that year having a lower 

multifamily mix than any of the years 

between 1991 and 2002. Sensitivity analyses 

are conducted to show the effects of 

multifamily mixes less than the previous 

lows of 11 percent in 1992 and 2002.

As discussed earlier, several commented 

that HUD had understated the UPB/unit, 

which caused HUD to overstate the share of 

newly-mortgage multifamily dwelling units. 

Section C.5 explains that HUD’s UPB/unit 

estimates for recent years are likely too low 

but that could be offset by low estimates of 

originations. To allow for different views 

about the volume of mortgage originations 

and the UPB/unit, Sections F–H will conduct 

sensitivity analyses with lower multifamily 

mixes than suggested by the mid-points of 

the likely ranges in Table D.5b. The third 

column of Table D.5b lists the ‘‘mid-point’’

MF mixes while the fourth column of Table 

D.5b lists the lower MF mixes used in 

Sections F–H. Over the 1995–2002 period, 

the average MF mix ranged from 13.9 percent 

(the lower MF mix approach) to 16.2 percent 

(the mid-point MF mix approach).16 Over the 

more recent period, the averages have ranged 

from 12.6 percent to 14.5 percent for 1999–

2002, from 15.1 percent to 17.5 percent for 

recent home purchase years, and from 11.2 

percent to 12.9 percent for the refinance 

years of 1998, 2001, and 2002.

The impact of the lower MF mix on the 

UPB/unit assumption can be illustrated for 

the case of 2001, which assumed a loanamount-per-unit figure of $34,000. Reducing 

the MF mix from 13.5 percent to 12.0 percent 

is consistent with increasing the UPB/unit 

from $34,000 to $39,075 (holding constant 

mortgage originations at $67 billion). Of 

course, the lower MF mix of 12.0 percent is 

consistent with a lower volume of mortgage 

originations if the initial UPB/unit of $34,000 

is retained. 

Fannie Mae (op.cit., page I–29) developed 

three sets of UPB-per-unit figures for 1997 to 

2002; below Fannie Mae’s estimates are 

compared with the UPB-per-unit figures that 

result from HUD’s model that uses the lower 

MF mixes.

Fannie Mae’s Estimates HUD’s Lower 

MF Mix 

High Low Baseline Model 

1997 ................................................................................................................. $35,063 $28,488 $31,776 $33,582 

1998 ................................................................................................................. 40,155 32,626 36,390 37,492 

1999 ................................................................................................................. 42,430 33,992 38,211 36,260 

2000 ................................................................................................................. 45,797 37,210 41,504 38,142 

2001 ................................................................................................................. 48,363 39,295 43,829 39,075 

2002 ................................................................................................................. 53,507 43,474 48,491 44,009 

Average ............................................................................................................ 44,219 35,847 40,033 38,093 

Three points stand out. First, there is a 

rather large differential between Fannie 

Mae’s Low and High UPB-per-unit figures, 

reflecting the lack of available data. Second, 

HUD’s UPB-per-unit estimates based on its 

lower MF mix model are in between Fannie 

Mae’s Low and Baseline estimates. Third, the 

differentials between HUD’s and Fannie 

Mae’s Baseline estimates are largest during 

the two heavy refinance years of 2001 and 

2002. 

HUD’s conducting its market share analysis 

with the lower MF mixes (as well as with the 

mid-point MF mixes) recognizes different 

views about the size of the mortgage market 

and the UPB/unit. This does not mean that 

the HUD’s range of MF mixes includes 

estimates as low as those suggested by ICF 

(Freddie Mac’s contractor) and Fannie Mae. 

ICF’s estimates of multifamily shares for 

the 1994–2002 were lower than those that 

HUD used (as reported in Table D.5b). ICF’s 

Best Estimates and Lower Bound Estimates 

were as follows:17

Best

estimates

(percent) 

Lower

bound

estimates

(percent) 

1994 .................. 17.2 14.0 

1995 .................. 16.5 14.0 

1996 .................. 13.7 11.5 

1997 .................. 14.4 12.3 

1998 .................. 11.3 9.9 

1999 .................. 12.3 10.7 

2000 .................. 13.8 11.7 

2001 .................. 10.8 9.0 

2002 .................. 10.2 8.5 

Various averages of ICF’s Best Estimates 

are calculated in Table D.4b. Over the 1995–

2002 period, ICF’s Best Estimates averaged 

12.9 percent, while HUD’s mid-point 

estimates averaged 16.2 percent and HUD’s 

lower MF mix estimates averaged 13.9 

percent. Thus, the average of ICF’s Best 

Estimates is slightly lower (one percentage 

point) than the average of HUD’s lower MF 

mixes. Over the more recent 1999–2002 

period, ICF’s Best Estimates averaged 11.8 

percent, while HUD’s mid-point estimates 

averaged 14.5 percent and its lower MF mix 

estimates averaged 12.6 percent. 

ICF also produces lower bound estimates 

of the multifamily share of the market (see 

above list for 1994 to 2002). ICF’s lower 

bound estimates for the multifamily mix 

averaged 11.3 percent between 1994 and 

2002. It is interesting that ICF’s lower bound 

estimates are in some cases either similar or 

less than the multifamily shares of Fannie 

Mae’s business. The multifamily share of 

Fannie Mae’s business was 9.9 percent in 

1999 (versus ICF’s lower bound estimate for 

the market of 10.7 percent), 13.3 percent in 

2000 (versus ICF’s lower bound of 11.7 

percent), and 10.9 percent in 2001 (versus 

ICF’s lower bound market estimate of 9.0 

percent). Even though these Fannie Mae data 

include both their seasoned and current-year 

purchases, it is surprising that ICF’s market 

estimates would be similar or less than 

Fannie Mae’s multifamily shares, given that 

Fannie Mae purchased practically no small 

(less-than-50-unit-property) multifamily 

loans during this period.

In its comments, Fannie Mae also provided 

various historical estimates of the MF mix 

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18The multifamily origination data in this 

paragraph reflect a recent release of the RFS; other 

single-family and multifamily data in this section 

draw from an earlier version of the RFS. HUD will 

continue its analysis of the RFS data as new 

versions are released by the Census Bureau.

(see its Appendix I, pages I–29 and I–30). 

First, without giving the details of its 

analysis, Fannie Mae asserts that ‘‘Fannie 

Mae’s analysis shows an average multifamily 

share of 10.2 percent for the 1997–2002 

period, compared with HUD’s 14 to 15 

percent range’’ (page I–30). Fannie Mae’s 

estimate of 10.2-percent is below ICF’s Best 

Estimate (12.1 percent), HUD’s lower MF mix 

estimate (13.1 percent), and HUD’s mid-point 

MF mix estimate (15.2 percent). (See Table 

D.5b.) Fannie Mae’s estimate of 10.2 percent 

is practically the same as ICF’s Lower Bound 

Estimate, which averaged 10.4 percent 

between 1997 and 2002; of course, this raises 

the same issue mentioned above with respect 

to ICF’s Lower Bound Estimates. 

Fannie Mae also provided various 

estimates of UPB per unit (see above) and 

applied its ‘‘Low UPB per Unit Assumption’’

and its ‘‘High UPB per Unit Assumption’’ to 

HUD’s likely range of MF mortgage 

originations (as reported in column 11 of 

Table D.4). For the period 1997–2002, Fannie 

Mae obtained: (A) a range of 12.7–13.8 

percent using its ‘‘Low UPB per Unit 

Assumption’’ and (B) a range of 10.5–11.5 

percent using its ‘‘High UPB per Unit 

Assumption.’’ (See Fannie Mae’s Table I.6 on 

page I–30.) Fannie Mae’s (A) results are 

similar to HUD’s lower MF mix estimates, 

which averaged 13.1 percent over the 1997–

2002 period; its (B) results are slightly higher 

than ICF’s Lower Bound Estimates, which 

averaged 10.4 percent over the 1997–2002 

period. 

Finally, Fannie Mae notes that its baseline 

analysis shows that the multifamily share 

dropped to 5.6 percent in 2003 and that 

HUD’s MF assumptions (e.g., 13.5 percent) 

clearly overstate typical multifamily shares 

and therefore the likely market opportunity 

for the GSEs (page I–30). HUD recognizes that 

the MF mix will be lower during heavy 

refinance years such as 2003, making it more 

difficult for the GSEs to achieve the housing 

goals; HUD’s Advance Notice of Proposed 

Rulemaking (described in the Preamble) 

seeks proposals on how to treat heavy 

refinance years in the goals determination 

process. The range of MF mixes (13.5–15.0–

16.0 percent) in HUD’s projection model 

apply to a home purchase environment, not 

a heavy refinance environment. 

As discussed in Section C.8 below, HUD 

will continue to use a 15 percent MF Mix as 

its baseline. In their comments on the 

proposed rule, both Fannie Mae and Freddie 

Mac expressed the view that HUD’s 15 

percent baseline estimate of the multifamily 

share of the conventional mortgage market 

was too high. As described earlier in this 

section, those organizations argued that 

HUD’s estimates of multifamily loan 

originations were too high, that HUD’s 

estimates of multifamily UPB/unit were too 

low, and that these two errors together 

combined to produce an estimate multifamily 

market share that was one to four percentage 

points too high. A trade organization reached 

similar conclusions in their comments on the 

multifamily mix. 

The Department has carefully considered 

these comments and the analysis supporting 

them. But HUD’s conclusion is that the 15.0-

percent baseline multifamily mix 

appropriately reflected the estimates and 

analysis appearing in the May 2004 Proposed 

Rule. The Department’s responses to 

critiques of the individual components of the 

multifamily mix calculation appear earlier in 

this section. In addition, the Department’s 

confidence that a 15 percent estimate for 

multifamily’s share of conventionally 

financed is not too high is bolstered by data 

from the newly released 2001 Residential 

Finance Survey (RFS). As discussed in the 

next section, the RFS indicates a long-run 

market share for multifamily that is 

considerably higher than 15 percent. After 

presenting the RFS results, Section C.8 will 

return to the discussion of the baseline MF 

mix used in HUD’s projection model. 

7. Evidence on the Multifamily Mix from the 

2001 Residential Finance Survey 

Subsequent to the Department issuing the 

proposed rule in May, 2004, the Census 

Bureau released the 2001 Residential Finance 

Survey (RFS). The RFS provides new 

information on the size and composition of 

the residential mortgage market. As noted by 

Fannie Mae, Freddie Mac, and other 

organizations commenting on the draft rule, 

the RFS is an important and unique data 

source of data, because it is designed to 

provide comprehensive, nationally 

representative estimates on the volume and 

characteristics of single-family and 

multifamily mortgage loans and the 

properties they finance. Some organizations 

urged that the Rule not be finalized until data 

from the RFS has been analyzed. 

The RFS data suggest a mortgage market 

somewhat different in size and composition 

from that estimated by most analysts based 

on partial data. Beginning with multifamily 

lending, the multifamily mortgage market is 

considerably larger than most analysts have 

thought, according to the RFS. For example, 

the RFS estimate of total mortgage debt 

outstanding on properties with five of more 

housing units is $608 billion dollars. The 

only other comprehensive estimate comes 

from the Federal Reserve Board’s ‘‘Flow of 

Funds’’ accounts, which draw on data from 

multiple sources and on judgments by the 

Fed staff. The Flow of Funds estimate of 

multifamily debt outstanding as of 2002Q2 

(the quarter most comparable to reporting 

dates of RFS respondents) was only $457 

billion. In other words, the RFS estimates a 

stock of multifamily mortgage debt 32 

percent larger than Federal Reserve.

As with debt outstanding, multifamily loan 

originations in the RFS exceed most other 

estimates. Over the period 1998–2001, 

annual originations averaged $66 billion 

according to the RFS, and conventional 

originations (total less FHA insured) 

averaged $61 billion. HUD’s estimates of 

conventional multifamily originations for 

these years, as summarized in Table D.2 of 

the proposed rule, averaged only $56 billion. 

In commenting on the proposed rule, Fannie 

Mae and Freddie Mac offered estimates of 

market size considerably below these.18

The single-family mortgage estimates from 

the 2001 RFS, like the multifamily estimates, 

are at odds with those from some other 

sources. For example, total mortgage debt on 

1-to-4 family residences, according to the 

RFS, was $5.032 trillion, whereas the Flow 

of Funds estimate for 2002Q1 was a much 

higher $6.546 billion. 

In summary, the RFS estimates a somewhat 

smaller residential mortgage market than the 

Flow of Funds—19 percent smaller as 

measured by total debt outstanding. 

Furthermore, multifamily debt is a much 

larger part of the total residential market in 

the RFS than in the Flow of Funds. 

The RFS also records the number of 

housing units at each surveyed property, 

providing an opportunity to measure directly 

the number of housing units financed instead 

of relying on indirect methods. The RFS 

estimates indicate that, as with debt 

outstanding, the mix of mortgage lending by 

the measure of units financed is more heavily 

multifamily than previously thought. This is 

shown in Table D.5c, where units financed 

are presented for the loan origination years 

2000 and 2001. These are the years for which 

the estimates are least likely to be biased by 

refinancing between the loan origination date 

and the survey. The estimates for 2001 are 

incomplete, because approximately 10 

percent of the survey respondents reported as 

of dates prior to December 31, 2001 and loans 

subsequently originated on those properties 

would not be included. This undercount 

should affect single-family and multifamily 

reporting about proportionally, with little 

effect on the market share calculations. 

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19Estimates of new MF units were created by 

comparing the historical estimates of numbers of 

units added by HUD and REIS, creating a ratio, and 

then applying that ratio to the REIS’ future 

projections.

By the housing goals’ metric of number of 

conventionally financed, conforming housing 

units, the 2001 Residential Finance Survey 

indicates a multifamily market share 

substantially above the pre-RFS estimates of 

HUD and GSEs. As detailed in Table D.5b, 

the multifamily share estimated for 2001 is 

0.197, or 19.7%, and the share for 2000 is a 

striking 0.254, or 25.4%. These high figures 

are particularly noteworthy because the year 

2001 was marked by high levels of 

refinancings, which have been viewed as 

boosting single-family lending proportionally 

more than multifamily. HUD’s estimate of the 

multifamily share for 2000, for example, was 

only 13%–14%, as derived elsewhere in this 

rule. 

There are several reasons for accepting the 

RFS estimates as an accurate portrayal of the 

residential mortgage market. First, the 

estimates are generated from a national 

representative sample of properties as drawn 

by experts at the U.S. Census Bureau. 

Second, the survey forms were designed in 

consultation with industry experts. Third, 

participation in the survey was mandatory, 

because it was conducted in conjunction 

with the 2000 Census. And fourth, data 

processing and editing at the Census Bureau 

prior to public release of census and survey 

results is meticulous. 

Nonetheless, for the specific reasons noted, 

results from the RFS should be interpreted 

cautiously. First, loan originations for any 

year will be understated, because the RFS 

will record only those loans still outstanding 

as of the late 2001 or early 2002 survey date. 

Loans originated in, for example, 1998, will 

be recorded only if those loans have not been 

refinanced, repaid, or charged off prior to the 

RFS survey date. For this reason, the RFS 

unit count and especially the market share 

estimates for 2001 are more reliable than 

those for 2000 and earlier years. Second, 

some of the results of the RFS are 

substantially at odds with other evidence and 

industry perceptions, as noted already. 

Another example of a surprising RFS finding 

is the time path of multifamily loan 

originations. According to the RFS, 

originations were roughly 50 percent greater 

in 1998–1999 than in 2000–2001, whereas 

most other evidence points to originations in 

2000–2001 that at least equaled, and likely 

exceeded, the volume of 1998–1999. 

Lastly, in response to user feedback and its 

own data checks, the Census Bureau has 

revised the RFS estimates three times since 

the initial data release in early July 2004. The 

possibility remains that additional errors will 

be found and that the resulting revisions to 

the data will significantly change the RFS 

portrayal of the multifamily mortgage market. 

HUD will continue its analysis of the RFS as 

new versions are released. 

On balance, the Department views the RFS 

as providing strong additional evidence that 

the Department’s baseline multifamily mix 

percentage of 15% is not an overestimate. 

The RFS data, weighed alone, would have 

that percentage set much higher. 

8. Multifamily Mix in HUD’s Model—Further 

Discussion 

As noted above, the ‘‘multifamily mix’’ is 

the number of multifamily units financed 

each year as a percentage share of the total 

(both single-family and multifamily) number 

of dwelling units financed each year. Because 

of the high goals-qualifying shares of 

multifamily housing, the multifamily mix is 

an important parameter in HUD’s projection 

model for the overall market; other things 

equal, a higher multifamily mix (or 

conversely, a lower share of single-family 

loans) leads to a higher estimate of goalsqualifying loans in the overall mortgage 

market.

The multifamily share of the conforming 

conventional market (or ‘‘multifamily mix’’) 

is utilized below as part of HUD’s analysis of 

the share of units financed each year meeting 

each of the housing goals. The proposed rule 

considered multifamily mixes of 13.5 

percent, 15.0 percent, and 16.5 percent, as 

well as even lower multifamily mixes for 

heavy refinance environments such as 2001–

03. The 15.0 percent level was considered as 

the baseline based on analysis of multifamily 

shares during home purchase environments 

of the 1990s. In the market sections below, 

HUD continues to focus on the baseline 15.0 

percent but also considers a range of 

estimates, including those provided by 

commenters on the proposed rule. Comments 

by Fannie Mae and ICF are summarized 

below. 

In its projection model, Fannie Mae uses a 

multifamily mix of 12.3 percent (see Table 

1.6 on page 11). As noted in Section C.6 

above, Fannie Mae estimated an average 

multifamily mix of only 10.2 percent over the 

1997–2002 period. Fannie Mae notes that 

HUD’s 13.5–16.5 range is ‘‘well above the 

range of estimates suggested by an 

examination of all available data and is 

inconsistent with the current weak 

fundamentals in the multifamily market.’’

(Fannie Mae, p. 15) Fannie Mae’s views 

about the future mortgage market were 

discussed on pages I–14 to I–17 in its 

Appendix I (‘‘Comments on HUD’s Analysis 

of the Statutory Factors’’) to its comments. As 

discussed earlier, Fannie Mae’s somewhat 

pessimistic views about the future market 

were driven by the current high vacancy rates 

for multifamily properties and the fact that 

the high-renter age group (the so-called ‘‘echo 

boom’’ aged 20–34) will not begin to increase 

until after 2007. Fannie Mae also emphasized 

that the recent spike in multifamily 

originations (beginning in 2001) means that 

a large portion of today’s holders of 

multifamily mortgages have already 

refinanced and therefore will have only 

limited ability and incentive to refinance 

over the next several years, due to yield 

maintenance provisions on their existing 

multifamily mortgages. According to Fannie 

Mae, these loans will not begin to exit their 

yield maintenance periods until sometime 

between 2008 and 2010, with the result being 

that the 2005–2008 period appears to have 

relatively limited prospects for multifamily 

refinancing. Fannie Mae notes that singlefamily lending is not subject to these 

constraints and is more likely to undergo 

modest refinance waves as a result of interest 

rate fluctuations. Based on its analysis, 

Fannie Mae concludes that a multifamily 

share of 12.3 percent is ‘‘consistent with 

reasonable estimates’’ of the multifamily 

market (Fannie Mae Appendix, Table I.15, p. 

I–42). 

Based on its analysis of the multifamily 

market, ICF, Freddie Mac’s contractor, 

offered higher projections of the MF mix. 

Specifically, ICF provided the following 

estimates of the multifamily mix during the 

projection period, 2005–08, as follows:

ICF MF Mix

(percent) 

2005 ...................................... 13.7 

2006 ...................................... 14.5 

2007 ...................................... 14.7 

2008 ...................................... 13.9 

average ................................. 14.2 

Thus, ICF’s 14.2-percent average estimate 

is a little less than HUD’s baseline (15.0 

percent), standing at the mid-point of HUD’s 

13.5 and 15.0 figures. For a discussion of 

ICF’s methodology for estimating the 

multifamily mix, and their actual use of their 

estimated multifamily mixes in projecting 

overall market estimates for the three housing 

goal categories, see pages 126–140 of their 

technical appendix, entitled ‘‘Analysis of the 

Proportion of the Mortgage Market that Meets 

the GSEs’’ Affordable Housing Goals: Issues 

of Variability and Uncertainty: Technical 

Appendix’’ (July 15, 2004). According to ICF, 

they projected the number of multifamily 

(MF) units based on the existing number of 

units likely to be refinanced (rollover) and 

the expected number of MF units that would 

be added to the housing stock (new 

completions). The amount of rollover was 

estimated as the average of the number of 

units financed 8, 9, and 10 years ago. ICF 

used these time periods because 10-year 

balloon mortgages are the most common MF 

mortgages, and MF loans typically include a 

yield maintenance period to limit 

prepayments.19 In their basic report, they 

state that they view the above estimates from 

their MF projection model as ‘‘our core, or 

our most likely forecast for 2005 through 

2008’’ (ICF Report, p. 40). While they state 

that ‘‘our [ICF] multifamily projections for 

2005 through 2008 have a sound empirical 

basis owing to the nature of multifamily 

mortgages and new multifamily 

construction,’’ ICF also reminds readers of 

the uncertainty of its MF projections when it 

states ‘‘while we believe the core range is the 

best and most likely estimate of the future 

market, we [ICF] recognize that it is possible 

that the actual outcomes may be outside this 

range, either higher or lower’’ (ICF Report, p. 

40). The ICF basic report is entitled 

‘‘Analysis of the Proportion of the Mortgage 

Market that Meets the GSEs’’ Affordable 

Housing Goals: Issues of Variability and 

Uncertainty: Technical Appendix’’ (July 15, 

2004). Because the basic report and the 

appendix are paginated differently, they will 

be referenced separately—ICF’s basic report 

will be referred to as the ‘‘ICF Report’’, while 

their appendix will be referred to as the ‘‘ICF 

Appendix’’.

As discussed earlier, the 2001 RFS 

provides higher estimates of the MF mix for 

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20The HMDA data reported in this section ignore 

HMDA loans with ‘‘non-applicable’’ for owner type.

21These data without subprime loans are 

presented merely to provide a sense of the likely 

changes if one excludes subprime investor loans. 

Three comments should be made about them. First, 

HUD’s procedure is to drop one-half of subprime 

loans as a proxy for B&C loans, which one reduce 

the one-half percentage point differential 

mentioned in the text to a one-quarter point 

percentage differential. Second, the comparisions in 

Table D.6a do not deduct single-family owner 

subrpime loans; doing that would raise the investor 

shares from those in middle portion of the table. 

Third, HUD’s model starts with investor and owner 

property shares that include subprime loans (such 

as those in the top portion of Table D.6a) and then 

excludes the subprime loans as part of the 

derivations within the model. See Section F for an 

explanation of this procedure.

1999–2001 than either Fannie Mae or ICF. 

The RFS data suggest that 15.0 percent is a 

reasonable baseline, particularly for a home 

purchase environment. Thus, the market 

analysis of the housing goals in Sections F–

H will continue to use 15.0 percent as the 

baseline MF mix. To reflect the uncertainty 

with the MF data, market projections will 

also be provided for alternative MF mixes of 

12.25 percent (approximating Fannie Mae’s 

projection of 12.3 percent), 13.5 percent (the 

low-end projection for a home purchase 

environment used in HUD’s 2004 proposed 

rule), 14.25 percent (approximating the 12.2 

percent average of ICF’s best projections of 

MF mixes between 2005 and 2008), and 16.0 

percent (a half percentage point below the 

high-end projection for a home purchase 

environment used in HUD’s 2004 proposed 

rule). Based on ICF’s best projection and 

HUD’s analysis of the 2001 RFS, the bottom 

end of the range probably should not go 

below 13.5 percent for a home purchase 

environment. However, results are provided 

for the 12.25 percent in order to show the 

sensitivity of the market sizing to the 

assumption made by Fannie Mae in its 

analysis. Of course, it is recognized that the 

multifamily mix will be significantly lower 

during heavy refinancing periods such as 

2001–2003. Therefore, additional sensitivity 

analyses will be conducted to show the 

effects of even lower multifamily mixes. But 

as explained in the Preamble of this Final 

Rule, in its goals scoring, HUD will reduce 

refinance loans so they account for not more 

than 40 percent of combined home purchase 

and refinance loans. This addresses the 

problem of a low MF mix during a heavy 

refinancing period reducing the ability of the 

GSEs to meet the new goal targets. 

D. Single-Family Owner and Rental Mortgage 

Market Shares 

1. Available Data on Investor Share 

As explained later, HUD’s market model 

will also use projections of mortgage 

originations on single-family (1–4 unit) 

properties. Current mortgage origination data 

combine mortgage originations for the three 

different types of single-family properties: 

Owner-occupied, one-unit properties (SF-O); 

2–4 unit rental properties (SF 2–4); and 1–

4 unit rental properties owned by investors 

(SF-Investor). The fact that the goal 

percentages are much higher for the two 

rental categories argues strongly for 

disaggregating single-family mortgage 

originations by property type. This section 

discusses available data for estimating the 

relative size of the single-family rental 

mortgage market. 

The Residential Finance Survey (RFS) and 

HMDA are the data sources for estimating the 

relative size of the single-family rental 

market. The 2001 RFS provides mortgage 

origination estimates for each of the three 

single-family property types, as it includes 

mortgages originated during 2001, as well as 

surviving mortgages that were originated in 

earlier years such as 1999 and 2000. HMDA 

divides newly-originated single-family 

mortgages into two property types:20

(1) Owner-occupied originations, which 

include both SF-O and SF 2–4. 

(2) Non-owner-occupied mortgage 

originations, which include SF Investor. 

The percentage distributions of singlefamily mortgages from HMDA and the 2001 

RFS are provided in Table D.6a and D.6b. 

HMDA data will be discussed first. Because 

HMDA combines the first two categories (SFO and SF 2–4), the comparisons between the 

data bases must necessarily focus on the SF 

investor category. The following points stand 

out from Table D.6.a: 

• The investor share of all single-family 

loans has ranged from 5.7 percent (1993) to 

9.1 percent (2000), with an average of 7.8 

percent. Over the more recent 1999–2003 

period, the investor share has averaged 8.3 

percent. 

• The investor share is much higher for 

home purchase loans than for refinance 

loans. The investor share of home purchase 

loans averaged 9.6 percent between 1993 and 

2003, as compared with a 6.8 percent average 

for refinance loans.

• The investor share for home purchase 

loans recently increased, rising from slightly 

above 9.0 percent during 1999 to around 10.0 

percent during 2000–2001 to 12.0–13.0 

percent during 2002 and 2003. The average 

investor share for home purchase loans was 

11.2 percent between 1999 and 2003. 

• In its comments, Fannie Mae noted that 

HUD should deduct subprime loans from 

investor loans. As shown in the middle 

portion of Table D.6a, deducting investor 

subprime loans reduces the overall investor 

share by approximately one-half percentage 

point (e.g., 1999–2003 average is reduced 

from 8.3 percent to 7.7 percent).21

• HMDA data for metropolitan areas 

(bottom portion of Table D.6.a) show a 

slightly lower investor share than HMDA 

data for both metropolitan and nonmetropolitan areas (top portion of Table 

D.6a). Between 1993 and 2003, the investor 

share in metropolitan areas averaged 7.5 

percent, as compared with 7.8 percent for the 

U.S. as a whole. During the more recent 

1999–2003 period, the differential was 

slightly higher, 7.8 percent versus 8.3 

percent. 

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Table D.6b provides information on 

investor loans from the 2001 RFS. During 

2001, investors accounted for 13.4 percent of 

all new single-family mortgages. Similar to 

the pattern in HMDA, the RFS-reported 

investor share of home purchase loans (15.7 

percent) was higher than the investor share 

(9.0 percent) of refinance loans (see Table 

D.6b). The RFS-based investor shares were 

similar for single-family mortgages originated 

in earlier years that had also survived (i.e.,

not prepaid) until the time of the RFS survey 

in 2001; for example, the investor share was 

13.0 percent for surviving 1999 mortgages 

and 14.0 percent for surviving year 2000 

mortgages. 

For comparison purposes, Table D.6c 

provides investor shares of the single-family 

mortgages purchased by the GSEs. Between 

1999 and 2003, the investor share of Fannie 

Mae’s single-family mortgage purchases 

ranged from 4.2 percent (1999) to 7.8 percent 

(2000). Freddie Mac’s investor share has been 

lower, ranging from 3.0 percent (2003) to 4.8 

percent (2000). The low figure for 2003 was 

due to the heavy refinancing of owner loans 

in that year.

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22 Dixie M. Blackley and James R. Follain, ‘‘A 

Critique of the Methodology Used to Determine 

Affordable Housing Goals for the Government 

Sponsored Housing Enterprises,’’ report prepared 

for Office of Policy Development and Research, 

Department of Housing and Urban Development, 

October 1995; and ‘‘HUD’s Market Share 

Methodology and its Housing Goals for the 

Government Sponsored Enterprises,’’ unpublished 

paper, March 1996. 23Blackley and Follain (1996), p. 20.

The RFS investor share of 13.4 percent in 

2001 is substantially larger than the 

corresponding HMDA investor share of 7.8 

percent. In their comments on the 2004 

proposed rule, as well as in their comments 

on HUD’s earlier 1995 and 2000 GSE rules, 

the GSEs have argued that HUD should use 

the HMDA-reported SF investor share. In its 

1995 and 2000 rules and the 2004 proposed 

GSE rule, HUD’s baseline model assumed a 

10 percent share for the SF investor group—

only slightly higher than the HMDA-based 

estimates; alternative models assuming 8 

percent and 12 percent were also considered. 

At that time, HUD argued that its baseline 

projection of 10 percent was probably quite 

conservative; however, given the uncertainty 

around the data, it was difficult to draw firm 

conclusions about the size of the singlefamily investor market, which necessitated 

that HUD conduct sensitivity analyses using 

investor shares (e.g., 8 percent) less than 10 

percent. HUD’s argument that its 10 percent 

baseline work was probably conservative was 

based on earlier work by Blackley and 

Follain. It is interesting to briefly review their 

work because they focused on the differences 

between RFS and HMDA data. 

2. Blackley and Follain Analysis of Investor 

Market Share 

As mentioned, during the 1995 rulemaking, HUD asked the Urban Institute to 

analyze the differences between the RFS and 

HMDA investor shares and determine which 

was the more reasonable. The Urban 

Institute’s analysis of this issue is contained 

in reports by Dixie Blackley and James 

Follain.22 Blackley and Follain provide 

reasons why HMDA should be adjusted 

upward as well as reasons why the RFS 

should be adjusted downward. They find that 

HMDA may understate the investor share of 

single-family mortgages because of ‘‘hidden 

investors’’ who falsely claim that a property 

is owner-occupied in order to more easily 

obtain mortgage financing. RFS may overstate 

the investor share of the market because units 

that are temporarily rented while the owner 

seeks another buyer may be counted as rental 

units in the RFS, even though rental status 

of such units may only be temporary. The 

RFS’s investor share should be adjusted 

downward in part because the RFS assigns 

all vacant properties to the rental group, but 

some of these are likely intended for the 

owner market, especially among one-unit 

properties. Blackley and Follain’s analysis of 

this issue suggests lowering the investor 

share from the 1991 RFS-reported investor 

share of 17.3 percent to about 14–15 percent.

Finally, Blackley and Follain note that a 

conservative estimate of the SF investor share 

is advisable because of the difficulty of 

measuring the magnitudes of the various 

effects that they analyzed. In their 1996 

paper, they conclude that 12 percent is a 

reasonable estimate of the investor share of 

single-family mortgage originations.23

Blackley and Follain caution that uncertainty 

exists around this estimate because of 

inadequate data.

3. GSE Comments on SF Rental Shares in the 

Proposed Rule 

Fannie Mae, Freddie Mac, and ICF thought 

that the investor share should be lower than 

the 10 percent used by HUD. While they 

agreed with HUD that the RFS provided the 

most accurate estimate of the true investor 

share of the market, they emphasized that 

lender reporting of investor loans to the GSEs 

was best proxied by HMDA data (which, of 

course, are based on lender reports). That is, 

the actual opportunities available to the GSEs 

in the SF investor market are best measured 

by data that lenders report based on 

information from actual loan applications. 

Based on this argument, they concluded that 

HUD’s market sizing analysis should rely on 

HMDA data, not RFS data. 

For example, Fannie Mae argued that the 

most valid measure of the single-family 

rental market is the same measure (lenderreported data to HMDA) against which the 

GSEs’ performance is measured. Fannie Mae 

points out that that two (10 percent and 12 

percent) of the three scenarios that HUD uses 

exceed the highest investor share ever 

reported in HMDA. Fannie rejects HUD’s 

justification (the 1991 RFS and the BlackleyFollain analysis) for using the higher 

scenarios because the lender reporting to the 

GSEs is closer to HMDA data than to the 

reporting in the RFS. Fannie Mae argues that 

the 1995 Blackley and Follain analysis 

bolsters its case against the RFS measures. 

Fannie Mae notes that both HUD and 

Blackley and Follain conclude that there is 

a reporting bias in the HMDA data that is not 

present in the RFS. The bias is in part due 

to hidden investors. At the time of 

origination, the property may be owneroccupied or may be intended to be owneroccupied. In fact, the property may become 

rental shortly after origination. As a result, 

the RFS reports a more accurate higher 

percentage of rental housing because it is a 

snapshot of housing, not a collection of 

information at mortgage origination. Fannie 

Mae says HUD uses the RFS because it is the 

more accurate measure of the rental market 

at any moment in time. However, Fannie Mae 

argues that the same bias in HMDA also 

exists in its own reporting when it acquires 

mortgages. According to Fannie Mae, an 

apples to apples comparison would make 

sure that the GSE goals contain the same 

biases that the GSE reports contain, rather 

than no bias. Finally, Fannie says that even 

HMDA overstates the investor share of the 

single-family market because of second 

homes. Second homes are reported in HMDA 

as ‘‘not owner occupied’’ to determine 

investor status but are not goals eligible. 

Therefore, according to Fannie Mae, HUD’s 

use of HMDA would overestimate the goalseligible share of the single-family market. As 

a result of these data and methodology 

issues, Fannie believes HUD miscalculates 

the mix of units in the rental market and 

overstates the size of the goals-eligible 

portion of the rental market.

Similarly, Freddie Mac concluded that 

HUD overestimated the SF investor share of 

the market because it relied on the RFS rather 

than HMDA. Freddie Mac says investorowners have an incentive to claim falsely 

they are owner-occupants because of higher 

underwriting standards and higher interest 

rates on investor-owner properties. 

According to Freddie Mac, these incentives 

likely result in HMDA’s undercounting SF 

investor loans relative to the more accurate 

counts of investor loans from the RFS. 

Freddie Mac concludes as follows: 

This undercounting [on the part of HMDA], 

however, is exactly what is desired when 

estimating the goal share available to the 

GSEs. Because the GSEs’ information on their 

loans has the same ‘‘bias’’ as does the HMDA 

data. * * * The HMDA data, therefore, are 

more appropriate to estimating the market for 

goal setting than are the RFS data. (p.II–6) 

Essentially, Freddie Mac concludes that 

HUD’s market estimates should measure 

opportunities in the marketplace that are 

actually available to the GSEs. Such 

opportunities are best measured by lenderreported HMDA data, not the more accurate 

RFS data. ICF reaches a similar conclusion, 

as it states that ‘‘HMDA data, or its 

equivalent, are what the GSEs’ performance 

will be measured against and is therefore the 

appropriate metric for estimating market goal 

shares’’ (ICF Report, p.20). 

4. SF Investor Shares in the Final Rule 

In this final Rule, HUD has switched to a 

HMDA-based system and provides overall 

market share estimates for a range of singlefamily investor shares. For each year between 

1993 and 2003, the top-right-hand-side 

portion of Table D.6a shows the projected 

investor share in a ‘‘home purchase 

environment’’ assuming a refinance share of 

35 percent, 40 percent, and 45 percent. 

Refinance shares greater than 35 percent are 

included here because single-family investor 

loans typically have higher refinance shares 

than single-family-owner loans. As shown in 

Table D.6a, the average 1993–2003, HMDAbased investor share would have been 8.5 

(8.4) percent if the investor refinance share 

had been 40 (45) percent during this period. 

During the more recent 1999–2003 period, 

which was characterized by particularly high 

HMDA-reported investor shares for home 

purchase loans, the average investor share 

would have been 9.4 (9.2) percent if the 

investor refinance share had been 40 (45) 

percent during this period. As noted earlier, 

the HMDA-reported investor shares for 

metropolitan areas are slightly lower than 

those for the entire U.S. As shown in the 

bottom-right-hand portion of Table D.6a, the 

average 1999–2003, HMDA-based investor 

share for metropolitan areas would have been 

8.9 (8.7) percent if the investor refinance 

share had been 40 (45) percent during this 

period. 

The above analysis suggests that the 

HMDA-reported investor share of a future 

home purchase market will probably be 

around 8.5–9.0 percent, or possibly higher if 

the recent figures for home purchase loans 

hold up (in this case, around 9.5 percent). 

63842 Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 

Thus, HUD’s analysis of market shares in 

Sections F–H will report overall market 

estimates for a range of SF investor shares—

8.0 percent, 8.5 percent, 9.0 percent, and 10.5 

percent. 

5. Single-Family Market in Terms of Unit 

Shares 

The market share estimates for the housing 

goals need to be expressed as percentages of 

units rather than as percentages of mortgages. 

Since a SF 2–4 and a SF-investor mortgage 

finances more than one dwelling unit, 

adjustments reflecting units-per-mortgage 

have to be made in order to arrive at the 

distribution of newly-financed single-family 

dwelling units. From HMDA, one can obtain 

the share of investor mortgages (those 

reported in Table D.6a) and the share of 

owner mortgages (obtained by subtracting the 

share of investor mortgages from 100 

percent). HMDA does not disaggregate the 

SF-owner (SF–O) mortgage category into its 

two components: SF–O 1-Unit mortgages and 

SF–O 2–4 mortgages. To arrive at shares of 

SF financed dwelling units, two sets of 

adjustments have to be made to the HMDA 

data. 

First, the owner-occupied HMDA data have 

to be disaggregated between SF–O 1-Unit and 

SF 2–4 mortgages. HUD’s 2004 proposed GSE 

rule assumed that SF 2–4 mortgages 

accounted for 2.0 percent of all single-family 

mortgages. Based on the 2001 RFS data, this 

percentage is reduced to about 1.6 percent in 

this Final Rule. In 2001, the RFS shows the 

following distribution across the three singlefamily mortgage types: (a) 85.1 percent for 

SF–O 1-Unit mortgages; (b) 1.5 percent for 

SF–O 2–4 mortgages; and (c) 13.4 percent for 

SF-Investor mortgages (see Table D.6b). Thus, 

according to 2001 RFS data, SF 2–4 

mortgages represent 1.73 percent of all 

single-family-owner mortgages (obtained by 

dividing (b) by the sum of (a) and (b)). In the 

market projection models, the SF-investor 

mortgage share is assumed to be lower than 

the RFS-reported figure of 13.4 percent. If the 

SF-investor share is 8.5 percent, then the SF–

O share is 91.5 percent, which is split as 

follows: 1.58 percent for SF–O 2–4 mortgages 

(obtained by multiplying 0.0173 by 91.5 

percent) and 89.92 percent for SF–O 1-Unit 

mortgages (obtained by subtracting 1.58 

percent for the overall SF–O share of 91.5 

percent). Thus, in this scenario, the 

distribution across SF mortgage types would 

be as follows: (d) 89.92 percent for SF–O 1-

Unit mortgages; (b) 1.58 percent for SF–O 2–

4 mortgages; and (c) 8.50 percent for SFInvestor mortgages. Table D.6d shows the 

distribution of SF mortgages under the 

various assumptions assumed in Sections F–

H. For comparison purposes, the SF–O 2–4 

shares for the GSEs are reported in Table 

D.6c. The 1999 to 2003 shares for Fannie Mae 

are approximately 2.0 percent while those for 

Freddie Mac are approximately 1.5 percent. 

Thus, the Fannie Mae shares are consistent 

with the 2.0 percent assumption used in the 

2004 proposed rule while the Freddie Mac 

shares are consistent with the 1.6 percent 

assumption used in this Final Rule. 

Sensitivity analyses in Sections F–G will 

show the effects of using the 2.0 percent 

assumption (as compared with the 1.6 

percent baseline). 

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Second, the resulting mortgage-based 

distributions have to be shifted to unit-based 

distributions by applying the unit-permortgage assumptions. The 2004 proposed 

GSE rule assumed the following: 2.25 units 

per SF 2–4 property and 1.35 units per SF 

investor property. Based on RFS data, these 

numbers are reduced slightly to the 

following: 2.2 units per SF 2–4 property and 

1.3 units per SF investor property. These 

figures are based on 1999–2001 averages from 

the RFS. The corresponding 2001 figures 

from the RFS were 2.1 and 1.4, respectively. 

As shown in Table D.6d, the GSE data has 

consistently been around the figures in the 

2004 proposed GSE rule, which were 2.25 

and 1.35, respectively. Thus, it was decided 

to use the 1999–01 RFS averages which drop 

each units-per-mortgage figure by 0.05. 

Sensitivity analysis shows that this issue 

(whether to use the 1999–01 combination of 

2.2/1.3 or to use the 2001 combination of 2.1/

1.4) has little impact on the market sizing 

results. 

Based on these calculations, the percentage 

distribution of newly-mortgaged singlefamily dwelling units was derived for each of 

the various estimates of the investor share of 

single-family mortgages. The results are 

presented in Table D.6e for investor 

percentage shares of 8.0, 8.5, 9.0, and 9.5. 

Three points should be made about these 

data.

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24 It should be mentioned that ICF’s 12.0 percent 

assumption for the SF rental share seems at odds 

with ICF’s Exhibit 6.4, which suggests that ICF’s 

1994–2002 average SF rental share is 14.9 percent. 

A 14.9 percent SF rental share would be consistent 

with a 12 percent investor mortgage share.

First, notice that the rental categories 

represent a larger share of the unit-based 

market than they did of the mortgage-based 

market reported earlier. For example, when 

the SF-investor category represents 8.5 

percent of all SF mortgages, it represents 10.6 

percent of all SF units financed. This, of 

course, follows directly from applying the 

loan-per-unit expansion factors. 

Second, notice that the ‘‘All SF-Rental 

Units’’ column highlights the share of the 

single-family mortgage market accounted for 

by all single-family rental units, for both SF–

O 2–4 properties and SF-Investor properties. 

For example, when the investor mortgage 

share is 8.5 percent, single-family rental units 

(in SF 2–4 properties as well as in SF 

investor properties) account for 12.4 percent 

of all newly-mortgaged SF units. This singlefamily rental share compares with 15.1 

percent under the baseline assumptions of 

the 2004 proposed GSE Rule; the 15.1 

percent figure is reported in Table D.6b of the 

2004 proposed GSE rule. If the single-family 

investor share is 9.0 (9.5) percent, then 

single-family rental units account for account 

for 13.0 (13.6) percent of all newly-mortgaged 

SF units. 

ICF projected that SF rental units would 

account for 12.0 percent of all single-familyfinanced units during the 2005–2008 

projection period (ICF Appendix, p.126). 

Under the units-per-mortgage and SF–O 2–4 

share assumptions that ICF was using (2.25 

for SF–O 2–4 and 1.35 for SF–Investor and 

a 2.0 percent share for SF–O 2–4 mortgages), 

ICF’s 12-percent assumption for single-family 

rental units translates back to an investor 

mortgage share of 7.5 percent.24

In its projections, Fannie Mae assumes 8.0 

percent for the investor share of mortgages, 

a figure Fannie Mae says is consistent with 

HMDA data (Fannie Mae Appendix I, Table 

1.11, p. I–38). Under the 2001 RFS 

assumptions (see above), this translates into 

a single-family rental share (on a units basis) 

of 11.8 percent. Under the units-per-loan and 

SF–O 2–4 assumptions of the proposed rule, 

this translates into a single-family rental 

share (expressed on a units basis) of 12.7 

percent. 

Third, if the investor mortgage share were 

13 percent (the 2001 figure from the RFS), 

single-family rental units would account for 

over 17 percent of all newly-mortgaged 

single-family units. 

The unit distributions reported for the 

GSEs in Table D.6f will be discussed in the 

next section.

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25The property distribution reported in Table D.1 

is an example of the output of the market share 

model. Thus, this section completes Step 1 of the 

three-step procedure outlined above in Section B.

26According to estimates by the Mortgage 

Bankers Association of America (MBAA), the 

conventional share of the 1–4 family market was 

between 86 and 88 percent of the market from 1993 

to 1999, with a one-time low of 81 percent in 1994. 

Calculated from ‘‘1–4 Family Mortgage 

Originations’’ tables (Table 1—Industry and Table 

2—Conventional Loans) from ‘‘MBAA Mortgage and 

Market Data,’’ at www.MBAAa.org/marketkdata/ as 

of July 13, 2000. More recent unpublished estimates 

by MBAA are slightly higher. As discussed in the 

text, the market sizing shares are affected by 

parameters other than this one, such as the 

multifamily share of newly-mortgaged dwelling 

units.

27 In its August 17, 2004 forecast, Fannie Mae 

projected approximately $1.6 billion for 2005 and 

2006 while the MBAA projected $1.8 billion for 

2005 in its August 13, 2004 forecast. As discussed 

later, single-family originations could differ from 

$1,700 billion during the 2005–2008 period that the 

goals will be in effect. As recent experience shows, 

market projections often change. For example, in 

January 2003, the MBAA projected $1,246 billion 

for 2003; of course, actual 2003 mortgage 

originations were triple the latter amount. (See 

http://www.MBAAa.org/marketdata/forecasts for 

January 2003 Mortgage Finance Forecasts.) While 

Sections F–H will report the effects on the market 

estimates of alternative estimates of single-family 

mortgage originations, it should be emphasized that 

the important parameter for the market sizing 

estimates is the share of single-family-owner units 

relative to the share of single-family and 

multifamily rental units, not the absolute level of 

single-family originations.

28The model requires an estimated refinance rate 

because purchase and refinance loans can have 

different shares of goals-qualifying units. In 2003, 

the refinance rate was almost 70 percent. In its 

August 13, 2004 forecast, the MBAA projects 25 

percent for 2005, as did Fannie Mae in its August 

17, 2004 forecast. The baseline model uses a higher 

refinance rate of 35 percent because conforming 

conventional loans tend to refinance at a higher rate 

than the overall market. Sensitivity analyses for 

alternative refinance rates are presented in Sections 

F–H.

29The average 2002 purchase loan amount is 

estimated at $135,060 for owner occupied units 

using 2002 HMDA average loan amounts for singlefamily home purchase loans in metropolitan areas. 

A small adjustment is made to this figure to account 

for a small number of two-to-four and investor 

properties (see Section D above). This produces an 

average purchase loan size of $133,458 for 2002 

which is then inflated 3 percent a year for three 

years and then rounded to arrive at an estimated 

$146,000 average loan size for home purchase loans 

in 2005.

30The average refinance loan amount is estimated 

by averaging the relationship between HMDA 

average purchase and refinance loan amounts for 

1999 and 2000, which were non-refinance 

environments. Applying this average of 90 percent 

(refinance loan amount/purchase loan amount) to 

the $146,000 average loan amount for purchase 

loans gives a rounded estimate of $131,000 for 

average refinance loan amounts. When refinance 

environments are used, $146,000 average loan 

amounts are used for both purchase and refinance 

loans. This relationship is consistent with the 

observed relationship in past refinance years such 

as 1998, 2001, and 2002.

31Based on the 2001 RFS, there is an average of 

2.2 housing units per mortgage for 2–4 properties 

and 1.3 units per mortgage for single-family 

investor properties. See earlier discussion.

E. HUD’s Market Share Model 

This section integrates findings from the 

previous two sections about the size of the 

multifamily mortgage market and the relative 

distribution of single-family owner and rental 

mortgages into a single model of the mortgage 

market. The section provides the basic 

equations for HUD’s market share model and 

identifies the remaining parameters that must 

be estimated. 

The output of this section is a unit-based 

distribution for the four property types 

discussed in Section B.25 Sections F–H will 

apply goal percentages to this property 

distribution in order to determine the size of 

the mortgage market for each of the three 

housing goals.

1. Basic Equations for Determining Units 

Financed in the Mortgage Market 

The model first estimates the number of 

dwelling units financed by conventional 

conforming mortgage originations for each of 

the four property types. It then determines 

each property type’s share of the total 

number of dwelling units financed. 

a. Single-Family Units 

This section estimates the number of 

single-family units that will be financed in 

the conventional conforming market, where 

single-family units (SF–UNITS) are defined 

as:

SF–UNITS = SF–O + SF 2–4 + SF–

INVESTOR

First, the dollar volume of conventional 

conforming single-family mortgages 

(CCSFM$) is derived as follows:

(1) CCSFM$ = CONV% * CONF% * SFORIG$ 

where

CONV% = conventional mortgage 

originations as a percent of total 

mortgage originations; estimated to be 

88%.26

CONF% = conforming mortgage originations 

(measured in dollars) as a percent of 

conventional single-family originations; 

forecasted to be 80% by industry. 

SFORIG$ = dollar volume of single-family 

one-to-four unit mortgages; $1,700 

billion is used here as a starting 

assumption to reflect market conditions 

during the years 2005–2008.27 While 

alternative assumptions will be 

examined, it must be emphasized that 

the important concept for deriving the 

goal-qualifying market shares is the 

relative importance of single-family 

versus multifamily mortgage originations 

(the ‘‘multifamily mix’’ discussed in 

Section C) rather than the total dollar 

volume of single-family originations 

considered in isolation.

Substituting these values into (1) yields an 

estimate for the conventional conforming 

market (CCSFM$) of $1,197 billion. 

Second, the number of conventional 

conforming single-family mortgages 

(CCSFM#) is derived as follows:

(2) CCSFM# = (CCSFM$ * (1–REFI)/

PSFLOAN$) + (CCSFM$ * REFI)/

RSFLOAN$) 

where 

REFI= the refinance rate, assumed to be 35 

percent for the baseline.28

PSFLOAN$ = the average conventional 

conforming purchase mortgage amount 

for single-family properties; estimated to 

be $146,000.29

RSFLOAN$ = the average conventional 

conforming refinance mortgage amount 

for single-family properties; estimated to 

be $131,000.30

Substituting these values into (2) yields an 

estimate of 8.5 million mortgages. 

Third, the total number of single-family 

mortgages is divided among the three singlefamily property types. Using the 89.9/1.6/8.5 

percentage distribution for single-family 

mortgages (see Section D), the following 

results are obtained:

(3a) SF–OM# = 0.899 * CCSFM# = number 

of owner-occupied, one-unit mortgages = 

7.642 million. 

(3b) SF–2–4M# = 0.016 * CCSFM# = number 

of owner-occupied, two-to-four unit 

mortgages = 0.136 million. 

(3c) SF–INVM# = 0.085 * CCSFM# = number 

of one-to-four unit investor mortgages = 

0.723 million.

Fourth, the number of dwelling units 

financed for the three single-family property 

types is derived as follows:

(4a) SF–O = SF–OM# + SF–2–4M# = number 

of owner-occupied dwelling units 

financed = 7.778 million. 

(4b) SF 2–4 = 1.2 * SF–2–4M# = number of 

rental units in 2–4 properties where an 

owner occupies one of the units = 0.163 

million.31

(4c) SF–INVESTOR= 1.3 * SF–INVM# = 

number of single-family investor 

dwelling units financed = 0.940 million.

Fifth, summing equations 4a–4c gives the 

projected number of newly-mortgaged singlefamily units (SF–UNITS):

(5) SF–UNITS = SF–O + SF 2–4 + SF–

INVESTOR = 8.915 million 

b. Multifamily Units 

The number of multifamily dwelling units 

(MF–UNITS) financed by conventional 

conforming multifamily originations is 

calculated by the following series of 

equations:

(5a) TOTAL = SF–UNITS + MF–UNITS 

(5b) MF–UNITS = MF–MIX * TOTAL = MF–

MIX * (SF–UNITS + MF–UNITS) = [MF–

MIX/(1 ¥ MF–MIX)] * SF–UNITS where 

MF–MIX = the ‘‘multifamily mix’’, or the 

percentage of all newly-mortgaged 

dwelling units that are multifamily; as 

discussed in Section C, alternative 

estimates of the multifamily market will 

be included in the analysis. As explained 

in Section C above, the baseline model 

assumes a multifamily mix of 15 percent; 

results are also presented in the basic 

market tables of Sections F–H for a 

higher (16.0 percent) multifamily mix 

and for lower (12.25 percent, 13.5 

percent and 14.25 percent) multifamily 

mixes. In addition, further sensitivity 

analyses are reported in those sections 

for even lower multifamily mixes that 

could occur during periods of heavy 

single-family refinancing activity.

Assuming a multifamily mix of 15 percent 

and solving (5b) yields the following:

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32The share of the mortgage market accounted for 

by owner occupants is (SF–O)/TOTAL; the share of 

the market accounted for by all single-family rental 

units is SF–RENTAL/TOTAL; and so on.

(5c) MF–UNITS = [0.15/0.85] * SF–UNITS = 

0.176 * SF–UNITS = 1.6 million. 

c. Total Units Financed 

The total number of dwelling units 

financed by the conventional conforming 

mortgage market (TOTAL) can be expressed 

in three useful ways:

(6a) TOTAL = SF–UNITS + MF–UNITS = 

10.6 million (or more precisely, 

10,632,145 units) 

(6b) TOTAL = SF–O + SF 2–4 + SF–

INVESTOR + MF– UNITS 

(6c) TOTAL = SF–O + SF–RENTAL + MF–

UNITS where SF–RENTAL equals SF–2–

4 plus SF–INVESTOR 

2. Dwelling Unit Distributions by Property 

Type 

The next step is to express the number of 

dwelling units financed for each property 

type as a percentage of the total number of 

units financed by conventional conforming 

mortgage originations.32

The projections used above in equations 

(1)–(6) produce the following distributions of 

financed units by property type:

% Share 

SF–O .......................................... 74.5

SF 2–4 ........................................ 1.5

SF INVESTOR ............................ 9.0

% Share 

MF–UNITS .................................. 15.0

Total ..................................... 100.0

or 

SF–O .......................................... 74.5

SF–RENTER .............................. 10.5

MF–UNITS .................................. 15.0

Total ............................................ 100.0

Sections C and D discussed alternative 

projections for the mix of multifamily 

originations and the investor share of singlefamily mortgages. This appendix will report 

results for multifamily mixes of 13.5 percent, 

15.0 percent, and 16.0 percent but sensitivity 

analyses for two other multifamily mix 

assumptions (e.g., the 12.3 percent 

assumption used by Fannie Mae and the 14.2 

assumption used by ICF) will also be 

reported. Under the baseline 15.0 percent 

multifamily mix, the newly-mortgaged unit 

distribution would be 74.5 percent for SingleFamily Owner, 10.5 percent for SingleFamily Renter, and 15.5 percent for 

Multifamily-Units. The analysis in sections 

F–H will focus on goals-qualifying market 

shares for this property distribution as well 

as the ones noted above. 

As discussed in Section D, the basic tables 

providing the goals-qualifying market 

estimates in this appendix will report results 

for the following investor shares of singlefamily mortgages—8 percent, 8.5 percent, 9.0 

percent, and 9.5 percent. For reasons 

discussed in Section D, these investor 

mortgage shares are lower than the range (8.0 

percent, 10.0 percent, and 12.0 percent) 

considered in the 2004 proposed GSE rule. 

The middle values (8.5 percent and 9.0 

percent) are probably the ones that should be 

considered as ‘‘baseline’’ projections; the 

above example used a mortgage share of 8.5 

percent, but 9.0 percent could also have been 

used to characterize a home purchase 

environment. However, HUD recognizes the 

uncertainty of projecting origination volume 

in markets such as single-family investor 

properties; therefore, the analysis in Sections 

F–H considers market assumptions other 

than these baseline assumptions. 

Table D.7 reports the unit-based 

distributions produced by HUD’s market 

share model for different combinations of 

these projections. Unit-based distributions 

are reported for each combination of a 

multifamily mix (12.25, 13.5, 14.25, 15.0, and 

16.0) and investor mortgage share (8.0, 8.5, 

9.0, and 9.5). The effects of the different 

projections can best be seen by examining the 

owner category which varies by 4.8 

percentage points, from a low of 72.6 percent 

(multifamily mix of 16.0 percent coupled 

with an investor mortgage share of 9.5 

percent) to a high of 77.4 percent 

(multifamily mix of 12.25 percent coupled 

with an investor mortgage share of 8.0 

percent). The overall rental share is also 

highlighted in Table D.7, varying from 22.6 

percent to 27.4 percent. 

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33Because of rounding, the two rental component 

shares do not add to the overall rental share.

34Because of rounding, the two rental component 

shares do not add to the overall rental share.

35HMDA data are expressed in terms of number 

of loans rather than number of units. In addition, 

HMDA data do not distinguish between owneroccupied one-unit properties and owner-occupied 

2–4 properties. This is not a particular problem for 

this section’s analysis of owner incomes.

36Sensitivity analyses will focus on how the 

results change during a heavy refinancing 

environment.

The baseline projection of newlymortgaged units in the 2004 proposed GSE 

rule was 72.2 percent for owner units, 12.8 

percent for single-family rental units, and 

15.0 percent for multifamily units. In this 

Final Rule, the baseline projection is 74.5 

percent for owner units, 10.5 percent for 

single-family rental units, and 15.0 percent 

for multifamily units, if an investor mortgage 

share of 8.5 percent is used. If an investor 

share of 9.0 percent is used, then the baseline 

projection is 74.0 percent for owner units, 

11.0 percent for single-family rental units, 

and 15.0 percent for multifamily units. Either 

way, compared with the 2004 proposed GSE 

rule, the rental share of financed dwelling 

units has dropped by approximately two 

percentage points due to the lower HMDAbased investor shares used in the Final Rule. 

The unit distribution in ICF’s projection 

model for 2005–2008 averaged 75.5 percent 

for owner units, 10.3 percent for singlefamily rental units, and 14.2 percent for 

multifamily units, which produces an overall 

rental share of 24.5 percent, a figure closed 

to those reported above (25.5–26.0 percent). 

The unit distribution used by Fannie Mae 

was approximately 77.4 percent for owner 

units, 10.4 percent for single-family rental 

units, and 12.3 percent for multifamily units, 

which produces an overall rental share of 

22.6 percent,33 a figure less than used by ICF 

(24.5 percent) or HUD (25.0–26.0 percent). 

Notice that Fannie Mae and ICF assume 

similar single-family rental shares (about 10.3 

percent), but ICF assumes a larger 

multifamily mix than Fannie Mae (14.2 

percent versus 12.3 percent). HUD’s singlefamily rental shares (10.5–11.0 percent) are 

slightly higher than the shares (about 10.3 

percent) used by ICF and Fannie Mae. HUD’s 

multifamily baseline share (15.0 percent) is 

slightly higher than the average (14.2 

percent) of ICF’s best estimate, and 

significantly higher than Fannie Mae’s 

assumed multifamily mix (12.3).

As discussed in Sections C and D, the 

Residential Finance Survey is the only 

mortgage data source that provides unitbased property distributions directly 

comparable to those reported below. Based 

on RFS data for 2001, HUD estimated that, 

of total dwelling units in properties financed 

by recently acquired conventional 

conforming mortgages, 68.3 percent were 

owner-occupied units, 16.5 percent were 

single-family rental units, and 15.2 percent 

were multifamily rental units. Thus, the RFS 

presents a much lower owner share than does 

HUD’s, ICF’s, or Fannie Mae’s models. See 

Sections C and D for further discussion of the 

RFS. 

Finally, it is interesting to compare the 

above market-based distributions of financed 

units with the distributions of units financed 

by mortgages purchased by Fannie Mae and 

Freddie Mac. As shown in Table D.6f, the 

1993–2003 averages (unweighted) for Fannie 

Mae were 81.0 percent for owner units, 9.0 

percent for single-family rental units, and 

10.0 percent for multifamily units, which 

produces an overall rental share of 19.0 

percent. During the year 2000, Fannie Mae’s 

overall rental share did reach a peak of 24.1 

percent. Freddie Mac’s rental shares have 

been markedly lower than Fannie Mae’s. The 

1993–2003 averages (unweighted) for Freddie 

Mac were 84.3 percent for owner units, 6.3 

percent for single-family rental units, and 9.3 

percent for multifamily units, which 

produces an overall rental share of 15.7 

percent.34 Freddie Mac’s rental share did 

peak at 17.5 percent in 2000. Still, it is clear 

that the market-based distributions project 

much higher rental shares than Freddie Mac 

has been purchasing. For example, the HUD 

projection of a 25-percent rental share is over 

nine percentage points higher than Freddie 

Mac’s 1999–2003 average rental share (15.7 

percent) and over seven percentage points 

higher than Freddie Mac’s peak rental share 

(17.5 percent in 2000). The 31.7-percent 

rental share from the RFS is 16 percentage 

points higher than Freddie Mac’s 1999–2003 

average rental share (15.7 percent) and over 

14 percentage points higher than Freddie 

Mac’s peak rental share (17.5 percent in 

2000).

F. Size of the Conventional Conforming 

Mortgage Market Serving Low- and ModerateIncome Families 

This section estimates the size of the lowand moderate-income market by applying 

low- and moderate-income percentages to the 

property shares given in Table D.7. This 

section essentially accomplishes Steps 2 and 

3 of the three-step procedure discussed in 

Section B.2. 

Technical issues and data adjustments 

related to the low- and moderate-income 

percentages for owners and renters are 

discussed in the first two subsections. Then, 

estimates of the size of the low- and 

moderate-income market are presented along 

with several sensitivity analyses. Based on 

these analyses, HUD concludes that 51–56 

percent is a reasonable estimate of the 

mortgage market’s low- and moderate-income 

share for the four years (2005–2008) when 

the new goals will be in effect. 

1. Low- and Moderate-Income Percentage for 

Single-Family-Owner Mortgages 

a. HMDA Data 

The most important determinant of the 

low- and moderate-income share of the 

mortgage market is the income distribution of 

single-family borrowers. HMDA reports 

annual income data for families who live in 

metropolitan areas and purchase a home or 

refinance their existing mortgage.35 The data 

cover conventional mortgages below the 

conforming loan limit, which was $322,700 

in 2003. Table D.8a gives the percentage of 

mortgages originated for low- and moderateincome families for the years 1992–2003. 

Data are presented for home purchase, 

refinance, and all single-family-owner loans. 

The discussion below will often focus on 

home purchase loans because they typically 

account for the majority of all single-familyowner mortgages.36 For each year, a low- and 

moderate-income percentage is also reported 

for the conforming market without B&C 

loans. 

Table D.8a also reports similar data for 

very-low-income families (that is, families 

with incomes less than 60 percent of area 

median income). As discussed in Section H, 

very-low-income families are the main 

component of the special affordable mortgage 

market.

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37The annual averages of the goals-qualifying 

mortgages reported in this appendix are unweighted 

averages; for analyses using weighted averages see

Appendix A.

Two trends in the income data should be 

mentioned—one related to the growth in the 

market’s funding of low- and moderateincome families during the 1990s (and 

particularly the growth since 1998 which was 

the last year analyzed in HUD’s 2000 GSE 

Rule); and the other related to changes in the 

borrower income distributions for refinance 

and home purchase mortgages. Throughout 

this appendix, ‘‘low- and moderate-income’’

will often be referred to as ‘‘low-mod’. 

Recent Trends in the Market Share for 

Lower Income Borrowers. First, focus on the 

percentages in Table D.8a for the total (both 

home purchase and refinance) conforming 

market. After averaging about 30 percent 

during 1992–93, the percentage of borrowers 

with less than area median income jumped 

to 41.0 percent in 1994, and remained above 

40 percent through 2003. Over the ten-year 

period, 1994 to 2003, the low-mod share of 

the total market averaged 42.9 percent (or 

42.2 percent if B&C loans are excluded from 

the market totals).37 The share of the market 

accounted for by very-low-income borrowers 

followed a similar trend, increasing from 6–

7 percent in 1992–93 to about 12 percent in 

1994 and averaging 13.2 percent during the 

1994-to-2003 period (or 12.7 percent if B&C 

loans are excluded).

Next, consider the percentages for home 

purchase loans. The share of the home loan 

market accounted for by less-than-medianincome borrowers increased from 34.4 

percent in 1992 to 44.7 percent in 2003. 

Within the 1994-to-2002 period, the low-mod 

share of the home purchase market averaged 

44.4 percent between 1999 and 2003, 

compared with 42.1 percent between 1994 

and 1998. Similarly, the very-low-income 

share of the home purchase market was also 

higher during the 1999-to-2002 period than 

during the 1994-to-1998 period (14.1 percent 

versus 12.6 percent). Note that within the 

more recent period, the low-mod share for 

home purchase loans was particularly high 

during 1999 (45.2 percent) and 2000 (44.3 

percent) before falling slightly in 2001 (43.2 

percent), only to rebound again in 2002 (44.8 

percent) and 2003 (44.7 percent). As shown 

in Table D.8a, the low-mod shares do not 

change much when B&C home loans are 

excluded from the market definition; this is 

because B&C loans are mainly refinance 

loans. 

It appears that the affordable lending 

market for home purchase loans is even 

stronger today than when HUD wrote the 

2000 Rule, which covered market data 

through 1998. The very-low-income and lowmod percentages were higher during 1999 to 

2003 than they were during the earlier 

period. In addition, when HUD wrote the 

2000 Rule, there had been five years (1994–

98) of solid affordable lending for lowerincome borrowers. Now, with five additional 

years of data for 1999–2003, there have been 

ten years of strong affordable lending. 

Of course, it is recognized that lending 

patterns could change with sharp changes in 

interest rates and the economy. However, the 

fact that lending to low-income families has 

remained at a high level for ten years 

demonstrates that the market has changed in 

fundamental ways from the mortgage market 

of the early 1990s. The numerous innovative 

products and outreach programs that the 

industry has developed to attract lowerincome families into the homeownership and 

mortgage markets appear to be working and 

there is no reason to believe that they will 

not continue to assist in closing troubling 

homeownership gaps that exist today. As 

explained in Appendix A, the demand for 

homeownership on the part of minorities, 

immigrants and non-traditional borrowers 

should help to maintain activity in the 

affordable portion of the mortgage market. 

Thus, while economic recession or higher 

interest rates would likely reduce the lowand moderate-income share of mortgage 

originations, there is evidence that the lowmod market might not return to the low 

levels of the early 1990s. There is also 

evidence that the affordable lending market 

increased slightly since 1998, although it is 

recognized that this could be due to the 

recent period of historically low interest 

rates. 

Refinance Mortgages. In the 2000 Rule, 

HUD’s market projection model assumed that 

low-mod borrowers represented a smaller 

share of refinance mortgages than they do of 

home purchase mortgages. However, as 

shown in Table D.8a, the income 

characteristics of borrowers refinancing 

mortgages seem to depend on the overall 

level of refinancing in the market. During the 

refinancing wave of 1992 and 1993, 

refinancing borrowers had much higher 

incomes than borrowers purchasing homes. 

For example, during 1993 low- and 

moderate-income borrowers accounted for 

29.3 percent of refinance mortgages, 

compared to 38.9 percent of home purchase 

borrowers. While this same pattern was 

exhibited during the two recent refinancing 

periods (1998 and 2001–2002–2003), the 

differentials were much smaller—during 

2001–2002–2003 (1998), low-mod borrowers 

accounted for 41.5 (39.7) percent of refinance 

loans, compared with 44.2 (43.0) percent of 

home purchase loans. However, the refinance 

effect was still evident, as can be seen by the 

almost ten percentage point drop in the lowmod percentage for refinance loans between 

2000 (a low refinance year) and 2001 (a high 

refinance year). 

On the other hand, for recent years 

characterized by a low level of refinancing, 

the low-mod share of refinance mortgages has 

been about the same or even greater than that 

of home purchase mortgages. As shown in 

Table D.8a, there was little difference in the 

very-low-income and low-mod shares of 

refinance and home purchase loans during 

1995 and 1996. In 1997, 1999, and 2000, the 

two lower-income shares (i.e., very-lowincome and low-mod shares) of refinance 

mortgages were significantly higher than the 

lower-income shares of home purchase loans. 

To a certain extent, this pattern was 

influenced by the growth of subprime loans, 

which are mainly refinance loans. If B&C 

loans are excluded from the market 

definition, the home purchase and refinance 

percentages are approximately the same in 

1997 and 1999, as well as in 1995 and 1996. 

(See Table D.8a.) Even after excluding all 

subprime loans from the market definition in 

1997 and 1999, the very-low-income and 

low-mod shares for refinance loans are only 

slightly less (about one percentage point) 

than those for home purchase loans. 

The year 2000 stands out because of the 

extremely high lower-income shares for 

refinance loans. In that year, the low-mod 

(very-low-income) share of refinance loans 

was 7.0 (4.4) percentage points higher than 

the low-mod (very-low-income) share of 

home purchase loans; this differential is 

reduced to 5.4 (3.3) percent if B&C loans are 

excluded from the market definition (see 

Table D.8a). The differential for 2000 is 

reduced further to 2.8 (1.5) percent if all 

subprime loans (both A-minus and B&C) are 

excluded from the market definition (not 

reported). While the projection model 

(explained below) for years 2005–08 will 

input low-mod percentages for the entire 

conforming market, the model will exclude 

the effects of B&C loans. Sensitivity analyses 

will also be conducted showing the effects on 

the overall market estimates of excluding all 

subprime loans as well as other loan 

categories such as manufactured housing 

loans. 

2000 Census Data and New OMB 

Metropolitan Area Definitions. Going 

forward, HUD will be re-benchmarking its 

median incomes for metropolitan areas and 

non-metropolitan counties based on 2000 

Census median incomes, and will be 

incorporating the effects of the new OMB 

metropolitan area definitions. Thus, under 

the new housing goals, the GSEs’

performance will be scored based on 2000 

Census data and new OMB definitions of 

metropolitan areas (labeled ‘‘CBSA 

definitions’’). One issue concerns whether 

the new data and the new definitions will 

result in lower or higher low-mod 

percentages relative to historical low-mod 

percentages based on the 1990 Census and 

earlier OMB definitions of metropolitan areas 

(labeled ‘‘MSA definitions’’). HUD projected 

the effects of these two changes on the lowand moderate-income shares of the singlefamily-owner market for the years 1999–

2003. The middle portion of Table D.8b 

reports low-mod shares for single-familyowner loans under the MSA and CBSA 

approaches for the years 1999–2003. Except 

for 2003, the low-mod shares for both home 

purchase and total SFO loans are lower 

under the new CBSA approach than under 

the old MSA approach. Because the results 

for 1999–2002 differed from the results for 

2003, these two periods are considered 

separately. Under the historical data, the 

average low-mod share of the conventional 

conforming market was 44.4 percent for 

home purchase loans (unweighted average of 

1999–2002 percentages in Table D.8a); the 

corresponding average with the projected 

data was 43.2 percent, yielding a differential 

of 1.2 percentage points. For total (both home 

purchase and refinance) loans, the average 

low-mod share of the conventional 

conforming market based on historical data 

was 44.6 percent (unweighted average of 

1999–2002 percentages); the corresponding 

average with the projected data was 43.4 

63854 Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 

38Between 1999 and 2002, the average singlefamily-owner differential between the historical and 

projected low-mod percentages was 1.1 percentage 

point for Fannie Mae and 1.3 percentage point for 

Freddie Mac.

percent, again yielding a differential of 1.2 

percentage points, with the same pattern 

exhibited for the annual differentials.38 It 

appears that the low-mod share for singlefamily-owners in the conventional conforming market will be at least one 

percentage point less due to the rebenchmarking of area median incomes and 

the new OMB definitions of metropolitan 

areas.

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39See Randall M. Scheessele, 1998 HMDA 

Highlights, op. cit. and ‘‘HUD Subprime and 

Manufactured Home Lender List’’ at http://

www.huduseer.org/datasets/manu.html.

40Since most HMDA data are for loans in 

metropolitan areas and a substantial share of 

manufactured homes are located outside 

metropolitan areas, HMDA data may not accurately 

state the goals-qualifying shares for loans on 

manufactured homes in all areas.

41While many fewer manufactured home loans 

were identified in the 2002 and 2003 HMDA data, 

the loans showed similar goals-qualifying shares: 

low-mod (77.6 percent and 75.4 percent, 

respectively), special affordable (45.0 percent and 

47.1 percent, respectively), and underserved areas 

(46.9 percent and 45.2 percent, respectively).

Based on the above analysis of 1999–2002 

data, it would appear the low-mod share of 

the conventional conforming market is about 

one percentage point less when based on 

projected data, as compared with historical 

data. However, the data for 2003 suggest a 

different picture. As shown in Table D.8b, 

the 2003 CBSA-based low-mod share for 

home purchase loans is 45.8 percent, which 

is 1.1 percentage points higher than the 

corresponding MSA-based percentage of 44.7 

percent. Similarly, the CBSA-based 

percentage is 1.1 percentage point higher 

when all owner loans are considered. Thus, 

the more recent 2003 data suggest that the 

GSEs will be scored higher than they have 

historically been scored. 

Table A.18 in Appendix A reported similar 

MSA and CBSA data for home purchase 

loans acquired by Fannie Mae and Freddie 

Mac. Again, the low-mod shares for the GSEs’

purchases of both home purchase and total 

SFO loans were lower under the new CBSA 

approach than under the old MSA approach 

for 1999–2002, but not for 2003. The 

proposed GSE rule accounted for the 1999–

2002 discrepancy by reducing the overall 

low-mod estimates by one percentage point. 

Given the 2003 results, which show higher 

low-mod shares under the new CBSA 

approach, that procedure is questionable. 

This Final Rule follows a different procedure. 

The actual CBSA-based low-mod shares for 

owners (reported in Table D.8b) are 

incorporated directly into the analysis. 

The projection model will initially assume 

that refinancing is 35 percent of the singlefamily mortgage market; this will be followed 

by projection models that reflect heavy 

refinance environments. Given the volatility 

of refinance rates from year to year, it is 

important to conduct sensitivity tests using 

different refinance rates. However, as 

explained in the preamble, HUD has 

included a provision in this Final Rule that 

eliminates the negative effects of heavy 

refinancing periods on the GSEs’ goals 

performance. 

b. Manufactured Housing Loans 

Because manufactured housing loans are 

such an important source of affordable 

housing, they are included in the mortgage 

market definition in this appendix—or at 

least that portion of the manufactured 

housing market located in metropolitan areas 

is included, as HMDA doesn’t adequately 

cover non-metropolitan areas. The GSEs have 

questioned HUD’s including these loans in 

its market estimates; therefore, following the 

same procedure used in the 2000 Rule and 

the 2004 proposed GSE Rule, this Appendix 

will report the effects of excluding 

manufactured home loans from the market 

estimates. As explained later, the effect of 

manufactured housing on HUD’s 

metropolitan area market estimate for each of 

the three housing goals is approximately one 

percentage point or less. 

As discussed in Appendix A, the 

manufactured housing market increased 

rapidly during the 1990s, as units placed in 

serviced increased from 174,000 in 1991 to 

374,000 in 1998. However, due to various 

problems in the industry such as lax 

underwriting and repossessions, volume has 

declined in recent years, falling to 192,000 in 

2001, to 172,000 in 2002, and to 135,000 in 

2003. Still, the affordability of manufactured 

homes for lower-income families is 

demonstrated by their average price of 

$48,800 in 2001, a fraction of the median 

price for new ($175,000) and existing 

($147,800) homes. Many households live in 

manufactured housing because they simply 

cannot afford site-built homes, for which the 

construction costs per square foot are much 

higher. 

Although manufactured home loans cannot 

be identified in the HMDA data, Randy 

Scheessele at HUD identified 21 lenders that 

primarily originated manufactured home 

loans in 2001 and likely account for most of 

these loans in the HMDA data for 

metropolitan areas.39 HMDA data on home 

loans originated by these lenders indicate 

that: 40

• A very high percentage of these loans—

75 percent in 2001—would qualify for the 

Low- and Moderate-Income Goal, 

• A substantial percentage of these loans—

42 percent in 2001—would qualify for the 

Special Affordable Goal, and 

• Almost half of these loans—47 percent in 

2001—would qualify for the Underserved 

Areas Goal (defined in terms of the 1990 

Census data).41

Thus an enhanced presence in this market 

by the GSEs would benefit many lowerincome families. It would also contribute to 

their presence in underserved rural areas, 

especially in the South. 

2. Low- and Moderate-Income Percentage for 

Renter Mortgages

Following the 2000 Rule, measures of the 

rent affordability of the single-family rental 

and the multifamily rental markets are 

obtained from the American Housing Survey 

(AHS) and the Property Owners and 

Managers Survey (POMS). As explained 

below, the AHS provides rent information for 

the stock of rental properties while the POMS 

provides rent information for flow of 

mortgages financing that stock. As discussed 

below, the AHS and POMS data provide very 

similar estimates of the low- and moderateincome share of the rental market. 

a. American Housing Survey Data 

The American Housing Survey does not 

include data on mortgages for rental 

properties; rather, it includes data on the 

characteristics of the existing rental housing 

stock and recently completed rental 

properties. Current data on the income of 

prospective or actual tenants has also not 

been readily available for rental properties. 

Where such income information is not 

available, FHEFSSA provides that the rent of 

a unit can be used to determine the 

affordability of that unit and whether it 

qualifies for the Low- and Moderate-Income 

Goal. A unit qualifies for the Low- and 

Moderate-Income Goal if the rent does not 

exceed 30 percent of the local area median 

income (with appropriate adjustments for 

family size as measured by the number of 

bedrooms). Thus, the GSEs’ performance 

under the housing goals is measured in terms 

of the affordability of the rental dwelling 

units that are financed by mortgages that the 

GSEs purchase; the income of the occupants 

of these rental units is not considered in the 

calculation of goal performance. For this 

reason, it is appropriate to base estimates of 

market size on rent affordability data rather 

than on renter income data. 

A rental unit is considered to be 

‘‘affordable’’ to low- and moderate-income 

families, and thus qualifies for the Low- and 

Moderate-Income Goal, if that unit’s rent is 

equal to or less than 30 percent of area 

median income. Table D.14 of Appendix D in 

HUD’s 2000 Rule reported AHS data on the 

affordability of the rental housing stock for 

the survey years between 1985 and 1997. The 

1997 AHS showed that for 1–4 unit 

unsubsidized single-family rental properties, 

94 percent of all units and of units 

constructed in the preceding three years had 

gross rent (contract rent plus the cost of all 

utilities) less than or equal to 30 percent of 

area median income. For multifamily 

unsubsidized rental properties, the 

corresponding figure was 92 percent. The 

AHS data for the other survey years were 

similar to the 1997 data. 

b. Property Owners and Managers Survey 

(POMS) 

As discussed in the 2000 GSE Rule, there 

were concerns about using AHS data on rents 

from the outstanding rental stock to proxy 

rents for newly mortgaged rental units. HUD 

investigated that issue further using the 

POMS. 

POMS Methodology. The affordability of 

multifamily and single-family rental housing 

backing mortgages originated in 1993–1995 

was calculated using internal Census Bureau 

files from the American Housing SurveyNational Sample (AHS) from 1995 and the 

Property Owners and Managers Survey from 

1995–1996. The POMS survey was 

conducted on the same units included in the 

AHS survey, and provides supplemental 

information such as the origination year of 

the mortgage loan, if any, recorded against 

the property included in the AHS survey. 

Monthly housing cost data (including rent 

and utilities), number of bedrooms, and 

metropolitan area (MSA) location data were 

obtained from the AHS file. 

In cases where units in the AHS were not 

occupied, the AHS typically provides rents, 

either by obtaining this information from 

property owners or through the use of 

imputation techniques. Estimated monthly 

housing costs on vacant units were therefore 

calculated as the sum of AHS rent and utility 

costs estimated using utility allowances 

published by HUD as part of its regulation of 

the GSEs. Observations where neither 

Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 63857

42 In 2002, 75 percent of GSE purchases of singlefamily rental units and 89 percent of their 

purchases of multifamily units qualified under the 

Low- and Moderate-Income Goal, excluding the 

effects of missing data.

43Applying Fannie Mae’s weights to data from 

the 2003 AHS produces low-mod shares of slightly 

over 90 percent for both single-family and 

multifamily rental properties.

monthly housing cost nor monthly rent was 

available were omitted, as were observations 

where MSA could not be determined. Units 

with no cash rent and subsidized housing 

units were also omitted. Because of the 

shortage of observations with 1995 

originations, POMS data on year of mortgage 

origination were utilized to restrict the 

sample to properties mortgaged during 1993–

1995. POMS weights were then applied to 

estimate population statistics. Affordability 

calculations were made using 1993–95 area 

median incomes calculated by HUD. 

POMS Results. The rent affordability 

estimates from POMS of the affordability of 

newly-mortgaged rental properties are quite 

consistent with the AHS data on the 

affordability of the rental stock (discussed 

above). Ninety-six (96) percent of singlefamily rental properties with new mortgages 

between 1993 and 1995 were affordable to 

low- and moderate-income families, as were 

96 percent of newly-mortgaged multifamily 

properties. Thus, these percentages for 

newly-mortgaged properties from the POMS 

are similar to those from the AHS for the 

rental stock. 

Further Results and Comments. The 

baseline projection from HUD’s market share 

model assumes that 90 percent of newlymortgaged, single-family rental and 

multifamily units are affordable to low- and 

moderate-income families.42 As noted above, 

the analysis of AHS and POMS data from the 

mid-1990s supports the use of a 90 percent 

low-mod figure, and also supports using 

rental stock data from the AHS as a proxy for 

the affordability characteristics of new 

mortgages financing rental properties. 

Updating these results using the 2001 and 

2003 AHS produced similar (over 90 percent) 

low-mod estimates for both the single-family 

rental stock and the multifamily rental stock. 

For example, using ICF’s assumptions for an 

AHS analysis (see ICF Appendix, p. 45), the 

2003 AHS showed that 94 (93) percent of 

single-family (multifamily) rental units 

would qualify as being affordable to low- and 

moderate-income families. While ICF used 90 

percent for multifamily, ICF concluded that 

87.5 percent should be used for single-family 

rentals. HUD’s updated analysis of the AHS, 

which is explained in more detail in Section 

H below, does not support using ICF’s 87.5 

percent assumption, except for sensitivity 

analysis. Since single-family rental units 

account for approximately 10 percent all 

financed units in both ICF’s and HUD’s 

market share models, the effect on the overall 

low-mod goal of using 87.5 percent instead 

of 90.0 percent would be only 0.25 

percentage point. (the 2.5 percentage point 

low-mod differential multiplied by the 0.10 

property share for single-family rental 

properties).

Based on its analysis of the AHS (see 

Fannie Mae Appendix, I–31–I–32), Fannie 

Mae concluded that the low-mod shares for 

both single-family and multifamily properties 

had fallen from 90 percent in 1997 to 86 

percent in 2001. In its analysis, Fannie Mae 

provides a weight of 0.07 to the low-mod 

share (74.8 percent) of recently-constructed 

single-family rental units in the AHS, and the 

residual 0.93 weight to the low-mod share 

(91.8 percent) of the remaining existing units 

in the AHS. While Fannie Mae appears to use 

a low-mod share of 86 percent for singlefamily rentals in its market sizing models, 

applying these weights to the 2001 AHS data 

(reported by Fannie Mae in Table I.7 on p. 

I–32) yields approximately 90 percent for the 

low-mod share of single-family rental 

properties. Similarly, for multifamily 

properties, Fannie Mae provides a weight of 

0.11 to the low-mod share (75.3 percent) of 

recently-constructed multifamily rental units 

in the AHS, and the residual 0.89 weight to 

the low-mod share (91.3 percent) of the 

remaining existing units in the AHS. Again, 

while Fannie Mae appears to use a low-mod 

share of 86 percent for multifamily rentals in 

its market sizing models, applying the above 

weights to the 2001 AHS data also yields 

approximately 90 percent for the low-mod 

share of multifamily rental properties. Since 

single-family and multifamily rental units 

combined account for about 25 percent of all 

financed units in the market sizing models, 

the effect on the overall low-mod share of 

using 86 percent instead of 90 percent would 

be about one percentage point. (the 4.0 

percentage point low-mod differential 

multiplied by the 0.25 property share for 

single-family and multifamily rental 

properties).43 Fannie Mae expressed 

particular concern with HUD’s Case 3, which 

assumed an even higher 95.0 percent lowmod share for rental properties; HUD has 

reduced this assumption to 92.5 percent in 

the Case 3 analysis below. HUD’s Case 2 will 

also consider a low-mod percentage of 87.5 

percent.

The low-mod characteristics of the GSEs’

own purchases can also be examined. 

Between 1999 and 2003, 86.4 percent of 

Fannie Mae’s single-family rental purchases 

qualified as low-mod, as did 87.3 percent of 

Freddie Mac’s purchases. During the same 

period, 90.7 percent of Fannie Mae’s 

multifamily rental purchases qualified as 

low-mod, as did 92.6 percent of Freddie 

Mac’s purchases. One issue discussed below 

concerns the impact on the GSEs’ low-mod 

performance of switching to 2000 Census 

data and the new OMB metropolitan area 

definitions. The above GSE percentages were 

recalculated after applying the new data and 

new OMB definitions back to 1999. Similar 

low-mod results were obtained for both 

single-family and multifamily rentals. Thus, 

the 2000 Census data and the new OMB 

metropolitan area definitions will have no 

impact on the low-mod scoring of the GSEs’

rental purchases.

Most of ICF’s and the GSEs’ concerns about 

HUD’s estimates of the affordability of rental 

housing properties related to the sizing of the 

special affordable market. Therefore, more 

detail treatment of these issues will be 

provided in Section H below. 

3. Size of the Low- and Moderate-Income 

Mortgage Market 

This section provides estimates of the size 

of the low- and moderate-income mortgage 

market. Subsection 3.a presents new 

estimates of the low-mod market while 

Subsection 3.b reports the sensitivity of the 

new estimates to changes in assumptions 

about economic and mortgage market 

conditions. 

a. Estimates of the Low- and ModerateIncome Market 

This section provides HUD’s estimates for 

the size of the low- and moderate-income 

mortgage market that will serve as a proxy for 

the four-year period (2005–2008) when the 

new housing goals will be in effect. The 

estimates are compared with recent 

experience in the low-mod market since 

1999. As discussed in Sections C and D, 

market estimates will be presented for 

different combinations of the investor 

mortgage share (8.8, 8.5, 9.0, and 9.5) and the 

multifamily mix (12.25, 13.5, 14.25, 15.0, and 

16.0). This range reflects uncertainty about 

the data and future conditions in these rental 

markets. As discussed in Section C, HUD 

continues to use a multifamily (MF) mix of 

15.0 percent as its baseline for a home 

purchase environment; this is strongly 

supported by RFS analysis. While results are 

reported for Fannie Mae’s MF mix of 12.3 

percent, HUD does not believe the MF mix 

will fall to that level in a home purchase 

environment; rather, the results are reported 

to gauge the effects on the market size of 

alternative assumptions supported by Fannie 

Mae. Three alternative sets of projections 

about rental property low- and moderateincome percentages are given in Table D.9. 

Case 1 projections represent the baseline and 

intermediate case; for example, it assumes 

that the low-mod share of rental loans is 90 

percent. Case 1 will be the focus of the 

market analysis in this section. Case 2 

assumes slightly lower goals-qualifying 

shares (e.g., an 85 percent low-mod share) for 

rental properties while Case 3 assumes 

slightly higher goals-qualifying shares (e.g., a 

92.5 percent low-mod share). 

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Because single-family-owner units account 

for about 75 percent of all newly mortgaged 

dwelling units, the low- and moderateincome percentage for owners is the most 

important determinant of the total market 

estimate. Thus, Table D.10 provides market 

estimates for different low-mod percentages 

for the owner market as well as for different 

MF mix percentages and investor mortgage 

shares. In a home purchase environment, the 

most likely MF mix is 15.0 percent and the 

most likely investor mortgage share is in the 

8.5–9.0 percent range. For simplicity, the 

combination of a 15.0-percent MF mix and a 

8.5-percent investor share will be labeled the 

baseline when presenting the results below. 

Including a 9.0-percent investor mortgage 

share as the baseline would increase the lowmod market estimate by about 0.2–0.3 

percentage point. The low-mod market 

estimates in Table D.10 exclude B&C loans, 

as explained below. 

Table D.10 assumes a refinance rate of 35 

percent, which means that the table reflects 

home purchase or low-refinancing 

environments. After presenting these results, 

market estimates reflecting heavy refinance 

environments will be presented. Because of 

the increase in single-family mortgages, the 

multifamily share of the mortgage market 

typically falls during a heavy refinance 

environment; therefore, several sensitivity 

analyses using lower multifamily mixes are 

examined below.

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In the 2000 Rule, HUD assumed that the 

low-mod share of refinance loans was three 

percentage points lower than the low-mod 

share of borrowers purchasing a home. 

However, as discussed earlier, the low-mod 

share of refinance loans has equaled or been 

greater than the low-mod share of home 

purchase loans during recent home purchase 

environments such as 1995–97 or 1999–2000; 

thus, the assumption of a lower low-mod 

shares for refinance loans is initially dropped 

for this analysis but will be reintroduced 

during the sensitivity analysis and during the 

discussion of heavy refinance environments. 

There are two ways to view the singlefamily-owner low-mod percentages reported 

in the first column of Table D.10. A first 

approach would be to view them as 

representing low-mod percentages of only the 

home purchase market. For example, a lowmod percentage for home purchase loans of 

43 percent—combined with the assumption 

of an equal low-mod share for refinance loans 

(i.e., also 43 percent) and with the other 

model assumptions (such as a multifamily 

mix of 15 percent and an investor share of 

8.5 percent)—produces an estimate of 54.6 

percent for the low-mod share of the overall 

(owner and rental) market, excluding B&C 

loans. Thus, the reader can view Table D.10 

as showing the overall low-mod market 

estimate once the reader specifies his or her 

views about the low-mod share of the singlefamily home purchase market (given the 

other model assumptions). In this case, if the 

reader believes that the low-mod share of 

refinance loans should be lower than that for 

home purchase loans, the reader simply has 

to multiply the differential amount by 0.35 

(which is the refinance share of singlefamily-owner loans) and 0.745 (which is the 

single-family-owner share of all dwelling 

units in the model that assumes a 15 percent 

multifamily mix and 8.5 percent investor 

mortgage share). For example, applying the 

assumption in the 2000 Rule that the lowmod share is three percentage points lower 

for refinance loans would reduce the overall 

low-mod share of the market by 0.78 

percentage points (3.0 times 0.35 times 

0.745); if the low-mod share of refinance 

loans is one percentage point below that of 

home purchase loans, then the overall lowmod market estimate falls by 0.26 percentage 

point. In this manner, the reader can easily 

adjust the market estimates reported in Table 

D.10 to incorporate his or her own views 

about differences in the low-mod share of 

home purchase and refinance loans. 

A second approach would be to view the 

low-mod percentages (in the first column of 

Table D.10) as representing low-mod shares 

for the overall single-family-owner market, 

including both home purchase and refinance 

loans. This approach does not specify 

separate low-mod percentages for home 

purchase and refinance loans, but rather 

focuses on the overall single-family-owner 

environments. Thus, it allows for mortgage 

market environments where the low-mod 

share of refinance loans is greater than the 

low-mod share for home purchase loans. For 

example, a low-mod percentage for singlefamily-owner loans of 47 percent would 

reflect the year 2000 environment, which had 

a low-mod home purchase percentage of 44.3 

percent combined with a higher low-mod 

refinance percentage of 51.3 percent. Of 

course, the 47 percent low-mod share for the 

overall single-family-owner market could be 

consistent with other combinations of lowmod shares for home purchase and refinance 

loans. In this case, a 47 percent assumption 

for the overall single-family-owner market 

produces an estimate of 57.8 percent for the 

low-mod share of the overall (owner and 

rental) market, excluding B&C loans. 

While both approaches will be discussed 

below, most of the discussion will focus on 

the first approach. It should be noted that 

several low-mod percentages of the owner 

market are given in Table D.10 to account for 

different perceptions of that market. 

Essentially, HUD’s approach throughout this 

appendix is to provide several sensitivity 

analyses to illustrate the effects of different 

views about the goals-qualifying share of the 

single-family-owner market. This approach 

recognizes that there is some uncertainty in 

the data and that there can be different 

viewpoints about the various market 

definitions and other model parameters. 

Market Estimates. Considering a 15.0-

percent MF mix and a 8.5-percent investor 

mortgage share, the low-mod market 

estimates reported in Table D.10 are: 55.7 

percent if the owner percentage is 44.4 

percent (average home purchase share for 

1999–2003); 56.2 percent if the owner 

percentage is 45 percent (home purchase 

share for 1999, 2002, and 2003); 55.4 percent 

if the owner percentage is 44 percent (home 

purchase share for 2000); 54.6 percent if the 

owner percentage is 43 percent (home 

purchase share for 1998 and 2001); and 53.8 

percent if the owner percentage is 42 percent 

(home purchase average from 1994–97). 

Considering a range of 13.5–16.0 for the MF 

mix and a range of 8.5–9.0 for the investor 

mortgage share, the low-mod market 

estimates reported in Table D.10 are: 55.6–

57.1 percent if the owner percentage is 45 

percent; 54.8–56.1 percent if the owner 

percentage is 44 percent; 54.0–55.3 percent if 

the owner percentage is 43 percent; and 

53.1–54.5 percent if the owner percentage is 

42 percent. If the low-mod percent is at its 

1999–2003 average (44.4 percent), the market 

range is 54.3–56.9 percent. If the low- and 

moderate income percentage for home 

purchase loans fell to 38 percent—or five 

percentage points from its 1994–2003 average 

level of 43 percent—then the overall market 

estimate would be about 51 percent. Thus, 51 

percent is consistent with a rather significant 

decline in the low-mod share of the singlefamily home purchase market. Under the 

baseline projection, the home purchase 

percentage can fall as low as 36 percent—

about four-fifths of the 1994–2003 average—

and the low- and moderate-income market 

share would still be 49 percent.

Table D.8b reported so-called ‘‘CBSAbased’’ low-mod shares for single-family 

owner loans that reflect the new 2000 Census 

data and the new OMB metropolitan area 

definitions. Since these differed slightly from 

the historical ‘‘MSA-based’’ low-mod shares, 

it is useful to repeat the above analysis in 

terms of these new data, which will serve as 

the basis for scoring the GSEs’ performance 

under the new housing goals. As shown in 

Table D.8b, the CBSA-based low-mod shares 

of home purchase loans averaged almost 44 

percent between 1999 and 2003, suggesting 

an overall low-mod goal of 55.4 percent 

under the baseline, with a range from 54.8 

percent to 56.1 percent. The CBSA-based 

measures of the low-mod share varied from 

approximately 42 percent (41.8 percent in 

2001) to almost 46 percent (45.8 percent in 

2003). Under baseline assumptions, an owner 

share of 42 percent translates into a 53.8 

percent overall low-mod share while a 46 

percent owner figure translates into a 57.0 

percent low-mod share. 

Case 2 (see Table D.9) considered a smaller 

low- and moderate-income percentage (85 

percent) for both SF and MF rental 

properties, as compared with the baseline 

Case 1, which assumed 90 percent. 

Incorporating the Case 2 assumption reduces 

the low-mod market shares by about 1.3 

percentage points. For example, if the SFO 

home purchase share is 45 percent, the 

overall low-mod market estimate is 54.9 

percent under Case 2, as compared with 56.2 

percent under Case 1 (see Table D.10). ICF 

considered a different option, as it reduced 

only the SF rental percentage from 90.0 

percent to 87.5 percent. Since SF rental units 

account for about 10 percent of all financed 

units, this change reduces the overall lowmod market estimates by about 0.25 

percentage points. As discussed earlier, the 

baseline Case 1 assumption of 90 percent is 

a reasonable approach for estimating the lowmod market shares. 

Multifamily Mix. The volume of 

multifamily activity is also an important 

determinant of the size of the low- and 

moderate-income market. HUD is aware of 

the uncertainty surrounding projections of 

the multifamily market and consequently 

recognizes the need to conduct sensitivity 

analyses to determine the effects on the 

overall market estimate of different 

assumptions about the size of that market. 

Section C of this appendix provided HUD’s 

rationale for its baseline MF mix of 15.0 

percent and for its 13.5–16.0 percent range of 

MF mixes. Assuming a 13.5 percent 

multifamily mix reduces the overall low-mod 

market estimates by 0.6–0.7 percentage 

points compared with a 15 percent mix, and 

by 1.0–1.2 percentage points compared with 

a 16.0 percent mix. For example, when the 

low-mod share of the home purchase market 

is at 44 percent (its CBSA-based average for 

1999–2003), the low-mod share of the overall 

market is 54.8 percent assuming a 13.5 

percent multifamily mix, compared with 55.4 

(56.8) percent assuming a 15 (16.0) percent 

multifamily mix. 

As shown in Table D.10, ICF’s MF mix of 

14.2 percent produces results intermediate 

between HUD’s 13.5 percent and 15.0 

percent. Estimates of the low-mod market 

based on a MF mix of 14.2 percent are only 

0.3–0.4 percentage points less than those 

based on a MF mix of 15.0 percent. 

Fannie Mae’s model combined an even 

lower MF mix of 12.3 percent with an 

investor mortgage share of 8.0 percent. If the 

low-mod share of home purchase loans is 44 

percent (the average for 1999–2003), then the 

estimate for the overall low-mod market is 

54.0 percent based on Fannie Mae’s 

63862 Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 

44These three estimates were initially reported in 

HUD’s 2000 Final Rule, and repeated in Table D.9 

of Appendix D of the 2004 proposed GSE rule.

45 Given that the midpoints of the multifamily 

mixes for 1995–1997 are in the high 18–20 percent 

range (see Table D.5b), three percentage points were 

dropped in the sensitivity analysis.

46To provide some confirmation for these 1995–

1997 estimates, HUD went back and re-estimated 

the model for 1997. As shown in Table D.9 of the 

2004 GSE Proposed Rule (as well as in Table D.15 

of the 2000 GSE Rule), HUD had earlier estimated 

a low-mod share of 57.5 percent for 1997 (which 

was about the same as the 57.3-percent low-mod 

share estimated for 1995 and 1996). With a lower 

investor share (8.4 percent instead of 10.0 percent) 

and other changes mentioned in the text, the new 

estimate for the 1997 low-mod market was 56.4 

assuming a multifamily mix of 19.3 percent. If the 

multifamily mix is reduced to 17.3 (16.3) percent, 

the low-mod share of the 1997 market is 55.5 (55.0) 

percent. The 55.0–56.4 percent range for 1997 is the 

same as the range reported in the text for 1995–

1997.

assumptions. In contrast, HUD’s estimates 

(with a MF mix of 15.0 percent and 8.5–9.0 

percent investor share) are 55.4–55.7 

percent—about one and a half percentage 

points higher. If the low-mod share of home 

purchase loans is 45 percent (which is below 

the CBSA-based percentage of 45.8 for 2003), 

then Fannie Mae’s assumptions result in a 

market estimate of 54.8 percent while HUD’s 

assumptions (see previous sentence) result in 

market estimates of 56.2–56.5 percent.

Investor Mortgage Share. As shown in 

Table D.10, increasing the investor mortgage 

share by one percentage point from 8.0 

percent to 9.0 percent increases the low-mod 

market estimate by approximately 0.5–0.6 

percentage point. If the 10.0 percent baseline 

from the 2004 proposed GSE rule were used 

in this analysis, the market estimates would 

be approximately 0.6 (0.4) percentage points 

higher relative to the results reported in 

Table D.10 for a baseline of 8.5 (9.0) percent. 

Examples of Home Purchase Years. The 

above projection results for a home purchase 

environment can be compared with actual 

results for the two most recent home 

purchase years, 1999 and 2000, as well as 

results from earlier home purchase years 

(1995–1997). According to the Mortgage 

Bankers Association of America, the 

refinance rate was 21 percent in 1995, 29 

percent in 1996 and 1997, 34 percent in 

1999, and 29 percent in 2000. 

For 1999, the baseline model assumed a 

multifamily mix of 16.0 percent (see Section 

C) and a mortgage investor share of 8.2 

percent (see Section D). Under these 

assumptions, the 1999 market estimate is 

56.9 percent; if the 1999 MF mix was lower—

for example, 15.0 (14.0) percent instead of 

16.0 percent—then the estimate of the 1999 

low-mod market share would be 56.4 (55.9) 

percent. 

The 2004 proposed rule (Table D.9 in 

Appendix D) reported a higher baseline 

market estimate for 1999 of 58.2 percent, as 

compared with the 56.9 percent reported in 

the previous paragraph. The difference is 

largely due to the treatment of single-family 

rental mortgages. For example, using the 

proposed rule’s 10-percent assumption for 

the mortgage investor share (instead of the 

lower 8.2 percent HMDA-based mortgage 

investor shares reported in the text) would 

increase the 1999 estimate to 57.7 percent, 

only 0.5 percentage points lower than the 

58.2 percent reported in the proposed rule. 

Other minor changes that lower the market 

estimate included: (a) Further reducing the 

SF mortgage investor share by excluding B&C 

investor loans from the HMDA data (see

Section C); (b) using 1.6 percent (instead of 

2.0 percent) for the mortgage share of singlefamily 2–4 property owners; and (c) using 

slightly lower dwelling-units-per-mortgage 

assumptions for SF 2–4 properties (2.20 

instead of 2.25) and for SF investor mortgages 

(1.30 instead of 1.35). 

The above changes also affect the 1995-to1997 estimates reported in Table D.9 of 

Appendix D of the proposed rule for the 

three home purchase environments prior to 

1999. These estimates were 57.3 percent for 

both 1995 and 1996 and 57.5 percent for 

1997, with an average of 57.4 percent.44

Given (a)–(c) in the previous paragraph and 

the fact that the HMDA-reported mortgage 

investor share was approximately eight 

percent during these three years (instead of 

the assumed 10 percent), these estimates 

should be reduced by about one percentage 

point, placing their average at 56.4. Allowing 

for a multifamily mix of three percentage 

points below the baseline estimates (similar 

to the approach used for 1999 and 2000 

above) would drop the 1995–1997 low-mod 

estimates by approximately 1.4 percentage 

points.45 Thus, the 1995–1997 average would 

range from about 55.0 percent (with a MF 

mix of three percentage points below the 

baseline estimate) to 56.4 percent (with the 

baseline MF mix).46

For 2000, the baseline model assumed a 

multifamily mix of 17.2 percent and a 

mortgage investor share of 9.1 percent. Under 

these assumptions, the 2000 low-mod market 

is estimated to be 57.9 percent. A lower MF 

mix—for example, 16.0 (15.0) percent instead 

of 17.2 percent—would reduce the estimated 

2000 low-mod market share to 57.4 (57.0) 

percent. The baseline 57.9 percent estimate 

for 2000 is about one percentage point lower 

than the 59.1 percent share reported in Table 

D.9 of the proposed rule, mainly for the 

reasons discussed in the previous paragraph. 

The above market estimates for 1999 and 

2000 are slightly lower if the projected CBSA 

data are used instead of the historical 1990-

based MSA data. The projected CBSA-based 

low-mod estimate was 56.2 percent for 1999, 

or 0.7 percentage points lower than the 56.9 

percent estimate based on the historical MSA 

data. In this case, the low-mod estimate falls 

to 55.8 (55.4) percent if the MF mix is 15.0 

(14.0) percent. Incorporating the CBSA data 

lowered the estimate for 2000 by 0.5 

percentage points to 57.4 percent, and to 56.9 

(56.5) percent if the MF mix is 16.0 (15.0) 

percent. 

To summarize, the historical MSA-based 

low-mod share for all recent home purchase 

environments (1995–97 and 1999–2000) 

averaged from 55.6 percent (with a two- to 

three-percentage point lower MF mix than 

the baseline) to 56.8 percent (with the 

baseline MF mix). The averages (56.5 to 57.4) 

for the two most recent home purchase years, 

1999 and 2000, were higher than those (55.0 

to 56.4) for the earlier home purchase years, 

1995–1997. When the data are expressed on 

a CBSA basis, the average low-mod shares for 

1999 and 2000 decline slightly to 56.0 

percent (with a two-percentage point lower 

MF mix than the baseline) and to 56.8 

percent (with the baseline MF mix). 

By comparison, ICF’s best (lower bound) 

estimates for these home purchase years were 

52 (49) percent for 1996, 55 (52–53) percent 

for 1997 and 1999, 56 (53) percent for 1995, 

and 57 (54) percent for 2000 (ICF Appendix, 

p. 66). Emphasizing the variability of these 

estimates, ICF also reported numerous other 

low-mod shares for these years, based on 

various simulations and assumptions. Some 

seem rather strange, or suggested that their 

analysis simply reduced the various input 

parameters to show that low estimates of the 

low-mod market could be the output. For 

example, ICF reports an overall market share 

of 46.9 percent share for 2000 (p. 66), which 

is about the same as the HMDA-reported 

single-family-owner percentage of 47.0 

percent for 2000 (Table D.8a); it is difficult 

to imagine what scenario would result in the 

low-mod share of the rental market being in 

the less-than-fifty-percent range (although it 

is recognized that ICF was probably using an 

owner share less than 47 percent). ICF’s 

report is full of such low estimates (e.g., 46.4 

percent for 1996 on page 67, another 49.6 

percent for 2000 on page 61) without any 

attempt to justify them, other than to argue 

that everything is variable and possible—an 

approach that is not very convincing if it 

produces a 46.9 percent low-mod share for 

the year 2000. 

Heavy Refinancing Environments. The lowmod share of the market will decline during 

a period of heavy refinancing due to (a) a 

decline in the low-mod share of single-family 

refinance mortgages as middle- and upperincome borrowers dominate the refinance 

market; (b) a decline in the relative 

importance of the subprime market; and (c) 

a decline in the share of multifamily 

mortgages. For example, during 2002, the 

refinance share of low-mod loans was 41.8 

percent (compared with 47–51 percent 

during the two home purchase years of 1999 

and 2000); the subprime share of the singlefamily market was 8.6 percent (compared 

with 13 percent during 1999 and 2000); and 

the multifamily share of the market was 11 

percent or less (compared with 16 percent or 

more during 1999 and 2000). Although there 

is some uncertainty with the data, the 

multifamily mix for 2003 could have been as 

low as 6 or 7 percent. 

Table D.11 shows the impact on the lowmod market share under different 

assumptions about a refinancing 

environment. The table reports the results for 

a 65 percent refinance environment, which 

has been characteristic of recent (2002 and 

2003) refinance waves. Refinancing 

environments are characterized by lower MF 

mixes because single owner properties 

dominate the market; therefore Table D.11 

considers MF mixes from 6 to 12 percent. 

Most likely, a MF mix of 12–13 percent 

characterized 2001, 9–11 percent 

characterized 2002, and less than 7 percent 

characterized 2003; there is some uncertainty 

Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 63863

with these estimates, as discussed in Section 

C of this appendix. In a refinancing wave, the 

low-mod percent is typically lower for 

refinance loans than home purchase loans, as 

middle- and high-income borrowers take 

advantage of reduced interest rates. With 

respect to the low-mod characteristics of SF 

owner loans, two scenarios were considered: 

(A) Scenario A represents the average lowmod percentages for the last four refinance 

years (1998, 2001, 2002, and 2003)—43 

percent for home purchase loans and 40 

percent for refinance loans; and (B) Scenario 

B represents the average low-mod 

percentages for the two most recent refinance 

years (2002, and 2003)—44.5 percent for 

home purchase loans and 40.5 percent for 

refinance loans. Thus, there is a 3–4 

percentage point differential between home 

purchase loans and refinance loans in a 

heavy refinancing environment. This analysis 

assumed an investor mortgage share of 8.0 

percent (average for these refinancing years) 

and a subprime market share of 8.5 percent 

(instead of the 12-percent assumption in the 

baseline model). 

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47The baseline estimates for 1998 (51.9 percent), 

2001 (53.4 percent) and 2002 (53.2 percent) are 

lower than those (53.8 percent, 54.9 percent and 

54.1 percent, respectively) reported in Table D.9 of 

Appendix D of the proposed rule. As explained 

earlier, the differences between the results in the 

proposed rule and this Final Rule are mainly due 

to the treatment of single-family rental mortgages. 

(In addition, the SF owner percentages for 2002 

were also lowered by approximately 0.5 percentage 

point in the Final Rule.) Notice that in 1998, the 

investor mortgage share dropped to 6.8 percent, or 

3.2 percentage points lower than that assumed in 

the proposed rule; this differential accounts for the 

reduction of 1.9 percentage points (53.8 percent to 

51.9 percent) in the low-mod market estimate for 

1998.

Under Scenario A, the low-mod shares 

varied by approximately three percentage 

points, from 51.6 percent with a 12 percent 

MF mix to 48.9 percent with a 6 percent MF 

mix. Under Scenario B, the low-mod 

percentages are all 0.7 percent higher, and 

the pattern is from 52.3 percent with a MF 

mix of 12 percent to 49.6 percent with a MF 

mix of 6 percent. Notice that under Scenario 

B, the low-mod share remains in the 50–51 

percent range even if the MF mix falls to 6–

8 percent. These low-mod market shares are 

4–7 percentage points lower than the lowmod shares reported in Table D.10 for HUD’s 

baseline home purchase environment. In 

addition to higher-income borrowers 

dominating the single-family market, the 

share of the ‘‘goals rich’’ rental market 

declines in a refinancing wave, which tends 

to further reduce the low-mod share of 

market activity. The right-hand column of 

Table D.11 shows that the rental share falls 

to the 17–22 percent range, or 4–9 percentage 

points less that the almost 26-percent rental 

share in HUD’s baseline model. 

Model estimates were also made for the 

recent refinancing years of 1998, 2001, 2002, 

and 2003. The Mortgage Bankers Association 

of America estimated that the refinance rate 

was 50 percent in 1998, 55 percent in 2001, 

59 percent in 2002, and 66 percent in 2003. 

The year 2003 stands out not only for its high 

rate of refinancing but also for the sheer 

volume of refinancing ($2.5 trillion), which 

led to record single-family mortgage 

originations ($3.8 trillion) that year. 

For 1998, the baseline model assumed a 

multifamily mix of 14.0 percent (see Section 

C) and a mortgage investor share of 6.8 

percent (see Section D). Under these 

assumptions, the 1998 market estimate is 

51.9 percent. If the MF mix for 1998 had been 

13.0 (12.0) percent, instead of the baseline of 

14.0 percent, then the estimated low-mod 

market share for 1998 would be 51.3 (50.8) 

percent. For 2001, the baseline model 

assumed a multifamily mix of 13.5 percent 

and a mortgage investor share of 7.8 percent. 

Under these assumptions, the 2001 market 

estimate is 53.4 percent. If the MF mix for 

2001 had been 12.5 (12.0) percent, instead of 

the baseline of 13.5 percent, then the 

estimated low-mod market share for 2001 

would be 52.9 (52.7) percent. For 2002, the 

baseline model assumed a multifamily mix of 

slightly over 11.0 percent and a mortgage 

investor share of 7.8 percent. Under these 

assumptions, the 2002 low-mod market is 

estimated to be 53.2 percent.47 A lower MF 

mix—for example, 10.5 (9.5) percent instead 

of 11 percent—would reduce the estimated 

2002 low-mod market share to 53.1 (52.5) 

percent.

Using the projected CBSA data (instead of 

the historical 1990-based MSA data) lowered 

the 2001 and 2002 low-mod estimates by 

approximately one percentage point. The 

2001 market estimates are reduced to 52.3 

percent (13.5 MF mix), 51.8 percent (12.5 MF 

mix), and 51.6 percent (12.0 MF mix). The 

2002 market estimates are reduced to 52.1 

percent (11.1 MF mix), 52.0 (10.5 MF mix), 

and 51.4 percent (9.5 MF mix). 

By comparison, ICF’s best estimates for 

these refinancing years are one or two 

percentage points lower than the above 

estimates: 49.7 percent for 1998, 51.1 percent 

for 2001, and 50.9 percent for 2002; because 

of the unavailability of 2003 HMDA data, no 

estimate was provided by ICF for that year. 

(See ICF Appendix, p. 60). ICF’s lower bound 

estimates for these three years were in the 

47–48 percent range. But as noted earlier, ICF 

also produces a number of even lower 

estimates without discussion of what 

circumstances might lead to them—examples 

include their 45.2 percent market estimate for 

2001 when the SFO low-mod share was 42.3 

percent (see Table D.8a) and their 44.9 

percent estimate for 2002 when the SFO lowmod share was 42.7 percent. (See ICF 

Appendix, p. 66.) 

For the years 1999 to 2002, Fannie Mae 

estimated a low-mod market share of 52–53 

percent. (This is their estimate assuming no 

missing data; see their Table I.9, page I–34.) 

This compares with HUD’s estimate of 53.7 

percent to 54.5 percent. As discussed in 

Section C.6, Fannie Mae assumes a rather 

low MF mix (approximately 10 percent) in 

the model that generates its historical 

estimates. 

Given that HUD did not receive 2003 

HMDA data until August 2004, it was not 

possible to develop a complete projection 

model for 2003. Still, HUD developed some 

rough projections for 2003. Given the huge 

volume of single-family originations ($3.8 

trillion), the 1998 MF mix was likely rather 

low. In fact, Fannie Mae estimates the MF 

mix dropped to five percent in 2003. Thus, 

the estimates of the low-mod market share for 

2003 are presented for different assumptions 

about the MF mix, recognizing that firm data 

on the 2003 multifamily market are not 

available. Combining an investor mortgage 

share of 8.2 from HMDA (from HMDA) with 

different MF mixes produces the following 

estimates: 51.9 percent (MF mix of 8 

percent); 51.4 percent (MF mix of 7 percent); 

and 51.0 percent (MF mix of 6.0 percent). 

As shown by both the simulation results 

and by the actual experience during 1998 and 

2001–2003, the low-mod share declines 

when refinances dominate the mortgage 

market. The above estimates place the lowmod average during these four years of heavy 

refinancing at 52 percent, with practically all 

of the estimates of annual low-mod shares 

varying between 51 and 53 percent. As noted 

above, the estimates for 2003 (around 51 

percent) are somewhat speculative.

The various market estimates presented in 

Table D.10 for a home purchase environment 

and reported above for a refinance 

environment are not all equally likely. Most 

of them equal or exceed 51 percent. In the 

home purchase environment, estimates 

below 51 percent would require the low-mod 

share of the single-family-owner market for 

home purchase loans to drop to 38 percent, 

which would be five percentage points below 

the 1994–2003 average of 43 percent. Thus, 

51 percent is consistent with a rather 

significant decline in the low-mod share of 

the single-family home purchase market. 

Sensitivity analyses of different refinance 

environments and model estimates for 1998, 

and 2001–2003 suggest that it would require 

a particularly heavy period of refinancing to 

fall below a 51-percent low-mod market 

share. 

b. Economic Conditions and the Feasibility of 

the Low- and Moderate-Income Housing Goal 

Commenters expressed a general concern 

that the market share estimates and the 

housing goals failed to recognize the 

volatility of housing markets and the 

existence of macroeconomic cycles. There 

was particular concern that the market shares 

and housing goals were based on a period of 

economic expansion accompanied by record 

low interest rates and high housing 

affordability. This section continues the 

discussion of these issues, noting that the 

Secretary can consider shifts in economic 

conditions when evaluating the performance 

of the GSEs on the goals, and noting further 

that the market share estimates can be 

examined in terms of less favorable market 

conditions than have existed during the 1993 

to 2003 period. As also explained below, 

HUD is publishing in the Federal Register an 

Advance Notice of Proposed Rulemaking that 

advises the public of HUD’s intention to 

consider by separate rulemaking a provision 

that recognizes and takes into consideration 

the impact of high volumes of refinance 

transactions on the GSEs’ ability to achieve 

the housing goals in certain years, and 

solicits proposals on how such a provision 

should be structured and implemented. 

Volatility of the Market. Changing 

economic conditions can affect the validity of 

HUD’s market estimates as well as the 

feasibility of the GSEs’ accomplishing the 

housing goals. The volatile nature of the 

mortgage market in the past few years 

suggests a degree of uncertainty around 

projections of the origination market. Large 

swings in refinancing, consumers switching 

between adjustable-rate mortgages and fixedrate mortgages, and increased first-time 

homebuyer activity due to record low interest 

rates, have all characterized the mortgage 

market during the nineties. These conditions 

are beyond the control of the GSEs but they 

would affect their performance on the 

housing goals. A mortgage market dominated 

by heavy refinancing on the part of middleincome homeowners would reduce the GSEs’

ability to reach a specific target on the Lowand Moderate-Income Goal, for example. A 

jump in interest rates would reduce the 

availability of very-low-income mortgages for 

the GSEs to purchase. But on the other hand, 

the next few years may be favorable to 

achieving the goals because of the high 

refinancing activity in 2001, 2002, and 2003. 

A period of low-to-moderate interest rates 

would sustain affordability levels without 

causing the rush to refinance seen earlier in 

63866 Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 

48Section 1336(b)(3)(A).

49Estimates of the subprime market for all years 

since 1995 are as follows (dollar and market share): 

1995 ($65 billion, 10 percent); 1996 ($96.5 billion, 

12.3 percent); 1997 ($125 billion, 15 percent); 1998 

($150 billion, 10 percent; 1999 ($160 billion, 12.5 

percent); 2000 ($138 billion, 12.1 percent); 2001 

($174 billion, 8.5 percent); 2002 ($213 billion, 8.6 

percent), and 2003 ($332 billion, 8.7 percent). The 

uncertainty about what these various estimates 

include should be emphasized; for example, they 

may include second mortgages and home equity 

loans as well as first mortgages, which are the focus 

of this analysis. The source for these estimates is 

Inside Mortgage Finance (various years).

50The one-half assumption for A-minus loans is 

conservative because it probably underestimates 

(overestimates) the share of A-minus (B&C) loans. 

According to data obtained by the Mortgage 

Information Corporation (see next footnote), 57 

percent of all subprime loans were labeled A-minus 

(as of September 30, 2000). According to Inside B&C 

Lending, which is published by Inside Mortgage 

Finance, the A-minus share of the subprime market 

was 61.6 percent in 2000, 70.7 percent in 2001 (see 

March 11, 2002 issue), 75 percent in 2002 (see the 

September 15, 2003 issue), and 82 percent during 

the first nine months of 2003 (see the December 8, 

2003 issue). A more recent analysis by Inside 

Mortgage Finance found that 81.4 percent of 

subprime loans originated during the first quarter 

of 2002 were A-minus or better (see Inside B&C 

Lending, Vol. 9, Issue 12, June 14, 2004).

51The Mortgage Information Corporation (MIC) 

reports the following serious delinquency rates 

(either 90 days past due or in foreclosure) by type 

of subprime loan: 3.36 percent for A-minus; 6.67 

percent for B; 9.22 percent for C; and 21.03 percent 

for D. The D category accounted for only 2 percent 

of subprime loans and of course, is included in the 

‘‘B&C’’ category referred to in this appendix. By 

comparison, MIC reports a seriously delinquent rate 

of 3.63 percent for FHA loans. See MIC, The Market 

Pulse, Winter 2001, page 6.

1998 and 2001–2003. A high percentage of 

potential refinancers have already done so, 

and are less likely to do so again. However, 

these same predictions were made after the 

1998 refinance wave, which indicates the 

uncertainty of making predictions about the 

mortgage market. 

Recent years have been characterized by 

record affordability conditions due to low 

interest rates and economic expansion. Thus, 

as Section F.3.a indicates, HUD also 

examined potential changes in the market 

shares under very different macroeconomic 

environments, including periods of 

recession, high interest rates, and heavy 

refinancing (accompanied by low interest 

rates). A recessionary environment would 

likely be characterized by a reduction in 

single-family activity (or an increase in the 

multifamily share of the market) and a 

reduction in the low-mod shares of the 

single-family-owner market. The home 

purchase percentage can fall as low as 36 

percent—about four-fifths of the 1994–2003 

average—and the low- and moderate-income 

market share would still be 49 percent. If the 

low-mod share of the owner market were 

reduced more modestly to 39 percent, the 

low-mod share for the overall market would 

fall to 51.5 percent, assuming a multifamily 

mix of 15.0 percent. (See Table D.10.) 

As discussed in Appendix A, record low 

interest rates, a more diverse socioeconomic 

group of households seeking 

homeownership, and affordability initiatives 

of the private sector have encouraged firsttime buyers and low-income borrowers to 

enter the market since the mid-1990s. Over 

the past eight years, the affordable lending 

market has demonstrated an underlying 

strength that suggests it will continue, 

particularly given demographic projections of 

increased minorities and immigrants in the 

mortgage market. However, a significant 

increase in interest rates over recent levels 

would reduce the presence of low-income 

families in the mortgage market and the 

availability of low-income mortgages for 

purchase by the GSEs. As noted above, the 

51–56 percent range for the low-mod market 

share covers economic and market 

affordability conditions much less favorable 

than recent conditions of low interest rates 

and economic expansion. The low-mod share 

of the single-family home purchase market 

could fall to 38 percent, which is five 

percentage points lower than its 1995–2003 

average level of 43 percent, and the low-mod 

market share would only be slightly below 51 

percent. The above analysis of 1998 and the 

2001–2003 period suggests that 51 percent is 

a reasonable minimum low-mod share for 

years of heavy refinancing.

Feasibility Determination. As stated in the 

2000 Rule, HUD is well aware of the 

volatility of mortgage markets and the 

possible impacts on the GSEs’ ability to meet 

the housing goals. FHEFSSA allows for 

changing market conditions.48 If HUD has set 

a goal for a given year and market conditions 

change dramatically during or prior to the 

year, making it infeasible for the GSE to 

attain the goal, HUD must determine 

‘‘whether (taking into consideration market 

and economic conditions and the financial 

condition of the enterprise) the achievement 

of the housing goal was or is feasible.’’ This 

provision of FHEFSSA clearly allows for a 

finding by HUD that a goal was not feasible 

due to market conditions, and no subsequent 

actions would be taken. As HUD noted in 

both the 1995 and 2000 GSE Rules, it does 

not set the housing goals so that they can be 

met even under the worst of circumstances. 

Rather, as explained above, HUD has 

conducted numerous sensitivity analyses for 

economic and market affordability 

environments much more adverse than has 

existed in recent years. If macroeconomic 

conditions change even more dramatically, 

the levels of the goals can be revised to 

reflect the changed conditions. FHEFSSA 

and HUD recognize that conditions could 

change in ways that require revised 

expectations.

HUD received a number of public 

comments seeking a regulatory solution to 

the issue of the ability of the GSEs to meet 

the housing goals during a period when 

refinances of home mortgages constitute an 

unusually large share of the mortgage market. 

As explained in the Preamble, HUD is not 

addressing the refinance issue in this final 

rule. Elsewhere in this Federal Register, 

HUD is publishing an Advance Notice of 

Proposed Rulemaking that advises the public 

of HUD’s intention to consider by separate 

rulemaking a provision that recognizes and 

takes into consideration the impact of high 

volumes of refinance transactions on the 

GSEs’ ability to achieve the housing goals in 

certain years, and solicits proposals on how 

such a provision should be structured and 

implemented. HUD believes that it would 

benefit from further consideration and 

additional public input on this issue. HUD 

also notes (see above) that FHEFSSA 

provides a mechanism by which HUD can 

take into consideration market and economic 

conditions that may make the achievement of 

housing goals infeasible in a given year. (See

12 U.S.C. 1336(b)(e).) 

c. Treatment of B&C Loans and Other 

Technical Market Issues 

B&C Mortgages. As discussed in Appendix 

A, the market for subprime mortgages has 

experienced rapid growth over the past 6–7 

years, rising from an estimated $65 billion in 

1995 to $174 billion in 2001, $213 billion in 

2002 and $332 billion in 2003.49 In terms of 

credit risk, subprime loans include a wide 

range of mortgage types. ‘‘A-minus’’ loans, 

which represent at least half of the subprime 

market, make up the least risky category.50

As discussed in Appendix A, the GSEs are 

involved in this market both through specific 

program offerings and through purchases of 

securities backed by subprime loans 

(including B&C loans as well as A-minus 

loans). The B&C loans experience much 

higher delinquency rates than A-minus 

loans.51

The market estimates reported in Section 

F.3.a–b exclude the B&C portion of the 

subprime market; or conversely, they include 

the A-minus portion of the subprime market. 

This section explains how these ‘‘adjusted’’

market shares are calculated from 

‘‘unadjusted’’ market shares that include B&C 

loans. 

There are two possible approaches for 

adjusting for the effects of B&C owner loans 

in the projection model. First, readers could 

choose a single-family low-mod percentage 

(that is, one of the percentages in the first 

column in Table D.10) that they believe is 

adjusted for B&C loans and then obtain a 

rough estimate of the overall market estimate 

from the second to fourth columns 

corresponding to different multifamily mixes. 

For instance, if one believes the appropriate 

single-family-owner percentage adjusted for 

B&C loans (or adjusted for any other market 

sectors that the reader thinks appropriate) is 

44 percent, then the low-mod market 

estimate is 55.4 percent assuming a 

multifamily mix of 15 percent. While 

intuitively appealing, such an approach 

would provide inaccurate results, as 

explained next. 

Second, readers could choose a singlefamily-owner percentage directly from 

HMDA data that is unadjusted for B&C loans 

and then rely on HUD’s methodology 

(described below) for excluding the effects of 

B&C loans. This is the approach taken in 

Table D.10. The advantage of the second 

approach is that HUD’s methodology makes 

the appropriate adjustments to the various 

property shares (i.e., the owner versus rental 

percentages) that result from excluding 

single-family B&C loans from the analysis. 

According to HUD’s methodology, dropping 

B&C loans would reduce the various lowmod market estimates by less than half of a 

Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 63867

52The goals-qualifying percentages for subprime 

lenders are much higher than the percentages for 

the overall single-family conventional conforming 

market; for example, the 1999–2003 average lowmod percentage for all single-family owner loans 

was 44 percent. For further analysis of subprime 

lenders, see Randall M. Scheessele, 1998 HMDA 

Highlights, Housing Finance Working Paper No. 

HF–009. Office of Policy Development and 

Research, U.S. Department of Housing and Urban 

Development, October 1999.

53 Dropping B&C loans in the manner described 

in the text results in the goals-qualifying 

Continued

percentage point. This minor effect is due to 

(a) the fact that the low-mod share of B&C 

loans is similar to that of the overall market; 

and (b) the offsetting effects of the increase 

in the rental market share when single-family 

B&C loans are dropped from the market 

totals. 

As noted above, if one assumes the singlefamily-owner percentages in the first column 

of Table D.10 are unadjusted for B&C loans, 

then the overall low-mod market estimates 

must be adjusted to exclude these loans. The 

effects of deducting the B&C loans from the 

projection model can be illustrated using an 

example of a low-mod percentage of 44 

percent for single-family-owner loans. Again, 

as explained earlier, this 44 percent figure 

could reflect a mortgage market environment 

where home purchase and refinance loans 

had similar low-mod percentages (i.e., 44 

percent) or a mortgage market environment 

where home purchase and refinance loans 

had different low-mod market percentages 

that together resulted in a 44 percent average 

for the single-family-owner market. 

As Table D.10 shows, a 44 percent lowmod share for owner mortgages translates 

into an overall low-mod market share of 55.4 

percent. It is assumed that the subprime 

market accounts for 12 percent of all 

mortgages originated, which would be $204 

billion based on $1,700 billion for the 

mortgage market. This $204 billion estimate 

for the subprime market is reduced by 20 

percent to arrive at $163.2 billion for 

subprime loans that will be less than the 

conforming loan limit. Dividing this figure by 

the average loan amount for subprime loans 

gives 1,256,361 subprime loans in the 

conventional market. HMDA data indicate 

that six percent of these are SF investor loans 

(75,382) and the remaining ones are SF 

owner loans (1,180,979). Since this analysis 

retains half of subprime loans (i.e., the Aminus portion of that market), these figures 

are reduced by one-half to arrive at 590,489 

owner B&C loans and 37,691 investor B&C 

loans. The investor loans are placed on a unit 

basis by multiplying by 1.3 (units per 

mortgage), yielding 48,998 financed dwelling 

units in the investor B&C market. 

HMDA data was used to provide an 

estimate of the portion of the 590,489 owner 

B&C loans that would qualify for each of the 

housing goals. HMDA data does not identify 

subprime loans, much less divide them into 

their A-minus and B&C components. As 

explained in Appendix A, Randall 

Scheessele in HUD’s Office of Policy 

Development and Research has identified 

almost 200 HMDA reporters that primarily 

originate subprime loans. Based on 1999–

2002 HMDA data, the goals-qualifying 

percentages of loans originated by these 

subprime lenders were as follows: 58.6 

percent qualified for the low-mod goal, 28.0 

percent for the special affordable goal, and 

52.0 percent for the underserved areas goal.52

Applying the goals-qualifying percentages to 

the 590,489 owner B&C loans gives the 

following estimates of B&C owner loans that 

qualified for each of the housing goals: Lowmod (346,027), special affordable (165,337), 

and underserved areas 614,109. The process 

for the smaller number (48,998) of investor 

B&C loans is similar. It is assumed that 90 

percent (44,098) of these B&C rental units 

qualify for the low-mod goal, 58 percent 

(28,419) qualify for the special affordable 

goal, and 74 percent (36,259) qualify for the 

underserved areas goal (based on 2000 

Census data).

Adjusting HUD’s model to exclude B&C 

owner loans and B&C financed rental units 

involves subtracting the above eight figures—

two for the overall owner and rental B&C 

market and six for B&C owner units and 

rental units that qualify for each of the three 

housing goals—from the corresponding 

figures estimated by HUD for the total singlefamily and multifamily market inclusive of 

B&C owner loans and B&C dwelling units. 

HUD’s model projects that 10,478,681 singlefamily and multifamily units will be 

financed; of these, 5,842,313 (55.8 percent) 

qualified for the low-mod goal, 2,801,179 

(26.7 percent) for the special affordable goal, 

and 3,983,005 (38.0 percent) for the 

underserved areas goal. Deducting the B&C 

owner and rental market estimates produces 

the following adjusted market estimates: A 

total market of 9,839,193, of which 5,452,188 

(55.4 percent) qualified for the low-mod goal, 

2,607,423 (26.5 percent) for the special 

affordable goal, and 3,639,692 (37.0 percent) 

for the 2000-based underserved areas goal. 

The low-mod market share estimate 

exclusive of B&C loans (55.4 percent) is only 

slightly lower than the original market 

estimate (55.8 percent from above), as is also 

the special affordable market estimate (26.7 

percent versus 26.5 percent). This occurs 

because the B&C owner loans that were 

dropped from the analysis have similar lowmod and special affordable percentages as 

the overall (both single-family and 

multifamily) market. For example, the lowmod share of B&C loans was projected to be 

58.6 percent and HUD’s market model 

(unadjusted for B&C loans) projected the 

overall low-mod share to be practically the 

same, 55.8 percent. Thus, dropping B&C 

owner loans from the market totals does not 

significantly reduce the overall low-mod 

share of the market. Because they qualify at 

such a high rate (e.g., 90 percent on lowmod), dropping B&C rental loans tends to 

reduce the market share estimates. However, 

they are relatively small in number—B&C 

owner loans dominate the results because 

they account for 92.3 percent (590,489 

divided by 639,487) of the total B&C owner 

and rental units dropped from the market 

totals.

The situation is different for the 

underserved areas goal. Underserved areas 

account for 52.0 percent of the B&C owner 

loans, which is a higher percentage than the 

underserved area share of the overall market 

(38.0 percent). Thus, dropping the B&C 

owner loans (as well as the smaller number 

of B&C rental units) leads to a reduction in 

the underserved areas market share of 1.0 

percentage points, from 38.0 percent to 37.0 

percent. (If this analysis were conducted in 

terms of 1990-Census data, the onepercentage point reduction would be from 

about 33.0 percent to 32.0 percent.) 

Dropping B&C loans from HUD’s projection 

model changes the mix between rental and 

owner units in the final market estimate; 

rental units accounted for 26.7 percent of 

total units after dropping B&C loans 

compared with 25.6 percent before dropping 

B&C loans. Since practically all rental units 

qualify for the low-mod goal, their increased 

importance in the market partially offsets the 

negative effects on the goals-qualifying shares 

of any reductions in B&C owner loans. Thus, 

another way of explaining why the goalsqualifying market shares are not affected so 

much by dropping B&C owner loans is that 

the rental share of the overall market 

increases as the B&C owner units are 

dropped from the market. Since rental units 

have very high goals-qualifying percentages, 

their increased importance in the market 

partially offsets the negative effects on the 

goals-qualifying shares of any reductions in 

B&C owner loans. In fact, this rental mix 

effect would come into play with any 

reduction in owner units from HUD’s model. 

A similar analysis can be used to 

demonstrate the effects of deducting the 

remaining, A-minus portion of the subprime 

market from the market estimates. Of course, 

deducting A-minus loans as well as B&C 

loans is equivalent to deducting all subprime 

loans from the market. In the example given 

above (44 percent low-mod percentage for 

owners), deducting all subprime loans would 

further reduce the overall low-mod market 

estimate to 55.0 percent. Thus, the 

unadjusted low-mod market estimate is 55.8 

percent, the estimate adjusted for B&C loans 

is 55.4 percent (reported in Table D.10), and 

the estimate adjusted for all subprime loans 

is 55.0 percent. 

As discussed in the 2000 Rule, there are 

caveats that should be mentioned concerning 

the above adjustments for the B&C market. 

The adjustment for B&C loans depends on 

several estimates relating to the single-family 

mortgage market, derived from various 

sources. Different estimates of the size of the 

B&C market or the goals-qualifying shares of 

the B&C market could lead to different 

estimates of the goals-qualifying shares for 

the overall market. The goals-qualifying 

shares of the B&C market were based on 

HMDA data for selected lenders that 

primarily originate subprime loans; since 

these lenders are likely originating both Aminus and B&C loans, the goals-qualifying 

percentages used here may not be accurately 

measuring the goals-qualifying percentages 

for only B&C loans. The above technique of 

dropping B&C loans also assumes that the 

coverage of B&C and non-B&C loans in 

HMDA’s metropolitan area data is the same; 

however, it is likely that HMDA coverage of 

non-B&C loans is higher than its coverage of 

B&C loans.53 Despite these caveats, it also 

63868 Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 

percentages for the non-B&C market being 

underestimated since HMDA coverage of B&C loans 

is less than that of non-B&C loans and since B&C 

loans have higher goals-qualifying shares than nonB&C loans. For instance, the low-mod shares of the 

market reported in the text underestimate (to an 

unknown extent) the low-mod shares of the market 

inclusive of B&C loans; so reducing the low-mod 

owner shares by dropping B&C loans in the manner 

described in the text would provide an 

underestimate of the low-mod share of the non-B&C 

owner market. A study of 1997 HMDA data in 

Durham County, North Carolina by the Coalition for 

Responsible Lending (CRL) found that loans by 

mortgage and finance companies are often not 

reported to HMDA. For a summary of this study, 

see ‘‘Renewed Attack on Predatory Subprime 

Lenders’’ in Fair Lending/CRA Compass, June 9, 

1999.

appears that reasonably different estimates of 

the various market parameters would not 

likely change, in any significant way, the 

above estimates of the effects of excluding 

B&C loans in calculating the goals-qualifying 

shares of the market. As discussed in other 

sections, HUD provides a range of estimates 

for the goals-qualifying market shares to 

account for uncertainty related to the various 

parameters included in its projection model 

for the mortgage market.

Manufactured Housing Loans and Small 

Loans. HUD includes the effects of 

manufactured housing loans (at least those 

financing properties in metropolitan areas) in 

its market estimates. However, sensitivity 

analyses are conducted to determine the 

effects of excluding these loans. Excluding 

manufactured housing loans as well as small 

loans (loans less than $15,000) reduces the 

overall market estimates reported in Table 

D.10 by about one percentage point. This is 

estimated as follows. First, excluding these 

loans reduces the low-mod percentage for 

single-family-owner mortgages in 

metropolitan areas by about 1.9 percentage 

points, based on analysis of recent home 

purchase environments (1995–97 and 1999 

and 2000). Multiplying this 1.9 percentage 

point differential by the property share 

(0.745) of single-family-owner units yields 

1.4 percentage points, which serves as a 

proxy for the reduction in the overall lowmod market share due to dropping 

manufactured home loans from the market 

analysis. The actual reduction will be 

somewhat less because dropping 

manufactured home loans will increase the 

share of rental units, which increases the 

overall low-mod market share, thus partially 

offsetting the 1.4 percent reduction. The net 

effect is probably a reduction of about one 

percentage point. 

The effects can be considered separately. 

Dropping only manufactured housing loans 

would reduce the market estimates by 

approximately three-quarters of a percentage 

point. ICF argued that loans with less than 

$15,000 should be excluded. The impact of 

doing this on the market estimates would be 

less than half a percentage point. ICF also 

considered scenarios where one-half of 

manufactured loans would be dropped, as 

well as small loans less than $15,000. The 

impact of doing this on the market estimates 

would be less than three-quarters of a 

percentage point.

The estimated reductions in goalsqualifying shares due to excluding 

manufactured housing would be even lower 

during the heavy refinance years such as 

1998 and 2001–2003. It should also be 

mentioned that manufactured housing in 

non-metropolitan areas is not included in 

HUD’s analysis due to lack of data; including 

that segment of the market would increase 

the goals-qualifying shares of the overall 

market. Thus, the analyses of manufactured 

housing reported above and throughout the 

this final rule pertain only to manufactured 

housing loans in metropolitan areas, as 

measured by loans originated by the 21 

manufactured housing lenders identified by 

Randy Scheessele at HUD. 

The above analyses of the effects of less 

affordable market conditions, different 

assumptions about the size of the rental 

market, and dropping different categories of 

loans from the market definition suggest that 

51–56 percent is a reasonable range of 

estimates for the low- and moderate-income 

market. This range covers markets without 

B&C and allows for market environments that 

would be much less affordable than recent 

market conditions. The next section presents 

additional analyses related to market 

volatility and affordability conditions. 

d. Conclusions About the Size of Low- and 

Moderate-Income Market 

Based on the above findings as well as 

numerous sensitivity analyses, HUD 

concludes that 51–56 percent is a reasonable 

range of estimates of the mortgage market’s 

low- and moderate-income share for the year 

2005 and beyond. The range covers much 

more adverse economic and market 

affordability conditions than have existed 

recently, allows for different assumptions 

about the single-family and multifamily 

rental markets, and excludes the effects of 

B&C loans. HUD recognizes that shifts in 

economic conditions and refinancing could 

increase or decrease the size of the low- and 

moderate-income market during that period. 

G. Size of the Conventional Conforming 

Market Serving Central Cities, Rural Areas, 

and Other Underserved Areas 

The following discussion presents 

estimates of the size of the conventional 

conforming market for the Central City, Rural 

Areas, and other Underserved Areas Goal; 

this housing goal will also be referred to as 

the Underserved Areas Goal. The first three 

sections, which analyze historical data going 

back to the early 1990’s, necessarily used 

1990 Census geography to define 

underserved census tracts and underserved 

counties. The first two sections focus on 

underserved census tracts in metropolitan 

areas, as Section 1 presents underserved area 

percentages for different property types while 

Section 2 presents market estimates for 

metropolitan areas. Section 3 discusses B&C 

loans and rural areas. But as explained in 

Appendix B, HUD will be defining 

underserved areas based on 2000 Census 

geography beginning in 2005, the first year 

covered by this final rule. Therefore, Section 

4 repeats much of the analyses in Sections 1–

3 but in terms of 2000 Census geography, 

rather than 1990 Census geography 

1. Underserved Areas Goal Shares by 

Property Type 

For purposes of the Underserved Areas 

Goal, underserved areas in metropolitan 

areas are defined as census tracts with: 

(a) Tract median income at or below 90 

percent of the MSA median income; or 

(b) A minority composition equal to 30 

percent or more and a tract median income 

no more than 120 percent of MSA median 

income. 

Owner Mortgages. The first set of numbers 

in Table D.12 are the percentages of singlefamily-owner mortgages that financed 

properties located in underserved census 

tracts of metropolitan areas between 1992 

and 2003. There are several interesting 

patterns in these data. During 1999 and 2000, 

28–30 percent of mortgages (both home 

purchase and refinance loans) financed 

properties located in these areas; this 

percentage fell to 25.7 percent in 2001, 25.0 

percent in 2002, and 25.3 percent in 2003, 

figures that were slightly below the average 

(26.8 percent) between 1994 and 1998. In 

1992 and 1993, the underserved areas share 

of single-family-owner mortgages was only 

20 percent. 

BILLING CODE 4210–27–P

Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 63869

In most years, refinance loans are more 

likely than home purchase loans to finance 

properties located in underserved census 

tracts. Between 1994 and 2003, 27.3 percent 

of refinance loans were for properties in 

underserved areas, compared to 25.5 percent 

of home purchase loans. This 1.8 percentage 

point refinance-home-purchase differential is 

mostly due to the influence of subprime 

loans. Excluding B&C (all subprime) loans 

and considering the same time period, 26.1 

(24.9) percent of refinance loans were for 

properties in underserved areas, compared to 

25.1 (24.6) percent of home purchase loans. 

Thus, excluding B&C (subprime) loans 

reduces the differential from 1.8 percentage 

points to 1.0 (0.3) percentage point. In the 

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excluding B&C (all subprime) loans reduced 

the refinance-home-purchase differential 

from 8.1 percent to 6.9 (5.7) percent; in this 

case, a significant differential remained after 

excluding B&C (subprime) loans. In the 

heavy refinance years of 1998, 2001, 2002, 

and 2003 underserved areas accounted for 

about 25 percent of total (both home 

purchase and refinance) owner loans. 

The underserved areas share for home 

purchase loans has been in the 25–26 percent 

range since 1995, except for 2000 and 2002 

when it increased to over 27 percent, and in 

2003 when it increased to 28.5 percent. 

Considering all (both home purchase and 

refinance) loans during recent ‘‘home 

purchase’’ environments, the underserved 

areas share was a high 28–30 percent during 

1999–2000, compared with a 27 percent 

average between 1995 and 1997; excluding 

B&C and other (i.e. A-minus) subprime loans 

places 1999 on par with the earlier years, 

with only the year 2000 showing a higher 

level of underserved area lending than 

occurred during 1995–97. These data 

indicate that the single-family-owner market 

in underserved areas has remained strong 

since the 2000 Rule was written. While it is 

recognized that economic and housing 

affordability conditions could change and 

reduce the size of the underserved areas 

market, it appears that the underserved 

market has certainly maintained itself at a 

high level over the past four years. 

Renter Mortgages. The second and third 

sets of numbers in Table D.12 are the 

underserved area percentages for singlefamily rental mortgages and multifamily 

mortgages, respectively. Based on HMDA 

data for single-family, non-owner-occupied 

(i.e., investor) loans, the underserved area 

share of newly-mortgaged single-family 

rental mortgages has averaged about 44 

percent (over nine or ten years). HMDA data 

also show that about half of newly-mortgaged 

multifamily rental units are located in 

underserved areas. HUD’s baseline assumes 

that 42.5 percent of single-family investor 

loans and 48 percent of multifamily loans are 

located in underserved areas. The GSEs and 

ICF argued that HUD had overstated these 

underserved area percentages; Section G.4 

below, which focuses on the 2000-based 

underserved area percentage, will discuss 

and respond to their concerns. Fannie Mae 

also said that subprime (or B&C) loans should 

be taken out of the SF investor loans. As 

shown in Table D.12, deducting B&C loans 

reduces the underserved area percentage for 

SF investor mortgages by almost one 

percentage point (the 1993–2003 unweighted 

average falls from 44.0 percent to 43.1 

percent). HUD’s model excludes B&C 

investor loans in the same manner it 

excludes B&C owner loans (see earlier 

explanation). 

2. Market Estimates for Underserved Areas in 

Metropolitan Areas 

Table D.13 reports HUD’s estimates of the 

market share for underserved areas based on 

the projection model discussed earlier. The 

estimates in Table D.13 exclude the effects of 

B&C owner loans and B&C investor loans. 

The percentage of single-family-owner 

mortgages financing properties in 

underserved areas is the most important 

determinant of the overall market share for 

this goal. Therefore, Table D.13 reports 

market shares for different single-familyowner percentages ranging from 30 percent 

(2000 level) to 20 percent (1993 level) to 19 

percent. Considering a 15.0-percent MF mix 

and a 8.5-percent investor mortgage share, 

the market share estimate is 31–32 percent if 

the overall (both home purchase and 

refinance) single-family-owner percentage for 

underserved areas is at its 1994–2003 HMDA 

average of 26.6 percent. The overall market 

share for underserved areas peaks at 35 

percent when the single-family-owner 

percentage is at its 2000 level of 30 percent.

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54Table D.15 of the 2000 GSE Rule also reported 

underserved area shares of 33.9 percent for 1995 

and 1997 and 33.4 percent for 1996. These 

estimates, after adjustments for a lower HMDAbased mortgage investor share and a lower-thanbaseline MF mix, would still remain in the 32–33 

percent range. To provide some confirmation for 

this, HUD went back and re-estimated the model for 

1997. As shown in Table D.15 of the 2000 GSE 

Rule, HUD had earlier estimated an underserved 

areas share of 33.9 percent for 1997 (which was the 

same as the 33.9-percent underserved areas estimate 

for 1995 and similar to the 33.4-percent estimate for 

1996). With a lower investor share (8.4 percent 

instead of 10.0 percent) and other changes 

mentioned in the text, the new estimate for the 1997 

underserved areas market was 32.7 assuming a 

multifamily mix of 19.3 percent. If the multifamily 

mix is reduced to 17.3 (16.3) percent, the 

underserved areas share of the 1997 market is 32.3 

(32.0) percent. Thus, this 32.0–32.7 percent range 

for 1997 is consistent with a 32–33 percent range 

for 1995–1997.

55The baseline 34.9 percent estimate for 2000 is 

0.4 percentage points lower than the 35.3 percent 

share reported in Table D.9 of the proposed rule. 

The difference is mostly explained by the different 

treatment of single-family rental mortgages.

56For the years 1999 to 2002, Fannie Mae 

estimated an underserved areas share of 32–33 

percent. (See their Table I.9, page I–34.) This 

compares with HUD’s estimate of 32.5 percent to 

32.9 percent for the same period.

The analysis can also be conducted in 

terms of the home purchase percentages 

reported in Table D.13. Again, considering a 

15.0-percent MF mix and an 8.5-percent 

investor mortgage share, the underserved 

area market estimates reported in Table D.13 

are: 33.3 percent if the owner percentage is 

28.5 percent (home purchase share for 2003); 

32.1 if the owner percentage is 27 percent 

(home purchase share in 2000 and 2002 

slightly above the 1999–2003 average home 

purchase share of 26.8 percent); 31.3 percent 

if the owner percentage is 26 percent (home 

purchase share for 1999 and 2001); and 30.5 

percent if the owner percentage is 25 percent 

(home purchase average from 1994–98). This 

analysis assumes that the underserved areas 

share of refinance loans is the same as those 

listed above for home purchase loans. But, as 

Table D.12 shows, the underserved areas 

share of refinance loans tends to be higher 

than that for home purchase loans. And in 

the year 2000, the overall underserved areas 

share for owner loans reached 30 percent; as 

noted in the previous paragraph, the overall 

market estimate is 34.6 percent in this case. 

However, the next highest overall owner 

share is the 28.2 percent share in 1999, 

which yields a market estimate of 

approximately 33 percent. 

Sensitivity Analyses. Unlike the Low- and 

Moderate-Income and Special Affordable 

Goals, the market estimates differ only 

slightly as one moves from a 13.5 percent MF 

mix to 16.0 percent MF mix. For example, 

reducing the assumed multifamily mix from 

16.0 percent to 13.5 percent reduces the 

overall market projection for underserved 

areas by only 0.5–0.6 percentage points. This 

is because the underserved area differentials 

between owner and rental properties are not 

as large as the low- and moderate-income 

differentials reported earlier. 

Similarly, the market estimates differ only 

slightly with changes in the investor 

mortgage share. Reducing the investor mix 

from 9.5 percent to 8.0 percent reduces the 

overall market projection for underserved 

areas by only 0.2–0.3 percentage points. 

Case 2 (see Table D.9) considered slightly 

smaller underserved area percentages for 

rental properties (40 percent for SF rentals 

and 46 percent for MF rentals), as compared 

with the baseline Case 1, which assumed 

42.5 percent and 46.0 percent, respectively. 

Incorporating these Case 2 assumptions 

reduces the underserved areas market 

estimate by only 0.6 percentage points. For 

example, if the SFO home purchase share is 

28 percent, then the overall underserved area 

estimate is 32.3 percent under Case 2, as 

compared with 32.9 percent under Case 1 

(see Table D.13). 

Examples of Home Purchase and 

Refinance Environments. The above 

projection results for a home purchase 

environment can be compared with actual 

results for two home purchase years, 1999 

and 2000 (see earlier description of these two 

years in the low-mod section, F.3.a). For 

1999, the baseline model assumed a 

multifamily mix of 16.0 percent and a 

mortgage investor share of 8.2 percent. Under 

these assumptions, the projected 1999 market 

estimate (based on 1990-Census data) is 33.1 

percent; if the 1999 MF mix was lower at 

15.0 (14.0), then the estimate of the 1999 

underserved areas market share would be 

only slightly lower at 32.9 (32.6) percent.54

For 2000, the baseline model assumed a 

multifamily mix of 17.2 percent and a 

mortgage investor share of 9.1 percent. Under 

these assumptions, the 2000 underserved 

areas market is estimated to be 34.9 percent. 

A lower MF mix of 16.0 (15.0) percent would 

reduce the estimated 2000 underserved areas 

market share slightly to 34.6 (34.4) percent.55

The heavy refinance scenarios discussed 

for the low-mod market were also projected 

for the underserved areas market. Since the 

impact of a heavy refinancing period on the 

underserved areas market share will be 

covered in Section G.4, which incorporates 

2000 Census data, there is no need for a 

detailed discussion in this section’s analysis 

based on 1990 Census data. Still, it is useful 

to provide a quick review of the 1990-based 

underserved area estimates for three heavy 

refinancing environments (1998, 2001, 2002, 

and 2003). For 1998, the baseline model 

assumed a multifamily mix of 14.0 percent 

and a mortgage investor share of 6.8 percent. 

Under these assumptions, the 1998 market 

estimate is 29.9 percent. If the MF mix for 

1998 had been 12.0 percent, instead of the 

baseline of 14.0 percent, then the estimated 

underserved area market share for 1998 

would be 29.4 percent. For 2001, the baseline 

model assumed a multifamily mix of 13.5 

percent and a mortgage investor share of 7.8 

percent. Under these assumptions, the 2001 

market estimate is 32.1 percent, dropping to 

31.7 percent if the MF mix was 12.0 percent. 

For 2002, the baseline model assumed a 

multifamily mix of slightly over 11.0 percent 

and a mortgage investor share of 7.8 percent. 

Under these assumptions, the 2002 

underserved areas market is estimated to be 

31.6 percent, dropping to 31.1 percent if the 

MF mix is 9.5 percent. This analysis suggests 

that the underserved areas market based on 

1990 Census data will be about 29–32 

percent range during periods of heavy 

refinancing.56

Additional sensitivity analyses were 

conducted to reflect the volatility of the 

economy and mortgage market. Recession 

and high interest rate scenarios assumed a 

significant drop in the underserved area 

percentage for single-family-owner 

mortgages. The single-family-owner 

percentage can go as low as 24 percent—

which is 3 percentage points lower than the 

1995–2003 average of 27 percent—and the 

estimated market share for underserved areas 

remains at almost 30 percent. In a more 

severe case, the overall underserved market 

share would be 27.5 percent if the singlefamily-owner share fell to 21 percent (its 

1992 level), which is 7–9 percentage points 

lower than its 1999–2000 levels.

3. Adjustments: B&C Loans, the Rural 

Underserved Areas Market, and 

Manufactured Housing Loans 

B&C Loans. The procedure for dropping 

B&C loans from the projections is the same 

as described in Section F.3.b for the Lowand Moderate-Income Goal. The underserved 

area percentage for B&C loans is 44.5 percent, 

which is much higher than the projected 

percentage for the overall market (which 

peaks at 35 percent as indicated in Table 

D.13). Thus, dropping B&C loans will reduce 

the overall market estimates. Consider the 

case of a single-family-owner percentage of 

27 percent, which yields an overall market 

estimate for underserved areas of 33.1 

percent, including B&C loans. When B&C 

loans are excluded from the projection 

model, the underserved areas market share 

falls by 0.9 percentage points to 32.2 percent, 

which is the figure reported in Table D.13. 

Non-metropolitan Areas. Underserved 

rural areas are non-metropolitan counties 

with: 

(a) County median income at or below 95 

percent of the greater of statewide nonmetropolitan median income or nationwide 

non-metropolitan income; or 

(b) A minority composition equal to 30 

percent or more and a county median income 

no more that 120 percent of statewide nonmetropolitan median income. 

HMDA’s limited coverage of mortgage data 

in non-metropolitan counties makes it 

impossible to estimate the size of the 

mortgage market in rural areas. However, all 

indicators suggest that underserved counties 

in non-metropolitan areas comprise a larger 

share of the non-metropolitan mortgage 

market than the underserved census tracts in 

metropolitan areas comprise of the 

metropolitan mortgage market. For instance, 

underserved counties within rural areas 

include 54 percent of non-metropolitan 

homeowners; on the other hand, underserved 

census tracts in metropolitan areas account 

for only 34 percent of metropolitan 

homeowners. 

During 1999–2003, 38.3 percent of the 

GSEs’ single-family-owner (SFO) purchases 

in non-metropolitan areas were in 

underserved counties while 23.1 percent of 

their SFO purchases in metropolitan areas 

were in underserved census tracts. These 

figures suggest the market share for 

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57Between 1999 and 2001, the non-metropolitan 

portion of the Underserved Areas Goal contributed 

1.1 to 1.4 (0.7 to 1.3) percentage points to Freddie 

Mac’s (Fannie Mae’s) overall performance (i.e., 

including both metro and non-metro loans), 

compared with a goals-counting system that only 

included metropolitan areas.

58These data do not include loans originated by 

lenders that specialize in manufactured housing 

loans, as well as estimated B&C loans.

59Federal Housing Finance Board data.

60Mortgage Interest Rate Survey (MIRS) data 

reported by the Federal Housing Finance Board 

separate conventional home purchase loans by their 

metropolitan and non-metropolitan location. The 

average non-metropolitan share between 1999 and 

2002 was about 13 percent.

underserved counties in rural areas is higher 

than the market share for underserved census 

tracts in metropolitan areas. Thus, using a 

metropolitan estimate to proxy the overall 

market for this goal, including rural areas, is 

conservative.57

The limited HMDA data available for nonmetropolitan counties also suggest that the 

underserved areas market estimate would be 

higher if complete data for non-metropolitan 

counties were available. According to 

HMDA, underserved counties accounted for 

41.6 percent of SFO mortgages originated in 

non-metropolitan areas between 1999 and 

2003. By contrast, underserved census tracts 

accounted for approximately 24.9 percent of 

SFO mortgages originated in metropolitan 

areas between 1999 and 2003.58 Since nonmetropolitan areas account for 13 percent of 

all single-family-owner mortgages 59 and 

estimating that the single-family-owner 

market accounts for 74.5 percent of newlymortgaged dwelling units, then the nonmetropolitan underserved area differential of 

16.7 percent would raise the overall market 

estimate by 1.6 percentage point—16.7 

percentage points times 0.13 (nonmetropolitan area mortgage market share) 

times 0.745 (single-family owner mortgage 

market share). Based on this calculation, if 

the 16.7 point differential reflected actual 

market conditions, then the underserved 

areas market share estimated using 

metropolitan area data should be increased 

by 1.6 percentage points to account for the 

effects of underserved counties in nonmetropolitan areas.60 A more conservative 

adjustment of 1.25 percentage points was 

made in Table D.13 for the 2005–2008 

projection model. The non-metropolitan area 

issue will be discussed further in Section G.4 

below, which incorporates the effects of the 

new 2000 Census data.

Small Loans and Manufactured Housing 

Loans. Excluding manufactured housing 

loans and small loans (less than $15,000) 

reduces the overall underserved area market 

estimates reported in Table D.13 by less than 

one percentage point. This is estimated as 

follows. First, excluding these loans reduces 

the unadjusted underserved areas percentage 

for single-family-owner mortgages in 

metropolitan areas by about 1.2 percentage 

points, based on analysis of recent home 

purchase environments (1995–97 and 1999 

and 2000). Multiplying this 1.2 percentage 

point differential by the property share of 

single-family-owner units (74.5 percent) 

yields 0.9 percentage points, which serves as 

a proxy for the reduction in the overall 

underserved area market share due to 

dropping manufactured home loans from the 

market analysis. The actual reduction will be 

somewhat less because dropping 

manufactured home loans will increase the 

share of rental units, which increases the 

overall underserved areas market share, thus 

partially offsetting the 0.8 percent reduction. 

The net effect is probably a reduction of 

about three-quarters of a percentage point. 

The small loan and manufactured housing 

effects can be considered separately. 

Dropping only manufactured housing loans 

would reduce the market estimates by 

approximately three-fourths of a percentage 

point. ICF argued that loans with less than 

$15,000 should be excluded. The impact of 

doing this on the market estimates would be 

about one-third of a percentage point. ICF 

also considered scenarios where one-half of 

manufactured loans would be dropped, as 

well as small loans less than $15,000. The 

impact of doing this on the market estimates 

would be three-fifths of a percentage point.

The next section discusses changes as a 

result of switching from 1990 to 2000 Census 

geography. 

4. 2000-Based Underserved Area Market 

Shares 

The above analysis has concluded that 29–

34 percent would be a reasonable market 

range for the Geographically Targeted Goal 

based on past origination activity in 

underserved areas and on scenarios that 

cover a variety of economic and mortgage 

market conditions. That analysis, which 

included historical data going back to the 

early 1990’s, necessarily used 1990 Census 

geography to define underserved census 

tracts. As explained in Appendix B, HUD 

will be defining underserved areas based on 

2000 Census geography beginning in 2005, 

the first year covered by this final rule. 

Appendix B also explains that the number of 

census tracts in metropolitan areas covered 

by HUD’s underserved area definition will 

increase from 21,587 tracts (based on 1990 

Census) to 26,959 tracts (based on 2000 

Census and OMB’s respecification of 

metropolitan areas). This increase in the 

number of tracts defined as underserved 

means that the market estimate for the 

Geographically Targeted Goal will be about 

five percentage points higher than the 1990-

based market estimate. Thus, this section 

provides a new range of market estimates for 

underserved areas defined in terms of 2000 

Census data. 

For the years 1999 to 2003, Table D.14a. 

reports the underserved areas share of the 

mortgage market for single-family-owner, 

investor (non-owner), and multifamily 

properties, with comparisons between 1990-

based and 2000-based measures of 

underserved areas. HMDA data, which is the 

source of the mortgage data, were reported in 

terms of 1990 census tracts. For the years 

1999 to 2002, HUD used various 

apportionment techniques to re-allocate 

1990-based HMDA mortgage data into census 

tracts as defined by the 2000 Census; 2003 

HMDA data were defined in terms of 2000 

Census tracts, so no reallocation was 

required. The 1990-based underserved area 

market shares reported in Table D.14.a. are 

the same data reported earlier in Table D.12, 

while the 2000-based underserved area 

market shares result from re-allocating 1999–

2002 HMDA data into 2000 Census 

geography. In addition, the data are defined 

in terms of the new OMB metropolitan area 

definitions. 

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61 ICF incorrectly said HUD’s baseline 

underserved areas share for MF rentals was 60 

percent, rather than 58 percent (ICF Appendix, p. 

47).

62Freddie Mac says ‘‘ICF estimates the 

multifamily underserved share to be just 56 percent 

and the single-family renter underserved area share 

to be just 50 percent’’ (at Appendix IV–24). 

However, ICF uses a 50 percent share in its 

projection model (ICF Appendix, p. 133); therefore, 

55 percent is used here as the ICF number. Also, 

ICF’s lower (upper bound) projection was 47 (53) 

percent for SF rental properties and 56 (58) percent 

for multifamily properties.

63 ‘‘Affordability and Geographic Distribution of 

the Housing Stock and the Use of Mortgage 

Finance,’’ Abt Associates, October 22, 2001.

64As shown in Table D.12, excluding B&C 

investor loans reduces the market’s underserved 

area share for SF investor loans. An adjustment for 

B&C investor loans is made within HUD’s model, 

along the same lines as that B&C owner loans are 

excluded from the analysis. See Section F.3.c for 

further explanation.

65 It is encouraging that the RFS underserved area 

percentage (31.7 percent) for SF-owner mortgages 

originated in metropolitan areas during 2001 was 

similar to the corresponding percentage (31.0 

percent) reported by HMDA.

Single-Family-Owner Loans. First, consider 

the market shares for single-family-owner 

properties in the top portion of Table D.14a. 

In 2002, the underserved area percentage for 

home purchase loans increases from 27.1 

percent (1990-based) to 32.8 percent (2000-

based), an increase of 5.7 percentage points; 

the corresponding percentages for refinance 

loans were 24.2 percent (1990-based) and 

29.4 percent (2000-based), or an increase of 

5.2 percentage points. Considering total 

owner loans (i.e., both home purchase and 

refinance owner loans), the weighted average 

of the ‘‘Differences’’ reported in Table D.14a. 

is 5.4 percentage points in 2002 for the 

conforming market. Between 1999 and 2003, 

30.3 percent of mortgage originations were 

originated in underserved areas based on 

2000 geography, compared with 25.2 percent 

based on 1990 geography—yielding the 

overall differential of 5.1 percentage points. 

(The unweighted 1999–2003 differential is 

4.9 percent.) 

The first column of Table D.14a. reports 

the 2000-based underserved areas share for 

home purchase loans for the years, 1999 to 

2003. The share was about 31 percent in 1999 

and 2001 and in the 32.6–33.7 percent range 

during 2000, 2002, and 2003. Notice that the 

peak share (33.7 percent) for home purchase 

loans occurred in the most recent year, 2003. 

It should be recalled that there was no need 

to re-apportion the 2003 data from 1990-

based tracts to 2000-based tracts, as these 

2003 data were already defined in terms of 

2000 census geography. Whether this fact 

affects the various differentials between 2003 

and earlier years is not clear. The years 1999 

and 2000 exhibited higher underserved area 

shares for refinance loans than for home 

purchase loans; as discussed earlier, this 

pattern was largely, but not entirely, due to 

subprime refinance loans. 

Single-Family Rental and Multifamily 

Loans. Next, consider the underserved area 

market shares reported for single-family 

rental (or non-owner) and multifamily 

properties in the middle and bottom portions 

of Table D.14a. In 2002, the underserved area 

percentage for home purchase investor loans 

increases from 42.0 percent (1990-based) to 

47.7 percent (2000-based), an increase of 5.7 

percentage points; the corresponding 

percentages for refinance loans were 45.6 

percent (1990-based) and 50.8 percent (2000-

based), or an increase of 5.3 percentage 

points. The multifamily differentials are 

somewhat higher at approximately 7–8 

percentage points. Between 1999 and 2003, 

60 percent (unweighted average) of 

multifamily originations were originated in 

underserved areas based on 2000 geography, 

compared with 52.6 percent based on 1990 

geography. 

In the 2004 proposed GSE Rule, HUD made 

the following 2000-based assumptions with 

respect to the underserved areas shares of 

single-family rental properties: 52.0% for 

Case 1 (baseline), 50.0% for Case 2, and 

54.0% for Case 3. With respect to multifamily 

properties, the following assumptions were 

made with respect to underserved areas 

shares: 58.0% for Case 1 (baseline), 56.0% for 

Case 2, and 59.0% for Case 3. ICF criticized 

HUD’s baseline assumptions (52 percent for 

SF investors and 58 percent for MF rentals) 

as being too high.61 ICF’s best estimate was 

50 percent for SF investors and 55 percent for 

MF rentals.62 Since SF rentals account for 

10.6 percent of financed units, reducing the 

underserved area share by two percentage 

points from HUD’s 52 percent to ICF’s 50 

percent would reduce the overall 

underserved areas goal by 0.21 percentage 

point. Since MF rentals account for 15.0 

percent of financed units (in HUD’s baseline 

model), reducing the underserved area share 

by three percentage points from HUD’s 58 

percent to ICF’s 55 percent would reduce the 

overall underserved areas goal by an 

additional 0.45 percentage point. Thus, the 

combined effect of ICF’s assumptions would 

be a 0.66 percentage point reduction in the 

underserved areas goal. Fannie Mae did not 

comment directly on this parameter other 

than to emphasize that HUD’s Case 2 is the 

‘‘most likely set of assumptions’’ for 

estimating the underserved areas share 

(Fannie Mae Appendix, p. I–38). HUD’s Case 

2 (see above) would drop the baseline 

underserved area share for both SF and MF 

by two percentage points; therefore, Fannie 

Mae’s assumptions are similar to ICF’s.

In this analysis supporting the Final Rule, 

HUD is retaining the same underserved areas 

shares for SF and MF rental properties that 

it used in the 2004 proposed GSE rule. HUD 

conducted several additional analyses that 

support its SF rental baseline of 52 percent 

and its MF rental baseline of 58 percent. 

These analyses are summarized below. 

A report by Abt Associates 63 calculated 

1990-based underserved areas shares using 

the 1995 AHS and POMS data, for (a) all SF 

rental properties, (b) all SF rental properties 

with a mortgage, (c) all SF rental properties 

with a conventional conforming mortgage, (d) 

all SF Rental properties with a new first 

mortgage, and (e) all SF rental properties 

with a new conventional conforming first 

mortgage. The underserved areas share for 

each of the groups of SF rental properties was 

approximately 50 percent. Adding a five 

percent adjustment to reflect 2000-based 

geography (see Table D.14a) would increase 

these estimates to 55 percent. While this 

information is dated, it is consistent with 

HUD’s 52.0 percent baseline and its 54.0 

percent assumption in Case 3. Abt Associates 

also reported similar data for MF rental 

categories (a)–(c). In this case the 

underserved areas share ranged from 51–54 

percent; adding 7–8 percent adjustment to 

reflect 2000-based geography would increase 

these estimates to 55–62 percent, again 

providing support for HUD’s baseline (58 

percent) and Case 3 (59 percent) 

assumptions.64

HUD had Census Bureau staff use the 

geocoded 2003 AHS file to calculate the 

distribution of the rental housing stock across 

served and underserved areas. This analysis, 

which was conducted in terms of 1990-

Census geography, showed that 55.8 percent 

of the SF rental housing stock was located in 

underserved areas, as was 51.4 percent of the 

MF rental housing stock. Adding a five (7–

8) percent adjustment to reflect 2000-based 

geography would increase these SF (MF) 

rental estimates to 60.8 (58.4–59.4) percent. 

HUD also had Census Bureau staff use the 

geocoded, 2001 Residential Finance Survey 

(RFS) to calculate the distribution of rental 

mortgages and financed units across served 

and underserved areas. (See Table D.14b.) 

Unlike the AHS analysis mentioned above, 

this analysis was conducted in terms of 2000 

Census geography. In 2001, 54.1 percent of 

newly-mortgaged SF rental units were 

located in underserved areas, as were 61.5 of 

newly mortgaged MF rental units. Similar 

underserved area percentages were obtained 

for SF investor and MF loans that were 

originated in 1999 and 2000 and still 

surviving at the time of the RFS survey in 

2001. 65

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66 In this case, the 2000-based underserved area 

percentages for years prior to 1999 (i.e. 1994 to 1998 

in this example) are estimated by adding 4.9 

percent to the corresponding 1990-based 

underserved area percentages reported in Table 

D.12. The 4.9 percent is the unweighted difference 

of the 2000-based and 1990-based underserved area 

shares for total (home purchase and refinance) SFO 

owner loans reported in Table D.14. This procedure 

will be used throughout this section.

Finally, HUD examined the GSEs’ own 

data. Between 1999 and 2003, 58 percent of 

the SF rental units financed by GSE 

purchases were located in underserved areas. 

Between 1999 and 2002, 57 percent of the 

multifamily units financed by GSE purchases 

were located in underserved areas. 

Based on the above analyses, HUD retained 

the assumptions from the 2004 GSE proposed 

rule concerning underserved areas location of 

SF and MF rental properties. Specifically, the 

baseline underserved area share for SF rental 

units is 52 percent and that for MF rental 

units is 58 percent. 

2000-Based Underserved Area Market 

Estimates. Table D.15 reports the results of 

the projection model assuming 2000 

geography. Since Table D.15 has the same 

interpretation as Table D.13, there is no need 

for a detailed explanation of it. Considering 

a 15.0-percent MF mix and a 8.5-percent 

investor mortgage share, the market share 

estimate is 36.9 percent if the overall (both 

home purchase and refinance) single-familyowner percentage for underserved areas is 31 

percent, which is the estimated 1994–2003 

HMDA average as well as the recent 1999–

2003 HMDA average.66


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67The market share estimates are interpolated 

from Table D.15. For example, the overall market 

estimate for a SFO percentage of 32.3 percent is 

obtained by adding [.3*(38.4 minus 37.6)] to 37.6, 

to obtain the 37.6 figure reported in the text.

68The baseline 39.7 percent estimate for 2000 is 

0.7 percentage points lower than the 40.4 percent 

share reported in Section G.4 of Appendix D of the 

proposed rule, mainly for the reasons discussed in 

the previous footnote. The difference is mostly 

explained (a) by the different treatment of singlefamily rental mortgages and (b) by a 0.4 percentage 

point decline in HUD’s projections (in terms of the 

2000 Census data) of the 2000 underserved areas 

percentage for SF owners.

69As explained earlier in Section G.2, HUD reestimated the underserved areas share for 1997 

under the new assumptions (e.g., a lower, HMDAbased mortgage share for investor loans), obtaining 

a range of 32.0 percent (with a 16.3 MF mix) to 32.7 

percent (with a 19.3 percent MF mix). These 

estimates assume 1990 Census geography. Adding 

five percentage points to reflect 2000 Census 

geography yields estimates of 37.0 percent to 37.7 

percent for the 1997 underserved areas market.

The above results are based on averages 

across both home purchase and heavy 

refinance environments. The analysis can 

also be conducted in terms of home purchase 

environments, focusing on the underserved 

area percentages for home purchase loans 

reported in the first column of Table D.15. 

Again, considering a 15.0-percent MF mix 

and a 8.5-percent investor mortgage share, 

the underserved area market estimates 

reported in Table D.15 are: 37.8 percent if the 

SFO owner underserved area percentage is 

32.3 percent (1999–2003 average home 

purchase share); 67 37.6 if the SF owner 

percentage is 31.8 percent (estimated average 

home purchase share from 1994–2003); 36.9 

percent if the owner percentage is 31 percent 

(approximate home purchase share in 1999 

and 2001); 38.0 percent if the owner 

percentage is 32.5 percent (approximate 

home purchase percentage in 2000 and 

2002); and 39.0 percent if the owner 

percentage is 33.7 percent (home purchase 

percentage in 2003). This analysis assumes 

that the underserved areas share of refinance 

loans is the same as those listed above for 

home purchase loans. But, as Table D.14a 

shows, in recent home purchase 

environments, the underserved areas share of 

refinance loans has been higher than that for 

home purchase loans, largely but not totally 

due to subprime refinance loans (see earlier 

discussion). In the year 2000, for example, 

the overall underserved areas share for SFO 

owner loans reached 34.2 percent; in this 

case, the market estimate is 39.4 percent in 

this case. However, the next highest overall 

(both home purchase and refinance loans) 

owner share is the 31.9 percent share in 

1999, which yields at an overall market 

estimate of approximately 37.5 percent.

Fannie Mae reports its estimates of the 

2000-Census-based underserved areas market 

in Table I.13 on page I–40. For SFO 

percentages of 30 percent and 32 percent 

(obtained by adding five percentage points to 

Fannie Mae’s 1990-Census-based SFO 

percentages of 25 percent and 27 percent, 

respectively), Fannie Mae projects 

underserved area market shares of 35.1 

percent and 36.8 percent, respectively. (It is 

interesting that these are the exact same 

market shares projected by HUD in Table 

D.15 for the ‘‘Fannie Mae assumptions’’ of 

12.2-percent MF mix and an 8.0-percent 

investor mortgage share—suggesting that 

Fannie Mae’s model produces the same 

results as HUD’s model when the input 

assumptions are the same.) Fannie Mae 

concluded that the higher 36.8 percent 

market share was not appropriate because the 

SFO percentage of 32 percent was too high. 

However, as shown in Table D.14a, the 2000-

based underserved area percentage for SFO 

home loans was greater than 32 percent in 

2000, 2002, and 2003. 

Multifamily Mix. As discussed earlier, 

compared with the low-mod and special 

affordable market estimates, the underserved 

area market estimates exhibit less variation as 

one moves from a 13.5 percent MF mix to 

16.0 percent MF mix. For example, reducing 

the assumed multifamily mix from 16.0 

percent to 13.5 percent reduces the overall 

market projection for underserved areas by 

only 0.6–0.7 percentage points. This smaller 

MF mix effect occurs because the 

underserved area differentials between owner 

and rental properties are not as large as the 

low- and moderate-income and special 

affordable differentials reported earlier. For 

example, the 1999–2003 average SF-owner 

underserved areas share (30.3 percent in 

Table D.14a) is only 22 percentage points less 

than the baseline SF-Rental underserved 

areas share (52.0); on the other hand, the 

1999–2003 average SF-owner special 

affordable share (15.7 percent) is about 42 

percentage points less than the baseline SFRental special affordable share (58.0 percent). 

As shown in Table D.15, ICF’s MF mix of 

14.25 percent produces results intermediate 

between HUD’s 13.5 percent and 15.0 

percent. Estimates of the underserved areas 

based on a MF mix of 14.2 percent are only 

0.2 percentage points less than those based 

on a MF mix of 15.0 percent.

Investor Mortgage Share. Similarly, the 

market estimates differ only slightly with 

changes the investor mortgage share. 

Reducing the investor mix from 9.5 percent 

to 8.0 percent reduces the overall market 

projection for underserved areas by only 0.2–

0.4 percentage points. If the 10.0 percent 

baseline from the 2004 proposed GSE rule 

were used in this analysis, the market 

estimates would be approximately 0.3 (0.2) 

percentage points higher relative to the 

results reported in Table D.15 for a baseline 

of 8.5 (9.0) percent. Fannie Mae’s model 

combined a MF mix of 12.3 percent with an 

investor mortgage share of 8.0 percent. If the 

underserved area share of home purchase 

loans is 32.3 percent (the average for 1999–

2003), then the estimate for the overall 

underserved areas market is 37.0 percent 

based on Fannie Mae’s assumptions. In 

contrast, HUD’s estimates (with a MF mix of 

15.0 percent and 8.5 percent investor share) 

are 37.8 percent—almost one percentage 

point higher. If the underserved areas share 

of home purchase loans is at its 2003 level 

(33.7 percent), then Fannie Mae’s 

assumptions result in a market estimate of 

38.3 percent while HUD’s assumptions (see 

previous sentence) result in a market 

estimate of 39.0 percent. In its projection 

model, ICF assumed an underserved areas 

share of 31.5 percent for SF owner loans and 

produced an estimate of almost 37 percent 

for the overall underserved areas market 

during 2005–2008 (ICF Appendix, p.133). 

Different Underserved Area Shares for 

Rental Properties. Case 2 (see Table D.9) 

considered slightly smaller underserved area 

percentages for rental properties (50 percent 

for SF rentals and 56 percent for MF rentals), 

as compared with the baseline Case 1, which 

assumed 52 percent and 58 percent, 

respectively. Case 2 includes ICF’s 

assumption (50 percent) for SF Rentals and 

is close to ICF’s assumption (55 percent) for 

MF Rentals. Incorporating these Case 2 

assumptions reduces the underserved areas 

market estimate by only 0.5 percentage 

points. For example, if the SFO home 

purchase share is 33 percent, then the overall 

underserved area estimate is 37.9 percent 

under Case 2, as compared with 38.4 percent 

under Case 1 (see Table D.15). As discussed 

earlier, the baseline Case 1 assumptions offer 

a reasonable approach for estimating the 

underserved area market shares. 

Examples of Home Purchase Years. The 

above projection results for a home purchase 

environment can be compared with actual 

results for two home purchase years, 1999 

and 2000 (see earlier description of these two 

years in the low-mod section, F.3.a). For 

1999, the baseline model assumed a 

multifamily mix of 16.0 percent (see Section 

C) and a mortgage investor share of 8.2 

percent (see Section D). Under these 

assumptions, the projected 1999 market 

estimate (based on 2000-Census data) is 37.6 

percent; lowering the MF mix to 15.0 (14.0) 

percent instead of 16.0 percent reduces the 

estimate only slightly to 37.3 (36.9) percent. 

For 2000, the baseline model assumed a 

multifamily mix of 17.2 percent and a 

mortgage investor share of 9.1 percent. Under 

these assumptions, the 2000 underserved 

areas market is estimated to be 39.7 percent. 

A lower MF mix—for example, 16.0 (15.0) 

percent instead of 17.2 percent—would 

reduce the estimated 2000 underserved areas 

market share slightly to 39.4 (39.2) percent.68

For 1999, the 2000-based underserved area 

estimate (37.6 percent) is 4.8 percentage 

points greater than the earlier-reported 1990 

based estimate (32.8 percent); for the year 

2000, the differential is 5.0 percentage points 

(39.7 versus 34.7). This approximately five 

percentage point differential can be used to 

obtain estimates of 2000-based underserved 

area shares for the earlier home purchase 

years, 1995 to 1997. Table D.9 of the 

proposed GSE rule reported 1990-based 

underserved area shares of 33.9 percent for 

1995 and 1997 and 33.4 percent for 1996. 

These estimates, after adjustments for a lower 

HMDA-based mortgage investor share and a 

lower-than-baseline MF mix, would remain 

in the 32–33 percent range. Adding five 

percentage points would place these 

estimates in the 37–38 percent range in terms 

of 2000 Census geography.69 ICF’s best 

estimates were approximately 37 percent for 

1994–1997 and 39 percent for 1999 (ICF 

Appendix, p. 77); its lower bound estimates 

were approximately 34 percent during 1994–

1997 and 1999, and 37 percent in 2000 (ICF 

Appendix, p.82). As noted earlier, ICF fills its 

report with numerous minimums that often 

Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 63879

70The baseline 36.9 percent estimate for 2001 is 

0.8 percentage point lower than the 37.7 percent 

share reported in Section G.4 of Appendix D of the 

proposed rule. The difference is mostly explained 

(a) by the different treatment in this Final Rule of 

single-family rental mortgages and (b) by a 0.2 

percentage point decline in HUD’s projections (in 

terms of the 2000 Census data) of the 2001 

underserved areas percentage for SF owners.

71The baseline 36.2 percent estimate for 2002 is 

one percentage point lower than the 37.2 percent 

share reported in Section G.4 of Appendix D of the 

proposed rule. The difference is mostly explained 

(a) by the different treatment in this Final Rule of 

single-family rental mortgages and (b) by a 0.4 

percentage point decline in HUD’s projections (in 

terms of the 2000 Census data) of the 2002 

underserved areas percentage for SF owners.

72For the years 1999 to 2002, Fannie Mae 

estimated a 2000-Census-based underserved areas 

share of 37–38 percent, obtained by adding five 

percentage points to Fannie Mae’s 32–33 percent 

estimate for the underserved areas market based on 

1990 Census data. (See their Table I.9, page I–34.) 

This compares with HUD’s estimate of 37.1 percent 

to 37.6 percent for the same period.

73The differentials reported in Table D.14 for the 

three individual property types tend to be greater 

than five percentage points, which raises the 

question of why the overall differential is only five 

percentage points. As explained later, the upward 

adjustment to account for underserved areas in nonmetropolitan areas is about 0.65 percentage point 

less using the 2000-based Census data than it was 

using the 1990-based Census data.

appear unbelievable, such as the 32.8 percent 

projection for the overall underserved market 

in 2000 (ICF Appendix, p. 83), a time when 

the SF owner underserved areas percentage 

was 35.7 percent itself (see Table 14a)—in 

this case, the rental portion of the market was 

below the underserved share for owners, 

rather than the typical case where the rental 

portion is more ‘‘goals rich’’ than the owner 

portion.

Market Volatility. Additional sensitivity 

analyses were conducted to reflect the 

volatility of the economy and mortgage 

market. Recession and high interest rate 

scenarios assumed a significant drop in the 

underserved area percentage for singlefamily-owner mortgages. The single-familyowner home purchase percentage can go as 

low as 29 percent—which is almost 2.8 

percentage points lower than the 1994–2003 

average of 31.8 percent, 3.3 percentage points 

lower than the 1999–2003 average of 32.3 

percent, and 4.7 percentage points lower than 

the underserved areas share of home 

purchase loans in 2003—and the estimated 

market share for underserved areas remains 

about 35 percent. In a more severe case, the 

overall underserved market share would be 

33–34 percent if the single-family-owner 

home purchase share fell to 27 percent (its 

1992 level), which is 5.3 percentage points 

lower than its 1999–2002 average. 

Table D.11 shows the impact on the 

underserved areas market share under 

different assumptions about a refinancing 

environment. See the earlier discussion of 

the low-mod goal in Section F.2b for an 

explanation of the various model 

assumptions necessary to simulate a heavy 

refinance environment. The discussion 

focuses on the 65-percent refinance rate since 

that has characterized recent refinance 

waves. With respect to the underserved area 

characteristics of SF owner loans, two 

scenarios were considered: (A) Scenario A 

represents the average underserved area 

percentages for the last four refinance years 

(1998, 2001, 2002, and 2003)—32 percent for 

home purchase loans and 30 percent for 

refinance loans; and (B) Scenario B 

represents the average underserved 

percentages for the two most recent refinance 

years (2002, and 2003)—33 percent for home 

purchase loans and 29 percent for refinance 

loans. Thus, there is a 2–4 percentage point 

differential between home purchase loans 

and refinance loans in a heavy refinancing 

environment.

Under Scenario A, the underserved areas 

market shares varied by almost two 

percentage points (i.e., 1.6 percent), from 

36.0 percent with a 12 percent MF mix to 

34.4 percent with a 6 percent MF mix. These 

underserved area market shares are 3–5 

percentage points lower than the 

underserved areas shares reported in Table 

D.15 for HUD’s baseline home purchase 

environment. (The results were similar for 

Scenario B.) Notice that under Scenario A, 

the underserved areas share remains in the 

34–35 percent range even if the MF mix falls 

to 6–8 percent. In addition to higher-income 

borrowers dominating the single-family 

market, the share of the ‘‘goals rich’’ rental 

market declines in a refinancing wave, which 

tends to further reduce the underserved areas 

share of market activity. The right-hand 

column of Table D.11 shows that the rental 

share falls to the 17–22 percent range, or 4–

9 percentage points less than the almost 26-

percent rental share in HUD’s baseline 

model. This contributes to the underserved 

areas share of the market typically falling to 

34–36 percent during a heavy refinancing 

period. 

Model estimates were also made for the 

recent refinancing years of 2001, 2002, and 

2003. For 2001, the baseline model assumed 

a multifamily mix of 13.5 percent and a 

mortgage investor share of 7.8 percent. Under 

these assumptions, the 2001 market estimate 

is 36.9 percent.70 If the MF mix for 2001 had 

been 12.5 (12.0) percent, then the estimated 

underserved areas market share for 2001 

would be 36.6 (36.4) percent. For 2002, the 

baseline model assumed a multifamily mix of 

slightly over 11.0 percent and a mortgage 

investor share of 7.8 percent. Under these 

assumptions, the 2002 underserved areas 

market is estimated to be 36.2 percent.71 A 

lower MF mix—for example, 10.5 (9.5) 

percent instead of 11 percent—would reduce 

the estimated 2002 underserved areas market 

share to 36.0 (35.7) percent. ICF’s best 

estimates for 1998, 2001, and 2002 were in 

the 34–35 percent range while its lowerbound estimates were in the 32–33 percent 

range.72

As noted in Section F.3.b, HUD did not 

receive 2003 HMDA data until early August 

2004 and therefore HUD has not been able to 

develop a complete projection model for 

2003. Still, some rough projections for 2003 

are provided here for different assumptions 

about the MF mix, recognizing that firm data 

on the 2003 multifamily market are not 

available. Combining an investor mortgage 

share of 8.2 from HMDA with different MF 

mixes produces the following estimates of 

the underserved areas market for 2003: 35.1 

percent (MF mix of 8 percent); 34.7 percent 

(MF mix of 7 percent); and 34.4 percent (MF 

mix of 6.0 percent). 

As shown by both the simulation results in 

Table D.10 and by the actual experience 

during 2001–2003, the underserved area 

share declines when refinances dominate the 

mortgage market. The above estimates 

suggest that the underserved areas share will 

not likely fall below 35 percent, although, as 

noted above, the estimates for 2003 (around 

35 percent) are somewhat speculative. 

Similar to 1999 and 2000, the 2001 and 

2002 differences between the 1990-based and 

2000-based underserved area market 

estimates are about five percentage points. 

For 2001, the 2000-based baseline estimate 

(36.9 percent) is 5.0 percentage points greater 

than the earlier-reported 1990 based estimate 

of 31.9 percent); for the year 2002, the 

differential is 4.9 percentage points (36.2 

versus 31.3).73

The analysis in this section suggests that a 

reasonable range for the overall market share 

for underserved areas based on 2000 

geography might be 35–39 percent, which is 

consistent with the 30–34 percent range 

estimated earlier based on 1990-based 

geography. 

Feasibility of Underserved Areas Goal in a 

Period of Heavy Refinancing. HUD received 

a number of public comments seeking a 

regulatory solution to the issue of the ability 

of the GSEs to meet the housing goals during 

a period when refinances of home mortgages 

constitute an unusually large share of the 

mortgage market. As explained in the 

Preamble, HUD is not addressing the 

refinance issue in this final rule. Elsewhere 

in the Federal Register, HUD is publishing 

an Advance Notice of Proposed Rulemaking 

that advises the public of HUD’s intention to 

consider by separate rulemaking a provision 

that recognizes and takes into consideration 

the impact of high volumes of refinance 

transactions on the GSEs’ ability to achieve 

the housing goals in certain years, and 

solicits proposals on how such a provision 

should be structured and implemented. HUD 

believes that it would benefit from further 

consideration and additional public input on 

this issue. HUD also notes that FHEFSSA 

provides a mechanism by which HUD can 

take into consideration market and economic 

conditions that may make the achievement of 

housing goals infeasible in a given year. (See 

12 U.S.C. 1336(b)(e).) 

B&C Loans. The procedure for dropping 

B&C loans from the projections is the same 

as described in Section F.3.c for the Low- and 

Moderate-Income Goal. The underserved 

areas percentage for B&C loans is 52.0 

percent, which is larger than the projected 

percentages for the overall market given in 

Table D.15. Thus, dropping B&C loans (as 

well as all subprime loans) will appreciably 

reduce the overall market estimates. Consider 

the case of a single-family-owner percentage 

of 32 percent, which yields an overall market 

estimate for the underserved areas of 38.6 

percent if B&C loans are included in the 

analysis. Dropping B&C loans from the 

projection model reduces the market share by 

one percentage point to 37.6 percent, as 

reported in Table D.15. Dropping all 

63880 Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 

74Between 1999 and 2002, 2000-based 

underserved census tracts accounted for 31.4 

percent (unweighted annual average) of all 

mortgages in metropolitan areas. This 1999–02 

average percentage for single-family owners in 

metropolitan area is lower than the underserved 

area percentage reported in previous paragraphs. To 

be comparable with the non-metropolitan data, 

these metropolitan area data do not include loans 

originated by lenders that specialize in 

manufactured housing loans and B&C loans, 

excluding these loans lowers the underserved areas 

share.

75There are two LIHTC thresholds: at least 20 

percent of the units are affordable at 50 percent of 

AMI or at least 40 percent of the units are affordable 

at 60 percent of AMI.

subprime loans (A-minus as well as B&C) 

would reduce the underserved areas market 

projection to 37.4 percent. 

Non-metropolitan Areas. As explained in 

Section G.3, in order to account for the much 

larger coverage of underserved areas in nonmetropolitan areas, 1.25 percent was added 

to the market share based on metropolitan 

area data, in order to arrive at a nationwide 

estimate of the market share for underserved 

areas. According to HMDA, underserved 

counties accounted for 42.7 percent of singlefamily-owner mortgages originated in nonmetropolitan areas during the 1999-to-2002 

period, based on 1990 geography. With 2000 

geography and the new tract-based definition 

of underserved areas in non-metropolitan 

areas, the market share falls by 2.3 percentage 

points to 39.6 percent. This 2000-based 

underserved areas percentage of 39.6 percent 

for non-metropolitan areas is about eight 

percentage points less than the comparable 

percentage for metropolitan areas.74 This 

eight-point differential is lower than the 16-

point differential used in the earlier 1990-

based Census analysis. Assuming that nonmetropolitan areas account for 13 percent of 

all single-family-owner mortgages and 

estimating that the single-family-owner 

market accounts for 74.5 percent of newlymortgaged dwelling units, then the nonmetropolitan underserved area differential of 

8 percent would raise the overall market 

estimate by 0.78 percentage point—8 

percentage points times 0.13 (nonmetropolitan area mortgage market share) 

times 0.745 (single-family owner mortgage 

market share). Based on this calculation, if 

the 8 point differential reflected actual 

market conditions, then the underserved 

areas market share estimated using 

metropolitan area data should be increased 

by 0.78 percentage point to account for the 

effects of underserved counties in nonmetropolitan areas, based on 2000 geography. 

A more conservative adjustment of 0.65 

percentage points was made in Table D.15, 

which reports the results of the projection 

model.

Section G.3 reported that excluding 

manufactured housing loans (as well as small 

loans less than $15,000) reduced the overall 

underserved area market estimates based on 

1990 geography by less than one percentage 

point (roughly three-quarters of a percentage 

point). Excluding manufactured housing 

loans leads to a similar reduction for the 

market estimates based on 2000 geography. 

As reported earlier, the small loan and 

manufactured housing effects can be 

considered separately. Dropping only 

manufactured housing loans would reduce 

the market estimates by approximately threefourths of a percentage point. ICF argued that 

loans with less than $15,000 should be 

excluded. The impact of doing this on the 

market estimates would be about one-third of 

a percentage point. ICF also considered 

scenarios where one-half of manufactured 

loans would be dropped, as well as small 

loans less than $15,000. The impact of doing 

this on the market estimates would be threefifths of a percentage point. 

The above analyses of the effects of less 

affordable market conditions, different 

assumptions about the size of the rental 

market, and dropping different categories of 

loans from the market definition suggest that 

the 35–39 percent range described earlier is 

a reasonable range for the market estimate for 

underserved areas based on the projection 

model described earlier. This range 

incorporates market affordability conditions 

that are more adverse than have existed 

recently and it excludes B&C loans from the 

market estimates.

5. Conclusions 

Based on the above findings as well as 

numerous sensitivity analyses, HUD 

concludes that 35–39 percent is a reasonable 

estimate of mortgage market originations that 

would qualify toward achievement of the 

Geographically Targeted Goal if purchased by 

a GSE. The 35–39 percent range is higher 

than the market range in the 2000 Rule 

mainly because it is based on 2000 Census 

geography which includes more underserved 

census tracts than 1990 Census geography. 

HUD recognizes that shifts in economic and 

housing market conditions could affect the 

size of this market; however, the market 

estimate allows for the possibility that 

adverse economic conditions can make 

housing less affordable than it has been in 

the last few years. In addition, the market 

estimate incorporates a range of assumptions 

about the size of the multifamily market and 

excludes B&C loans. 

H. Size of the Conventional Conforming 

Market for the Special Affordable Housing 

Goal 

This section presents estimates of the 

conventional conforming mortgage market for 

the Special Affordable Housing Goal. The 

special affordable market consists of owner 

and rental dwelling units which are occupied 

by, or affordable to: (a) Very-low-income 

families; or (b) low-income families in lowincome census tracts; or (c) low-income 

families in multifamily projects that meet 

minimum income thresholds patterned on 

the low-income housing tax credit (LIHTC).75

HUD estimates that the special affordable 

market is 23–27 percent of the conventional 

conforming market.

HUD is proposing to establish each GSE’s 

special affordable multifamily subgoal as 1.0 

percent of its average annual dollar volume 

of total (single-family and multifamily) 

mortgage purchases over the 2000–2002 

period. In dollar terms, the Department’s 

proposal is $5.49 billion per year in special 

affordable multifamily purchases for Fannie 

Mae, and $3.92 billion for Freddie Mac. The 

multifamily special affordable goal, as well as 

the special affordable home purchase 

subgoal, are discussed further in Appendix C. 

Section F described HUD’s methodology 

for estimating the size of the low- and 

moderate-income market. Essentially the 

same methodology is employed here except 

that the focus is on the very-low-income 

market (0–60 percent of Area Median 

Income) and that portion of the low-income 

market (60–80 percent of Area Median 

Income) that is located in low-income census 

tracts. Data are not available to estimate the 

number of renters with incomes between 60 

and 80 percent of Area Median Income who 

live in projects that meet the tax credit 

thresholds. Thus, this part of the Special 

Affordable Housing Goal is not included in 

the market estimate. 

1. Special Affordable Shares by Property 

Type 

The basic approach involves estimating for 

each property type the share of dwelling 

units financed by mortgages that are 

occupied by very-low-income families or by 

low-income families living in low-income 

areas. HUD combined mortgage information 

from HMDA, the American Housing Survey, 

the Property Owners and Managers Survey 

and the recently released 2001 Residential 

Finance Survey in order to estimate these 

special affordable shares. 

a. Special Affordable Owner Percentages 

HMDA data for the percentage of singlefamily-owners that qualify for the Special 

Affordable Goal are reported in Table D.16. 

That table also reports data for the two 

components of the Special Affordable Goal—

very-low-income borrowers and low-income 

borrowers living in low-income census tracts. 

Focusing first on home purchase loans, 

HMDA data show that the special affordable 

share of the market has followed a pattern 

similar to that discussed earlier for the lowand moderate-income loans. The percentage 

of special affordable borrowers increased 

significantly between 1992 and 1994, from 

10.4 percent of the conforming market in 

1992 to 12.6 percent in 1993, and then to 

14.1 percent in 1994. Between 1995 and 

1998, the special affordable market was in 

the 14–16 percent range, averaging 15.1 

percent. Over the past five years (1999–2003), 

the special affordable share of the home 

purchase loans has averaged 16.4 percent. It 

was about 17 percent during 1999 and 2000 

and 16 percent during the most recent three 

years, 2001 to 2003. 

BILLING CODE 4210–27–P

Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 63881

Considering all (home purchase and 

refinance) loans during recent ‘‘home 

purchase’’ environments, the special 

affordable share averaged 18.7 percent during 

1999–2000, over three percentage points 

more than the 15.4 percent average between 

1995 and 1997. Excluding B&C (all subprime) 

loans from the analysis reduces this 

differential only slightly to 2.8 (2.4) 

percentage points. As mentioned earlier, 

lending patterns could change with sharp 

changes in the economy, but the fact that 

there have been several years of strong 

affordable lending suggests that the special 

affordable market has changed in 

fundamental ways from the mortgage market 

of the early 1990s. 

Except for the four years of heavy 

refinancing (1998, 2001, 2002, and 2003), the 

special affordable share of the refinance 

market has recently been higher than the 

special affordable share of the home purchase 

market—a pattern discussed in Section F for 

low-mod and very-low-income loans. During 

1999 (2000), for example, the special 

affordable share of the refinance market was 

19.2 (22.6) percent, compared with 17.3 

(16.9) percent for the home loan market. The 

higher special affordable percentages for 

refinance loans are reduced or even 

eliminated if subprime loans are excluded 

from the analysis. As shown in Table D.16, 

excluding B&C loans from the data 

practically eliminates the refinance-homepurchase differential for 1999 and reduces 

the differential for 2000 to 4.2 percentage 

points (from 5.7 percentage points). Going 

further and excluding A-minus loans from 

the year 2000 data would reduce the 

differential to 2.7 percentage points. HUD’s 

projection model excludes B&C loans and 

sensitivity analyses will show the effects on 


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76As noted earlier, this discrepancy could be due 

to mis-measurement from the technique for 

apportioning 2003 data, which is defined in 2000-

census geography, to a 1990-based geography.

77Affordability was calculated as discussed 

earlier in Section F, using AHS monthly housing 

cost, monthly rent, number of bedrooms, and MSA 

location fields. Low-income tracts were identified 

using the income characteristics of census tracts 

from the 1990 Census of Population, and the census 

tract field on the AHS file was used to assign units 

in the AHS survey to low-income tracts and other 

tracts. POMS data on year of mortgage origination 

were utilized to restrict the sample to properties 

mortgaged during 1993–1995.

78 During the 1995 rule-making process, HUD 

examined the rental housing stock located in lowincome zones of 41 metropolitan areas surveyed as 

part of the AHS between 1989 and 1993. While the 

low-income zones did not exactly coincide with 

low-income tracts, they were the only proxy readily 

available to HUD at that time. Slightly over 13 

percent of single-family rental units were both 

affordable at the 60–80 percent of AMI level and 

located in low-income zones; almost 16 percent of 

multifamily units fell into this category.

the overall special affordable market of 

excluding all single-family subprime loans. 

New 2000-Based Census Geography and 

New OMB Metropolitan Area Definitions.

Going forward, HUD will be re-benchmarking 

its median incomes for metropolitan areas 

and non-metropolitan counties based on 

2000 Census incomes, will be defining lowincome census tracts (which are included in 

the definition of special affordable) in terms 

of the 2000 Census geography, and will be 

incorporating the effects of the new OMB 

metropolitan area definitions. As discussed 

earlier in Section F, HUD projected the 

effects of these three changes on the special 

affordable shares of the market for the years 

1999–2003; the results for special affordable 

loans are reported in the top portion of Table 

D.8b. Under the historical MSA-based data, 

the (unweighted) average special affordable 

share of the conventional conforming market 

was 16.4 (16.3) percent for home purchase 

(total) loans (see Table D.16); the 

corresponding average with the CBSA-based 

projected data was 16.4 (16.4) percent, or 

practically the same. Given these small 

differences there is no need to adjust the 

overall market estimates reported below to 

account for the new data. However, it should 

be noted that the most recent year of 2003 

does show a rather larger difference—the 

special affordable share of home purchase 

loans under the projected CBSA approach is 

16.9 percent, which is a full percentage point 

higher than the special affordable share of 

15.9 percent under historical data.76

For the other two property types (singlefamily rental and multifamily), comparisons 

between projected and historical special 

affordable percentages were made using the 

GSEs’ data. For single-family rental 

mortgages, the weighted average of Fannie 

Mae’s (Freddie Mac’s) special affordable 

percentage for the years 1999 to 2003 was 

48.2 (48.7) percent using the historical data, 

compared with 49.6 (49.5) percent using the 

projected data. For multifamily mortgages, 

the weighted average of Fannie Mae’s 

(Freddie Mac’s) special affordable percentage 

for the years 1999 to 2003 was 50.9 (48.7) 

percent using historical data, compared with 

51.6 (51.5) percent using the projected data. 

These comparisons suggest little difference 

between the historical and projected special 

affordable shares for rental properties. HUD 

also projected the overall special affordable 

percentage for each GSE. For the overall 

special affordable goal (considering all three 

property types), the unweighted average of 

Fannie Mae’s (Freddie Mac’s) special 

affordable percentage for the years 1999 to 

2002 was 20.0 (18.9) percent using the 

projected data, compared with 20.0 (18.9) 

percent using the historical data. There is 

little difference in the GSEs’ average special 

affordable performance between the 

projected and historical data. 

b. Very-Low-Income Rental Percentages 

Table D.14 in Appendix D of the 2000 Rule 

reported the percentages of the single-family 

rental and multifamily stock affordable to 

very-low-income families. According to the 

AHS, 59 percent of single-family units and 53 

percent of multifamily units were affordable 

to very-low-income families in 1997. The 

corresponding average values for the AHS’s 

six surveys between 1985 and 1997 were 58 

percent and 47 percent, respectively. As 

discussed earlier in Section F, an important 

issue concerns whether rent data based on 

the existing rental stock from the AHS can be 

used to proxy rents of newly mortgaged 

rental units. HUD’s analysis of POMS data 

during the 2000 rule-making process 

suggested that it could—estimates from 

POMS of the rent affordability of newlymortgaged rental properties are quite 

consistent with the AHS data on the 

affordability of the rental stock. Fifty-six (56) 

percent of single-family rental properties 

with new mortgages between 1993 and 1995 

were affordable to very-low-income families, 

as were 51 percent of newly-mortgaged 

multifamily properties. These percentages for 

newly-mortgaged properties from the POMS 

are similar to those reported above from the 

AHS for the rental stock. Based on this POMS 

analysis, HUD’s baseline model in the 2004 

proposed GSE rule assumed that 50 percent 

of newly-mortgaged, single-family rental 

units, and 47 percent of multifamily units, 

were affordable to very-low-income families. 

(See further discussion of this issue in 

Section H.1.d) 

c. Low-Income Renters in Low-Income Areas 

HMDA does not provide data on lowincome renters living in low-income census 

tracts. As a substitute, HUD used the POMS 

and AHS data. As explained in the 2000 GSE 

Rule, the share of single-family and 

multifamily rental units affordable to lowincome renters at 60–80 percent of area 

median income (AMI) and located in lowincome tracts was calculated using the 

internal Census Bureau AHS and POMS data 

files.77 The POMS data showed that 8.3 

percent of the 1995 single-family rental stock, 

and 9.3 percent of single-family rental units 

receiving financing between 1993 and 1995, 

were affordable at the 60–80 percent level 

and were located in low-income census 

tracts. The POMS data also showed that 12.4 

percent of the 1995 multifamily stock, and 

13.5 percent of the multifamily units 

receiving financing between 1993 and 1995, 

were affordable at the 60–80 percent level 

and located in low-income census tracts.78

The baseline analysis in HUD’s proposed 

GSE rule assumed that 8 percent of the 

single-family rental units and 11.0 percent of 

multifamily units are affordable at 60–80 

percent of AMI and located in low-income 

areas.

Combining the assumed very-low-income 

percentage of 50 percent (47 percent) for 

single-family rental (multifamily) units with 

the assumed low-income-in-low-income-area 

percentage of 8 percent (11 percent) for 

single-family rental (multifamily) units yields 

the special affordable percentage of 58 

percent (58 percent) for single-family rental 

(multifamily) units. This was the baseline 

case in the 2004 proposed GSE rule. 

d. Comments on the Special Affordable 

Rental Share and Additional Analysis 

Both ICF and Fannie Mae commented that 

HUD overstated the special affordable share 

of the single-family rental and multifamily 

rental markets. They argued that updated 

2001 AHS data showed that the affordability 

of the rental housing stock had declined 

since HUD had conducted its POMS and 

AHS analyses in 1995 and 1997, respectively. 

For both single-family (SF) and multifamily 

(MF) rentals, ICF used a special affordable 

range of 47–53 percent, with a baseline of 50 

percent. ICF’s special affordable range is 

much less than both HUD’s 53–61 percent 

range (58 percent baseline) for single-family 

rentals and HUD’s 54–62 percent range for 

multifamily rentals (also a 58 percent 

baseline). Since SF and MF rentals account 

for about 25 percent of financed units in 

HUD’s model, reducing the SF and MF 

baselines from 58 percent (HUD’s baseline) to 

50 percent (ICF’s baseline) would reduce the 

overall special affordable market estimate by 

two percentage points. Thus, this is an 

important issue. 

Based on its analysis of the AHS (see 

Fannie Mae Appendix, I–31–I–32), Fannie 

Mae concluded that the very-low-income 

share for single-family rental properties had 

fallen from 58.3 percent in 1997 to 53.0 

percent in 2001; similarly, the very-lowincome (VLI) share of multifamily rental 

properties had fallen from 52.0 percent to 

44.9 percent over this same period. (By 

comparison, ICF estimated that 47 percent of 

the SF rental stock and 42 percent of the MF 

rental stock were affordable to VLI families.) 

In its analysis, Fannie Mae provides a weight 

of 0.07 to the VLI share (25.7 percent) of 

recently-constructed single-family rental 

units in the AHS, and the residual 0.93 

weight to the VLI share (53.6 percent) of the 

remaining existing units in the AHS. While 

Fannie Mae uses a VLI share of 46 percent 

for single-family rentals in its market sizing 

models, applying these weights to the 2001 

AHS data (reported by Fannie Mae in Table 

I.7 on p. I–32) yields approximately 52 

percent for the VLI share of single-family 

rental properties. Similarly, for multifamily 

properties, Fannie Mae provides a weight of 

0.11 to the VLI share (22.2 percent) of 

recently-constructed multifamily rental units 

in the AHS, and the residual 0.89 weight to 

the VLI share (45.7 percent) of the remaining 

existing units in the AHS. In this case, 

Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 63883

79Fannie Mae’s data exhibited some variation, 

standing at 33 percent in 2001 and 19 percent in 

2001. Freddie Mac’s percentage was 29 percent in 

both years.

80These adjustments for low-income renters 

living in low-income areas may be conservative. For 

SF (MF) rentals, the 2001 and 2002 figures for the 

GSEs were in the nine (eight) percent range.

81This is obtained by multiplying (a) 1.0 

percentage point by (b) the refinance rate of 0.35 by

(c) the 0.745 property share for SF owner loans.

applying the above weights to the 2001 AHS 

data yields 43 percent for the VLI share of 

multifamily rental properties—a figure 

similar to the 41-percent VLI share that 

Fannie Mae uses in its market sizing models. 

After computing a VLI share of 46 percent for 

SF rentals, Fannie Mae adds 8 percent to 

account for low-income renters living in lowincome census tracts (the second component 

of the special affordable category); this yields 

54 percent for the special affordable share of 

SF rentals. After computing a VLI share of 41 

percent for MF rentals, Fannie Mae adds 11 

percent to account for low-income renters 

living in low-income census tracts; this 

yields 52 percent for the special affordable 

share of MF rentals. Thus, Fannie Mae’s 

estimates are intermediate between ICF’s (50 

percent) and HUD’s (58 percent). Since SF 

rentals account for 10.6 percent of financed 

units in HUD’s model, reducing the SF 

baseline from 58 percent (HUD’s baseline) to 

54 percent (Fannie Mae’s baseline) would 

reduce the overall special affordable market 

estimate by 0.42 percentage points. Since MF 

rentals account for 15.0 percent of financed 

units in HUD’s model, reducing the MF 

baseline from 58 percent (HUD’s baseline) to 

52 percent (Fannie Mae’s baseline) would 

reduce the overall special affordable market 

estimate by 0.90 percentage points. 

Combining these two reductions yields a 1.32 

percentage point reduction in the overall 

special affordable market. 

HUD is retaining its baseline of 58 percent 

for the special affordable share of both SF 

and MF rentals. Several sets of analyses led 

to this decision. 

HUD updated its analysis with 2001 and 

2003 AHS data. Using ICF’s assumptions for 

an AHS analysis (see ICF Appendix, p. 45), 

the 2003 AHS data showed that 57 percent 

(67 percent) of single-family (multifamily) 

rental units would qualify as being affordable 

to VLI families. This analysis of the 2003 

AHS used a new geocoded file that identified 

the specific metropolitan area or county 

location for each observation in the AHS. 

This allowed HUD to link accurate area 

median incomes (used to determine 

affordability) to each AHS observation, 

which represents a substantial improvement 

over previous AHS analyses that did not have 

the specific household location and thus had 

to rely on estimates of area median income 

in order to compute affordability ratios. This 

more accurate approach appears to produce 

higher affordability estimates than earlier 

analyses based on the non-geocoded AHS. 

To derive an overall special affordable 

percentage, one must add the second 

component of the special affordable 

category—low-income renters living in lowincome areas—to the VLI share. HUD’s 

analysis of POMS data and its analysis of 

2003 AHS geocoded data suggest that lowincome SF renters in low-income areas 

account for 22 percent of all SF low-income 

renters; GSE data for 2001 and 2002 suggest 

a slightly higher percentage.79 With respect 

to MF properties, HUD’s analysis of POMS 

data and its analysis of 2003 AHS geocoded 

data suggest that low-income MF renters in 

low-income areas accounted for 24–25 

percent of all MF low-income renters; GSE 

data for 2001 and 2002 suggest a slightly 

lower percentage (21 percent). These shares 

can be applied to the 2003 AHS results for 

low-income renters. For SF rentals, the 22 

percent share for low-income renters living 

in low-income census tracts can be 

multiplied by the 20 percent figure that the 

2003 AHS produces for low-income SF 

renters, yielding estimate of 4.4 percent. This 

4.4 percent is added to the VLI percentage of 

67 percent for SF rentals to arrive at a special 

affordable estimate of 71 percent, based on 

the 2003 AHS. For MF rentals, the 25 percent 

share for low-income renters living in lowincome census tracts can be multiplied by 

the 27 percent figure that the 2003 AHS 

produces for low-income MF renters, 

yielding an estimate of 6.7 percent.80 This 6.7 

percent is added to the VLI percentage of 57 

percent for MF rentals to arrive at a special 

affordable estimate of 63 percent, based on 

the 2003 AHS. These 2003 AHS special 

affordable shares—67 percent for SF rental 

units and 63 percent for MF rental units—

support HUD’s use of a 58-percent baseline 

as the special affordable share of both SF and 

MF rental properties.

It is interesting to compare HUD’s 58-

percent baseline with the actual performance 

of Fannie Mae and Freddie Mac. For singlefamily rental mortgages, the weighted average 

of both Fannie Mae’s and Freddie Mac’s 

special affordable percentage for the years 

1999 to 2003 was about 50 percent using 

projected CBSA data. For multifamily 

mortgages, the weighted average of Fannie 

Mae’s special affordable percentage for the 

same years was 49 percent, while Freddie 

Mac’s percentage was 52 percent. As ICF 

notes, the GSEs’ below market performance 

may be due to their limited participation in 

the small multifamily market (ICF Appendix, 

p. 47). 

2. Size of the Special Affordable Market 

The size of the special affordable market 

depends in large part on the size of the 

single-family rental and multifamily markets 

and on the special affordable percentages of 

both owners and renters. Therefore, this 

section conducts several sensitivity analyses 

around these market parameters. As in the 

previous sections, this section initially 

assumes a refinance rate of 35 percent, which 

means that it initially focuses on a home 

purchase or low-refinancing environments. 

After presenting these results, market 

estimates reflecting a heavy refinance 

environment will be presented. In the 2000 

GSE Rule, HUD assumed that the special 

affordable share of refinance loans was 1.4 

percentage points lower than the special 

affordable share of borrowers purchasing a 

home. However, as discussed earlier, the 

special affordable share of refinance loans 

equaled or was greater than the special 

affordable share of home purchase loans 

during home purchase environments such as 

1995–97 or 1999–2000; thus, the assumption 

of a lower special affordable share for 

refinance loans is initially dropped from the 

analysis but will be reintroduced during the 

sensitivity analysis and the discussion of 

heavy refinancing environments. If the 

special affordable share of refinance loans 

were assumed to be one percentage point less 

than that of home purchase loans, then the 

market shares in Table D.17 would be 

approximately one-quarter percentage point 

lower.81

Considering a 15.0-percent MF mix and a 

8.5-percent investor mortgage share, the 

special affordable market estimates reported 

in Table D.17 are: 27.3 percent if the owner 

percentage is 17 percent (home purchase 

share for 1999 and 2000); 26.8 if the owner 

percentage is 16.4 percent (average home 

purchase share from 1999–2003); 26.5 

percent if the owner percentage is 16 percent 

(home purchase share for 1998, 2001, 2002, 

and 2003); and 25.7 percent if the owner 

percentage is 15 percent (home purchase 

average from 1995–97). Considering a range 

of 13.5–16.0 for the MF mix and a range of 

8.5–9.0 for the investor mortgage share, the 

special affordable market estimates reported 

in Table D.17 are: 26.7–27.9 percent if the 

owner percentage is 17 percent; 26.2–27.4 

percent if the owner percentage is 16.4 

percent; 25.9–27.1 percent if the owner 

percentage is 16 percent; and 25.1–26.3 

percent if the owner percentage is 15 percent. 

BILLING CODE 4210–27–P

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BILLING CODE 4210–27–C


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82These three estimates were initially reported in 

HUD’s 2000 Final Rule, and repeated in Table D.9 

of Appendix D of the 2004 proposed GSE rule.

83To provide some confirmation for these 1995–

1997 estimates, HUD went back and re-estimated 

the model for 1997. As shown in Table D.9 of the 

2004 GSE Proposed Rule (as well as in Table D.15 

of the 2000 GSE Rule), HUD had earlier estimated 

a special affordable share of 28.8 percent for 1997 

(which was practically the same as the 28.9-percent 

share estimated for 1995 and the 28.7-percent share 

estimated for 1996). With a lower investor share 

(8.4 percent instead of 10.0 percent) and other 

changes mentioned in the text, the new estimate for 

the 1997 special affordable market was 28.0 

assuming a multifamily mix of 19.3 percent. If the 

multifamily mix is reduced to 17.3 (16.3) percent, 

the special affordable share of the 1997 market is 

27.1 (26.7) percent. The 26.7–28.0 percent range for 

1997 is consistent with the 1995–1997 ranges 

reported in the text.

84Using the projected CBSA data (instead of the 

historical 1990-based MSA data) did not change the 

special affordable market estimate in either 1999 or 

2000.

If the special affordable percentage for 

home purchase loans fell to 13 percent—or 

by three percentage points below its 1995–

2003 average level of approximately 16 

percent—then the overall market estimate 

would be about 24 percent under the baseline 

assumptions. Thus, 24 percent is consistent 

with a rather significant decline in the 

special affordable share of the single-family 

home purchase market. A 24 percent market 

estimate allows for the possibility that 

adverse economic and housing affordability 

conditions could keep special affordable 

families out of the housing market. On the 

other hand, if the special affordable home 

purchase percentage stays at its recent levels 

(15–17 percent), the market estimate is in the 

26–27 percent range. 

Different Special Affordable Shares for 

Rental Properties. Case 2 (see Table D.9) 

considered smaller special affordable 

percentages for rental properties (53 percent 

for SF rentals and 54 percent for MF rentals), 

as compared with the baseline Case 1, which 

assumed 58 percent for both property types. 

Case 2 assumptions are close to Fannie Mae’s 

assumptions—54 percent for SF Rentals and 

52 for MF Rentals. Incorporating the Case 2 

assumptions reduces the special affordable 

market estimate by 1.2 percentage points. For 

example, if the SFO home purchase share is 

17 percent, then the overall special affordable 

estimate is 26.1 percent under Case 2, as 

compared with 27.3 percent under Case 1 

(see Table D.17). 

ICF’s assumptions were even lower, 50 

percent for both SF and MF rentals, a figure 

that is eight percentage points lower than 

HUD’s baseline Case 1 assumption of 58 

percent for each of these two property types. 

Given that these two property types account 

for 25 percent of all financed dwelling units, 

using ICF’s 50-percent assumption (instead of 

HUD’s 58-percent assumption) would reduce 

the overall special affordable market shares 

in Table D.17 by two percentage points. As 

discussed above, HUD’s baseline Case 1 

assumptions offer a reasonable approach for 

estimating the special affordable market 

shares. 

Multifamily Mix. The volume of 

multifamily activity is also an important 

determinant of the size of the special market. 

While Section C explained the rationale for 

HUD’s 15.0 percent range, it is useful, given 

the uncertainty surrounding the size of the 

multifamily market, to consider the effects of 

lower multifamily mix assumptions, even in 

a home purchase environment. Assuming a 

13.5 percent MF mix reduces the overall 

special affordable market estimates by 0.4 

percentage points compared with a 15 

percent MF mix, and by 1.0 percentage point 

compared with a 16.0 percent mix. For 

example, when the special affordable share of 

the home purchase market is at 16.4 percent 

(its 1999–2003 average), the special 

affordable share of the overall market is 26.2 

percent assuming a 13.5 percent multifamily 

mix, compared with 26.8 (27.4) percent 

assuming a 15 (16.0) percent multifamily 

mix. 

As shown in Table D.17, the ICF’s MF mix 

of 14.2 percent produces results intermediate 

between HUD’s 13.5 percent and 15.0 

percent. Estimates of the special affordable 

market based on a MF mix of 14.2 percent 

are only 0.3 percentage points less than those 

based on a MF mix of 15.0 percent. Fannie 

Mae’s model combined an even lower MF 

mix of 12.3 percent with an investor 

mortgage share of 8.0 percent. If the special 

affordable share of home purchase loans is 

16.4 percent (the 1999–2003 average), then 

the estimate for the overall special affordable 

market is 25.2 percent based on Fannie Mae’s 

assumptions. In contrast, HUD’s estimates 

(with a MF mix of 15.0 percent and 8.5–9.0 

percent investor share) are 26.8–27.0 percent 

‘‘about one and a half percentage points 

higher. If the special affordable share of home 

purchase loans is 16 percent (its recent 2001–

2003 level), then Fannie Mae’s assumptions 

result in a market estimate of 25.2 percent 

while HUD’s assumptions (see previous 

sentence) result in market estimates of 26.5–

26.7 percent.

Investor Mortgage Share. As shown in 

Table D.17, increasing the investor mortgage 

share by one percentage point from 8.0 

percent to 9.0 percent increases the special 

affordable market estimate by approximately 

0.4–0.5 percentage point. If the 10.0 percent 

baseline from the 2004 proposed GSE rule 

were used in this analysis, the market 

estimates would be approximately 0.6 (0.4) 

percentage points higher relative to the 

results reported in Table D.15 for a baseline 

of 8.5 (9.0) percent. 

Examples of Home Purchase Years. The 

above projection results for a home purchase 

environment can be compared with actual 

results for two home purchase years, 1999 

and 2000, which were characterized by 

refinance rates of 34 percent and 29 percent, 

respectively. For 1999, the baseline model 

assumed a multifamily mix of 16.0 percent 

and a mortgage investor share of 8.2 percent. 

Under these assumptions, the 1999 market 

estimate is 27.9 percent; if the 1999 MF mix 

was lower—for example, 15.0 (14.0) percent 

instead of 16.0 percent—then the estimate of 

the 1999 special affordable market share 

would be 27.5 (27.2) percent. 

The 2004 proposed rule (Table D.9 in 

Appendix D) reported a higher baseline 

market estimate for 1999 of 29.2 percent, as 

compared with the 27.9 percent reported in 

the previous paragraph—a differential of 1.3 

percentage points. The difference is largely 

due to the treatment of single-family rental 

mortgages. For example, using the proposed 

rule’s 10-percent assumption for the 

mortgage investor share (instead of the lower 

8.2 percent HMDA-based mortgage investor 

shares reported in the text) would increase 

the 1999 estimate by 0.8 percentage points to 

28.7 percent, only 0.5 percentage points 

lower than the 29.2 percent reported in the 

proposed rule. Other more minor changes 

that lower market estimate included: (a) 

Further reducing the SF mortgage investor 

share by excluding B&C investor loans from 

the HMDA data (see Section C); (b) using 1.6 

percent (instead of 2.0 percent) for the 

mortgage share of single-family 2–4 property 

owners; and (c) using slightly lower 

dwelling-units-per-mortgage assumptions for 

SF 2–4 properties (2.20 instead of 2.25) and 

for SF investor mortgages (1.30 instead of 

1.35). These changes, leading to this 1.3 

percentage point differential, also affect the 

estimates reported in Table D.9 of Appendix 

D of the proposed rule for the three home 

purchase environments prior to 1999—28.9 

percent for 1995, 28.7 for 1996, and 28.8 

percent for 1997.82 Given (a)–(c) and the fact 

that the HMDA-reported mortgage investor 

share was approximately eight percent 

during these three years (instead of the 

assumed 10 percent in the earlier 1995–97 

analysis), these estimates should probably be 

reduced by the above-mentioned 1.3 

percentage points, which would place them 

at 27–28 percent assuming no adjustment in 

the baseline MF mix, and at 26–27 percent 

assuming a MF mix three percentage points 

lower than the baseline MF mix.83

For 2000, the baseline model assumed a 

multifamily mix of 17.2 percent and a 

mortgage investor share of 9.1 percent. Under 

these assumptions, the 2000 special 

affordable market is estimated to be 29.1 

percent. A lower MF mix—for example, 15.0 

percent instead of 17.2 percent—would 

reduce the estimated 2000 low-mod market 

share to 28.2 percent.84

ICF’s best estimates for the special 

affordable market were 25–26 percent in 

1995, 1997, 1999, and 2000, and a 

particularly low 23 percent for 1996 (ICF 

Appendix, p. 94). Its lower bound estimates 

were 22–23 percent for 1997 and 1999, 24 

percent for 1995 and 2000, and 21 percent for 

1996 (ICF Appendix, p. 99). As discussed 

earlier, two percentage points of the HUD–

ICF differential involves ICF’s lower 

assumptions about the special affordable 

characteristics of rental loans. Given that the 

SFO percentage was 18–19 percent during 

1999 and 2000 (see Table D.16), ICF’s 23–24 

estimates for 1999 and 2000 are in need of 

further explanation. 

Heavy Refinancing Environments. The 

special affordable share of the overall market 

declines when refinances dominate the 

market. Section F.3c, which presents the lowmod market estimates, explained the 

assumptions for incorporating a refinance 

environment into the basic projection model 

for 2005–08. Briefly, they are: the refinance 

share of single-family mortgages was 

increased to 65 percent (from 35 percent); the 

multifamily mix was allowed to vary from 6 

to 12 percent; the market share for subprime 

63886 Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 

85The baseline estimates for 2001 (25.0 percent) 

and 2002 (24.3 percent) are lower than those (26.5 

percent and 25.8 percent, respectively) reported in 

Table D.9 of Appendix D of the proposed rule. As 

explained earlier, the differences between the 

results in the proposed rule and this Final Rule are 

mainly due to the treatment of single-family rental 

mortgages. In addition, the SF0 percentage for home 

purchase loans originated during 2002 was lowered 

by approximately 0.2 percentage point in the Final 

Rule.

86Using the projected CBSA data (instead of the 

historical 1990-based MSA data) resulted in only 

small changes in the special affordable market 

estimates for 2001 (a 0.1 percentage point decline) 

and 2002 (a 0.5 percentage point decline).

87For the years 1999 to 2002, Fannie Mae 

estimated a special affordable market share of 23–

25 percent. (This is their estimate assuming no 

missing data; see their Table I.9, page I–34.) This 

compares with HUD’s estimate of 25.9 percent to 

26.6 percent. As discussed in Section C.6, Fannie 

Mae assumes a rather low MF mix (approximately 

10 percent) in the model that generates its historical 

estimates.

loans was reduced to 8.5 percent (from 12 

percent); and the mortgage investor share was 

set at 8.0 percent (its average during recent 

refinancing waves). With respect to MF 

mixes, it is likely that an 11–12 percent MF 

mix characterized 2001, 9–11 percent 

characterized 2002, and less than 7 percent 

characterized 2003, although there is some 

uncertainty with these estimates. In a 

refinancing wave, the special affordable 

percent is typically lower for refinance loans 

than home purchase loans, as middle- and 

high-income borrowers dominate the market. 

With respect to the special affordable 

characteristics of SF owner loans, the 

refinancing analysis assumed 16 percent for 

home purchase loans and 14 percent for 

refinance loans, which were the average 

special affordable percentage for the last four 

refinance years (1998, 2001, 2002, and 2003). 

There has been a two percentage point 

differential between home purchase loans 

and refinance loans during a heavy 

refinancing environment. 

As shown in Table D.11, the special 

affordable shares varied by over two 

percentage points, from 24.1 percent with a 

12 percent MF mix to 21.7 percent with a 6 

percent MF mix. These special affordable 

market shares are 3–5 percentage points 

lower than the special affordable shares 

reported in Table D.17 for HUD’s baseline 

home purchase environment. Notice that the 

special affordable share remains in the 22–23 

percent range even if the MF mix falls to 6–

8 percent. In addition to higher-income 

borrowers dominating the single-family 

market, the share of the ‘‘goals rich’’ rental 

market declines in a refinancing wave, which 

tends to further reduce the special affordable 

of market activity. The right-hand column of 

Table D.11 shows that the rental share falls 

to the 17–22 percent range, or 4–9 percentage 

points less that the almost 26-percent rental 

share in HUD’s baseline model. 

Model estimates were also made for the 

recent refinancing years of 1998, 2001, 2002, 

and 2003. For 1998, the baseline model 

assumed a multifamily mix of 14.0 percent 

and a mortgage investor share of 6.8 percent. 

Under these assumptions, the 1998 market 

estimate is 24.0 percent. If the MF mix for 

1998 had been 13.0 (12.0) percent then the 

estimated special affordable market share for 

1998 would be 23.5 (23.1) percent. For 2001, 

the baseline model assumed a multifamily 

mix of 13.5 percent and a mortgage investor 

share of 7.8 percent. Under these 

assumptions, the 2001 market estimate for 

special affordable loans is 25.0 percent. If the 

MF mix for 2001 had been 12.0 percent, 

instead of the baseline of 13.5 percent, then 

the estimated special affordable market share 

for 2001 would be 24.4 percent. For 2002, the 

baseline model assumed a multifamily mix of 

slightly over 11.0 percent and a mortgage 

investor share of 7.8 percent. Under these 

assumptions, the 2002 special affordable 

market is estimated to be 24.3 percent.85 A 

lower MF mix—for example, 10.5 (9.5) 

percent instead of 11 percent—would reduce 

the estimated 2002 special affordable market 

share to 24.2 (23.7) percent. 86 87

As explained in Section F.3b, HUD has not 

yet completed its analysis of 2003 data. 

However, HUD developed some rough 

projections for different assumptions about 

the MF mix. Combining an investor mortgage 

share of 8.2 from HMDA with different MF 

mixes (ranging from 6 percent to 8 percent) 

produced estimates of 22.6 percent (MF mix 

of 6 percent) to 23.5 percent (MF of 8 

percent). 

As shown by both the simulation results in 

Table D.17 and the actual experience during 

2001–2003, the special affordable share of the 

overall market declines when refinances 

dominate the market. The special affordable 

share was approximately 24 percent during 

2001 and 2002 and 23 percent in 2003 

(although there is some uncertainty with the 

2003 estimate). 

The various market estimates presented in 

Table D.17 for a home purchase environment 

and reported above for a refinance 

environment are not all equally likely. Most 

of them equal or exceed 23 percent. In the 

home purchase environment, estimates 

below 23 percent would require the special 

affordable share for home purchase loans to 

drop to 12 percent which would be 4 

percentage points lower than the 1995–2003 

average for the special affordable share of the 

home purchase market. As shown in Table 

D.11, dropping below 23 percent would be 

more likely in a heavy refinance 

environment, particularly those characterized 

by extremely low MF mixes of 7 percent or 

less. 

As stated in Sections F and G above, HUD 

received a number of public comments 

seeking a regulatory solution to the issue of 

the ability of the GSEs to meet the housing 

goals during a period when refinances of 

home mortgages constitute an unusually 

large share of the mortgage market. As 

explained in the Preamble, HUD is not 

addressing the refinance issue in this final 

rule. Elsewhere in the Federal Register, HUD 

is publishing an Advance Notice of Proposed 

Rulemaking that advises the public of HUD’s 

intention to consider by separate rulemaking 

a provision that recognizes and takes into 

consideration the impact of high volumes of 

refinance transactions on the GSEs’ ability to 

achieve the housing goals in certain years, 

and solicits proposals on how such a 

provision should be structured and 

implemented. HUD believes that it would 

benefit from further consideration and 

additional public input on this issue. HUD 

also notes (see above) that FHEFSSA 

provides a mechanism by which HUD can 

take into consideration market and economic 

conditions that may make the achievement of 

housing goals infeasible in a given year. (See

12 U.S.C. 1336(b)(e).) 

B&C Loans. The procedure for dropping 

B&C loans from the projections is the same 

as described in Section F.3.c for the Low- and 

Moderate-Income Goal. The special 

affordable percentage for B&C loans is 28.0 

percent, which is similar to the projected 

percentages for the overall market given in 

Table D.17. Thus, dropping B&C loans (as 

well as all subprime loans) does not 

appreciably reduce the overall market 

estimates. Consider the case of a singlefamily-owner percentage of 16 percent, 

which yields an overall market estimate for 

Special Affordable Goal of 26.7 percent if 

B&C loans are included in the analysis. 

Dropping B&C loans from the projection 

model reduces the special affordable market 

share by 0.2 percentage points to 26.5, as 

reported in Table D.17. Dropping all 

subprime loans (A-minus as well as B&C) 

would reduce the special affordable market 

projection to 26.2 percent. 

Manufactured Housing Loans and Small 

Loans. Excluding manufactured housing 

loans and small loans (loans less than 

$15,000) reduces the overall market estimates 

reported in Table D.17 by less than one 

percentage point. This is estimated as 

follows. First, excluding these loans reduces 

the special affordable percentage for singlefamily-owner mortgages in metropolitan 

areas by about 1.5 percentage points, based 

on analysis of recent home purchase 

environments (1995–97 and 1999 and 2000). 

Multiplying this 1.5 percentage point 

differential by the property share (0.745) of 

single-family-owner units yields 1.1 

percentage points, which serves as a proxy 

for the reduction in the overall special 

affordable market share due to dropping 

manufactured home loans from the market 

analysis. The actual reduction will be 

somewhat less because dropping 

manufactured home loans will increase the 

share of rental units, which increases the 

overall special affordable share, thus partially 

offsetting the 1.1 percent reduction. The net 

effect is probably a reduction of threequarters to one percentage point. 

The effects can be considered separately. 

Dropping only manufactured housing loans 

would reduce the market estimates by 

approximately one-half of a percentage point. 

ICF argued that loans with less than $15,000 

should be excluded. The impact of doing this 

on the market estimates would be about onethird to four-fifths of a percentage point. ICF 

also considered scenarios where one-half of 

manufactured loans would be dropped, as 

well as small loans less than $15,000. The 

impact of doing this on the market estimates 

would be three-fifths to three-quarters of a 

percentage point. 

The above analyses of the effects of less 

affordable market conditions, different 

assumptions about the size of the rental 

market, and dropping different categories of 

Federal Register / Vol. 69, No. 211 / Tuesday, November 2, 2004 / Rules and Regulations 63887

loans from the market definition suggest that 

23–27 percent is a reasonable range of 

estimates for the low- and moderate-income 

market. This range covers markets without 

B&C and allows for market environments that 

would be much less affordable than recent 

market conditions. 

Tax Credit Definition. Data are not 

available to measure the increase in market 

share associated with including low-income 

units located in multifamily buildings that 

meet threshold standards for the low-income 

housing tax credit. Currently, the effect on 

GSE performance under the Special 

Affordable Housing Goal is rather small. For 

instance, adding the tax credit condition 

increased Fannie Mae’s performance as 

follows: 0.42 percentage point in 1999 (from 

17.20 to 17.62 percent); 0.59 percentage point 

in 2000 (from 18.64 to 19.23 percent); and 

0.43 percent point in 2001 (from 19.29 to 

19.72 percent). The increases for Freddie Mac 

have been lower (ranging from 0.24 to 0.38 

percentage point during the same period). 

3. Conclusions 

Sensitivity analyses were conducted for the 

market shares of each property type, for the 

very-low-income shares of each property 

type, and for various assumptions in the 

market projection model. These analyses 

suggest that 23–27 percent is a reasonable 

estimate of the size of the conventional 

conforming market for the Special Affordable 

Housing Goal. This estimate excludes B&C 

loans and allows for the possibility that 

homeownership will not remain as affordable 

as it has over the past six years. In addition, 

the estimate covers a range of projections 

about the size of the multifamily market.

[FR Doc. 04–24101 Filed 11–1–04; 8:45 am] 

BILLING CODE 4210–27–P