65044 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

DEPARTMENT OF HOUSING AND

URBAN DEVELOPMENT

24 CFR Part 81

[Docket No. FR–4494–F–02]

RIN 2501–AC60

HUD’s Regulation of the Federal

National Mortgage Association (Fannie

Mae) and the Federal Home Loan

Mortgage Corporation (Freddie Mac)

AGENCY: Office of the Assistant

Secretary for Housing ‘‘ Federal Housing

Commissioner, HUD.

ACTION: Final rule.

SUMMARY: This final rule establishes

new housing goal levels for the Federal

National Mortgage Association (Fannie

Mae) and the Federal Home Loan

Mortgage Corporation (Freddie Mac)

(collectively, the ‘‘Government

Sponsored Enterprises,’’ or the ‘‘GSEs’’)

for the years 2001 through 2003. The

new housing goal levels are established

in accordance with the Federal Housing

Enterprises Financial Safety and

Soundness Act of 1992 (FHEFSSA), and

govern 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. Specifically, the

final rule increases the Low- and

Moderate-Income Housing Goal to 50

percent, the Geographically Targeted

Goal to 31 percent, and the Special

Affordable Housing Goal to 20 percent

of units backing each GSE’s annual

eligible mortgage transactions. The

Special Affordable Multifamily Subgoal

increases to one percent of each GSE’s

average annual total dollar mortgage

purchases in 1997 through 1999. This

rule also establishes new provisions and

clarifies certain other provisions of

HUD’s rules for counting different types

of mortgage purchases towards the

goals, including provisions regarding

the use of bonus points for mortgages

that are secured by certain single family

rental properties and small multifamily

properties; and the disallowance of

goals credit for mortgage loans with

predatory characteristics.

While Fannie Mae and Freddie Mac

have been successful in providing

stability and liquidity in the market for

certain types of mortgages, their share of

the affordable housing market is

substantially smaller than their share of

the total conventional, conforming

mortgage market. There are several

reasons for these disparities, related to

the GSEs’ purchase and underwriting

guidelines; and to their relatively low

level of activity in specific mortgage

markets that provide financing for

housing serving low- and moderateincome families, including small

multifamily rental properties, single

family owner-occupied rental

properties, manufactured housing, and

markets for seasoned mortgages on

properties with affordable housing. As

the GSEs continue to grow their

businesses, the new goals will provide

strong incentives for the two enterprises

to more fully address the housing

finance needs for very low-, low- and

moderate-income families and residents

of underserved areas and, thus, more

fully realize their public purposes.

In addition, as government sponsored

enterprises and market leaders, Fannie

Mae and Freddie Mac have a public

responsibility to help eliminate

predatory mortgage lending practices

which are inimical to the home

financing and homeownership

objectives that the GSEs were

established to serve. Fannie Mae and

Freddie Mac have adopted policies

stating that they will not purchase

mortgage loans with certain predatory

characteristics. This final rule affirms

the GSEs’ actions by disallowing

housing goals credit for mortgages

having features that the GSEs

themselves have identified as

unacceptable.

EFFECTIVE DATE: January 1, 2001.

FOR FURTHER INFORMATION CONTACT:

Director, Office of Government

Sponsored Enterprises Oversight, Office

of Housing, Room 6182, telephone 202–

708–2224. For questions on data or

methodology, contact John L. Gardner,

Director, Financial Institutions

Regulation Division, Office of Policy

Development and Research, Room 8234,

telephone (202) 708–1464. For legal

questions, contact Kenneth A. Markison,

Assistant General Counsel for

Government Sponsored Enterprises/

RESPA, Office of the General Counsel,

Room 9262, telephone 202–708–3137.

The address for all of these persons is

Department of Housing and Urban

Development, 451 Seventh Street, SW.,

Washington, DC 20410. Persons with

hearing and speech impairments may

access the phone numbers via TTY by

calling the Federal Information Relay

Service at (800) 877–8399.

SUPPLEMENTARY INFORMATION

I. General

A. Purpose

This final rule revises existing

regulations implementing the

Department of Housing and Urban

Development’s (the ‘‘Department’’ or

‘‘HUD’’) authority to regulate the GSEs.

The authority exercised by the

Department is established under:

(1) The Federal National Mortgage

Association Charter Act (‘‘Fannie Mae

Charter Act’’), which is Title III of the

National Housing Act, section 301 et

seq. (12 U.S.C. 1716 et seq.);

(2) The Federal Home Loan Mortgage

Corporation Act (‘‘Freddie Mac Act’’),

which is Title III of the Emergency

Home Finance Act of 1970, section 301

et seq. (12 U.S.C. 1451 et seq.); and

(3) FHEFSSA, enacted as Title XIII of

the Housing and Community

Development Act of 1992 (Pub. L. 102–

550, approved October 28, 1992) (12

U.S.C. 4501–4641).

(4) Section 7(d) of the Department of

Housing and Urban Development Act

(42 U.S.C. 3535(d)), which provides that

the Secretary may make such rules and

regulations as may be necessary to carry

out his functions, powers, and duties,

and may delegate and authorize

successive redelegations of such

functions, powers, and duties to officers

and employees of the Department.

FHEFSSA substantially changed the

Department’s regulatory authorities

governing the GSEs by establishing a

separate safety and soundness regulator

within the Department and clarified and

expanded the Department’s regulation

of the GSEs’ missions. Regulations first

implementing the Department’s

authorities with respect to the GSEs’

missions under FHEFSSA were issued

on December 1, 1995 (24 CFR part 81).

This rule revises certain portions of

those regulations concerning the GSEs’

affordable housing goals and provisions

related to how mortgage loans are

treated in the calculation of

performance under the housing goals.

The remaining part of the preamble

contains several endnotes. These

endnotes appear at the end of the

preamble.

B. Background

1. Fannie Mae and Freddie Mac

Fannie Mae and Freddie Mac engage

in two principal businesses: investing in

residential mortgages and guaranteeing

securities backed by residential

mortgages. Fannie Mae and Freddie Mac

are chartered by Congress as

Government Sponsored Enterprises to:

(1) Provide stability in the secondary

market for residential mortgages; (2)

respond appropriately to the private

capital market; (3) provide ongoing

assistance to the secondary market for

residential mortgages (including

activities relating to mortgages on

housing for low- and moderate-income

families involving a reasonable

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65045

11 11. Sec. 1321.

economic return that may be less than

the return earned on other activities) by

increasing the liquidity of mortgage

investments and improving the

distribution of investment capital

available for residential mortgage

financing; and (4) promote access to

mortgage credit throughout the nation

(including central cities, rural areas, and

other underserved areas) by increasing

the liquidity of mortgage investments

and improving the distribution of

investment capital available for

residential mortgage financing.1

Fannie Mae and Freddie Mac receive

significant explicit benefits through

their status as GSEs that are not enjoyed

by any other shareholder-owned

corporations in the mortgage market.

These benefits include: (1) Conditional

access to a $2.25 billion line of credit

from the U.S. Treasury; 2 (2) exemption

from the securities registration

requirements of the Securities and

Exchange Commission and the States; 3

and (3) exemption from all State and

local taxes except property taxes.4

Additionally, although the securities

the GSEs guarantee and the debt

instruments they issue are not backed

by the full faith and credit of the United

States, and nothing in this final rule

should be construed otherwise, such

securities and instruments trade at

yields only a few basis points over those

of U.S. Treasury securities and at yields

lower than those for securities issued by

comparable firms that are fully private

but may be higher capitalized. The

market prices for GSE debt and

mortgage-backed securities, and the fact

that the market does not require that

those securities be rated by a national

rating agency, suggest that investors

perceive that the government implicitly

backs the GSEs’ debt and securities.

This perception evidently arises from

the GSEs’ relationship to the Federal

Government, including their public

purposes, their Congressional charters,

their potential direct access to U.S.

Department of Treasury funds, and the

statutory exemptions of their debt and

mortgage-backed securities (MBS) from

otherwise mandatory security laws.

Consequently, each GSE enjoys a

significant implicit benefit—its cost of

doing business is significantly less than

that of other firms in the mortgage

market. According to a U.S. Department

of Treasury 1996 study, the benefits of

federal sponsorship are worth almost $6

billion annually to Fannie Mae and

Freddie Mac. Of this amount, reduced

operating costs (i.e., exemption from

SEC filing fees and from state and local

income taxes) represent approximately

$500 million annually. These estimates

are broadly consistent with estimates by

the Congressional Budget Office and

General Accounting Office. According

to the Department of the Treasury,

Fannie Mae and Freddie Mac appear to

pass through part of these benefits to

consumers through reduced mortgage

costs and retain part for their own

stockholders.5

The GSEs have achieved an important

part of their mission: providing stability

and liquidity to large segments of the

housing finance markets. As a result of

the GSEs’ activities, many home buyers

have benefited from lower interest rates

and increased access to capital,

contributing, in part, to a record

national homeownership rate of 66.8

percent in 1999. While the GSEs have

been successful in providing stability

and liquidity to certain portions of the

mortgage market, the GSEs must further

utilize their entrepreneurial talents and

power in the marketplace and ‘‘lead the

mortgage finance industry’’ to ‘‘ensure

that citizens throughout the country

enjoy access to the public benefits

provided by these federally related

entities.’’ 6

Despite the record national

homeownership rate in 1999, lower

homeownership rates have prevailed for

certain minorities, especially for

African-American households (46.3

percent) and Hispanics (45.5 percent).

These gaps are only partly explained by

differences in income, age, and other

socioeconomic factors. Disparities in

mortgage lending are a contributing

factor to lower homeownership rates

and are reflected in loan denial rates of

minority groups when compared to

white applicants. Denial rates for

conventional (non-government-backed)

home purchase mortgage loans in 1998

were 54 percent for African Americans,

53 percent for Native American

applicants, 39 percent for Hispanic

applicants, 26 percent for White

applicants, and 12 percent for Asian

applicants.7 Despite strong economic

growth, low unemployment, low

mortgage interest rates, and relatively

stable home prices, housing problems

continue to persist for low-income

families and certain minorities.

In addition to disparities across racial

groups, populations who live in certain

types of housing have not benefited to

the same degree as have others from the

advantages and efficiencies provided by

Fannie Mae and Freddie Mac. The GSEs

have been much less active in

purchasing mortgages in markets where

there is a need for additional financing

to address persistent housing needs

including financing for small

multifamily rental properties,

manufactured housing, single family

owner-occupied rental properties,

seasoned affordable housing mortgages,

and older housing in need of

rehabilitation.

While HUD recognizes that the GSEs

have played a significant role in the

mortgage finance industry by providing

a secondary market and liquidity for

mortgage financing for certain segments

of the mortgage market, it is this

recognition of their ability, along with

HUD’s comprehensive analyses of the

size of the mortgage market and the

opportunities available, America’s

unmet housing needs, identified credit

gaps, and HUD’s consideration of the

statutory factors under FHEFSSA that

causes HUD to increase the level of the

housing goals so that as the GSEs grow

their businesses so they will address

new markets and persistent housing

finance needs.

2. Regulation of the GSEs

In 1968, Congress assigned HUD

general regulatory authority over Fannie

Mae,8 and in 1989, Congress granted the

Department essentially identical

regulatory authority over Freddie Mac.9

Under the 1968 law, HUD was

authorized to require that a portion of

Fannie Mae’s mortgage purchases be

related to the national goal of providing

adequate housing for low- and

moderate-income families. Accordingly,

the Department established two housing

goals—a goal for mortgages on low- and

moderate-income housing and a goal for

mortgages on housing located in central

cities—by regulation, for Fannie Mae in

1978.10 Each goal was established at the

level of 30 percent of mortgage

purchases. Similar housing goals for

Freddie Mac were proposed by the

Department in 1991 but were not

finalized before October 1992, when

Congress revised the Department’s GSE

regulatory authorities including

requirements for new housing goals.

In 1992, Congress enacted the Federal

Housing Enterprises Financial Safety

and Soundness Act (FHEFSSA) as Title

XIII of the Housing and Community

Development Act of 1992 (Pub. L. 102–

550, approved October 28, 1992) (12

U.S.C. 4501–4641), which established

the Office of Federal Housing Enterprise

Oversight (OFHEO) as the GSEs’ safety

and soundness regulator and affirmed,

clarified and expanded the Secretary of

Housing and Urban Development’s

responsibilities for GSE mission

regulation. FHEFSSA provided that,

except for the specific authority of the

Director of OFHEO, the Secretary

retained general regulatory power over

the GSEs.11 FHEFSSA also detailed and

expanded the Department’s specific

65046 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

powers and authorities, including the

power to establish, monitor, and enforce

housing goals for the GSEs’ purchases of

mortgages that finance housing for lowand moderate-income families; housing

located in central cities, rural areas, and

other underserved areas; and special

affordable housing, affordable to very

low-income families and low-income

families in low-income areas.12 The

Department is required to establish each

of the goals after consideration of

certain prescribed factors relevant to the

particular goal.13

FHEFSSA provided for a transition

period during 1993 and 1994 and

required HUD to establish interim goals

for the transition period (58 FR 53048;

October 13, 1993) (59 FR 61504;

November 30, 1994). In November 1994,

HUD extended the interim goals

established for 1994 for both GSEs

through 1995 while the Department

completed its development of post

transition goals.

The Department issued proposed and

final rules in 1995 establishing and

implementing the housing goals for the

years 1996 through 1999. The rule

provided that the housing goals for 1999

would continue beyond 1999 if the

Department did not change the goals,

and further provided that HUD may

change the level of the goals for the

years 2000 and beyond based upon

HUD’s experience and in accordance

with HUD’s statutory authority and

responsibility.

In addition to establishing the level of

the housing goals, the 1995 final rule

included counting requirements for

purposes of calculating performance

under the housing goals. The new

regulations also prohibited the GSEs

from discriminating in any manner on

any prohibited basis in their mortgage

purchases, implemented procedures by

which HUD exercises its authority to

review new programs of the GSEs,

required reports from the GSEs,

established a public use data base on the

GSEs’ mortgage purchase activities

while providing protections for

confidential and proprietary

information, and established

enforcement procedures under

FHEFSSA.

C. The Proposed Rule

On March 9, 2000,14 HUD published

a rule proposing new housing goal

levels for Fannie Mae and Freddie Mac.

The rule proposed to increase the level

of the housing goals for the purchase by

Fannie Mae and Freddie Mac of

mortgages financing low- and moderateincome housing, special affordable

housing, and housing in central cities,

rural areas, and other underserved areas.

The rule also proposed to clarify HUD’s

guidelines for counting different types

of mortgage purchases under the

housing goals, including treatment of

missing affordability data and purchases

of seasoned mortgage loans; use of

bonus points for goals credit for

purchases of mortgages secured by

single family rental and small

multifamily properties; and providing

greater public access to certain types of

mortgage data on the GSEs’ mortgage

purchases in HUD’s public use database.

The rule also solicited public comments

on several other issues related to the

housing goals including the appropriate

role of credit enhancements in

furthering affordable housing lending

and whether the use of credit

enhancements should be considered in

calculating housing goal performance.

D. This Final Rule

In response to the proposed rule, HUD

received over 250 comments. The

comments came from the GSEs;

individuals; representatives of lending

institutions; non-profit organizations;

community, consumer groups and civil

rights organizations; local and State

governments; and others. Following full

consideration of the comments, HUD

developed this final rule. The final rule

is consistent with the approach

announced in the proposed rule but

does include some revisions adopted in

light of the comments received. The

final rule: (1) Increases the level of the

housing goals for the years 2001 through

2003 as a result of HUD’s review of the

statutory factors under FHEFSSA to

ensure that the GSEs continue and

strengthen their efforts to carry out

Congress’ intent that the GSEs provide

the benefits of the secondary market to

families throughout the nation—the

Low- and Moderate-Income Housing

Goal increases to 50 percent, the

Geographically Targeted Goal increases

to 31 percent, the Special Affordable

Housing Goal increases to 20 percent;

and the Special Affordable Multifamily

Subgoal increases to the respective

average of one percent of each GSE’s

total mortgage purchases over 1997

through 1999; (2) establishes the use of

bonus points for small multifamily

properties with 5 to 50 units and for

single family owner-occupied rental

properties for the years 2001 through

2003; (3) establishes a temporary

adjustment factor for Freddie Mac’s

multifamily mortgage purchases for the

years 2001 through 2003; (4) prohibits

the counting of high cost mortgage loans

with predatory features for goals credit;

(5) provides or clarifies counting rules

for the treatment of missing affordability

data, purchases of seasoned mortgage

loans, purchases of federally insured

mortgage loans and purchases of

mortgage loans on properties with

expiring assistance contracts; (6)

provides for HUD’s review of

transactions to determine appropriate

goal treatment; and (7) includes certain

definitional and technical corrections to

the regulations issued in 1995.

Specific changes included in the Final

Rule from the provisions included in

the Proposed Rule are as follows:

(1) The period covered by the housing

goals is 2001 through 2003 and there is

no transition year. The proposed rule

had suggested the goals cover the period

from 2000 through 2003 with 2000

serving as a transition year.

(2) The Special Affordable

Multifamily Subgoal uses the average of

1997 through 1999 as the base period for

establishing the level of the goal over

the 2001 through 2003 period, rather

than 1998 as the base period, as

proposed. The subgoal remains a fixed

dollar amount for each year of the

period covered by the housing goals

base equal to one percent of each GSE’s

average total mortgage purchases in

1997 through 1999.

(3) The final rule does not allow goals

credit for predatory mortgage loans, and

the rule describes specific

characteristics, in addition to the

HOEPA definition suggested in the

proposed rule, to determine what types

of loans are considered predatory. The

final rule also identifies good lending

practices with which mortgages should

conform in order to count towards goals

credit.

(4) The proposed provisions for the

treatment of missing affordability data

are retained but the final rule includes

a five percent ceiling on the use of

estimated affordability information for

multifamily units.

(5) The guidance provided on how to

determine if seasoned mortgage loan

purchases meet the recycling

requirements of the Special Affordable

Housing Goal was expanded to (1)

include additional types of lending

organizations with affordable housing

missions that are presumed to meet the

recycling requirements; (2) adjust the

Community Reinvestment Act (CRA)

examination requirement for Federally

regulated financial institutions to one

‘‘Satisfactory’’ rating for financial

institutions with assets of $250 million

or less to accommodate a less frequent

examination schedule; and (3) specify

requirements that a seller must meet for

purposes of evaluating whether the

seller meets the recycling requirements

of 12 U.S.C. 4563(b)(1)(B).

(6) The final rule does not make

changes to the definition of underserved

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65047

area other than the inclusion of tribal

lands in underserved areas and does not

address the public availability of

mortgage data in the public use data

base. As explained below, HUD will

publish a decision on which data

elements will be accorded proprietary

and non-proprietary treatment by

separate Order following publication of

this final rule.

The analysis of Fannie Mae’s and

Freddie Mac’s affordable housing

performance, which is the basis for

many of the changes in the final rule, is

primarily based on data from 1997, 1998

and 1999. The GSEs’ actual performance

is presented through 1999. However,

Home Mortgage Disclosure Act (HMDA)

data which provides data on the

conventional, conforming market was

not available for 1999 at the time HUD

prepared its analysis supporting this

final rule. As HMDA data for 1999 were

not available, comparisons between the

GSEs and the market as a whole for that

year are not possible. Further, as 1998

was a year with a large percentage of

refinance mortgage transactions, at

times 1997 data is utilized as it presents

a more normal year in terms of home

purchase mortgage transactions.

In finalizing these regulations, the

Department is guided by and affirms the

following principles established in the

1995 rulemaking:

(1) To fulfill the intent of FHEFSSA,

the GSEs should lead the industry in

ensuring that access to mortgage credit

is made available for very low-, lowand 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 goals and

close the gap between the secondary

mortgage market and the primary

mortgage market. This approach is

consistent with Congress’ recognition

that ‘‘the enterprises will need to stretch

their efforts to achieve’’ the goals.15

(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 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

should allow the GSEs to maintain their

flexibility and their ability to respond

quickly to market opportunities. At the

same time, the Department must ensure

that the GSEs’ strategies serve families

in underserved markets and address

unmet credit needs. The addition of

bonus points to the regulatory structure

provides an additional means of

encouraging the GSEs’ affordable

housing activities to address identified,

persistent credit needs while leaving the

specific approaches to meeting these

needs to the GSEs.

(3) Discrimination in lending—albeit

sometimes subtle and unintentional—

has denied racial and ethnic minorities

the same access to credit to purchase a

home that has been available to

similarly situated non-minorities. The

GSEs have a central role and

responsibility to promote access to

capital for minorities and other

identified groups and to demonstrate

the benefits of such lending to industry

and borrowers alike. The GSEs also have

an integral role in eliminating mortgage

lending practices that are predatory.

(4) In addition to the GSEs’ purchases

of single family home loans, the GSEs

also must continue to assist in the

creation of an active secondary market

for multifamily loans. Affordable rental

housing is essential for those families

who cannot afford or choose not to

become homeowners. The GSEs must

assist in making capital available to

assure the continued development of

rental housing.

II. Discussion of Public Comments

A. Overview

1. Public Comment

Of the over 250 comments received,

by far the most detailed were the

submissions of the two directly affected

GSEs—Fannie Mae and Freddie Mac.

Each GSE’s comments were in large

measure supportive of the overall goal

structure proposed by the Department.

The GSEs, however, did provide

extensive appendices questioning 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.

Other commenters included national

and regional industry related groups,

non-profit organizations, state and local

government officials, lenders, and

individuals. In large measure, these

commenters were also supportive of the

Department’s proposal to increase the

affordable housing goals and the related

provisions designed to streamline the

counting rules used to calculate

performance under the housing goals.

Other than the goals framework, the

areas generating the largest response

from commenters were the treatment of

high cost mortgages, the role of credit

enhancements in affordable lending

transactions, and the availability of data

on the public use data base. It should be

noted that in evaluating these comments

a large number of comments were

received that included substantially

similar responses, in both language and

tone, to those submitted by Fannie Mae.

In addressing the appropriate goals

treatment for high cost mortgages, one

group of commenters, comprised

primarily of non-profit and housing

advocacy groups, felt the provisions

included in the proposed rule

disallowing credit for loans that meet

the HOEPA definition should be

strengthened. Other commenters,

consistent with the comments provided

by Fannie Mae, opposed any limitation

of goals credit for predatory mortgage

loans.

With regard to credit enhancements, a

substantial majority of commenters

noted that credit enhancements are a

critical component of many affordable

housing transactions. There was little

support for limiting goals credit for

affordable housing transactions that

include credit enhancements without a

better understanding of how to ensure

that there are not negative implications

for affordable housing transactions.

The Department received comments

supporting both increased data

availability and limited availability of

data. One group of commenters,

including non-profit organizations and

academic researchers, felt the provisions

included in the proposed rule should be

adopted and, in some instances,

expanded in order to fully understand

and challenge the GSEs on their

affordable housing activities. Again,

another group of commenters,

consistent with the comments provided

by Fannie Mae, opposed the availability

of additional data on the public use data

base. This group of commenters

included both lenders and non-profit

organizations which felt the additional

data would release confidential business

information and could compromise the

privacy of individuals, respectively.

This final rule does not, however,

address the availability of data on the

public use data base.

A discussion of the general and

specific comments on the rule follows

in subsequent sections. While

comments are summarized, not all of

the comments are addressed explicitly

in this preamble. HUD fully considered

all of the comments and HUD’s response

is either explicit in this final rule or

implicit in the general discussion of the

rule or other comments. HUD is

appreciative of the full range of public

comments received and acknowledges

the value of all of the comments

65048 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

submitted in response to the proposed

rule.

2. Other Public Input

As part of the public comment

process, the Department conducted

extensive outreach to educate and

inform interested parties of the nature

and extent of the GSEs’ affordable

housing activities. The outreach was

undertaken in order to encourage

comments on the proposed rule from a

wide range of individuals, organizations

and businesses that are interested in or

are affected by Congress’ charge to the

GSEs to further the financing needs of

underserved families and

neighborhoods. The Department’s

outreach in this regard included two

forums, three subject matter meetings,

and meetings with various industry

trade groups and non-profit

organizations to discuss the provisions

of the proposed rule. These sessions are

described below. Further, additional

information on these meetings is

contained in the public docket file of

this rule in Room 10276 at HUD

Headquarters.

a. Forums. The Department

conducted two forums designed to give

participants an in-depth look at how

well the GSEs are supporting affordable

housing activities in local communities.

One forum was held in Hartford,

Connecticut and the other in Durham,

North Carolina. Each forum had

approximately 125 participants. In

addition to sessions held at both forums

that reviewed the GSEs’ progress in

meeting the affordable housing needs in

the respective region, each forum had a

session that addressed issues and needs

specific to the region. In Hartford, a

session was held on the role of

multifamily housing in meeting

affordable housing needs. Research was

presented on how small multifamily

properties disproportionately serve lowincome families and data was provided

on the extent of the GSEs’ purchases of

mortgages on small multifamily

properties. Panel members discussed

the unique problems of financing small

multifamily properties and how Fannie

Mae and Freddie Mac can better serve

these markets. In Durham, a session was

held on predatory lending. Panel

members identified abusive practices

and discussed the impacts that

predatory lenders were having

particularly on the elderly and in

minority neighborhoods. Serious

questions were raised as to whether

Fannie Mae and Freddie Mac should be

involved in this market.

b. Subject Matter Meetings. HUD also

held three smaller discussion group

sessions designed to address specific

subject matters included in the

proposed rule. Subject matter meetings

were held on the availability of data on

the public use data base, issues related

to identifying and meeting the credit

needs of non-metropolitan areas, and

the role of credit enhancements in

affordable housing lending.

c. Other Meetings. In addition to the

meetings described above, the

Department met with various industry

trade groups and non-profit

organizations to present the changes

suggested in the proposed rule and the

rationale for the changes. HUD also met

with Fannie Mae and Freddie Mac to

discuss their concerns regarding the

proposed rule.

B. Subpart A—General

HUD proposed to revise the

definitions of ‘‘median income,’’

‘‘metropolitan area,’’ and ‘‘underserved

area’’ in order to provide greater clarity,

consistency and technical guidance. The

few comments received on these

definitions were supportive of the

proposed technical changes. HUD also

proposed certain changes to several

aspects of the definition of underserved

area to solicit public input on how best

to identify the areas that are

underserved by the mortgage credit

markets.

1. Median Income

HUD proposed to change the

definition of ‘‘median income’’ to

require the GSEs to use HUD estimates

of median family income to further

clarify the appropriate process for the

GSEs’ determination of area incomes.

HUD has implemented this change in

this final rule. As part of this change to

the definition of ‘‘median income,’’

HUD will provide the GSEs, on an

annual basis, information specifying

how HUD’s published median family

income estimates are to be applied. This

change is needed because, in some

cases, HUD publishes area median

family income estimates for portions of

areas rather than whole metropolitan

statistical areas (MSAs) or primary

metropolitan statistical areas (PMSAs).

2. Metropolitan Area

HUD proposed to clarify the

definition of ‘‘metropolitan area’’ by

revising the description of the relevant

area for determining median incomes to

eliminate the reference in § 81.2 to

consolidated metropolitan statistical

areas (CMSAs). HUD has implemented

this change in the final rule.

‘‘Metropolitan area’’ was defined in

§ 81.2 under the 1995 final rule as an

MSA, a PMSA, or a CMSA, designated

by the Office of Management and

Budget of the Executive Office of the

President. This definition raised

questions as to the definition of

‘‘underserved area’’ and the

denominator of the affordability ratio

used to compute the Low- and

Moderate-Income Housing Goal and the

Special Affordable Housing Goal

regarding whether to use the median

income of the CMSA or the PMSA. HUD

has consistently relied upon median

incomes of PMSAs in defining

underserved areas and determining

denominators for the other goals and

this final rule clarifies this point.

3. Underserved Area

a. Technical Definition. HUD

proposed to revise the definition of

‘‘underserved area’’ to clarify the

parameters of rural underserved areas.

The definition under HUD’s 1995 final

rule omitted the requirement for a

comparison between the ‘‘greater of the

State non-metropolitan median income

or nationwide non-metropolitan median

income’’ from the ‘‘income/minority’’

provision even though it had provided

for this comparison when qualifying

mortgage purchases under the ‘‘incomeonly’’ provision. HUD proposed to add

the comparative language to the

‘‘income/minority’’ provision for rural

underserved areas. The revision applies

the same median income standard to

both the ‘‘income-only’’ and the

‘‘income/minority’’ definitions. HUD

has implemented this change in § 81.2

of this final rule. (HUD also proposed

other changes to the definition of

‘‘underserved areas.’’ These are

discussed in Subpart B—Housing

Goals.)

b. Other Changes Proposed and/or

Comments Requested. The proposed

rule described additional changes to the

definition of underserved area relating

to tribal lands and requested comments

on possible changes to the income and

minority requirements of the definition.

(1) Tribal Lands. HUD proposed to

revise the definition of ‘‘underserved

areas’’ in § 81.2 to designate all

qualifying Indian reservations and trust

lands as underserved areas.

c. Summary of Comments. Fannie

Mae stated that it is ‘‘particularly

appropriate’’ to include these lands in

the definition of underserved areas.

Fannie Mae added that it ‘‘does not

think it is feasible, practical, or

appropriate to split trust lands between

served and underserved designations,

depending on the designation of the

surrounding tracts or counties.’’ Fannie

Mae further commented that HUD’s

proposal could lead to ‘‘split or

proportional treatment of any one trust

land,’’ and that such areas should be

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65049

included as underserved areas ‘‘without

regard to income or minority status.’’

Fannie Mae added that HUD should

consider postponing this change until

‘‘the new boundary files and data files’’

become available from the 2000 Census.

Fannie Mae further stated that HUD’s

proposal to define some underserved

areas in terms of income and minority

composition for the balance of a county

or census tract excluding the area

within any Federal or State American

Indian reservation or tribal or individual

trust land ‘‘raises operational issues that

will be difficult to overcome.’’

Freddie Mac stated that ‘‘In principal

[sic], Freddie Mac has no objection to

treating an American Indian Reservation

or tribal land as a geographic whole’’ for

determining underserved areas. It

added, however, that ‘‘adoption of a

definition that would involve geocoding

rural loans at the subcounty level could

present formidable practical problems.’’

Freddie Mac recommended that HUD

‘‘designate entire tracts in metropolitan

areas and entire counties in

nonmetropolitan areas that contain

qualifying reservations and trust lands

as underserved.’’

Other commenters were generally

supportive of the Department’s

proposal. One commenter called for an

expansion of the proposal to include

tribal service areas and urban living

Native Americans.

d. HUD’s Determination. HUD

believes that treating tribal lands as

separate geographic entities implies that

the balance of counties or tracts

excluding such areas would logically be

treated as separate entities, but it

recognizes Fannie Mae’s argument that

this could raise ‘‘operational issues.’’

HUD will issue operational guidance on

this matter prior to the effective date of

this Final Rule.

HUD evaluated Fannie Mae’s

recommendation to classify all

American Indian and Alaskan Native

(AIAN) areas as underserved areas,

without regard to income or minority

status, in light of the problems involved

in obtaining a mortgage on even the very

few higher-income (or low minority)

tribal lands. HUD analyzed data on 1989

median incomes and minority

concentrations for AIAN areas provided

by the U.S. Bureau of the Census. HUD’s

analysis showed that, out of 248 AIAN

areas with sufficient population to

determine an area median family

income, 19 areas, or 6.7 percent, would

be classified as served and 265 areas, or

93.3 percent, as underserved. The 19

areas include some with very low

minority concentrations and some with

very high median incomes. HUD

concludes that implementation of

Fannie Mae’s recommendation would,

in a small but significant number of

instances, substantially breach the

principle that underserved areas are

areas with low median incomes and/or

high minority concentrations, as

established in the 1995 Final Rule.

Accordingly, HUD has not implemented

Fannie Mae’s recommendation.

HUD believes that designating entire

tracts or counties that contain qualifying

tribal lands as underserved areas is not

appropriate. The purpose of the

definitional change in underserved

areas to include all tribal lands is to

focus attention on the mortgage

financing needs of Native American

communities. By designating the entire

county or census tract as underserved

by virtue of the presence of tribal lands

in a portion of it, this focus is lost. HUD

believes that any geocoding problems

arising from this proposal can be

resolved. HUD will issue operational

guidance on this matter prior to the

effective date of this final rule.

HUD believes that underserved areas

must have relatively fixed definitions—

tribal service areas are evolving over

time. The underserved areas goal is

defined broadly by both geographic and

area wide demographic features so that

borrowers living in underserved areas

benefit from the increased attention

paid to lending in such areas as a result

of HUD’s geographic goal.

(2) Enhanced Tract Definition. In the

proposed rule, comments were sought

on possible changes to the current

metropolitan underserved areas

definition to better target underserved

areas with higher mortgage denial rates

and thereby promote better access to

mortgage credit for these areas.

Specifically, HUD proposed changing

the current tract income ratio to an

‘‘enhanced’’ tract income ratio requiring

that for tracts to qualify as underserved

they must have a tract income ratio at

or below the maximum of 80 percent of

area median income or 80 percent of

U.S. median income in metropolitan

areas. The proposed change would make

the underserved areas definition used

by the GSEs consistent with the

requirements of Federally insured

depository institutions under the

Community Reinvestment Act (CRA).

The Department believes the concept

has substantial merit, and there was a

sizeable group of commenters that

supported the concept, at least in part.

However, there were a number of

commenters, including the GSEs, that

said that since the redesignation of

census tracts as underserved would be

based on data from the 1990 Census,

and since data from the 2000 Census

would not be available for a few years,

it would not be appropriate to make

such a change at this time. Rather, they

suggested that the Department wait until

updated information from the 2000

Census is available to analyze. The

Department agrees that, with more

current information to become available

from the 2000 Census in the near future,

the timing is not optimal to make a

change in the underserved areas

designation. Once information from the

2000 Census is available, the

Department will determine whether this

proposal merits consideration.

(3) Minority Composition. Similarly,

the proposed rule requested comment

on another approach to target high

mortgage denial rate areas. The

alternative approach would be to

increase the minority component

required to identify an area as

underserved by increasing the

requirement from 30 percent to 50

percent minority. Several commenters

noted that increasing the minority

component of a census tract to qualify

as underserved would have a

disproportionately negative impact on

the Hispanic population. Commenters

observed that Hispanic residential living

patterns are not as concentrated as those

of other minority groups. In addition,

comments were provided suggesting

that any changes in this area be

considered once data from the 2000

Census is available before making a final

determination in this regard. The

Department has determined that it will

obtain and analyze 2000 Census data

and consider various minority

population patterns and their

relationship to the availability of

mortgage credit before deciding whether

this proposal continues to merit

consideration.

(4) Rural Areas. The proposed rule

requested comments on how best to

define underserved rural areas, posing

questions on whether the underserved

rural areas should be identified by

census tract or by county. HUD received

comments that supported both

approaches. Again, the commenters

raised the issue of the 2000 Census.

Consistent with the Department’s other

determinations regarding significant

changes to the definition of underserved

areas, HUD will not make any changes

at this time in defining underserved

rural areas and will wait for the

opportunity to analyze the data from the

2000 Census.

C. Subpart B—Housing Goals

1. Overview

Comments received overwhelmingly

supported the Department’s proposal to

increase the level of the affordable

65050 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

housing goals. Both GSEs commented

that, while meeting these goals will be

a challenge (particularly the

Underserved Areas Goal), they are

committed to doing so. While some

commenters, including the GSEs,

expressed concern that the market

scenarios used by HUD did not

adequately consider an economic

downturn, those commenters still felt

that higher goals levels were

appropriate. This section of the final

rule reviews the statutory factors the

Department must consider in setting the

level of the housing goals, specific

comments on the housing goals

including the market methodology, and

the determination made with regard to

the level for each of the housing goals.

2. Statutory Considerations in Setting

the Level of the Housing Goals

In establishing the housing goals,

FHEFSSA requires the Department to

consider six factors—national housing

needs; economic, housing and

demographic conditions; performance

and effort of the GSEs toward achieving

the goal in previous years; size of the

conventional mortgage market serving

the targeted population or areas, relative

to the size of the overall conventional

mortgage market; ability of the GSEs to

lead the industry in making mortgage

credit available for the targeted

population or areas; and the need to

maintain the sound financial condition

of the GSEs. These factors are discussed

in more detail in the following sections

of this preamble and in the Appendices

to this rule. A summary of HUD’s

findings relative to each factor follows:

a. National Housing Needs. Analysis

and research by HUD and others in the

housing industry indicate that there are,

and will continue to be in the

foreseeable future, substantial unmet

housing needs among lower-income and

minority families. Data from the

American Housing Surveys demonstrate

that there are substantial unmet housing

needs among lower-income families.

Many households are burdened by high

homeownership costs or rent payments

and will likely continue to face serious

housing problems, given the dim

prospects for earnings growth in entrylevel occupations. According to HUD’s

‘‘Worst Case Housing Needs’’ report, 21

percent of owner households faced a

moderate or severe cost burden in 1997.

Affordability problems were even more

common among renters, with 40 percent

paying more than 30 percent of their

income for rent in 1997.16

Despite the growth during the 1990s

in affordable housing lending,

disparities in the mortgage market

remain, with certain minorities,

particularly African-American and

Hispanic families, lagging the overall

market in rate of homeownership. In

addition, there is evidence that the

aging stocks of single family rental

properties and small multifamily

properties with 5–50 units, which play

a key role in lower-income housing,

have experienced difficulties in

obtaining financing. The ability of the

nation to maintain the quality and

availability of the existing affordable

housing stock and to stabilize

neighborhoods depends on an adequate

supply of affordable credit to

rehabilitate and repair older units.

(1) Single Family Mortgage Market.

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 several

years, economic expansion,

accompanied by low interest rates and

increased outreach on the part of the

mortgage industry, has improved

affordability conditions for lowerincome families. Between 1994 and

1999, record numbers of lower-income

and minority families purchased homes.

First time homeowners have become a

major driving force in the home

purchase market over the past five

years. Thus, the 1990s have seen the

development of a strong affordable

lending market. Despite the growth of

lending to minorities, disparities in the

mortgage market remain. For example,

African-American applicants are still

twice as likely to be denied a loan as

white applicants, even after controlling

for income.

(2) Multifamily Mortgage Market.

Since the early 1990s, the multifamily

mortgage market has become more

closely integrated 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

mortgages on single family properties.

Property values, vacancy rates, and

market rents of multifamily properties

appear to be highly correlated with local

job market conditions, creating greater

sensitivity of loan performance to

economic conditions than may be

experienced for single family mortgages.

There is a need for an on-going GSE

presence in the multifamily secondary

market both to increase liquidity and to

further 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.

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. Increased

GSE presence would provide greater

liquidity to lenders, i.e., a viable ‘‘exit

strategy,’’ that in turn would serve to

increase their lending. It appears that

the financing of small multifamily rental

properties with 5–50 units, where a

substantial portion of the nation’s

affordable housing stock is

concentrated, have been adversely

affected by excessive borrowing costs.

Multifamily properties with significant

rehabilitation needs also appear to have

experienced difficulty gaining access to

mortgage financing. Moreover, the flow

of capital into multifamily housing for

seniors has been historically

characterized by a great deal of

volatility.

b. Economic, Housing, and

Demographic Conditions. Studies

indicate that changing population

demographics will result in a need for

the mortgage market to meet

nontraditional credit needs and to

respond to diverse housing preferences.

The U.S. population is expected to grow

by an average of 2.4 million persons per

year over the next 20 years, resulting in

1.1 to 1.2 million new households per

year. In particular, the continued influx

of immigrants will increase the demand

for rental housing while those who

immigrated during the 1980s will be in

the market to purchase owner-occupied

housing. The aging of the baby-boom

generation and the entry of the small

baby-bust generation into prime home

buying age is expected, however, to

result in a lessening of housing demand.

Non-traditional households have, and

will, become more important as overall

household formation rates slow down.

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

fastest growing household groups have

been single parent and single person

households. With continued house price

appreciation and favorable mortgage

terms, ‘‘trade-up buyers’’ will also

increase their role in the housing

market. There will also be increased

credit needs from new and expanding

market sectors, such as manufactured

housing and housing for senior citizens.

These demographic trends will lead to

greater diversity in the homebuying

market, which, in turn, will require

greater adaptation by the primary and

secondary mortgage markets.

As a result of the above demographic

forces, housing starts are expected to

average 1.5 million units annually

between 2000 and 2003, essentially the

same as in 1996–99.17 Refinancing of

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65051

existing mortgages, which accounted for

50 percent of originations in 1998 and

34 percent in 1999, is expected to return

to lower levels during 2000. The

mortgage market remained strong with

$1.3 trillion dollars in originations

during 1999. A lower number of

originations is expected in 2000 with

approximately $962 billion in

originations being projected by the

Mortgage Bankers Association of

America.

c. Performance and Effort of the GSEs

Toward Achieving the Goal in Previous

Years. Both Fannie Mae and Freddie

Mac have improved their affordable

housing loan performance since the

enactment of FHEFSSA in 1992 and

HUD’s establishment of housing goals

under the law. However, the GSEs’

mortgage purchases continue to lag the

overall market in providing financing

for affordable housing to low- and

moderate-income families, underserved

borrowers and their neighborhoods,

indicating that there is more that the

GSEs can do to improve their

performance. In addition, a large

percentage of the lower-income loans

purchased by the GSEs have relatively

high down payments, which raises

questions about whether the GSEs are

adequately meeting the needs of those

lower-income families who have little

cash for making large down payments

but can fully meet their monthly

payment obligations. The discussion of

the performance and effort of the GSEs

toward achieving the housing goals in

previous years is specific to each of the

three housing goals. This topic is

discussed below and further details are

provided in the Appendices to this rule.

d. Size of the Mortgage Market

Serving the Targeted Population or

Areas, Relative to the Size of the Overall

Conventional, Conforming Mortgage

Market. The Department’s analyses

indicate that the size of the

conventional, conforming market

relative to each housing goal is greater

than earlier estimates (based mainly on

HMDA data for 1992 through 1994) used

in establishing the 1996–1999 housing

goals. The discussion of the size of the

conventional mortgage market serving

targeted populations or areas relative to

the size of the overall conventional,

conforming mortgage market is specific

to each of the three housing goals. The

Department’s estimate of the size of the

conventional mortgage market is

discussed below and further details are

provided in the Appendices to this rule.

e. Ability of the GSEs To Lead the

Industry in Making Mortgage Credit

Available for the Targeted Population or

Areas. Research concludes that the

GSEs have generally not been leading

the market, but have lagged behind the

primary market in financing housing for

lower-income families and housing in

underserved areas. However, the GSEs’

state-of-the-art technology, staff

resources, share of the total

conventional, conforming market, and

their financial strength suggest that the

GSEs have the ability to lead the

industry in making mortgage credit

available for lower-income families and

underserved neighborhoods.

The legislative history of FHEFSSA

indicates 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.18 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.’’ 19 FHEFSSA, therefore,

specifically required 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 Community

Reinvestment Act 20 and fair lending

laws 21 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 establishing

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 housing needs, as well as for

underserved urban and rural areas.

While the GSEs cannot be expected to

solve all of the nation’s housing

problems, the efforts of Fannie Mae and

Freddie Mac have not matched the

opportunities that are available in the

primary mortgage market. Although the

GSEs were directed by Congress to lead

the mortgage finance industry in making

mortgage credit available for low- and

moderate-income families, depository

and other lending institutions have been

more successful than the GSEs in

providing affordable loans to lowerincome borrowers and in historically

underserved neighborhoods. In 1998 for

example, very low-income borrowers

accounted for 9.9 percent of Freddie

Mac’s acquisitions of home purchase

mortgage loans, 11.4 percent of Fannie

Mae’s acquisitions, 15.2 percent of such

mortgage loans originated and retained

by depository institutions, and 13.3

percent of such mortgage loans

originated in the overall conventional,

conforming market. Similarly, mortgage

purchases on properties located in

underserved areas accounted for 20.0

percent and 22.5 percent of Freddie

Mac’s and Fannie Mae’s purchases of

home purchase loans, respectively, 26.1

percent of home purchase mortgages

originated and retained by depository

institutions and 24.6 percent of home

purchase mortgages originated in the

overall conventional, conforming

market.

Between 1993 and 1998, Fannie Mae

improved its affordable lending

performance and made progress toward

closing the gap between its performance

and that of the overall mortgage market.

During that period Freddie Mac showed

less improvement and, as a result, did

not make as much progress in closing

the gap between its performance and

that of the overall market for home

loans. However, during 1999, Freddie

Mac’s purchases of goals qualifying

home loans increased significantly

relative to Fannie Mae’s purchases and,

as a result Freddie Mac now matches or

out-performs Fannie Mae in several

affordable lending categories. For

example, during 1999, very low-income

borrowers accounted for 11.0 percent of

Freddie Mac’s purchases of home loans

in metropolitan areas, compared with

10.8 percent of Fannie Mae’s. Similarly,

mortgages on properties in underserved

census tracts accounted for 21.2 percent

of Freddie Mac’s acquisitions of home

purchase mortgage loans in

metropolitan areas, compared with 20.6

percent of Fannie Mae’s. The extent to

which Freddie Mac has closed its

performance gap relative to depositories

and the overall market will be clarified

once HUD has the opportunity to

analyze 1999 HMDA data for

metropolitan areas.

The Department estimates the GSEs

provided financing for 55 percent of

units financed by conventional,

conforming mortgages in 1998.22

However, the GSEs’ mortgage market

presence varies significantly by property

type. While the GSEs accounted for

about 68 percent of the owner-occupied

units financed in the primary market in

that year, their role was much less in the

market for mortgages on rental

properties. Specifically, HUD estimates

that Fannie Mae and Freddie Mac

accounted for only about 24 percent of

rental units financed in 1998. Thus, the

GSEs’ presence in the rental mortgage

market was well under half their

presence in the market for mortgages on

65052 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

single family owner-occupied

properties.

Within the rental category, GSE

purchases have accounted for 29

percent of the multifamily dwelling

units that were financed in 1998. The

GSEs have yet to play a major role in

financing mortgages for rental units in

single family rental properties (those

with at least one rental unit and no

more than four units in total), where

their market share was only 19 percent.

As noted above, the GSEs continue to

lag the overall conforming, conventional

market in providing affordable home

purchase loans to lower-income families

and for properties in underserved

neighborhoods. Additionally, a large

percentage of the lower-income loans

purchased by both GSEs have relatively

high down payments, which raises

questions about whether the GSEs are

adequately meeting the needs of those

lower-income families who find it

difficult to raise enough cash for a large

down payment. Also, while rental

properties are an important source of

low- and moderate-income rental

housing, they represent only a small

portion of the GSEs’ business.

The appendices to this rule provide

more information on HUD’s analysis of

the extent to which the GSEs have

lagged the mortgage industry in funding

loans to underserved borrowers and

neighborhoods. From this analysis of

the GSEs’ performance in comparison

with the primary mortgage market and

with other participants in the mortgage

markets, it is clear that the GSEs need

to improve their performance relative to

the primary market of conventional,

conforming mortgage lending. The need

for improvements in the GSEs’

performance is especially apparent with

respect to the single family and

multifamily rental markets.

f. Need To Maintain the Sound

Financial Condition of the GSEs. Based

on HUD’s economic analysis and

discussions with the Office of Federal

Housing Enterprise Oversight, HUD has

concluded that the level of the goals as

proposed would not adversely affect the

sound financial condition of the GSEs.

Further discussion of this issue is found

in Appendix A.

3. Determinations Regarding the Level

of the Housing Goals

There are several reasons the

Department, having considered all the

statutory factors, is increasing the level

of the housing goals.

a. Market Needs and Opportunities.

First, the GSEs appear to have

substantial room for growth in serving

the affordable housing mortgage market.

For example, as discussed above, the

Department estimates that the two GSEs’

mortgage purchases accounted for 55

percent of the total (single family and

multifamily) conventional, conforming

mortgage market during 1998. In

contrast, GSE purchases comprised only

44 percent of the low- and moderateincome mortgage market in 1998, 46

percent of the underserved areas market,

and, a still smaller, 33 percent of the

special affordable market. As discussed

above, the GSE presence in mortgage

markets for rental properties, where

much of the nation’s affordable housing

is concentrated, is far below that in the

single family owner-occupied market.

The GSEs’ role in the mortgage market

varies somewhat from year to year in

response to changes in interest rates,

mortgage product types, and a variety of

other factors. Underlying market trends,

however, show a clear and significant

increase in the GSEs’ role. Specifically,

OFHEO estimates that the share (in

dollars) of single family mortgages

outstanding accounted for by mortgagebacked securities issued by the GSEs

and by mortgages held in the GSEs’

portfolios has risen from 31 percent in

1990 to 42 percent in 1999. In absolute

terms, the GSEs’ presence has grown

even more sharply, as the total volume

of single family mortgage debt

outstanding has increased rapidly over

this period.

The GSEs have indicated that they

expect their role in the mortgage market

to continue to increase in the future, as

they develop new products, refine

existing products, and enter markets

where they have not played a major role

in the past. The Department’s housing

goals for the GSEs also anticipate that

their involvement in the mortgage

market will continue to increase.

There are a number of segments of the

multifamily, single family owner, and

single family rental markets that the

GSEs have not tapped in which the

GSEs might play an enhanced role

thereby increasing their shares of

targeted loans and their performance

under the housing goals. Six such areas

are discussed below.

(1) Small Multifamily Properties. One

sector of the multifamily mortgage

market where the GSEs could play an

enhanced role involves loans on small

multifamily properties—those

containing 5–50 units. These loans

account for 39 percent of the units in

recently mortgaged multifamily

properties, according to the 1991 Survey

of Residential Finance. However, the

GSEs typically purchase relatively few

of these loans. HUD estimates that the

GSEs acquired loans financing only

three percent of units in small

multifamily properties originated during

1998. This is substantially less than the

GSEs’ presence in the overall

multifamily mortgage market, which the

Department estimates was 29 percent in

1998.

Increased purchases of small

multifamily mortgages would make a

significant contribution to performance

under the goals, since the percentages of

these units qualifying for the incomebased housing goals are high—in 1999,

95 percent of units backing Fannie

Mae’s multifamily mortgage transactions

qualified for the Low- and ModerateIncome Housing Goal, with a

corresponding figure of 90 percent for

Freddie Mac. That year, 43 percent of

units backing Freddie Mac’s multifamily

transactions qualified for the Special

Affordable Housing Goal, with a

corresponding figure of 56 percent for

Fannie Mae.

(2) Multifamily Rehabilitation Loans.

Another multifamily market segment

holding potential for expanded GSE

presence involves properties with

significant rehabilitation needs.

Properties that are more than 10 years

old are typically classified as ‘‘C’’ or

‘‘D’’ properties, and are considered less

attractive than newer properties by

many lenders and investors.

Multifamily rehabilitation loans

accounted for only 0.5 percent of units

backing Fannie Mae’s 1998 mortgage

purchases and for 1.6 percent in 1999.

These loans accounted for 1.9 percent of

Freddie Mac’s 1998 multifamily

mortgage purchase total (with none

indicated in 1999).

(3) Single Family Rental Properties.

Studies show that single family rental

properties are a major source of

affordable housing for lower-income

families, yet these properties are only a

small portion of the GSEs’ overall

business.

HUD estimates that approximately

203,000 mortgages were originated on

owner-occupied single family rental

properties in 1998. These mortgages

financed a total of 458,000 units—the

owners’ units plus an additional

254,000 rental units.23 Data submitted to

HUD by the GSEs indicate that, in 1998,

together the GSEs acquired mortgages

backed by 188,000 such units, 41

percent of the number of units financed

in the primary market, well below the

GSEs’ overall 1998 market share of 55

percent.24

There is ample room for an enhanced

GSE role in this goal-rich market. For

the GSEs combined, 65 percent of the

units in these properties qualified for

the Low- and Moderate-Income Housing

Goal in 1999, 32 percent qualified for

the Special Affordable Housing Goal,

and 54 percent qualified for the

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65053

Geographically Targeted Goal. Thus,

significant gains could be made in

performance on all of the goals if Fannie

Mae and Freddie Mac played a larger

role in the market for mortgages on

single family owner-occupied rental

properties (two to four units).

(4) Manufactured Homes. The

Manufactured Housing Institute, in its

Annual Survey of Manufactured Home

Financing, reported that 116 reporting

institutions originated $15.6 billion in

consumer loans on manufactured homes

in 1998, and that, with an average loan

amount of about $30,000, approximately

520,000 loans were originated.

While the GSEs have traditionally

played a minimal role in financing

manufactured housing, they have

recently stepped up their activity in this

market. However, even with their

increased level of activity, the GSEs’

purchases probably accounted for less

than 15 percent of total loans on

manufactured homes in 1998—a figure

well below their overall market

presence of 55 percent.

There is ample room for an enhanced

GSE role in this market, with its high

concentration of goals qualifying

mortgage loans. In 1998, for loans

reported by 21 manufactured housing

lenders (that are required by HMDA to

report loan data), 76 percent qualified

for the Low- and Moderate-Income

Housing Goal in 1998, 42 percent

qualified for the Special Affordable

Housing Goal, and 47 percent qualified

for the Geographically Targeted Goal.

Thus, manufactured housing has

significantly higher shares of goal

qualifying loans than all single family

owner-occupied properties, though

purchases of these loans are not quite as

goal-rich as loans on multifamily

properties. In general, goal performance

could be enhanced substantially if the

GSEs were to play an increased role in

the manufactured housing mortgage

market.

(5) A-minus Loans. Industry sources

estimate that subprime mortgage

originations amounted to about $160

billion in 1999, and that these loans are

divided evenly between the more

creditworthy (‘‘A-minus’’) borrowers

and less creditworthy (‘‘B,’’ ‘‘C,’’ and

‘‘D’’) borrowers. Based on HMDA data

for 200 subprime lenders, the

Department estimates that 58 percent of

the units financed by subprime loans

qualified for the Low- and ModerateIncome Housing Goal in 1998, 29

percent qualified for the Special

Affordable Housing Goal, and 45

percent qualified for the Geographically

Targeted Goal.

Freddie Mac has estimated that 10 to

30 percent of subprime borrowers

would qualify for a prime conventional

loan. Fannie Mae Chairman Franklin

Raines has stated that half of all

mortgages in the high cost subprime

market are candidates for purchase by

Fannie Mae. Both Fannie Mae and

Freddie Mac recently introduced

programs aimed at borrowers with past

credit problems that would lower the

interest rates for those borrowers that

were timely on their mortgage

payments. Freddie Mac has also

purchased subprime loans through

structured transactions that limit

Freddie Mac’s risk to the ‘‘A’’ piece of

a senior-subordinated transaction.

However, there may be ample room

for further enhancement of both GSEs’

roles in the A-minus market. A larger

role by the GSEs might help standardize

mortgage terms in this market, possibly

leading to lower interest rates.

(6) Seasoned Mortgages. Over the past

five years, depository institutions (banks

and thrifts) have been expanding their

affordable loan programs and, as a

result, have originated substantial

numbers of loans to low-income and

minority borrowers and to low-income

and predominantly minority

neighborhoods, under the incentive of

the Community Reinvestment Act

(CRA),25 which requires many

depository institutions to help meet the

credit needs of their communities. As

the GSEs noted in their comments, some

of these loans, when originated, may not

have met the GSEs’ underwriting

guidelines. A large number of the ‘‘CRAtype’’ loans that have been recently

originated remain in thrift and bank

portfolios; selling these loans on the

secondary market would free up capital

for depositories to originate new CRA

loans. Given its enormous size, the CRA

market segment provides an opportunity

for Fannie Mae and Freddie Mac to

expand their affordable housing

financing programs. The Department

recognizes that purchasing these loans

may present some challenges for the

GSEs. However, it appears these loans

are beginning to be purchased by GSEs

after the loans have seasoned and

through various structured transactions.

As explained in Appendix A, Fannie

Mae’s purchases of seasoned loans

improved its performance on the

housing goals in 1997 and 1998.

Seasoned loan purchases did not have a

similar impact in 1999. Freddie Mac, on

the other hand, has not been as active

as Fannie Mae in purchasing seasoned

CRA type loans. With billions of dollars

worth of CRA loans in bank portfolios,

the early experience of Fannie Mae

suggests that purchasing these loans

could be an important strategy for

reaching the housing goals and provide

needed liquidity for a market that is

serving the needs of low-income and

minority homeowners.

(7) Lending to Minority Borrowers.

The GSEs have an opportunity to play

a leadership role in making mortgage

credit more widely available to African

American and other minority borrowers,

who represent yet another underserved

market. In 1998, for example, African

American borrowers accounted for five

percent of conventional, conforming

single family mortgage loans originated

in metropolitan areas, as shown in

Appendix A.26 By contrast, African

American borrowers accounted for only

3.1 percent of Fannie Mae’s

metropolitan area mortgage purchases

and three percent of Freddie Mac’s

mortgage purchases. Hispanic borrowers

accounted for 5.2 percent of the

metropolitan area conventional,

conforming mortgage market in 1998,

4.8 percent of Fannie Mae’s mortgage

purchases and 4.4 percent of Freddie

Mac’s mortgage purchases.27

b. Market Share Higher than Goal

Levels. The shares of the mortgage

markets that would qualify for each of

the housing goals are higher than the

goal levels as they were set through

1999. Specifically, the Low- and

Moderate-Income Housing Goal for 1997

through 1999 was 42 percent, but the

market share for low- and moderateincome mortgages has been estimated at

50–55 percent. The Geographically

Targeted Goal for 1997 through 1999

was 24 percent, but the estimated

market share of geographically targeted

mortgages has been estimated at 29–32

percent. The Special Affordable

Housing Goal for 1997 through 1999

was 14 percent, but the estimated

special affordable market share is 23–26

percent.28 Thus, the increases in the

housing goals implemented in this final

rule and described below will

significantly reduce the disparities that

existed between the previous housing

goals and HUD’s market estimates.

HUD’s analysis indicates that the goal

levels established in the final rule are

reasonable and feasible and that its

market estimates reflect significantly

more adverse economic environments

than have recently existed. Reasons for

the remaining disparity between the

GSE housing goals established in this

final rule and the respective shares of

the overall mortgage market qualifying

for each of the housing goals are

discussed below. See Appendix D for

further discussion of these issues.

c. Need for Increased Affordable

Single Family Mortgage Purchases.

Higher housing goals are needed to

assure that both Fannie Mae and

Freddie Mac increase their purchases of

65054 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

single family mortgages for lowerincome families. The GSEs lag behind

depository institutions and other

lenders in the conventional, conforming

market in providing mortgage funds for

underserved families and their

neighborhoods. Numerous studies have

concluded that Fannie Mae and Freddie

Mac have room to increase their

purchases of affordable loans originated

by primary lenders. The single family

affordable market, which had only

begun to grow when HUD set housing

goals in 1995, has now established itself

with seven straight years (1993–1999) of

solid performance. Current projections

suggest that the demand for affordable

housing by minorities, immigrants, and

non-traditional households will be

maintained in the post-1999 period,

leading to additional opportunities for

the GSEs to support mortgage lending

benefiting families targeted by the

housing goals.

d. Market Disparities. Despite the

recent growth in affordable lending,

there are many groups who continue to

face problems obtaining mortgage credit

and who would benefit from a more

active and targeted secondary market.

Homeownership rates for lower-income

families, certain minorities, and central

city residents are substantially below

those of other families, and the

disparities cannot simply be attributed

to differences in income. Immigrants

represent a ready supply of potential

first-time home buyers and need access

to mortgage credit. Special needs in the

market, such as rehabilitation of older

two- to four-unit properties, could be

helped by new mortgage products and

more flexibility in underwriting and

appraisal guidelines. The GSEs, along

with primary lenders and private

mortgage insurers, have been making

efforts to reach out to these underserved

portions of the markets. However, more

needs to be done, and the proposed

increases in the housing goals are

intended to encourage additional efforts

by Fannie Mae and Freddie Mac.

e. Impact of Multifamily Mortgage

Purchases. When the 1996–99 goals

were established in December 1995,

Freddie Mac had only recently

reentered the multifamily mortgage

market, after an absence from the market

in the early 1990s. Freddie Mac has

made progress in rebuilding its

multifamily mortgage purchase

program, with its purchases of these

loans rising from $191 million in 1993

to $7.6 billion in 1999. Freddie Mac’s

limited role in the multifamily market

was a significant constraint when HUD

set the level of the housing goals for

1996 through 1999. While Freddie Mac

has made progress in recent years in

significantly increasing its multifamily

mortgage purchases, Freddie Mac’s

smaller multifamily portfolio relative to

that of Fannie Mae has meant fewer

refinance opportunities from within its

portfolio. Accordingly, the Department

is providing Freddie Mac with a

temporary adjustment factor for

purchases of mortgages in multifamily

properties with more than 50 units

under the 2001–2003 goals as it

continues to increase its multifamily

mortgage purchases, as discussed in

more detail, below.

f. Financial Capacity to Support

Affordable Housing Lending. A wide

variety of quantitative and qualitative

indicators 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 decade, from

$677 million in 1987 to over six billion

dollars in 1999. 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.

g. Closing the Gap Between the GSEs

and the Market. This section discusses

the relationship between the housing

goals, the GSEs’ performance and HUD’s

market estimates; and identifies key

segments of the affordable market in

which the GSEs have had only a weak

presence. To lay the groundwork for this

discussion, the following table

summarizes the Department’s findings

regarding GSE performance under the

1997–2000 goals and the new goal levels

for 2001–2003 as compared to HUD’s

estimates for 1995–1998 markets as well

as HUD’s projected market estimates for

2001–2003:

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65055

It is evident from this table that the

new goal levels for the Low- and

Moderate-Income Housing Goal and

Special Affordable Housing Goal are

below HUD’s projected market estimate

for the years covered by the new

housing goals. One reason for this

disparity can be discerned by

disaggregating GSE purchases by

property type, which shows that the

GSEs have little presence in some

important segments of the affordable

housing market. For example, as shown

in Figure 1, in 1998, the GSEs

purchased loans representing only 19

percent of rental units in single family

rental properties, and only three percent

of units in small multifamily properties

mortgaged that year. Figure 2 provides

additional detail providing unit data

comparing the GSEs’ with the

conventional, conforming market.

Typically, about 90 percent of rental

units in single family rental and small

multifamily properties qualify for the

Low- and Moderate-Income Housing

Goal. One reason that the GSEs’

performance under the Low- and

Moderate-Income Housing Goal falls

short of HUD’s market estimate is that

the GSEs have had only a weak and

inconsistent presence in financing these

important sources of affordable housing,

notwithstanding that these market

segments are important components in

the market estimate. In the overall

conventional, conforming mortgage

market, rental units in single family

properties and in small multifamily

properties are expected to represent

approximately 21 percent of the overall

mortgage market, and 33 percent of

units backing mortgages qualifying for

the Low- and Moderate-Income Housing

Goal. Yet in 1999, units in such

properties accounted for 6.6 percent of

the GSEs’ overall purchases, and only

11.5 percent of the GSEs’ purchases

meeting the Low- and Moderate-Income

Housing Goal. The continuing weakness

in GSE purchases of mortgages on single

65056 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

family rental and small multifamily

properties is a major factor explaining

the shortfall between GSE performance

and that of the primary mortgage

market.

For a variety of reasons, the GSEs

have historically viewed the single

family rental and small multifamily

market segments as more difficult for

them to penetrate than the single family

owner-occupied mortgage market. In

order to provide the GSEs with an

incentive to enter these markets and to

provide this housing the benefits of

greater financing through the secondary

market, HUD is proposing to award

‘‘bonus points’’ for the GSEs’ purchases

of mortgages on owner-occupied single

family rental properties and small

multifamily properties in calculating

credit toward the housing goals. The

bonus points will make the

Department’s increased housing goals

easier for the GSEs to attain if they

devote resources to affordable market

segments where their past role has been

limited and there are significant needs

for greater secondary market

involvement.

BILLING CODE 4210–27–P

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65057

65058 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

4. Summary of Comments on HUD’s

Analysis of Statutory Factors

HUD received several comments on

the factors for determining the goal

levels. Fannie Mae and Freddie Mac

provided numerous technical comments

on HUD’s analyses in the appendices to

the proposed rule. Most of the

comments focused on two related topics

concerning HUD’s market methodology:

(a) HUD’s model for the determining the

market size for each of the three housing

goals; and (b) HUD’s analysis of the

GSEs’ performance in the single family

owner-occupied portion of the

conventional, conforming mortgage

market. Section A of Appendices A, B

and C and Section B of Appendix D

provide a more extensive discussion of

HUD’s response to the various questions

raised by the GSEs about the factors for

determining the housing goals.

a. Market Share Methodology. In

Appendix D, HUD estimates the

following market shares for the three

housing goals during 2001–2003: 50–55

percent for the Low-Mod Goal, 23–26

percent for the Special Affordable Goal,

and 29–32 percent for the

Geographically Targeted Goal. Neither

GSE objected to HUD’s basic approach

to calculating these market shares,

which involves estimating (1) the share

of the market (in dwelling units) by type

of property (single family owneroccupied, single family rental, and

multifamily), (2) the proportion of

dwelling units financed by mortgages

for each type of property meeting each

goal, and (3) projecting the size of the

total market by weighting each such

goal share by the corresponding market

share. In fact, both Fannie Mae and

Freddie Mac stated that HUD’s market

share model was a reasonable approach

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65059

for estimating the goals qualifying

shares of the mortgage market. Freddie

Mac stated that the Department took the

correct approach in estimating the size

of the conventional, conforming market

by examining several different data sets,

using alternative methodologies, and

conducting sensitivity analyses. Fannie

Mae expressed similar sentiments

asserting that HUD’s model for assessing

the size of the affordable housing market

is reasonable.

Both GSEs were critical, however, of

HUD’s implementation of its market

methodology. Their major comments on

the market methodology fall into two

general areas. First, the GSEs expressed

concern about HUD’s assumptions and

use of specific data elements both in

constructing the distribution of property

shares among single family owneroccupied, single family rental, and

multifamily properties and in estimating

the goals qualifying shares for each

property type. The GSEs contended that

HUD chose assumptions and data

sources that resulted in an

overstatement of the market estimate for

each of the housing goals. In particular,

the GSEs claimed that HUD overstated

the importance of rental properties (both

single family and multifamily) in its

market model and overstated the Lowand Moderate-Income, Special

Affordable, and Geographically Targeted

shares of the single family owner

market. Second, both GSEs argued that

HUD’s market estimates depended

heavily on a continuation of recent

conditions of economic expansion and

low interest rates. According to the

GSEs, HUD’s range of market estimates

did not include periods of adverse

economic and affordability conditions

such as those which existed in the early

1990s.

b. GSEs’ Performance in Single Family

Owner-Occupied Market. Both GSEs

differed with HUD’s conclusions that

they lag the conventional, conforming

market in funding mortgages for the

goals qualifying segments of the single

family owner-occupied market. Rather,

the GSEs hold strongly that they have

led the mortgage market, from both

quantitative and qualitative

perspectives. The GSEs expressed

concern about HUD’s assumptions and

treatment of HMDA data in estimating

the goals qualifying shares for single

family owner-occupied mortgages. The

GSEs assert that certain portions of the

conforming mortgage market (such as

manufactured housing loans and

selected CRA loans)—those market

segments where they have not been very

active—should be excluded from HUD’s

definition of the owner market. From

their own analysis that excludes these

markets from HMDA data, the GSEs

conclude that they match or exceed the

market in funding affordable loans.

It should be noted that the GSEs

extend their criticism to other

researchers that have examined this

issue of their leading the market with

HMDA and related data. Appendix A

summarizes findings of several research

studies that have reached the same

conclusion as HUD—that the GSEs have

lagged the market in affordable lending

c. Volatility of the Mortgage Market.

Both GSEs claimed that HUD had not

adequately considered the impact that

changes in the national economy could

have on the size of the affordable

lending market and that HUD should

significantly lower its market estimates

to reflect adverse economic conditions.

The GSEs commented that HUD based

its market estimates on the unusually

favorable economic and housing market

conditions that have existed since 1995.

The GSEs relied on a Freddie Mac

funded study by PriceWaterhouseCoopers (PWC) which concluded that

the low- and moderate-income share of

the mortgage market was heavily

influenced by interest rate movements

and changes in the rate of economic

growth.30 PWC claims that the low-mod

share of the market ranged from 35

percent to 56 percent during the 1990s,

with a mean of 46 percent. HUD’s

analysis, on the other hand, finds that

the low- and moderate-income share of

the market averaged 53 percent during

the 1990s.

In HUD’s view, a major shortcoming

of the PWC report is that it

underestimates the size of the

multifamily mortgage market by relying

on multifamily originations reported in

HMDA data. While HMDA is for many

purposes a preeminent data source on

single family lending, its usefulness as

a multifamily data source is much more

limited due to severe underreporting of

loan originations. Indeed, HMDA is not

widely used as a multifamily data

source in published works by highly

regarded independent researchers, nor

by Fannie Mae in its comments

submitted in response to HUD’s

proposed rule.

The discussion of single family

lending in the PWC document initially

appears to contradict HUD’s analysis in

Appendix D of the proposed rule, but

this is mainly because HUD’s analysis is

based upon the conforming,

conventional mortgage market, whereas

PWC includes FHA loans and loans

above the conforming loan limit, at least

in the same years.31 Because the GSEs

are prohibited from purchasing loans

above the conforming limit, and because

HUD is directed by statute to focus on

the conventional market in setting the

housing goals, it is necessary to restrict

analyses of the mortgage market to the

conventional, conforming market for

purposes of establishing the housing

goals.

As explained in Appendices A and D,

HUD is aware that the mortgage market

is dynamic in character and susceptible

to significant changes in conditions that

would affect the overall level of

affordable lending to lower-income

families. In response to concerns

expressed about the volatility of the

mortgage markets over time, HUD has

estimated a range of market shares for

each of the housing goals for the years

2001–2003 of 50–55 percent for the

Low- and Moderate-Income Housing

Goal, 23–26 percent for the Special

Affordable Housing Goal, and 29–32

percent for the Geographically Targeted

Goal—that reflect economic

environments significantly more

adverse than those which existed during

the period between 1995 and 1998,

when the units financed in the

conventional, conforming market

meeting the Low- and Moderate-Income

Housing Goal averaged 56 percent, the

Special Affordable Housing Goal, 28

percent, and the Geographically

Targeted Goal, 33 percent.

HUD conducted detailed sensitivity

analyses for each of the housing goals to

reflect affordability conditions that are

less conducive to lower-income

homeownership than those that existed

during the mid- to late-1990s. For

example, the low- and moderate-income

percentage for single family home

purchase loans can fall to as low as 34

percent—or four-fifths of its 1995–98

average of over 42 percent—before the

projected low- and moderate-income

share of the overall market would fall

below 50 percent. Additional sensitivity

analyses examining recession and

proportionately higher refinance

scenarios and varying other key

assumptions, such as the size of the

multifamily market, show that HUD’s

market estimates consider a range of

mortgage market and affordability

conditions and provide a sound basis

for setting housing goals for the years

2001–03.

HUD recognizes that under certain

adverse circumstances, the goals

qualifying market shares could fall

below its estimates. However, as HUD

stated in its 1995 GSE Rule, while the

housing goals must be feasible, setting

goals so that they can be met even under

the very worst of circumstances is

unreasonable. As HUD stated in its 1995

Final GSE Rule, policy should not be

based on market estimates that include

the worst possible economic scenarios.

65060 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

HUD believes that the range for the

market shares should be broad enough

to reflect the likely scenarios including

an expected range of volatility in the

mortgage market over the period during

which the new housing goals will be in

effect.

FHEFSSA and HUD recognize that

conditions could change in ways that

would require revised expectations.

Thus, HUD is given the statutory

discretion to revise the goals if the need

arises. Further, current regulations

require that, if a GSE fails or if there is

a substantial probability that a GSE will

fail one or more of the housing goals,

notice be provided to the GSE and an

opportunity provided for the GSE to

explain the reason for the failure, or

potential failure, and to provide

information as to the feasibility of

achieving the housing goal. The

Department then makes a

determination, taking into consideration

market and economic conditions and

the financial condition of the GSE, as to

whether the goal was feasible. If the goal

is determined not to be feasible, no

further action is taken. If the goal is

determined to be feasible, the GSE is

given the opportunity to submit, for

HUD’s approval, a housing plan

demonstrating how the goal will be

achieved in the future. Thus, there are

adequate protections for the GSEs if

they are unable to achieve one or more

of their housing goals due to a dramatic

downturn in the market.

d. Shortcomings of Mortgage Market

Data Bases. Major mortgage market data

bases such as HMDA and the American

Housing Survey (AHS) are used to

implement HUD’s market share model.

The GSEs made extensive criticisms of

these data bases, concluding from their

critiques that the ranges for the

estimates of the goals-qualifying market

shares should be wider to reflect

uncertainty due to inadequate data.

Examples of problems asserted by the

GSEs include: overstating of low-income

loans in HMDA data; inability of HMDA

data to identify important segments of

the market (such as subprime lenders);

underreporting of multifamily

mortgages in HMDA data and generally

unreliable reporting of rental mortgages

in other data bases; underreporting of

income in the AHS; and the fact that

some important mortgage market data

bases such as the 1991 Residential

Mortgage Finance Survey are dated.

HUD agrees that a single

comprehensive source of information on

mortgage markets is not available.

Nevertheless, HUD considered and

analyzed a number of data sources for

the purpose of estimating market size,

since no single source could provide all

the data elements needed for its market

model. In the appendices, HUD

carefully defines the range of

uncertainty associated with each data

source, pulls together estimates of

important market parameters from

independent sources, and conducts

sensitivity analyses to show the effects

of various assumptions. In fact, Freddie

Mac noted that ‘‘We support the

Department’s approach for addressing

the empirical challenges of setting the

goals by examining several different

data sets, using alternative

methodologies, and conducting

sensitivity analysis.’’

While HUD recognizes the

shortcomings of the various data and the

inability to derive precise point

estimates of various market parameters,

HUD does not believe that these

limitations call for expanding the range

of the market estimates, as suggested by

the GSEs. One purpose of the

appendices is to demonstrate that

careful consideration of independent

data sources can lead to reliable ranges

of estimates for the goals-qualifying

shares of the mortgage market. HUD

demonstrates the robustness of its

market estimates by reporting the results

of numerous sensitivity analyses that

examine a range of assumptions about

the existing data on the rental and

owner markets. It should also be

emphasized that while there are some

problems with existing mortgage market

data, there is a wealth of information on

important components of the market.

For example, HMDA data provide wide

coverage of the single family owner

market in metropolitan areas, yielding

important information on the borrower

income and census tract (underserved

area) characteristics of that market, and

thus providing useful information on

the affordability characteristics of the

single family rental and multifamily

housing stock.

HUD’s specific responses to the GSEs’

comments on data are included mainly

in Section A of Appendices A, B and C

and Section B of Appendix D. For

example, as noted there, HUD disagrees

with the GSEs’ assertions regarding the

seriousness of the bias problem (i.e.,

overstating low-income loans) in HMDA

data. HUD does not rely heavily on

some of the data bases that the GSEs

criticize (e.g., the borrower income data

from the AHS and the 1991 Residential

Finance Survey).

e. Size of the Multifamily Market.

Because a high proportion of

multifamily units qualify for the

housing goals (e.g., 90 percent typically

qualify for the Low- and ModerateIncome Housing Goal and about 50

percent for the Special Affordable Goal),

the size of the multifamily market is an

important determinant of the overall

market shares for the housing goals, as

estimated by HUD’s model. Both GSEs

commented that HUD overstated the

role of multifamily financing, which

they asserted led to HUD’s overstated

estimated market shares. Freddie Mac

and PriceWaterhouseCoopers, in

particular, advocated the use of HMDA

data for measuring the size of the

multifamily market.

As explained in Appendix D, HUD

disagrees with Freddie Mac’s and PWC’s

analysis of the multifamily market. That

appendix contains a detailed discussion

of the size of the multifamily mortgage

market that considers a number of

alternative data sources providing

ample evidence on multifamily

origination volume over the years 1990

to 1999. HUD finds that newly

mortgaged multifamily units represent

an average of 16–17 percent of units

financed during the 1990s. HUD’s

estimated multifamily market shares

exceed estimates prepared by PWC

(averaging 8.7 percent for 1991–1998);

Appendix D outlines what HUD regards

as errors in the PWC study that led to

its unrealistically low estimates of the

multifamily origination market. The

three multifamily market shares—13.5

percent, 15 percent, and 16.5 percent—

that HUD emphasizes in its market

share model accommodates the

possibility of a recession or heavy

refinance year.

f. GSEs’ Affordable Lending

Performance—Defining the Relevant

Market. As noted earlier, HUD uses

HMDA data to show that even though

the GSEs have improved their

performance since 1993, they have

lagged depositories and others in the

conventional, conforming market in

funding affordable loans, both since

1993 and particularly during the more

recent 1996–98 period when the new

housing goals were in effect. In their

analyses, the GSEs reach the opposite

conclusion—each concludes that they

already match or even lead the market,

depending on the affordable category

being considered. The GSEs obtain this

result by adjusting HMDA market data

to exclude single family loans that they

perceive as not being available for them

to purchase.

Both GSEs provided numerous

comments concerning the types of

mortgages that HUD should exclude

from the definition of the single family

owner market. Fannie Mae states that it

‘‘can only purchase or securitize

mortgages that primary market lenders

are willing to sell’’ and that ‘‘HUD fails

to adjust for those housing markets that

are not fully available to Fannie Mae

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65061

and Freddie Mac.’’ Freddie Mac states

that it ‘‘has not achieved, and is

unlikely to achieve in the near term, the

same penetration in the subprime and

manufactured housing segments of the

market as it has achieved in the

conventional, conforming market’’ and,

therefore, HUD should not include these

segments in its market definition.

According to the GSEs, markets that are

‘‘not available’’ to them or where they

are not a ‘‘full participant’’ should be

excluded from HUD’s market definition.

In addition to the subprime and

manufactured housing markets,

examples of market segments mentioned

by the GSEs for exclusion consisted of

the following: low-down payment

mortgages (those with loan-to-value

ratios greater than 80 percent) without

private mortgage insurance or some

other credit enhancement; loans

financed through state and local

housing finance agencies; below-marketinterest-rate mortgages; specialized CRA

mortgages; and portions of depository

portfolios that are not available for

purchase by the GSEs at the time of

mortgage origination.

HUD disagrees with the comments

offered by the GSEs advocating

exclusion of those market segments that

they have not yet been able to penetrate.

The conventional, conforming market

represents the appropriate benchmark

for evaluating GSE performance as

discussed previously, even if this is not

the market that the GSEs perceive as

available for them to purchase.

However, with respect to the subprime

market, HUD believes that the risky,

B&C portion of that market should be

excluded from the market estimates for

each of the housing goals. Thus, HUD

includes only the A-minus portion of

the subprime market in its overall

estimates of the goals-qualifying market

shares.

Excluding other important segments

of the mortgage market as the GSEs

recommend would render the resulting

market benchmark useless for

evaluating the GSEs’ performance. The

loans that the GSEs would exclude are

important sources of goals credit and, in

fact, are the very loans the GSEs are

supposed to be reaching out to finance.

A recent report by the Department of

Treasury demonstrated the targeting of

CRA-type loans to lower-income and

minority families. Numerous studies

have shown that the manufactured

home sector is an important source of

low-income housing. In many of these

markets, a more active secondary market

could encourage lending to traditionally

underserved borrowers. While HUD

recognizes that some segments of the

market may be more challenging for the

GSEs to enter than others, the data

reported in Figure 2 of this Appendix

show that the GSEs have ample

opportunities to purchase goalsqualifying mortgages. Furthermore,

HUD recognizes the challenge of

reaching segments of these markets by

not setting each goal at the very top of

its market estimate range.

Finally, it should also be noted that

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—after these

loans have seasoned and the GSEs have

had the opportunity to observe their

payment performance.

g. HUD’s Determination. HUD

carefully examined the comments on its

analysis of the statutory factors used to

determine the appropriate level of the

housing goals, particularly the

methodology used to establish the

market share for each of the goals. Based

on that evaluation, as well as HUD’s

additional analysis of its estimates, HUD

determined that its basic methodology is

a reasonable and valid approach to

estimating market share and that the

percentage ranges for each of the three

market share estimates do not need to be

adjusted from those provided in the

proposed rule. While a number of

technical changes have been made in

this final rule in response to the

comments, the approach for

determining market size has not been

modified substantially. The detailed

evaluations show that the methodology,

as modified, produces conservative

estimates of the market share for each

goal. 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.

5. Period Covered by the Housing Goals

This final rule establishes housing

goals for the years 2001 through 2003.

The proposed rule would have

established housing goals for the GSEs

for the year 2000 as well as 2001–2003,

with higher housing goals than

currently required for 2000, a transition

year, and still higher goals for 2001–

2003.

The GSEs commented that since the

proposed rule would have set

transitional goals for 2000, if the goals

are established later in 2000, then 2001

should become the transition year.

HUD has considered the issue and

concluded that while it could establish

higher ‘‘transitional’’ goals for 2000 as

were proposed late in the year, and

require that the GSEs perform at the

new goal levels, given the publication

date of this final rule, HUD will not

require that the GSEs meet higher goals

for 2000.

At the same time, HUD has

determined that establishing 2001 as a

transition year is unnecessary and

unwarranted. The goal levels for the

years 2001–2003, and 2000, were

announced in July 1999 and formally

proposed earlier this year, providing the

GSEs ample notice of the goal levels

expected for these years. Indeed, data

indicate that the GSEs have increased

their efforts in 2000 in light of the

proposed 2001–2003 levels. Moreover,

the Department’s analysis of the

statutory factors supports establishment

of the goals for 2001–2003 at the levels

proposed as both reasonable and

feasible. Accordingly, the housing goals

for 2000 shall remain at the levels

previously established in accordance

with §§ 81.12(c)(3), 81.13(c)(3), and

81.14(c)(3) of the regulations as they

existed prior to the effectiveness of this

final rule. The housing goals for 2001–

2003 are established at the levels HUD

proposed.

The Department believes the new goal

levels established by this rule to be

appropriate based upon consideration of

the statutory factors and comments

received. Setting the goal levels for

years 2001–2003 provides the GSEs

with a level of predictability to enable

them to develop and implement

business strategies to achieve the goals.

6. 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 for each of the years 2001–

2003 that are affordable to low- and

moderate-income families at 50 percent.

A short discussion of the statutory

factors received follows. 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.’’

65062 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

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 50–55 percent

of total units financed in the overall

conventional, conforming mortgage

market during the period 2001 through

2003. HUD has developed a reasonable

range, rather than a point estimate, that

accounts for significantly more adverse

economic conditions than have existed

recently.

b. Past Performance of the GSEs

under the Low- and Moderate-Income

Housing Goal. During the transition

period from 1993 through 1995, Fannie

Mae’s performance under the Low- and

Moderate-Income Housing Goal jumped

sharply in one year, from 34.2 percent

in 1993 to 44.8 percent in 1994, before

declining to 42.3 percent in 1995. It

then stabilized at just over 45 percent in

1996 and 1997. Fannie Mae’s

performance in 1998 declined to 44.1

percent due in large measure to the high

volume of refinance loans that Fannie

Mae funded in 1998, before rising to

45.9 percent in 1999.

During the same period, Freddie Mac

demonstrated more consistent gains in

performance under the Low- and

Moderate-Income Housing Goal, from

29.7 percent in 1993 to 37.4 percent in

1994 and 38.9 percent in 1995. Freddie

Mac then achieved 41.1 percent in 1996,

and 42.6 percent and 42.9 percent in

1997 and 1998, respectively. In 1999,

Freddie Mac’s performance increased

sharply to 46.1 percent.

The housing goals that have been in

effect prior to this final rule specified

that in 1996 at least 40 percent of the

number of units financed by mortgage

purchases of the GSEs and eligible to

count toward the Low- and ModerateIncome Goal should qualify as low- and

moderate-income, and at least 42

percent should qualify as such in each

year from 1997 through 1999. Fannie

Mae surpassed these goal levels by 5.6

percentage points in 1996, 3.7

percentage points in 1997, 2.1

percentage points in 1998, and 3.9

percentage points in 1999. Freddie Mac

surpassed the goals by 1.1 percentage

points, 0.6 percentage points, 0.9

percentage points and 4.1 percentage

points in 1996, 1997, 1998 and 1999,

respectively.

Fannie Mae’s performance on the

Low- and Moderate-Income Housing

Goal has surpassed Freddie Mac’s in

every year but one, 1999, when Freddie

Mac slightly outperformed Fannie Mae

(46.1 percent versus 45.9 percent).

However, Freddie Mac’s 1999

performance represented a 55 percent

increase over its 1993 level, exceeding

the 34 percent increase by Fannie Mae

over the same period, recognizing,

however, that Fannie Mae’s 1993

performance was significantly greater

than Freddie Mac’s.

The GSEs’ performance under the

Low- and Moderate-Income Housing

Goal for the 1996 through 1999 period

is summarized below:

SUMMARY OF GSES’ PERFORMANCE UNDER THE LOW- AND MODERATE-INCOME HOUSING GOAL 1996–1999 32

[In percentages]

1996 1997 1998 1999

Required Goal Level ........................................................................................................ 40 42 42 42

Fannie Mae: Percent Low- and Moderate-Income .......................................................... 45.6 45.7 44.1 45.9

Freddie Mac: Percent Low- and Moderate-Income ......................................................... 41.1 42.6 42.9 46.1

Freddie Mac’s improved performance

since 1993 is due mainly to its increased

purchases of multifamily loans as it has

again become active in this market.

Some housing industry observers

believe that the establishment of the

Low- and Moderate-Income Housing

Goal has been an important factor in

explaining Freddie Mac’s re-entry into

the multifamily market. In fact, as

indicated above, multifamily mortgage

purchases represent a significant

component of both GSEs’ activities in

meeting the Low- and Moderate-Income

Housing Goal, even though multifamily

loans comprise a relatively small

portion of the GSEs’ business activities.

In 1999, while Fannie Mae’s

multifamily purchases represented only

nine percent of its total mortgage

acquisition volume measured in terms

of dwelling units, these purchases

comprised 20 percent of units qualifying

for the Low- and Moderate-Income

Housing Goal. Multifamily purchases

were eight percent of the units financed

by Freddie Mac’s 1999 mortgage

purchases but represented 17 percent of

the units comprising Freddie Mac’s lowand moderate-income mortgage

purchases.

c. Summary of Comments. A number

of commenters recommended that the

Low- and Moderate-Income Housing

Goal include separate goals targeting a

portion of the GSEs’ business to

multifamily housing and a portion to

single family housing. While there are

distinctly different issues relevant to the

single family market and the

multifamily market, the Department

does not believe that it is necessary or

appropriate to establish separate goals

for those two markets. First, the

increased level of the Low- and

Moderate-Income Housing Goal in this

final rule will require an increase in

both single family and multifamily

mortgage purchases. HUD’s present

analysis of these markets indicates that

a unitary goal will best achieve

increased performance in both markets.

Second, this final rule adopts a number

of incentives to encourage the GSEs to

move into markets with unmet needs

including the financing of smaller

multifamily properties. HUD will,

however, continue to examine market

needs and evaluate the effects of the

goal structure established in this final

rule on the GSEs’ single family and

multifamily mortgage purchase

performance. Based on this ongoing

review, HUD may at a future date

consider separate single family and

multifamily goals or subgoals under the

Low- and Moderate-Income Housing

Goal, as warranted.

Fannie Mae expressed no objection to

the higher goal level, provided the

Department retains the proposed

housing goals framework, including the

proposed changes to the counting rules,

in the final rule. Freddie Mac supports

the goal framework included in the

proposed rule and is committed to

meeting the new goal levels. The

Department’s response to the issues

raised by Fannie Mae and Freddie Mac

relative to HUD’s market share

methodologies and its analysis of the

statutory factors are discussed above.

Overall, other commenters were

supportive of the proposed increase in

the Low- and Moderate-Income Housing

Goal. One group of commenters thought

that, since the GSEs are mandated to

lead the market, the level of the Lowand Moderate-Income Housing Goal

should be increased further. Another

group of commenters supported the

increased level of the goal, but felt the

Department needed to be prepared to

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65063

accommodate shifts in economic

conditions that may have a negative

impact on the GSEs’ ability to meet the

housing goals.

d. HUD’s Determination. The Lowand Moderate-Income Housing Goal

established in this final rule is

reasonable and appropriate having

considered the factors set forth in

FHEFSSA. 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, demonstrates that

the number of mortgages secured by

housing for low- and 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 2001 to

2003, 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

50 percent of eligible units financed in

each of the years 2001, 2002 and 2003.

The new goal level will increase the

GSEs’ current level of performance to a

level that is consistent with reasonable

estimates of the low- and moderateincome housing market.

7. Central Cities, Rural Areas, and Other

Underserved Areas Goal, § 81.13

This section discusses the

Department’s consideration of the

statutory factors in arriving at and

comments received on the proposed

new housing goal level for the Central

Cities, Rural Areas, and Other

Underserved Areas Housing Goal (the

Geographically Targeted Goal).

The Geographically Targeted Goal

focuses on areas currently underserved

by the mortgage finance system. The

1995 Final Rule provided that mortgage

purchases count toward the

Geographically Targeted Goal if such

purchases finance properties that are

located in underserved census tracts. In

§ 81.2, HUD defined ‘‘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 percent of

a tract’s population is calculated by

dividing the tract’s minority population

by its total population.

For properties in non-metropolitan

(rural) areas, mortgage purchases count

toward the Geographically Targeted

Goal where such purchases finance

properties that are located in

underserved counties. These are defined

as counties where either: (1) The

median income in the county does not

exceed 95 percent of the greater of the

state or nationwide non-metropolitan

median income; or (2) minorities

comprise at least 30 percent of the

residents and the median income in the

county does not exceed 120 percent of

the state non-metropolitan median

income.

After analyzing the statutory factors

and considering the comments, this

final rule establishes the goal for the

percentage of dwelling units financed

by each GSE’s mortgage purchases on

properties that are located in

underserved areas for each of the years

2001–2003 be 31 percent. A short

discussion of the statutory factors

follows. Additional information

analyzing each of the statutory factors is

provided in Appendix B, ‘‘Departmental

Considerations to Establish the Central

Cities, Rural Areas, and Other

Underserved Areas Goal,’’ and

Appendix D, ‘‘Estimating the Size of the

Conventional Conforming Market for

Each Housing Goal.’’

a. Market Estimate for the

Geographically Targeted Goal. The

Department estimates that dwelling

units in underserved areas will account

for 29–32 percent of total units financed

in the overall conventional, conforming

mortgage market during the period 2001

through 2003. HUD has developed a

reasonable range, rather than a point

estimate, that accounts for significantly

more adverse economic conditions than

have existed recently.

b. Past Performance of the GSEs

under the Geographically Targeted

Goal. The housing goals that have been

in effect prior to this final rule required

that in 1996 at least 21 percent of the

units financed by the GSEs’ mortgage

purchases should count toward the

Geographically Targeted Goal, and at

least 24 percent in 1997 through 1999.

Fannie Mae surpassed the goal by 7.1

percentage points in 1996, 4.8

percentage points in 1997, 3.0

percentage points in 1998, and 2.8

percentage points in 1999. Freddie Mac

surpassed the goal by 4.0, 2.3, 2.1 and

3.5 percentage points in 1996, 1997,

1998, and 1999, respectively. The GSEs’

performance for the 1996–99 period is

summarized below:

SUMMARY OF GSE PERFORMANCE UNDER THE GEOGRAPHICALLY TARGETED GOAL 1996–1999 33

[In percentages]

1996 1997 1998 1999

Required Goal Level ........................................................................................................ 21 24 24 24

Fannie Mae: Percent Geographically Targeted .............................................................. 28.1 28.8 27.0 26.8

Freddie Mac: Percent Geographically Targeted ............................................................. 25.0 26.3 26.1 27.5

Although both GSEs have improved

their performance in underserved areas,

on average, their mortgage purchases

continue to lag the primary market in

providing financing for housing in these

areas. On average, during the 1996–1998

period, mortgage purchases on

properties in underserved areas

accounted for 19.9 percent of Freddie

Mac’s purchases of single family home

purchase mortgages, compared with

22.9 percent of Fannie Mae’s purchases,

25.8 percent of mortgages retained by

portfolio lenders, and 24.9 percent of all

home purchase mortgages originated in

the conventional, conforming market.

These figures indicate that Freddie Mac

has been less likely than Fannie Mae to

purchase mortgages on properties in

underserved neighborhoods. Through

1998, Freddie Mac had not made

progress in reducing the gap between its

performance and that of the overall

market. In 1992, underserved areas

accounted for 18.6 percent of Freddie

Mac’s purchases of home purchase

mortgages and for 22.2 percent of such

mortgage loans originated in the

conforming market, which yields a

‘‘Freddie Mac-to-Market’’ ratio 34 of

65064 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

0.84. By 1998, the ‘‘Freddie Mac-toMarket’’ ratio had actually fallen to 0.81.

During the same period, the ‘‘Fannie

Mae-to-Market’’ ratio increased from

0.82 to 0.93. However, in 1999, Freddie

Mac’s purchase share for underserved

area loans increased while Fannie Mae’s

declined. In 1999, underserved areas

accounted for 21.2 percent of Freddie

Mac’s home purchase mortgage loan

acquisitions, compared with 20.6

percent for Fannie Mae.35

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

performance in underserved areas must

be derived from mortgages on housing

for lower income families. In 1999,

housing for above median-income

households accounted for about half of

the single family owner-occupied

mortgages the GSEs purchased in

underserved areas.

c. Summary of Comments. Fannie

Mae expressed no objection to the

higher goal level provided the

Department retains the proposed

housing goals framework, including the

proposed changes to the counting rules,

in the final rule. Freddie Mac supported

the overall goal framework included in

the proposed rule but recommended

that the Geographically Targeted Goal

be set at 30 percent. Freddie Mac noted

that it was committed to stretching to

meet the proposed new goal levels, but

believed that the level of the

Geographically Targeted Goal was set

too far toward the high end of the

market estimate, making it more

difficult to achieve. The Department’s

response to the issues raised by both

Fannie Mae and Freddie Mac relative to

HUD’s estimates of the markets and its

analysis of the statutory factors used to

set the level of the goals was discussed

above.

Overall, other commenters were

supportive of the proposed increase in

the Geographically Targeted Goal.

Certain commenters noted that by

placing the level of the goal around the

midpoint of the estimate of market size,

the GSEs will be encouraged to move

into a market leadership position.

Another group of commenters

supported the increased level of the

goal, but felt the Department needed to

be prepared to accommodate changes in

economic circumstances that may have

a negative impact on the GSEs’ ability

to meet the housing goals. d. HUD’s Determination. The

Geographically Targeted Goal

established in this final rule is

reasonable and appropriate, considering

the factors set forth in FHEFSSA. The

Department’s market share estimates for

the Geographically Targeted Goal

accommodate a variety of economic

scenarios. In addition, a current

examination of the gaps in the mortgage

markets, along with the estimated size

of the market available to the GSEs,

demonstrates the opportunities for the

GSEs to purchase mortgages secured by

housing in underserved areas of the

nation.

Therefore, having considered all

statutory factors including housing

needs, projected economic and

demographic conditions for 2001 to

2003, the GSEs’ past performance, the

size of the market for central cities, rural

areas and other underserved areas, and

the GSEs’ ability to lead the market

while maintaining a sound financial

condition; HUD is establishing the

annual goal for mortgage purchases

qualifying under the Geographically

Targeted Goal to be 31 percent of

eligible units financed in each of the

years 2001, 2002 and 2003. The new

goal level will increase the GSEs’

current level of performance to a level

that is consistent with reasonable

estimates of the housing market in

underserved areas.

8. 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 counts mortgages

on housing for very low-income families

and low-income families living in lowincome areas. After consideration of

these factors and the comments

received, this final rule establishes the

goal for the percentage of the total

number of dwelling units financed by

each GSE’s mortgage purchases for

housing affordable to very low-income

families and low-income families living

in low-income areas for each of the

years 2001–2003 at 20 percent. A short

discussion of the statutory factors

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 lowincome areas will account for 23–26

percent of total units financed in the

overall conventional, conforming

mortgage market during the period 2001

through 2003. HUD has developed a

reasonable range, rather than a point

estimate, that accounts for significantly

more adverse economic conditions than

have existed recently.

b. Past Performance of the GSEs

under the Special Affordable Housing

Goal. The Special Affordable Housing

Goal is designed to ensure that the GSEs

serve the very low- and low-income

portion of the housing market. However,

analysis of HMDA data shows that the

shares of mortgage loans for very lowincome homebuyers are smaller for the

GSEs’ mortgage purchases than for

depository institutions and others

originating mortgage loans in the

conforming conventional market. HUD’s

analysis suggests that the GSEs should

improve their performance in providing

financing for the very low-income

housing market.

The housing goals that have been in

effect prior to this final rule specified

that in 1996 at least 12 percent of the

number of units eligible to count toward

the Special Affordable Housing Goal

should qualify as special affordable, and

at least 14 percent in 1997 through

1999. As indicated below, Fannie Mae

surpassed the goal by 3.4 percentage

points in 1996, 3.0 percentage points in

1997, 0.3 percentage points in 1998 and

3.6 percentage points in 1999. Freddie

Mac surpassed the goal by 2.0, 1.2, 1.9,

and 3.2 percentage points in 1996, 1997,

1998, and 1999, respectively. The GSEs’

performance for the 1996–99 period is

summarized below:

SUMMARY OF GSE PERFORMANCE UNDER THE SPECIAL AFFORDABLE HOUSING GOAL 1996–1999 36

1996

(in percent)

1997

(in percent)

1998

(in percent)

1999

(in percent)

Required Goal Level ........................................................................................................ 12 14 14 14

Fannie Mae:

Percent Low-and Moderate-Income ......................................................................... 15.4 17.0 14.3 17.6

Freddie Mac:

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65065

SUMMARY OF GSE PERFORMANCE UNDER THE SPECIAL AFFORDABLE HOUSING GOAL 1996–1999 36—Continued

1996

(in percent)

1997

(in percent)

1998

(in percent)

1999

(in percent)

Percent Low-and Moderate-Income ......................................................................... 14.0 15.2 15.9 17.2

As noted above, HMDA and GSE data

for metropolitan areas show that both

GSEs lag depository institutions and

other lenders in providing financing for

home loans that qualify for the Special

Affordable Housing Goal. Special

affordable loans, which include loans

for very low-income borrowers and lowincome borrowers living in low-income

areas, accounted for 9.8 percent of

Freddie Mac’s purchases of home

purchase mortgages during 1996–98,

11.9 percent of Fannie Mae’s purchases,

16.7 percent of newly originated loans

retained by depository institutions, and

15.3 percent of all new originations in

the conventional, conforming market.

While Freddie Mac has improved its

special affordable lending since the

housing goals were put in place in 1993,

up until 1999 it had not made as much

progress as Fannie Mae in closing the

gap with depository institutions and

other lenders in the home loan market.

In 1998, Freddie Mac’s special

affordable performance was 73 percent

of the primary market proportion of

home loans that would qualify under

the Special Affordable Housing Goal,

compared to Fannie Mae’s performance

of 85 percent during the same period. In

1999, Freddie Mac did match Fannie

Mae, as special affordable loans

accounted for 12.5 percent of its home

loan purchases versus 12.3 percent of

Fannie Mae’s home loan purchases.

Market data for 1999 are not yet

available.

The multifamily market 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

multifamily units meeting the Special

Affordable Housing Goal. For example,

in 1999, 56 percent of units financed by

Fannie Mae’s multifamily mortgage

purchases met the Special Affordable

Housing Goal, representing 31 percent

of units counted toward the Special

Affordable Housing Goal, at a time

when multifamily units represented

only nine percent of its total purchase

volume.37

c. Summary of Comments. Fannie

Mae expressed no objection to the

higher goal level, provided the

Department retains the proposed

housing goals framework, including the

proposed changes to the counting rules,

in the final rule. Freddie Mac supported

the goal framework included in the

proposed rule and is committed to

stretching to meet the new goal levels.

The Department’s response to the issues

raised by both Fannie Mae and Freddie

Mac relative to HUD’s market share

methodologies and its analysis of the

statutory factors used to set the level of

the goals was discussed above.

Overall, other commenters were

supportive of the proposed increase in

the Special Affordable Housing Goal.

One group of commenters thought that,

since the GSEs are mandated to lead the

market, the level of the Special

Affordable Housing Goal should be

increased even more, at a minimum, to

the lower range of the Department’s

market share, at 23–24 percent. Another

group of commenters supported the

increased level of the goal but felt the

Department needed to be prepared to

accommodate changes in economic

circumstances that may have a negative

impact on the GSEs’ ability to meet the

housing goals.

d. HUD Determination. The Special

Affordable Housing Goal established in

the final rule is reasonable and

appropriate, considering the factors set

forth in FHEFSSA. The market share

estimates for this goal reflect a variety

of economic scenarios significantly

more adverse than have existed

recently. 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

housing for special affordable families is

more than sufficient for the GSEs to

achieve the goal.

Having considered all statutory

factors including housing needs,

projected economic and demographic

conditions for 2001 to 2003, the GSEs’

past performance, the size of the market

serving very low-income families and

low-income families living in lowincome areas, and the GSEs’ ability to

lead the market while maintaining a

sound financial condition; HUD is

establishing the annual goal for

mortgage purchases qualifying under

the Special Affordable Housing Goal at

20 percent of eligible units financed by

each GSE in each of the years 2001,

2002 and 2003. This new goal level will

increase the GSEs’ current level of

performance to a level that is consistent

with reasonable estimates of the special

affordable housing market.

e. Special Affordable Housing Goal:

Multifamily Subgoal. This final rule

modifies the proposed rule by

implementing a multifamily subgoal

based upon each GSE’s respective

average mortgage purchase volume for

the years 1997 through 1999. The

proposed rule suggested that the subgoal

be established at 0.9 percent of each

GSE’s dollar volume of combined 1998

mortgage purchases in 2000 and at 1.0

percent of combined 1998 mortgage

purchases from 2001 through 2003. In

this final rule, the level of the subgoal

is established at a fixed level of one

percent of the average of each GSE’s

respective dollar volume of combined

(single family and multifamily)

mortgage purchases in the years 1997,

1998 and 1999. This level is $2.85

billion for Fannie Mae and $2.11 billion

for Freddie Mac, in each of the years

2001 through 2003.

f. Summary of Comments. Both

Fannie Mae and Freddie Mac opposed

establishing the special affordable

multifamily subgoal as a percentage of

their 1998 transaction volumes, stating

that 1998 was in some respects an

unusual year in the mortgage markets.

Instead, they both recommended that

the special affordable multifamily

subgoal be established as a percentage of

a five year average of each GSE’s

transactions volume. Freddie Mac

commented further that HUD’s

proposed subgoal was unreasonably

high.

Many other commenters supported

the multifamily subgoal, although they

questioned whether 1998 was the

appropriate base year upon which to

establish the subgoal. Some commenters

asserted that the proposed subgoal was

too high, in light of an expected decline

in multifamily origination volume.

Other commenters noted that the

subgoal was too low, based on the needs

of very low- and low-income families

and those in rural areas. Yet, others

agreed the subgoal should continue to

be percentage based, but argued that the

baseline year should move from year to

year. Still other commenters felt that the

multifamily subgoal should be

eliminated, as it no longer appears to

serve a purpose, particularly since

Freddie Mac has re-entered the

multifamily market.

65066 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

g. HUD’s Determination. Both the

multifamily mortgage market and

Freddie Mac’s multifamily transactions

volume have grown significantly during

the 1990’s, indicating both increased

opportunity and capacity to grow by

Freddie Mac. While Freddie Mac

continues to lag behind Fannie Mae

somewhat in its multifamily volume, it

appears to be within reach of catching

up with its larger competitor with

regard to the multifamily proportion of

total purchases. In 1999, Fannie Mae’s

multifamily mortgage purchases were

9.5 percent of its total mortgage

purchases and Freddie Mac’s

multifamily mortgage purchases were

8.3 percent of its total mortgage

purchases.

Freddie Mac’s multifamily special

affordable transactions volume was $2.7

billion in 1998 and $2.3 billion in 1999,

which demonstrates Freddie Mac’s

capacity to generate significant

multifamily special affordable volume

in a favorable market environment.

However, the Department is mindful of

the fact that the multifamily market

conditions experienced during 1998

were very favorable and may not be

fully representative of future years. HUD

expects conventional multifamily

volume in 2001 through 2003 to be

somewhat lower than the level reached

during 1998.

The Special Affordable Housing

Multifamily Subgoal established in this

final rule is 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 lowerincome families and families in lowincome areas to a greater extent than

single family 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 lowerincome families. Now that more recent

data is available, it is apparent that

taking 1999 mortgage volume into

consideration, along with that of 1997

and 1998, more accurately corresponds

to the relative size and respective

capabilities of the GSEs over the 2001–

2003 goals period. Accordingly, as

noted above, this final rule establishes

each GSE’s special affordable

multifamily subgoal at the respective

average of one percent of that GSEs’

combined mortgage purchases over 1997

through 1999.

h. Multifamily Subgoal Alternatives.

In the proposed rule, HUD identified

three alternative approaches for

specifying multifamily subgoals for the

GSEs based on a (i) minimum number

of units; (ii) minimum percentage of

multifamily acquisition volume; and

(iii) minimum number of mortgages

acquired. While some of these proposals

did receive support from commenters,

HUD does not see any compelling

reason to alter the dollar based structure

of the multifamily subgoal as

established in the regulations, which

can be updated and adapted to the

current market environment by basing it

upon recent acquisition volume. It is

noteworthy that the Special Affordable

Housing Goal, as a percentage of

business goal based on the number of

units financed, combines elements of

options (i) and (iii). HUD’s decision to

award bonus points toward the housing

goals for GSE transactions involving

small multifamily properties with 5–50

units will achieve some of the intended

policy objectives associated with option

(iii).

9. Bonuses and Subgoals

a. Overview. The Department

proposed to introduce a system of bonus

points to encourage the GSEs to increase

their activity in specified underserved

markets that serve low- and moderateincome families and families in

underserved areas. Bonus points were

specifically proposed to encourage

increased involvement by the GSEs

under goals established for the years

2000–2003 for purchases of mortgages

financing small multifamily properties

(5–50 units) and two to four unit owneroccupied properties that contain rental

units. The areas for which bonus points

were suggested are areas in which the

GSEs’ mortgage purchases have

traditionally played a minor role but

which provide significant sources of

affordable housing and for which the

need for mortgage credit persists. As a

regulatory incentive to encourage the

GSEs to increase their mortgage

purchase activity in underserved

markets, the Department proposed the

use of bonus points for mortgage

purchases in these important segments

of the housing market. HUD also sought

comments on the utility of applying

bonus points and other regulatory

incentives such as subgoals to other

underserved segments of the market

including manufactured housing,

multifamily properties in need of

rehabilitation, and properties in tribal

areas.

This final rule incorporates the use of

bonus points for small multifamily

properties and owner-occupied single

family rental properties as proposed for

the years 2001 through 2003.

b. Summary of Comments. Fannie

Mae and Freddie Mac commented in

detail on the use of bonus points and

subgoals. Fannie Mae supported the use

of bonus points to provide incentives to

expand its presence in the markets for

both the small multifamily and single

family owner-occupied, 2–4 unit

property. Fannie Mae opposed the use

of subgoals for that purpose, however,

arguing that they would result in

micromanagement of its business

operation. Fannie Mae added that

‘‘these two property types pose great

difficulties for the secondary market to

serve and will require new channels,

new products, new modes of operation,

and significant investments to better

understand the risks.’’ Fannie Mae also

recommended that if the Department

adopts bonus points, the points should

continue beyond 2003.

Freddie Mac supported using bonus

points and opposed using subgoals for

small multifamily and single family

owner-occupied, 2–4 unit property

mortgage acquisitions. As with Fannie

Mae, Freddie Mac commented that

subgoals would result in

micromanagement of its business.

Freddie Mac also recommended

calculating the threshold for 2–4 unit

properties based on the period from

1995–1999 instead of using a five-year

rolling average. Overall, Freddie Mac

commented that it would prefer bonus

points to subgoals for any targeted

market segments.

Other commenters were generally

supportive of the use of bonus points,

with many noting that bonus points

were preferable to additional subgoals.

This group of commenters felt that

additional subgoals would result in

micromanagement of the GSEs’ business

operations but felt that bonus points

provided an incentive rather than a

mandate to move into markets that were

underserved.

One group of commenters was

opposed to bonus points. Among many

of these commenters, however, there

was support for incentives for the GSEs

to purchase mortgages on small rental

properties, noting that the market is

underserved and provides an excellent

source of affordable rental housing.

Specific comments regarding the use of

bonus points concluded that bonus

points would: (a) Allow the GSEs to

meet the goals with less effort and that

they might lead the GSEs to relax their

single family efforts; and (b) inflate goal

performance numbers. It was suggested

by several commenters that subgoals

would be a more appropriate vehicle to

encourage the GSEs’ involvement in

those segments of the market as well as

other segments, e.g., mortgages made to

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65067

minority borrowers and home purchase

mortgages. Some commenters suggested

that since there was evidence that the

small multifamily mortgage market is

well served by community banks, thrifts

and small life insurance companies,

there is no need for HUD to award

bonus points as an incentive for the

GSEs to enter that market.

c. HUD’s Determination. This final

rule adopts the two categories for bonus

points that were proposed by the

Department. Bonus points are a

temporary incentive for the GSEs to step

up their efforts to serve this particular

need. Availability of bonus points for

this purpose beyond 2003, therefore,

will require a determination by the

Department that the bonus points

continue to serve this need. HUD’s

research and analysis indicates that

there is substantial unmet need in these

two areas and believes that these are

markets the GSEs should serve better.

While HUD has determined to establish

bonus points in the two market areas

proposed, HUD does not believe that

either the use of subgoals, that would be

unenforceable under FHEFSSA (except

for the Special Affordable Housing

Goal), or bonus points amounts to

micromanagement of the GSEs. By

utilizing bonus points the GSEs can

choose whether to increase their

presence in these markets, and by

evaluating the impact of these

incentives on the GSEs’ mortgage

purchase patterns, the Department can

evaluate the reasonableness and

effectiveness of bonus points as a tool to

increase activity in specific markets.

d. Additional Bonus Points and

Subgoals. Commenters suggested a wide

variety of other areas to consider for

either bonus points and/or subgoals

including those for which views were

invited. Suggestions by commenters for

subgoals included home purchase

mortgages and mortgages to minority

borrowers. Commenters also suggested

either bonus points and/or subgoals for

reverse mortgages, groups with low

homeownership rates, rural multifamily

housing programs, manufactured

housing, and expiring Section 8

assistance contracts, among other types

of transactions. While there was some

support for directing bonus points for

encouraging GSE financing for

minorities there was, however, no

consensus among the commenters for

this or other specific categories that

bonus points and subgoals should

address. Since HUD believes that the

increased goals under this rule will

result in increased financing of

affordable housing and increased home

ownership opportunities for minorities

and other families in underserved areas,

HUD has determined to establish bonus

points only in the two categories

proposed at this time. As indicated

above, HUD will, however, monitor the

effectiveness of these bonus points

closely, based on these results and

future housing needs, may establish

bonus points for other mortgage

purchases in the future.

10. Temporary Adjustment Factor for

Freddie Mac

a. Overview. To overcome any

lingering effects of Freddie Mac’s

decision to dismantle and then

cautiously reestablish a multifamily

mortgage purchase program in the early

1990s, the Department proposed an

incentive for Freddie Mac to further

expand its scope of multifamily

operations through the use of a

temporary adjustment factor for its

multifamily mortgage purchases in

calculating its performance under the

Low- and Moderate-Income Housing

Goal and the Special Affordable

Housing Goal. In determining Freddie

Mac’s performance for each of these two

goals, the Department proposed that

each unit in a property with more than

50 units meeting either of these two

housing goals would be counted as 1.2

units in the numerator of the respective

housing goal percentage. The temporary

adjustment factor would be limited to

properties with more than 50 units to

avoid overlap with the proposal to

award bonus points for multifamily

properties with 5–50 units. Comments

were requested on whether the

proposed temporary adjustment factor

for Freddie Mac was set at an

appropriate level and whether such an

adjustment factor should be phased out

prior to 2003.

This final rule incorporates the

temporary adjustment factor for Freddie

Mac for multifamily properties, other

than those small multifamily units

receiving bonus credit, as proposed for

the years 2001 through 2003.

b. Summary of Comments. Fannie

Mae and Freddie Mac commented in

detail on the application of a temporary

adjustment factor for Freddie Mac’s

multifamily business. Fannie Mae

opposed the application of a temporary

adjustment factor for Freddie Mac’s

multifamily business. Fannie Mae stated

that Freddie Mac made a business

decision to leave the multifamily market

and HUD’s action would effectively

punish Fannie Mae for staying in the

market. Fannie Mae recommended that

instead of a temporary adjustment

factor, HUD should lower Freddie Mac’s

goals to levels that would represent a

similar ‘‘stretch’’ as the higher goal

levels that would be established for

Fannie Mae.

Freddie Mac supported the idea of a

temporary adjustment factor but

recommended that it be set at a

multiplier of 1.35 instead of 1.2. Noting

that the difference in size and age

between Freddie Mac’s and Fannie

Mae’s multifamily portfolios makes goal

achievement easier for Fannie Mae,

Freddie Mac also recommended that the

temporary adjustment factor apply to all

three goals. Freddie Mac also opposed

any phasing out or elimination of the

adjustment factor.

Other comments on the proposal were

mixed. While there were many

comments in support of the proposal, a

number of commenters objected to the

proposal, observing that by providing

the temporary adjustment factor, HUD

would be rewarding Freddie Mac for

leaving the multifamily mortgage market

in previous years. Commenters also

suggested that the same objective could

be achieved through the Special

Affordable Multifamily Subgoal or by

establishing separate housing goals for

the single family and multifamily

market. Many of these commenters said

that, if the temporary adjustment factor

were adopted for Freddie Mac, it should

be phased out over a period of time.

c. HUD’s Determination. In the period

since HUD’s interim housing goals took

effect in January 1993, the volume of

Freddie Mac’s multifamily mortgage

purchase transactions has grown

significantly, both in absolute terms and

as a proportion of its total mortgage

purchases. Freddie Mac’s 1993

multifamily transactions volume was

only $191 million, compared with $7.6

billion in 1999. In 1999, Freddie Mac’s

multifamily transactions volume

represented 8.3 percent of units backing

its total mortgage purchases, close to the

Fannie Mae proportion of 9.5 percent.

Thus, while Freddie Mac continues to

lag behind Fannie Mae somewhat in its

multifamily volume, it appears to be

within reach of catching up with Fannie

Mae with regard to the multifamily

proportion of total purchases.

In discussing the Department’s

appropriations for fiscal year 2000, the

Conference Report stated in October,

1999 that ‘‘* * * the stretch affordable

housing efforts required of each of

Freddie Mac and Fannie Mae should be

equal, so that both enterprises are

similarly challenged in attaining the

goals. This will require the Secretary to

recognize the present composition of

each enterprise’s overall portfolio in

order to ensure regulatory parity in the

application of regulatory guidelines

measuring goal compliance.’’ 38

65068 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

Consistent with Congress’ October

1999 guidance, HUD’s analysis indicates

that a 1.2 adjustment factor applied to

Freddie Mac’s mortgage purchases for

multifamily properties of more than 50

units for purposes of the Low- and

Moderate-Income and Special

Affordable Housing Goals, as proposed,

is sufficient both to overcome any

lingering effects of Freddie Mac’s

decision to leave the multifamily market

in the early 1990s and to ‘‘ensure

regulatory parity,’’ taking account of the

recent magnitude of difference between

the GSEs’ respective multifamily shares

of business and the multifamily market

projections detailed in Appendix D.

Therefore, while the goals are set at the

same levels, the Department has

decided to implement the temporary

adjustment factor as proposed. The

temporary adjustment factor of 1.2 will

be applied to the Low- and ModerateIncome Housing Goal and the Special

Affordable Housing Goal. The

temporary adjustment factor will

terminate December 31, 2003. The

temporary adjustment factor will not

apply to Fannie Mae.

11. High Cost Mortgages

a. Overview. The proposed rule

requested comments on whether HUD

should disallow goals credit for high

cost mortgage loans, and if so, whether

HUD should define high cost mortgage

loans using the Home Ownership and

Equity Protection Act (HOEPA) 39 or an

alternative definition. HOEPA defines

high cost mortgages as those that meet

an annual percentage rate (APR)

threshold (more than 10 percentage

points above the yield on Treasury

securities of comparable maturity; the

Federal Reserve Board can adjust the

threshold down to 8 percent or up to 12

percent), or a threshold for points and

fees charged (exceeding the greater of 8

percent of the loan amount or $400—

adjusted for inflation to $451 for the

year 2000). HOEPA requires additional

disclosures and restricts certain loan

terms (e.g., prepayment penalties,

balloon payments, and negative

amortization) and practices (e.g. failing

to consider a borrower’s ability to repay)

for those mortgages.40

The proposed rule also requested

comments on the potential benefits, if

any, associated with the GSEs’ presence

in the various higher cost mortgage

markets, such as the standardization of

underwriting guidelines or reductions

in interest rates, as well as the potential

dangers, if any, associated with the

GSEs’ presence in those markets.

Finally, the proposed rule requested

comments on what additional data

would be useful for the purposes of

monitoring the GSEs’ activities in this

area and on whether certain of these

data elements should be included in the

public use data base. The proposed rule

noted that possible data elements that

could be collected from the GSEs for

monitoring include loan level data on

the annual percentage rate, debt-toincome ratio, points and fees, and

prepayment penalties.

b. HUD/Treasury Report. On June 20,

2000, HUD and the Department of

Treasury jointly released a report

entitled ‘‘Curbing Predatory Home

Mortgage Lending,’’ which detailed

predatory or abusive lending practices

in connection with higher cost loans in

the subprime mortgage market. These

practices include charging excessive

fees, lending to borrowers without

regard to their ability to repay,

establishing prepayment penalties that

prevent high cost borrowers from

refinancing into lower cost loans,

abusive terms and conditions that

include packing loans with products

such as single premium credit

insurance, and other practices,

including failing to steer borrowers to

the lowest-cost product for which they

qualify and incomplete reporting of

borrowers’ payment history to credit

bureaus. The report recommended

legislative and regulatory action to

combat predatory lending while

maintaining access to credit for lowand moderate-income borrowers.

Respecting the secondary mortgage

market, the report recommended that

HUD restrict the GSEs from funding

loans with predatory features since such

loans may undermine homeownership

by low- and moderate-income families.

HUD and Treasury noted ‘‘while the

GSEs currently play a relatively small

role in the subprime market today, they

are beginning to reach out with new

products in this marketplace.’’

Recently the GSEs have each

announced corporate policies against

the purchase of loans with certain

features. Fannie Mae has established

greater limitations than Freddie Mac,

although Fannie Mae has been less

involved in the subprime market to

date. Fannie Mae announced that ‘‘[f]or

loans delivered to Fannie Mae, the

points and fees charged to a borrower

should not exceed 5 percent, except

where this would result in an

unprofitable origination,’’ and that

Fannie Mae will not purchase high cost

mortgages as defined under HOEPA.

Fannie Mae announced further that it

‘‘will not purchase or securitize any

mortgage for which a prepaid singlepremium credit life insurance policy

was sold to the borrower,’’ and that it

will generally only allow prepayment

penalties under the terms of a

negotiated contract and where the

lender adheres to the following criteria:

A mortgage that has a prepayment

penalty should provide some benefit to

the borrower (such as a rate or fee

reduction for accepting the prepayment

premium); the borrower also should be

offered the choice of another mortgage

product that does not require payment

of such premium; the terms of the

mortgage provision that requires a

prepayment penalty should be

adequately disclosed to the borrower,

and the prepayment penalty should not

be charged when the mortgage debt is

accelerated as a result of the borrower’s

default in making his or her mortgage

payments.

Fannie Mae also announced that it

will not purchase loans from lenders

who steer borrowers to higher cost

products if those borrowers qualify for

lower cost products. Freddie Mac

announced that it will not purchase

HOEPA loans, nor will it purchase

mortgage loans with single-premium

credit life insurance. Both GSEs have

announced that they will require

lenders who sell them loans to file

monthly full-file credit reports on every

borrower. While the GSEs’ policies

differ somewhat in their scope and

specificity, both have publicly

expressed strong concern about

predatory lending practices and have

adopted policies requiring them to look

harder at particular loan terms and their

seller/servicers’ business practices, and

restricting their purchases of loans

originated with such terms and

practices. However, the GSEs’ broad

guidelines describing the characteristics

of loans that they intend to make

ineligible for purchase lack important

details and are subject to changes in

corporate direction, or other changes.

Therefore, HUD and Treasury

recognized in the report that such

corporate policies may not be sufficient

and that regulations would be needed to

address this issue.

c. Summary of Comments. Many

commenters on the proposed rule

supported the disallowance of credit

under the GSE housing goals for high

cost mortgages. Some of these

commenters commended the GSEs for

beginning to offer quality loan products

to credit-impaired borrowers. Those

commenters argued, however, that

restrictions on goals credit for certain

loans would not prohibit the GSEs from

purchasing all subprime loans but

merely those that are likely to be

predatory and wealth-stripping. Other

commenters argued that without

adequate controls, the GSEs’ forays into

the subprime market will not translate

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65069

into lower costs for borrowers, but will

only lower the cost of capital for

subprime lenders.

Some commenters wrote that the

GSEs should not receive credit under

the housing goals for high cost

mortgages that are subject to HOEPA.

Many other commenters felt that such a

standard would not go far enough, and

that the GSEs should not receive goals

credit for purchasing loans with certain

features. Such features would include

fees greater than 3 percent of the loan

amount, prepayment penalties on high

cost loans, and prepaid single premium

credit life insurance that is to be

financed in the loan. Commenters also

provided additional features for which

the GSEs should not receive goals

credit, including negative amortization

and accelerating indebtedness, fees to

renew or modify, balloon payments,

yield spread premiums, mandatory

arbitration, or high cost loans for which

the borrower did not receive

homeownership counseling.

One commenter suggested that the

Department should treat loans

purchased from an institution that

engages in predatory lending the same

as loans that actually have predatory

features in order to send a message that

such lenders are not responsible

business partners and to restrict further

the availability of mortgage credit for

such loans. Other commenters suggested

that the GSEs should not be allowed to

purchase subprime loans at all, so that

they will have an incentive to develop

conventional mortgage products to

reach out to those borrowers. Another

suggestion was that the GSEs should be

affirmatively penalized for purchasing

certain abusive mortgages (i.e., by

subtracting points from the numerator

but fully counting such loans in the

denominator).

A number of commenters suggested

that GSEs should be required to conduct

fair lending reviews of subprime loans

before they purchase them in order to

receive credit. Such reviews would

include determining whether the

lending institution is reporting

borrowers’ full payment histories to

credit bureaus.

Many of the commenters that

supported the disallowance of goals

credit for high cost loans and loans with

certain harmful features asserted that

the GSEs’ support of such lending poses

great risks. These commenters argued

that the types of mortgage products that

strip equity out of homes and lead to

higher foreclosures are not consistent

with the GSEs’ public mission. Further,

to the extent that defaults on these loans

lead to losses, these commenters

asserted that the GSEs’ financial

condition will likely be affected.

With regard to data collection and

reporting, several commenters suggested

that the GSEs should be required to

provide full information on their

subprime loans, including the APR,

total closing costs, points, and fees

(including financed credit insurance

premiums), delinquency and foreclosure

rates, and the length of time between

purchase and refinance on an aggregate

basis.

Both GSEs and a large group of

commenters objected to the

Department’s proposal regarding the

disallowance of goals credit for

purchases of high cost mortgages. Many

of those commenting in this regard

provided substantially similar responses

to those submitted by Fannie Mae.

These commenters emphasized the

difference between legitimate subprime

lending and lending through the use of

abusive and predatory practices such as

those outlined in the HUD/Treasury

report. Several of these commenters

expressed concern that the Department

should not take any action that would

discourage the GSEs from serving the

subprime market. The GSEs both

remarked that they are using enhanced

technology (e.g., their respective

automated underwriting systems) to

allow them to offer products targeted

toward borrowers with impaired credit,

and that they are, therefore, able to

move into the legitimate subprime

market in a responsible and prudent

manner, bringing liquidity,

standardization, and efficiency to that

market. The GSEs argue that disallowing

goals credit for high cost mortgages will

provide a disincentive for them to reach

out to those borrowers and will do

nothing to combat the predatory lending

practices about which the Department is

concerned. Indeed, Fannie Mae argued

that disallowing goals credit for high

cost mortgages would simply drive

predatory lending ‘‘into the government

market or to secondary market sources

who are less responsible than Fannie

Mae on this issue.’’

Fannie Mae argued that disallowing

goals credit for high cost mortgages is

inconsistent with the Department’s

inclusion of A-minus mortgages in the

market estimates to which the

Department compares the GSEs’

performance. Fannie Mae further argued

that the Department would need to

‘‘recalibrate the goals’’ in order to

implement a system of disallowing goals

credit for high cost mortgages, which

would be ‘‘extremely difficult, if not

impossible’’ due to ‘‘the lack of reliable

market data on loan costs.’’

Nonetheless, Fannie Mae urged the

Department to work with other

regulatory agencies to collect more data

on the problem. Freddie Mac urged the

Department to await the outcome of any

Federal legislative or regulatory

initiatives that may arise as a result of

the widespread concern and focus on

these issues among members of

Congress and regulatory agencies.

The GSEs also both objected to any

additional reporting requirements

related to monitoring their purchases of

high cost mortgages. Fannie Mae argued

that the relevant information is not now

captured in the primary market, and

that collecting and reporting this

information would force a ‘‘tremendous

change to the way the market operates.’’

Freddie Mac similarly argued that the

required data elements are not stored

uniformly across lenders, and collecting

and reporting such data elements would

require ‘‘substantial investments,’’ the

economic impacts of which would

likely be considerable.

d. HUD’s Determination. After

considering the issues raised by the

commenters, the Department has

determined that, in accordance with the

Secretary’s authority under section

1336(a)(2) of FHEFSSA, the GSEs

should not be assigned credit toward the

Affordable Housing Goals for

purchasing certain high cost mortgages

including mortgages with certain

unacceptable features. The GSEs have a

statutory responsibility to lead the

industry in making mortgage credit

available to low and moderate income

families and underserved areas. In

carrying out this responsibility, the

GSEs should seek to make the lowest

cost credit available while ensuring that

they do not purchase loans that actually

harm borrowers and support unfair

lending practices. The HUD/Treasury

report recommended regulatory and/or

legislative restrictions that would go

beyond the matter of goals credit and

would prohibit the GSEs from

purchasing certain types of loans with

high costs and/or predatory features

altogether. These proposals stem from

the concern that mortgages with

predatory features undermine

homeownership by low-and moderateincome families in derogation of the

GSEs’ Charter missions. As pointed out

in the HUD/Treasury Report, ‘‘While the

secondary market could be viewed as

part of the problem of abusive practices

in the subprime mortgage market, it may

also represent a large part of the

solution to the problem. If the secondary

market refuses to purchase loans that

carry abusive terms, or loans originated

by lenders engaging in abusive

practices, the primary market might

65070 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

react to the resulting loss of liquidity by

ceasing to make these loans.’’

Accordingly, consistent with and

combining restrictions already

voluntarily undertaken by both GSEs,

this final rule restricts credit under the

goals for purchases of high cost loans

including mortgages with certain

unacceptable terms and resulting from

unacceptable practices. Specifically, the

GSEs will not receive credit toward any

of the Affordable Housing Goals for

dwelling units financed by mortgages

that come within HOEPA’s thresholds

for high cost mortgages, nor will they

receive credit for mortgages with certain

unacceptable features or resulting from

unacceptable practices. The housing

goals provide incentives to encourage

GSE efforts to finance housing for low

and moderate income families, housing

in underserved areas, and special

affordable housing. Therefore, HUD has

determined that the GSEs should not

receive the incentive of goals credit for

purchasing high cost mortgages

including mortgages with unacceptable

features.

(1) Mortgages that Come Within

HOEPA’s Thresholds. The final rule

disallows goals credit for dwelling units

financed by mortgages that come within

HOEPA’s thresholds, i.e., with an APR

of 10 percentage points or higher above

the yield on Treasury securities of

comparable maturity, or with points and

fees that are above the greater of 8

percent of the loan amount or $451.

HOEPA’s thresholds provide a

discernible and standard industry

measure of a class of loans that are very

high cost, that present a very high risk

that their borrowers will lose their

homes, and that the GSEs themselves

have determined not to purchase. While

originating such loans is not illegal, but

rather made subject to additional

disclosures and protections under

HOEPA, loans at these levels should not

be encouraged by receiving credit under

the goals. In incorporating the HOEPA

high cost loan standards in this rule, the

thresholds are subject to adjustment by

the Federal Reserve Board 41 or

Congress. This rule is established to

encompass such adjustments unless the

GSEs are otherwise notified in writing

by HUD. While HOEPA itself only

covers closed end loans made to

refinance existing mortgages and closed

end home equity loans, this final rule

also applies the HOEPA thresholds to

home purchase mortgages.

(2) Mortgages with Unacceptable

Terms or Conditions or Resulting from

Unacceptable Practices. This final rule

also disallows goals credit for dwelling

units financed by mortgages with

features that the GSEs themselves, either

through announced policies or

practices, have identified as unfair to

borrowers and unacceptable.

Specifically, these include mortgages

with:

(a) Excessive fees, where the total

points and fees charged to a borrower

exceed 5 percent of the loan amount,

except where this restriction would

result in an unprofitable origination. For

such cases, involving small loans, this

rule provides a maximum dollar amount

of $1000, or such other amount as may

be requested by a GSE and determined

appropriate by the Secretary, as an

alternative to the 5 percent limit. For

purposes of this provision, points and

fees include: (i) Origination fees, (ii)

underwriting fees, (iii) broker fees, (iv)

finder’s fees, and (v) charges that the

lender imposes as a condition of making

the loan—whether they are paid to the

lender or a third party. For purposes of

this provision, points and fees would

not include: (i) Bona fide discount

points; (ii) fees paid for actual services

rendered in connection with the

origination of the mortgage, such as

attorneys’ fees, notary’s fees, and fees

paid for property appraisals, credit

reports, surveys, title examinations and

extracts, flood and tax certifications,

and home inspections; (iii) the cost of

mortgage insurance or credit-risk price

adjustments; (iv) the costs of title,

hazard, and flood insurance policies; (v)

state and local transfer taxes or fees; (vi)

escrow deposits for the future payment

of taxes and insurance premiums; and

(vii) other miscellaneous fees and

charges that, in total, do not exceed 0.25

percent of the loan amount.

This restriction on goals credit for

mortgages with excessive fees does not,

of course, supplant the restriction on

goals credit for HOEPA loans. If a

mortgage has fees that exceed 5 percent

of the loan amount as described in the

immediately preceding paragraph, but

do not exceed the 8 percent/$451

threshold under HOEPA, the mortgage

would not receive credit toward the

goals. HUD, Treasury, the GSEs, and

many others have recognized that

mortgages with excessive fees are a

particularly onerous problem and

disproportionately affect the low- and

moderate-income borrowers that the

GSEs are to serve. Therefore, this final

rule will remove any incentive under

the goals for the GSEs to purchase loans

with excessive fees as described above.

Having said that, the HUD/Treasury

report called upon the Federal Reserve

Board to expand the HOEPA ‘‘points

and fees’’ threshold to include certain

additional types of fees, including (i)

fees and amounts imposed by third

party closing agents (except payments

for escrow and primary mortgage

insurance), (ii) prepayment penalties

that are levied on a refinancing, and (iii)

all compensation received by a mortgage

broker in connection with the mortgage

transaction. As mentioned above, if the

Federal Reserve changes the HOEPA

thresholds, such changes will be

encompassed within HUD’s housing

goals, unless HUD notifies the GSEs

otherwise.

(b) Prepayment penalties, except

where: (i) the mortgage provides some

benefits to the borrower (e.g., such as

rate or fee reduction for accepting the

prepayment premium); (ii) the borrower

is offered the choice of a mortgage that

does not contain such a penalty; (iii) the

terms of the mortgage provision

containing the prepayment penalty are

adequately disclosed to the borrower;

and (iv) the prepayment penalty is not

charged when the mortgage debt is

accelerated as the result of the

borrower’s default in making his or her

mortgage payments.

(c) Single premium credit life

insurance products sold in connection

with the origination of the mortgage.

(d) Evidence that the lender did not

adequately consider the borrower’s

ability to make payments, i.e., mortgages

that are originated with underwriting

techniques that focus on the borrower’s

equity in the home, and do not give full

consideration to the borrower’s income

and other obligations. Ability to repay

must be based upon relating the

borrower’s income, assets, and liabilities

to the mortgage payments.

(3) Mortgages Contrary to Good

Lending Practices. As the GSEs have

recognized in their own policies and

many of the commenters pointed out as

well, while good mortgage lending

practices can reduce costs to borrowers,

contrary practices can result in loans

that are higher cost to borrowers in ways

that are not directly reflected in the

interest rate, points, or fees. Therefore,

to remove any goals incentive for the

GSEs to purchase mortgages or

categories of mortgages regarding which

there is evidence that lenders engaged

in specific practices contrary to good

lending practices identified in the rule,

this rule provides that the GSEs may not

receive goals credit for such loans or

categories of loans. These specific

practices identified in this rule that

lenders employ to avoid abusive lending

include regularly reporting complete

borrower information to credit agencies,

avoiding steering borrowers to higher

cost products, and complying with fair

lending requirements.

FHEFSSA and HUD’s GSE regulations

at 24 CFR 81.41, prohibit the GSEs from

discriminating in any manner in making

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65071

any mortgage purchases because of race,

color, religion, sex, handicap, familial

status, age or national origin. Since

abusive lenders often specifically target

and aggressively solicit homeowners in

predominantly lower-income and

minority communities who may lack

sufficient access to mainstream sources

of credit, it is essential that the GSEs

scrutinize lender practices to protect

against buying loans that are the result

of unlawful discrimination. For

example, good lending practices that

help lenders avoid unlawful

discrimination include employee

training programs, periodic loan

sampling, specifically tailored

recordkeeping and reporting

requirements, and other reviews. The

GSEs have reported, consistent with

their pledges not to buy certain harmful

loans, that they will be looking closer at

the lending practices of entities with

which they do business, and HUD

commends those efforts. HUD will

review the processes the GSEs employ

to ascertain positive practices to avoid

unlawful discrimination and steering

borrowers to higher cost products, as

well as monthly credit reporting. This

final rule provides that where HUD

finds evidence that loans or categories

of loans do not conform to such positive

practices, HUD may deny goals credit

for such loans in accordance with

§ 81.16(d) of this rule.

HUD recognizes that the particular

loan terms and practices that are

identified as abusive and unacceptable

may change as some unscrupulous

actors adjust to new restrictions and as

the GSEs and HUD gain experience with

abuses. Accordingly, to allow flexibility

this rule allows the Department to

modify the list of terms and practices

that will not receive goals credit, by

providing that the GSEs may request

modifications to the list and that the

Secretary will after reviewing such

submissions determine whether or not

to change the abuses for which goals

credit will be restricted. HUD also will

continue to monitor the mortgage

industry with regard to abusive lending

practices and may determine that future

modifications are necessary and require

further rulemaking.

The restrictions and provisions in

sections (1), (2), and (3), above, address

terms and practices that are harmful to

mortgage borrowers. Accordingly, these

restrictions and provisions in this rule

apply to mortgages purchased through

the GSEs’ ‘‘flow’’ business, as well as

mortgages purchased or guaranteed

through structured transactions. Since

these restrictions and provisions are

consistent with the GSEs’ own

measures, the Department does not

believe that any of these restrictions will

provide a disincentive for the GSEs to

provide financing for borrowers with

slightly impaired credit through

innovative products that can bring

competition and efficiencies to the

legitimate subprime market.

While the GSEs themselves will

presumably be obtaining certain

additional data and information to carry

out their previously announced

purchase restrictions and to monitor

lending practices, HUD is not

establishing any requirements for

additional data to carry out these

provisions under this rule.

Subsequently, HUD plans to request

only such additional data as is

necessary. In this regard, HUD will

consult with the GSEs, as practicable, to

develop reasonable data reporting

requirements that will not present an

undue additional burden.

12. Data On Unit Affordability, § 81.15

The GSEs have reported that at times

it can be difficult and costly for them to

obtain the data on incomes and rents

that is necessary to establish

affordability for goals purposes,

especially for seasoned loan

transactions and some negotiated

transactions. HUD proposed to allow (1)

the use of estimation techniques to

approximate unit rents in multifamily

properties where current rental

information is unavailable and (2) the

exclusion of units, both single family

and multifamily, from goal calculations

where it is impossible to obtain full data

or estimate values, subject to certain

limits.

As has been discussed, GSE purchases

of mortgages on rental properties

disproportionately serve the affordable

housing market. Typically, around 90

percent of rental units backing GSE

mortgage purchases would count

towards the Low- and Moderate-Income

Housing Goal and around 50 percent

would meet the affordability

requirements of the Special Affordable

Housing Goal (excluding missing data).

HUD did not want the lack of data on

affordability to act as a disincentive for

the GSEs to purchase mortgages in these

important sectors, which have been

identified by HUD as having substantial

unmet credit needs in the mortgage

market. While single family owneroccupied units are also affected by

missing data, these units are typically

not as affordable as the GSEs’ rental

purchases. Consequently, the provision

in the proposed rule to exclude units

from the numerator and denominator for

single family owner-occupied properties

is limited to properties located in lower

income areas and is subject to a cap.

a. Multifamily Rental Units.

(1) Overview. The Department

proposed allowing the use of estimated

rents for multifamily units with missing

data, subject to HUD review and

approval of the data sources and

methodologies used in computing them.

The Department asked for comment on

whether it should establish a percentage

ceiling on the use of estimated rents.

HUD further proposed that, in cases

where multifamily rents are missing and

where application of estimated rents is

not possible, such units be excluded

from both the denominator and

numerator for purposes of calculating

performance under the Low- and

Moderate-Income Housing Goal and the

Special Affordable Housing Goal. The

Department requested comment on

whether it should establish a percentage

ceiling for the exclusion of multifamily

units with missing data from the

denominator for goal calculation

purposes.

(2) Summary of Comments. Several

commenters endorsed the concept of

using estimated data to calculate

performance toward the Low- and

Moderate-Income Housing Goal and the

Special Affordable Housing Goal when

multifamily rent data are missing. No

commenters indicated opposition to

allowing the use of estimated rents.

In its comments, Fannie Mae stated

that HUD should, in order to provide

operational certainty, incorporate an

approved methodology into the

regulations for estimating rents on

multifamily properties where actual rent

data are missing. Freddie Mac

commented that the GSEs should be

given the choice of whether to provide

estimated rents or to exclude units from

the denominator for purposes of

calculating goals performance in

instances of missing multifamily rent

data.

In cases where calculation of

estimated rents is not feasible, a number

of commenters wrote in support of

excluding the units in question from the

denominator as well as the numerator

for purposes of calculating performance

toward the Low- and Moderate-Income

Housing Goal and the Special

Affordable Housing Goal. One

commenter opposed such exclusion,

noting that by including all multifamily

units in the denominator whether or not

the GSEs have the required income and

rent data places a more serious burden

on the GSEs to obtain the data and focus

on affordable lending in the multifamily

area.

With regard to the issue of percentage

ceilings, Freddie Mac suggested a twopercent (2%) ceiling on the exclusion of

multifamily units from the denominator

65072 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

because of missing rents. Other

commenters suggested alternative

limits, e.g., a half-of-one percent (0.5%)

ceiling or a one-percent (1%) ceiling for

the combined total of multifamily units

with estimated rent and units excluded

from the denominator. Only Fannie Mae

indicated opposition to such a ceiling,

writing that ‘‘Enforcement of percentage

ceilings will perpetuate penalties

against and create a disincentive for

Fannie Mae to engage in the very

business that HUD has identified for

expanded penetration—single family,

owner-occupied, 2–4 unit housing and

small multifamily rental properties.’’

(3) HUD’s Determination. In order to

promote liquidity in the multifamily

mortgage market, including mortgages

on properties which may not have

current data on the affordability of such

units the Department believes that it is

reasonable for the GSEs to provide

estimated affordability data for such

properties, which would be utilized for

purposes of calculating performance

toward the Low- and Moderate-Income

Goal and the Special Affordable

Housing Goal as long as the data sources

and methodology are reliable. The data

sources and methodology used by a GSE

to estimate affordability data are,

therefore, subject to HUD review and

approval. Estimated affordability data

may be used up to a maximum of five

(5) percent of units backing GSE

multifamily purchases in any given

year.

In its evaluation of whether to accept

a proposed methodology for estimating

affordability data, the Department will

seek to determine: (a) The reliability of

the data source(s) used including the

size of the sample used; (b) the accuracy

of the calculations; and (c) the

reasonableness of the proposed

methodology with regard to providing

an unbiased measure of GSE

performance toward the Low- and

Moderate-Income Housing Goal and the

Special Affordable Housing Goal,

including the degree to which the

methodology accurately predicts

affordability information and goals

performance on units backing GSE

acquisitions in cases where current

affordability data are known. The GSEs

will be required to certify that any

proposed estimated affordability

methodology meets these standards.

Methodologies that tend to understate

actual rents, or which otherwise tend to

overstate the affordability of GSE

multifamily mortgage purchases or

exaggerate GSE goals performance

relative to actual performance, will not

be considered acceptable by HUD.

Once a methodology is approved, the

Department will closely monitor its

implementation and its effects on

calculated goals performance.

Withdrawal of Departmental approval of

an estimated affordability methodology

could be warranted if evidence becomes

available indicating that use of

estimated affordability methodologies is

unreliable or has undermined GSE

incentives to collect and maintain rent

data.

HUD does not believe it is necessary

to codify in the regulations the specific

methodology for estimating affordability

data. The concept of estimating

affordability data is new relative to the

affordable housing goals. Both HUD and

the GSEs need to evaluate the

implications of the methodology

proposed, monitor performance over

time using such data, evaluate new data

sources that may become available and

become more predictive. HUD needs the

flexibility to make changes and

refinements to the approved

methodology based on experience,

without unnecessary limitations. In

approving any methodology and data

sources, HUD will, of course, be

mindful of the GSEs’ needs for

operational certainty in making

determinations.

With regard to circumstances where

estimation of affordability on

multifamily properties with missing

data is not feasible, HUD believes it is

reasonable to exclude such units from

the denominator as well as the

numerator for purposes of calculating

performance toward the Low- and

Moderate-Income Goal and the Special

Affordable Housing Goal. The

Department does not believe that a

percentage ceiling on the exclusion of

multifamily units with missing data

from the denominator is needed in order

to preserve incentives for data

collection, and could actually be

harmful from the standpoint of the

reliability of the housing goals as a

measure of actual GSE performance.

Because the percent of multifamily units

qualifying for the Low- and-Moderate

Income Goal is so much higher than the

average across all property types (over

90 percent for multifamily, compared

with approximately 45 percent overall),

an incentive will remain in place for the

GSEs to collect rent data or obtain

reliable estimated rents wherever it is

feasible to do so. For the same reason,

the Department believes that applying a

ceiling on exclusion of units from the

denominator as well as the numerator

for goal calculation purposes would

undermine the reliability of the Lowand Moderate Income Goal as a measure

of actual GSE performance, since

multifamily units above the ceiling

would be counted as not being

affordable when, in fact, there is

approximately a 90 percent probability

that such units do meet the

requirements of the Low- and ModerateIncome Housing Goal. Similar

arguments could be made with regard to

the Special Affordable Housing Goal.

Therefore, a percentage ceiling on

removal of units from the denominator

as well as the numerator is not

necessary or warranted at this time.

b. Single Family Rental Units.

(1) Overview. The Department further

proposed to exclude rental units in 1–

4 unit properties with missing rent data

from the denominator as well as the

numerator in calculating performance

under the Low- and Moderate-Income

Housing Goal and the Special

Affordable Housing Goal. HUD asked for

comment on whether it should establish

a percentage ceiling for such exclusions.

This final rule retains the provision

excluding rental units in 1–4 unit

properties with missing rent data from

the numerator and the denominator in

calculating performance under the two

goals. These properties

disproportionately serve affordable

housing markets and the GSEs should

be active in this segment of the market.

As the Department is awarding bonus

points for the units in owner-occupied

single family rental properties, the GSEs

have a large incentive to obtain the

required affordability data. When the

data is not available, however, the

Department does not wish to create a

disincentive to purchase mortgages on

these properties simply because

affordability data is not available.

(2) Summary of Comments. A number

of commenters wrote in favor of

excluding rental units in 1–4 unit

properties from the denominator as well

as the numerator for purposes of

calculating performance toward the

Low- and Moderate-Income Housing

Goal and the Special Affordable

Housing Goal when rent data are

missing. No commenters indicated

opposition to such exclusion.

Writing in support of the ceiling

concept, Freddie Mac suggested a twopercent (2%) ceiling on the exclusion of

single family rental units from the

denominator. Fannie Mae objected to

such a ceiling, commenting that a

ceiling was unnecessary given that it is

in Fannie Mae’s interest to obtain rent

data on single family rental properties

when it is cost effective to do so. Other

commenters endorsed a percentage

ceiling on the number of single family

rental units that would be excluded

from the denominator as well as the

numerator for purposes of calculating

performance toward the Low- and

Moderate-Income Housing Goal and the

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65073

Special Affordable Housing Goal when

rent data are missing.

Fannie Mae and Freddie Mac both

suggested that the use of estimated rents

should be permitted for single family

rental properties with missing data.

(3) HUD’s Determination. With regard

to single family rental units with

missing rent data, HUD believes it is

reasonable to remove such units from

the denominator as well as the

numerator for purposes of calculating

performance toward the Low- and

Moderate-Income Goal and the Special

Affordable Housing Goal. Because of the

high degree of affordability of single

family rental units, the Department does

not believe that a percentage ceiling on

exclusion of single family rental units

with missing data from the denominator

is needed in order to preserve incentives

for data collection, and could actually

be harmful from the standpoint of the

reliability of the housing goals as a

measure of actual GSE performance.

HUD will monitor the GSEs’ use of

missing data provisions to ensure that

they are being used in a reasonable way.

The Department has determined not

to permit the use of estimated

affordability data where it is missing for

single family rental units. There are

several reasons why HUD believes this

a reasonable and prudent decision.

A decision to exclude units with

missing affordability data from the

numerator as well as the denominator

for certain goals calculation purposes on

single family rental properties removes

a potential disincentive to an expanded

GSE presence in the markets for

mortgages on single family rental

properties at the same time. The

Department believes this segment of the

market has unmet credit needs. To

encourage the GSEs to move into this

market, it is awarding bonus points for

the rental and owner-occupied units in

owner-occupied single family rental

properties. The use of bonus points will

serve as an additional incentive to the

GSEs to obtain the necessary

affordability data in order to obtain

bonus credit.

Furthermore, HUD calculates

affordability of single family rental units

for purposes of the housing goals using

origination-year rents, in contrast to

multifamily, where acquisition year

rents are used. While acquisition year

rents on multifamily properties may

sometimes be difficult to provide on

seasoned and negotiated transactions

where lenders have not continued to

collect annual rent data following loan

origination, this situation does not

apply to single family rental properties,

since information on rent at the time of

loan origination is ordinarily required

by lenders and secondary market

institutions as part of the loan

underwriting process.

The Department’s decision to allow

the estimation of affordability data with

the limitations provided in this rule for

multifamily rental units affords an

opportunity to pilot the estimated rent

methodology in an appropriately

controlled environment.

c. Single Family Owner-Occupied

Units.

(1) Overview. The Department also

proposed to exclude single family

owner-occupied units from the

denominator as well as the numerator

for purposes of calculating performance

toward the Low- and Moderate-Income

Housing Goal and the Special

Affordable Housing Goal when data on

borrower income are missing, provided

the unit is located in a census tract with

median income less than or equal to

area median. HUD proposed to restrict

this exclusion up to a ceiling of one

percent (1%) of the total number of

single family, owner-occupied dwelling

units eligible to be counted toward the

respective housing goal.

This final rule retains the provision to

exclude single family owner-occupied

mortgages from both the numerator and

the denominator when borrower income

is missing for properties located in

lower income areas subject to a one

percent maximum.

(2) Summary of Comments. A number

of commenters wrote in favor of

excluding at least some single family

owner-occupied units from the

denominator as well as the numerator

for purposes of calculating performance

toward the Low- and Moderate-Income

Housing Goal and the Special

Affordable Housing Goal when income

data are missing. One commenter

indicated opposition to such exclusion.

Both Fannie Mae and Freddie Mac

expressed opposition to restricting the

exclusion of single family owneroccupied units with missing income

data from the denominator only in

lower-income areas. They recommended

a two percent ceiling without these

geographic restrictions.

In its comments, Fannie Mae stated

that ‘‘the place-based restriction that

HUD proposes implies an unreasonable

assumption that all the units that are

missing data outside of the low-income

census tracts are not affordable. The

effect of the cap is to deny credit for

units that are missing data and even

when those units have some statistical

likelihood of serving loans to low- and

moderate-income borrowers. HUD’s

proposed methodology treats loans to

low- and moderate-income borrowers

differently simply because the borrower

chose to purchase a property in a

higher-income area.’’ While opposed, in

principle, to the concept of a ceiling on

the exclusion of missing single family

owner-occupied units from the

denominator for goals calculation

purposes, Fannie Mae stated that any

ceiling established by the Department

should be set at ‘‘not less than two

percent.’’

Similarly, Freddie Mac wrote that ‘‘A

substantial fraction of mortgages in

above-average income tracts are made to

low- and moderate-income families’

citing 1998 HMDA data in support of

this contention. Consequently,

‘‘geographic restrictions would

erroneously exclude many low- and

moderate-income loans from

performance measures.’’

Several commenters endorsed HUD’s

proposed one percent ceiling on

exclusion of single family owneroccupied units with missing data from

the denominator although some

commenters thought the ceiling should

be lower than one percent. A number of

other commenters expressed opposition

to this ceiling. No comments were

received on the geographic restrictions

aside from those from the GSEs.

(3) HUD’s Determination.

With regard to single-family owneroccupied units with missing income

data, HUD believes it is reasonable to

remove such units from the

denominator as well as the numerator

up to one percent of the eligible total for

purposes of calculating performance

toward the Low- and Moderate-Income

Goal and the Special Affordable

Housing Goal provided such units are

located in tracts where median income

is less than or equal to area median

income.

The percentage ceiling and the

restriction to tracts where median

income is less than or equal to area

median income are both necessary in

order to ensure that the exclusion does

not result in undue exaggeration of GSE

performance as calculated in achieving

the housing goals as compared to actual

performance. Because single family

owner-occupied units are significantly

less affordable than all other property

types in the conventional, conforming

mortgage market according to HUD’s

estimates (approximately 36 percent

single family owner-occupied units

meet the Low-and Moderate-Income

Housing Goal, compared with 45

percent overall), excluding single family

owner-occupied units with missing data

from the denominator as well as the

numerator could significantly raise the

proportion of GSE acquisitions counting

toward the Low-and Moderate-Income

and Special Affordable Housing Goals

65074 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

above actual performance. The onepercent ceiling on exclusion of single

family owner-occupied units from the

denominator places a limit on the

degree to which such exclusions bias or

affect the data, and the restriction to

tracts with income less than area

median serves to increase the likelihood

that the affordability characteristics of

the excluded units resembles that of the

‘‘typical’’ GSE purchase, further limiting

the bias that would otherwise be

introduced.

In HUD’s view, the proposed

geographic restriction on the exclusion

of missing single family owner-occupied

units from the denominator as well as

the numerator for certain goals

calculation purposes is, therefore,

reasonable and necessary to correct for

the bias that would otherwise be

introduced even with a one-percent

ceiling. Fannie Mae’s contention that

‘‘the place-based restriction that HUD

proposes implies an unreasonable

assumption that all the units that are

missing data outside of the low-income

census tracts are not affordable’’ is not

pertinent to HUD’s determination. The

Department made no such assumption.

HUD is well aware that many lowincome borrowers choose to live in

tracts with median income above the

area median, as pointed out by Fannie

Mae. Conversely, however, a significant

number of above median-income

borrowers choose to live in tracts with

median income below the area median.

HMDA data does, however, show a

strong correlation between borrower

income as a percent of area median and

tract income as a percent of area

median, suggesting that tract income

serves as a useful predictor of borrower

of income. For example, in 1998, 55

percent of conforming, conventional

owner-occupied loans in tracts where

median income was less than area

median were to low-and moderateincome borrowers. In contrast, only 33

percent of loans in high-income tracts

were to low-and moderate-income

borrowers. (Overall, 42 percent of single

family owner-occupied loans in HMDA

data were to low-and moderate-income

borrowers.) HUD’s analysis of GSE loanlevel data reveal a similar correlation

between borrower income as a percent

of area median and tract income as a

percent of area median, although the

low-mod percentage of GSE acquisitions

is lower than in HMDA data.

Accordingly, HMDA findings support

the conclusions that HUD’s proposed

geographic restrictions on the exclusion

of missing single family owner-occupied

data will (i) result in goals calculations

that more accurately track actual

performance than would otherwise be

the case and (ii) respond appropriately

to any perceived weakening of

incentives for the GSEs to collect

affordability data to the extent feasible.

d. Other Matters. Freddie Mac argued

that units with missing census tract data

should be excluded from the

denominator as well as the numerator

for purposes of calculating performance

toward the Underserved Areas Goal up

to a maximum of 0.5 percent of the total.

The Department has not determined,

however, that it is reasonable to remove

units with missing geographic

information from the denominator as

well as the numerator for purposes of

calculating performance toward the

Underserved Areas Goal. In those

limited instances where census tract (for

metropolitan areas) or county (for

nonmetropolitan areas) cannot be

determined using automated methods,

manual methods can be used.

13. Seasoned Mortgage Loan Purchases

‘‘Recycling’’ Requirement

a. Overview. Under section

1333(b)(1)(B) of FHEFSSA, 42 special

rules apply for counting purchases of

portfolios of seasoned mortgages under

the Special Affordable Housing Goal.

Specifically, the statute requires that

purchases of seasoned mortgage

portfolios receive full credit toward the

achievement of the Special Affordable

Housing Goal if ‘‘(i) the seller is engaged

in a specific program to use the

proceeds of such sales to originate

additional loans that meet such goal;

and (ii) such purchases or refinancings

support additional lending for housing

that otherwise qualifies under such goal

to be considered for purposes of such

goal.’’ 43 HUD refers to this provision as

the ‘‘recycling requirement.’’

The proposed rule suggested changes

to § 81.14(e)(4) of the current

regulations. The proposed language was

intended to provide guidance to the

GSEs with regard to the recycling

requirements described above and to

provide new, simpler rules when it is

evident based on the characteristics of a

mortgage seller that the recycling

requirements would likely be met.

The rule proposed that certain

categories of lenders could be presumed

to conduct a lending program meeting

the recycling requirements of the statute

and regulations. These categories

include federally regulated financial

institutions with satisfactory ratings on

recent Community Reinvestment Act

examinations and specific categories of

lenders with affordable housing

missions.

b. Guidance Provided on Recycling

Requirements. Commenters were

generally supportive of the overall

guidance proposed by the Department

with regard to determining when

recycling requirements were met in

order to count purchases of seasoned

mortgage loans toward the Special

Affordable Housing Goal, assuming they

otherwise qualified for the goal. These

provisions are included in the final rule

with three specific changes based on the

comments received. The changes made

in the proposed language relate to the

satisfactory CRA requirement for

Federally insured financial institutions,

identification of other institutions and/

or organizations presumed to meet the

recycling requirements, and the

treatment of third party originations

under the recycling provision. Changes

made in the final rule on these three

aspects are discussed in more detail

below.

c. CRA Requirement.

(1) Summary of Comments. Overall

commenters supported the proposed

changes identifying specific criteria and

standards for the recycling

requirements. However, many

commenters disagreed with HUD’s

requirement that a financial institution

subject to CRA examinations must have

received ‘‘at least a satisfactory

performance evaluation rating for at

least the two most recent examinations

under the Community Reinvestment

Act’’ to be presumed to meet the

recycling requirements.

Fannie Mae, Freddie Mac and several

other commenters suggested that a

satisfactory performance evaluation

rating on the most recent examination is

sufficient, as opposed to the two most

recent examinations, since the period

between examinations can be as long as

60 months. A number of commenters

noted that this could be a particularly

difficult requirement for small

institutions, who are examined much

less frequently.

Other commenters suggested that two

consecutive outstandings is a more

suitable standard, as 78 percent of banks

received satisfactory ratings in their

1999 CRA exams and about 75 percent

received these ratings in previous years.

Still other commenters were

supportive of HUD’s proposal of at least

a satisfactory performance evaluation

rating for at least the two most recent

examinations under the Community

Reinvestment Act because it would

reduce the compliance burden of both

the GSEs and depository institutions,

allowing them to spend more time on

the business of financing housing loans.

(2) HUD’s Determination. HUD has

reviewed these comments and noted

that the proposed rule, in establishing

the CRA examinations and ratings of

financial depository institutions as a

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65075

basis for determining that a financial

institution met the recycling

requirements for seasoned loan

purchases under the Special Affordable

Housing Goal, did not make a

distinction between small and large

depository institutions as intended and

reflected in the CRA regulation 44 and

the Gramm-Leach-Bliley Act of 1999. 45

The 1995 CRA regulation distinguishes,

for examination purposes, four different

types of financial institutions based on

their size, structures, and operations:

Small banks, large banks, wholesale

banks, and limited purpose banks.

Accordingly, the 1995 regulation

provides different performance

procedures, standards, ratings, and

cycles for small banks, large banks,

wholesale banks, and limited purpose

banks. All of the procedures reflect the

intent of the regulation to establish

performance-based CRA examinations

that are complete and accurate but, to

the maximum extent possible, mitigate

the compliance burden for institutions.

Under section 712 of the GrammLeach-Bliley Act, small banks with

aggregate assets of not more than $250

million will be subject to routine

examination:

• Not more than once every 60

months for an institution that has

achieved a rating of ‘‘outstanding record

of meeting community credit needs’’ at

its most recent examination;

• Not more than once every 48

months for an institution that has

received a rating of ‘‘satisfactory record

of meeting community credit needs’’ at

its most recent examination.

• As deemed necessary by the

appropriate federal financial

supervisory agency for an institution

that has received a rating of ‘‘less than

satisfactory record of meeting

community credit needs’’ at its most

recent examination.

In view of the comments received and

based on its analysis of the 1995 CRA

regulations and the Gramm-Leach-Bliley

Act of 1999, this rule includes the

recycling requirement that a financial

institution have ‘‘at least a satisfactory

performance evaluation rating for at

least the two most recent examinations

under the Community Reinvestment

Act’’ for large banks and wholesale

banks that are subject to CRA

examinations. Limited purpose banks

are not making home mortgage loans

and therefore are not relevant for this

analysis. This final rule adds a

provision for small institutions with

assets of no more than $250 million that

such institutions must have received ‘‘a

satisfactory performance evaluation

rating for the most recent examination

under the Community Reinvestment Act

to be presumed to meet the

requirements in paragraphs (e)(4)(i)

through (e)(4)(iv) of this section for

seasoned loans.’’ This safe harbor

provision will also apply to the affiliates

of depository institutions, provided that

these affiliates are subject to the CRA

examinations.

With regard to the suggestion that the

standard for CRA examinations be two

consecutive outstanding ratings, the

Department believes that such a

standard would be counterproductive.

The purpose of the standard is to

identify those financial institutions that

are in the business of serving affordable

housing markets. Using a satisfactory

CRA examination rating achieves that

purpose and is retained in the final rule.

d. Classes or Categories of

Organizations Presumed to Meet

Recycling Requirement.

(1) Summary of Comments. With

regard to other additional classes of

institutions or organizations that should

be recognized as meeting the recycling

requirements, most commenters,

including the GSEs, agreed with HUD’s

proposal that State Housing Finance

Agencies or Special Affordable Housing

Loan Consortia should be presumed to

meet the recycling requirements.

However, both GSEs urged that HUD

provide them with ‘‘as much flexibility

as possible on this provision.’’ Fannie

Mae opposed HUD approval of

additional lending institutions or

organizations and, instead

recommended that HUD provide a list of

HUD-approved institutions, and criteria

for the GSEs to qualify lenders or certain

kinds of lending or transactions. Freddie

Mac suggested HUD ‘‘broaden the

regulatory presumption of recycling to

all sellers of mortgages so long as they

originate or purchase qualifying special

affordable housing goal mortgages in the

ordinary course of business.’’

A great number of commenters

suggested that HUD’s list also include

other ‘‘non-traditional lenders’’ who

serve targeted communities and who

could potentially benefit from the

liquidity that the change could provide.

These commenters mentioned the

following institutions: Community

development financial institutions,

minority owned lenders, women owned

lenders, non-profit lenders, and public

revolving loan funds.

Other commenters urged HUD to

include all credit unions in HUD’s list

because credit unions originate low-cost

residential loans that make housing

affordable to millions of credit union

members even though they are exempt

from CRA requirements. At a minimum,

it was suggested that ‘‘seasoned loans

purchased from community

development credit unions, which are

chartered to serve low-income

communities, should qualify for goal

credit.

(2) HUD’s Determination. HUD has

reviewed the above comments and

agreed to expand the safe harbor

provision to include the following

institutions or classes of institutions

that the GSEs may presume meet the

recycling requirements as long as these

institutions have an affordable housing

mission: State housing finance agencies;

affordable housing loan consortia;

Federally insured credit unions that are

either (a) community development

credit unions, or (b) credit unions that

are members of the Federal Home Loan

Bank System and meet the first-time

homebuyer standard of the Community

Support Program; community

development financial institutions;

public loan funds; and non-profit

lenders. The final rule retains the

requirement that any additional classes

of institutions or organizations must be

approved by the Department. The final

rule establishes a reasonable set of

lender characteristics that are presumed

to meet the recycling provisions that

cover a large portion of the affordable

lending market. For those lenders falling

outside of these parameters, the final

rule provides the GSEs with broad

guidance as to what a recycling program

should include if a lender does not fall

into an accepted category. The GSEs

have broad latitude to evaluate the

circumstances of a particular lender in

counting seasoned loan purchases

toward the Special Affordable Housing

Goal. A GSE does not have to get prior

approval to do business with a lender

that does not fall into the presumptive

category as long as the GSE verifies and

monitors that the lender is conducting

an affordable lending program

consistent with the guidelines provided.

Prior approval is only required if a seller

of loans falls outside the boundaries

established in the final rule and the GSE

wants them designated among the

category of institutions already

identified and presumed to meet the

requirements. The Department does not

anticipate that such action will limit the

GSEs ability to conduct business in any

material way, but rather will relieve the

burden of having to verify and monitor

the lending programs of those entities

presumed to meet the recycling

requirements.

e. Third Party Transactions.

(1) Overview. In the proposed rule,

HUD solicited comments on the

treatment under the recycling

provisions of structured transactions

where the mortgage loans included in

the transaction were originated by a

65076 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

depository institution or mortgage

banker engaged in mortgage lending on

special affordable housing but acquired,

packaged and re-sold by a third-party,

e.g., an investment banking firm that is

not in the business of affordable housing

lending.

(2) Summary of Comments. Fannie

Mae believes that ‘‘the appropriate

approach is to extend the streamlined

application to third party deliveries.’’

Fannie Mae argues that when it

purchases loans delivered by third

parties, it ‘‘is supporting the

marketplace dynamic that provides

liquidity,’’ and therefore ‘‘the

intermediate step in no way degrades

the liquidity support provided to the

institutions or the mortgage products.’’

Freddie Mac did not address this

issue directly but pointed out that

Congressional intent underlying the

seasoned, recycling requirement was ‘‘to

ensure that the proceeds will be used in

a manner that increases the availability

of mortgage credit for the benefit of lowincome families.’’ According to Freddie

Mac, Congress’ interest was to ensure

that ‘‘mortgage proceeds were funneled

back into the mortgage market, not that

specific types of lending programs

should be used to recycle these

proceeds.’’ Thus, Freddie Mac

recommends that HUD include all

mortgage sellers that regularly engage in

originating or purchasing mortgages that

meet the special affordable housing goal

criteria. The alternative, according to

Freddie Mac, would be ‘‘adoption of the

BIF/SAIF regulatory presumption while

maintaining the current regulatory

scheme.’’

(3) HUD’s Determination. HUD

recognizes that Congress intended that

the housing goals generally and the

recycling provisions specifically were to

expand the availability of affordable

housing with particular emphasis on the

purchase of loans that are originated in

conjunction with affordable housing

programs, the creation of innovative

product lines, or the building of

institutional capacity and infrastructure

among others in the industry.46 If the

mortgages were, in fact, originated by an

entity that meets the new recycling

presumptions, i.e., is regularly in the

business of mortgage lending; is a BIFinsured or SAIF-insured depository

institution; and is subject to, and has

received at least a satisfactory

performance evaluation rating under the

Community Reinvestment Act, or is

among the enumerated class or classes

of organizations whose primary

business is financing affordable housing

mortgages; but the mortgages were

delivered to the GSEs by a third party

seller after a relatively short holding

period, the purchase of such mortgages

would meet the intent of Congress and

fulfill the spirit of the recycling

requirement. Therefore, in this final

rule, HUD will allow mortgages

delivered by such third party sellers to

meet the recycling presumptions in

§ 81.14(e)(4)(vi) and (vii) of this final

rule if the mortgages were originated by

an entity that comes within the

recycling presumptions; and the seller

acted for, or in conjunction with, such

entity in the transaction with the GSE.

A seller that holds loans itself for more

than six months is not presumed to be

acting for, or in conjunction with, such

an entity. Accordingly, the final rule

excepts such sellers from the benefit of

the presumption. Notwithstanding, a

seller that otherwise meets the tests of

the recycling provisions may qualify

under the rules on its own behalf.

Moreover, in any case, if the mortgages

were originated by an entity that does

not meet the recycling presumptions,

the GSEs can still get goals credit under

the Special Affordable Housing Goal if

they verify and monitor that the

originator, acting in conjunction with a

seller, meets the recycling requirements

in § 81.14(e)(4)(i) through (iv).

14. Counting Federally Insured

Mortgages Including HECMs, Mortgages

on Housing in Tribal Areas and

Mortgages Guaranteed by the Rural

Housing Service Under the Housing

Goals

a. Overview. Under § 81.16(b)(3) of

HUD’s regulations prior to this final

rule, non-conventional mortgages—

mortgages that are guaranteed, insured

or otherwise obligations of the United

States—did not generally count under

the three housing goals. However,

mortgage loans under the Home Equity

Conversion Mortgage (HECM) Program

and the RHS’s Guaranteed Rural

Housing Loan Program have received

credit under the Special Affordable

Housing Goal. FHEFSSA specifically

provides that mortgages that cannot be

readily securitized through the

Government National Mortgage

Association (GNMA) or another Federal

agency and for which a GSE’s

participation substantially enhances the

affordability should receive full credit

under the Special Affordable Housing

Goal. On this basis, those two categories

of mortgages would count under that

goal if they finance housing for very

low-income families or low-income

families in low-income areas and meet

recycling requirements if seasoned.

In the proposed rule, HUD proposed

to amend § 81.16(b)(3) to count and give

full credit for the following types of

mortgage loans toward all three housing

goals: mortgage loans under the HECM

Program, mortgages guaranteed by RHS,

and mortgage loans made under FHA’s

Section 248 program and HUD’s Section

184 program for properties in tribal

lands. (This section has also been

amended as described herein at

paragraph 14, Expiring Assistance

Contracts.) HUD also proposed that

other types of mortgages involving

Federal guarantees, insurance or other

Federal obligation may be eligible for

credit under the goals if a GSE submits

documentation to HUD that supports

eligibility for HUD’s approval and the

Department determines, in writing, that

the financing needs addressed by such

programs are not well served and that

the mortgage purchases under such

program should count under the

housing goals.

b. Summary of Comments.

Commenters other than the GSEs

generally supported the proposed

change allowing goals credit for the

GSEs’ purchases of HECMs and rural

and tribal mortgages. They stressed the

need for liquidity for such programs and

for encouraging the GSEs to better serve

these markets. They pointed out that

these markets are still undeveloped and

underserved.

Fannie Mae supported the proposed

changes with regard to government

loans, but Freddie Mac made no

comment.

A few commenters recommended that

HUD count all reverse mortgages, not

just HECMs, toward the three goals.

Other commenters suggested that loans

guaranteed by the RHS’ Sections 538

and 515 programs should also receive

goals credit as they provide high quality

affordable multifamily housing for

lower-income families in rural areas.

Some commenters suggested that

HUD also should include all mortgages

that are supported in some way by state

and local governments. Others

recommended that predevelopment

grants or loans, interim development or

bridge financing, and permanent

financing be considered.

Fannie Mae objected to the proposal

for HUD’s review and approval of goals

credit for other types of government

loan programs and requested that HUD

provide a set of criteria for the GSEs to

apply and make their own

determinations. According to Fannie

Mae, the GSEs should receive goal

credit for the purchase of specialized

government program loans if two

conditions are met: (1) Loans are made

under any federally-insured programs

(except for FHA loans insured under

section 203(b) or VA loans insured

under the VA single family insurance

program); and (2) the GSEs add valuable

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65077

liquidity, lower costs, additional credit

enhancements, or some other value to

the financing of these loans.

c. HUD’s Determination. In view of

this general support for the proposed

changes and based upon its review of

data on the GSEs’ mortgage purchases of

HECMs, RHS mortgages and loans made

to Native Americans under FHA’s

Section 248 program and HUD’s Section

184 program, this final rule amends

§ 81.16(b)(3) to except mortgages under

the HECM program, single-family

mortgages guaranteed by RHS under the

Section 502 program, and loans made

under FHA’s Section 248 program and

HUD’s Section 184 program on

properties in tribal lands from the

general exclusion from goals credit for

non-conventional loans. This final rule

allows goal credit for those specific

Federally insured or guaranteed

mortgage loans.

As proposed, the final rule provides

that HUD will review other types of

mortgages involving Federal guarantees,

insurance or other Federal obligation for

goals credit. HUD’s review of the GSEs’

non-conventional mortgage purchases is

needed, among other reasons, to ensure

compliance with FHEFSSA, which

permits mortgages that cannot be readily

securitized through GNMA or another

Federal agency and for which a GSE’s

participation substantially enhances

liquidity, to receive full credit under the

Special Affordable Housing Goal. In

view of the ample liquidity among the

great majority of FHA loans, HUD must

exercise ongoing responsibility to

evaluate whether the GSEs’ mortgage

purchases under non-conventional

mortgage programs (other than HECM

program, specified RHS mortgage

programs, and FHA’s Section 248

program and HUD’s Section 184

program on properties in tribal lands)

should count under the Special

Affordable Housing Goal. Beyond its

responsibility under the Special

Affordable Housing Goal, HUD must

continually determine whether goals

credit should be provided for particular

GSE purchases. HUD has evaluated and

considered the specific programs

enumerated above and, at this time, is

able to determine that goals credit

should be given for the GSEs purchases

of mortgages under these programs

because these purchases will address

credit needs that are not well served.

For other programs, HUD must make the

same careful and complete evaluation

before it can decide in accordance with

FHEFSSA whether goals credit is

warranted.

This final rule retains a provision that

to the extent categories of nonconventional mortgage purchases that

now count toward the goals, they no

longer will be excluded from the

denominator of the GSEs’ mortgage

purchases as are other non-conventional

loans that do not receive credit under

the goals.

15. Expiring Section 8 Assistance

Contracts

a. Overview. Over 900,000 housing

units in approximately 10,000

multifamily projects have been financed

with FHA-insured mortgages and

supported by project based Section 8

housing assistance contracts.47 Many of

these contracts will expire over the next

five years. A significant portion of these

contracts currently provide for rents for

assisted units that substantially exceed

the rents for comparable unassisted

units in the local market. Simply

reducing rents to a level which may not

support the project’s debt service would

risk likely defaults on the FHA-insured

mortgage payments resulting in

substantial claims to FHA’s insurance

funds.

In October 1997, Congress enacted the

Multifamily Assisted Housing Reform

and Affordability Act of 1997 (MAHRA;

42 U.S.C. 1737f) specifically to address

the problem of expiring contract for

project-based Section 8 rent subsidies

for certain multifamily rental projects,

most of which are insured by FHA.

MAHRA authorized a new Mark-toMarket Program designed to preserve

low-income rental housing affordability

while reducing the long-term costs of

Federal rental assistance for these

projects.48 MAHRA establishes

processes and standards for debt

restructuring under the program where

it is determined that such restructuring

is appropriate and necessary.

MAHRA also amended section

1335(a) of FHEFSSA (12. U.S.C.

4565(a)(5)) to require Fannie Mae and

Freddie Mac to ‘‘assist in maintaining

the affordability of assisted units in

eligible multifamily housing projects

with expiring contracts.’’ MAHRA

amendments further stipulate that such

actions shall constitute part of the

contribution of each GSE toward

meeting its housing goals as determined

by the Secretary. In the proposed rule,

HUD proposed to provide partial to full

credit under the housing goals as

determined by HUD for actions that

maintain the affordability of assisted

units in eligible multifamily housing

projects with expiring contracts include

the restructuring or refinancing of

mortgages, and credit enhancements or

risk-sharing arrangements to modified

or refinanced mortgages. HUD solicited

comments on how and to what extent

the GSEs should receive credit for such

actions.

b. Summary of Comments.

Commenters who addressed this issue

were generally supportive of HUD’s

proposal to award credit for these

activities. Although Freddie Mac did

not express an opinion in its comments,

Fannie Mae expressed some support for

HUD’s approach. However, Fannie Mae

requested that HUD consider some

revisions to its proposal. Specifically,

Fannie Mae suggested that HUD

broaden its definition of actions which

would receive credit to include the

purchase of FHA-insured mortgages,

mortgage revenue bonds and equity

investments, including Low Income

Housing Tax Credits. Fannie Mae

suggested that HUD strike the language

‘‘* * * as determined by HUD’’ from

the final rule to avoid a regulatory

process that requires prior HUD

approval for determining goals credit.

Fannie Mae also suggested that actions

qualifying for credit under this section

should always receive full, rather than

partial, credit.

c. HUD’s Determination. HUD has

determined that it is both appropriate

and consistent with the statutory

mandates of FHEFSSA and MAHRA

that actions taken by the GSEs to assist

in maintaining the affordability of

assisted multifamily units with expiring

contracts receive goals credit as part of

the GSEs’ contributions in meeting their

housing goals as determined by the

Secretary. HUD’s current counting rules

permit the GSEs to receive full credit for

purchases of mortgages or interests in

mortgages as set forth in 24 CFR 81.16.

Those rules address goals eligibility

standards for credit enhancements, the

purchase of refinanced mortgages,

mortgage revenue bonds and risksharing. Because HUD intends that goals

credit for actions in conjunction with

expiring assistance contracts should

conform to actions that are already

awarded credit in other transactions,

HUD has determined that it is not

necessary to restate these rules with

respect to eligibility of actions for goals

credit that assist the Mark-to-Market

program. Accordingly, this final rule

revises the language to eliminate

redundancies by referencing current

regulations.

HUD agrees with Fannie Mae that the

purchase of FHA-insured mortgages

resulting from restructured financings of

projects with expiring assistance

contracts is an appropriate activity to

include in actions eligible for goals

credit. Accordingly, HUD has amended

§ 81.14(e)(3) to specify that purchases of

mortgages on projects with expiring

assistance contracts that meet the

65078 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

requirements of 12 U.S.C.

4563(b)(1)(A)(i) and (ii) will receive full

credit toward achievement of the special

affordable housing goal.

This final rule also clarifies the

counting treatment for actions a GSE

takes to modify or restructure the terms

of mortgages with expiring assistance

contracts which it may hold in portfolio,

provided such restructuring results in

lower debt service costs to the project’s

owner. HUD has added § 81.16(c)(9)(ii)

to provide full credit under any housing

goal for these activities.

HUD has reviewed comments from

Fannie Mae, Freddie Mac, and others

regarding awarding goals credit for

equity investments, particularly Low

Income Housing Tax Credits (LIHTCs).

These comments, while not necessarily

offered in response to this section of the

proposed rule, indicate a continuing

interest in counting these transactions

under the goals. The Department agrees

that the GSEs’ participation in LIHTCs

plays a vital role in the development of

affordable housing. By excluding these

investments from goals credit HUD does

not intend to convey any lack of

appreciation for their importance.

However, FHEFSSA imposes certain

standards on what can and cannot be

counted towards the housing goals.49

Specifically, only mortgage purchases

as defined in FHEFSSA and the

implementing regulation meet the

standard for eligibility. As described in

the preamble to HUD’s 1995 regulation,

the purchase of LIHTCs is not a

mortgage purchase or the equivalent of

a mortgage purchase and, therefore, is

not eligible for goals credit under HUD’s

general counting requirements as set

forth in the implementing regulation.

While MAHRA does provide that

actions to maintain the affordability of

assisted units under MAHRA will count

under the goals, MAHRA does not

specifically impose standards for

counting actions with respect to

expiring assistance contracts under the

goals but leaves this matter to HUD’s

determination. In determining whether

actions count under the goals, HUD will

generally be guided by definitions and

counting conventions set forth in the

implementing regulation. In instances

where a GSE engages in actions not

specified in the implementing

regulation but which it believes warrant

goals credit, or where a GSE provides

more than one form of assistance for a

single project, the GSE must submit the

transaction to HUD for a determination

on the appropriate level of credit to be

awarded if the goals credit is sought. In

making a determination, HUD will

award counting treatment for those

actions that are required under MAHRA

and that may count under FHEFSSA.

A few commenters expressed concern

about the counting treatment for

mortgage purchases on projects with

expiring contracts that ‘‘opt out’’ of the

assisted program. One commenter

suggested that HUD impose additional

affordability requirements as a

condition of awarding goals credit for

such transactions. However, HUD finds

that the issue of affordability relative to

goals credit is already well established.

HUD’s current regulations address the

income requirements for determining

how mortgage purchases are counted

under any of the housing goals. There

are other statutory provisions that also

address long-term affordability. Projects

that rely upon or intend to rely upon

equity investments from the LIHTC

program must meet tax code

requirements for affordability for a 15-

year period.50 Mortgages secured by

projects subject to restructuring plans

must provide for a Use Agreement that

includes affordability restrictions and

remains in effect for at least 30 years.51

HUD believes that the current counting

rules and statutory definitions under

FHEFSSA and MAHRA are sufficient to

ensure that goals credit is awarded

appropriately for mortgage purchases

that meet prescribed housing

affordability standards.

16. Provision for HUD to Review New

Activities To Determine Appropriate

Counting Under the Housing Goals

a. Overview. In order to address

confusion about whether a given

transaction will receive credit under the

housing goals, HUD proposed adding a

provision at § 81.16(d) to further clarify

its position regarding HUD’s authority

review new activities, or classes of

transactions, to determine appropriate

counting treatment under the housing

goals.

While the GSEs participate in

transactions and activities that support

community and housing development

in general, FHEFSSA is clear that only

‘‘mortgage purchases’’ count toward

performance on the housing goals.

Section 81.16(a) of the regulations

stipulates that the Secretary shall

consider whether a transaction or

activity of the GSE is substantially

equivalent to a mortgage purchase and

either creates a new market or adds

liquidity to an existing market. As

provided in § 81.16(b), HUD has

determined that certain transactions do

not meet those criteria and, therefore,

will not count toward a GSE’s housing

goals performance. Examples include

equity investments in housing

development projects; commitments,

options or rights of first refusal to

acquire mortgages; mortgage purchases

financing secondary residences;

purchases of non-conventional

mortgages and government housing

bonds except under certain

circumstances. As provided in

§ 81.16(c), HUD has determined that

certain other transactions, including

credit enhancements in certain

situations, REMIC purchases and

guarantees in certain circumstances, and

others, do count as mortgage purchases.

HUD believes that, in order to meet

higher goal levels, the GSEs will need to

continue to develop new products and

approaches while also remaining

mindful of FHEFSSA’s requirements.

HUD invited comment on this proposal.

b. Summary of Comments.

Commenters who addressed this issue

generally offered support for the

proposal. Some commenters, however,

confused HUD’s proposal to review

classes of transactions for goals counting

treatment with the Department’s New

Programs Approval authority as set forth

in § 81.51 which relates to HUD’s

review of a new GSE activity to

determine whether it is a new program

and whether it is authorized under the

GSE’s charter and in the public interest.

The provision in § 81.16(d) of the

proposed rule concerns instead whether

a class of transactions counts as

mortgage purchases that will receive

credit under the housing goals. In

HUD’s proposed rule, no regulatory

changes to the New Programs Approval

authority were proposed.

Of the comments received, Fannie

Mae addressed the issue of counting

classes of transactions under the goals

in some detail. Generally, Fannie Mae

expressed an overall objection to any

regulatory provisions that would require

prior HUD approval for goals counting

purposes, believing instead that HUD

should codify clear but flexible rules

that remove all uncertainty regarding

goals counting treatment. Fannie Mae

further stated that prior HUD review

could ‘‘put in place a disincentive to the

development of new and innovative

products.’’ Fannie Mae did not suggest

any specific examples of classes of

transactions or characteristics that HUD

should exclude from a prior review

process nor did it specify how

regulatory guidance could be

constructed to address future events.

However, Fannie Mae did suggest that

HUD impose a 30-day time frame for

review after which the transaction(s)

would be approved for goals credit

unless HUD had notified the GSE

otherwise during the review period.

Another commenter expressed

concern that HUD intends to count

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65079

transactions that are not formally

mortgages if HUD believes they serve a

new market or add liquidity to an

existing market, thereby potentially

allowing the GSEs to expand their

activities into areas now served by

others.

c. HUD’s Determination. In assessing

these concerns, HUD believes that

Fannie Mae’s suggestions for additional

codified regulatory guidance in lieu of

any HUD review are impractical and

unnecessary. The regulation already

includes numerous provisions that

address eligible transactions and their

counting treatment. In fact, virtually all

transactions in current use which could

be substantially equivalent to a

mortgage purchase have been addressed

elsewhere in the counting rules.

Nevertheless, given the pace of

innovation in the mortgage and

investment markets and the likelihood

that the GSEs will devise new lending

and marketing approaches in the future,

providing a prior-review requirement to

address goals counting treatment for

these future transactions is both an

efficient and practical solution while a

more prescriptive approach may not be

sufficiently foresighted or encompassing

thereby disadvantaging both the public’s

and the GSEs’ interests.

HUD regards concerns that by adding

§ 81.16(d) to the regulation, HUD is

opening the door to counting nonmortgage transactions towards the goals

as unwarranted. The regulatory

language is explicit in stating that, in

order to count towards goals

performance, transactions must be

‘‘mortgage purchases’’ in accordance

with FHEFSSA. The regulatory language

does not use ‘‘liquidity’’ as a criteria for

review and approval to count

transactions for goals credit, and

‘‘liquidity’’ is not a defining element of

‘‘mortgage purchase’’ under this

regulation. Further, the regulation

explicitly states which classes of

transactions are currently ineligible, and

it provides guidance on criteria

necessary for qualifying other classes of

transactions. Thus the plain meaning of

the regulations including the counting

rule conventions set forth in the

regulation would preclude a broader

interpretation of § 81.16(d).

HUD has further determined that

establishment of a time limit for HUD

review of GSE requests to count

transactions is unnecessary. While HUD

is aware of the need for responsive

action to a GSE’s request for guidance

and will respond to such requests

reasonably, rigid time frames may not

provide sufficient review of complex

transactions to best serve the public

interest. Accordingly, HUD has

implemented § 81.16(d) as originally

proposed.

17. Counting Rules—Clarifying

Technical Provisions

a. Especially Low Income. Section

81.14(d)(1)(i) of the regulations provides

that dwelling units in a multifamily

property will count toward the Special

Affordable Housing Goal if 20 percent of

the units are affordable to families

whose incomes do not exceed 50

percent of the area median income.

HUD’s regulations at §§ 81.17 through

81.19 stipulate that the income

requirements are to be adjusted based

on family size and provide adjustment

tables for qualifying family income

where incomes do not exceed from 60

percent to 100 percent of area median

income. However, there has been no

similar adjustment table provided for

families whose incomes do not exceed

50 percent of area median income. HUD

proposed to amend those sections to

provide additional adjustment tables for

such families. To be consistent, HUD

also proposed to designate such families

as ‘‘especially low-income families’’ for

purposes of the Department’s GSE

regulations and to reflect this change in

§ 81.14. HUD received no comments on

these proposals. Therefore, this final

rule implements the changes as

proposed in § 81.14 and §§ 81.17

through 81.19.

b. Defining the ‘‘Denominator’’. HUD

proposed amending the calculation of

‘‘Denominator’’ to clarify that the

denominator does not include GSE

transactions or activities that are not

mortgages or transactions that are

specifically excluded. HUD received no

comments on this proposed change, and

this final rule implements the change as

proposed in 81.14(a)(2).

c. Balloon Note Conversions. HUD

proposed to amend the definition of

‘‘Refinancing’’ at § 81.2 to exclude a

conversion of a balloon mortgage note

on a single family property to a fully

amortizing mortgage note provided the

GSE already owns or has an interest in

the balloon note at the time of the

conversion. HUD also proposed

amending the counting rules at

§ 81.16(b)(9) to exclude these

transactions from the denominator.

Fannie Mae suggested deleting other

proposed language which sought to

clarify that single family loans with

conversion features which had already

been exercised prior to purchase by the

GSE would count as new purchases.

Fannie Mae believed this additional

language created confusion and was

unnecessary stating that the revised

definition of ‘‘Refinancing’’ at § 81.2

already provided sufficient clarification.

HUD agrees with this comment.

Accordingly, this final rule implements

the proposed changes to § 81.2 and to

§ 81.16(b)(9), with slight revisions to

§ 81.16(b)(9) to avoid any potential

confusion.

d. Title I. HUD proposed awarding the

GSEs half credit for purchases of

mortgage loans insured under HUD’s

Title I property improvement and

manufactured homes program. Fannie

Mae and one other commenter asked

that the Department award full credit for

Title I mortgages saying that these

mortgages support affordable housing

needs. Fannie Mae noted that purchases

of these loans were difficult transactions

to undertake and for this reason should

receive more than half credit. One other

commenter recommended that no goals

credit be given for Title I loans,

asserting that such loans do not directly

support affordable housing needs.

Given the limited number of

comments and their conflicting nature,

the Department decided to retain the

provision in the final rule that

purchases of Title I loans will receive

half credit under the housing goals. As

explained in more detail in the

appendices to this final rule, HUD has

determined that such loans finance an

important source of affordable housing

and an enhanced GSEs role could

improve the affordability of such loans

for lower-income families.

18. Credit Enhancements

a. Overview. The GSEs utilize a large

variety of credit enhancements, for both

single family and multifamily mortgage

purchases, to reduce the credit risk to

which they might otherwise be exposed.

For example, the GSEs generally require

the use of mortgage insurance on single

family loans with loan-to-value ratios

exceeding 80 percent. While more

common in the multifamily mortgage

market, seller-provided credit

enhancements may also be required for

GSE purchases of single family mortgage

loans. Other types of credit

enhancements include arrangements

such as credit enhancements in

structured transactions where a GSE

may acquire a pool of loans, mortgagebacked securities (MBS), or real estate

mortgage investment conduits

(REMICs), and then create separate

senior and subordinated securities,

structured so that the subordinated

securities absorb credit losses; spread

accounts, in which a GSE may create a

special class of unguaranteed securities

where pass-through payments will cease

in the event of default of the underlying

mortgage collateral; acquisition of senior

tranches of REMIC securities by the

GSEs which are enhanced by the

65080 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

presence of subordinate tranches and

where the collateral is already credit

enhanced prior to purchase; and agency

pool insurance coverage provided by a

mortgage seller.

Since enactment of FHEFSSA in 1992,

HUD’s regulations have awarded full

goals credit for the purchase of most

mortgages or interests in mortgages that

otherwise qualify under the definition

for each goal regardless of the level of

credit risk a GSE might bear in the

transaction. However, the increasing

complexity of, and prevalence in, the

use of credit enhancements have raised

questions about whether the GSEs

should receive full credit towards the

goals for transactions where their credit

risk exposure is minimal. In the

proposed rule, HUD sought comments

on various questions regarding the

appropriate goals treatment for

transactions with credit enhancements.

For example, assuming credit risk can

be measured, HUD asked commenters to

consider whether HUD should establish

a sliding scale from 0 to 100 percent for

awarding goals credit depending on the

GSE’s risk exposure in a transaction.

HUD also asked for comments on other

issues including whether a minimum

risk threshold should be established in

order for a transaction to receive any

goals credit as well as comments on

whether HUD should measure

counterparty risk on seller-provided

credit enhancements.

b. Summary of Comments. The

overwhelming majority of commenters,

including Fannie Mae and Freddie Mac,

responded with strong opposition to the

concept of basing goals credit on the

level of credit risk borne by a GSE in the

transaction. Freddie Mac expressed

concern that, in addition to being

inconsistent with the Freddie Mac Act

and FHEFSSA, discounting goals credit

for protections against default cost

would lead to a host of unintended

consequences and practical problems,

including measurement problems. For

example, with regard to multifamily

mortgages especially, Freddie Mac

stated that ‘‘when cross-default or crosscollateralization techniques are used to

price credit enhancements, there is no

ready and straightforward method of

allocating default cost protection to the

risks presented by the individual

mortgages, let alone to the housing units

that are financed by each of those

mortgages.’’

Fannie Mae also strongly opposed any

goals scoring approach based on the

level of credit enhancement. Fannie

Mae stated that credit enhancements are

essential to its safe and sound operation

and, in fact, are explicitly recognized

under OFHEO’s risk-based capital

standard as an important risk

management tool. Fannie Mae further

stated that reducing goals credit based

on the level of credit enhancement ‘‘is

contrary to our charter, misconstrues the

purpose of Fannie Mae, distorts the

efficient functioning of the capital

markets, increases the cost of

homeownership, restricts the

availability of capital, and weakens the

financial soundness of Fannie Mae.’’

Commenters representing state and

local housing finance agencies, forprofit and non-profit advocacy and

consumer groups, trade associations,

and the mortgage lending and

investment industry were nearly

unanimous in voicing objections to any

regulatory approach that considered

levels of credit enhancements in

assigning goals credit. The recurring

objection held that such an approach

would undermine the purpose of the

housing goals regulation by disrupting

the risk-sharing partnerships that are

critical to making affordable housing

lending a reality, thereby resulting in a

negative consequence to

homeownership. For example, some

commenters expressed concern that

such an approach could interfere with

the GSEs’ incentive to develop new

affordable mortgage products using risksharing arrangements while others felt

that reducing goals credit based on the

level of risk would have the effect of

reducing the amount and liquidity of

funds available for affordable housing

lending rather than force the GSEs to

take on more risk than they felt they

could effectively manage. These

commenters remarked that since risk

sharing arrangements allow more

industry partners to bring more capital

to the mortgage market, they were

concerned that the affordable housing

market would be adversely impacted if

HUD adopted a regulatory counting

scheme that penalized the GSEs for

sharing risk.

Two commenters, however, suggested

there may be instances in which goals

credit should be limited and suggested

further review and study of the issue.

One commenter stated that the financial

benefits of GSE status can and should

function as an offset for the assumption

of some amount of credit risk but also

cautioned that HUD must carefully

consider the effects of any regulatory

change in this area, especially how

OFHEO and the financial markets

would view encouraging the GSEs to

assume certain credit risks and what

effect this approach could have on

mortgage rates. Another commenter

suggested that HUD establish an

industry working group to examine

these issues in greater detail. This

commenter also supported limiting

goals credit on the GSEs’ purchase of

seasoned mortgages when the selling

institution provides a credit

enhancement beyond customary

representations and warranties, and also

supported some limitation on goals

credit for loans securitized in

commercial mortgage-backed securities

(CMBS) and REMIC structures to the

risk level of the tranches purchased by

the GSEs.

One commenter suggested that, in

assigning goals credit based on the

GSEs’ actual involvement in facilitating

the flow of private capital into low/mod

communities, there may be a useful

prototype in the CRA provisions for

allotting goals credit based upon the

type of mortgage purchase transaction,

i.e., the purchase of newly originated

loan versus other mortgage investments.

HUD appreciates this suggestion and

plans to consider it further.

c. HUD’s Determination. HUD has

taken the position that GSE credit

enhancement transactions provide

needed liquidity to the mortgage

markets and play a key role in

affordable housing lending. As

explained in a study HUD has

undertaken with the Urban Institute to

assess recent innovations in the

secondary market for low- and

moderate-income lending, the GSEs’

purchase of interests in CRA loans is

identified as one approach to how the

enterprises facilitate liquidity for loans

that do not conform to standard

guidelines.52 Investment analysts also

report that the GSEs’ credit

enhancement of CRA REMIC securities

results in a more attractive debt

instrument for investors and a higher

return for issuers which benefits lenders

seeking to liquidate their CRA portfolios

and ultimately borrowers.

HUD recognizes there also are other

valid reasons to grant the GSEs full

credit under the housing goals for

mortgage purchase transactions

involving credit enhancements even

where the enterprises bear relatively

minimal credit risk. For example, in the

absence of private mortgage insurance

for multifamily mortgages, seller

provided credit enhancements

apparently are a viable means by which

secondary market purchasers may

delegate certain of their underwriting

responsibilities and share risks. When a

GSE purchases a mortgage subject to a

recourse agreement or similar

arrangement with the lender, the GSE

still retains credit risk with respect to

holders of the GSEs’ mortgage-backed

security or, where the mortgage is held

in portfolio, for its own account. Of

course, even if the GSE is not bearing

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65081

substantial credit risk, the GSE may still

be bearing other types of risk. For

example, the protection afforded to the

GSE under recourse agreements is

dependent on the soundness of the

party to whom the GSE has recourse. In

addition, the GSE assumes interest rate

risk for mortgages that are retained in

portfolio.

In analyzing credit enhancement

issues, thus far, there has emerged no

clear approach to establishing an

appropriate ‘‘risk threshold’’ associated

with mortgages purchased by a GSE,

below which credit toward the goals

should not be granted. Under typical

recourse agreements or similar

arrangements, GSEs rarely divest

themselves of credit risk associated with

mortgage purchases in clear-cut

percentages of risk. Some arrangements

have time or dollar limits. The relative

risk assumed by the GSE on one loan

compared to another relates not only to

the relative risk management

characteristics (including mortgage

insurance and recourse arrangements),

but also to loan-to-value ratios,

multifamily debt coverage ratios,

interest rate risk, and many other

parameters. Moreover, whether there is

subsequent securitization or

resecuritization of a GSE interest also

bears upon the degree of credit risk

retained by the GSE in a transaction.

Any determination about discounting

goals credit based on the level of risk

borne by a GSE in the transaction also

must take into account consistency with

the GSEs’ Charter Acts which require

the GSEs to obtain mortgage insurance

or its equivalent for certain single family

mortgages, and must consider the

financial safety and soundness

requirements under FHEFSSA as well as

its housing goals provisions.

Accordingly, HUD has determined,

based on its analysis of available

information on the GSEs’ credit

enhanced transactions, comments and

other input received on the proposed

rule, as well as its analysis of the law,

the complexity of these issues requires

additional evaluations before changes

are made to these rules. These

evaluations will further assess the

extent to which the GSEs’ use of credit

enhancements add value and liquidity

to the marketplace, especially for

affordable housing lending, as well as

the impact their use has on the GSEs’

mandate to play a leadership role in the

mortgage markets. To assist its

evaluations, HUD is undertaking further

review and analysis on credit

enhancements. Topics being covered in

this review include the GSEs’ use of

credit enhancements provided by sellerservicers, third party vendors, and

buyers of subordinated debt in the

GSEs’ single family and multifamily

mortgage transactions. In addition, HUD

will continue its assessments of credit

enhancement structures including

newly introduced structures to

determine how and to what extent, if

any, HUD’s goal counting rules should

be modified in the future.

19. Public Use Data Base and Public

Information

Section 1323 of FHEFSSA requires

that HUD make available to the public

data relating to the GSEs’ mortgage

purchases. In the legislative history of

FHEFSSA, Congress indicated its intent

that the GSE public use data base is to

supplement HMDA data.53 The purpose

of the GSE data base is to assist the

public, including mortgage lenders,

planners, researchers, and housing

industry groups, as well as HUD and

other government agencies, in studying

the GSEs’ mortgage activities and the

flow of mortgage credit and capital into

the nation’s communities. At the same

time, section 1326 of FHEFSSA protects

from public access and disclosure,

proprietary data and information that

the GSEs submit to the Department and

requires HUD to protect such data or

information by order or regulation.

To comply with FHEFSSA, HUD

established a public use data base to

collect and make available to the public,

loan-level data on the GSEs’ single

family and multifamily mortgage

purchases. In Appendix F to the

December 1, 1995 final rule, the

Department specified the structure of

the GSE public use data base and

identified the data to be withheld from

public use.

The single family data was to be

disclosed in three separate files—a

Census Tract File (with geographic

identifiers down to the census tract

level), a National File A (with mortgagelevel data on owner-occupied 1-unit

properties), and a National File B (with

unit-level data on all single family

properties). The national files do not

have geographic indicators. The

multifamily data was to be disclosed in

two separate files ‘‘a Census Tract File

and a National File. Each file consists of

two parts, one part containing mortgage

loan level data and the other containing

unit level data for all multifamily

properties. For each file, Appendix F

identified data elements that were

considered proprietary and those that

were not proprietary and available to

the public, and specified further that

certain proprietary elements would be

recoded or categorized into ranges to

protect the proprietary information and

to permit the release of non-proprietary

information to the public. This multifile structure was designed to allow the

greatest dissemination of loan-level

data, without disclosing proprietary

data of the GSEs and causing

competitive harm by, for example,

allowing competitors to determine the

GSEs’ marketing and pricing strategies

at the local level.

On October 17, 1996, a Final Order

describing each data element submitted

by the GSEs and the proprietary or

nonproprietary nature of each element

was published in the Federal Register.

The Final Order also recoded, adjusted,

and categorized in ranges certain

proprietary loan-level data elements to

protect proprietary GSE information.

HUD released the recoded data elements

and the data elements that were

identified as non-proprietary

information to the public.

In the fall of 1996, the Department

released the first publicly available GSE

loan level data base, containing nonproprietary information on every

mortgage purchased by the GSEs from

1993 to 1995. Subsequently, HUD has

made the 1996, 1997, 1998, and 1999

databases available to the public. In

addition, HUD issued an order

determining that certain aggregations of

data that may otherwise be proprietary

at the loan level is not proprietary at an

aggregated level. Through that order, it

is possible for HUD to make available to

the public specific tables of

nonproprietary information about the

GSEs’ activities and housing goal

performance.

After consideration of the current

structure of the GSE public use data

base, the Department proposed several

changes to its classifications of the

GSEs’ mortgage data. Those proposed

changes were either technical in nature

or would, by reclassifying certain data

from proprietary to non-proprietary,

make available to the public the same

data from the GSEs that is made

available by primary lenders under the

Home Mortgage Disclosure Act (HMDA).

HUD received comments from both

GSEs as well as trade organizations,

advocacy groups, researchers, and

lenders on this issue. Comments were

almost evenly divided between those

groups approving of increased data

disclosure at the loan-level and those

that opposed the proposals, mostly out

of concern for protecting the privacy of

borrowers’ and lenders’ business

strategies. Both GSEs were strongly

opposed to increased disclosure, citing

competitive issues resulting from the

release of what each GSE considered to

be proprietary, confidential business

information. Fannie Mae and Freddie

Mac expressed general concern that

65082 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

recoding certain loan-level data as nonproprietary at either the census tract or

national file level would reveal

information about lender relationships,

pricing arrangements, and management

of credit and interest rate risks. Fannie

Mae also took issue with HUD’s efforts

to conform data available in the GSE

public use data base to HMDA data for

research purposes, contending that both

databases are fundamentally different

and cannot be readily reconciled.

Lenders expressed a similar concern

about the potential for additional public

data to reveal business strategies,

commenting that the more data HUD

makes available through the public use

data base, the more likely that other

lenders would be able to discern the

competition’s lending strategies.

Some trade organizations viewed the

proposed changes as potentially harmful

to consumers. Their viewpoints were

representative of similar concerns

expressed by lenders and the GSEs. One

organization wrote that exposing more

detailed information about the

consumer to the general public will only

enhance the ability of sellers of credit to

take unfair advantage of the consumer,

particularly the urban and minority

consumer.’’ Another urged that HUD be

‘‘sensitive to emerging technology when

deciding what data elements to make

public on the [public use data base]

files. Consumer financial and credit

information privacy must be a

paramount concern to the Department.’’

A third organization strongly opposed

releasing additional data out of concern

for borrowers’ privacy and ‘‘potential

exposure of association members’

confidential business information.’’

Another commenter, however,

supported increased disclosure of data,

contending that access to more data

should lead to a better understanding of

the affordable housing market and to

reduced costs for those operating in the

market.

Housing and community

organizations generally viewed HUD’s

proposed changes as a series of

improvements that would make the

public use data base more compatible

with HMDA data and, therefore, more

valuable as a research tool. One

commenter also supported bringing the

public use data base into conformity

with HMDA stating that comparisons

between the two databases are

‘‘extremely important’’ in evaluating the

GSEs’’ mandate to lead the primary

market.

HUD recognizes the potential harm

that the release of truly proprietary data

could have on the GSEs as well as their

lending partners and is cognizant of its

responsibilities under FHEFSSA to

preserve and protect such data from

public disclosure. Also, any implication

that additional disclosure of GSE data

might in fact facilitate a further loss of

borrower privacy or encourage

predatory lending practices are issues

that HUD believes warrant especially

close scrutiny.

In recognition of its responsibilities to

proceed with the utmost caution in

releasing data, HUD follows a rigorous

six-factor determination process in

considering whether to accord

proprietary treatment to mortgage data.

For every data element under

consideration for non-proprietary

treatment, HUD evaluates:

(1) The type of data or information

involved and the nature of the adverse

consequences to the GSE, financial or

otherwise, that could result from

disclosure;

(2) The existence and applicability of

any prior determinations by HUD, any

other Federal agency, or a court,

concerning similar data or information;

(3) The measures taken by the GSE to

protect the confidentiality of the

mortgage data and similar data before

and after its submission to the Secretary;

(4) The extent to which the mortgage

data is publicly available including

whether the data or information is

available from other entities, from local

government offices or records, including

deeds, recorded mortgages, and similar

documents, or from publicly available

data bases;

(5) The difficulty that a competitor,

including a seller/servicer, would face

in obtaining or compiling the mortgage

data; and

(6) Such additional facts and legal and

other authorities as the Secretary may

consider appropriate, including the

extent to which particular mortgage

data, when considered together with

other information, could reveal

proprietary information.

Section 1326 of FHEFSSA and § 81.75

of the regulations provide that the

Department may, by regulation or order,

issue a list of information that shall be

accorded proprietary treatment. HUD

utilized the proposed rule to suggest

changes to the proprietary treatment of

certain GSE data. The comments

received in response offered useful

insights into concerns of many different

organizations including the GSEs’

respecting the proposed changes.

Based on the comments received,

HUD is not making a determination on

this matter as part of this rulemaking.

HUD will issue a decision on which

data elements will be accorded

proprietary and non-proprietary

treatment by separate order following

publication of this final rule in

accordance with the Department’s

regulations at §§ 81.72 through 81.74.

20. Other Considerations

a. Data Reporting. Many of the

changes included in the final rule

involve changes in data reporting

requirements. The Department will not

establish those requirements in this

final rule, but rather will establish them

in accordance with FHEFSSA and 24

CFR part 81, considering the proprietary

concerns of the GSEs and other

considerations in the public interest.

Specific areas where additional data

will need to be collected include but are

not limited to indicators for mortgages

located in tribal lands, identification of

units with estimated affordability data

mortgage loans receiving bonus points

and the temporary adjustment factor,

and mortgages relating to Section 8

assistance contracts.

One area in particular that will

require additional data elements is high

cost mortgage loans. In order to monitor

and enforce the restrictions included in

this final rule, new data and reporting

requirements may be required, as

appropriate. The Department notes that

the HUD/Treasury report recommended

that the Federal Reserve amend its

regulations to require the collection of

similar data items under the Home

Mortgage Disclosure Act (HMDA),

including information on loan price

(APR and cost of credit) and borrower

debt-to-income ratio for HOEPA loans. If

such recommendations are

implemented, it may affect the data

reporting required under this rule.

b. Comments Regarding Regional

Issues. Several commenters offered

comments on the need to inform various

communities and regions around the

country of the GSEs’ affordable housing

goal performance in those areas.

Separate from this rulemaking, as

described above, HUD has recently

taken steps to make more MSA level

information, on an aggregated basis,

about the GSEs mortgage purchases

available to the public. HUD encourages

the residents of local communities and

regions of the country to increase their

knowledge of the roles the GSEs’ play in

their areas and, toward that end, HUD

will make available information to build

understanding of the GSEs’ activities.

c. Technical Correction. Section

81.76(d) describes the protection of GSE

information by HUD officers and

employees. That section has cited

HUD’s Standards of Conduct regulations

in 24 CFR part 0. HUD’s Standards of

Conduct regulations in part 0 were,

however, largely superseded by new

financial disclosure regulations codified

in 5 CFR part 2634, new executive

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65083

branch-wide Standards of Conduct

codified in 5 CFR part 2635, and

supplemental HUD-specific Standards

of Conduct codified in 5 CFR part 7501.

Consequently, in 1996, HUD removed

the current text of 24 CFR part 0 and

replaced it with a single section (§ 0.1)

that provides cross-references to those

provisions. (See final rules published in

the Federal Register on April 5, 1996

(61 Fed. Reg. 15,350), and on July 9,

1996 (61 Fed. Reg. 36,246).) In order to

correct § 81.76(d), this final rule will

revise the references to those provisions

accordingly.

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 economically

significant under E.O. 12866. Any

changes made to this final rule

subsequent to its submission to OMB

are identified in the docket file, which

is available for public inspection

between 7:30 a.m. and 5:30 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.

Congressional Review of Major Final

Rules

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

in Chapter 8 of 5 U.S.C. The rule has

been 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 the Office of

Management and Budget (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

In accordance with 24 CFR 50.19(c)(1)

of HUD’s regulations, this final rule

would not direct, provide for assistance

or loan and mortgage insurance for, or

otherwise govern or regulate real

property acquisition, disposition, lease,

rehabilitation, alteration, demolition, or

new construction; nor would it

establish, revise, or provide for

standards for construction or

construction materials, manufactured

housing, or occupancy. Therefore, this

final rule is categorically excluded from

the requirements of the National

Environmental Policy Act.

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 final regulation is applicable only

to the GSEs, which are not small entities

for purposes of the Regulatory

Flexibility Act, and, thus, does not have

a significant economic impact on a

substantial number of small entities.

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 final 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 (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 final rule would not impose any

Federal mandates on any State, local, or

tribal governments, or on the private

sector, within the meaning of the

UMRA.

Endnotes to Preamble

1. See sec. 301 of the Federal National

Mortgage Association Charter Act (Fannie

Mae Charter Act) (12 U.S.C. 1716); sec. 301(b)

of the Federal Home Loan Mortgage

Corporation Act (Freddie Mac Act) (12 U.S.C.

1451 note).

2. Secs. 306(c)(2) of the Freddie Mac Act

and 304(c) of the Fannie Mae Charter Act.

3. Secs. 306(g) of the Freddie Mac Act and

304(d) of the Fannie Mae Charter Act.

4. Secs. 303(e) of the Freddie Mac Act and

309(c)(2) of the Fannie Mae Charter Act.

5. U.S. Department of Treasury,

Government Sponsorship of the Federal

National Mortgage Association and the

Federal Home Loan Mortgage Corporation

(1996), page 3.

6. S. Rep. No. 282, 102d Cong., 2d Sess. 34

(1992).

7. FFIEC Press Release, July 29, 1999.

8. Section 802(ee) of the Housing and

Urban Development Act of 1968 (Pub. L. 90–

448, approved August 1, 1968; 82 Stat. 476,

541).

9. See sec. 731 of the Financial Institutions

Reform, Recovery, and Enforcement Act of

1989 (FIRREA) (Pub. L. 101–73, approved

August 9, 1989), which amended the Freddie

Mac Act.

10. See 24 CFR 81.16(d) and 81.17 (1992

codification).

11. Sec. 1321.

12. See generally secs. 1331–34.

13. Secs. 1332(b), 1333(a)(2), 1334(b).

14. 65 FR 12632–12816

15. S. Rep. No. 282, 102d Cong., 2d Sess.

34 (1992) at 35.

16. Rental Housing Assistance—The

Worsening Crisis: A Report to Congress on

Worst Case Housing Needs, Department of

Housing and Urban Development, Office of

Policy Development and Research, (March

2000).

17. Standard & Poor’s DRI Review of the

U.S. Economy. (June 2000), p. 57.

18. See, e.g., S. Rep. at 34.

19. S. Rep. at 34.

20. 12 U.S.C. 2901 et seq.

21. See section 1335(3)(B).

22. The following discussion is based on

analysis of conventional, conforming

mortgage loans which were originated in

1998, and which may have been acquired by

the GSEs in 1998 or 1999. Appendix A

contains further details regarding GSE

acquisitions of 1997 originations as well.

HUD will analyze GSE purchases in relation

to the 1999 mortgage market once HUD has

the opportunity to analyze 1999 HMDA data

for metropolitan areas.

23. Totals do not add due to rounding.

24. This percentage differs from the GSEs’

19 percent market share for rental units in

single family rental properties financed in

1998 chiefly because the 41 percent figure

reported here includes owner-occupied units

in 2–4 unit properties which also have rental

units.

25. A recent Treasury-sponsored report on

CRA found that banks and thrifts increased

the share of their mortgage originations to

low-income borrowers and communities

from 25 percent in 1993 to 28 percent in

1998. See Robert E. Litan, Nicolas P.

Retsinas, Eric S. Belsky, and Susan White

Haag, The Community Reinvestment Act

After Financial Modernization: A Baseline

Project, U.S. Department of Treasury, April

25, 2000.

26. African American borrowers accounted

for 6.5 percent of all conforming home loans,

including FHA and VA loans, in

metropolitan areas in 1998. Further

information on the GSEs’ purchases of

mortgage loans to minority borrowers may be

found in Appendix A.

27. Hispanic borrowers were 6.7 percent of

all conforming metropolitan area home loans,

including FHA and VA loans, in 1998.

Further information on the GSEs’ purchases

of mortgage loans to minority borrowers may

be found in Appendix A.

65084 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

28. The low- and moderate-income market

share is the estimated proportion of newly

mortgaged units in the market serving lowand moderate-income families. The two other

shares are similarly defined. HUD’s

conservative range of estimates (such as 50–

55 percent) reflects uncertainty about future

market conditions.

29. Appendix D explains the specific

reasons for the 1995–98 market estimates for

the low-mode and special affordable housing

goals are higher than the upper end of HUD’s

market projections for the years 2001–2003.

Based on average 1993–1998 experience,

HUD’s projection model assumes that

refinance borrowers have higher incomes

than home purchase borrowers; however,

between 1995 and 1997, refinance borrowers

had lower incomes. On average, the 1995–98

period also exhibited a slightly higher

percentage of rental units financed than

assumed in HUD’s projection model. See

Appendix D for other reasons the 1995–1998

average market estimates are higher than

those projected for the years 2001–2003.

30. PriceWaterhouse-Coopers, ‘‘The Impact

of Economic Conditions on the Size and the

Composition of the Affordable Housing

Market’’ (April 5, 2000).

31. In 1998, PWC estimates the size of the

single family mortgage market at $1.5 trillion.

This estimate is identical to the widely used

estimate by the Mortgage Bankers

Association for the entire single family

mortgage market, including FHA and jumbo

loans.

32. The figures presented for goal

performance are based on HUD analysis of

the GSEs’ loan level data. Some results differ

marginally from the corresponding figures

presented by Fannie Mae and Freddie Mac in

their respective Annual Housing Activities

Reports (AHARs) to HUD, reflecting

differences in application of counting rules.

33. The figures presented for goal

performance are based on HUD’s analysis of

the GSEs’ loan level data. Some results differ

marginally from the corresponding figures

presented by the GSEs in their AHARs,

reflecting differences in application of

counting rules.

34. GSE to market ratio is calculated by

dividing the performance of the respective

GSE by the performance of the market.

35. Freddie Mac-to-Market and Fannie

Mae-to-Market ratios cannot be calculated

until 1999 HMDA data is available.

36. The figures presented for goal

performance are based on HUD’s analysis of

the GSEs’ loan level data. Some results differ

from the corresponding figures presented by

Fannie Mae in its AHARs by one to two

percentage points. The difference largely

reflects differences between HUD and Fannie

Mae in application of counting rules relating

to counting of seasoned mortgage loans for

purposes of this goal. Freddie Mac’s AHAR

figures for this goal differ marginally from the

official figures presented above, also

reflecting differences in application of

counting rates.

37. The percentage of Freddie Mac’s

multifamily transactions counting toward the

Special Affordable Goal was unusually low

in 1999 relative to previous years, but the

multifamily sector still contributed

significantly to Freddie Mac’s performance

on the Special Affordable Goal. In 1999, 43

percent of units backing Freddie Mac’s

multifamily transactions met the Special

Affordable Goal, representing 22% of units

counted toward the Goal. Multifamily units

were eight per cent of Freddie Mac’s total

purchase volume in 1999.

38. U.S. House of Representatives,

Congressional Record. (October 13, 1999), p.

H10014.

39. 15 U.S.C. 1601 note; Title I, Subtitle B

of the Riegle Community Development and

Regulatory Improvement Act of 1994, Pub. L.

103–325 (Sept. 23, 1994); 108 Stat. 2190–98.

40. Currently, HOEPA covers refinancings

of mortgages. 15 U.S.C. 1601(aa)(1).

41. As mentioned above, HOEPA grants the

Federal Reserve Board authority to lower the

APR trigger to 8 percentage points over

comparable treasuries (or to raise it to 12

percentage points above), 15 U.S.C.

1602(aa)(2), and to broaden the class of costs

counted toward the fees trigger, 15 U.S.C.

1602(aa)(4)(D).

42. 12 U.S.C. 4563(b)(1)(B).

43. Id.

44. CRA regulations were published as a

joint final rule on May 4, 1995. The

regulation is codified at 12 CFR Part 25, CFR

Parts 228 and 203, 12 CFR Part 345, and 12

CFR Part 563e for the Office of the

Comptroller of the Currency, the Federal

Reserve Board, the Federal Deposit Insurance

Corporation, and the Office of Thrift

Supervision, respectively.

45. Pub. L. 106–102; approved November

12, 1999.

46. See S. Rep. No. 282, 102nd Cong., 2nd

Sess. 39 (1992); H.R. Rept. 206, 102nd Cong.,

1st Sess. 59 (1991)

47. Ibid.

48. 24 CFR parts 401 and 402, Multifamily

Housing Mortgage and Housing Assistance

Restructuring Program (Mark-to-Market):

Final Rule, March 22, 2000.

49. The 1992 House committee report on

the bill that later became FHEFSSA

emphasizes that ‘‘the goals included in this

legislation are specifically not to include

purchases of equity for low-income housing

tax credits.’’ (House of Representatives

Report 102–206, 102d Congress, 1st Session,

p. 60.)

50. Handbook of Housing and

Development Law, 1996, p. 10–8 and IRC

Sec. 42 (i)(1).

51. 42 U.S.C. 1437f, sec. 514(e)(6)

52. Kenneth Temkin, Jennifer E. H.

Johnston, and Charles Calhoun, An

Assessment of Recent Innovations in the

Secondary Market for Low- and ModerateIncome Lending, report submitted to the U.S.

Department of Housing and Urban

Development (March 2000).

53. See S. Rep. No. 282, 102d Cong., 2d

Sess. 39 (1992).

List of Subjects in 24 CFR Part 81

Accounting, Federal Reserve System,

Mortgages, Reporting and recordkeeping

requirements, Securities.

Accordingly, 24 CFR part 81 is

amended 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; 42 U.S.C. 3535(d) and

3601–3619.

2. Section 81.2, is amended by

revising the definitions of ‘‘Median

income’’ ‘‘Metropolitan area,’’ and

‘‘Underserved area,’’ by adding a new

paragraph (7) to the definition of

‘‘Refinancing,’’ and by adding new

definitions for ‘‘HOEPA mortgage,’’

‘‘Mortgages contrary to good lending

practices,’’ and ‘‘Mortgages with

unacceptable terms or conditions or

resulting from unacceptable practices,’’

to read as follows:

§ 81.2 Definitions.

* * * * *

‘‘HOEPA mortgage’’ means a mortgage

for which the annual percentage rate (as

calculated in accordance with the

relevant provisions of section 107 of the

Home Ownership Equity Protection Act

(HOEPA) (15 U.S.C. 1606)) exceeds the

threshold described in section

103(aa)(1)(A) of HOEPA (15 U.S.C.

1602(aa)(1)(A)), or for which the total

points and fees payable by the borrower

exceed the threshold described in

section 103(aa)(1)(B) of HOEPA (15

U.S.C. 1602(aa)(1)(B)), as those

thresholds may be increased or

decreased by the Federal Reserve Board

or by Congress, unless the GSEs are

otherwise notified in writing by HUD.

Notwithstanding the exclusions in

section 103(aa)(1) of HOEPA, for

purposes of this part, the term ‘‘HOEPA

mortgage’’ includes all types of

mortgages as defined in this section,

including residential mortgage

transactions as that term is defined in

section 103(w) of HOEPA (15 U.S.C.

1602(w)), but does not include reverse

mortgages.

* * * * *

Median income means, with respect

to an area, the unadjusted median

family income for the area as most

recently determined and published by

HUD. HUD will provide the GSEs

annually with information specifying

how HUD’s published median family

income estimates for metropolitan areas

are to be applied for the purposes of

determining median family income.

Metropolitan area means a

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

primary metropolitan statistical area

(‘‘PMSA’’), or a portion of such an area

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65085

for which median family income

estimates are published annually by

HUD.

* * * * *

‘‘Mortgages contrary to good lending

practices’’ means a mortgage or a group

or category of mortgages entered into by

a lender and purchased by a GSE where

it can be shown that a lender engaged

in a practice of failing to:

(1) Report monthly on borrowers’

repayment history to credit repositories

on the status of each GSE loan that a

lender is servicing;

(2) Offer mortgage applicants products

for which they qualify, but rather steer

applicants to high cost products that are

designed for less credit worthy

borrowers. Similarly, for consumers

who seek financing through a lender’s

higher-priced subprime lending

channel, lenders should not fail to offer

or direct such consumers toward the

lender’s standard mortgage line if they

are able to qualify for one of the

standard products;

(3) Comply with fair lending

requirements; or

(4) Engage in other good lending

practices that are:

(i) Identified in writing by a GSE as

good lending practices for inclusion in

this definition; and

(ii) Determined by the Secretary to

constitute good lending practices.

‘‘Mortgages with unacceptable terms

or conditions or resulting from

unacceptable practices’’ means a

mortgage or a group or category of

mortgages with one or more of the

following terms or conditions:

(1) Excessive fees, where the total

points and fees charged to a borrower

exceed the greater of 5 percent of the

loan amount or a maximum dollar

amount of $1000, or an alternative

amount requested by a GSE and

determined by the Secretary as

appropriate for small mortgages.

(i) For purposes of this definition,

points and fees include:

(A) Origination fees;

(B) Underwriting fees;

(C) Broker fees;

(D) Finder’s fees; and

(E) Charges that the lender imposes as

a condition of making the loan, whether

they are paid to the lender or a third

party.

(ii) For purposes of this definition,

points and fees do not include:

(A) Bona fide discount points;

(B) Fees paid for actual services

rendered in connection with the

origination of the mortgage, such as

attorneys’ fees, notary’s fees, and fees

paid for property appraisals, credit

reports, surveys, title examinations and

extracts, flood and tax certifications,

and home inspections;

(C) The cost of mortgage insurance or

credit-risk price adjustments;

(D) The costs of title, hazard, and

flood insurance policies;

(E) State and local transfer taxes or

fees;

(F) Escrow deposits for the future

payment of taxes and insurance

premiums; and

(G) Other miscellaneous fees and

charges that, in total, do not exceed 0.25

percent of the loan amount.

(2) Prepayment penalties, except

where:

(i) The mortgage provides some

benefits to the borrower (e.g., such as

rate or fee reduction for accepting the

prepayment premium);

(ii) The borrower is offered the choice

of another mortgage that does not

contain payment of such a premium;

(iii) The terms of the mortgage

provision containing the prepayment

penalty are adequately disclosed to the

borrower; and

(iv) The prepayment penalty is not

charged when the mortgage debit is

accelerated as the result of the

borrower’s default in making his or her

mortgage payments.

(3) The sale or financing of prepaid

single-premium credit life insurance

products in connection with the

origination of the mortgage;

(4) Evidence that the lender did not

adequately consider the borrower’s

ability to make payments, i.e., mortgages

that are originated with underwriting

techniques that focus on the borrower’s

equity in the home, and do not give full

consideration of the borrower’s income

and other obligations. Ability to repay

must be determined and must be based

upon relating the borrower’s income,

assets, and liabilities to the mortgage

payments; or

(5) Other terms or conditions that are:

(i) Identified in writing by a GSE as

unacceptable terms or conditions or

resulting from unacceptable practices

for inclusion in this definition; and

(ii) Determined by the Secretary as an

unacceptable term or condition of a

mortgage for which goals credit should

not be received.

* * * * *

Refinancing means * * *

* * * * *

(7) A conversion of a balloon

mortgage note on a single family

property to a fully amortizing mortgage

note where the GSE already owns or has

an interest in the balloon note at the

time of the conversion.

* * * * *

Underserved area means:

(1) For purposes of the definitions of

‘‘Central city’’ and ‘‘Other underserved

area,’’ a 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 median income of the

metropolitan area and a minority

population of 30 percent or greater; or

(ii) A median income at or below 90

percent of median income of the

metropolitan area.

(2) For purposes of the definition of

‘‘Rural area’’:

(i) In areas other than New England,

a whole county, a Federal or State

American Indian reservation or tribal or

individual trust land, or the balance of

a county excluding the area within any

Federal or State American Indian

reservation or tribal or individual trust

land, having:

(A) 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

(B) A median income at or below 95

percent of the greater of the State nonmetropolitan median income or

nationwide non-metropolitan median

income.

(ii) In New England, a whole county

having the characteristics in paragraphs

(2)(i)(A) or (2)(i)(B) of this definition; a

Federal or State American Indian

reservation or tribal or individual trust

land, having the characteristics in

paragraphs (2)(i)(A) or (2)(i)(B) of this

definition; or the balance of a county,

excluding any portion that is within any

Federal or State American Indian

reservation or tribal or individual trust

land, or metropolitan area where the

remainder has the characteristics in

paragraphs (2)(i)(A) or (2)(i)(B) of this

definition.

(3) Any Federal or State American

Indian reservation or tribal or individual

trust land that includes land that is both

within and outside of a metropolitan

area and that is designated as an

underserved area by HUD. In such

cases, HUD will notify the GSEs as to

applicability of other definitions and

counting conventions.

* * * * *

3. Section 81.12 is amended as

follows:

a. Paragraph (b) is amended by

revising the last sentence; and

b. Paragraph (c) is revised, to read as

follows:

65086 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

§ 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 October 31,

2000.

(c) Goals. The annual goals for each

GSE’s purchases of mortgages on

housing for low-and moderate-income

families are:

(1) For each of the years 2001–2003,

50 percent of the total number of

dwelling units financed by that GSE’s

mortgage purchases in each of those

years unless otherwise adjusted by HUD

in accordance with FHEFSSA; and

(2) For the year 2004 and thereafter

HUD shall establish annual goals.

Pending establishment of goals for the

year 2004 and thereafter, the annual

goal for each of those years shall be 50

percent of the total number of dwelling

units financed by that GSE’s mortgage

purchases in each of those years.

4. Section 81.13 is amended as

follows:

a. Paragraph (b) is amended by

revising the last sentence; and

b. Paragraph (c) is revised, 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 October 31, 2000.

(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 each of the years 2001–2003,

31 percent of the total number of

dwelling units financed by that GSE’s

mortgage purchases in each of those

years unless otherwise adjusted by HUD

in accordance with FHEFSSA; and

(2) For the year 2004 and thereafter

HUD shall establish annual goals.

Pending establishment of goals for the

year 2004 and thereafter, the annual

goal for each of those years shall be 31

percent of the total number of dwelling

units financed by that GSE’s mortgage

purchases in each of those years.

* * * * *

5. Section 81.14 is amended as

follows:

a. Paragraph (b) is amended by

revising the last sentence;

b. Paragraph (c) is revised;

c. Paragraph (d) is amended by

revising paragraph (d)(1)(i);

d. Paragraph (e) is amended by

revising paragraphs (e)(2), (e)(3), and

(e)(4);

e. Paragraph (f) is redesignated as

paragraph (g) and the last sentence of

the newly redesignated paragraph (g) is

revised; and

f. A new paragraph (f) is added; 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 October 31, 2000.

(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 each of the years 2001, 2002,

and 2003, 20 percent of the total number

of dwelling units financed by that GSE’s

mortgage purchases in each of those

years unless otherwise adjusted by HUD

in accordance with FHEFSSA. The goal

for each year 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

1997, 1998 and 1999, unless otherwise

adjusted by HUD in accordance with

FHEFSSA; and

(2) For the year 2004 and thereafter

HUD shall establish annual goals.

Pending establishment of goals for the

year 2004 and thereafter, the annual

goal for each of those years shall be 20

percent of the total number of dwelling

units financed by that 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 1997, 1998 and 1999.

(d) * * *

(1) * * *

(i) 20 percent of the dwelling units in

the particular multifamily property are

affordable to especially low-income

families; or

* * * * *

(e) * * *

(2) Mortgages insured under HUD’s

Home Equity Conversion Mortgage

(‘‘HECM’’) Insurance Program, 12 U.S.C.

1715 z–20; mortgages guaranteed under

the Rural Housing Service’s Single

Family Housing Guaranteed Loan

Program, 42 U.S.C. 1472; mortgages on

properties on tribal lands insured under

FHA’s Section 248 program, 12 U.S.C.

1715 z–13, HUD’s Section 184 program,

12 U.S.C. 1515 z–13a, or Title VI of the

Native American Housing Assistance

and Self-Determination Act of 1996, 25

U.S.C. 4191–4195; meet the

requirements of 12 U.S.C.

4563(b)(1)(A)(i) and (ii).

(3) HUD will give full credit toward

achievement of the Special Affordable

Housing Goal for the activities in 12

U.S.C. 4563(b)(1)(A), provided the GSE

submits documentation to HUD that

supports eligibility under 12 U.S.C.

4563(b)(1)(A) for HUD’s approval.

(4)(i) For purposes of determining

whether a seller meets the requirement

in 12 U.S.C. 4563(b)(1)(B), a seller must

currently operate on its own or actively

participate in an on-going, discernible,

active, and verifiable program directly

targeted at the origination of new

mortgage loans that qualify under the

Special Affordable Housing Goal.

(ii) A seller’s activities must evidence

a current intention or plan to reinvest

the proceeds of the sale into mortgages

qualifying under the Special Affordable

Housing Goal, with a current

commitment of resources on the part of

the seller for this purpose.

(iii) A seller’s actions must evidence

willingness to buy qualifying loans

when these loans become available in

the market as part of active, on-going,

sustainable efforts to ensure that

additional loans that meet the goal are

originated.

(iv) Actively participating in such a

program includes purchasing qualifying

loans from a correspondent originator,

including a lender or qualified housing

group, that operates an on-going

program resulting in the origination of

loans that meet the requirements of the

goal, has a history of delivering, and

currently delivers qualifying loans to

the seller.

(v) The GSE must verify and monitor

that the seller meets the requirements in

paragraphs (e)(4)(i) through (e)(4)(iv) of

this section and develop any necessary

mechanisms to ensure compliance with

the requirements, except as provided in

paragraph (e)(4)(vi) and (vii) of this

section.

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65087

(vi) Where a seller’s primary business

is originating mortgages on housing that

qualifies under this Special Affordable

Housing Goal such seller is presumed to

meet the requirements in paragraphs

(e)(4)(i) through (e)(4)(iv) of this section.

Sellers that are institutions that are:

(A) Regularly in the business of

mortgage lending;

(B) A BIF-insured or SAIF-insured

depository institution; and

(C) Subject to, and has received at

least a satisfactory performance

evaluation rating for

(1) At least the two most recent

consecutive examinations under, the

Community Reinvestment Act, if the

lending institution has total assets in

excess of $250 million; or

(2) The most recent examination

under the Community Reinvestment Act

if the lending institutions which have

total assets no more than $250 million

are identified as sellers that are

presumed to have a primary business of

originating mortgages on housing that

qualifies under this Special Affordable

Housing Goal and, therefore, are

presumed to meet the requirements in

paragraphs (e)(4)(i) through (e)(4)(iv) of

this section.

(vii) Classes of institutions or

organizations that are presumed have as

their primary business originating

mortgages on housing that qualifies

under this Special Affordable Housing

Goal and, therefore. are presumed in

paragraphs (e)(4)(i) through (e)(4)(iv) of

this section to meet the requirements are

as follows: State housing finance

agencies; affordable housing loan

consortia; Federally insured credit

unions that are:

(A) Members of the Federal Home

Loan Bank System and meet the firsttime homebuyer standard of the

Community Support Program; or

(B) Community development credit

unions; community development

financial institutions; public loan funds;

or non-profit mortgage lenders. HUD

may determine that additional classes of

institutions or organizations are

primarily engaged in the business of

financing affordable housing mortgages

for purposes of this presumption, and if,

so will notify the GSEs in writing.

(viii) For purposes of paragraph (e)(4)

of this section, if the seller did not

originate the mortgage loans, but the

originator of the mortgage loans fulfills

the requirements of either paragraphs

(e)(4)(i) through (e)(4)(iv), paragraph

(e)(4)(vi) or paragraph (e)(4)(vii) of this

section; and the seller has held the loans

for six months or less prior to selling the

loans to the GSE, HUD will consider

that the seller has met the requirements

of this paragraph (e)(4) and of 12 U.S.C.

4563(b)(1)(B).

* * * * *

(f) Partial credit activities. Mortgages

insured under HUD’s Title I program,

which includes property improvement

and manufactured home loans, shall

receive one-half credit toward the

Special Affordable Housing Goal until

such time as the Government National

Mortgage Association fully implements

a program to purchase and securitize

Title I loans.

(g) No credit activities. * * * For

purposes of this paragraph (g),

‘‘mortgages or mortgage-backed

securities portfolios’’ includes

mortgages retained by Fannie Mae or

Freddie Mac and mortgages utilized to

back mortgage-backed securities.

6. In § 81.15, paragraph (a) is revised,

paragraph (d) is amended by revising

the second sentence and by adding two

new sentences at the end, and paragraph

(e) is amended by re-designating

paragraph (e)(6) as (e)(7), and by adding

a new paragraph (e)(6), to read as

follows:

§ 81.15 General requirements.

(a) Calculating the numerator and

denominator. Performance under each

of the housing goals shall be measured

using a fraction that is converted into a

percentage.

(1) The numerator. The numerator of

each fraction is the number of dwelling

units financed by a GSE’s mortgage

purchases in a particular year that count

toward achievement of the housing goal.

(2) The denominator. The

denominator of each fraction is, for all

mortgages purchased, the number of

dwelling units that could count toward

achievement of the goal under

appropriate circumstances. The

denominator shall not include GSE

transactions or activities that are not

mortgages or mortgage purchases as

defined by HUD or transactions that are

specifically excluded as ineligible under

§ 81.16(b).

(3) Missing data or information. When

a GSE lacks sufficient data or

information to determine whether the

purchase of a mortgage originated after

1992 counts toward achievement of a

particular housing goal, that mortgage

purchase shall be included in the

denominator for that housing goal,

except under the circumstances

described in paragraphs (d) and (e)(6) of

this section.

* * * * *

(d) Counting owner-occupied units.

* * * To determine whether mortgagors

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. When the

income of the mortgagors is not

available to determine whether the

purchase of a mortgage originated after

1992 counts toward achievement of the

Low- and Moderate-Income Housing

Goal or the Special Affordable Housing

Goal, a GSE may exclude single family

owner-occupied units located in census

tracts with median income less than or

equal to area median income according

to the most recent census from the

denominator as well as the numerator,

up to a ceiling of one percent of the total

number of single family owner-occupied

dwelling units eligible to be counted

toward the respective housing goal in

the current year. Mortgage purchases in

excess of the ceiling will be included in

the denominator and excluded from the

numerator if they are missing data.

(e) * * *

(6) Affordability data unavailable. (i)

Multifamily. When information

regarding the affordability of a rental

unit is not available, a GSE’s

performance with respect to such a unit

may be evaluated with estimated

affordability information, so long as the

Department has reviewed and approved

the data source and methodology for

such estimated data. The use of

estimated information to determine

affordability may be used up to a

maximum of five percent of the total

number of units backing the GSEs’

multifamily mortgage purchases in the

current year, adjusted for REMIC

percentage and participation percent.

When the application of affordability

data based on an approved market rental

data source and methodology is not

possible, and therefore the GSE lacks

sufficient information to determine

whether the purchase of a mortgage

originated after 1992 counts toward the

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

Affordable Housing Goal, HUD will

exclude units in multifamily properties

from the denominator as well as the

numerator in calculating performance

under those goals.

(ii) Rental units in 1–4 unit single

family properties. When neither the

income of prospective or actual tenants

of a rental unit in a 1–4 unit single

family property nor actual or average

rent data is available, and, therefore, the

GSE lacks sufficient information to

determine whether the purchase of a

mortgage originated after 1992 counts

toward achievement of the Low- and

Moderate-Income Housing Goal or the

65088 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

Special Affordable Housing Goal, a GSE

may exclude rental units in 1–4 unit

single family properties from the

denominator as well as the numerator in

calculating performance under those

goals.

* * * * *

7. Section 81.16 is amended as

follows:

a. Paragraph (a) is revised;

b. Paragraph (b) is amended by

revising paragraphs (b)(3) and (b)(9) and

by adding a new paragraph (b)(10);

c. Paragraph (c) is amended by adding

introductory text, by revising paragraph

(c)(6), and by adding new paragraphs

(c)(9), (c)(10), (c)(11), (c)(12), and (c)(13);

and

d. A new paragraph (d) is added; to

read as follows:

§ 81.16 Special counting requirements.

(a) General. HUD shall determine

whether a GSE shall receive full, partial,

or no credit for a transaction toward

achievement of any of the housing goals.

In this determination, HUD will

consider whether a transaction or

activity of the GSE is substantially

equivalent to a mortgage purchase and

either creates a new market or adds

liquidity to an existing market, provided

however that such mortgage purchase

actually fulfills the GSE’s purposes and

is in accordance with its Charter Act.

(b) * * *

(3) Purchases of non-conventional

mortgages except:

(i) Where such mortgages are acquired

under a risk-sharing arrangement with a

Federal agency;

(ii) Mortgages insured under HUD’s

Home Equity Conversion Mortgage

(‘‘HECM’’) insurance program, 12 U.S.C.

1715z–20; mortgages guaranteed under

the Rural Housing Service’s Single

Family Housing Guaranteed Loan

Program, 42 U.S.C. 1472; mortgages on

properties on lands insured under

FHA’s Section 248 program, 12 U.S.C.

1715z–13, or HUD’s Section 184

program, 12 U.S.C. 1515z–13a, or Title

VI of the Native American Housing

Assistance and Self-Determination Act

of 1996, 25 U.S.C. 4191–4195; and

mortgages with expiring assistance

contracts as defined at 42 U.S.C. 1737f;

(iii) Mortgages under other mortgage

programs involving Federal guarantees,

insurance or other Federal obligation

where the Department determines in

writing that the financing needs

addressed by the particular mortgage

program are not well served and that the

mortgage purchases under such program

should count under the housing goals,

provided the GSE submits

documentation to HUD that supports

eligibility and that HUD makes such a

determination, or

(iv) As provided in § 81.14(e)(3)

* * * * *

(9) Single family mortgage

refinancings that result from conversion

of balloon notes to fully amortizing

notes, if the GSE already owns or has an

interest in the balloon note at the time

conversion occurs.

(10) Any combination of factors in

paragraphs (b)(1) through (9) of this

section.

(c) Other special rules. Subject to

HUD’s primary determination of

whether a GSE shall receive full, partial,

or no credit for a transaction toward

achievement of any of the housing goals

as provided in paragraph (a) of this

section, the following supplemental

rules apply:

* * * * *

(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

the GSE has already counted the

mortgage under a housing goal

applicable to 1993 or any subsequent

year, or where the Department

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.

* * * * *

(9) Expiring assistance contracts. In

accordance with 12 U.S.C. 4565(a)(5),

actions that assist in maintaining the

affordability of assisted units in eligible

multifamily housing projects with

expiring contracts shall receive credit

under the housing goals as provided in

paragraph (b)(3)(ii) and in accordance

with paragraphs (b) and (c)(1) through

(c)(9) of this section.

(i) For restructured (modified)

multifamily mortgage loans with an

expiring assistance contract where a

GSE holds the loan in portfolio and

facilitates modification of loan terms

that results in lower debt service to the

project’s owner, the GSE shall receive

full credit under any of the housing

goals for which the units covered by the

mortgage otherwise qualify.

(ii) Where a GSE undertakes more

than one action to assist a single project

or where a GSE engages in an activity

that it believes assists in maintaining

the affordability of assisted units in

eligible multifamily housing projects

but which is not otherwise covered in

paragraph (c)(9)(i) of this section, the

GSE must submit the transaction to

HUD for a determination on appropriate

goals counting treatment.

(10) Bonus points. The following

transactions or activities, to the extent

the units otherwise qualify for one or

more of the housing goals, will receive

bonus points toward the particular goal

or goals, by receiving double weight in

the numerator under a housing goal or

goals and receiving single weight in the

denominator for the housing goal or

goals. Bonus points will not be awarded

for the purposes of calculating

performance under the special

affordable housing multifamily subgoal

described in § 81.14(c). All transactions

or activities meeting the following

criteria will qualify for bonus points

even if a unit is missing affordability

data and the missing affordability data

is treated consistent with

§ 81.15(e)(6)(i). Bonus points are

available to the GSEs for purposes of

determining housing goal performance

for each year 2001 through 2003.

Beginning in the year 2004, bonus

points are not available for goal

performance counting purposes unless

the Department extends their

availability beyond December 31, 2003

for one or more types of activities and

notifies the GSEs by letter of that

determination.

(i) Small multifamily properties. HUD

will assign double weight in the

numerator under a housing goal or goals

for each unit financed by GSE mortgage

purchases in small multifamily

properties (5 to 50 physical units),

provided, however, that bonus points

will not be awarded for properties that

are aggregated or disaggregated into 5–

50 unit financing packages for the

purpose of earning bonus points.

(ii) Units in 2–4 unit owner-occupied

properties. HUD will assign double

weight in the numerator under the

housing goals for each unit financed by

GSE mortgage purchases in 2- to 4-unit

owner-occupied properties, to the extent

that the number of such units financed

by mortgage purchases are in excess of

60 percent of the yearly average number

of units qualifying for the respective

housing goal during the five years

immediately preceding the year of

mortgage purchase.

(11) Temporary adjustment factor for

Freddie Mac. In determining Freddie

Mac’s performance on the Low- and

Moderate-Income Housing Goal and the

Special Affordable Housing Goal, HUD

will count each qualifying unit in a

property with more than 50 units as 1.2

units in calculating the numerator and

as one unit in calculating the

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65089

denominator, for the respective housing

goal. HUD will apply this temporary

adjustment factor for each year from

2001 through 2003; for the year 2004

and thereafter, this temporary

adjustment factor will no longer apply.

(12) HOEPA mortgages and mortgages

with unacceptable terms and

conditions. HOEPA mortgages and

mortgages with unacceptable terms or

conditions as defined in § 81.2 will not

receive credit toward any of the three

housing goals.

(13) Mortgages contrary to good

lending practices. The Secretary will

monitor the practices and processes of

the GSEs to ensure that they are not

purchasing loans that are contrary to

good lending practices as defined in

§ 81.2. Based on the results of such

monitoring, the Secretary may

determine in accordance with paragraph

(d) of this section that mortgages or

categories of mortgages where a lender

has not engaged in good lending

practices will not receive credit toward

the three housing goals.

(d) HUD review of transactions. HUD

will determine whether a class of

transactions counts as a mortgage

purchase under the housing goals. If a

GSE seeks to have a class of transactions

counted under the housing goals that

does not otherwise count under the

rules in this part, the GSE may provide

HUD detailed information regarding the

transactions for evaluation and

determination by HUD in accordance

with this section. In making its

determination, HUD may also request

and evaluate additional information

from a GSE with regard to how the GSE

believes the transactions should be

counted. HUD will notify the GSE of its

determination regarding the extent to

which the class of transactions may

count under the goals.

8. Section 81.17 is amended by

adding a new paragraph (d), to read as

follows:

§ 81.17 Affordability—Income level

definitions—family size and income known

(owner-occupied units, actual tenants, and

prospective tenants).

* * * * *

(d) Especially-low-income means, in

the case of rental units, where the

income of actual or prospective tenants

is available, income not in excess of the

following percentages of area median

income corresponding to the following

family sizes:

Number of persons in family

Percentage of

area median

income

1 ............................................ 35

2 ............................................ 40

Number of persons in family

Percentage of

area median

income

3 ............................................ 45

4 ............................................ 50

5 or more .............................. (*)

* 50% plus (4.0% multiplied by the number

of persons in excess of 4).

9. Section 81.18 is amended by

adding a new paragraph (d), to read as

follows:

§ 81.18 Affordability—Income level

definitions—family size not known (actual

or prospective tenants).

* * * * *

(d) For especially-low-income, income

of prospective tenants shall not exceed

the following percentages of area

median income with adjustments,

depending on unit size:

Unit size

Percentage of

area median

income

Efficiency .............................. 35

1 bedroom ............................ 37.5

2 bedrooms ........................... 45

3 bedrooms or more ............. (*)

* 52% plus (6.0% multiplied by the number

of bedrooms in excess of 3).

10. In § 81.19, paragraph (d) is redesignated as paragraph (e), a new

paragraph (d) is added and the second

sentence of the newly re-designated

paragraph (e) is revised, to read as

follows:

§ 81.19 Affordability—Rent level

definitions—tenant income is not known.

* * * * *

(d) For especially-low-income,

maximum affordable rents to count as

housing for especially-low-income

families shall not exceed the following

percentages of area median income with

adjustments, depending on unit size:

Unit size

Percentage of

area median

income

Efficiency .............................. 10.5

1 bedroom ............................ 11.25

2 bedrooms ........................... 13.5

3 bedrooms or more ............. (*)

* 15.6% plus (1.8% multiplied by the number

of bedrooms in excess of 3).

* * * * *

(e) Missing Information. * * * If a

GSE makes such efforts but cannot

obtain data on the number of bedrooms

in particular units, in making the

calculations on such units, the units

shall be assumed to be efficiencies

except as provided in § 81.15(e)(6)(i)

11. In § 81.76, paragraph (d) is revised

to read as follows:

§ 81.76 FOIA requests and protection of

GSE information.

* * * * *

(d) Protection of information by HUD

officers and employees. The Secretary

will institute all reasonable safeguards

to protect data or information submitted

by or relating to either GSE, including,

but not limited to, advising all HUD

officers and employees having access to

data or information submitted by or

relating to either GSE of the legal

restrictions against unauthorized

disclosure of such data or information

under the executive branch-wide

standards of ethical conduct, 5 CFR part

2635, and the Trade Secrets Act, 18

U.S.C. 1905. Officers and employees

shall be advised of the penalties for

unauthorized disclosure, ranging from

disciplinary action under 5 CFR part

2635 to criminal prosecution.

* * * * *

Dated: October 16, 2000.

William C. Apgar,

Assistant Secretary for Housing—Federal

Housing Commissioner.

Note: The Following Appendices Will Not

Appear in the Code of Federal Regulations.

Appendix A—Departmental

Considerations To Establish the Lowand Moderate-Income Housing Goal

A. Introduction and Response to Comments

Sections 1 and 2 provide a basic

description of the rule process. Section 3

discusses comments on the proposed rule

and the Department’s responses. Section 4

discusses conclusions based on consideration

of the factors.

1. Establishment of 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.

65090 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

2. Underlying Data

In considering the statutory factors in

establishing these goals, HUD relied on data

from the 1995 American Housing Survey

(AHS), the 1990 Census of Population and

Housing, the 1991 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

Moderate-Income Housing Goal in previous

years, HUD analyzed the loan-level data on

all mortgages purchased by the GSEs for

1993–99 in accordance with the goal

counting provisions established by the

Department in the December 1995 rule (24

CFR part 81).

3. Response to Comments

a. Introduction

Fannie Mae and Freddie Mac provided

detailed comments on HUD’s discussion of

the factors for determining the goal levels in

Appendix A of the proposed rule. A major

portion of their substantive comments

concerned HUD’s analysis of the GSEs’

performance relative to the market. Both

GSEs disagreed with HUD’s conclusions that

they lag the conventional conforming market

in funding mortgages for the goals-qualifying

segments (low-mod borrowers, special

affordable borrowers, and underserved

neighborhoods) of the single-family owner

market. The GSEs argued strongly that they

have led the mortgage market, from both

quantitative and qualitative perspectives

(explained below). The GSEs expressed

concern about HUD’s assumptions and

treatment of specific data in estimating the

goals-qualifying shares for single-family

owner mortgages. The GSEs concluded that

HUD chose assumptions and data sources

that result in an overstatement of the lowmod, special affordable, and underserved

areas shares of owner mortgages.

It should be noted that the GSEs extended

their criticisms to other researchers who have

examined this issue of their targeted lending

performance relative to the overall mortgage

market. Section E.3 of this appendix

summarizes findings of several independent

studies that have also concluded that the

GSEs have lagged the market in affordable

lending. For the most part, these studies have

used the same HMDA-based methodology

described in Section E.2 of this appendix.

The GSEs focused many of their comments

on the adequacy of HMDA data, the main

source for the goals-qualifying shares of the

conventional conforming market, against

which the GSEs are compared. The GSEs

argued that HMDA data are biased (i.e.,

overstate the goals-qualifying shares of the

market) and that significant portions of

HMDA data are not relevant for calculating

the market standard for evaluating GSE

performance in the conventional conforming

market. These and related comments of the

GSEs are discussed below in subsections b–

f.

Both GSEs also argued that HUD’s analysis

and conclusions depended on a continuation

of recent conditions of economic expansion

and low interest rates. According to the

GSEs, HUD’s range of market estimates did

not include periods of adverse economic and

affordability conditions, such as existed in

the early 1990s. HUD discusses the GSEs’

comments on economic volatility in Section

B of Appendix D. As explained there, HUD’s

ranges of market estimates for each of the

housing goals are conservative, because they

allow for economic and interest rate

conditions much more adverse than existed

during the mid- to late-1990s.

The discussion that follows summarizes

HUD’s responses to the GSEs’ comments on

the ‘‘leading the market’’ analysis that HUD

has conducted in Section E.2 of this

appendix—that section fully develops the

various concepts referenced here. The final

two subsections, g and h, discuss additional

issues that the GSEs raised about HUD’s

analysis of the factors in Appendix A.

b. Overview of Leading the Owner Market—

Quantitative Analysis

The analysis of HMDA data in Section E.2

of this appendix indicates demonstrates that

even though the GSEs have improved their

performance since 1993, they have lagged

depositories and others in the conventional

conforming market in funding affordable

loans, both since 1993 and during the more

recent 1996–98 period when the new housing

goals have been in effect. For example,

underserved areas accounted for 22.9 (19.9)

percent of Fannie Mae’s (Freddie Mac’s)

purchases of home loans between 1996 and

1998, compared with 24.4 percent for the

entire conforming market (excluding B&C

loans). Based on comparisons such as these,

HUD concludes that the GSEs need to

continue improving their performance so that

they can match or exceed the overall market

in affordable lending.

In their comments, the GSEs reached the

opposite conclusion—each stated that they

already match or even lead the market,

depending on the affordable category being

considered. The GSEs also assert that HUD’s

analysis does not accurately reflect their

performance relative to the overall market.

Freddie Mac stated that ‘‘the shares of

Freddie Mac’s loan purchases serving lowand moderate-income families, families in

underserved areas and minority families

mirror those of the primary market’’. Freddie

Mac said that its market calculations

‘‘account for the limitations on loans we

[Freddie Mac] can purchase’’ (see below).

Similarly, Fannie Mae stated that ‘‘an

appropriate comparison between Fannie Mae

and the primary single-family market shows

that we [Fannie Mae] serve a higher

percentage of low- and moderate-income

borrowers, a higher percentage of minority

borrowers, and a higher percentage of

borrowers in underserved areas than does the

primary market’’.

Both the GSEs and HUD rely on HMDA

data for the market estimates. However, as

suggested by the GSEs’ comments, they

frequently adjust HMDA data to exclude

loans in the market that they perceive as not

being available for them to purchase. The

types of adjustments made by the GSEs, and

HUD’s response to those adjustments, are

discussed in the next subsection. HUD’s

conclusions about the appropriate definition

of the conventional conforming market are

also discussed in Section E of this appendix,

which provides a detailed analysis of the

GSEs’ goals-qualifying purchases in the

single-family-owner market, and in

Appendix D, which provides overall (both

single-family and multifamily) estimates of

the goals-qualifying shares of the market. In

Appendix D, HUD excludes B&C loans from

its overall estimates of the market. In this

appendix, HUD illustrates (to the extent

HMDA data allow) the effects of excluding

B&C loans on the GSE-market comparisons,

as well as the effects of excluding other loan

categories such as manufactured housing

loans. However, as explained below, HUD

does not believe that HMDA data for the

conventional conforming market should be

adjusted to reflect the GSEs’ perceptions

about the characteristics of loans that are

available for them to purchase.

c. Relevant Market for Single-Family Owner

Properties

Both GSEs provided numerous comments

concerning the types of mortgages that HUD

should exclude from the definition of the

single-family owner market, both when HUD

is evaluating the GSEs’ performance relative

to the conventional conforming owner

market (i.e., determining whether the GSEs’

lead or lag the market for single-family-owner

mortgages) and when HUD is calculating the

overall market shares for each housing goal

(as described in Appendix D). Fannie Mae

stated that it ‘‘can only purchase or securitize

mortgages that primary market lenders are

willing to sell’’ and that certain types of

products (such as ARMs) ‘‘are particularly

difficult to structure for sale to the secondary

market’’. Fannie Mae added that ‘‘HUD fails

to adjust for those housing markets that are

not fully available to Fannie Mae and Freddie

Mac’’. Freddie Mac stated that it ‘‘has not

achieved, and is unlikely to achieve in the

near term, the same penetration in the

subprime and manufactured housing

segments of the market as it has achieved in

the conventional, conforming market’’ and

therefore HUD should not include these

segments in its market definition. According

to the GSEs, markets that are ‘‘not available’’

to them or where they are not a ‘‘full

participant’’ should be excluded from HUD’s

market definition. In addition to the

subprime and manufactured housing

markets, examples of market segments

mentioned by the GSEs for exclusion

included: low-down payment mortgages

(those with loan-to-value ratios greater than

80 percent) without private mortgage

insurance or some other credit enhancement;

loans financed through state and local

housing finance agencies; below-marketinterest-rate mortgages; specialized CRA

mortgages; and portions of depository

portfolios that are not available at mortgage

origination for purchase by the GSEs.

To analyze the availability of loans

originated by depositories to the GSEs,

Fannie Mae funded a study by KPMG

Barefoot-Marrinan (KPMG). According to

Fannie Mae, KPMG found that the advent of

the Community Reinvestment Act (CRA) had

encouraged depositories to hold lowerincome loans in portfolio. Depositories may

not offer their products for sale on the

secondary market not only because they are

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65091

outside of the GSEs’ guidelines, but also

because of business and portfolio strategy

reasons (such as the interest-rate-duration

advantage of holding ARMs in portfolio).

Freddie Mac estimated the impacts on

HUD’s market estimates of excluding from

the market definition both specialized

community development (CRA-type) loans

and portions of depository portfolios. Based

on Freddie Mac’s analysis, the low-mod

(underserved areas) share of the owner

market would fall by four (three) percentage

points and HUD’s overall low-mod and

underserved areas market estimates would

each fall by about two percentage points. In

commenting on whether Freddie Mac leads

or lags depositories in affordable lending,

Freddie Mac said that the HMDA data for

depositories should be adjusted downward to

exclude depositories’ high-LTV loans

without private mortgage insurance, their

below-market rate loans, their subprime

loans, and coverage bias in HMDA (see the

next subsection). Based on these adjustments,

Freddie Mac reduced the 1998 HMDAreported underserved areas percentage for

depositories from 26.1 percent to 20.0, which

led Freddie Mac to conclude that its

performance equals or exceeds the

performance of depositories on loans that are

likely to be sold to Freddie Mac.

HUD’s Response. In general, HUD

disagrees with the comments offered by the

GSEs about excluding those market segments

that they haven’t yet been able to penetrate

fully. Congress stated that HUD was to

estimate the size of the conventional

conforming mortgage market, not the market

that the GSEs perceive as available for them

to purchase. However, with respect to the

subprime market, HUD believes that the

risky, B&C portion of that market should be

excluded from the market definition for each

of the housing goals. Thus, HUD includes

only the A-minus portion of the subprime

market in its overall estimates of the goalsqualifying market shares. In Appendix D,

HUD explains its methodology for adjusting

the overall market estimates to exclude B&C

loans. Section E.2 of this appendix uses

HMDA data and the GSEs’ loan-level data to

examine the GSEs’ performance in the singlefamily owner portion of the conventional

conforming mortgage market in metropolitan

areas. B&C loans are not identified in HMDA

data; however, HUD shows the effects of

adjusting the owner market definition for

subprime and B&C loans by using a list of

lenders that specialize in subprime loans (see

Table A.4b).

Excluding other important segments of the

lower-income mortgage market, as the GSEs

recommend, would render the resulting

market benchmark useless for evaluating the

GSEs’ performance. The loans that the GSEs

would exclude are important sources of

lower-income credit and, in fact, are among

the very loans the GSEs are supposed to be

funding. A recent report by the Department

of Treasury demonstrated the targeting of

CRA-type loans to lower-income and

minority families. Numerous studies have

shown that the manufactured home sector is

an important source of low-income housing.

In many of these markets, a more active

secondary market would encourage lending

to traditionally underserved borrowers.

While HUD recognizes that some segments of

the market may be more challenging for the

GSEs than others, the data reported in Tables

A.7a and A.7b of this Appendix show that

the GSEs have ample opportunities to

purchase goals-qualifying mortgages. As

market leaders, the GSEs should be looking

for innovative ways to pursue this business,

rather than suggesting that it is not available

to the secondary market. Furthermore, there

is evidence that the GSEs can earn reasonable

returns on their goals business. The

Economic Analysis that accompanies this

final rule provides evidence that the GSEs

have been earning financial returns on their

purchases of goals-qualifying loans that are

only slightly below their 20–25 percent

return on equity from their normal business.

HUD also disagrees with other specific

comments offered by the GSEs. For example,

HUD does not think that the data for

depositories should be adjusted downward as

proposed by Freddie Mac and Fannie Mae.

Both types of institutions receive government

benefits and both operate in the conventional

conforming market. Furthermore, if a GSE

makes a business decision to not pursue

certain types of goals-qualifying loans in one

segment of the market, they are free to pursue

goals-qualifying owner and rental property

mortgages in other segments of the market.

With respect to loans that are originated

without private mortgage insurance, the GSEs

have been quite innovative in structuring

transactions to provide alternative credit

enhancements. Between 1997 and 1999,

Freddie Mac was involved in 16 structured

transactions totaling $8.1 billion, with

Freddie Mac’s 1999 business accounting for

over $5 billion of this total.1 HUD gives full

goals credit for such credit-enhanced

transactions.

Finally, it should be noted that 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 experience in 1997–

98, the purchase of seasoned loans appears

to be one useful strategy for purchasing goalsqualifying loans. In Section E.2, HUD’s

comparisons of the GSEs’ single-family

performance with those of depositories and

the overall single-family market include the

GSEs’ purchases of prior-year as well newlyoriginated loans.

d. Bias in HMDA Data

Both GSEs refer to findings from a study

by Peter Zorn and Jim Berkovec concerning

potential bias in HMDA data.2 Based on a

comparison of the borrower and census tract

characteristics between Freddie Macpurchased loans (from Freddie Mac’s own

data) and loans identified in 1993 HMDA

data as sold to Freddie Mac, Zorn and

Berkovec conclude that HMDA data

overstates the percentage of conventional,

conforming loans originated for lowerincome borrowers and for properties located

in underserved census tracts. The data

reported in Table A.4a of this appendix,

which are based on more recent data than the

Zorn and Berkovec paper, do not appear to

support their findings. With respect to the

goals-qualifying percentages for GSE

purchases, comparing columns 2 and 4 for

Fannie Mae, and columns 6 and 8 for Freddie

Mac, show that the HMDA-reported goalsqualifying percentages for loans sold to the

GSEs are not always larger than the

corresponding percentages for loans the GSEs

report as purchased. In fact, the HMDAreported percentages are more likely to be

smaller than the GSE-reported percentages

for the Special Affordable and Underserved

Areas Goals, yielding conclusions different

from those drawn by Zorn and Berkovec with

regard to bias in the HMDA data. In addition,

as noted in Appendix D, other research has

concluded that a portion of lower-income

loan originations are not even reported to

HMDA. Thus it is not clear that more recent

and complete data would support the Zorn

and Berkovec findings.

e. Other Technical Comments Related to GSE

Performance in Single-Family Owner Market

MSA-Level Analysis. In its comments,

Fannie Mae raised several concerns about

HUD’s comparisons between Fannie Mae and

the primary market at the metropolitan

statistical area (MSA) level (see Table A.5 in

this appendix). Essentially, Fannie Mae

questioned the relevance of any analysis at

the local level, given that the housing goals

are national-level goals. HUD believes that its

metropolitan-area analyses support and

clarify the national analyses on GSE

performance. While official goal performance

is measured only at the national level, HUD

believes that analyses of, for example, the

numbers of MSAs where Fannie Mae and

Freddie Mac lead or lag the local market

increases public understanding of the GSEs’

performance. For example, if the national

aggregate data showed that one GSE lagged

the market in funding loans in underserved

areas, it would be of interest to the public to

determine if this reflected particularly poor

performance in a few large MSAs or if it

reflected shortfalls in many MSAs. In this

case, an analysis of individual MSA data

would increase public understanding of that

GSE’s performance.

Missing Data. Both GSEs mentioned the

increasing problem of missing information in

HMDA data and in their own data bases—

particularly with regard to borrower race/

ethnicity. HUD agrees that treatment of

missing data is an important issue when

measuring GSE performance and developing

estimates of the size of the affordable market.

Both Appendices A and D use several

techniques for situations where data are

limited or missing. HUD’s treatment of

missing data reflects a consistent

commitment to fair and reasonable analyses,

and is designed to permit ‘‘apples-to-apples’’

comparisons between the GSEs and the

market to the extent possible. When

calculating portfolio percentages for different

sectors of the mortgage market, HUD

followed its usual procedure of excluding

loans with missing data. In certain analyses

involving market shares, HUD used a variety

of techniques such as reallocating missing

data, making adjustments for undercoverage

by HMDA data, or using data from other

65092 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

sources to estimate the absolute number of

mortgage originations. In general, HUD

believes that methods for addressing missing

data are reasonable and appropriate.

Lender-Purchased Loans. When analyzing

HMDA data, Fannie Mae included loans

purchased by lenders, as well as loans

originated by lenders, in its market

definition. HUD included only HMDAreported mortgage originations in its market

definition—mortgages purchased by lenders

were not included in HUD’s market data. To

do so would involve double counting loan

originations in the HMDA data.

Prior-Year/Current-Year Analysis. Fannie

Mae raised a number of concerns about

HUD’s separation of its purchases into

‘‘prior-year’’ loans and ‘‘current-year’’ loans.

Section E.2 of this appendix discusses this

issue in some detail. Much of HUD’s analysis

is conducted along the lines that Fannie Mae

recommends—considering each GSE’s total

purchases (of both prior-year mortgages and

current-year mortgages) in a single calendar

year. For example, see the discussion of the

GSEs’ past performance in Section E of this

appendix and the data in Tables A.3 and A.4.

But HUD believes the GSEs’ performance

should also be analyzed by focusing on the

total number of mortgages from a particular

origination year that the GSEs have

purchased to date. Comparing the GSEs’

current-year purchases, including prior-year

originations, with newly-originated

mortgages would result in somewhat of an

‘‘apples-to-oranges’’ comparison. Hence, to

conduct more of an ‘‘apples-to-apples’’

comparison between the GSEs and the

market, it is necessary to restrict the analysis

to GSE loan acquisitions originated in a

particular year (see Tables A.7a and A.7b).

HUD recognizes some of the problems that

result from analyses that focus on a single

origination year. However, as indicated by

the variety of analyses provided in Appendix

A, HUD believes that both frameworks are

useful for understanding the GSEs’ role in the

affordable lending market.

f. Leading the Market—The Qualitative

Dimension

The GSEs commented that they make a

sizable contribution toward serving the

housing needs of a wide range of American

families through their innovative outreach

and the overall leadership they provide to the

affordable lending market. This ‘‘qualitative’’

dimension of market leadership comes from

their normal operations in the market. Each

GSE gave numerous examples of their market

leadership, similar to the discussion that

HUD provides in Section G of this appendix.

Fannie Mae noted its Trillion Dollar

Commitment, its programs with minority-and

women-owned lenders, its initiative with

Community Development Financial

Institutions, and its numerous initiatives in

the technology area. Freddie Mac noted

similar program initiatives and outreach

efforts, and stated that it has been a ‘‘leader

in removing historical barriers to mortgage

credit’’ and that a recent HUD-commission

study commended both Freddie Mac and

Fannie Mae for their leadership in the

liberalization of mortgage underwriting

standards.

HUD understands the important role that

the GSEs play in the market and applauds

their efforts to re-examine their underwriting

standards and to reach out to traditionally

underserved borrowers and neighborhoods.

This perspective is reflected in Section G of

this appendix, which discusses qualitative

dimensions of the GSEs’ ability to lead the

industry. HUD concludes that due to their

dominant role in the market, their ability to

influence the types of loans that lenders will

originate, their utilization of state-of-the-art

technology, and their financial strength, the

GSEs have the ability to lead the market in

affordable lending and to reach out to those

markets that have traditionally not received

the benefits of an active secondary market.

g. Linking Housing Needs to GSEs

Fannie Mae commented that HUD’s

analysis of housing needs in Appendix A

needed to more carefully identify the

appropriate roles for the public sector and

the GSEs. Similar to its comments on HUD’s

1995 rule, Fannie Mae expressed concern

that HUD did not distinguish between

general housing needs of low- and moderateincome households and those needs that the

GSEs can reasonably be expected to address.

In this appendix, HUD presents an analysis

of general housing needs to comply with

FHEFSSA, which requires the Secretary to

consider such needs when establishing the

housing goals. HUD’s examination of

national housing needs does not suggest that

the GSEs can or should meet all of those

needs. Rather, the analysis is intended to

provide background on the evolution and

current state of the housing markets for lowand moderate-income households. HUD

recognizes that the GSEs alone can not

mitigate some of the more extreme problems

identified in this analysis.

However, with more focused effort, the

GSEs can assist in addressing several

problems discussed in this appendix with

regard to single-family and multifamily

housing. On the single-family side, the GSEs

can develop secondary market programs for

‘‘untapped’’ markets such as 2–4 unit rental

properties and properties needing

rehabilitation in the nation’s inner cities. The

GSEs can increase their support of more

customized mortgage products and

underwriting, with greater outreach to those

families who have not been served with

traditional products, underwriting, and

marketing. Particularly important in this

regard, the GSEs can ensure that their

automated underwriting systems recognize

the special circumstances of lower-income

and minority borrowers. As discussed in

Section 3.d of this appendix, HUD and others

are concerned about potential negative effects

of mortgage scoring on industry efforts to

reach out to lower-income and minority

families.

On the multifamily side, with new product

development and partnerships, the GSEs can

more fully address the credit needs of the

current market for affordable rental housing.

This appendix cities several areas where the

GSEs can help. One segment that would

benefit from a more active secondary market

is small multifamily properties—an

important part of the rental housing market

that is currently not being adequately served

by the GSEs. The GSEs can work to improve

overall efficiency and stability in this market

by developing new products and promoting

increased standardization and streamlined

procedures.

The GSEs have been immensely successful

in the financing of traditional single-family

housing. HUD recognizes that ‘‘untapped’’

markets will present some difficulties and

challenges for the GSEs. But by helping

develop a secondary market in these areas,

the GSEs will bring increased liquidity,

added stability, and ultimately lower interest

rates and rents for lower-income families in

these segments of the market.

h. Barriers to Higher GSE Performance on the

Housing Goals

Fannie Mae raised concerns with respect to

the interplay of the housing goals and the

risk-based capital standard proposed by

OFHEO. Fannie Mae stated that ‘‘the riskbased capital proposal represents another

potentially significant barrier to meeting the

goals that was not analyzed by the

Department.’’ OFHEO previously addressed

this question in their notice of proposed

rulemaking, dated April 13, 1999, concluding

that ‘‘the risk-based capital standard will not

affect the Enterprises’ ability to purchase

affordable housing loans.’’ 3 In part, this

conclusion was based on the finding that in

1996 and 1997, Freddie Mac would have

enjoyed capital surpluses under OFHEO’s

proposed rule, despite increased purchases of

loans meeting the housing goals. OFHEO

concluded that even in more adverse

economic environments, ‘‘the capital cost of

single family loans meeting the Enterprises’

affordable housing goals should not be

materially different, on average, from the cost

of other loans.’’

Of the various issues mentioned by Fannie

Mae in relation to OFHEO’s proposed

regulation, implications of the rule for highLTV and multifamily lending are of the

greatest relevance with regard to affordable

lending and the GSEs’ housing goals.

High-LTV Lending. Fannie Mae stated

concerns regarding the impacts of the

proposed OFHEO regulation on high-LTV

lending:

The risk-based capital regulation as

proposed imposes disproportionately high

capital requirements on high-LTV loans.

These requirements will impair our ability

to serve those borrowers with limited

resources. High-LTV lending is critically

important to our affordable housing

initiatives and outreach to first-time

homebuyers.4

It is not apparent that OFHEO’s proposed

rulemaking would impose

‘‘disproportionate’’ capital requirements on

high-LTV loans. Because high-LTV loans

typically have higher default rates, it is

reasonable to require the GSEs to hold more

capital against high-LTV loans than against

low-LTV loans, other things being equal.

If Fannie Mae’s view is that the proposed

OFHEO regulation requires the GSEs to hold

more capital against high-LTV loans than is

the case for other financial institutions, their

comments submitted in response to HUD’s

proposed housing goals rule do not contain

any material documenting such a claim.

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65093

However, it is noteworthy that the GSEs

enjoy benefits not conferred on other

financial institutions (e.g., exemption from

state and local taxes and exemption from

securities registration). There is no evidence

that Congress intended for the GSE risk-based

capital requirements to be strictly

comparable to capital standards for other

regulated financial institutions.

OFHEO’s proposed rule would require the

GSEs to hold more capital against high-LTV

loans, assuming the GSEs charge the same

guarantee against such loans as they do

against low-LTV loans. In practice, however,

the GSEs implicitly charge higher guarantee

fees on high-LTV loans, mitigating the need

for additional capital beyond what is added

through the guarantee fee. In its discussion

of this issue, OFHEO concluded that ‘‘Both

Enterprises use internal capital models that

reflect the higher risk of high LTV loans and

already may incorporate higher capital costs

into the implicit fees charged for these

loans.’’ 5

In addition, OFHEO observed that

multifamily loans, which predominantly

benefit low-and moderate-income

households, act as a hedge against high-LTV

loans in a down-rate environment ‘‘so that

higher costs on high LTV single family loans

are substantially offset by lower costs on

multifamily loans,’’ reducing the amount of

capital that the GSEs would otherwise be

required to hold against high-LTV loans.

Multifamily Risk-Sharing. Fannie Mae

contends that, under the provisions of

OFHEO’s proposed rule, its Delegated

Underwriting and Servicing (DUS)

multifamily program ‘‘will be impaired

because of the onerous ‘‘haircuts’’ specified

in the proposed capital regulation.’’ The

‘‘haircuts’’ mentioned by Fannie Mae refer to

adjustments for counterparty risk proposed

by OFHEO under risk-sharing provisions

such as those governing the DUS program.

Because of the importance of counterparty

risk to GSE safety and soundness, it is

certainly reasonable and necessary for

OFHEO to take such risk into consideration

in formulating its risk-based capital

regulation for the GSEs. HUD notes that

OFHEO received extensive comments from

the GSEs and others on this issue in response

to its proposed rule. Because the OFHEO

capital standard is presently at the proposed

rule stage, and not a final rule, it would be

premature and inappropriate for HUD to

speculate at this time on the possible

implications of OFHEO’s capital standards

on GSE multifamily performance. The

multifamily market and the GSEs’

capabilities within it will continue to evolve

during and after the time period when

OFHEO revises and finalizes its proposed

capital regulation in response to comments.

Any implications of the OFHEO capital

standards for GSE activities related to

multifamily mortgages or affordable housing

will merit consideration in future rounds of

HUD’s GSE rulemaking.

4. 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) and is useful for

gauging the reasonableness of specific levels

of the Low- and Moderate-Income Housing

Goal. In addition, the severe housing

problems faced by lower-income families are

discussed.

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. 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.

The consideration of the factors in this

appendix has led the Secretary to the

following conclusions:

• Despite the record national

homeownership rate of 66.8 percent in 1999,

much lower rates prevailed for minorities,

especially for African-American households

(46.7 percent) and Hispanics (45.5 percent),

and these lower rates are only partly

accounted for by differences in income, age,

and other socioeconomic factors.

• Pervasive and widespread disparities in

mortgage lending continued across the nation

in 1998, when the loan denial rate was 10.2

percent for white mortgage applicants, but

23.9 percent for African Americans and 18.9

percent for Hispanics.6

• Despite strong economic growth, low

unemployment, the lowest mortgage rates in

1998–99 in 25 years, and relatively stable

home prices, there is clear and compelling

evidence of deep and persistent housing

problems for Americans with the lowest

incomes. The number of very-low-income

American households with ‘‘worst case’’

housing needs is at an all-time high—5.4

million.7

• 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 barriers that many immigrants

and minorities face. In addition, market

segments such as single-family rental

properties, small multifamily properties,

manufactured housing, and older inner city

properties would benefit from the additional

financing and pricing efficiencies of a more

active secondary mortgage market.

• The Low- and Moderate-Income Housing

Goals for both GSEs were 40 percent in 1996

and 42 percent in 1997–1999. Fannie Mae

surpassed these goals, with a performance of

45.6 percent in 1996, 45.7 percent in 1997,

44.1 percent in 1998, and 45.9 percent in

1999. Freddie Mac’s performance of 41.1

percent in 1996, 42.6 percent in 1997 and

42.9 percent in 1998 narrowly exceeded

these goals, but Freddie Mac’s performance

jumped sharply in 1999 to 46.1 percent,

exceeding Fannie Mae’s performance for the

first time, by a narrow margin.

• Several studies have shown that both

Fannie Mae and Freddie Mac lag behind

depository institutions and the overall

conventional conforming market in providing

affordable home loans to lower-income

borrowers and underserved neighborhoods.

Though 1998 Fannie Mae made efforts to

improve its performance, while Freddie Mac

made less improvement, and therefore fell

behind Fannie Mae, depositories, and the

overall market in serving lower-income and

minority families and their neighborhoods.

This indicated that there was room for both

GSEs (but particularly Freddie Mac) to

improve their funding of single-family home

mortgages for lower-income families and

underserved communities. Data on the

performance of depositories and the primary

market is not yet available for 1999, thus it

is not possible to determine if the GSEs

continued to lag these sectors of the market

last year. But, based on the data provided by

the GSEs to the Department, Freddie Mac’s

single-family low- and moderate-income

performance in 1999 exceeded Fannie Mae’s

performance. It remains to be seen whether

this represents a new trend, or a temporary

reversal of the pattern for the 1996–98

period.

• The GSEs’ presence in the goalqualifying market is significantly less than

their presence in the overall mortgage

market. Specifically, HUD estimates that they

accounted for 40 percent of all owneroccupied and rental units financed in the

primary market in 1997, but only 32 percent

of low- and moderate-income units financed.

Their role was even lower for low-and

moderate-income rental properties, where

they accounted for 26 percent of low- and

moderate-income multifamily units financed

and only 14 percent of low- and moderateincome single-family rental units financed.

These general patterns were also evident in

1998, a heavy refinance year, except that the

GSEs had a higher share of the single-family

owner market.

• Other issues have also been raised about

the GSEs’ affordable lending performance. A

large percentage of the lower-income loans

purchased by the enterprises have relatively

high down payments, which raises questions

about whether the GSEs are adequately

meeting the mortgage credit needs of lowerincome families who do not have sufficient

cash to make a high down payment. Also,

while single-family rental properties are an

important source of low- and moderateincome rental housing, they represent only a

small portion of the GSEs’ business.

• Freddie Mac has re-entered the

multifamily market, after withdrawing for a

time in the early 1990s. Thus, concerns

regarding Freddie Mac’s multifamily

capabilities no longer constrain their

performance with regard to the Low- and

Moderate-Income Housing Goal and the

Special Affordable Housing Goal to the same

degree that prevailed at the time the

Department issued its 1995 GSE regulations.

However, Freddie Mac’s multifamily

presence remains proportionately lower than

that of Fannie Mae. For example, units in

65094 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

multifamily properties accounted for 7.3

percent of Freddie Mac’s mortgage purchases

during 1994–99, compared with 11.8 percent

for Fannie Mae. Because a relatively large

proportion of multifamily units qualify for

the Low- and Moderate-Income Housing Goal

and the Special Affordable Housing Goal,

through 1998 Freddie Mac’s lower

multifamily presence was a major factor

contributing to its weaker overall

performance on these two housing goals

relative to Fannie Mae. But in 1999,

multifamily units accounted for 8.2 percent

of total units financed by Freddie Mac and

9.5 percent of total units financed by Fannie

Mae, the narrowest gap of the 1994–99

period.

• The overall presence of both GSEs in the

multifamily mortgage market falls short of

their involvement in the single-family

market. Specifically, the GSEs’ purchases of

1997 originations accounted for 50 percent of

the owner market, but only 24 percent of the

multifamily market. Further expansion of the

presence of both GSEs in the multifamily

market is needed in order for them to make

significant progress in closing the gaps

between the affordability of their mortgage

purchases and that of the overall

conventional market.

• The GSEs have proceeded cautiously in

expanding their multifamily purchases

during the 1990s. Fannie Mae’s multifamily

lending has been described by Standard &

Poor’s as ‘‘extremely conservative,’’ and

Freddie Mac has not experienced a single

default on the multifamily mortgages it has

purchased since 1993.8 By the end of 1999,

both GSEs’ multifamily performance had

improved to the point where multifamily

delinquency rates were lower than those for

single-family loans.9

• Because of the advantages conferred by

Government sponsorship, the GSEs are in a

unique position to provide leadership in

addressing the excessive cost and difficulty

in obtaining mortgage financing for

underserved segments of the multifamily

market, including small properties with 5–50

units and properties in need of rehabilitation.

B. Factor 1: National Housing Needs

This section reviews the general housing

needs of low- and moderate-income families

that exist today and are expected to continue

in the near future. In so doing, the section

focuses on the affordability problems of

lower- income families and on racial

disparities in homeownership and mortgage

lending. It also notes some special problems,

such as the need to rehabilitate our older

urban housing stock.

1. Homeownership Gaps

Despite a record national homeownership

rate, 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 at an all-time high of 67.1

percent in the first quarter of 2000, the rate

for minority households was lower. The

homeownership rate for African-American

households was 47.4 percent. Similarly, just

45.7 percent of Hispanic households owned

a home.

Importance of Homeownership.

Homeownership is one of the most common

forms of property ownership as well as

savings.10 Historically, home equity has been

the largest source of wealth for most

Americans. Only recently has stock equity

exceeded home equity as a share of total

household wealth. Even with stocks

appreciating faster than home prices over the

past decade, still 59 percent of all

homeowners in 1998 held more than half of

their net wealth in the form of home equity.

Among low-income homeowners (household

income less than $20,000), half held more

than 70 percent of their wealth in home

equity in 1995.11 Median net wealth for

renters was less than four percent of the

median net wealth for homeowners in 1998.

For low-income households, renter median

net wealth is less than two percent of

homeowner median net wealth.12 Thus a

homeownership gap translates directly into a

wealth gap.

Homeownership promotes social and

community stability by increasing the

number of stakeholders and reducing

disparities in the distributions of wealth and

income. There is growing evidence that

planning for and meeting the demands of

homeownership may reinforce the qualities

of responsibility and self-reliance. White and

Green 13 provide empirical support for the

association of homeownership with a more

responsible, self-reliant citizenry. Both

private and public benefits are increased to

the extent that developing and reinforcing

these qualities improve prospects for

individual economic opportunities.

Barriers to Homeownership. 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,

lower-income, and minority families who

were closed out of the housing market during

the 1980s re-entered the housing market

during the last decade. However, many

households still lack the financial resources

and earning power to take advantage of

today’s homebuying 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. Fully 45 percent of

the nation’s population between the ages of

25 and 34 have no advanced education and

are therefore at risk of being unable to afford

homeownership.14 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.

In addition to low income, high debts are

a primary reason households cannot afford to

purchase a home. According to a 1993

Census Bureau report, nearly 53 percent of

renter families have both insufficient income

and excessive debt problems that may cause

difficulty in financing a home purchase.15

High debt-to-income ratios frequently make

potential borrowers ineligible for mortgages

based on the underwriting criteria

established in the conventional mortgage

market.

An additional barrier to homeownership is

the fear and uncertainty about the buying

process and the risks of ownership. 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.16 Also, many feared the

obligations of ownership, because of

concerns about the risk of future

deterioration of the house or the

neighborhood.

Finally, discrimination in mortgage

lending continues to be a barrier to

homeownership. Disparities in treatment

between borrowers of different races and

neighborhoods of different racial makeup

have been well documented. These

disparities are discussed in the next section.

2. Disparities in Mortgage Financing

Disparities Between Borrowers of Different

Races. Research based on Home Mortgage

Disclosure Act (HMDA) data suggests

pervasive and widespread disparities in

mortgage lending across the Nation. For

1998, the denial rate for white mortgage

applicants was 10.2 percent, while 23.9

percent of African-American and 18.9

percent of Hispanic applicants were denied.

Even after controlling for income, the

African-American denial rate was

approximately twice that of white applicants.

A major study by researchers at the Federal

Reserve Bank of Boston found that mortgage

denial rates remained substantially higher for

minorities in 1991–93, even after controlling

for indicators of credit risk.17 AfricanAmerican and Hispanic applicants in Boston

with the same borrower and property

characteristics as white applicants had a 17

percent denial rate, compared with the 11

percent denial rate experienced by whites. A

subsequent study conducted at the Federal

Reserve Bank of Chicago reported similar

findings.18

Several possible explanations for these

lending disparities have been suggested. The

studies by the Boston and Chicago Federal

Reserve Banks found that racial disparities

cannot be explained by reported differences

in creditworthiness. 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

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65095

than devote effort to distinguishing the

creditworthiness of the individual

applicant.19 This violates the Fair Housing

Act.

Underwriting Rigidities. 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. Over the

past few years, lenders, private mortgage

insurers, and the GSEs have adjusted their

underwriting guidelines to take into account

these special circumstances of lower-income

families. Many of the changes recently

undertaken by the industry to expand

homeownership have focused on finding

alternative underwriting guidelines to

establish creditworthiness that do not

disadvantage creditworthy minority or lowincome applicants.

However, because of the enhanced roles of

credit scoring and automated underwriting in

the mortgage origination process, it is unclear

to what degree the reduced rigidity in

industry standards will benefit borrowers

who have been adversely impacted by the

traditional guidelines. 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. Thus lower-income

and minority loan applicants, who often have

lower credit scores than other applicants,

may be dependent on the willingness of

lenders to take the time to look beyond such

credit scores and consider any appropriate

‘‘mitigating factors,’’ such as the timely

payment of their bills, in the underwriting

process. For example, there is a concern in

the industry that a ‘‘FICO’’ score less than

620 means an automatic rejection of a loan

application without further consideration of

any such factors.20 This could

disproportionately affect minority applicants.

More information on the distribution of

credit scores and on the effects of

implementing automated underwriting

systems is needed.21

Disparities Between Neighborhoods.

Mortgage credit also appears to be less

accessible in low-income and high-minority

neighborhoods. As discussed in Appendix B,

1998 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 (19.4 percent

versus 10.3 percent). Numerous studies have

found that mortgage denial rates are higher

in low-income census tracts, even accounting

for other loan and borrower characteristics.22

These geographic 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.

Geographic disparities in mortgage lending

and the issue of mortgage redlining are

discussed further in Appendix B.

3. Affordability Problems and Worst Case

Housing Needs

The severe 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 report analyzes data from the 1997

AHS and focuses on the housing problems

faced by low-income renters, but some data

is also presented on families living in owneroccupied housing. In introducing the most

recent study, Secretary Cuomo noted that it

found that ‘‘despite the booming economy,

worst case housing needs continue to

increase’’ and such needs ‘‘have now reached

an all-time high of million households.’’ 23

The ‘‘Worst Cases’’ report measures three

types of problems faced by homeowners and

renters:

• 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’’);

• 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

• Crowded housing, where there is more

than one person per room in a residence.

The study reveals that in 1997, 5.4 million

households had ‘‘worst case’’ housing needs,

defined as housing costs greater than 50

percent of household income or severely

inadequate housing among unassisted

households.

a. Problems Faced by Owners

Of the 65.5 million owner households in

1997, 5.5 million (8.5 percent) confronted a

severe cost burden and another 8.3 million

(12.7 percent) faced a moderate cost burden.

There were 725,000 households with severe

physical problems and 916,000 which were

overcrowded. The report found that 25.4

percent of American homeowners faced at

least one severe or moderate problem.

Not surprisingly, problems were most

common among very low-income owners.24

More than a third of these households faced

a severe cost burden, and an additional 23

percent faced a moderate cost burden. And

7 percent of these families lived in severely

or moderately inadequate housing, while 2

percent faced overcrowding. Only 38 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 grown—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 1997. 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 1995.25 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.26 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 1997.

b. Problems Faced by Renters

Problems of all three types listed above are

more common among renters than among

homeowners. In 1997 there were 6.7 million

renter households (20 percent of all renters)

who paid more than 50 percent of their

income for rent.27 Another 6.8 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, 72

percent faced an affordability problem,

including 44 percent who paid more than

half of their income in rent. More than onethird 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 42 percent in 1997.

The share of families living in inadequate

housing in 1997 was higher for renters (12

percent) than for owners (4 percent), as was

the share living in overcrowded housing (6

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. But in 1997 there were also sharp

increases in the inadequate and crowded

shares of rental housing.

Other problems faced by renters discussed

in the ‘‘Worst Cases’’ report include the loss

between 1991 and 1997 of 370,000 rental

units affordable to very low-income families,

the increase in ‘‘worst case needs’’ among

working families between 1991 and 1997,

and the shortage of units affordable to very

low-income households (especially in the

West).

4. 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. This section presents

a brief discussion of three such areas and the

roles that the GSEs play or might play in

addressing the needs in these areas. Other

needs are discussed throughout these

appendices.

a. Single-family Rental Housing

The 1996 Property Owners and Managers

Survey reported that 51 percent of all rental

65096 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

housing units are located in ‘‘multifamily’’

properties—i.e, properties that contain 5 or

more rental units. The remaining 49 percent

of rental units are found in the ‘‘mom and

pop shops’’ of the rental market—’’singlefamily’’ rental properties, containing 1–4

units. These small properties are largely

individually-owned and managed, and in

many cases the owner-managers live in one

of the units in the property. They include

many properties in older cities, such as the

duplexes in Baltimore and the triple-deckers

in Boston. A number of these single-family

rental properties are in need of financing for

rehabilitation, discussed in the next

subsection.

Single-family rental units play an

especially important role in lower-income

housing. The 1997 AHS found that 59

percent of such units were affordable to very

low-income families—exceeding the

corresponding share of 53 percent for

multifamily units. These units also play a

significant role in the GSEs’ performance on

the housing goals, since 30 percent of the

single-family rental units financed by the

GSEs in 1999 were affordable to very lowincome 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. In 1999

the GSEs purchased $26 billion in mortgages

for such properties, but this represented 5

percent of the total dollar volume of each

enterprise’s 1999 business and 8 percent of

total single-family units financed by each

GSE. With regard to their market share, HUD

estimates that the GSEs have financed only

about 19 percent of all single-family rental

units that received mortgages in 1998, well

below the GSEs’ estimated market share of 68

percent for single-family owner properties.

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.28

b. Rehabilitation Problems of Older Areas

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.

The ability of the nation to maintain the

quality and availability of the existing

affordable housing stock and to stabilize the

neighborhoods where it is found depends on

an adequate supply of credit to rehabilitate

and repair older units. 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.29 However, extra

effort is required, due to the complexities of

rehabilitation financing, as there is still a

need to do more.

c. Small Multifamily Properties

There is evidence that small multifamily

properties with 5–50 units have been

adversely affected by differentials in the cost

of mortgage financing relative to larger

properties.30 While mortgage loans can

generally be obtained for most properties, the

financing that is available is relatively

expensive, with interest rates as much as 150

basis points higher than those on standard

multifamily loans. Loan products are

characterized by shorter terms and adjustable

interest rates. Borrowers typically incur costs

for origination and placement fees,

environmental reviews, architectural

certifications (on new construction or

substantial rehabilitation projects),

inspections, attorney opinions and

certifications, credit reviews, appraisals, and

market surveys.31 Because of a large fixed

element, these costs are usually not scaled

according to the mortgage loan amount or

number of dwelling units in a property and

consequently are often prohibitively high on

smaller projects.

d. Other Needs

Further discussions of other housing needs

and mortgage market problems are provided

in the following sections on economic,

housing, and demographic conditions. In the

single-family area, for example, an important

trend has been the growth of the subprime

market and the GSEs’ participation in the Aminus portion of that market. Manufactured

housing finance and rural housing finance

are areas that could be served more

efficiently with an enhanced secondary

market presence. In the multifamily area,

properties in need of rehabilitation represent

a market segment where financing has

sometimes been difficult. Other housing

needs and mortgage market problems are also

discussed.

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, homebuyer

characteristics, and the state of affordable

lending. Section D follows with a discussion

of the economic, housing, and demographic

conditions affecting the multifamily mortgage

market.

1. Recent Trends in the Housing Market

Solid economic growth, low interest rates,

price stability, and an unemployment rate of

4.2 percent, the lowest rate since 1969,

combined to make 1999 a very strong year for

the housing market. The employmentpopulation ratio reached a record 64.3

percent last year, and a broad measure of

labor market distress, combining the number

of unemployed and the duration of

unemployment, was down by 54 percent

from its 1992 peak.32 Rising real wages, a

strong stock market, and higher home prices

all contributed to a continuation of the rise

in net household worth, contributing to the

strong demand for 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. Major gains in ownership

have occurred over the last few years, with

the homeownership rate reaching a record

level of 66.8 percent in 1999, when the

number of households owning their own

home was 7 million greater than in 1994, an

unprecedented five-year increase.

Gains in homeownership have been

widespread in over the last six years.33 As a

result, the homeownership rate rose from:

• 42.0 percent in 1993 to 46.7 percent in

1999 for African American households,

• 39.4 percent in 1993 to 45.5 percent in

1999 for Hispanic households,

• 73.7 percent in 1993 to 77.6 percent in

1999 for married couples with children,

• 65.1 percent in 1993 to 67.2 percent in

1999 for household heads aged 35–44, and

• 48.9 percent in 1993 to 50.4 percent in

1999 for central city residents.

However, as these figures demonstrate,

sizable gaps in homeownership remain.

Sales of New and Existing Homes.34 New

home sales rose at a rate of 7.5 percent per

year between 1991 and 1999, and exceeded

the previous record level (set in 1998) by 2

percent in 1999. The market for new homes

has been strong throughout the nation, with

record sales in the South and Midwest during

1999. New home sales in the Northeast and

West, while strong, are running below the

peak levels attained during their strong job

markets of the mid-1980s and late-1970s,

respectively.

The National Association of Realtors

reported that 5.2 million existing homes were

sold in 1999, overturning the old record set

in 1998 by 5 percent. Combined new and

existing home sales also set a record of 6.2

million last year. Since existing homes

account for more than 80 percent of the total

market and sales of existing homes are strong

throughout the country, combined sales

reach record levels in three of the four major

regions of the nation and came within 97

percent of the record in the Northeast.

One of the strongest sectors of the housing

market in recent years has been shipments of

manufactured homes, which more than

doubled between 1991 and 1996, and

essentially leveled off at the 1996 record

during 1997–99. Two-thirds of manufactured

home placements were in the South, where

they comprised more than one-third of total

new homes sold in 1999.

Economy/Housing Market Prospects. As

noted above, the U.S. economy is coming off

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65097

several years of economic expansion,

accompanied by low interest rates and high

housing affordability. In fact, 1999 was a

record year for housing sales. The remainder

of this subsection discusses the future

prospects for the housing market.

According to Standard & Poor’s DRI, the

housing market is slowing down from the

record breaking pace of over five million

single-family existing homes sold during

1999.35 Sales of existing single-family homes

are on a pace of 4.5 million units for 2000.

Between 2001 and 2004, existing singlefamily home sales are expected to average 4.2

million units. Housing starts are expected to

average 1.5 million units over the same

period. Housing should remain affordable, as

indicated by out-of-pocket costs as a share of

disposable income, which are expected to

continue their downward trend through

2004, dipping below 24 percent by 2003.

According to Standard & Poor’s DRI, the 30-

year fixed rate mortgage rate is expected to

average 8.4 percent in 2000, and then trend

down to 7.7 percent by 2004.

The Congressional Budget Office (CBO) 36

projects that real Gross Domestic Product

will grow at an average rate of 2.7 percent

from 2001 through 2005, down from the

expected 4.9 percent growth rate during

2000. The ten-year Treasury rate is projected

to average 6.0 percent between 2001 and

2005. Inflation, as measured by the Consumer

Price Index (CPI) is projected to remain

modest during the same period, averaging 2.7

percent. The unemployment rate is expected

to remain low over the next four years,

averaging 4.3 percent.

Certain risks exist, however, which could

undermine the wellbeing of the economy.

The probability of a recession still exists for

the next couple of years. Under a pessimistic

scenario (10 percent probability), Standard &

Poor’s DRI predicts that if a stock-market

correction were to occur toward the end of

2000, housing starts could fall to 1.2 million

units. With relatively low inflation, DRI

anticipates that the Federal Reserve would

respond quickly by lower interest rates. This

would revive the housing market, although

the recovery would be slow, with starts not

returning to pre-recession levels until late

2004.37 An alternative scenario has a

recession arriving in 2002, resulting from a

Federal Reserve overreaction to higher

inflation and a stock market correction in late

2001 or early 2002 (which DRI predicts with

a probability of 35 percent). Under this

scenario, housing starts would fall to almost

one million units. As a result of lower

interest rates, the housing market would

rebound strongly, with starts reaching nearrecord levels by the end of 2004.38

In addition to DRI and CBO, the Mortgage

Bankers Association predicts that for 2000/

2001 housing starts will reach 1.6/1.5 million

units for 2000 and 2001 and the 30-year fixed

rate mortgage rate will average 8.5/9.0

percent.39 Fannie Mae predicts that the

Federal Reserve will successfully engineer a

soft landing, with real growth of the economy

slowing to a two to three percent pace in

2001. As a result, mortgage originations

should decline to $967 billion, 27 percent

less than the 1998 record level.40

2. Underlying Demographic Conditions

Over the next 20 years, the U.S. population

is expected to grow by an average of 2.4

million per year. This will likely result in 1.1

to 1.2 million new households per year,

creating a continuing need for additional

housing.41 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; ‘‘trade-up buyers;’’ nontraditional and single households; and the

growing income inequality between people

with different levels of education.

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 next decade due to the

aging of the baby-boom population.42

However, growing demand from immigrants

and non-traditional homebuyers will likely

fill in the void. The Joint Center for Housing

Studies recently projected that the share of

the U.S. population accounted for by racial

and ethnic minorities would increase from 25

percent to 30 percent by the year 2010.43 The

echo baby-boom (that is, children of the

baby-boomers) will also add to housing

demand later in the next decade. 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

homebuying 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 homebuying 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.44

As the youngest of the baby-boomers, those

born in the 1960s, reached their peak

homebuying 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.45

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 is

expected to lead to reduced housing demand

during the next decade, though, as discussed

below, other factors have kept the housing

market very strong in the 1990s. However,

the echo baby-boom generation (the children

of the baby-boomers, who were born after

1977), while smaller than the baby-boom

generation, will reach peak homebuying age

later in the first decade of the new

millennium, softening the blow somewhat.46

Immigrant Homebuyers. Past, present, and

future immigration will also help keep

homeownership growth at a respectable

level. During the 1980s, 6 million legal

immigrants entered the United States,

compared with 4.2 million during the 1970s

and 3.2 million during the 1960s.47 As a

result, the foreign-born population of the

United States doubled from 9.6 million in

1970 to 19.8 million in 1990, and is expected

to reach 31 million by 2010.48 While

immigrants tend to rent their first homes

upon arriving in the United States,

homeownership rates are substantially higher

among 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 67.4 percent for native-born households.

For foreign-born naturalized citizens, the

homeownership rate after six years was a

remarkable 66.9 percent.49

Immigration is projected to add even more

new Americans in the 1990s, which will help

offset declines in the demand for housing

caused by the aging of the baby-boom

generation. While it is projected that

immigrants will account for less than four

percent of all households in 2010, without

the increase in the number of immigrants,

household growth would be 25 percent lower

over the next 15 years. As a result of the

continued influx of immigrants and the aging

of the domestic population, household

growth over the next decade should remain

at or near its current pace of 1.1–1.2 million

new households per year, even though

population growth is slowing. If this high

rate of foreign immigration continues, it is

possible that first-time homebuyers will

make up as much as half of the home

purchase market over the next several

years.50

Past and future immigration will lead to

increasing racial and ethnic diversity,

especially among the young adult

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.

Trade-up Buyers. The fastest growing

demographic group in the early part of the

next millennium will be 45-to 65-year olds.

This will translate into a strong demand for

upscale housing and second homes. The

greater equity resulting from recent increases

in home prices should also lead to a larger

role for ‘‘trade-up buyers’’ in the housing

market during the next 10 to 15 years.

Nontraditional and Single Homebuyers.

While overall growth in new households has

slowed down, nontraditional households

have become more important in the

homebuyer market. With later marriages and

more divorces, single-person and singleparent households have increased rapidly.

First-time buyers include a record number of

never-married single households, although

65098 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

their ownership rates still lag those of

married couple households. According to the

Chicago Title and Trust’s Home Buyers

Surveys, the share of first-time homebuyers

who were never-married singles rose from 21

percent in 1991 to 37 percent in 1996, and

to a record 43 percent in 1997. However, in

1999 never-married singles fell to 30 percent

of first-time homebuyers.51 The shares for

divorced/separated and widowed first-time

homebuyers have stayed constant over the

period, at eight percent and one percent,

respectively.52 The National Association of

Realtors reports that ‘‘single individuals,

unmarried couples and minorities are

entering the market as first-time buyers in

record numbers.’’ 53 With the increase in

single person households, it is expected that

there will be a greater need for apartments,

condominiums and townhomes.

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 20 percent.

Single-parent households are also expected

to decline as the baby-boom generation ages

out of the childbearing years. For these

reasons, nontraditional homebuyers may

account for a smaller share of the housing

market in the future.

Growing Income Inequality. The Census

Bureau recently reported that the top 5

percent of American households received

21.4 percent of aggregate household income

in 1998, up sharply from 16.1 percent in

1977. The share accruing to the lowest 80

percent of households fell accordingly, from

56.5 percent in 1977 to 50.8 percent in 1998.

The share of aggregate income accruing to

households between the 80th and 95th

percentiles of the income distribution was

virtually unchanged over this period.54

The increase in income inequality over the

past two decades has been especially

significant between those with and those

without post-secondary education. The

Census Bureau reports that by 1997, the

mean income of householders with a high

school education (or less) was less than half

that for householders with a bachelor’s

degree (or more). According to the Joint

Center for Housing Studies, inflationadjusted median earnings of men aged 25 to

34 with only a high-school education

decreased by 14 percent between 1989 and

1995.55 So, while homeownership is highly

affordable, this cohort lacks 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 postsecondary education find their demand for

housing constrained.

3. Single-Family Owner Mortgage Market

The mortgage market has undergone a great

deal of growth and change over the past few

years. Low interest rates, modest increases in

home prices, and growth in real household

income have increased the affordability of

housing and resulted in a mortgage market

boom. Total originations of single-family

loans increased from $458 billion in 1990 to

$859 billion in 1997 and then jumped to a

record $1.507 trillion during the heavy

refinancing year of 1998, before declining to

$1.287 billion in 1999, the second highest

level recorded.56 There have also been many

changes in the structure and operation of the

mortgage market. Innovations in lending

products, added flexibility in underwriting

guidelines, the development of automated

underwriting systems and the rise of the

subprime market, have had impacts on both

the overall market and affordable lending

during the 1990s.

The section starts with a review of trends

in the market for mortgages on single-family

owner-occupied housing. Next, trends in

affordable lending, including new initiatives

and changes to underwriting guidelines and

the prospects for potential homebuyers are

discussed. The section concludes with a

summary of the activity of the GSEs relative

to originations in the primary mortgage

market.

a. Basic Trends in the Mortgage Market

Interest Rate Trends. The high and volatile

mortgage rates of the 1980s and early 1990s

have given way to a period with much lower

and more stable rates in the last six years.

Interest rates on mortgages for new homes

were above 12 percent as the 1980s began

and quickly rose to more than 15 percent.57

After 1982, they drifted downward slowly to

the 9 percent range in 1987–88, before rising

back into double-digits in 1989–90. Rates

then dropped by about one percentage point

a year for three years, reaching a low of 6.8

percent in October-November 1993 and

averaging 7.2 percent for the year as a whole.

Mortgage rates turned upward in 1994,

peaking at 8.3 percent in early 1995, but fell

to the 7.5 percent-7.9 percent range for most

of 1996 and 1997. However, rates began

another descent in late-1997 and averaged

6.95 percent for 30-year fixed rate

conventional mortgages during 1998, the

lowest level since 1968, before rising to an

average of 7.44 percent in 1999.58

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

rates are high, because they carry lower rates

than FRMs and because buyers may hope to

refinance to a FRM when mortgage rates

decline. Thus the Federal Housing Finance

Board (FHFB) reports that the ARM share of

the market jumped from 20 percent in the

low-rate market of 1993 to 39 percent when

rates rose in 1994.59 The ARM share has

since trended downward, falling to 22

percent in 1997 and a record low of 12

percent in 1998, before rising back to 22

percent in 1999.

In 1997 the term-to-maturity was 30 years

for 83 percent of conventional home

purchase mortgages. Other maturities

included 15 years (11 percent of mortgages),

20 years (2 percent), and 25 years (1 percent).

The average term was 27.5 years, up slightly

from 26.9 years in 1996, but within the

narrow range of 25–28 years which has

prevailed since 1975.

One dimension of the mortgage market

which has changed in recent years is the

increased popularity of low- or no-point

mortgages. FHFB reports that average initial

fees and charges (‘‘points’’) have decreased

from 2.5 percent of loan balance in the mid1980s to 2 percent in the late-1980s, 1.5

percent in the early 1990s, and less than 1.0

percent in 1995–97. In 1998, 21 percent of all

loans were no-point mortgages. These lower

transactions costs have increased the

propensity of homeowners to refinance their

mortgages.60

Another recent major change in the

conventional mortgage market has been the

proliferation of high loan-to-value ratio (LTV)

mortgages. 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. Loans with LTVs less

than or equal to 80 percent fell from threequarters of the market in 1989–91 to an

average of 56 percent of mortgages originated

in 1994–97. As a result, the average LTV rose

from 75 percent in 1989–91 to nearly 80

percent in 1994–97.61

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.

Mortgage Originations: Refinance

Mortgages. Mortgage rates affect the volume

of both home purchase mortgages and

mortgages used to refinance an existing

mortgage. The effects of mortgage rates on the

volume of home purchase mortgages are felt

through their role in determining housing

affordability, discussed in the next

subsection. However, the largest impact of

rate swings on single-family mortgage

originations is reflected in the volume of

refinancings.

During 1992–93, homeowners responded

to the lowest rates in 25 years by refinancing

existing mortgages. In 1989–90 interest rates

exceeded 10 percent, and refinancings

accounted for less than 25 percent of total

mortgage originations.62 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.63

Total single-family mortgage originations

bottomed out at $639 billion in 1995, when

the refinance share was only 15 percent. This

meant that refinance volume declined by

more than 80 percent in just two years.

A second surge in refinancings began in

late-1997, abated somewhat in early 1998,

but regained momentum in June 1998. The

refinance share rose above 30 percent in mid1997, exceeded 40 percent in late-1997, and

peaked at 64 percent in January, before

falling to 40 percent by May 1998. This share

increased steadily over the June–September

1998 period, and averaged 50 percent for

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65099

1998. The refi boom ended abruptly in early

1999, as the share of loans for refinancings

fell from 60 percent in the first quarter to 27

percent in the second quarter and 22 percent

in the third and fourth quarters. Total

originations, driven by the volume of

refinancings, amounted to $859 billion in

1997 and were $1.507 trillion in 1998, nearly

50 percent higher than the previous record

level of $1.02 trillion attained in 1993, before

falling to $1.287 trillion last year. Total

refinance mortgage volume in 1998 was

estimated to be nearly 10 times the level

attained in 1995. The refinance wave from

1997 through early 1999 reflects 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 (including the GSEs),

utilizing automated underwriting and

mortgage origination systems, to handle this

unprecedented volume expeditiously.

Mortgage Originations: Home Purchase

Mortgages. In 1972 the median price of

existing homes in the United States was

$27,000 and mortgage rates averaged 7.52

percent; thus with a 20 percent down

payment, a family needed an income of

$7,200 to qualify for a loan on a medianpriced home. Actual median family income

was $11,100, exceeding qualifying income by

55 percent. The National Association of

Realtors (NAR) has developed a housing

affordability index, calculated as the ratio of

median income to qualifying income, which

was 155 in 1972.

By 1982 NAR’s affordability index had

plummeted to 70, reflecting a 154 percent

increase in home prices and a doubling of

mortgage rates over the decade. That is,

qualifying income rose by nearly 400 percent,

to $33,700, while median family income

barely doubled, to $23,400. With so many

families priced out of the market, singlefamily mortgage originations amounted to

only $97 billion in 1982.

Declining interest rates and the moderation

of inflation in home prices have led to a

dramatic turnaround in housing affordability

in the last decade and a half. Remarkably,

qualifying income was $27,700 in 1993—

$6,000 less than it had been in 1982. Median

family income reached $37,000 in 1993, thus

the NAR’s housing affordability index

reached 133. 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.64 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.

The high affordability of housing, low

unemployment, and high consumer

confidence meant that home purchase

mortgages reached a record level in 1997.

However, this record was surpassed in 1998,

as a July 1998 survey by Fannie Mae found

that ‘‘every single previously cited barrier to

homeownership—from not having enough

money for a down payment, to not having

sufficient information about how to buy a

home, to the confidence one has in his job,

to discrimination or social barriers—has

collapsed to the lowest level recorded in the

seven years Fannie Mae has sponsored its

annual National Housing Survey.’’ 65

Specifically, the Mortgage Bankers

Association estimates that home purchase

mortgages rose to about $754 billion in 1998,

well above the previous record of $574

billion established in 1997. The boom

continued in 1999, with home purchase

mortgage volume increasing further, to $824

billion.

First-time Homebuyers. First-time

homebuyers have been the driving force in

the recovery of the nation’s housing market

over the past several years. First-time

homebuyers 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

1992.66 Even though this cohort is smaller,

first-time homebuyers increased their share

of home sales. First-time buyers accounted

for about 45 percent of home sales in 1999.

Participation rates for first-time homebuyers

so far this decade are all greater than or equal

to 45 percent. This follows participation rates

that averaged 40 percent in the 1980s,

including a low of 36 percent in 1985. The

highest first-time homebuyer participation

rate was achieved in 1977, when it was 48

percent.67

The Chicago Title and Trust Company

reports that the average first-time buyer in

1999 was 32 years old and spent 5 months

looking at 12 homes before making a

purchase decision. Most such buyers are

married couples, but in 1999 29 percent had

never been married, 9 percent were divorced

or separated, and 1 percent were widowed.

First-time buyers paid an average of 34

percent of after-tax income, or $1,090 per

month, on their mortgage payments in 1999,

and saved for 2.2 years to accumulate a down

payment. The National Association of

Realtors reports that the median mortgage

amount for first-time buyers was $104,000 in

1999, corresponding to an LTV of 97 percent,

compared with a median mortgage amount of

$150,000 and an average LTV of 81 percent

for repeat buyers.

GSEs’ Acquisitions as a Share of the

Primary Single-Family Mortgage Market. The

GSEs’ single-family mortgage acquisitions

have generally followed the volume of

originations in the primary market for

conventional mortgages, falling from 5.3

million mortgages in the record year of 1993

to 2.2 million mortgages in 1995, but

rebounding to 2.9 million mortgages in 1996.

In 1997, however, single-family originations

were essentially unchanged, but the GSEs’

acquisitions declined to 2.7 million

mortgages.68 This pattern was reversed in

1998, when originations rose by 73 percent,

but the GSEs’ purchases jumped to 5.8

million mortgages. In 1999 the GSEs’

acquired 4.8 million single-family mortgages,

a decline of 17 percent, which approximated

the 15 percent decline in single-family

originations.

Reflecting these trends, the Office of

Federal Housing Enterprise Oversight

(OFHEO) estimates that the GSEs’ share of

total originations in the 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

market rebounded sharply in 1998–99, to 43–

42 percent.

Mortgage Market Prospects. The Mortgage

Bankers Association (MBA) reports that

mortgage originations in 1999 were $1.3

trillion. This followed the record-breaking

year of 1998, with $1.5 trillion in mortgage

originations. Refinancing of existing

mortgages was down from 1998’s 50 percent

share of total mortgage originations to 34

percent in 1999, still higher than an average

year. Meanwhile, the ARM share in 1999

increased from 12 percent in 1998 to 22

percent of originations, reflecting the rise in

overall interest rates. The MBA predicts that

mortgage originations will amount to $962

billion and $912 billion, with refinancings

representing 16 and 12 percent of

originations, during 2000 and 2001, which is

more in line with a normal pace. ARMs are

expected to account for a larger share, 32

percent in 2000 and 34 percent in 2001, of

total mortgage originations.69 Fannie Mae

projects that mortgage originations will fall to

$967 billion for 2000, with 19 percent

coming from refinancings, while 30 percent

of originations will be in the form of ARMs.70

b. Affordable Lending in the Mortgage Market

In the past few years, conventional lenders,

private mortgage insurers and the GSEs have

begun implementing changes to extend

homeownership opportunities to lowerincome and historically underserved

households. The industry has started offering

more customized 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. This

section summarizes recent initiatives

undertaken by the industry to expand

affordable housing. The section also

discusses the significant role FHA plays in

making affordable housing available to

historically underserved groups.

Down Payments. GE Capital’s 1989

Community Homebuyer Program first

allowed homebuyers who completed a

program of homeownership counseling to

have higher than normal payment-to-income

qualifying ratios, while providing less than

the full 5-percent down payment from their

own funds. Thus the program allowed

borrowers to qualify for larger loans than

would have been permitted under standard

underwriting rules. Fannie Mae made this

Community Homebuyer Program a part of its

own offerings in 1990. Affordable Gold is a

similar program introduced by Freddie Mac

in 1992. Many of these programs allowed 2

percentage points of the 5-percent down

payment to come from gifts from relatives or

grants and unsecured loans from local

governments or nonprofit organizations.

65100 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

In 1994, the industry (including lenders,

private mortgage insurers and the GSEs)

began offering mortgage products that

required down payments of only 3 percent,

plus points and closing costs. Other industry

efforts to reduce borrowers’ up front costs

have 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

are required to put down only 3 percent of

the purchase price. The down payment, as

well as closing costs, can 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. While these programs started

out slowly, by November 1998 both GSEs’

programs reached volumes of $200 million

per month.

In early 1999, Fannie Mae announced that

it would introduce several changes to its

mortgage insurance requirements. The

planned result is to provide options for low

downpayment borrowers to reduce their

mortgage insurance costs. Franklin D. Raines,

Fannie Mae chairman and chief executive

officer stated, ‘‘Now, thanks to our

underwriting technology, our success in

reducing credit losses, and innovative new

arrangements with mortgage insurance

companies, we can increase mortgage

insurance options and pass the savings

directly on to consumers.’’ 71

Partnerships. 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’s

partnership offices in more than 40 central

cities, serving to coordinate Fannie Mae’s

programs with local lenders and affordable

housing groups, are an example of this

initiative. Another example is the

partnership Fannie Mae and the National

Association for the Advancement of Colored

People (NAACP) announced in January

1999.72 Under this partnership, Fannie Mae

will provide funding for technical assistance

to expand the NAACP’s capacity to provide

homeownership information and counseling.

It will also invest in NAACP-affiliated

affordable housing development efforts and

explore structures to assist the organization

in leveraging its assets to secure

downpayment funds for eligible borrowers.

Furthermore, Fannie Mae will provide up to

$110 million in special financing products,

including a new $50 million underwriting

experiment specifically tailored to NAACP

clientele.

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. Freddie Mac also

announced on January 15, 1999 that it

entered into a broad initiative with the

NAACP to increase minority

homeownership. Through this alliance,

Freddie Mac and the NAACP seek to expand

community-based outreach, credit counseling

and marketing efforts, and the availability of

low-downpayment mortgage products with

flexible underwriting guidelines. As part of

the initiative, Freddie Mac has committed to

purchase $500 million in mortgage loans.73

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.

Underwriting Flexibility. Lenders, mortgage

insurers, and the GSEs have also been

modifying their underwriting standards to

attempt 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 measure the

circumstances of lower-income households.

The changes to underwriting standards

include, for example:

• Using a stable income standard rather

than a stable job standard. 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.

• Making exceptions to the ‘‘declining

market’’ rule and clarifying the treatment of

mixed-use properties.74 These changes

benefit applicants from inner-city

underserved neighborhoods.

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.

Increase in Affordable Lending During the

1990s.75 Home Mortgage Disclosure Act

(HMDA) data suggest that the new industry

initiatives may be increasing the flow of

credit to underserved borrowers. Between

1993 and 1997 (prior to the heavy refinancing

during 1998), conventional loans to lowincome and minority families increased at

much faster rates than loans to higher income

and non-minority families. As shown below,

over this period home purchase originations

to African Americans and Hispanics grew by

almost 60 percent, and purchase loans to

low-income borrowers (those with incomes

less than 80 percent of area median income)

increased by 45 percent.

1993–97

(in percent)

1995–97

(in percent)

All Borrowers ........................................................................................................................................................... 28.1 11.1

African Americans/Hispanics. .................................................................................................................................. 57.7 ¥0.2

Whites ...................................................................................................................................................................... 21.9 8.9

Income Less Than 80% AMI ................................................................................................................................... 45.1 15.4

Income Greater Than 120% AMI ............................................................................................................................ 31.5 24.5

However, as also shown, in the latter part

of this period conventional lending for some

groups slowed significantly. Between 1995

and 1997, the slowing of the growth of home

purchase originations was much greater for

low-income borrowers than for higherincome borrowers. Moreover, even though

remaining at near-peak levels in 1997,

conventional home purchase originations to

African Americans and Hispanics actually

decreased by two-tenths of a percent over the

past three years. It should be noted, however,

that total loans (conventional plus

government) originated to African-American

and Hispanic borrowers increased between

1995 and 1997, but this was mainly the result

of a 40.0 percent increase in FHA-insured

loans originated for African-American and

Hispanic borrowers.

Affordable Lending Shares by Major

Market Sector. The focus of the different

sectors of the mortgage market on affordable

lending can be seen by examining Tables

A.1a, A.1b, and A.1c. Tables A.1a and A.1b

present affordable lending percentages for

FHA, the GSEs, depositories (banks and thrift

institutions), the conventional conforming

sector, and the overall market.76 The

discussion below will center on Table A.1a,

which provides information on home

purchase loans and thus, homeownership

opportunities. Table A.1b, which provides

information on total (both home purchase

and refinance) loans, is included to give a

complete picture of mortgage activity. Both

1997 and 1998 HMDA data are included in

these tables; the year 1997 represents a more

typical year of mortgage activity than 1998,

which was characterized by heavy refinance

activity. The tables also include GSE data for

1999; the 1999 HMDA data will be

incorporated when it is made available.

The affordable market shares reported in

parentheses for the conventional conforming

market in Tables A.1a and A.1b were derived

by excluding the estimated number of B&C

loans from the HMDA data. HUD’s method

for excluding B&C loans is explained in

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65101

Section F.3a of Appendix D. Because B&C

lenders operate mainly in the refinance

sector, excluding these loans from the market

totals has little impact on the home purchase

percentages reported in Table A.1a. The

reductions in the market shares are more

significant for total loans (reported in Table

A.1b) which include refinance as well as

home purchase loans.

The interpretation of the ‘‘distribution of

business’’ percentages, reported in Table

A.1a for several borrower and neighborhood

characteristics, can be illustrated using the

FHA percentage for low-income borrowers:

during 1997, 47.5 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. Table A.1c, on the other

hand, presents ‘‘market share’’ percentages

that measure the portion of all home

purchase loans for a specific affordable

lending category (such as low-income

borrowers) accounted for by a particular

sector of the mortgage market (FHA or the

GSEs). In this case, the FHA market share of

33 percent for low-income borrowers is

interpreted as follows: of all home purchase

loans originated in metropolitan areas during

1997, 33 percent were FHA-insured loans.

Thus, this ‘‘market share’’ percentage

measures the importance of FHA to the

market’s overall funding of loans for lowincome borrowers.

BILLING CODE 4210–27–P

65102 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65103

BILLING CODE 4210–27–C

Four main conclusions may be drawn from

the data presented in Tables A.1a and A.1c.

First, FHA places much more emphasis on

affordable lending than the other market

sectors. Low-income borrowers accounted for

47.5 percent of FHA-insured loans during

1997, compared with 21.2 percent of the

home loans purchased by the GSEs, 29.4

percent of home loans retained by

depositories, and 27.3 percent of

conventional conforming loans.77 Likewise,

41.3 percent of FHA-insured loans were

originated in underserved census tracts,

while only 22.1 percent of the GSEpurchased loans and 25.2 percent of

conventional conforming loans were

originated in these tracts.78 As shown in

Table A.1c, while FHA insured only 23

percent of all home purchase mortgages

originated in metropolitan areas during 1997,

it insured 33 percent of all mortgages

originated in underserved areas.79

Second, the affordable lending shares for

the conventional conforming sector are low

for minority borrowers, particularly AfricanAmerican borrowers. For example, AfricanAmerican borrowers accounted for only 5.0

percent of all conventional conforming home

purchase loans originated during 1997 and

1998, compared with over 14 percent of

FHA-insured loans and over 7.5 percent of all

home purchase loans originated in the

market. The African-American share of the

GSEs’ purchases is even lower than the

corresponding share for the conventional

conforming market. In 1998, home purchase

loans to African-Americans accounted for 3.2

percent of Freddie Mac’s purchases, 3.8

percent of Fannie Mae’s purchases, and 4.9

percent of loans originated in the

conventional conforming market (or 4.7

percent if B&C loans are excluded from the

market definition).80 As shown in Table A.1a,

the results change when other minority

borrowers are considered. Fannie Mae

purchased mortgages for minority borrowers

and their neighborhoods at higher rates than

these loans were originated by primary

lenders in the conventional conforming

market. During 1997, 17.7 percent of Fannie

Mae’s purchases were mortgages for minority

borrowers, compared with 16.5 percent of

conventional conforming loans. During 1998,

14.0 percent of Fannie Mae’s purchases

financed homes in high-minority census

tracts, compared with 14.1 percent of

conventional conforming loans (or 13.7

percent without B&C loans). However, as

suggested by the data presented above, the

minority lending performance of

conventional lenders has been subject to

much criticism in recent studies. These

studies contend that primary lenders in the

conventional market are not doing their fair

65104 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

share of minority lending which forces

minorities, particularly African-American

and Hispanic borrowers, to the more costly

FHA and subprime markets.81

Third, the GSEs, but particularly Freddie

Mac, lagged the conventional conforming

market in funding affordable loans for lowincome families and their neighborhoods

during 1997 and 1998—in 1998, for example,

low-income census tracts accounted for 7.9

percent of Freddie Mac’s purchases, 9.4

percent of Fannie Mae’s purchases, 12.1

percent of loans retained by depositories, and

10.7 percent of all home loans originated by

conventional conforming lenders. This

pattern of Freddie Mac lagging all market

participants during 1997 and 1998 holds up

for all of the borrower and neighborhood

categories examined in Table A.1a. One

encouraging trend for Freddie Mac is the

significant increases in its purchases of

affordable loans between 1997 and 1999—for

example, from 19.2 percent to 24.5 percent

for low-income borrowers, resulting in

Freddie Mac surpassing Fannie Mae in the

funding of home loans for low-income

families. With respect to the GSEs’ total

(combined home purchase and refinance)

purchases, Freddie Mac matched or outperformed Fannie Mae in 1999 on all

categories in Table A.1b except minority

borrowers. A more complete analysis of the

GSEs’ purchases of mortgages qualifying for

the housing goals is provided below in

Section E.

Finally, within the conventional

conforming market, depository institutions

stand out as important providers of

affordable lending for lower-income families

and their neighborhoods (see Table A.1a).82

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. Another

important factor influencing the types of

loans held by depository lenders is the

Community Reinvestment Act, which is

discussed next.

Seasoned CRA Loans. The Community

Reinvestment Act (CRA) requires depository

institutions to help meet the credit needs of

their communities. CRA provides an

incentive for lenders to initiate affordable

lending programs with underwriting

flexibility.83 CRA loans are typically made to

low- and moderate-income borrowers earning

less than 80 percent of median income for

their area, and in moderate-income

neighborhoods. They are usually smaller

than typical conventional mortgages and also

are likely to have a high LTV, high debt-toincome ratios, 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. On average, CRA

loans in a pool have three to four years

seasoning.84

However, because of the size, LTV and PMI

characteristics of CRA loans, they have

slower prepayment rates than traditional

mortgages, making them attractive for

securitization. CRA loan delinquencies also

have very high cure rates.85 For banks, selling

CRA pools will free up capital to make new

CRA loans. As a result, the CRA market

segment may provide an opportunity for

Fannie Mae and Freddie Mac to expand their

affordable lending programs. In mid-1997,

Fannie Mae launched its Community

Reinvestment Act Portfolio Initiative. Under

this pilot program Fannie Mae purchases

seasoned CRA loans in bulk transactions

taking into account track record as opposed

to relying just on underwriting guidelines. By

the end of 1997, Fannie Mae had financed $1

billion in CRA loans through this pilot.86

With billions of dollars worth of CRA loans

in bank portfolios the market for

securitization should improve. Section E,

below, presents data showing that Fannie

Mae’s purchases of CRA-type seasoned

mortgages have increased recently. Fannie

Mae also started another pilot program in

1998 where they purchase CRA loans on a

flow basis, as they are originated. Results

from this four-year $2 billion nationwide

pilot should begin to be reflected in the 1999

production data.87

c. Potential Homebuyers

While the growth in affordable lending and

homeownership has been strong in recent

years, attaining this Nation’s housing goals

will not be possible without tapping into the

vast pool of potential homebuyers. The

National Homeownership Strategy has set a

goal of achieving a homeownership rate of

67.5 percent by the end of the year 2000. Due

to the aging of the baby boomers, this rate

reached an annual record of 66.8 percent in

1999, and rose further to 67.1 percent in the

first quarter of 2000. This section discusses

the potential for further increases beyond

those resulting from current demographic

trends.

The Urban Institute estimated in 1995 that

there was a large group of potential

homebuyers among the renter population

who were creditworthy enough to qualify for

homeownership.88 Of 20.3 million renter

households having low- or moderateincomes, roughly 16 percent were better

qualified for homeownership than half of the

renter households who actually did become

homeowners over the sample period. When

one also considered their likelihood of

defaulting relative to the average expected for

those who actually moved into

homeownership, 10.6 percent, or 2.15

million, low- and moderate-income renters

were better qualified for homeownership,

assuming the purchase of a home priced at

or below median area home price. These

results indicate the existence of a significant

lower-income population of low-risk

potential homebuyer households that might

become homeowners with continuing

outreach efforts by the mortgage industry.

Other surveys conducted by Fannie Mae

indicate that renters desire to become

homeowners, with 60 percent of all renters

indicating in the July 1998 National Housing

Survey that buying a home ranks from being

a ‘‘very important priority’’ to their ‘‘numberone priority,’’ the highest level found in any

of the seven National Housing Surveys dating

back to 1992. Immigration is expected to be

a major source of future homebuyers—Fannie

Mae’s 1995 National Housing Survey

reported that immigrant renter household

were 3 times as likely as renter households

in general to list home purchase as their

‘‘number-one priority.’’

Further increases in the homeownership

rate also depend on whether or not recent

gains in the homeowning share of specific

groups are maintained. Minorities accounted

for 18 percent of homeowners in 1999, but

the Joint Center for Housing Studies has

pointed out that minorities account were

responsible for nearly 40 percent of the 6.9

million increase in the number of

homeowners between 1994 and 1999.

Minority demand for homeownership

continues to be high, as reported by the

Fannie Mae Foundation’s April 1998 Survey

of African Americans and Hispanics. For

example, 38 percent of African Americans

surveyed said it is fairly to very likely that

they will buy a home in the next 3 years,

compared with 25 percent in 1997.89 The

survey also reports that 67 percent of African

Americans and 65 percent of Hispanics cite

homeownership as being a ‘‘very important

priority’’ or ‘‘number-one priority.’’ 90

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 homeownership rate could reach 70

percent by 2010.

d. Automated Mortgage Scoring

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 manufactured

housing.

Automated mortgage scoring was

developed as a high-tech tool with the

purpose of identifying credit risks in a more

efficient manner. As time and cost are

reduced by the automated system, more time

can be devoted by underwriters to qualifying

marginal loan applicants that are referred by

the automated system for more intensive

review. Fannie Mae and Freddie Mac are in

the forefront of new developments in

automated mortgage scoring technology. Both

enterprises released automated underwriting

systems in 1995—Freddie Mac’s Loan

Prospector and Fannie Mae’s Desktop

Underwriter. Each system uses numerical

credit scores, such as those developed by

Fair, Isaac, and Company, and additional

data submitted by the borrower, such as loanto-value ratios and available assets, to

calculate a mortgage score that evaluates the

likelihood of a borrower defaulting on the

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.

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

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65105

and minority homebuyers will not score well

enough to be accepted by the automated

underwriting system resulting in fewer

getting loans. The second concern relates to

the ‘‘black box’’ nature of the scoring

algorithm. The scoring algorithm is

proprietary and therefore it is difficult, if not

impossible, for applicants to know the

reasons for their scores.

Federal Reserve Study. Four economists at

the Board of Governors of the Federal

Reserve System conducted a conceptual and

empirical study on the use of credit scoring

systems in mortgage lending.91 Their broad

assessment of the models was that:

‘‘[C]redit scoring is a technological

innovation which has increased the speed

and consistency of risk assessment while

reducing costs. Research has uniformly

found that credit history scores are

powerful predictors of future loan

performance. All of these features suggest

that credit scoring is likely to benefit both

lenders and consumers.’’ 92

The authors evaluated the current state-ofthe-art of development of credit scoring

models, focusing particularly on the

comprehensiveness of statistical information

used to develop the scoring equations. They

presented a conceptual framework in which

statistical predictors of default include

regional and local market conditions,

individual credit history, and applicants’

characteristics other than credit history. The

authors observed that the developers of credit

scoring models have tended to disregard

regional and local market conditions in

model construction, and such neglect may

tend to reduce the predictive accuracy of

scoring equations. To determine the extent of

the problem, they analyzed Equifax credit

scores together with mortgage payment

history data for households living in each of

994 randomly selected counties from across

the country. The authors used these data to

assess the variability of credit scores relative

to county demographic and economic

characteristics.

The authors found a variety of pieces of

evidence which confirmed their suspicions:

Credit scores tended to be relatively lower in

counties with relatively high unemployment

rates, areas that have experienced recent rises

in unemployment rates, areas with high

minority population, areas with lower

median educational attainment, areas with

high percentages of individuals living in

poverty, areas with low median incomes and

low house values, and areas with relatively

high proportions of younger populations and

lower proportions of older residents.

This analysis suggests the need for a twostep process of improvement of the equations

and their application, in which (a) new

statistical analyses would be performed to

incorporate the omitted environmental

variables, and (b) additional variables bearing

on individuals’ prospective and prior

circumstances will be taken into account in

determining their credit scores.

These authors also discussed the

relationship between credit scoring and

discrimination. They found a significant

statistical relationship between credit history

scores and minority composition of an area,

after controlling for other locational

characteristics. From this, they concluded

that concerns about potential disparate

impact merit future study. However, a

disparate impact study must include a

business justification analysis to demonstrate

the ability of the score card to predict

defaults and an analysis of whether any

alternative, but equally-predictive, score card

has a less disproportionate effect.

Urban Institute Study. The Urban Institute

submitted a report to HUD in 1999 on a fourcity reconnaissance study of issues related to

the single-family underwriting guidelines

and practices of Fannie Mae and Freddie

Mac.93 The study included interviews with

informants knowledgeable about mortgage

markets and GSE business practices on the

national level and in the four cities.

The study observed, as did the Fed study

summarized above, that minorities are more

likely than whites to fail underwriting

guidelines. Therefore, as a general matter the

GSEs’ underwriting guidelines—as well as

the underwriting guidelines of others in the

industry—do have disproportionate adverse

effects on minority loan applicants.94

Based on the field reconnaissance in four

metropolitan housing markets, the study

made several observations about the

operation of credit scoring systems in

practice, as follows: 95

• Credit scores are used in mortgage

underwriting to separate loans that must be

referred to loan underwriters from loans that

may be forwarded directly to loan officers;

for example, a 620 score was mentioned by

some respondents as the line below which

the loan officer must refer the loan for

manual underwriting. It is very difficult for

applicants with low credit scores to be

approved for a mortgage, according to the

lenders interviewed by the Urban Institute.

• Some respondents believe the GSEs are

applying cutoffs inflexibly, while others

believe that lenders are not taking advantage

of flexibility allowed by the GSEs.

• Some respondents believe that credit

scores may not be accurate predictors of loan

performance, despite the claims of users of

these scores. Respondents who voiced this

opinion tended to base these observations on

their personal knowledge of low-income

borrowers who are able to keep current on

payments, rather than on an understanding of

statistical validation studies of the models.

• Respondents indicate that the ‘‘black

box’’ nature of the credit scoring process

creates uncertainty among loan applicants

and enhances the intimidating nature of the

process for them.

Based on these findings, the authors

concluded that ‘‘the use of automated

underwriting systems and credit scores may

place lower-income borrowers at a

disadvantage when applying for a loan, even

though they are acceptable credit risks.’’

The Urban Institute report included several

recommendations for ongoing HUD

monitoring of the GSEs’ underwriting

including their use of credit scoring models.

One suggestion was to develop a data base on

the GSEs’ lending activities relevant for

analysis of fair lending issues. The data

would include credit scores to reveal the

GSEs’ patterns of loan purchase by credit

score. A second suggestion was to conduct

analyses of the effects of credit scoring

systems using a set of ‘‘fictitious borrower

profiles’’ that would reveal how the systems

reflect borrower differences in income, work

history, credit history, and other relevant

factors. HUD has begun following up on the

Urban Institute’s recommendations. For

instance, in February 1999, HUD requested

the information and data needed to analyze

the GSEs’ automated underwriting systems.

Concluding Observations. It is important to

note that both of the studies reviewed above

comment on the problem of correlation of

valid predictors of default (income, etc.) with

protected factors (race, etc.). Both studies

suggest that, ultimately, the question whether

mortgage credit scoring models raise any

problems of legal discrimination based on

disparate effects would hinge on a business

necessity analysis and analysis of whether

any alternative underwriting procedures with

less adverse disproportionate effect exist.

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. The three most predictive

factors are down payment, credit

performance or bureau score, and financial

cushion.

In response to criticisms aimed at using

FICO scores in mortgage underwriting,

Fannie Mae’s new version of Desktop

Underwriter (DU) 5.0 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.

e. Subprime Loans

Another major development in housing

finance has been the recent growth in

subprime loans. In the past borrowers

traditionally obtained an ‘‘A’’ quality (or

‘‘investment grade’’) mortgage or no

mortgage. However, an increasing share of

recent borrowers have obtained ‘‘subprime’’

mortgages, with their quality denoted as ‘‘Aminus,’’ ‘‘B,’’ ‘‘C,’’ or even ‘‘D.’’ The

subprime borrower typically is someone who

has experienced credit problems in the past

or has a high debt-to-income ratio.96 Through

the first nine months of 1998, ‘‘A-minus’’

loans accounted for 63 percent of the

subprime market, with ‘‘B’’ loans

representing 24 percent and ‘‘C’’ and ‘‘D’’

loans making up the remaining 13 percent.97

Because of the perceived higher risk of

default, subprime loans typically carry

mortgage rates that in some cases are

substantially higher than the rates on prime

mortgages. While in many cases these

perceptions about risk are accurate, some

housing advocates have expressed concern

that there are a number of cases in which the

perceptions are actually not accurate. The

Community Reinvestment Association of

North Carolina (CRA–NC), conducted a study

based on HMDA data, records of deeds, and

personal contacts with affected borrowers in

Durham County, NC. They found that

65106 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

subprime lenders make proportionally more

loans to minority borrowers and in minority

neighborhoods than to whites and white

neighborhoods at the same income level.

African-American borrowers represented 20

percent of subprime mortgages in Durham

County, but only 10 percent of the prime

market.98 As a result, these borrowers can

end up paying very high mortgage rates that

more than compensate for the additional

risks to lenders. High subprime mortgage

rates make homeownership more expensive

or force subprime borrowers to buy less

desirable homes than they would be able to

purchase if they paid lower prime rates on

their mortgages.

The HMDA database does not provide

information on interest rates, points, or other

loan terms that would enable researchers to

separate more expensive subprime loans

from other loans. However, the Department

has identified 200 lenders that specialize in

such loans, providing some information on

the growth of this market.99 This data shows

that mortgages originated by subprime

lenders, and reported in the HMDA data, has

increased from 104,000 subprime loans in

1993 to 210,000 in 1995 and 997,000 in 1998.

Most of the subprime loans reported in the

HMDA data are refinance loans; for example,

refinance loans accounted for 80 percent of

the subprime loans reported by the

specialized subprime lenders in 1997.

An important question is 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. It

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, its automated underwriting

system.100

Most of the subprime loans that were

purchased by the GSEs in past years were

purchased through structured transactions.

Under this form of transaction, whole groups

of loans are purchased, and not all loans

necessarily meet the GSEs’ traditional

underwriting guidelines. The GSEs typically

guarantee the so-called ‘‘A’’ tranche, which is

supported by a ‘‘B’’ tranche that covers

default costs.

An expanded GSE presence in the

subprime market could be of significant

benefit to lower-income families, minorities,

and families living in underserved areas.

HUD’s research shows that in 1998: AfricanAmericans comprised 5.0 percent of market

borrowers, but 19.4 percent of subprime

borrowers; Hispanics made up 5.2 percent of

market borrowers, but 7.8 percent of

subprime borrowers; very low-income

borrowers accounted for 12.1 percent of

market borrowers, but 23.3 percent of

subprime borrowers; and borrowers in

underserved areas amounted to 24.8 percent

of market borrowers, but 44.7 percent of

subprime borrowers.101

The GSEs. Fannie Mae and Freddie Mac

have shown increasing interest in the

subprime market throughout the latter half of

the 1990s. Both GSEs now purchase A-minus

and Alt-A mortgages on a flow basis.102 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.

Freddie Mac has been the more aggressive

GSE in the subprime market. In early 1996,

Freddie Mac stated that its interest in

subprime loans was for the development of

a subprime module for Loan Prospector

(Freddie Mac’s automated underwriting

system), a joint project with Standard &

Poor’s to score subprime mortgages.103

Freddie Mac increased its subprime business

through structured transactions, with Freddie

Mac guaranteeing the senior classes of

senior/subordinated securities backed by

home equity loans. Between 1997 and 1999,

Freddie Mac was involved in 16 transactions

totaling $8.1 billion, with Freddie Mac’s 1999

business accounting for over $5 billion of this

total.104 During 1999, Freddie Mac did four

transactions with Option One Mortgage,

including its largest subprime deal to date,

$930.4 million, in November of that year.

Freddie Mac also offers a product for Aminus borrowers through its Loan Prospector

system and it recently announced a product

similar to the ‘‘Timely Payment Rewards’’

mortgage offered by Fannie Mae. In total,

Freddie Mac 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.105

Freddie Mac is projecting to increase its

subprime purchases to $17.5 billion in the

year 2000, consisting of $9.5 billion in

subprime flow purchases and $8.0 billion in

security purchases.106

Fannie Mae has not focused on structured

transactions as Freddie Mac has. However,

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 A-minus loans.107

Because the GSEs have a funding

advantage over other market participants,

they have the ability to underprice their

competitors and increase their market

share.108 This advantage, as has been the case

in the prime market, could allow the GSEs

to eventually play a significant role in the

subprime market. 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. Since, as explained earlier in this

chapter, one could define a prime loan as one

that the GSEs will purchase, the difference

between the prime and subprime markets

will become less clear. This melding of

markets could occur even if many of the

underlying characteristics of subprime

borrowers and the market’s (i.e., non-GSE

participants) evaluation of the risks posed by

these borrowers remain unchanged.

Increased involvement by the GSEs in the

subprime market might result in more

standardized underwriting guidelines. As the

subprime market becomes more

standardized, market efficiencies might

possibly reduce borrowing costs. Lending to

credit-impaired borrowers will, in turn,

increasingly make good business sense for

the mortgage market.

f. Loans on Manufactured Housing

Manufactured housing provides low-cost,

basic-quality housing for millions of

American households, especially younger,

lower-income families in the South, West,

and rural areas of the nation. Many

households live in manufactured housing

because they simply cannot afford site-built

homes, for which the construction cost per

square foot is much higher. Because of its

affordability to lower-income families,

manufactured housing is one of the fastestgrowing parts of the American housing

market.109

The American Housing Survey found that

16.3 million people lived in 6.5 million

manufactured homes in the United States in

1997, and that such units accounted for 6.6

percent of the occupied housing stock, an

increase from 5.4 percent in 1985. Shipments

of manufactured homes rose steadily from

171,000 units in 1991 to 373,000 units in

1998, before tailing off to 348,000 units in

1999. The industry grew much faster over

this period in sales volume, from $4.7 billion

in 1991 to $15.3 billion in 1999, reflecting

both higher sales prices and a major shift

from single-section homes to multisection

homes, which contain two or three units

which are joined together on site.110

Despite their eligibility for mortgage

financing, only about 10–20 percent of

manufactured homes 111 are financed with

mortgages secured by the property, even

though half of owners hold title to the land

on which the home is sited. Most purchasers

of manufactured homes take out a personal

property loan on the home and, if they buy

the land, a separate loan to finance the

purchase of the land.

In 1995, the average loan size for a

manufactured home was $24,500, with a 15

percent down payment and term of 13 years.

Rates averaged about 3 percentage points

higher than those paid on 15-year fixed rate

mortgages, but borrowers benefit from very

rapid loan-processing and underwriting

standards that allow high debt payment-toincome (‘‘back-end’’) ratios.

Traditionally loans on manufactured

homes have been held in portfolio, but a

secondary market has emerged since trading

of asset-backed securities collateralized by

manufactured home loans was initiated in

1987. Investor interest has been reported as

strong due to reduced loan losses, low

prepayments, and eligibility for packaging of

such loans into real estate mortgage

investment conduits (REMICs). The GSEs’

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65107

underwriting standards allow them to buy

loans on manufactured homes that meet the

HUD construction code, if they are owned,

titled, and taxed as real estate.

The GSEs are beginning to expand their

roles in the manufactured home loan

market.112 A representative of the

Manufactured Housing Institute has stated

that ‘‘Clearly, manufactured housing loans

would fit nicely into Fannie Mae’s and

Freddie Mac’s affordable housing goals.’’ 113

Given that manufactured housing loans often

carry relatively high interest rates, an

enhanced GSE role could also improve the

affordability of such loans to lower-income

families.

D. Factor 2: Economic, Housing, and

Demographic Conditions: Multifamily

Mortgage Market

Since the early 1990s, the multifamily

mortgage market has become more closely

integrated with global capital markets,

approaching the same degree as the singlefamily mortgage market by the end of the

decade. In 1999, 58.8 percent of multifamily

mortgage originations were securitized,

compared with 60.8 percent of single-family

originations.114

Loans on multifamily properties are

typically viewed as riskier than their singlefamily counterparts. Property values, vacancy

rates, and market rents in multifamily

properties appear to be highly correlated

with local job market conditions, creating

greater sensitivity of loan performance to

economic conditions than may be

experienced in the single-family market.

Within much of the single-family mortgage

market, the GSEs occupy an undisputed

position of industrywide dominance, holding

loans or guarantees with an unpaid principal

comprising 39.0 percent of outstanding

single-family mortgage debt and guarantees

as of the end of 1999. In multifamily, the

overall market presence of the GSEs is more

modest. At the end of 1999, the GSEs’ direct

holdings and guarantees represented 17.3

percent of outstanding multifamily mortgage

debt.115 It is estimated that GSE acquisitions

of multifamily loans originated during 1997

represented 24 percent of the conventional

multifamily origination market.116

1. Special Issues and Unmet Needs

Recent studies have documented a pressing

unmet need for affordable housing. For

example, the Harvard University Joint Center

for Housing Studies, in its report State of the

Nation’s Housing 2000, points out that:

• Despite recent job and income growth,

renters in the bottom quarter of the income

distribution experienced a decline in real

income from 1996–1998, at a time when real

rents increased by 2.3 percent.

• Between 1993 and 1995, the number of

unsubsidized units affordable to very lowincome households decreased by nearly

900,000 units, or 8.6 percent.

• One-quarter of very low-income working

households paid 30 percent or more of their

incomes for housing.

• Rising home prices and interest rates are

raising the cost of homeownership.

• Reductions in federal subsidies may

contribute to further losses in the affordable

stock.

The affordable housing issues go beyond

the need for greater efficiency in delivering

capital to the rental housing market. In many

cases, subsidies are needed in order for lowincome families to afford housing that meets

adequate occupancy and quality standards.

Nevertheless, greater access to reasonably

priced capital can reduce the rate of losses

to the stock, and can help finance the

development of new or rehabilitated

affordable housing when combined with

locally funded subsidies. Development of a

secondary market for affordable housing is

one of many tools needed to address these

issues.

Recent scholarly research suggests that

more needs to be done to develop the

secondary market for affordable multifamily

housing.117 Cummings and DiPasquale (1998)

point to the numerous underwriting, pricing,

and capacity building issues that impede the

development of this market. They suggest the

impediments can be addressed through the

establishment of affordable lending

standards, better information, and industry

leadership.

• More consistent standards are especially

needed for properties with multiple layers of

subordinated financing (as is often the case

with affordable properties allocated Low

Income Housing Tax Credits and/or local

subsidies).

• More comprehensive and accurate

information, particularly with regard to the

determinants of default, can help in setting

standards for affordable lending.

• Leadership from the government or from

a GSE is needed to develop consensus

standards; it would be unprofitable for any

single purely private lender to provide

because costs would be borne privately but

competitors would benefit.

2. Underserved Market Segments

There is evidence that segments of the

multifamily housing stock have been affected

by costly, difficult, or inconsistent

availability of mortgage financing. Small

properties with 5–50 units represent an

example. The fixed-rate financing that is

available is typically structured with a 5–10

year term, with interest rates as much as 150

basis points higher than those on standard

multifamily loans, which may have adverse

implications for affordability.118 This market

segment appears to be dominated by thrifts

and other depositories who keep these loans

in portfolio. In part to hedge interest rate risk,

loans on small properties are often structured

as adjustable-rate mortgages.

Multifamily properties with significant

rehabilitation needs have experienced

difficulty in obtaining mortgage financing.

Properties that are more than 10 years old are

typically classified as ‘‘C’’ or ‘‘D’’ properties,

and are considered less attractive than newer

properties by many lenders and investors.119

Multifamily rehabilitation loans accounted

for only 0.5 percent of units backing Fannie

Mae’s 1998 purchases and for 1.6 percent in

1999. These loans accounted for 1.9 percent

of Freddie Mac’s 1998 multifamily total (with

none indicated in 1999).

Historically, the flow of capital into

housing for seniors has been characterized by

a great deal of volatility. A continuing lack

of long-term, fixed-rate financing jeopardizes

the viability of a number of some properties.

There is evidence that financing for new

construction remains scarce.120 Both Fannie

Mae and Freddie Mac offer Senior Housing

pilot programs.

Under circumstances where mortgage

financing is difficult, costly, or inconsistent,

GSE intervention may be desirable. Follain

and Szymanoski (1995) say that ‘‘a [market]

failure occurs when the market does not

provide the quantity of a particular good or

service at which the marginal social benefits

of another unit equal the marginal social

costs of producing that unit. In such a

situation, the benefits to society of having

one more unit exceeds the costs of producing

one more unit; thus, a rationale exists for

some level of government to intervene in the

market and expand the output of this

good.’’121 It can be argued that the GSEs have

the potential to contribute to the mitigation

of difficult, costly, or inconsistent availability

of mortgage financing to segments of the

multifamily market because of their funding

cost advantage, and even a responsibility to

do so as a consequence of their public

missions, especially in light of the limitations

on direct government resources available to

multifamily housing in today’s budgetary

environment.

3. Recent History and Future Prospects in

Multifamily

The expansion phase of the real estate

cycle been well underway for several years

now, at least insofar as it pertains to

multifamily. Rental rates have been rising,

and vacancy rates have been relatively stable,

contributing to a favorable environment for

multifamily construction and lending

activity.122 Delinquencies on commercial

mortgages reached an 18-year low in 1997.123

Some analysts have warned that recent

prosperity may have contributed to

overbuilding in some markets and

deterioration in underwriting standards.124 A

September 1998, report by the Office of the

Comptroller of the Currency anticipates

continued decline in credit standards at the

77 largest national banks as a consequence of

heightened competition between lenders, and

the Federal Deposit Insurance Corporation

has expressed similar concerns regarding

1,212 banks it examined.125

Growth in the multifamily mortgage market

has been fueled by investor appetites for

Commercial Mortgage Backed Securities

(CMBS). Nonagency securitization of

multifamily and commercial mortgages

received an initial impetus from the sale of

nearly $20 billion in mortgages acquired by

the Resolution Trust Corporation (RTC) from

insolvent depositories in 1992–1993.

Nonagency issuers typically enhance the

credit-worthiness of their offerings through

the use of senior-subordinated structures,

combining investment-grade senior tranches

with high-yield, below investment-grade

junior tranches designed to absorb any credit

losses.126

Because of their relatively low default risk

in comparison with loans on other types of

income property, multifamily mortgages are

often included in mixed-collateral financing

structures including other commercial

65108 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

property such as office buildings, shopping

centers, and storage warehouses. CMBS

volume reached $30 billion in 1996; $44

billion in 1997; $78 billion in 1998; and $67

billion in 1999. Approximately 25 percent of

each year’s total is comprised of multifamily

loans.127

During the financial markets turmoil in the

fall of 1998, investors expressed reluctance to

purchase the subordinated tranches in CMBS

transactions, jeopardizing the ability of

issuers to provide a cost-effective means of

credit-enhancing the senior tranches as

well.128 When investor perceptions regarding

credit risk on subordinated debt escalated

rapidly in August and September, the GSEs,

which do not typically use subordination as

a credit enhancement, benefited from a

‘‘flight to quality.’’ 129

Depository institutions and life insurance

companies, formerly among the largest

holders of multifamily debt, have

experienced a decline in their share of the

market at the expense of CMBS conduits.130

Increasingly, depositories and life insurance

companies are participating in multifamily

markets by holding CMBS rather than whole

loans, which are often less liquid, more

expensive, and subject to more stringent riskbased capital standards.131 In recent years a

rising proportion of multifamily mortgages

have been originated to secondary market

standards, a consequence of a combination of

factors including the establishment of a

smoothly functioning securitization

‘‘infrastructure;’’ the greater liquidity of

mortgage-related securities as compared with

whole loans; and the desire for an ‘‘exit

strategy’’ on the part of investors.132

Because of their limited use of mortgage

debt, increased equity ownership of

multifamily properties by REITs may have

contributed to increased competition among

mortgage originators, servicers and investors

for a smaller mortgage market than would

otherwise exist. During the first quarter of

1997, REITs accounted for 45 percent of all

commercial real estate transactions, and the

market capitalization of REITs at the end of

January 1998 exceeded that of outstanding

CMBS.133

Demographic factors will contribute to

continued steady growth in the new

construction segment of the multifamily

mortgage market. The number of apartment

households is expected to grow

approximately 1.1 percent per year over

2000–2005. Taking into consideration losses

from the housing stock, it has been projected

that approximately 250,000–275,000

additional multifamily units will be needed

in order to meet anticipated demand.134 This

flow is approximately half that of the mid1980s, but twice that of the depressed early

1990s. In 1999, 291,800 apartment units were

completed. 135

The high degree of volatility of multifamily

new construction experienced historically is

consistent with a view that this sector of the

housing market is driven more by

fluctuations in the availability of financing

than by demographic fundamentals. The

stability and liquidity of the housing finance

system is therefore a significant determinant

of whether the volume of new construction

remains consistent with demand.

Past experience suggests that the

availability of financing for all forms of

commercial real estate is highly sensitive to

the state of the economy. In periods of

economic uncertainty, lenders and investors

sometimes raise underwriting and credit

standards to a degree that properties that

would be deemed creditworthy under normal

circumstances are suddenly unable to obtain

financing. Ironically, difficulty in obtaining

financing may contribute to a fall in property

values that can exacerbate a credit crunch.136

The sensitivity of commercial real estate

markets to investor perceptions regarding

global volatility was demonstrated by the rise

in CMBS spreads in September, 1998.137

Thus, market disruptions could have adverse

implications on U.S. commercial and

residential mortgage markets.

4. Recent Performance and Effort of the GSEs

Toward Achieving the Low- and ModerateIncome Housing Goal: Role of Multifamily

Mortgages

The GSEs have rapidly expanded their

presence in the multifamily mortgage market

in the period since the housing goals were

established in 1993. Fannie Mae has played

a larger role in the multifamily market, with

a portfolio of $47.4 billion in retained loans

and outstanding guarantees, compared with

$16.8 billion for Freddie Mac.138 Freddie Mac

has successfully rebuilt its multifamily

program after a three-year hiatus during

1991–1993 precipitated by widespread

defaults.

Multifamily loans represent a relatively

small portion of the GSEs’ business activities.

For example, multifamily loans held in

portfolio or guaranteed by the GSEs at the

end of 1999 represented less than three

percent of their combined single- and multifamily holdings and guarantees. In

comparison, multifamily mortgages not held

or guaranteed by the GSEs represent

approximately ten percent of the overall nonGSE stock of mortgage debt.

However, the multifamily market

contributes disproportionately to GSE

purchases meeting both the Low- and

Moderate-Income and Special Affordable

Housing goals. In 1999, Fannie Mae’s

multifamily purchases represented 9.5

percent of their total acquisition volume,

measured in terms of dwelling units. Yet

these multifamily purchases comprised 20.4

percent of units qualifying for the Low- and

Moderate-Income Housing Goal, and 31.3

percent of units meeting the Special

Affordable goal. Multifamily purchases were

8.2 percent of units backing Freddie Mac’s

1999 acquisitions, 16.8 percent of units

meeting the Low- and Moderate-Income

Housing Goal, and 21.6 percent of units

qualifying for the Special Affordable Housing

Goal.139 The multifamily market therefore

comprises a significant share of units meeting

the Low- and Moderate-Income and Special

Affordable Housing Goals for both GSEs, and

the goals may have contributed to increased

emphasis by both GSEs on multifamily in the

period since the previous final rule took

effect in 1996.140

The majority of units backing GSE

multifamily transactions meet the Low- and

Moderate-Income Housing Goal because the

great majority of rental units are affordable to

families at 100 percent of median income, the

standard upon which the Low- and

Moderate-Income Housing Goal is defined.

For example, 38.5 percent of units securing

Freddie Mac’s 1999 single-family, one-unit

owner-occupied mortgage purchases met the

Low- and Moderate-Income Housing Goal,

compared with 90.0 percent of its

multifamily transactions. Corresponding

figures for Fannie Mae were 37.9 percent and

94.8 percent. 141 For this reason, multifamily

purchases represent a crucial component of

the GSEs’ efforts in meeting the Low- and

Moderate-Income Housing Goal.

Because such a large proportion of

multifamily units qualify for the Low- and

Moderate-Income Housing Goal and for the

Special Affordable Housing Goal, Freddie

Mac’s weaker multifamily performance

adversely affects its overall performance on

these two housing goals relative to Fannie

Mae. Units in multifamily properties

accounted for 7.2 percent of Freddie Mac’s

mortgage purchases during 1994–1999,

compared with 11.8 percent for Fannie Mae.

Fannie Mae’s greater emphasis on

multifamily is a major factor contributing to

the strength of its housing goals performance

relative to Freddie Mac.

5. A Role for the GSEs in Multifamily

Housing

By sustaining a secondary market for

multifamily mortgages, the GSEs can extend

the benefits that come from increased

mortgage liquidity to many more lowerincome families while helping private

owners to maintain the quality of the existing

affordable housing stock. In addition,

standardization of underwriting terms and

loan documents by the GSEs has the

potential to reduce transactions costs. As the

GSEs gain experience in areas of the

multifamily mortgage market affected by

costly, difficult, or inconsistent access to

secondary markets, they gain experience that

enables them to better measure and price

default risk, yielding greater efficiency and

further cost savings.

Ultimately, greater liquidity, stability, and

efficiency in the secondary market due to a

significant presence by the GSEs will benefit

lower-income renters by enhancing the

availability of mortgage financing for

affordable rental units—in a manner

analogous to the benefits the GSEs provide

homebuyers. Providing liquidity and stability

is the main role for the GSEs in the

multifamily market, just as in the singlefamily market.

Recent volatility in the CMBS market

underlines the need for an ongoing GSE

presence in the multifamily secondary

market. The potential for an increased GSE

presence is enhanced by virtue of the fact

that an increasing proportion of multifamily

mortgages are originated to secondary market

standards, as noted previously. While the

GSEs have also been affected by the widening

of yield spreads affecting CMBS, historical

experience suggests that agency spreads will

converge to historical magnitudes as a

consequence of the perceived benefits of

federal sponsorship.142 When this occurs, the

capability of the GSEs to serve and compete

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65109

in the multifamily secondary market will be

enhanced.143

6. Multifamily Mortgage Market: GSEs’

Ability To Lead the Industry

Holding 12.8 percent of the outstanding

stock of multifamily mortgage debt and

guarantees as of the end of 1999, Fannie Mae

is regarded as an influential force within the

multifamily market. Its Delegated

Underwriting and Servicing (DUS) program,

in which Fannie Mae delegates underwriting

responsibilities to originators in return for a

commitment to share in any default risk, now

accounts for more than half its multifamily

acquisitions, and has been regarded as highly

successful.

Freddie Mac’s presence in the multifamily

market is not as large as that of Fannie Mae.

Freddie Mac’s direct holdings of multifamily

mortgages and guarantees outstanding as of

the end of 1999, $16.8 billion, are much

smaller than that Fannie Mae’s $47.4 billion,

not only in absolute terms, but also a

percentage of all mortgage holdings and

guarantees. Freddie Mac’s multifamily

holdings and guarantees are 2.1 percent of its

total, compared with 4.3 percent for Fannie

Mae.144 However, Freddie Mac is credited

with rapidly rebuilding its multifamily

operations since 1993. The GSEs’ ability to

lead the multifamily industry is discussed

further below.

7. GSEs’ Performance in the Multifamily

Mortgage Market

GSE activity in the multifamily mortgage

market has expanded rapidly since 1993, as

noted previously. However, it is not clear

that the potential of the GSEs to lead the

multifamily mortgage industry has been fully

exploited. In particular, the GSEs’

multifamily purchases do not appear to be

consistently contributing to mitigation of

excessive cost of mortgage financing facing

small properties with 5–50 units. Based on

data from the Survey of Residential Finance

showing that 39.4 percent of units in recently

mortgaged multifamily properties were in

properties with 5–49 units, it appears

reasonable to assume that loans backed by

small properties account for 39.4 percent of

multifamily units financed each year. As a

share of units backing their multifamily

transactions, however, GSE purchases of

loans on small multifamily properties are

typically less than 5 percent, and have never

approached the estimated 39.4 percent

market share, as shown in Table A.2.

TABLE A.2.—GSE MULTIFAMILY TRANSACTIONS BY SIZE OF PROPERTY, 1994–1999 ACQUISITION YEAR

1994 1995 1996 1997 1998 1999

Fannie Mae:

Small (5–50 units) ..................................................... 8,717 45,488 5,838 8,111 64,753 12,351

As % Fannie Mae Multifamily Total .......................... 3.9 19.3 2.1 3.2 16.5 4.2

Freddie Mac:

Small (5–50 units) ..................................................... 1,165 5,461 4,100 3,963 10,244 4,068

As % Freddie Mac Multifamily Total ......................... 2.6 3.6 4.2 4.0 4.6 2.1

Source: GSE loan-level data.

In order to more usefully compare the

GSEs with the market, it is desirable to

supplement the data presented in Table A.2

by acquisition year with findings organized

by year of origination. Based on HUD’s

analysis of loans originated in 1997 and

acquired by the GSEs in 1997, 1998, and

1999, the GSEs have purchased loans backed

by 24 percent of units financed in the overall

conventional multifamily mortgage market in

1997, but their acquisitions of loans on small

multifamily properties have been only 2.3

percent of such properties financed that

year.145

GSE multifamily acquisitions tend to

involve larger properties than are typical for

the market as a whole.146 For example, the

average number of units in Fannie Mae’s

1997 multifamily transactions was 163, with

a corresponding figure of 158 for Freddie

Mac. Both of these averages are significantly

higher than the overall market average of 33.4

units per property on 1995 originations

estimated from the HUD Property Owners

and Managers (POMS) survey.147 A factor

possibly contributing to the GSEs’ emphasis

on larger properties is the relatively high

fixed multifamily origination costs, including

appraisal, environmental review, and legal

fees typically required under GSE

underwriting guidelines.148

A recent noteworthy development is

Fannie Mae’s announcement of a new

product through its Delegated Underwriting

and Servicing (DUS) program for multifamily

properties with 5–50 units. Features include

a streamlined underwriting process designed,

in part, to reduce borrower costs for thirdparty reports; use of FICO scores to evaluate

borrower creditworthiness; and recourse to

the borrower in the event of default.149

Another area underserved by mortgage

markets, in which the GSEs have not

demonstrated market leadership is

rehabilitation loans. Both GSEs’ relatively

weak performance in the multifamily

rehabilitation market segment is related to

the fact that, since the inception of the

interim housing goals in 1993, the great

majority of units backing GSE multifamily

mortgage purchases have been in properties

securing refinance loans with an established

payment history, in a proportion exceeding

80 percent in some years.150

The GSEs have been conservative in their

approach to multifamily credit risk.151 HUD’s

analysis of prospectus data indicates that the

average loan-to-value (LTV) ratio on pools of

seasoned multifamily mortgages securitized

by Freddie Mac during 1995 through 1996

was 55 percent. In comparison, the average

LTV on private-label multifamily conduit

transactions over 1995–1996 was 73 percent

based on HUD’s analysis of Commercial

Mortgage Backed Security data. Fannie Mae

utilizes a variety of credit enhancements to

further mitigate default risk on multifamily

acquisitions, including loss sharing, recourse

agreements, and the use of senior/

subordinated debt structures.152 Freddie Mac

is less reliant on credit enhancements than is

Fannie Mae, possibly because of a more

conservative underwriting approach.153

The GSEs’ ambivalence historically

regarding the perception of credit risk in

lending on affordable multifamily properties

is evident with regard to pilot programs

established in 1991 between Freddie Mac

and the Local Initiatives Managed Assets

Corporation (LIMAC), a subsidiary of the

Local Initiatives Support Corporation (LISC),

and in 1994 between Fannie Mae and

Enterprise Mortgage Investments (EMI), a

subsidiary of the Enterprise Foundation.

Cummings and DiPasquale (1998) conclude

that both initiatives had mixed results,

although the Fannie Mae/EMI pilot was more

successful in a number of regards. The

Freddie Mac/LIMAC initiative was

suspended after two years with only one

completed transaction, involving eight loans

with an aggregate loan amount of $4.6

million. As of June, 1997, 15 transactions

comprising $20.5 million had been

completed under the Fannie Mae/EMI pilot,

which is ongoing.

Both programs suffered initially from

documentation requirements that borrowers

perceived as burdensome. Cummings and

DiPasquale observe that ‘‘The smaller,

nonprofit, and CDC developers that these

programs intended to bring to the market

were unprepared, and perhaps unwilling or

unable, to meet the high costs of Freddie

Mac’s and Fannie Mae’s due diligence

requirements.’’

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 1993–99

period. The data presented are ‘‘official

results’’—i.e., they are based on HUD’s indepth analysis of the loan-level data

submitted to the Department 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 to the Department by

the GSEs in their Annual Housing Activities

Reports.

65110 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

Following this analysis, the GSEs’ past

performance in funding low- and moderateincome borrowers in the single-family

mortgage market is provided. Performance

indicators for the Geographically-Targeted

and Special Affordable Housing Goals are

also included in order to present a complete

picture in Appendix A of the GSEs’ funding

of single-family mortgages that qualify for the

three housing goals. In addition, the findings

from a wide range of studies—employing

both quantitative and qualitative techniques

to analyze several performance indicators

and conducted by HUD, academics, and

major research organizations—are

summarized below.

Organization and Main Findings. Section

E.1 reports the performance of Fannie Mae

and Freddie Mac on the Low- and ModerateIncome Housing Goal. Section E.2 uses

HMDA data and the loan-level data that the

GSEs provide to HUD on their mortgage

purchases to compare the characteristics of

GSE purchases of single-family loans with

the characteristics of all loans in the primary

mortgage market and of newly-originated

loans held in portfolio by depositories.

Section E.3 summarizes the findings from

several studies that have examined the role

of the GSEs in supporting affordable lending.

Section E.4 discusses the findings from a

recent HUD-sponsored study of the GSEs’

underwriting guidelines.154 Finally, Section

E.5 reviews the GSEs’ support of the singlefamily rental market.

The Section’s main findings with respect to

the GSEs’ single-family mortgage purchases

are as follows:

• Both Fannie Mae and Freddie Mac

surpassed the Low- and Moderate-Income

Housing Goals of 40 percent in 1996 and 42

percent in 1997–99.

• Both Fannie Mae and Freddie Mac have

improved their affordable lending 155

performance over the past seven years but, on

average, they have lagged the primary market

in providing mortgage funds for lowerincome borrowers and underserved

neighborhoods. This finding is based both on

HUD’s analysis of GSE and HMDA data as

well as on numerous studies by academics

and research organizations.

• The GSEs show very different patterns of

home loan lending.156 Through 1998,

Freddie Mac was less likely than Fannie Mae

to fund single-family home mortgages for

low-income families and their communities.

However, this pattern did not continue in

1999. The percentages of Freddie Mac’s

purchases through 1998 benefiting

historically underserved families and their

neighborhoods were also substantially less

than the corresponding shares of total market

originations. Through 1998 Freddie Mac had

not made much progress closing the gap

between its performance and that of the

overall home loan market. HMDA data to

analyze the affordable lending shares of the

primary market in 1999 were not available at

the time this appendix was prepared. But

since the GSEs are such major participants in

the mortgage market, the fact that Freddie

Mac surpassed Fannie Mae last year in many

dimensions of affordable lending suggests

that they may well have narrowed the gap

between their performance and that of the

primary market.

• Through 1998 Fannie Mae’s purchases

more nearly matched the patterns of

originations in the primary market than did

Freddie Mac’s. However, during the 1993–98

period as a whole and the 1996–98 period

during which the new goals were in effect,

Fannie Mae lagged depositories and others in

the conforming market in providing funding

for the lower-income borrowers and

neighborhoods covered by the three housing

goals. HMDA data are not currently available

to compare Fannie Mae’s performance

relative to the primary market for 1999.

• A large percentage of the lower-income

loans purchased by the GSEs have relatively

high down payments, which raises questions

about whether the GSEs are adequately

meeting the needs of lower-income families

who have little cash for making large down

payments.

• A study by The Urban Institute of lender

experience with the GSEs’ underwriting

standards finds that the enterprises have

stepped up their outreach efforts and have

increased the flexibility in their underwriting

standards, to better accommodate the special

circumstances of lower-income borrowers.

However, this study concludes that the GSEs’

guidelines remain somewhat inflexible and

that they are often hesitant to purchase

affordable loans. Lenders also told the Urban

Institute that Fannie Mae has been more

aggressive than Freddie Mac in market

outreach to underserved groups, in offering

new affordable products, and in adjusting

their underwriting standards.

• While single-family rental properties are

an important source of low-income rental

housing, they represent only a small portion

of the GSEs’ business. In addition, many of

the single-family rental properties funded by

the GSEs are one-unit detached units in

suburban areas rather than the older, 2–4

units commonly located in urban areas.

1. Past Performance on the Low- and

Moderate-Income Housing Goal

HUD’s goals specified that in 1996 at least

40 percent of the number of units eligible to

count toward the Low- and Moderate-Income

Goal should qualify as low-or moderateincome, and at least 42 percent should

qualify in 1997–99. Actual performance,

based on HUD’s analysis, was as follows:

1996 1997 1998 1999

Fannie Mae:

Units Eligible to Count Toward Goal ........................................................................ 1,831,690 1,710,530 3,468,428 2,925,347

Low- and Moderate-Income Units ............................................................................ 834,393 782,265 1,530,308 1,530,308

Percent Low- and Moderate-Income ........................................................................ 45.6 45.7 44.1 45.9

Freddie Mac:

Units Eligible to Count Toward Goal ........................................................................ 1,293,424 1,173,915 2,654,850 2,224,849

Low- and Moderate-Income Units ............................................................................ 532,219 499,590 1,137,660 1,024,660

Percent Low- and Moderate-Income ........................................................................ 41.1 42.6 42.9 46.1

Thus, Fannie Mae surpassed the goals by

5.6 percentage points and 3.7 percentage

points in 1996 in 1997, respectively, while

Freddie Mac surpassed the goals by 1.1 and

0.6 percentage points. In 1998 Fannie Mae’s

performance fell by 1.6 percentage points,

while Freddie Mac’s reported performance

continued to rise, by 0.3 percentage point.

Freddie Mac showed a sharp gain in

performance to 46.1 percent in 1999,

exceeding its previous high by 3.2 percentage

points. Fannie Mae’s performance was also at

a record level of 45.9 percent, which, for the

first time, slightly lagged Freddie Mac’s

performance.

The figures for 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 points in both

1996 and 1997, reflecting minor differences

in application of counting rules. These

differences also persisted for Freddie Mac for

1998–99, but the goal percentages shown

above for Fannie Mae for these two years are

the same as the results reported by Fannie

Mae to the Department.

Fannie Mae’s performance on the Low- and

Moderate-Income Goal jumped sharply in

just one year, from 34.1 percent in 1993 to

45.1 percent in 1994, before tailing off to 42.8

percent in 1995. As indicated, it then

stabilized at the 1994 level, just over 45

percent, in 1996 and 1997, before tailing off

to 44.1 percent in 1998, but rose to 45.9

percent last year. Freddie Mac has shown

more steady gains in performance on the

Low- and Moderate-Income Goal, from 30.0

percent in 1993 to 38.0 percent in 1994 and

39.6 percent in 1995, before surpassing 41

percent in 1996 and 42 percent 1997, and

rising to nearly 43 percent in 1998 and to 46

percent last year.

Fannie Mae’s performance on the Low- and

Moderate-Income Goal surpassed Freddie

Mac’s in every year through 1998. This

pattern was reversed last year, 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 is due to its

increased purchases of multifamily loans, as

it re-entered that market, and to increases in

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65111

the goal-qualifying shares of its single-family

mortgage purchases.

2. Comparisons With the Primary Mortgage

Market

This section summarizes several analyses

conducted by HUD on the extent to which

the GSEs’ loan purchases through 1998

mirror or depart from the patterns found in

the primary mortgage market. The GSEs’

affordable lending performance is also

compared with the performance of major

portfolio lenders such as commercial banks

and thrift institutions. Dimensions of lending

considered include the borrower income and

underserved area dimensions covered by the

three housing goals. In addition, this section

also analyzes Fannie Mae and Freddie Mac

purchases during 1999; however, market data

from HMDA were not available for 1999 at

the time this analysis was prepared.

Subsection a defines the primary mortgage

market, subsection b addresses some

questions that have recently arisen about

HMDA’s measurement of GSE activity, and

subsections c–e present the findings.157

The market analysis in this section is based

mainly on HMDA data for home purchase

loans originated in metropolitan areas during

the years 1992 to 1998. The discussion below

will often focus on the year 1997, as that year

represents more typical mortgage market

activity than the heavy refinancing year of

1998. Still, important shifts in mortgage

funding that occurred during 1998 will be

highlighted in order to offer a complete

analysis.

a. Definition of Primary Market

First it is necessary to define what is meant

by ‘‘primary market’’ in making these

comparisons. In this section this term

includes all mortgages on single-family

owner-occupied properties that are

originated in the conventional conforming

market.158 The source of this market

information is the data provided by loan

originators to the Federal Financial

Institutions Examination Council (FFIEC) in

accordance with the Home Mortgage

Disclosure Act (HMDA).

There is a consensus that the following

loans should be excluded from the HMDA

data in defining the ‘‘primary market’’ for the

sake of comparison with the GSEs’ purchases

of goal-qualifying mortgages:

• Loans with a principal balance in excess

of the loan limit for purchases by the GSEs—

$240,000 for a 1-unit property in most parts

of the United States in 1999.159 Loans not in

excess of this limit are referred to as

‘‘conforming mortgages’’ and larger loans are

referred to as ‘‘jumbo mortgages.’’ 160

• Loans which are backed by the Federal

government, including those insured by the

Federal Housing Administration and those

guaranteed by the Department of Veterans

Affairs, which are generally securitized by

the Government National Mortgage

Association (‘‘Ginnie Mae’’), as well as Rural

Housing Loans, guaranteed by the Farmers

Home Administration.161 Generally, the GSEs

do not receive credit on the housing goals for

purchasing loans with Federal government

backing. Loans without Federal government

backing are referred to as ‘‘conventional

mortgages.’’

Questions have arisen about whether loans

on manufactured housing should be

excluded when comparing the primary

market with the GSEs. As discussed

elsewhere in this Appendix, the GSEs have

not played a significant role in the

manufactured housing mortgage market in

the past. However, the manufactured home

mortgage market is changing in ways that

make a higher percentage of such loans

eligible for purchase by the GSEs, and the

GSEs are looking for ways to increase their

purchases of these loans. But more

importantly, the manufactured housing

sector is one of the most important providers

of affordable housing, which makes it

appropriate to include this sector in the

market definition. As discussed earlier in

Section A.3c, HUD believes that excluding

important low-income sectors such as

manufactured housing from the market

definition would render the resulting market

benchmark useless for evaluating the GSEs’

performance. For comparison purposes, data

are presented for the primary market defined

both to include and exclude mortgages

originated by manufactured housing lenders.

This issue of the market definition is

discussed further in Appendix D, which

calculates the market shares for each housing

goal.

Questions have also arisen about whether

subprime loans should be excluded when

comparing the primary market with the

GSEs. Appendix D, which examines this

issue in some detail, reports the effects of

excluding the B&C portion of the subprime

market from HUD’s estimates of the goalqualifying shares of the overall (combined

owner and rental) mortgage market. As

explained Section C.3.e of this appendix, the

low-income and minority borrowers in the Aminus 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 Freddie Mac has been purchasing

A-minus loans, both on a flow basis and

through negotiated transactions. Fannie Mae

recently introduced a new program targeted

at A-minus borrowers. Thus, HUD does not

believe that A-minus loans should be

excluded from the market definition.

Unfortunately, HMDA does not identify

subprime loans, much less separating them

into their A-minus and B&C components.

There is some evidence that many subprime

loans are not reported to HMDA but there is

nothing conclusive on this issue.162 Thus, it

is not possible to exclude B&C loans from the

comparisons reported below. However, HUD

staff has identified HMDA reporters that

primarily originate subprime loans.163 The

text below will report the effects of excluding

data for these lenders from the primary

market. The effects are minor mostly because

the analysis below focuses on home purchase

loans, which accounted for only twenty

percent of the mortgages originated by the

subprime lenders. During 1997 and 1998, the

subprime market was primarily a refinance

market.

b. Methods and Data for Measuring GSE

Performance

Several issues have arisen about the

methods and the data used to measure the

GSEs’ performance relative to the

characteristics of the mortgages being

originated in the primary market. While most

of these issues will be discussed throughout

the appendices, one issue, the reliability of

HMDA data in measuring GSE performance,

needs to be addressed before presenting the

market comparisons, which utilize the

HMDA data. Fannie Mae, in particular, has

raised questions about HUD’s reliance on

HMDA data for measuring its performance.

There are two sources of loan-level

information on 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 purchased loans are sold to

Fannie Mae, Freddie Mac or some other

entity. As discussed later, there have been

numerous studies by HUD staff and other

researchers that use the 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 the

HMDA data, which is widely available to the

public, provides an accurate measure of GSE

performance, as compared with the GSEs’

own data.164 Fannie Mae has argued that

HMDA data have understated its past

performance, where performance is defined

as the percentage of Fannie Mae’s mortgage

purchases accounted for by one of the goalqualifying categories such as underserved

areas. As explained below, HMDA provided

reliable national-level information through

1997 on the goals-qualifying percentages for

the GSEs’ purchases of newly-originated

loans but not for their purchases of prior-year

loans. In 1998, HMDA data differed from data

that the GSEs reported to HUD on their

purchases of newly-originated loans.

In any given calendar year, the GSEs can

purchase mortgages originated in that

calendar year or mortgages originated in a

prior calendar year. In 1997, purchases of

prior-year mortgages accounted for 30

percent of the single-family units financed by

Fannie Mae’s mortgage purchases and 20

percent of the single-family units financed by

Freddie Mac’s mortgage purchases.165 HMDA

data provides information mainly on newlyoriginated 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.166 The

implications of this for measuring GSE

performance can be seen in Tables A.3 and

A.4a.167

Table A.3 summarizes affordable lending

by the GSEs, depositories and the conforming

market for the six-year period between 1993

and 1998 and for the borrower and census

tract characteristics covered by the housing

goals. The GSE percentages presented in

Table A.3 are derived from the GSEs’ own

data that they provide to HUD, while the

depository and market percentages are taken

65112 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

from HMDA data. Annual data on the

borrower and census tract characteristics of

GSE purchases are provided in Table A.4a.

According to Fannie Mae’s own data, 9.9

percent of its purchases during 1997 were

loans for very low-income borrowers (see

Table A.4a). According to HMDA data (also

reported in Table A.4a), only 8.8 percent of

Fannie Mae’s purchases were loans for very

low-income borrowers.168 Thus, in this case

the HMDA data underestimate the share of

Fannie Mae’s mortgage purchases for very

low-income borrowers. Similarly, Fannie

Mae reports a very low-income percentage of

11.4 percent for its 1998 purchases while

HMDA reports only 9.2 percent.

BILLING CODE 4210–27–P

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65113

65114 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65115

BILLING CODE 4210–27–C

65116 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

The reason that HMDA data underestimate

those purchases can be seen by

disaggregating Fannie Mae’s purchases

during 1997 into their ‘‘Prior Year’’ and

‘‘Current Year’’ components. Table A.4a

shows that the overall figure of 9.9 percent

for very low-income borrowers is a weighted

average of 13.4 percent for Fannie Mae’s

purchases during 1997 of ‘‘Prior Year’’

mortgages and 8.7 percent for its purchases

of ‘‘Current Year’’ purchases. HMDA data

report that 8.8 percent of Fannie Mae’s 1997

purchases consisted of loans to very lowincome borrowers is based mainly on newlymortgaged (current-year originations) loans

that lenders report they sold to Fannie Mae.

Therefore, the HMDA data figure is similar in

concept to the ‘‘Current Year’’ percentage

from the GSEs’’ own data. As Table A.4a

shows, HMDA data and ‘‘Current Year’’

figures are practically the same in this case

(about nine percent). Thus, the relatively

large share of very low-income mortgages in

Fannie Mae’s 1997 purchases of ‘‘Prior Year’’

mortgages is the primary reason why Fannie

Mae’s own data show an overall (both prioryear and current-year) percentage of very

low-income loans that is higher than that

reported in HMDA data.

A review of the data in Table A.4a yields

the following insights about the reliability of

HMDA data at the national level for

metropolitan areas. First, comparing the

HMDA data on GSE purchases with the GSE

‘‘Current Year’’ data suggests that HMDA

data provided reasonable estimates of the

GSEs’ current year purchases through

1997.169 Second, the HMDA data percentages

through 1997 are actually rather close to

Freddie Mac’s overall percentages because

Freddie Mac’s prior-year purchases often

resembled their current-year originations.

Fannie Mae, on the other hand, was more apt

to purchase seasoned loans with a relatively

high percentage of low-income loans, which

means that HMDA data was more likely to

underestimate its overall performance.

However, this underestimation of the share of

Fannie Mae’s goal-qualifying loans in the

HMDA data first arose in 1997, when Fannie

Mae’s purchases of prior-year loans were

particularly targeted to affordable lending

groups. For the years 1993 to 1996, Fannie

Mae’s prior-year loan purchases more closely

resembled their current-year originations.170

Third, the 1998 data show that even the

GSEs’ ‘‘Current Year’’ data differ from the

HMDA-reported data on GSE purchases. For

example, special affordable loans accounted

for 12.1 percent of Fannie Mae’s current-year

purchases in 1998 compared with only 10.7

percent of Fannie Mae’s special affordable

purchases as reported by HMDA. Similarly,

underserved areas accounted for 21.0 percent

of Fannie Mae’s current-year purchases

compared with only 19.6 percent of Fannie

Mae’s underserved area purchases as

reported by HMDA. The same patterns exist

for Freddie Mac’s 1998 data for the special

affordable and underserved area categories.

Thus, 1998 HMDA data do not provide a

reliable estimate at the national level of the

goals-qualifying percentages for the GSEs’

purchases of current-year (newly-mortgaged)

loans. More research on this issue is

needed.171

The next section compares the GSE

performance with that of the overall market.

The fact that the GSE data includes prior-year

as well as current-year loans, while the

market data includes only current-year

originations, means that the GSE-versusmarket comparisons are defined somewhat

inconsistently for any particular calendar

year. Each year, the GSEs have newlyoriginated affordable loans available for

purchase, but they can also purchase loans

from a large stock of seasoned loans currently

being held in the portfolios of depository

lenders. Depository lenders have originated a

large number of CRA-type loans over the past

six years and many of them remain on their

books. In fact, HUD has encouraged the GSEs

to purchase seasoned, CRA-type loans that

have demonstrated their creditworthiness.

One method for making the 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 Table A.3.

c. Affordable Lending by the GSEs and the

Primary Market

Table A.3 summarizes goal-qualifying

lending by the GSEs, depositories and the

conforming market for the six-year period

between 1993 and 1998 and for the more

recent 1996–98 period, which covers the

period since the most recent housing goals

have been in effect. As noted above, the data

are aggregated over time to provide a clearer

picture of how the GSEs’ purchases of both

current-year and prior-year loans compare

with the types of mortgages that have been

originated during the past few years. All of

the data are for home purchase mortgages in

metropolitan areas. Several points stand out

concerning the affordable lending

performance of Freddie Mac and Fannie Mae

through 1998.

Freddie Mac—1993–98 Performance

Relative to Market. The data in Table A.3

show that Freddie Mac substantially lagged

both Fannie Mae and the primary market in

funding affordable home loans between 1993

and 1998. During that period, 7.6 percent of

Freddie Mac’s mortgage purchases were for

very low-income borrowers, compared with

9.2 percent of Fannie Mae’s purchases, 14.5

percent of loans originated and retained by

depositories, and 12.4 percent of loans

originated in the conforming market (or 10.7

percent if manufactured home loans are

excluded from the conforming market

definition).172 As shown by the annual data

reported in Table A.4a, Freddie Mac did

improve its funding of very low-income

borrowers during this period, from 6.0

percent in 1993 to 7.6 percent in 1997, and

then to 9.9 percent in 1998. However,

Freddie Mac did not make as much progress

as Fannie Mae (discussed below) in closing

the gap between its performance and that of

the overall market. During the 1996–98

period in which the new goals have been in

effect, the ratio of Freddie Mac’s average

performance (8.4 percent) to that of the

overall market (13.0 percent) was only 0.65;

this ‘‘Freddie-Mac-to-market’’ ratio remained

at only 0.76 even when manufactured homes

are excluded from the market definition.

A similar conclusion about Freddie Mac’s

performance can be drawn for the other goalqualifying categories presented in Tables A.3

and A.4a: Freddie Mac’s performance was

well below the market between 1993 and

1998. For example, during the recent 1996–

98 period, mortgages financing properties in

underserved areas accounted for only 19.9

percent of Freddie Mac’s purchases,

compared with 22.9 percent of the loans

purchased by Fannie Mae and 24.9 percent

of the mortgages originated in the conforming

market. Similarly, mortgages originated for

low- and moderate-income borrowers

represented 34.9 percent of Freddie Mac’s

purchases during that period, compared with

42.6 percent of all mortgages originated in

the conforming market.

One encouraging sign for Freddie Mac is

that the borrower-income categories showed

a rather large increase between 1997 and

1998, followed by another significant

increase between 1998 and 1999. Special

affordable (low-mod) loans increased from

9.0 (34.1) percent in 1997 to 11.3 (36.9)

percent in 1998 to 12.3 (40.0) percent in

1999. The reasons for this increase require

further study, but certainly, an interesting

question going forward is whether Freddie

Mac can continue this 1997–99 pattern and

thus further close its performance gap

relative to the overall market. It is somewhat

surprising that Freddie Mac’s purchases of

home loans in underserved areas did not

increase (in percentage terms) between 1997

and 1998; as shown in Table A.4a, the

underserved areas share of Freddie Mac’s

home loan purchases remained constant at

approximately 20 percent between 1994 and

1998 before rising to 21.2 percent in 1999.

Fannie Mae—1993–98 Performance

Relative to the Market. The data in Table A.3

show that Fannie Mae has also lagged

depositories and the primary market in the

funding of homes for lower-income

borrowers and underserved neighborhoods.

Between 1993 and 1998, 37.4 percent of

Fannie Mae’s purchases were for low- and

moderate-income borrowers, compared with

43.6 percent of loans originated and retained

by depositories and with 41.8 percent of

loans originated in the primary market. Over

the more recent 1996–98 period, 22.9 percent

of Fannie Mae’s purchases financed

properties in underserved neighborhoods,

compared with 25.8 percent of loans

originated by depositories and 24.9 percent

of loans originated in the conventional

conforming market.

However, Fannie Mae’s affordable lending

performance between 1993 and 1998 can be

distinguished from Freddie Mac’s. First,

Fannie Mae performed much better than

Freddie Mac on every goal-category

examined here. For example, home loans for

special affordable loans accounted for 13.2

percent of Fannie Mae’s purchases in 1998,

compared with only 11.3 percent of Freddie

Mac’s purchases (see Table A.4a). In that

same year, 22.9 percent of Fannie Mae’s

purchases were in underserved census tracts,

compared with only 20.0 percent of Freddie

Mac’s purchases.

Second, Fannie Mae improved its

performance between 1993 and 1998 and

made more progress than Freddie Mac in

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65117

closing the gap between its performance and

the market’s performance on the goalqualifying categories examined here. In fact,

by 1998, Fannie Mae’s performance was close

to that of the primary market for some

important components of affordable lending.

For example, in 1992, very low-income loans

accounted for 5.2 percent of Fannie Mae’s

purchases and 8.7 percent of all loans

originated in the conforming market, giving

a ‘‘Fannie Mae-to-market’’ ratio of 0.60. By

1998, this ratio had risen to 0.86, as very lowincome loans had increased to 11.4 percent

of Fannie Mae’s purchases and to 13.3

percent of market originations.

A similar trend in market ratios can be

observed for Fannie Mae on the underserved

areas category. Fannie Mae improved its

performance relative to the market; for

example, the ‘‘Fannie-Mae-to-market’’ ratio

for underserved areas increased from 0.82 in

1992 to 0.93 in 1998. This improved

performance relative to the overall market by

Fannie Mae is in sharp contrast to Freddie

Mac’s record during the same 1992 to 1998

period—the ‘‘Freddie-Mac-to-market’’ ratio

for underserved areas actually declined, from

0.84 in 1992 to 0.81 in 1998. As a result,

Fannie Mae approached the home loan

market in underserved areas while Freddie

Mac lost ground relative to overall primary

market.

B&C Home Purchase Loan. As explained

earlier, HMDA does not identify subprime

loans, much less separate them into their Aminus and B&C components. Randall

Scheessele at HUD has identified 200 HMDA

reporters that primarily originate subprime

loans and probably accounted for at least half

of the subprime market during 1998.173 As

shown in Table A.4b, excluding the home

purchase loans originated by these lenders

from the primary market data has only minor

effects on the goal-qualifying shares of the

market. The average market percentages for

1998 are reduced as follows: low- and

moderate-income (43.0 to 42.6 percent);

special affordable (15.5 to 15.2 percent); and

underserved areas (24.6 to 23.7 percent). As

explained earlier, the effects are minor

mostly because this analysis focuses on home

purchase loans, which accounted for only 20

percent of the mortgages originated by these

200 subprime lenders—the subprime market

has been mainly a refinance market.

GSEs’ Purchases of Home Loans in 1999.

Although market data are not yet available

for 1999, the GSEs have reported their

purchase data to HUD for that year. As

shown in Table A.4a, the 1993–98 pattern

discussed above of Freddie Mac lagging

behind Fannie Mae in funding affordable

loans changed in 1999, as Freddie Mac

matched or slightly out-performed Fannie

Mae on all three goals-qualifying categories.

For example, special affordable loans

accounted for similar percentages of Freddie

Mac’s (12.5 percent) and Fannie Mae’s (12.3

percent) purchases of home loans during

1999. Low-mod (underserved areas) loans

accounted for 40.0 (21.2) percent of Freddie

Mac’s 1999 purchases, compared with 39.3

(20.6) percent of Fannie Mae’s 1999

purchases. Between 1998 and 1999, Fannie

Mae’s shares of goals-qualifying home loans

declined in every case while Freddie Mac’s

goals-qualifying shares increased. For

example, the low-mod share of Freddie Mac’s

purchases of home loans increased by 3.1

percentage points from 36.9 percent to 40.0

percent between 1998 and 1999; this

compares to a decrease of 1.1 percentage

point for Fannie Mae, from 40.4 percent to

39.3 percent. Data from 1999 HMDA will

enable HUD to examine the extent to which

Freddie Mac has closed its performance gap

relative to the overall conventional

conforming market.

d. Prior-Year Loans

An important source of the past differential

in affordable lending between Fannie Mae

and Freddie Mac concerns the purchase of

prior-year loans. As shown in Table A.4a, the

prior-year mortgages that Fannie Mae was

purchasing through 1998 were much more

likely to be loans for lower-income families

and underserved areas than the newlyoriginated mortgages that they were

purchasing. For example, 30.1 percent of

Fannie Mae’s 1997 purchases of prior-year

mortgages were loans financing properties in

underserved areas, compared with 20.8

percent of its purchases of newly-originated

mortgages. These purchases of prior-year

mortgages were one reason Fannie Mae

improved its performance relative to the

primary market, which includes only newlyoriginated mortgages, in 1997. Sixteen

percent of its prior-year mortgages qualified

for the Special Affordable Goal, compared

with only 10.2 percent of its purchases of

newly-originated loans. The same patterns

are exhibited by the 1998 data. For example,

17.9 percent of Fannie Mae’s prior-year

purchases during 1998 qualified for the

Special Affordable Goal, compared with only

12.1 percent of its 1998 purchases of newlyoriginated loans. Through 1998, Fannie Mae

seem to be purchasing affordable loans that

were originated by portfolio lenders in

previous years.

Freddie Mac, on the other hand, does not

seem to be pursuing such a strategy, or at

least not to the same degree as Fannie Mae.

In 1997, 1998, and 1999, Freddie Mac’s

purchases of prior-year mortgages and its

purchases of newly-originated mortgages had

similar percentages of special affordable and

low-and moderate-income borrowers. As

Table A.4a shows, there is a small differential

between Freddie Mac’s prior-year and newlyoriginated mortgages for the underserved

areas category but it is much smaller than the

differential for Fannie Mae. Thus, during

1997 and 1998, Freddie Mac’s purchases of

prior-year mortgages were less likely to

qualify for the housing goals, and this was

one reason Freddie Mac’s overall affordable

lending performance was below Fannie

Mae’s during those years. In 1999, on the

other hand, there was surprisingly little

difference between the goals-qualifying

percentages for Fannie Mae’s prior-year and

its current-year purchases.

e. GSE Purchases of Total (Home Purchase

and Refinance) Loans

The above sections have 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, this section briefly

considers the GSEs’ purchases of all singlefamily-owner mortgages, including both

home purchase loans and refinance loans.174

As shown in Table A.4c, shifting the analysis

to consider all (home purchase and

refinance) mortgages does not change the

basic finding that both GSEs lag the primary

market in serving low-income borrowers and

underserved neighborhoods. For example, in

1998 underserved areas accounted for 21.2

(20.9) percent of Fannie Mae’s (Freddie

Mac’s) purchases, compared to

approximately 25 percent for both depository

institutions and the overall primary market.

Similarly, special affordable loans accounted

for 11.1 (10.9) percent of Fannie Mae’s

(Freddie Mac’s) purchases of single-familyowner loans, compared to 14.9 percent for

depository institutions and 14.2 percent for

the overall primary market.

There are two changes when one shifts the

analysis from only home purchase loans to

include all mortgages—one concerning the

relative performance of Fannie Mae and

Freddie Mac and one concerning the impact

of subprime mortgages on the goalsqualifying percentages. These are discussed

next.

Fannie Mae versus Freddie Mac

Performance—1997 to 1998. As indicated by

the above percentages for 1998, the borrowerincome and underserved area comparisons

between Fannie Mae and Freddie Mac

change when the analysis switches from their

acquisitions of only home purchase loans to

their acquisitions of total (both home

purchase and refinance) loans—in the case of

total loans, Freddie Mac’s performance

resembles Fannie Mae’s performance in 1998

and surpasses Fannie Mae’s performance in

1999 (see Table A.4c). These important shifts

in the relative performance of Fannie Mae

and Freddie Mac are best described by

analyzing the 1997 to 1998 changes that led

to Freddie Mac catching up with Fannie Mae

in overall affordable lending, and then

examining the 1998 to 1999 changes that led

to Freddie Mac surpassing Fannie Mae in

overall affordable lending.

Consider the special affordable income

category for 1997 and 1998. As shown earlier

in Table A.4a, special affordable loans

accounted for a much higher percentage of

Fannie Mae’s acquisitions of home purchase

loans than of Freddie Mac’s in each of these

two years. Similarly, in 1997, special

affordable loans accounted for 11.5 percent of

Fannie Mae’s total (both home purchase and

refinance) purchases, compared with 9.9

percent of Freddie Mac’s total purchases.

However, between 1997 and 1998, the special

affordable percentage of Freddie Mac’s total

purchases increased from 9.9 percent to 10.9

percent, while the corresponding percentage

for Fannie Mae actually declined from 11.5

percent to 11.1 percent. Thus, in 1998,

Freddie Mac’s overall special affordable

percentage (10.9 percent) was approximately

the same as Fannie Mae’s (11.1 percent). This

is reflected in Table A.4c by the ‘‘FannieMae-to-Freddie-Mac’’ ratio of 1.02 for the

special affordable category.

Further analysis shows that this

improvement of Freddie Mac relative to

65118 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

Fannie Mae was due to Freddie Mac’s better

performance on refinance loans during 1998.

The special affordable percentage of Fannie

Mae’s refinance loans fell from 11.1 percent

in 1997 to 9.7 percent in 1998, which is not

surprising given that middle-and upperincome borrowers typically dominate heavy

refinance markets such as 1998. But the

special affordable percentage of Freddie

Mac’s refinance loans did not drop very

much, falling from 11.3 percent in 1997 to

10.7 percent in 1998.175 Thus, Freddie Mac’s

higher special affordable percentage (10.7

percent versus 9.7 percent for Fannie Mae)

on refinance loans in 1998 enabled Freddie

Mac to close the gap between its overall

single-family performance and that of Fannie

Mae.

The GSEs’ low-mod and underserved areas

percentages followed a somewhat similar

pattern as their special affordable percentages

between 1997 and 1998. In 1997, Freddie

Mac’s underserved area percentage (21.6

percent) for total purchases was significantly

less than Fannie Mae’s (23.6), but in 1998,

Freddie Mac’s underserved areas percentage

(20.9) was about the same as Fannie Mae’s

(21.2 percent), as indicated by a ‘‘Fannie Mae

to Freddie Mac’’ ratio of 1.01. This

convergence was mainly due to a sharper

decline in Fannie Mae’s underserved area

percentage for refinance loans between 1997

and 1998.

Fannie Mae versus Freddie Mac

Performance—1998 to 1999. In 1998, the

‘‘Fannie-Mae-to-Freddie-Mac’’ ratios for all

three goals-qualifying categories were

approximately one, indicating similar

performance for the two GSEs. As shown in

Table A.4c, the 1999 ratios were 0.93 for

special affordable loans, 0.95 for low-mod

loans, and 0.93 for underserved areas loans—

indicating that Freddie Mac, for the first

time, had significantly surpassed Fannie Mae

in overall performance. For instance, in 1999,

underserved areas accounted for 21.8 percent

of Fannie Mae’s purchases, compared with

23.5 percent of Freddie Mac’s purchases. For

each of the three housing goal categories,

Fannie Mae’s performance increased between

1998 and 1999, but Freddie Mac’s increased

even more. For example, Fannie Mae’s

special affordable performance increased by

1.2 percentage points (from 11.1 percent to

12.3 percent) between 1998 and 1999 while

Freddie Mac’s performance increased 2.4

percentage points (from 10.9 percent to 13.3

percent).

B&C Loans. Table A.4b shows that the

estimates for the home purchase market do

not change much when loans for subprime

lenders were excluded from the HMDA

analysis; the reason was that these 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. As indicated in Table A.4c,

excluding 200 subprime lenders reduced the

goal-qualifying shares of the total market in

1998 as follows: special affordable (from 14.2

to 12.7 percent); low-mod (from 40.9 to 39.0

percent); and underserved areas (from 24.8 to

22.6 percent). As discussed earlier, the GSEs

have been entering the subprime market over

the past two years, particularly the A-minus

portion of that market. Industry observers

estimate that A-minus loans account for 50–

70 percent of all subprime loans while the

more risky B&C loans account for the

remaining 30–50 percent. Thus, one proxy

for excluding B&C loans originated by the

200 specialized lenders from the overall

market benchmark might be to reduce the

goal-qualifying percentages from the HMDA

data by half the above differentials;

accounting for B&C loans in this manner

would reduce the 1998 HMDA-reported goalqualifying shares of the total conforming

market as follows: special affordable (from

14.2 to 13.5 percent); low-mod (from 40.9 to

40.0 percent); and underserved areas (from

24.8 to 23.7 percent). However, as discussed

in Appendix D, much uncertainty exists

about the size of the subprime market and its

different components. More data and

research are obviously needed on this

growing sector of the mortgage market. 176

f. GSE Mortgage Purchases in Individual

Metropolitan Areas

While the above analyses, as well as earlier

studies, 177 concentrate on national-level

data, it is also instructive to compare the

GSEs’ purchases of mortgages in individual

metropolitan areas (e.g. MSAs). In this

section, the GSEs’ purchases of single-family

owner-occupied home purchase loans are

compared to the market in individual

MSAs. 178 To do so, total primary market

mortgage originations from three years, 1995,

1996 and 1997, are summed up by year, by

MSA, and for GSE purchases of these loans.

The GSEs’ purchases of 1995 originations

include all 1995 originations purchased by

each GSE between 1995 and 1998 from 324

MSAs. For their purchases of 1996

originations, all 1996 originations purchased

between 1996 and 1999 from 326 MSAs are

included. All 1997 originations purchased

between 1997 and 1999 from 328 MSAs are

included for 1997 originations. This should

cover 90 to 95 percent of the 1995 through

1997 originated loans that will be purchased

by the GSEs, thus making the GSE data

comparable to HMDA market data. The loans

are then grouped by the GSE housing goal

categories for which they qualify and the

ratio of the housing goal category originations

to total originations in each MSA is

calculated for each GSE and the market. The

GSE-to-market ratio is then calculated by

dividing each GSE ratio by the corresponding

market ratio. For example, if it is calculated

that one of the GSEs’ purchases of Low- and

Moderate-Income loans in a particular MSA

is 47 percent of their overall purchases in

that MSA, while 49 percent of all

originations in that MSA are Low-Mod, then

that GSE-to-market ratio is 47/49 (or 0.96).

Table A.5 shows the performance of the

GSEs by MSA for 1995, 1996 and 1997

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. 179 For the above example, that GSE

is considered to be lagging the market. These

results are then summarized in Table A.5,

which reports the number of MSAs in which

each GSE under-performs the market with

respect to the housing goal categories.

BILLING CODE 4210–27–P

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65119

BILLING CODE 4210–27–C

65120 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

For 1996 originations, Fannie Mae:

• Lagged the market in 268 (83 percent) of

the MSAs in the purchase of Underserved

Area loans,

• Lagged the market in 288 (88 percent) of

the MSAs in the purchase of Low- and

Moderate-Income loans, and

• Lagged the market in 295 (90 percent) of

the MSAs in the purchase of Special

Affordable loans.

Freddie Mac lagged the market to an even

greater extent in 1996. Specifically, the

market outperformed Freddie Mac in:

• 296 (91 percent) of the MSAs in the

purchase of Underserved Area loans,

• 322 (99 percent) of the MSAs in the

purchase of Low- and Moderate-Income

loans, and

• 323 (99 percent) of the MSAs in the

purchase of Special Affordable loans.

Thus Freddie Mac was behind Fannie Mae

in at least three-quarters of the MSAs for all

three goal categories. As shown in Table A.5,

the results for loans originated in 1995 and

1997 are similar.

g. High Down Payments on GSEs’ LowerIncome Loans

Recent studies have raised questions about

whether the lower-income loans purchased

by the GSEs are adequately meeting the

needs of some lower-income families. In

particular, the lack of funds for down

payments is one of the main impediments to

homeownership, particularly for many lowerincome families who find it difficult to

accumulate enough cash for a down

payment. As this section explains, a

noticeable pattern among lower-income loans

purchased by the GSEs is the predominance

of loans with high down payments.

HUD’s 1996 report to Congress on the

possible privatization of Fannie Mae and

Freddie Mac 180 found, rather surprisingly,

that the mortgages taken out by lower-income

borrowers and purchased by the GSEs were

as likely to have high down payments as the

mortgages taken out by higher-income

borrowers and purchased by the GSEs. For

example, considering the GSEs’ purchases of

home purchase loans in 1995, 58 percent of

very low-income borrowers made a down

payment of at least 20 percent, compared

with less than 50 percent of borrowers from

other groups. In addition, a surprisingly large

percentage of the GSEs’ first-time homebuyer

loans had high down payments. In 1995, 35

percent of Fannie Mae’s and 41 percent of

Freddie Mac’s first-time homebuyer loans

had down payments of 20 percent or more.

Table A.6 presents similar data for the

GSEs’ purchases of total loans during 1999.

Over three-fourths (75.1 percent) of the GSEs’

very low-income loans had a down payment

more than 20 percent, compared with 72.1

percent of their remaining purchases.

Essentially, the GSEs have been purchasing

lower-income loans with large down

payments. 181

BILLING CODE 4210–27–P

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65121

BILLING CODE 4210–27–C

These results are consistent with previous

studies that show that the proportion of large

down payment loans purchased by the GSEs

from lower-income borrowers is greater than

that for all loan purchases.182

As discussed in Section C, both Fannie

Mae and Freddie Mac have introduced highLTV products: ‘‘Flexible 97’’ and ‘‘Alt 97’’

respectively. By lowering the required down

payment to three percent and adding

flexibility to the source of the down payment,

these loans should be more affordable. The

down payment, as well as closing costs, can

come from, 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.

65122 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

However, in order to control default risk,

these loans also have stricter credit history

requirements.

Fed Study. An important study by three

economists—Glenn Canner, Wayne Passmore

and Brian Surette 183—at the Federal Reserve

Board showed the implications of the GSEs’

focus on high down payment loans. Canner,

Passmore, and Surette examined the degree

to which different mortgage market

institutions—the GSEs, FHA, depositories

and private mortgage insurers—are taking on

the credit risk associated with funding

affordable mortgages. The authors combined

market share and down payment data with

data on projected foreclosure losses to arrive

at an estimate of the credit risk assumed by

each institution for each borrower group.

This study found that Fannie Mae and

Freddie Mac together provided only 4 to 5

percent of the credit support for lowerincome and minority borrowers and their

neighborhoods. The relatively small role of

the GSEs providing credit support is due to

their low level of funding for these groups

and to the fact that they purchase mainly

high down payment loans. FHA, on the other

hand, provided about two-thirds of the credit

support for lower-income and minority

borrowers, reflecting FHA’s large market

shares for these groups and the fact that most

FHA-insured loans have less-than-fivepercent down payments.

3. 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.

a. Studies by Bunce and Scheessele

Harold Bunce and Randall Scheessele of

the Department have published two studies

of affordable lending. In December 1996, they

published a study titled The GSEs’ Funding

of Affordable Loans.184 This report analyzed

HMDA data for 1992–95, including a detailed

comparison of the GSEs’ purchases with

originations in the primary market. In July

1998, they updated their earlier study to

analyze the mortgage market and the GSEs’

activities in 1996.185 The findings were

largely similar in both studies: 186

• Both GSEs lagged the primary

conventional market, depositories, and

(particularly) FHA in funding mortgages for

lower-income and historically underserved

borrowers. FHA stands out as the major

funder of affordable loans. In 1996,

approximately 30 percent of FHA-insured

loans were for African-American and

Hispanic borrowers, compared with only 10

percent of the loans purchased by the GSEs

or originated in the conventional market.

• The two GSEs show very different

patterns of lending—Fannie Mae is much

more likely than Freddie Mac to serve

underserved borrowers and their

neighborhoods. Since 1992, Fannie Mae has

narrowed the gap between its affordable

lending performance and that of the other

lenders in the conforming market. Freddie

Mac’s improvement has been more mixed—

in some cases it has improved slightly

relative to the market but in other cases it has

actually declined relative to the market. The

findings with respect to Freddie Mac are

similar to those discussed earlier in Section

E.2.c.

b. Studies by Freddie Mac

In 1995 Freddie Mac published Financing

Homes for A Diverse America, which

contained a wide variety of statistics and

charts on the mortgage market. Several of the

exhibits contained comparisons between the

primary mortgage market and Freddie Mac’s

purchases in 1993 and 1994:

• While not asserting strict parity, this

report presented comparable frequency

distributions of primary market originations

and Freddie Mac’s purchases by borrower

and census tract income, concluding that

Freddie Mac ‘‘finances housing for

Americans of all incomes’’ and it ‘‘buys

mortgages from neighborhoods of all

incomes.’’

• With regard to minority share of census

tracts, the report stated that Freddie Mac’s

‘‘share of minority neighborhoods matches

the primary market.’’

• The report acknowledged that Freddie

Mac’s purchases did not match the primary

market in terms of borrower race. It found

that in 1994 African-Americans and

Hispanics each accounted for 4.9 percent of

the primary market but only 2.7 percent and

4.0 percent respectively of Freddie Mac’s

purchases. On the other hand, Whites and

Asian Americans accounted for 83.7 percent

and 3.2 percent of the primary market, but

86.3 percent and 3.9 percent respectively of

Freddie Mac’s acquisitions.

In its March 1998 Annual Housing

Activities Report (AHAR) submitted to the

Department and Congress, Freddie Mac

presented data on this issue for 1996 and

1997. This report stated that its purchases

‘‘essentially mirror[ed] the overall

distribution of mortgage originations in terms

of borrower income.’’ However, the data

underlying Exhibit 4 of the AHAR indicated

that the share of Freddie Mac’s 1997

purchases for borrowers with income (in

1996 dollars) less than $40,000 was more

than 4 percentage points below the

corresponding share for the primary market

in 1996. A similar pattern prevailed in terms

of census tract income—the data underlying

Exhibit 5 of the AHAR indicated that the

share of Freddie Mac’s 1997 purchases in

tracts with income in excess of 120 percent

of area median income exceeded the

corresponding share for the primary market

in 1996 by about 4 percentage points.

In its March 1998 AHAR, Freddie Mac

found a much closer match between the

distributions of home purchase mortgages by

down payment for Freddie Mac’s 1997

acquisitions and the primary market in 1997,

as the latter was reported by the Federal

Housing Finance Board. Specifically, Exhibit

6 of the AHAR reported that 42 percent of

borrowers in each category made down

payments of less than 20 percent.187

c. Studies by Fannie Mae

Fannie Mae has not published any studies

on the comparability of its mortgage

purchases with the primary market.

However, in an October 1998 briefing for

HUD staff, Fannie Mae presented the results

of several comparisons of its purchases,

based on the data supplied to the Department

by Fannie Mae, with loans originated in the

conventional conforming market, based on

the HMDA data. In these analyses, Fannie

Mae stated that:

• The percentage of Fannie Mae’s home

purchase loans serving minorities exceeded

the corresponding percentage in the

conventional conforming market by 2.6

percentage points in 1995, 2.0 percentage

points in 1996, and 2.7 percentage points

(18.6 percent vs. 15.9 percent) in 1997;

• The percentage of Fannie Mae’s home

purchase loans for low-and moderate-income

households exceeded the corresponding

percentage in the conventional conforming

market by 0.2 percentage point in 1995, fell

0.1 percentage point short of the market in

1996, but exceeded it again, by 1.2

percentage points (38.5 percent vs. 37.3

percent), in 1997;

• The percentage of Fannie Mae’s home

purchase loans for households in

underserved areas fell 0.04 percentage point

short of the conventional conforming market

in 1996, but exceeded the corresponding

percentage in the conventional conforming

market by 1.4 percentage points (25.5 percent

vs. 24.1 percent) in 1997;

• The percentage of Fannie Mae’s home

purchase loans for very low-income

households and low-income households in

low-income areas fell 1.0 percentage point

short of the conventional conforming market

in 1995 and 0.9 percentage point short in

1996, but exceeded the corresponding

percentage in the conventional conforming

market by 2.2 percentage points (12.7 percent

vs. 10.5 percent) in 1997.

Some of these findings by Fannie Mae

differ from those of other researchers. This is

due in part to the fact that most other studies

have utilized HMDA data for both the

primary market and sales to the GSEs, but

Fannie Mae compared the primary market,

based on HMDA data, with the patterns in

the GSE loan-level data submitted to the

Department.188 189

d. Other Studies

Lind. John Lind examines HMDA data in

order to compare the GSEs’ loan purchase

activity to mortgage originations in the

primary conventional conforming market.190

Like other studies, Lind presents an aggregate

comparison of GSE/primary market

correspondence for Black, Hispanic, lowincome borrowers, and low- and moderateincome Census tracts. Unlike other studies,

however, Lind also examines market

correspondence at the individual

metropolitan area and regional levels.

Lind finds that the GSEs are not leading

the market, but that Fannie Mae, in

particular, improved its performance

between 1993 and 1994. In 1994, Lind finds

that the shares of Fannie Mae’s home

purchase loans to minority and low-income

borrowers were comparable to the industry’s

shares. But the share of its home purchase

loans for low- and moderate-income census

tracts and the shares of Freddie Mac’s home

purchase loans for all categories examined

trailed those for the industry as a whole. For

refinance mortgages, on the other hand, both

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65123

GSEs trailed the industry in terms of the

shares of their loans for the groups analyzed.

In a subsequent study, Lind found that the

difference between the affordable lending

performance of Fannie Mae and Freddie Mac

was caused by differences in policy and

operating procedures of the GSEs, and not

differences in the make-up of their suppliers

of loans.191

Ambrose and Pennington-Cross. There

exists a wide variation in the market shares

of the GSEs, FHA and portfolio lenders

across geographic mortgage markets. Brent

Ambrose and Anthony Pennington-Cross

analyze FHA, GSE and portfolio lender

market shares to find insights into what

factors affect the market shares for FHA

eligible (under the FHA loan limit) loans.192

They hypothesize that the GSEs try to

mitigate higher perceived risks at the MSA

level by tightening lending standards,

generating a prediction of higher FHA market

share in locations with characteristically

higher or dynamically worsening risk. A

second hypothesis is that market share of

portfolio lenders increases in areas with

higher risk due to ‘‘reputation effects’’ and

GSE repurchase requirements. In their model,

they account for cyclical risk, permanent

risk, demographic, lender and regional

differences.

Ambrose and Pennington-Cross found that

the GSEs exhibit risk averse behavior as

evidenced by lower GSE market presence in

MSAs experiencing increasing risk and in

MSAs that historically exhibit high-risk

tendencies. FHA market shares, in contrast,

are associated with high or deteriorating risk

conditions. Portfolio lenders increase their

mortgage portfolios during periods of

economic distress, but increase the sale of

originations out of portfolio during periods of

increasing house prices. Lenders in MSAs

with historically high delinquency hold more

loans in portfolio. MSA risk is therefore

concentrated among portfolio lenders and in

FHA, with the GSEs bearing relatively little

credit risk of this kind. The study does find

that, other things being equal, the GSEs do

have a higher presence in underserved areas

and in areas where the minority population

is highly segregated.

MacDonald (1998). Heather MacDonald 193

examined the impact of the central city

housing goal from HUD’s 1993–1995 interim

housing goals. Census tracts were clustered

according to five variables (median house

value, median house age, proportion of

renters, percent minority and proportion of 2

to 4 units) argued to impede secondary

market purchases of homes in some

neighborhoods. Borrower characteristics and

lending patterns were compared across the

clusters of tracts, and across central city and

suburban tracts. Clustered tracts were found

to be more strongly related to a set of key

lending variables than are tracts divided

according to central city/suburban

boundaries. MacDonald concludes that

targeting affirmative lending requirements on

the basis of neighborhood characteristics

rather than political or statistical divisions

may provide a more appropriate framework

for efforts to expand access to credit.

MacDonald (1999). In a 1999 study,

Heather MacDonald investigated variations

in GSE market share among a sample of 426

nonmetropolitan counties in eight census

divisions.194 Conventional conforming

mortgage originations were estimated using

residential sales data, adjusted to exclude

government-insured and nonconforming

loans. Multivariate analysis was used to

investigate whether GSE market shares

differed significantly by location, after

controlling for the economic, demographic,

housing stock and credit market differences

among counties that could affect use of the

secondary markets. The study also

investigated whether there were significant

differences between the nonmetropolitan

borrowers served by Fannie Mae and those

served by Freddie Mac.

MacDonald found that space contributes

significantly to explaining variations in GSE

market shares among nonmetropolitan

counties, but its effects are quite specific.

One region—non-adjacent West North

Central counties—had significantly lower

GSE market shares than all others. The

disparity persisted when analysis was

restricted to underserved counties only. The

study also suggested significant disparities

between the income levels of the borrowers

served by each agency, with Freddie Mac

buying loans from borrowers with higher

incomes than the incomes of borrowers

served by Fannie Mae. An important

limitation on any study of nonmetropolitan

mortgages was found to be the lack of Home

Mortgage Disclosure Act data. This meant

that more precise conclusions about the

extent to which the GSEs mirror primary

mortgage originations in nometropolitan

areas could not be reached.

McClure. Kirk McClure examined the twin

mandates of FHEFSSA: to direct mortgage

credit to neighborhoods that have been

underserved by mortgage lenders; and to

direct mortgage credit to low-income and

minority households.195 Using the Kansas

City metropolitan area as a case study,

mortgages purchased by the GSEs in 1993–

96 were compared with mortgages held by

portfolio lenders in order to determine the

performance of the GSEs in serving these two

objectives. Kansas City provides a useful case

study area for this analysis, because it

includes a range of weak and strong housing

market areas where homebuyers have been

able to move easily to serve their housing,

employment, and neighborhood needs.

McClure found that borrowers are better

served if credit is directed to them

independent of location. Very low-income

and minority borrowers fared better, in terms

of the demographic, housing, and

employment opportunities of the

neighborhoods into which they located, than

borrowers in underserved neighborhoods,

suggesting that directing credit to lowincome and minority households has had the

desired effect of helping these households

purchase homes in areas where they would

find good homes and good employment

prospects. According to McClure, HUD’s

1996–99 housing goals defined underserved

tracts very broadly, such that nearly one-half

of the tracts in the Kansas City area are

categorized as underserved. Because the

definition of underserved is so broad,

directing credit to these tracts means only

increasing the flow of mortgage credit to the

lesser one-half of all tracts, which includes

many areas with stable housing stocks and

viable job markets.

The alternative approach of directing credit

to underserved areas was found to be helpful

only insofar as it has helped direct credit to

neighborhoods with slightly lower household

income levels and higher incidence of

minorities than found elsewhere in the

metropolitan area. McClure concluded that

neighborhoods that receive very low levels of

mortgage credit seemed to provide

insufficient housing or employment

opportunities to justify the effort that would

be required to direct additional mortgage

credit to them.

McClure concluded that whatever the

approach, the GSEs have not been performing

as well as the primary credit lenders in the

Kansas City metropolitan area. In terms of

helping underserved areas, the GSEs lagged

behind the industry in the proportion of

loans found in these areas. In terms of

helping low-income and minority borrowers,

the GSEs also lagged behind the industry.

However, to the extent that the GSEs served

these targeted populations, these households

used this credit to move to neighborhoods

with better housing and employment

opportunities than were generally present in

the underserved areas.

Williams.196 This study looks at mortgage

lending in underserved markets in the

primary and secondary mortgage markets for

the MSAs in Indiana. A more extensive

analysis is provided for South Bend/St.

Joseph County, Indiana that looks at the GSE

purchases in underserved markets by type of

primary market lender in both 1992 and

1996. It shows the percentage of loans bought

by the GSEs and the loan they did not buy.

This study found that the GSEs were more

aggressive in closing the gap in St. Joseph

County than in other MSAs in Indiana. It also

found that Fannie Mae’s underserved market

performance was slightly better than Freddie

Mac’s performance.

Williams compared the GSEs performance

in underserved markets and CRA institutions

between 1992 and 1995. It shows that the

GSEs have narrowed the gap between

themselves and lenders while CRA

institutions have lost ground relative to nonCRA lenders. A pattern observed across all

Indiana MSAs is that the GSEs do not appear

to lead the market but rather almost perfectly

mirrored the performance of mortgage

companies.

Williams looked at the impact of size and

location of lenders on the home mortgage

market. Large lenders were more likely to

finance mortgages for very low-income and

African American borrowers than smaller

lenders. Lenders headquartered in Indiana

were more likely to purchase mortgages in

underserved areas than lenders who only had

branches or no apparent physical presence in

Indiana. This suggest that served markets

might benefit more than underserved areas

from increased competition from non-local

lenders.

Gyourko and Hu. This study focuses on the

GSEs’ housing goals looking at the intrametropolitan distribution of mortgage

acquisitions by Fannie Mae and Freddie Mac

65124 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

and the spatial distribution of households

within 22 MSAs.197 The data on the GSEs’

mortgage purchases is provided by the

Census Tract File of Public Use Data Base

and data on households is provided by the

1990 census. The study found that the

distribution of goal-qualifying loan purchases

by the GSEs does not match the distribution

of goal-qualifying households. On average 44

percent of Low- and Moderate-Income Goal

and 46 percent of Special Affordable Goal

qualifying households are located in central

cities. This compares to the GSEs’ mortgage

purchases where 26 percent of Low- and

Moderate-Income Goal and 36 percent of

Special Affordable Goal were located in

central cities.

This study develops criteria for evaluating

the GSEs’ mortgage purchasing performance

in census tracts. The first measure is a ratio.

The numerator of the ratio is the share of the

GSEs’ mortgage purchases that qualify for the

Special Affordable Housing Goal in the

census tract. The denominator is the share of

households that are targeted by the Special

Affordable Housing Goal in the census tract.

A ratio is also computed for the Low- and

Moderate-Income Housing Goal. If the ratio is

less than 0.80 then the census tract is called

under-represented, meaning that the share of

the GSEs’ mortgage purchases which qualify

for the housing goal is less than 80 percent

of the share of the households that the goal

targets. The analysis of these ratios shows

that: (1) Central cities are more likely to be

under-represented in terms of the share of

affordable loans purchased by the GSEs, (2)

in suburbs, the larger the census tracts’

percent minority the greater the probability

that affordable loan purchases are underrepresented, and (3) the higher the tract’s

median income, the greater the likelihood

that census tract is over-represented.

Gyourko and Hu’s results are broadly

consistent across the 22 MSAs analyzed;

however, some noteworthy exceptions are

made. In a few MSAs, particularly Miami and

New York, the mismatch of affordable GSE

purchases to affordable households is much

less severe. In Boston, Los Angeles and New

York, census tracts with higher relative

median incomes are more likely to be underrepresented.

Case and Gillen. This study provides a

descriptive analysis of market share and

logistic regression analysis of the GSEs’

mortgage purchase patterns in 44

metropolitan areas over the period from 1993

to 1996.198 The study compares the GSEs and

the market along several borrower and

neighborhood characteristics.

This descriptive analysis of market shares

finds that, compared with mortgages

originated in the market, the GSEs’ are less

likely to purchase loans made to lowerincome borrowers, minority borrowers,

borrowers in lower-income neighborhoods,

and borrowers in central city neighborhoods.

The GSEs are more likely to purchase loans

made to higher income borrowers, white

borrowers, borrowers in higher income

neighborhoods, and suburban borrowers than

the non-GSEs. Case and Gillen find that

Fannie Mae provides a higher proportion of

total GSE funding for mortgage lending to

lower-income and minority borrowers and to

borrowers living in lower income,

predominantly minority, central city, and

geographically targeted areas than Freddie

Mac.

A logistic regression analysis was

conducted to look at the influence of specific

borrower and neighborhood characteristics

on the probability that a loan is purchased by

one the GSEs. The results support the

findings of the descriptive analysis with

some exceptions. In contrast to the

descriptive analysis, the impact of

geographically targeted census tracts and

neighborhood minority composition on the

GSEs’ purchasing behavior was inconsistent

over the 44 areas. 199, 200

The logistic regression analysis was

extended to test for changes in the GSEs’

purchasing behavior over time (1993–1996).

Changes in the GSEs’ purchasing activity are

observed, but no systematic time trend was

found. One explanation that was given for

this result was that changes in the GSEs’

purchases over time might be related to

changes in overall market activity rather than

changes in purchasing behavior by either of

the GSEs.

Myers. Earlier studies have shown that

racial minority groups—particularly African

Americans and Latinos—are less likely to be

approved for home mortgage loans than

members of majority populations. It has been

suggested that primary lenders may use the

difficulty of selling loans to the GSEs on the

secondary market as a pretext for not

approving loans to racial minority group

members. This study uses the residual

difference approach to measure racial

discrimination in mortgage lending and

estimates differential treatment by the GSEs

of minority and nonminority first-time

homeowner loans in the 23 largest

metropolitan statistical areas (MSAs).201

The residual difference approach

decomposes racial gaps in HMDA-reported

loan-rejection rates between the component

that can be explained and that which cannot

be explained by racial differences in

characteristics. Characteristics Myers uses to

explain poor credit history and denial rates

include borrower, neighborhood, and loan

variables from HMDA, the GSE Public Use

Data Base, and Census 1990.202 Myers

interprets the unexplained gap as being

‘‘discrimination’’. The residual difference

method permits the estimation of minority

loan rejection rates when minorities are

treated like equally qualified white borrowers

(i.e. equal treatment values).

There are three main findings of this study.

First, there are unexplained disparities in

loan-rejection rates between black and white

applicants for home mortgage loans in

HMDA data; that is, blacks have higher

denial rates than whites even after

controlling for variables such as income.

Second, the probability that a loan won’t sell

on the secondary market systematically

increases the probability that a loan will be

rejected by the lender.203 Third, African

American and Hispanic loans are often less

likely to sell on the secondary market than

white loans.

The study also looks at whether the GSEs’

purchasing behavior explains racial gaps in

loan rejection rates. It compares the residual

difference on racial disparities in loan

rejection rates with and without controlling

for GSE decisions. If the equal-treatment

rejection rate is higher than the equaltreatment rejection rate that accounts for the

GSE effect, then the purchase policies of the

GSEs ‘‘explains part of the lending gap’’. If

the equal-treatment value without accounting

for racial difference in GSE effects is equal

to or lower than the corresponding value

than accounts for racial difference in GSE

effect, then GSEs effect does not explain

racial lending gaps.

Myers concludes that there are no

consistent patterns for the GSE effect, either

across racial groups or across MSAs—that is,

the GSE discussions do not systematically

explain the observed racial disparities in loan

rejection rates. In many MSAs, the GSE effect

can account for some of the high rejection

rates of blacks and ‘‘others’’. Among other

racial groups, however, there are as many

MSAs where there is no such finding as there

are ones where the effect seems to hold. But

even in those cases where the effect seems to

hold the amount explained is small. Myers

finds that the impact is so small that even

large differences in actual probabilities that

loans are not sold to GSEs cannot explain the

substantial racial difference in loan-rejection

rates.

Bradford. In a case study comparison of the

Chicago and Washington D.C. mortgage

markets, Bradford found that minority areas

received considerably lower levels of GSE

purchases than white areas in the Chicago

market, but about equal and sometimes

higher levels of GSE purchases in the D.C.

study area.204 Bradford’s interprets this

finding as partially the result of the

exceptionally large minority population in

the D.C. area living in new development and

suburban areas when compared to the

minority population distribution in the

Chicago market. In his view, the fact that

many minority homeowners in the D.C. area

reside in suburban and new growth areas

provides for increasing housing values and

high levels of demand that help mitigate the

effects of mortgage default by providing

borrowers with more options to refinance or

sell their homes to escape from foreclosure.

This makes the minority market in the D.C.

area generally more attractive to lenders and

secondary market investors.

Bradford argues that the role of individual

lenders is an important factor in explaining

the disparate racial patterns between the

Chicago and D.C. study areas. The large GSE

lenders and the large lenders serving

minority markets tend to be the same lenders

in the D.C. market. He contends that the

parity in the racial markets in the D.C. area

would disappear and would be replaced by

levels of disparity comparable to those in the

Chicago market if just a handful of large GSE

lenders in the minority areas reduced their

GSE levels to the norm for the entire market.

Bradford also examines differences

between Fannie Mae and Freddie Mac in the

two study areas. Both Fannie Mae and

Freddie Mac showed lower levels of

purchases in minority areas than in white

areas in the Chicago market, based on his

research. While there were some instances

where Freddie Mac made improvements

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65125

relative to Fannie Mae (notably in the

Chicago market in 1996), Fannie Mae’s

relative performance in different racial

markets was better than that of Freddie Mac.

In the Chicago market, for example, Fannie

Mae had higher levels of market shares in the

racially changing areas than in the white

areas while Freddie Mac always had lower

market shares in the racially changing areas

compared to the white areas. In the D.C.

market, Bradford found that while the GSEs

as a whole showed relative parity in the

different racial markets, this was largely due

to Fannie Mae’s performance that countered

the systematic disparities in the Freddie Mac

purchases.

Harrison, et. al. Theories of ‘‘information

externalities,’’ supported by recent empirical

evidence, suggest that property transactions

in a particular market area generate

information making similar future

transactions in that same market area less

risky for prospective lenders. Specifically,

home sales generate information useful to

independent appraisers in generating more

precise value estimates. This increased

precision, in turn, reduces the uncertainty

(risk) faced by lenders, and hence, may

increase acceptance rates and the flow of

funds to the given market area.

Using a sample of GSE purchasing

activities across twelve Florida counties,

Harrison et al. find some evidence that both

Fannie Mae and Freddie Mac are more active

in neighborhoods with historically low

transaction volume than they are in other

neighborhoods.205 In addition, the results of

their investigation are generally consistent

with the previous literature suggesting

Fannie Mae outperformed Freddie Mac in

historically underserved market segments in

1993–95.

4. GSEs’ Underwriting Guidelines

Most studies on affordability of mortgage

loans are quantitative using HMDA data,

HUD’s GSE Public Use Database or some

other related database. To complement these

studies, HUD commissioned a study by the

Urban Institute (UI) to examine recent trends

in the GSEs’ underwriting criteria and to seek

attitudes and opinions of informed players in

four local mortgage market markets (Boston,

Detroit, Miami and Seattle).206 Interviews

were conducted with mortgage lenders,

community advocates and local government

officials—all local actors who would be

knowledgeable about the impact of the GSEs’

underwriting policies on their ability to fund

affordable loans for lower-income

borrowers.207

The UI report reveals three major trends in

the GSEs’ underwriting that affects affordable

lending. These include increased flexibility

in standard 208 underwriting and appraisal

guidelines, the introduction of affordable

lending products, and the introduction of

automated underwriting and credit scores in

the loan application process. Through these

trends, Fannie Mae and Freddie Mac have

attempted to increase their capacity to serve

low- and moderate-income homebuyers.

They are also eliminating practices that could

potentially have had disparate impacts on

minority homebuyers. While both GSEs have

made progress, ‘‘most [of those interviewed]

thought Fannie Mae has been more

aggressive than Freddie Mac in outreach

efforts, implementing underwriting changes

and developing new products.’’ 209

While the GSEs improved their ability to

serve low- and moderate-income borrowers,

it does not appear that they have gone as far

as some primary lenders to serve these

borrowers and to minimize the

disproportionate effects on minority

borrowers. From previous published analyses

of the GSEs’ mortgage purchases, differences

between the income characteristics and racial

composition of borrowers served by the

primary mortgage market and the purchase

activity of the GSEs were found. ‘‘This means

that the GSEs are not serving lower-income

and minority borrowers to the extent these

families receive mortgages from primary

lenders.’’ 210 From UI’s discussions with

lenders, it was revealed that primary lenders

are originating mortgages to lower-income

borrowers using underwriting guidelines that

allow lower down payments, higher debt-toincome ratios and poorer credit histories than

allowed by the GSEs’ guidelines. These

mortgages are originated to a greater extent to

minority borrowers who have lower incomes

and wealth. From this evidence, UI

concludes that the GSEs appear to be lagging

the market in servicing low- and moderateincome and minority borrowers.

Furthermore, UI 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. Moreover, the use of automated

underwriting systems and credit scores may

place lower-income borrowers at a

disadvantage when applying for a loan, even

though they are acceptable credit risks.’’ 211

5. The GSEs’ Support of the Mortgage Market

for Single-family Rental Properties

Single-family rental housing is an

important part of the housing stock because

it is an important source of housing for

lower-income households. Based on the 1996

Property Owners and Managers Survey, 49

percent of all rental units are in properties

with fewer than five units and the 1997

American Housing Survey found that

approximately 59 percent of the stock of

single-family rental units are affordable to

very-low income families (i.e., families

earning 60 percent or less of the area median

income). Of the GSEs’ mortgage purchases in

1999, around 30 percent of the single-family

rental units financed were affordable to verylow income households.

While single-family rental properties are a

large segment of the rental stock for lowincome families, they make up a small

portion of the GSEs’ overall business. In

1999, Fannie Mae and Freddie Mac

purchased more than $26 billion in

mortgages for these properties. These

purchases represented less than 5 percent of

the total dollar amount of their overall 1999

business.

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.7b in Section G shows that

in 1998 the GSEs financed 68 percent of

owner-occupied dwelling units but only 19

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 are

important for the GSEs housing goals,

especially for meeting the needs of lowerincome families. In 1999 around 73 percent

of single-family rental units qualified for the

Low- and Moderate-Income Goals, compared

with 38 percent of one-family owneroccupied properties. This heavy focus on

lower-income families meant that singlefamily rental properties accounted for 15

percent of the units qualifying for the Lowand Moderate-Income Goal, even though they

accounted for 8 percent of the total units

(single-family and multifamily) financed by

the GSEs. Single-family rental properties

account for 16 percent of the geographicallytargeted and 23 percent of the special

affordable housing goals.

A comparison of the GSEs’ single-family

rental and one-family owner-occupied

mortgage purchases reveals the following

broad patterns of borrower and neighborhood

characteristics. Borrowers for single-family

rental properties are more likely to be

minorities than borrowers for one-family

owner-occupied properties. Mortgages

purchased by the GSEs for single-family

rental properties compared with one-family

owner-occupied properties are more likely to

be located in lower-income and higher

minority neighborhoods. More single-family

rental than one-family owner-occupied

mortgages were refinance or prior-year loans.

A closer look at borrower characteristics

for single-family rental properties shows the

following. First, based on ethnic/racial

characteristics, borrowers for investor-owned

properties are similar to borrowers for onefamily owner-occupied properties. Second,

borrowers for single-family rental properties,

especially owner-occupied 2- to 4-unit

properties, are more likely to be nonwhite

than are borrowers for one-family owneroccupied and investor-owned properties.

About 35 percent of the borrowers for owneroccupied 2- to 4-unit properties are nonwhite compared with around 17 percent for

both one-family and investor-owned

properties. For one-family owner-occupied

and investor-owned properties about 5

percent of borrowers are African American,

compared with 9 percent for owner-occupied

2- to 4-unit properties. A similar comparison

applies for Hispanic borrowers, 6 percent and

15 percent respectively.

With regard to neighborhood

characteristics, a comparison of different

65126 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

types of rental properties purchased by the

GSEs shows that investor 1-unit properties

were more likely to be located in higherincome neighborhoods than were units in 2-

to 4-unit rental properties. For units in

investor 1-unit properties, about 18 percent

were in low-income neighborhoods,

compared with 31 percent from units in 2-

to 4-unit rental properties. About 40 percent

of the units in investor properties were in

high-minority neighborhoods, compared to

only a slightly lower 37 percent for owneroccupied 2- to 4-unit properties.

The GSEs can mitigate risk by purchasing

mortgages which are seasoned or refinanced.

The data show that mortgages on properties

with additional risk components such as

being investor-owned, in low- income

neighborhoods, and/or in high-minority

neighborhoods are more likely to be seasoned

or refinanced. For the GSEs’ mortgage

purchases, in general, mortgages on investorowned properties are more likely to be prioryear than mortgages on owner-occupied 2- to

4-unit properties (based on unit counts).

These patterns are consistent with the notion

that investor properties are more risky than

owner-occupied 2- to 4-unit properties.

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 50–55 percent of

total units financed in the overall

conventional conforming mortgage market

during 2001–2003, the period for which the

Low- and Moderate-Income Housing Goal is

established. The market estimates exclude

B&C loans and allow for much more adverse

economic 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].’’ 212

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 other

lenders in funding lower-income borrowers

and their communities. 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 50–55 percent,

which is substantially higher than the GSEs’

performance on that goal.

This section provides another perspective

on the GSEs’ performance by examining the

share of the total mortgage market and the

share of the goal-qualifying markets (lowmod, special affordable, and underserved

areas) accounted for by the GSEs’ purchases.

This analysis, which is conducted by product

type (single-family owner, single-family

rental, and multifamily), shows the relative

importance of the GSEs in each of the goalqualifying markets.

1. GSEs’ Role in Major Sectors of the

Mortgage Market

Table A.7 compares GSE mortgage

purchases with HUD’s estimates of the

numbers of units financed in the

conventional conforming market during

1997(A.7a) and 1998 (A.76).213 Because 1997

was a more typical year then the heavy

refinance year of 1998, the following

discussion will focus on 1997. HUD

estimates that there were 7,306,950 owner

and rental units financed by new mortgages

in 1997. Fannie Mae’s and Freddie Mac’s

mortgage purchases financed 2,948,112

dwelling units, or 40 percent of all dwelling

units financed. As shown in Table A.7a, the

GSEs play a much smaller role in the goalsqualifying markets than they do in the overall

market. During 1997, new mortgages were

originated for 4,201,287 dwelling units that

qualified for the Low- and Moderate-Income

Goal; the GSEs low-mod purchases financed

1,330,516 dwelling units, or only 32 percent

of the low-mod market. Similarly, the GSEs’

purchases accounted for only 25 percent of

the special affordable market and 34 percent

of the underserved areas market.214

Obviously, the GSEs are not leading the

industry in financing units that qualify for

the three housing goals.

BILLING CODE 4210–27–P

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65127

65128 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

BILLING CODE 4210–27–C

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. As shown in Table A.7a, the

GSEs accounted for 50 percent of the singlefamily owner market in 1997 but only 24

percent of the multifamily market and 14

percent of the single-family rental market (or

a combined share of 20 percent of the rental

market).

Single-family Owner Market. This market

is the bread-and-butter of the GSEs’ business,

and based on the financial and other factors

discussed below, they 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 been lagging behind the market in

their funding of single-family owner loans

that qualify for the housing goals, as

discussed in Section E.2.c. Between 1996 and

1998, low- and moderate-income borrowers

accounted for 34.9 percent of Freddie Mac’s

mortgage purchases and 38.4 percent of

Fannie Mae’s mortgage purchases, but 42.6

percent of primary market originations in

metropolitan areas. The market share data

reported in Table A.7. for the single-family

owner market tell the same story. The GSEs’

purchases of single-family owner loans

represented 50 percent of all newlyoriginated owner loans in 1997, but only 43

percent of the low-mod loans that were

originated, 35 percent of the special

affordable loans, and 48 percent of the

underserved area loans. Thus, the GSEs need

to improve their performance and it appears

that there is ample room in the non-GSE

portions of the goals-qualifying markets for

them to do so. For instance, the GSEs are not

involved in almost two-thirds of special

affordable owner market.

Single-family Rental Market. Single-family

rental housing is a major source of low- and

moderate-income housing. As discussed in

Appendix D, data on the size of the primary

market for mortgages on these properties is

limited, but information from the American

Housing Survey on the stock of such units

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65129

and plausible rates of refinancing indicate

that the GSEs are much less active in this

market than in the single-family owner

market. As shown in Table A.7a, HUD

estimates that the GSEs’ purchases have

totaled only 14 percent of newly-mortgaged

single-family rental units that were affordable

to low- and moderate-income families.

Many of these properties are ‘‘mom-andpop’’ 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 Department believes that

there is room for such an enhanced role.

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 five 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 17 percent all (singlefamily and multifamily) dwelling units

financed during 1997.215 By comparison,

multifamily acquisitions represented 13.5

percent all units backing Fannie Mae’s

purchases of mortgages originated in 1997,

with a corresponding figure of only 8.8

percent for Freddie Mac.216 In other words,

the GSEs place more emphasis on singlefamily mortgages than they do on

multifamily mortgages.

The GSEs role in the multifamily market is

significantly smaller than in single-family. As

shown in Table A.7a, the GSEs’ purchases

have accounted for only 24 percent of newly

financed multifamily units during 1997—a

market share much lower than their 50

percent share of the single-family owner

market. Thus, these data suggest that a

further enlargement of the GSEs’ role in the

multifamily market seems feasible and

appropriate in the future.

There are a number of submarkets, such as

the market for mortgages on 5–50 unit

multifamily properties, where the GSEs have

particularly lagged the market. As mentioned

above, the GSEs acquired loans representing

24 percent units multifamily units receiving

conventional financing in 1997, but their

acquisitions of loans on small multifamily

properties represented only about 2 percent

of such properties financed that year.

Certainly the GSEs face a number of

challenges in better meeting the needs of the

multifamily secondary 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. 217

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.

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

As discussed in Section C of this

Appendix, the GSEs’ single-family mortgage

acquisitions have generally followed the

volume of originations in the primary market

for conventional mortgages. However, in

1997, single-family originations rose by

nearly 10 percent, while the GSEs’

acquisitions declined by 7 percent. As a

result, the Office of Federal Housing

Enterprise Oversight (OFHEO) estimates that

the GSEs’ share of single-family mortgage

originations declined from 37 percent in

1996 to 32 percent in 1997. The GSEs’ singlefamily mortgage share jumped to an

estimated 43 percent in 1998 and 42 percent

in 1999, but that is still well below the peak

of 51 percent attained in 1993.

The GSEs’ high shares of originations

during the 1990s led to a rise in their share

of total conventional single-family mortgages

outstanding, including both conforming

mortgages and jumbo mortgages.218 OFHEO

estimates that the GSEs’ share of such

mortgages outstanding jumped from 34

percent at the end of 1991 to 40 percent at

the end of 1994 and an estimated 45 percent

at the end of 1998.219 All of the increase in

the GSEs’ relative role between 1991 and

1998 was due to the growth in their portfolio

holdings as a share of mortgages outstanding,

from 5 percent at the end of 1991 to 17

percent at the end of 1998; relative holdings

of the GSEs’ mortgage-backed securities by

others actually declined as a share of

mortgages outstanding, from 29 percent at the

end of 1991 to 28 percent at the end of 1998.

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, who accounted for 58 percent of all

single-family loans in 1997, sell virtually all

of their conventional conforming loans to the

GSEs.220 Private mortgage insurers are closely

linked to the GSEs, because mortgages

purchased by the enterprises that have loanto-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.221 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. The GSEs, through

their automated underwriting systems, have

started adapting their underwriting for

subprime loans and other loans that have not

met their traditional underwriting standards.

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 low- and moderate-income

borrowers and underserved neighborhoods.

Congress realized the crucial role played by

the GSEs’ underwriting guidelines when it

required each enterprise to submit a study on

its guidelines to the Secretary and to

Congress in 1993, and when it called for the

Secretary to ‘‘periodically review and

comment on the underwriting and appraisal

guidelines of each enterprise.’’ Some of the

conclusions from a study of the GSEs’ singlefamily underwriting guidelines prepared for

the Department by the Urban Institute have

been discussed in Section E.

c. State-of-the-Art Technology

Both GSEs are in the forefront of new

developments in mortgage industry

technology. Each enterprise released an

automated underwriting system in 1995—

Freddie Mac’s ‘‘Loan Prospector’’ and Fannie

Mae’s ‘‘Desktop Underwriter.’’ Both systems

rely on numerical credit scores, such as those

developed by Fair, Isaac, and Company, and

additional data submitted by the borrower, to

obtain a mortgage score. The mortgage score

indicates to the lender either that the GSE

will accept the mortgage, based on the

application submitted, or that more detailed

manual underwriting is required to make the

loan eligible for GSE purchase.

It is estimated that 25–40 percent of the

GSEs’ purchases were based on automated

underwriting in 1999. These systems have

also been adapted for FHA and jumbo loans.

They have the potential to reduce the cost of

loan origination, particularly for low-risk

loans, but the systems are so new that no

comprehensive studies of their effects have

been conducted. As discussed earlier,

concerns about the use of automated

underwriting include the impact on

minorities 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 FannieMaps, a

computerized mapping service offered to

lenders, nonprofit organizations, and state

and local governments to help them

implement community lending programs.

65130 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

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. They also contract with outside

leaders in the finance industry for technical

expertise not available in-house and for

advice on a wide variety of issues.

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 has increased from

$376 million in 1987 to $1.6 billion in 1992,

$3.1 billion in 1997, $3.4 billion in 1998 and

$3.9 billion in 1999—an average annual rate

of increase of 22 percent. Through the fourth

quarter of 1998, Fannie Mae has recorded 48

consecutive quarters of increased net income

per share of common equity. Fannie Mae’s

return on equity averaged 24.0 percent over

the 1995–99 period—far above the rates

achieved by most financial corporations.

Investors in Fannie Mae’s common stock

have seen their annual dividends per share

more than double since 1993, rising from

$1.84 to $4.32 in 1999. If dividends were

fully reinvested, an investment of $1000 in

Fannie Mae common stock on December 31,

1987 would have appreciated to $27,983.98

by December 31, 1997. This annualized total

rate of return of 39.5 percent over the decade

exceeded that of many leading U. S.

corporations, including Intel (35.9 percent),

Coca-Cola (32.4 percent), and General

Electric (24.3 percent).

Freddie Mac. Freddie Mac has shown

similar trends. Freddie Mac’s net income has

increased from $301 million in 1987 to $622

million in 1992, $1.4 billion in 1997, $1.7

billion in 1998 and $2.2 billion in 1999—an

average annual rate of increase of 18 percent.

Freddie Mac’s return on equity averaged 23.4

percent over the 1995–99 period—also well

above the rates achieved by most financial

corporations.

Investors in Freddie Mac’s common stock

have also seen their annual dividends per

share more than double since 1993, rising

from $0.88 to $2.40 in 1999. If dividends

were fully reinvested, an investment of $1000

in Freddie Mac common stock on December

29, 1989 would have appreciated to

$8,670.20 by December 31, 1997, for an

annualized total rate of return of 31.0 percent

over this period. This was slightly higher

than the annual return on Fannie Mae

common stock (29.9 percent) and

substantially higher than the average gain in

the S&P Financial-Miscellaneous index (24.1

percent) over the 1990–97 period.222

Other indicators. Additional indicators of

the strength of the GSEs are provided by

various rankings of American corporations.

One survey found that at the end of 1999

Fannie Mae was third of all companies in

total assets and Freddie Mac ranked 14th.223

Business Week has reported that among

Standard & Poor’s 500 companies in 1999,

Fannie Mae and Freddie Mac respectively

ranked 49th and 88th in market value, and

24th and 43rd in total profits.224

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 discussions with the Office of

Federal Housing Enterprise Oversight, 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 established at 50

percent of eligible units financed in each of

calendar years 2001, 2002 and 2003. This

goal will remain in effect for 2004 and

thereafter, unless changed by the Secretary

prior to that time. The goal represents an

increase over the 1996 goal of 40 percent and

the 1997–99 goal of 42 percent. These goals

are in the lower portion of the range of

market share estimates of 50–55 percent,

presented in Appendix D. 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

Data from the 1990 Census and the

American Housing Surveys demonstrate that

there are substantial housing needs among

low- and moderate-income families,

especially among lower-income and minority

families in this group. Many of these

households are burdened by high

homeownership costs or rent payments and

will likely continue to face serious housing

problems, given the dim prospects for

earnings growth in entry-level occupations.

According to HUD’s ‘‘Worst Case Housing

Needs’’ report, 21 percent of owner

households faced a moderate or severe cost

burden in 1997. Affordability problems were

even more common among renters, with 40

percent paying more than 30 percent of their

income for rent in 1997.225

Single-family Mortgage Market. Many

younger, minority and lower-income 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 seven 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.

Between 1993 and 1999, record numbers of

lower-income and minority families

purchased homes. First-time homeowners

have become a major driving force in the

home purchase market over the past five

years. Thus, the 1990s have seen the

development of a strong affordable lending

market. Despite this growth in affordable

lending to minorities, disparities in the

mortgage market remain. For example,

African-American applicants are still twice

as likely to be denied a loan as white

applicants, even after controlling for income.

Several demographic changes will affect

the housing finance system over the next few

years. First, the U.S. population is expected

to grow by an average of 2.4 million per year

over the next 20 years, resulting in 1.1 to 1.2

million new households per year. 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 1980’s will be in the

market for owner-occupied housing. 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. With continued house

price appreciation and favorable mortgage

terms, ‘‘trade-up buyers’’ will increase their

role in the housing market. These

demographic trends will lead to greater

diversity in the homebuying market, which

will require adaptation by the primary and

secondary mortgage markets.

As a result of the above demographic

forces, housing starts are expected to average

1.5 million units between 2000 and 2004,

essentially the same as in 1996–99.226

Refinancing of existing mortgages, which

accounted for 50 percent of originations in

1998 and 34 percent in 1999 are returning to

lower levels during 2000 and 2001 (16 and

12 percent respectively).

Multifamily Mortgage Market. Since the

early 1990s, the multifamily mortgage market

has become more closely integrated with

global capital markets, although not to the

same degree as the single-family mortgage

market. Loans on multifamily properties

remain viewed as riskier than their singlefamily counterparts. Property values, vacancy

rates, and market rents in multifamily

properties appear to be highly correlated

with local job market conditions, creating

greater sensitivity of loan performance to

economic conditions than may be

experienced for single-family mortgages.

Volatility during 1998 in the market for

Commercial Mortgage Backed Securities

(CMBS), an important source of financing for

multifamily properties, underlines the need

for an ongoing GSE presence in the

multifamily secondary market. The potential

for an increased GSE presence is enhanced

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65131

by virtue of the fact that an increasing

proportion of multifamily mortgages is now

originated in accordance with secondary

market standards.

The GSEs have the capability to increase

the availability of long-term, fixed rate

financing, thereby contributing greater

liquidity in market segments where increased

GSE presence can provide lenders with a

more viable ‘‘exit strategy’’ than what is

presently available. It appears that the cost of

mortgage financing on properties with 5–50

units, where much of the nation’s affordable

housing stock is concentrated, may be higher

than warranted by the degree of inherent

credit risk.227 Presently, however, the GSEs

purchase only about 5 percent of units in 5–

50 unit properties financed annually.

Borrowers have also experienced difficulty

obtaining mortgage financing for multifamily

properties with significant rehabilitation

needs. Historically the flow of capital into

multifamily housing for seniors has,

moreover, been characterized by a great deal

of volatility.

2. Past Performance and Ability To Lead the

Industry

The GSEs have played a major role in the

conventional single-family mortgage market

in the 1990s. The GSEs’ purchases of singlefamily-owner mortgages accounted for 42

percent of mortgages originated in the singlefamily market during 1999. Many industry

observers believe that the role of the GSEs in

the late-1980s and 1990s is a major reason

why the decline of the thrift industry had

only minor effects on the nation’s housing

finance system. Additionally, the American

mortgage market was not impacted adversely

in any way by the volatility in world

financial markets in late 1998.

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,

including activities involving subprime

mortgages and mortgages on manufactured

housing.

The GSEs’ performance on the Low- and

Moderate-Income Housing Goal has also

improved significantly in recent years, as

shown in Figure A.1. Fannie Mae’s

performance increased from 34.2 percent in

1993 to 42.3 percent in 1995, 45.6 percent in

1996, and 45.7 percent in 1997, then falling

slightly to 44.1 percent in 1998, but rising to

45.9 percent in 1999. Freddie Mac’s

performance also increased, from 29.7

percent in 1993 to 38.9 percent in 1995, 41.1

percent in 1996, 42.6 percent in 1997, 42.9

percent in 1998, and 46.1 percent in 1999.

Freddie Mac’s low- and moderate-income

shares were below Fannie Mae’s shares in

every year through 1998, but its goal

performance slightly exceeded Fannie Mae’s

performance in 1999. This increase in

Freddie Mac’s relative performance on the

Low- and Moderate-Income Housing Goal

resulted from its increased role in the

multifamily mortgage market and the

increase in the goal-qualifying share of its

single-family mortgages.

BILLING CODE 4210–27–P

65132 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65133

65134 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

BILLING CODE 4210–27–C

Single-family Affordable Lending Market.

Despite these gains in goal performance, the

Department remains concerned about the

GSEs’ support of lending for the lowerincome end of the market. As shown in

Figures A.2 and A.3, the lower-income shares

of the GSEs’ purchases are too low,

particularly when compared with the

corresponding shares for portfolio lenders

and the primary market.

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65135

This appendix has reached the following

findings with respect to the GSEs’ purchases

of affordable loans for low- and moderateincome families and their communities.

• While Fannie Mae and Freddie Mac have

both improved their support for the singlefamily affordable lending market over the

past seven years, they have generally lagged

the overall single-family market in providing

affordable loans to lower-income borrowers.

This finding is based on HUD’s analysis of

GSE and HMDA data and on numerous

studies by academics and research

organizations.

• The GSEs show somewhat different

patterns of mortgage purchases—through

1998, Freddie Mac was less likely than

Fannie Mae to fund mortgages for lowerincome families. As a result, the percentage

of Freddie Mac’s purchases benefiting

historically underserved families and their

neighborhoods was less than the

corresponding shares of total market

originations, while Fannie Mae’s purchases

were closer to the patterns of originations in

the primary market (see Figure A.3).

However, in 1999, Freddie Mac’s purchases

of home loans included a higher percentage

of low-mod loans than Fannie Mae’s

purchases (40.0 percent and 39.3 percent,

respectively). It remains to be seen whether

this represents a new trend for Freddie Mac,

or a temporary reversal of the pattern for the

1996–98 period.

• A study by The Urban Institute of lender

experience with the GSEs’ underwriting

guidelines finds that the enterprises had

stepped up their outreach efforts and

increased the flexibility in their standards to

better accommodate the special

circumstances of lower-income borrowers.

However, this study concluded that the

GSEs’ guidelines remain somewhat inflexible

and that the enterprises are often hesitant to

purchase affordable loans. Lenders also told

The Urban Institute that Fannie Mae has been

more aggressive than Freddie Mac in market

outreach to underserved groups, in offering

new affordable products, and in adjusting its

underwriting standards.

• A large percentage of the lower-income

loans purchased by the enterprises have

relatively high down payments, which raises

questions about whether the GSEs are

adequately meeting the needs of lowerincome families have difficulty raising

enough cash for a large down payment.

• There are important parts of the singlefamily market where the GSEs have played

a minimal role. For example, single-family

rental properties are an important source of

low-income housing, but they represent only

a small portion of the GSEs’ business. GSE

purchases have accounted for only 14

percent of the single-family rental units that

received financing in 1997. An increased

presence by Fannie Mae and Freddie Mac

would bring lower interest rates and liquidity

to this market, as well as improve their goals

performance.

• The above points can be summarized by

examining the GSEs’ share of the singlefamily mortgage market. The GSEs’ total

purchases have accounted for 44 percent of

all single-family (both owner and rental)

units financed during 1997; however, their

low-mod purchases have accounted for only

34 percent of the low- and moderate-income

single-family units that were financed during

that year.

In conclusion, the Department’s analysis

suggests that the GSEs are not leading the

single-family market in purchasing loans that

qualify for the Low- and Moderate-Income

Goal. There is room for Fannie Mae and

Freddie Mac to improve their performance in

purchasing affordable loans at the lowerincome end of the market. Moreover,

evidence suggests that there is a significant

population of potential homebuyers who

might respond well to aggressive outreach by

the GSEs. Specifically, both Fannie Mae and

the Joint Center for Housing Studies expect

immigration 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.

Multifamily Market. 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.228

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 $7.6 billion in 1999. As a result,

concerns regarding Freddie Mac’s

multifamily capabilities no longer constrain

their performance with regard to low- and

moderate-income families in the manner that

prevailed at the time of the December 1995

rule.

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.

Holding 12.8 percent of the outstanding stock

of multifamily mortgage debt and guarantees

as of the end of 1999, Fannie Mae is regarded

as an influential force within the multifamily

market. Fannie Mae’s multifamily

underwriting standards have been widely

emulated throughout the multifamily

mortgage market.

The increased role of Fannie Mae and

Freddie Mac in the multifamily market has

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 Table A.7a,

the GSEs’ purchases (through 1999) have

accounted for only 24 percent of the

multifamily units that received financing

during 1997. Standard & Poor’s recently

described both GSEs’ multifamily lending as

‘‘extremely conservative.’’ 229 In particular,

their multifamily purchases to date do not

appear to be contributing to mitigation of the

excessive cost of mortgage financing for small

multifamily properties, nor have the GSEs

demonstrated market leadership with regard

to rehabilitation loans, a segment where

financing has sometimes been difficult to

obtain. In conclusion, it appears that both

GSEs can make improvements in their

underwriting policies and procedures and

introduce new products that will enable

them to more effectively serve segments of

the multifamily market that can benefit from

greater liquidity.

3. 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 50 to 55 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 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.

4. The Low- and Moderate-Income Housing

Goals for 2001–03

There are several reasons why the

Secretary is increasing the Low- and

Moderate-Income Housing Goal from 42

percent in 1997–99 to 50 percent of eligible

units financed in each of calendar years

2001, 2002 and 2003.

First, when the 1996–99 goals were

established in December 1995, Freddie Mac

had only recently reentered the multifamily

mortgage market, after its absence in the early

1990s. Freddie Mac has rebuilt its

multifamily acquisition program over the

past several years, with its 1999 purchases at

a level more than eight times what they were

in 1994 (in dollar terms). The limited role of

Freddie Mac in the multifamily market was

a significant constraint in setting the Lowand Moderate-Income Housing Goals for

1996–99. Freddie Mac’s return as a major

participant in the multifamily market was an

important factor in the improvement in its

performance on the Low- and ModerateIncome Housing Goal, as shown in Figure

A.1, and it removes an impediment to higher

goals for both GSEs. These goals will create

new opportunities for the GSEs to further

step up their support of mortgages on

properties with rents affordable to low- and

moderate-income families. However, as

discussed in the Preamble, to encourage

Freddie Mac to further step up its role in the

multifamily market, the Secretary is

proposing a ‘‘temporary adjustment factor’’

for its purchases of loans on properties with

more than 50 units. Specifically, each unit in

such properties would be weighted as 1.2

units in the numerator of the housing goal

percentage for both the Low and Moderate

Income Goal and the Special Affordable

Housing Goal for the years 2001–2003.

65136 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

Second, the single-family affordable market

had only recently begun to grow in 1993 and

1994, the latest period for which data was

available when the 1996–99 goals were

established in December 1995. But the

historically high low-and moderate-income

share of the primary mortgage market

attained in 1994 has been maintained over

the 1995–98 period. The three-year average

estimate of the low- and moderate-income

share of the single-family owner mortgage

market was 38 percent for 1992–94, but 42

percent for 1995–98 and 41 percent for the

1992–98 period as a whole. The continued

high affordability of housing suggests that a

strong low-income market continued for a

sixth straight year in 1999. Current economic

forecasts suggest that housing affordability

could be maintained in the post-2000 period,

leading to additional opportunities for the

GSEs to support mortgage lending benefiting

low- and moderate-income families.230 And

various surveys indicate that the demand for

homeownership by minorities, immigrants,

and younger households will remain strong

for the foreseeable future.

Although single-family owner 1-unit

properties comprise the ‘‘bread-and-butter’’

of the GSEs’ business, evidence presented

above demonstrates that the shares of their

loans for low- and moderate-income families

taking out loans on such properties lag the

corresponding shares for the primary market.

For example, in 1997 the Department finds

that these shares amounted to 34.1 percent

for Freddie Mac, 37.6 percent for Fannie

Mae, and 42.5 percent for the primary

market; as shown in Figure A.3, a similar

pattern holds for 1998. Thus 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 outreach efforts,

(2) their incorporation of greater flexibility

into their underwriting guidelines, (3) their

purchases of seasoned CRA loans, (4) their

entry into new single-family mortgage

markets such as loans on manufactured

housing, (5) their increased purchases of

loans on small multifamily properties, and

(6) their increased presence in other rental

markets where they have had only a limited

presence in the past.

Third, one particular area where the GSEs

could play a greater role is in the mortgage

market for single-family rental dwellings.

These properties, containing 1–4 rental units,

are an important source of housing for lowand moderate-income families, but the GSEs

have not played a major role in this mortgage

market—they accounted for only 6.5 percent

of units financed by Fannie Mae and 6.4

percent of units financed by Freddie Mac in

1997. The Department believes that the GSEs’

role in financing loans on such properties,

which are generally owned by ‘‘mom and

pop’’ businesses, can and should be

enhanced, though it recognizes that singlefamily rental properties are very

heterogeneous, making it more difficult to

develop standardized underwriting standards

for the secondary market. But the Secretary

believes that the GSEs can do more to play

a leadership role in providing financing for

such properties.231

Finally, a wide variety of quantitative and

qualitative indicators indicate that the GSEs’

have the financial strength to improve their

affordable lending performance. For example,

combined net income has risen steadily over

the last decade, from $1.244 billion in 1989

to $6.135 billion in 1999, an average annual

growth rate of 17 percent per year. This

financial strength provides the GSEs with the

resources to lead the industry in supporting

mortgage lending for units affordable to lowand moderate-income families.

Summary. Figure A.7a 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 have provided financing for

2,948,712 (or 40 percent) of the 7,306,950

single-family and multifamily units that were

financed in the conventional conforming

market during 1997. However, in the lowand moderate-income part of the market, the

1,330,516 units that were financed by GSE

purchases represented only 32 percent of the

4,201,287 dwelling units that were financed

in the market. Thus, there appears to ample

room for the GSEs to increase their purchases

of loans that qualify for the Low- and

Moderate-Income Goal. Examples of specific

market segments that would particularly

benefit from a more active secondary market

have been provided throughout this

appendix.

BILLING CODE 4910–27–P

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65137

BILLING CODE 4910–27–C

65138 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

5. Conclusions

Having considered the projected mortgage

market serving low- and moderate-income

families, economic, housing and

demographic conditions for 2001–03, and the

GSEs’ recent performance in purchasing

mortgages for low- and moderate-income

families, the Secretary has determined that

the annual goal of 50 percent of eligible units

financed in each of calendar years 2001, 2002

and 2003 is feasible. Moreover, the Secretary

has considered the GSEs’ ability to lead the

industry as well as the GSEs’ financial

condition. The Secretary has determined that

the goal is necessary and appropriate.

Endnotes to Appendix A

1See ‘‘Freddie’s Subprime Wrap Business

Blooms in 1999’’, Inside B&C Lending,

December 27, 1999, pages 8–9. 2See 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. 3U.S. Department of Housing and Urban

Development, Office of Federal Housing

Enterprise Oversight, ‘‘Risk-Based Capital’’

(Notice of Proposed Rulemaking), Federal

Register, April 13, 1999, p. 18116. 4Fannie Mae (2000), p. 102.

5OFHEO NPR, Ibid. 6Mortgage denial rates are based on 1998

HMDA data; manufactured housing lenders

are excluded from these comparisons. 7U.S. Department of Housing and Urban

Development. Rental Housing Assistance—

The Worsening Crisis: A Report to Congress

on Worst Case Housing Needs. (March 2000). 8 ‘‘Final Report of Standard & Poor’s to the

Office of Federal Housing Enterprise

Oversight,’’ February 3, 1997; Freddie Mac,

1998 Annual Report to Shareholders, p. 6. 9Freddie Mac reported delinquency rates

of 0.14% for multifamily and 0.39% for

single-family in 1999 (1999 Annual Report to

Shareholders, p. 23.) Fannie Mae reported

‘‘serious delinquency rates’’ of 0.12% for

multifamily and 0.48% for single-family in

1999 (1999 Annual Report to Shareholders,

p. 27). 10According to the National Association of

Realtors, Housing Market Will Change in New

Millennium as Population Shifts, (November

7, 1998), 45 percent of U.S. household wealth

is in the form of home equity in 1998. Since

1968, home prices have increased each year,

on average, at the rate of inflation plus up to

two percentage points. 11 Joint Center for Housing Studies of

Harvard University. State of the Nation’s

Housing 2000. (2000), p. 9. 12 Joint Center for Housing Studies of

Harvard University. (2000), p. 33. 13Michelle J. White, and Richard K. Green.

‘‘Measuring the Benefits of Homeowning:

Effects on Children,’’ Journal of Urban

Economics. 41 (May 1997), pp. 441–61. Also

see ‘‘The Social Benefits and Costs of

Homeownership: A Critical Assessment of

the Research,’’ Working Paper No. 00–01,

Research Institute for Housing America, May

2000. 14 Joint Center for Housing Studies of

Harvard University. State of the Nation’s

Housing 1998 (1998). 15Howard Savage and Peter Fronczek, Who

Can Afford to Buy A House in 1991?, U.S.

Bureau of the Census, Current Housing

Reports H121/93–3, (July 1993), p. ix. 16 Donald S. Bradley and Peter Zorn. ‘‘Fear

of Homebuying: Why Financially Able

Households May Avoid Ownership,’’

Secondary Mortgage Markets (1996). 17Munnell, Alicia H., Geoffrey M. B.

Tootell, Lynn E. Browne, and James

McEneaney, ‘‘Mortgage Lending in Boston:

Interpreting HMDA Data,’’ American

Economic Review. 86 (March 1996). 18William C. Hunter. ‘‘The Cultural

Affinity Hypothesis and Mortgage Lending

Decisions,’’ WP–95–8, Federal Reserve Bank

of Chicago, (1995). In addition, a study

undertaken for HUD also found higher denial

rates among FHA borrowers for minorities

after controlling for credit risk. See Ann B.

Schnare and Stuart A. Gabriel. ‘‘The Role of

FHA in the Provision of Credit to

Minorities,’’ ICF Incorporated, Prepared for

the U.S. Department of Housing and Urban

Development, (April 25, 1994). 19See Charles W. Calomiris, 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.’’ 20The FICO score, developed by Fair, Isaac

and Company, is summary index of an

individual’s credit history. The FICO score is

based on elements from the applicant’s credit

report, such as number of delinquencies in

the past year, number of trade lines, and the

amount owed on trade lines as compared to

the available maximum credit limits. The

FICO score is said to reflect the credit risk

of the applicant and a score of 620 is often

cited as a threshold between being an

acceptable and an unacceptable credit risk. 21Section 3.b of this appendix provides a

further discussion of automated

underwriting. 22Robert 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). 23Rental Housing Assistance—The

Worsening Crisis: A Report to Congress on

Worst Case Housing Needs, Department of

Housing and Urban Development, (March

2000), p. i. All statistics in this subsection are

taken from this report, except as noted. 24 Very low-income households are defined

in the report as those whose income, adjusted

for family size, is less than 50 percent of 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. 25Edward N. Wolff, ‘‘Recent Trends in the

Size Distribution of Household Wealth,’’ The

Journal of Economic Perspectives, 12( 3),

(Summer 1998), p. 137. 26 Joint Center for Housing Studies, The

State of the Nation’s Housing: 2000, June

2000, p. 24. 27Rent is measured in this report as gross

rent, defined as contract rent plus the cost of

any utilities which are not included in

contract rent.

28A detailed discussion of the GSEs’

activities in this area is contained in Theresa

R. Diventi, The GSEs’ Purchases of SingleFamily Rental Property Mortgages, Housing

Finance Working Paper No. HF–004, Office

of Policy Development and Research,

Department of Housing and Urban

Development, (March 1998). 29One program that shows promise is

Fannie Mae’s HomeStyle Home Improvement

Mortgage Loan Product. Under this program,

Fannie Mae will purchase mortgages that

finance the purchase and rehabilitation of 1-

to 4-unit properties in ‘‘as-is’’ condition. The

mortgage amount is limited to 90 percent of

the appraised ‘‘as-completed’’ value, with the

rehab amount not to exceed 50 percent of this

value. 30See Drew Schneider and James Follain,

‘‘A New Initiative in the Federal Housing

Administration’s Office of Multifamily

Housing Programs: An Assessment of Small

Projects Processing,’’ Cityscape: A Journal of

Policy Development and Research 4 (1),

(1998), pp. 43–58; and William Segal and

Christopher Herbert, Segmentation of the

Multifamily Mortgage Market: The Case of

Small Properties, paper presented to annual

meetings of the American Real Estate and

Urban Economics Association, (January

2000). 31These costs have been estimated at

$30,000 for a typical transaction.

Presentation by Jeff Stern, Vice President,

Enterprise Mortgage Investments, HUD GSE

Working Group, July 23, 1998. The most

comprehensive account of the multifamily

housing finance system as it relates to small

properties is contained in Schneider and

Follain (see above reference). 32This measure is discussed in Paul B.

Manchester, ‘‘A New Measure of Labor

Market Distress,’’ Challenge, (November/

December 1982). 33Homeownership 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. 34All of the home sales data in this section

are obtained from U.S. Housing Market

Conditions, 1st Quarter 2000, U.S.

Department of Housing and Urban

Development, (May 2000). 35Existing home sales, housing starts,

housing affordability and 30-year fixed rate

mortgage rate forecasts are obtained from

Standard & Poor’s DRI, The U.S. Economy.

(June 2000), pp. 55–7. While DRI provides

forecasts through 2004, one should obviously

interpret them with care. 36Real GDP, unemployment, inflation, and

treasury note interest rate projections are

obtained for fiscal years 2000–2009 from The

Economic and Budget Outlook: An Update,

Washington DC: Congressional Budget Office,

(July 2000). 37Standard & Poor’s DRI, The U.S.

Economy. (June 2000), pp. 31 and 56. 38Standard & Poor’s DRI, The U.S.

Economy. (June 2000), p. 56. 39Mortgage Bankers Association of

America. MBA Mortgage Finance Forecast,

(July 14, 2000). 40Fannie Mae. Berson’s Housing and

Economic Report, (June 2000). 41National Association of Realtors.

Housing Market Will Change in New

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65139

Millennium as Population Shifts. (November

7, 1998). 42Homeownership rates do not peek until

population groups reach 65 to 74 years of

age. Since the baby-boom population is such

a large cohort, even though they will be past

their homebuying peak, it is possible they

will still have an impact. 43 Joint Center for Housing Studies of

Harvard University. State of the Nation’s

Housing 2000. (2000), p. 11. 44 Joint Center for Housing Studies of

Harvard University. State of the Nation’s

Housing 1998. (1998), p. 14. 45 Joint Center for Housing Studies of

Harvard University. (1998), p. 15. 46National Association of Realtors.

Housing Market Will Change in New

Millennium As population Shifts. (November

7, 1998). 47 Joint Center for Housing Studies of

Harvard University. (1998). 48 John R. Pitkin and Patrick A. Simmons.

‘‘The Foreign-Born Population to 2010: A

Prospective Analysis by Country of Birth,

Age, and Duration of U.S. Residence,’’

Journal of Housing Research. 7(1) (1996), pp.

1–31. 49Fred Flick and Kate Anderson. ‘‘Future

of Housing Demand: Special Markets,’’ Real

Estate Outlook. (1998), p. 6. 50Mark A. Calabria. ‘‘The Changing Picture

of Homebuyers,’’ Real Estate Outlook. (May

1999), p. 10. 51Chicago Title and Trust Family of

Insurers, Who’s Buying Homes in America.

(2000). 52Chicago Title and Trust Family of

Insurers, Who’s Buying Homes in America.

(1998 and 2000). 53Calabria. (May 1999), p. 11. 54U.S. Census Bureau, Current Population

Reports, P60–206, Money Income in the

United States: 1998, U.S. Government

Printing Office, Washington, DC, (1999). 55 Joint Center for Housing Studies of

Harvard University. State of the Nation’s

Housing 1998. (1998). 56 Data for 1990–97 from U.S. Housing

Market Conditions, 1st Quarter 1999, U.S.

Department of Housing and Urban

Development, (May 1999), Table 17; data for

1998–99 from the Mortgage Bankers

Association. 57 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. 58U.S. Housing Market Conditions, 1st

Quarter 2000, (May 2000), Table 14. 59All statistics in this section are taken

from the Federal Housing Finance Board’s

MIRS. 60This 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).

61Other 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. 62Refinancing data is taken from Freddie

Mac’s monthly Primary Mortgage Market

Survey. 63There is some evidence that lowerincome borrowers did not participate in the

1993 refinance boom as much as higherincome 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. 64Housing affordability varies markedly

between regions, ranging in May 2000 from

147 in the Midwest to 93 in the West, with

the South and Northeast falling in between. 65Fannie Mae, http://

www.fanniemae.com/news/housingsurvey/

1998, (July 16, 1998). 66U.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). 67Chicago Title and Trust Family of

Insurers, Who’s Buying Homes in America,

(1998). 68Single-family originations rose by 10

percent in dollar terms in 1997, but the

Mortgage Bankers Association estimates that

they fell by 0.6 percent in terms of the

number of loans. 69Mortgage market projections obtained

from the MBA’s MBA Mortgage Finance

Forecast, (July 14, 2000). 70Fannie Mae. Berson’s Housing and

Economic Report, (June 2000). 71Speech before the annual convention of

the National Association of Home Builders in

Dallas TX, (January 1999). 72Fannie Mae News Release (January

1999). 73Freddie Mac News Release (January 15,

1999). 74Standard underwriting procedures

characterize a property in a declining

neighborhood as one at high risk of losing

value. Implicitly, these underwriting

standards presume that the real estate market

is inefficient in economic terms, that is,

prices do not reflect all available information. 75For an update of this analysis to include

1998, see Randall M. Scheessele, 1998 HMDA

Highlights, Housing Finance Working Paper

HF–009, Office of Policy Development and

Research, U.S. Department of Housing and

Urban Development, (October 1999). 76The ‘‘overall’’ market is defined as all

loans (including both government and

conventional) below the 1997 conforming

loan limit of $214,600 and the 1998

conforming loan limit of $227,150. 77The percentages reported in Table A.1a

for the year 1998 are similar; in that year,

low-income borrowers accounted for 49.1

percent of FHA-insured loans, 23.9 percent of

GSE purchases, and 27.8 percent of home

purchase mortgages originated in the

conventional conforming market. 78FHA, which focuses on first-time

homebuyers and low down payment loans,

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. 79FHA’s role in the market is particularly

important for African-American and

Hispanic borrowers. As shown in Table A.1c,

FHA insured 44 percent of all 1997 home

loan originations for these borrowers. 80 It should be noted that Tables A.1a and

A.1b include only the GSEs’ purchases of

conventional loans; the same tables in the

proposed rule also included the GSEs’

purchases of government (particularly FHAinsured) loans. 81See 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). 82However, as shown in Table A.1a,

depository institutions resemble other

conventional lenders in their relatively low

level of originating loans for AfricanAmerican, Hispanic and minority borrowers. 83For an analysis of the impact of CRA

agreements signed by lending institutions,

see Alex Schwartz, ‘‘From Confrontation to

Collaboration? Banks, Community Groups,

and the Implementation of Community

Reinvestment Agreements’’, Housing Policy

Debate, 9(3), (1998), pp. 631–662. Also see

the Department of Treasury CRA study by

Litan et al., op cit. 84 ‘‘With Securities Market Back on Track,

Analysts Expect Surge in CRA Loan

Securitization in 1999,’’ Inside MBS & ABS.

(February 19, 1999), pp. 11–12. 85 Inside MBS & ABS. (February 19, 1999),

p. 12. 86Fannie Mae. 1997 Annual Housing

Activities Report, (1998), p. 28. 87For an analysis of the GSEs’ CRA

purchases, see the HUD-sponsored study by

the Urban Institute, An Assessment of Recent

Innovations in the Secondary Market for

Low- and Moderate-Income Lending, by

Kenneth Temkin, Jennifer E.H. Johnson, and

Charles Calhoun, March 2000. 88 George Galster, Laudan Y. Aron, Peter

Tatain and Keith Watson. Estimating the

Size, Characteristics, and Risk Profile of

Potential Homebuyers. Washington: The

Urban Institute, (1995). Report Prepared for

the Department of Housing and Urban

Development. 89Fannie Mae Foundation. African

American and Hispanic Attitudes on

Homeownership: A Guide for Mortgage

Industry Leaders, (1998), p. 3. 90Fannie Mae Foundation. (1998), p. 14. 91Robert B. Avery, Raphael W. Bostic, Paul

S. Calem, and Glenn B. Canner, Credit

Scoring: Issues and Evidence from Credit

Bureau Files, mimeo., (1998). 92Avery et al. (1998), p. 24. 93 Kenneth Temkin, Roberto Quercia,

George Galster, and Sheila O’Leary, A Study

of the GSEs’ Single Family Underwriting

Guidelines: Final Report. Washington DC:

65140 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

U.S. Department of Housing and Urban

Development, (April 1999). This study

involves an analysis of the GSEs’

underwriting guidelines in general. This

section reviews only the aspects of the study

related to mortgage scoring. A broader review

of this paper is provided below in section

E.4. 94Temkin, et al. (1999), p. 2. 95Temkin, et al. (1999), p. 5; pp. 26–27. 96Standard & Poor’s B and C mortgage

guidelines can be used to illustrate that

underwriting criteria in the subprime market

becomes more flexible as the grade of

borrower moves from the most creditworthy

A-borrowers to the riskier D borrowers. For

Example, the A-grade borrower is allowed to

be delinquent 30 days on his mortgage twice

in the last year whereas the D grade borrower

is allowed to be delinquent 30 days on his

mortgage credit five times in the last year.

Moreover, the A-borrower is permitted to

have a 45 percent debt-to-income ratio

compared to the D grade borrower’s 60

percent. 97 ‘‘Subprime Product Mix, Strategies

Changed During a Turbulent 1998,’’ Inside

B&C Lending. (December 21, 1998), p. 2. 98 ‘‘Renewed Attack on ‘Predatory’

Subprime Lenders.’’ Fair Lending/CRA

Compass, (June 1999) and http://cracn.home.mindspring.com. 99See Randall M. Scheessele. 1998 HMDA

Highlights, Housing Finance Working Paper

HF–009, Office of Policy Development and

Research, U.S. Department of Housing and

Urban Development, (October 1999).

Nonspecialized lenders such as banks and

thrifts also make subprime loans, but no data

is available to estimate the number of these

loans. 100Freddie Mac, We Open Doors for

America’s Families, Freddie Mac’s Annual

Housing Activities Report for 1997, (March

16, 1998), p. 23. 101The statistics cited for the ‘‘market’’

refer to all conforming conventional

mortgages (both home purchase and

refinance). The data for the subprime market

are for 200 lenders that specialize in such

loans; see Scheessele, op. cit. 102Alternative—A (or Alt—A) mortgages

are made to prime borrowers who desire low

down payments or do not want to provide

full documentation for loans. 103Freddie Mac and Standard & Poor’s

tested the new module in a pilot during early

1996 and marketed it to lenders at the end

of that year. 104See ‘‘Freddie’s Subprime Wrap

Business Blooms in 1999’’, Inside B&C

Lending, December 27, 1999, pages 8–9. 105 David A. Andrukonis, ‘‘Entering the

Subprime Arena,’’ Mortgage Banking, May

2000, pages 57–60. 106The figures include their purchases of

Alt A mortgages. Inside B&C Lending, May

22, 2000, page 12. 107See Lederman, et al., op cit. 108For an explanation of the GSEs funding

advantage see ‘‘Government Sponsorship of

FNMA and FHLMC,’’ United States

Department of the Treasury, July 11, 1996. 109A detailed discussion of manufactured

housing is contained in Kimberly Vermeer

and Josephine Louie, The Future of

Manufactured Housing, Joint Center for

Housing Studies, Harvard University,

(January 1997). 110 Data on industry shipments and sales

has been obtained from ‘‘U.S. Housing

Market Conditions,’’ U.S. Department of

Housing and Urban Development (May,

2000), p. 49. 111Although the terms are sometimes used

interchangeably, manufactured housing and

mobile homes differ in significant ways

relative to construction standards, mobility,

permanence, and financing (These

distinctions are spelled out in detail in

Donald S. Bradley, ‘‘Will Manufactured

Housing Become Home of First Choice?’’

Secondary Mortgage Markets, (July 1997)).

Mobile homes are not covered by national

construction standards, though they may be

subject to State or local siting requirements.

Manufactured homes must be built according

to the National Manufactured Housing

Construction Safety and Standards Act of

1974. In accordance with this act, HUD

developed minimum building standards in

1976 and upgraded them in 1994.

Manufactured homes, like mobile homes, are

constructed on a permanent chassis and

include both axles and wheels. However,

with manufactured housing, the axles and

wheels are intended to be removed at the

time the unit is permanently affixed to a

foundation. Manufactured homes, unlike

mobile homes, are seldom, if ever, moved.

Mobile homes are financed with personal

property loans, but manufactured homes are

eligible for conventional-mortgage financing

if they are located on land owned by or under

long-term lease to the borrower. Other types

of factory-built housing, such as modular and

panelized homes, are not included in this

definition of ‘‘manufactured housing.’’ These

housing types are often treated as ‘‘site built’’

for purposes of eligibility for mortgage

financing. 112Freddie Mac, the Manufactured

Housing Institute and the Low Income

Housing Fund have formed an alliance to

utilize manufactured housing along with

permanent financing and secondary market

involvement to bring affordable, attractive

housing to underserved, low- and moderateincome urban neighborhoods. Origination

News. (December 1998), p. 18. 113 Mortgage-Backed Securities Letter.

(September 7, 1998), p. 3. 114The Mortgage Market Statistical Annual

for 2000 (Washington, DC: Inside Mortgage

Finance Publications), 1, 286. A conventional

multifamily mortgage market of $46 billion is

assumed in this calculation. B and C

mortgages are excluded from the calculation. 115The Mortgage Market Statistical Annual

for 2000 (Washington, DC: Inside Mortgage

Finance Publications), 1, 286. A conventional

multifamily mortgage market of $46 billion is

assumed in this calculation. B and C

mortgages are excluded from the calculation. 116This calculation incorporates GSE

multifamily transactions involving loans

originated during 1997 and acquired during

1997–1999. A multifamily conventional

origination market of $38 billion and a perunit loan amount of $27,266 is assumed per

Appendix D. 117 Jean L. Cummings and Denise

DiPasquale, ‘‘Developing a Secondary Market

for Affordable Rental Housing: Lessons From

the LIMAC/Freddie Mac and EMI/Fannie

Mae Programs,’’ Cityscape: A Journal of

Policy Development and Research, 4(1),

(1998), pp. 19–41. 118 Drew Schneider and James Follain

assert that interest rates on small property

mortgages are as high as 300 basis points over

comparable maturity Treasuries in ‘‘A New

Initiative in the Federal Housing

Administration’s Office of Multifamily

Housing Programs: An Assessment of Small

Projects Processing,’’ Cityscape: A Journal of

Policy Development and Research 4(1): 43–

58, 1998. Berkshire Realty, a Fannie Mae

Delegated Underwriting and Servicing (DUS)

lender based in Boston, was quoting spreads

of 135 to 150 basis points in ‘‘Loans

Smorgasbord,’’ Multi-Housing News,

August–September 1996. Additional

information on the interest rate differential

between large and small multifamily

properties is contained in William Segal and

Christopher Herbert, Segmentation of the

Multifamily Mortgage Market: The Case of

Small Properties, paper presented to annual

meetings of the American Real Estate and

Urban Economics Association, (January

2000). 119On the relation between age of property

and quality classification see Jack Goodman

and Brook Scott, ‘‘Rating the Quality of

Multifamily Housing,’’ Real Estate Finance,

(Summer, 1997). 120W. Donald Campbell. Seniors Housing

Finance, prepared for American Association

of Retired Persons White House Conference

on Aging Mini-Conference on Expanding

Housing Choices for Older People, (January

26–27, 1995). 121 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), p. 154. 122 Despite sustained economic expansion,

however, the rise in homeownership, has not

fallen below 9 percent in recent years. (Regis

J. Sheehan, ‘‘Steady Growth,’’ Units,

(November/December 1998), pp. 40–43).

Regarding rents and vacancy rates see also

Ted Cornwell. ‘‘Multifamily Lending

Approaches Record Level,’’ National

Mortgage News, (September 23, 1996); and

David Berson, Monthly Economic and

Mortgage Market Report, Fannie Mae,

(November 1998). 123American Council of Life Insurance

data reported in Inside MBS & ABS, (March

20, 1998). 124A November, 1998 ‘‘Review of the

Short-Term Supply/Demand Conditions for

Apartments’’ by Peter P. Kozel of Standard

and Poor’s concludes that ‘‘in some markets,

the supply of units exceeds the likely level

of demand, and in only a few MSAs should

the pace of development accelerate.’’ See also

‘‘Apartment Projects Find Lenders Are Ready

with Financing,’’ Lew Sichelman, National

Mortgage News, (April 14, 1997); Commercial

Lenders Warned That They Could Spur

Overbuilding, National Mortgage News,

(March 30, 1998); ‘‘Multifamily, Commercial

Markets Grow Up,’’ Neil Morse, Secondary

Marketing Executive, (February 1998);’’

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65141

‘‘Recipe for Disaster,’’ National Mortgage

News editorial, (July 6, 1998). 125 1998 Survey of Credit Underwriting

Practices, Comptroller of the Currency,

National Credit Committee. ‘‘For the fourth

consecutive year, underwriting standards for

commercial loans have eased,’’ states the

OCC report. ‘‘Examiners again cite

competitive pressure as the primary reason

for easing underwriting standards.’’ The

weakening of underwriting practices is

especially concentrated in commercial real

estate lending according to a the Federal

Deposit Insurance Corporation’s Report on

Underwriting Practices, (October 1997–

March 1998). See also Donna Tanoue,

‘‘Underwriting Concerns Grow,’’ National

Mortgage News, (September 21, 1998), and

‘‘Making the Risk-Takers Pay,’’ National

Mortgage News, (October 12, 1998). 126On the effects of multifamily mortgage

securitization see ‘‘Financing Multifamily

Properties: A Play With new Actors and New

Lines,’’ Donald S. Bradley, Frank E. Nothaft,

and James L. Freund, Cityscape, A Journal of

Policy Development and Research, vol. 4, No.

1 (1998); and ‘‘Financing Multifamily

Properties,’’ Donald S. Bradley, Frank E.

Nothaft, and James L. Freund, Urban Land

(November 1998). 127CMBS Database, Commercial Mortgage

Alert, Harrison-Scott Publications, Hoboken,

NJ.128 ‘‘New CMBS Headache: B-Piece Market

Softens,’’ Commercial Mortgage Alert,

(September 21, 1998); ‘‘Criimi Bankruptcy

Accelerates CMBS Freefall,’’ Commercial

Mortgage Alert, (October 12, 1998); ‘‘Capital

America Halts Lending Amid Woes,’’

Commercial Mortgage Alert, (October 12,

1998). 129On CMBS spreads see ‘‘Turmoil Hikes

Loan Rates’’ in Wall Street Mortgage Report,

(September 14, 1998). Regarding implications

for the GSEs of the conduit pullback see ‘‘No

Credit Crunch for First Mortgages’’ in

Commercial Mortgage Alert, (October 12,

1998). 130 ‘‘Financing Multifamily Properties: A

Play With New Actors and New Lines,’’

Donald S. Bradley, Frank E. Nothaft, and

James L. Freund, Cityscape: A Journal of

Policy Development and Research, 4(1),

(1998). 131The Impact of Public Capital Markets

on Urban Real Estate, Clement Dinsmore,

discussion paper, Brookings Institution

Center on Urban and Metropolitan Policy,

July 1998; ‘‘Capital Availability Fuels

Commercial Market Growth,’’ Marshall

Taylor, Real Estate Finance Today, (February

17, 1997). 132Board of Governors of the Federal

Reserve System and U.S. Securities and

Exchange Commission, Report to the

Congress on Markets for Small-Business-and

Commercial-Mortgage-Backed Securities,

(September 1998). 133 ‘‘REITs Tally Nearly Half of All Big CRE

Deals in First Quarter,’’ National Mortgage

News, (July 7, 1997); ‘‘Will REITs, MortgageBackeds Make Difference in Downturn,’’

Jennifer Goldblatt, American Banker,

(February 18, 1998). 134 ‘‘Apartment Demographics: Good for

the Long Haul?’’ Jack Goodman, Real Estate

Finance, (Winter 1997); ‘‘The Multifamily

Outlook,’’ Jack Goodman, Urban Land,

(November 1998). 135U.S. Housing Market Conditions, U.S.

Department of Housing and Urban

Development (May 2000), Table 4. 136Howard Esaki, a principal in CMBS

Research at Morgan Stanley Dean Witter

stated at a recent conference that volatility in

global markets contributed to a 10–20 percent

decline in commercial real estate values in

late 1998. John Hackett, ‘‘CRE Seen Down

10% to 20%,’’ National Mortgage News,

(November 23, 1998), p. 1. 137 John Holusha, ‘‘As Financing Pool Dries

Up, Some See Opportunity,’’ New York

Times, November 1, 1998. 138 Federal Reserve Bulletin, June 2000, A

35.139 1997 Annual Housing Activity Reports,

Table 1. 140William Segal and Edward J.

Szymanoski. The Multifamily Secondary

Mortgage Market: The Role of GovernmentSponsored Enterprises. Housing Finance

Working Paper No. HF–002, Office of Policy

Development and Research, Department of

Housing and Urban Development, (March

1997). 141HUD analysis of GSE loan-level data. 142 Fundingnotes, Vol. 3, Issue 9;

(September 1998), Eric Avidon,

‘‘PaineWebber Lauds Fannie DUS Paper,’’

National Mortgage News, (September 14,

1998),p. 21. 143There is evidence that the GSEs have

benefited from recent widening in CMBS

spreads because of their funding cost

advantage. See ‘‘No Credit Crunch for First

Mortgages,’’ Commercial Mortgage Alert,

(October 12, 1998); and ‘‘Turmoil a Bonanza

for Freddie,’’ Commercial Mortgage Alert,

(November 2, 1998). 144 Federal Reserve Bulletin, June 2000, A

35.145See Table A.7a for details. It is assumed

that units in small multifamily properties

represented approximately 39.4 percent of

multifamily units financed in 1997, per the

1991 Residential Finance Survey, as

discussed above. Additionally, it is assumed

that 1997 multifamily conventional

origination volume was $38 billion, as

discussed in Appendix D. An average loan

amount per unit of $27,266 is used, the GSE

average for 1997 acquisitions. 146Larger properties may be perceived as

less subject to income volatility caused by

vacancy losses. Scale economies in

securitization may also favor purchase of

larger multifamily mortgages by the GSEs.

Scale economies refer to the fixed costs in

creating a mortgage backed security, and the

smaller reduction in yield (higher security

price) if these costs can be spread over larger

unpaid principal balances. 147 1995 POMS data are used because 1995

represents the year with the most complete

mortgage origination information in the

Survey. 1996 GSE data are used because of

number of units of property exhibited

atypical behavior during 1995. 148These costs have been estimated at

$30,000 for a typical transaction.

Presentation by Jeff Stern, Vice President,

Enterprise Mortgage Investments, HUD GSE

Working Group, (July 23, 1998).

149 ‘‘Fannie Mae Announces New 5–50(SM)

Streamlined Mortgage for Small Multifamily

Properties is Now Available Through DUS

Lenders; 10-Year Volume Goal is $18

Billion,’’ Fannie Mae press release, May 10,

2000. 150 Data from the HUD Property Owners

and Managers Survey (POMS) suggests that,

in and of itself, the GSEs’ emphasis on

refinance loans may roughly track that of the

overall market. 151Standard & Poor’s described Fannie

Mae’s multifamily lending as ‘‘extremely

conservative’’ in ‘‘Final Report of Standard &

Poor’s to the Office of Federal Housing

Enterprise Oversight (OFHEO),’’ (February 3,

1997), p. 10. 152See William Segal and Edward J.

Szymanoski. ‘‘Fannie Mae, Freddie Mac, and

the Multifamily Mortgage Market,’’

Cityscape: A Journal of Policy Development

and Research, vol. 4, no. 1 (1998), pp. 59–

91.153Freddie Mac’s policy of re-underwriting

each multifamily acquisition is a response to

widespread defaults affecting its multifamily

portfolio during the late 1980s according to

Follain and Szymanoski (1995). 154A more detailed discussion of

underwriting guidelines is contained in the

analysis below regarding Factor 5, ‘‘The

GSEs’’ Ability to Lead the Industry.’’ 155The term ‘‘affordable lending’’ is used

generically here to refer to lending for lowerincome families and neighborhoods that have

historically been underserved by the

mortgage market. 156Throughout these appendices, the terms

‘‘home loan’’ or ‘‘home mortgage’’ will refer

to a ‘‘home purchase loan,’’ as opposed to a

‘‘refinance loan.’’ 157Subsections b–d of this section focus on

the single-family mortgage market for home

purchase loans, which is the relevant market

for analysis of homeownership opportunities.

Subsection e extends the analysis to include

single-family refinance loans. For a

discussion of past performance in the

multifamily mortgage market, see Section D

of this Appendix. 158Thus, the market definition in this

section is narrower than the data presented

earlier in Section C and Tables A.1a and

A.1b, which covered all loans (both

government and conventional) less than or

equal to the conforming loan limit. As in that

section, only the GSEs’ purchases of

conventional conforming loans are

considered; their purchases of FHA-insured,

VA-guaranteed, and Rural Housing Service

loans are excluded from this analysis. 159Higher limits apply for loans on 2-,

3-, and 4-unit properties and for properties in

Alaska, Hawaii, Guam, and the Virgin

Islands. 160 ‘‘Jumbo mortgages’’ in any given year

might become eligible for purchase by the

GSEs in later years as the loan limits rise and

the outstanding principal balance is reduced. 161However, in analyzing the provision of

mortgage finance more generally, it is often

appropriate to include government loans; see

Tables A.1a, A.1b and A.2 in Section C.3.b. 162 Fair Lending/CRA Compass, (June

1999), p. 3. 163Randall M. Scheessele developed a list

of 42 subprime lenders that was used by

65142 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

HUD and others in analyzing HMDA data

through 1997. In 1998, Scheessele updated

the list to 200 subprime lenders. For analysis

comparing various lists of subprime lenders,

see Appendix D of Scheessele (1999), op. cit.

That paper also discusses Scheessele’s lists of

manufactured housing lenders. 164See 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 underreporting 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. Section A.4 of this appendix also

discusses several issues regarding HMDA

data that were raised by the GSEs in their

comments on the proposed rule. 165Since 1993, the GSEs have increased

their purchases of seasoned loans. See Paul

B. Manchester, Characteristics of Mortgages

Purchased by Fannie Mae and Freddie Mac:

1996–1997 Update, Housing Finance

Working Paper HF–006, Office of Policy

Development and Research, Department of

Housing and Urban Development, (August

1998), p.17. 166For a discussion of the impact of the

GSEs’ seasoned mortgage purchases on

HMDA data coverage, see Scheessele (1998),

op. cit. 167Table A.4b, which reports similar GSE

information as Table A.4a, provides several

alternative estimates of the conventional

conforming market depending on the

treatment of small loans, manufactured

housing loans, and subprime loans. The data

in Table A.4b will be referenced throughout

the discussion. 168Any HMDA data reported in the

appendices on borrower incomes excludes

loans where the loan-to-borrower-income

ratio is greater than six. 169For example, in 1997 Fannie Mae

reported that 20.8 percent of the loans they

purchased, that were originated during 1997,

were for properties in underserved areas.

HMDA reports that 21.0 percent of the loans

sold to Fannie Mae during 1997 were for

properties in underserved areas. The

corresponding numbers for Freddie Mac, in

1997, are 19.3 percent reported by them and

18.6 percent reported by HMDA. During

1997, both Fannie Mae and HMDA reported

that approximately 37 percent of the ‘‘current

year’’ loans purchased by Fannie Mae were

for low- and moderate-income borrowers.

Freddie Mac reported that 34.2 percent of the

current year loans they purchased were for

low-mod borrowers, compared to the 35.4

low-mod percent that HMDA reported as sold

to Freddie Mac. 170Notice that while Fannie Mae’s 1998

purchases resembled their 1997 purchases

with prior-year loans having higher goalsqualifying percentages than current-year

loans, the pattern for 1999 was similar to that

for 1993 to 1996 when there were smaller

differentials between the goals-qualifying

percentages of prior-year and current-year

mortgages. 171Referencing the study by Peter Zorn and

Jim Berkovec, op cit., the GSEs argued in

their comments on the proposed rule that

HMDA overstates goals-qualifying loans. See

Section A.3d for HUD’s response which

questions the findings of the Zorn-Berkovec

study. 172The borrower income distributions in

Tables A.3 and A.4a for the ‘‘market without

manufactured housing’’ exclude loans less

than $15,000 as well as all loans originated

by lenders that primarily originate

manufactured housing loans. See Table A.4b

for market definitions that show the separate

effects of excluding small loans and

manufactured housing loans. Also, Table

A.4b shows that excluding subprime loans

has only a minor effect on the goalsqualifying percentages in the mortgage

market. 173See Scheessele (1999), op. cit. As

explained in Appendix D of Scheessele’s

paper, the number of subprime lenders varies

by year; the 200 figure cited in the text

applies to 1998. The number of loans

identified as subprime in these appendices is

the same as reported by Scheessele in Table

D.2b of his paper. 174Table A.1b in Section C.3.b provides

several comparisons of the GSEs’ total

purchases with primary market originations.

As shown there, many of the same patterns

described above for home purchase loans can

be seen in the data for the GSEs’ total

purchases. 175 In general, the HMDA-reported

affordability percentages for GSE purchases

of refinance loans have matched the

corresponding GSE-reported percentages. For

example, in 1997, both GSEs reported to

HUD that special affordable loans accounted

for about 11 percent of their purchases of

refinance loans in metropolitan areas; HMDA

reported the same percentage for each GSE.

Similarly, in 1998, both HMDA and Fannie

Mae reported that special affordable loans

accounted for 9.7 percent of Fannie Mae’s

refinance purchases. However, in 1998, the

Freddie-Mac-reported special affordable

percentage (10.7 percent) for its refinance

loans was significantly higher than the

corresponding percentage (9.5 percent)

reported in the HMDA data. The reasons for

this discrepancy require further study. 176The Mortgage Information Corporation

(MIC) has recently started publishing

origination and default performance data for

the subprime market. For an explanation of

their data and some early findings, see Dan

Feshbach and Michael Simpson, ‘‘Tools for

Boosting Portfolio Performance’’, Mortgage

Banking: The Magazine of Real Estate

Finance, (October 1999), pp. 137–150. 177For example, see Bunce and Scheessele

(1996 and 1998), op. cit. 178This analysis is limited to the

conventional conforming market. 179To test the robustness of these statistics,

this analysis was conducted where the ‘‘lag’’

determination is made at 95 percent instead

of 99 percent. The results are consistent with

those shown in Table A.5. For example, at

the 95 percent cutoff, Fannie Mae lagged the

market in 286 MSAs (88 percent) in the

purchase of 1996 originated Special

Affordable category loans. Likewise, Freddie

Mac lagged the market in 322 MSAs (99

percent). 180Privatization of Fannie Mae and

Freddie Mac: Desirability and Feasibility.

Office of Policy Development and Research,

Department of Housing and Urban

Development, (July 1996). 181The Treasury Department reached

similar conclusions in its 1996 report on the

privatization of the GSEs, Government

Sponsorship of the Federal National

Mortgage Association and the Federal Home

Loan Mortgage Corporation, U.S. Department

of the Treasury (July 11, 1996). Based on data

such as the above, the Treasury Department

questioned whether the GSEs were

influencing the availability of affordable

mortgages and suggested that the lowerincome loans purchased by the GSEs would

have been funded by private market entities

if the GSEs had not purchased them. 182See Glenn B. Canner, and Wayne

Passmore. ‘‘Credit Risk and the Provision of

Mortgages to Lower-Income and Minority

Homebuyers,’’ Federal Reserve Bulletin. 81

(November 1995), pp. 989–1016; Glenn B.

Canner, Wayne Passmore and Brian J.

Surette. ‘‘Distribution of Credit Risk among

Providers of Mortgages to Lower-Income and

Minority Homebuyers.’’ Federal Reserve

Bulletin. 82 (December 1996), pp. 1077–1102;

Harold L. Bunce, and Randall M. Scheessele,

The GSEs’ Funding of Affordable Loans: A

1996 Update, Housing Finance Working

Paper HF–005, Office of Policy Development

and Research, Department of Housing and

Urban Development, (July 1998); and

Manchester, (1998), p. 24. 183Canner, et al. (1996). 184Harold L. Bunce and Randall M.

Scheessele, The GSEs’ Funding of Affordable

Loans, Housing Finance Working Paper HF–

001, Office of Policy Development and

Research, U.S. Department of Housing and

Urban Development, (December 1996). 185Harold L. Bunce and Randall M.

Scheessele, The GSEs’ Funding of Affordable

Loans: A 1996 Update, Housing Finance

Working Paper HF–005, Office of Policy

Development and Research, U.S. Department

of Housing and Urban Development, (July

1998), pp. 15–16. 186Statistics cited are from Table B.1 of

Bunce and Scheessele, (1998) and are based

on sales to the GSEs as reported by lenders

in accordance with the HMDA. ‘‘Lagging the

market’’ means, for example, that the

percentage of the GSEs’ loans for very lowand low-income borrowers is less than the

corresponding percentage for the primary

market, depositories, and the FHA. 187Under their charter acts, loans

purchased by the GSEs with down payments

of less than 20 percent must carry private

mortgage insurance or a comparable form of

credit enhancement. 188 It is generally agreed that HMDA does

not capture all loans originated in the

primary market—for example, small lenders

need not report under HMDA. But Fannie

Mae believes that the undercount is not

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65143

spread uniformly across all borrower

classes—in particular, it argues that the

HMDA data exclude relatively more loans

made to minorities and lower-income

families. 189Bunce and Scheessele (1998) contained

a comparison (Table A.1) of HMDA-reported

and GSE-reported data on the characteristics

of GSE mortgage purchases in 1996. In most

cases the differences between the results

utilizing the two different data sources were

minimal, but in some cases (such as lending

in underserved areas) the evidence lent some

support to Fannie Mae’s assertion that the

HMDA data underreports their level of

activity. The discrepancies between HMDA

data and GSE data at the national level are

also due to the seasoned loan effect (see

Section E.2.e above and Table A.4a). 190 John E. Lind. Community Reinvestment

and Equal Credit Opportunity Performance of

Fannie Mae and Freddie Mac from the 1994

HMDA Data. San Francisco: Caniccor.

Report, (February 1996). 191 John E. Lind. A Comparison of the

Community Reinvestment and Equal Credit

Opportunity Performance of Fannie Mae and

Freddie Mac Portfolios by Supplier from the

1994 HMDA Data. San Francisco: Cannicor.

Report, (April 1996). 192Brent W. Ambrose and Anthony

Pennington-Cross, Spatial Variation in

Lender Market Shares, Research Study

submitted to the Office of Policy

Development and Research, Department of

Housing and Urban Development, (1999). 193Heather MacDonald. ‘‘Expanding

Access to the Secondary Mortgage Markets:

The Role of Central City Lending Goals,’’

Growth and Change. (27), (1998), pp. 298–

312. 194Heather MacDonald, Fannie Mae and

Freddie Mac in Non-metropolitan Housing

Markets: Does Space Matter, Research Study

submitted to the Office of Policy

Development and Research, Department of

Housing and Urban Development, (1999). 195 Kirk McClure, The Twin Mandates

Given to the GSEs: Which Works Best,

Helping Low-Income Homebuyers or Helping

Underserved Areas in the Kansas City

Metropolitan Area? Research Study

submitted to the Office of Policy

Development and Research, Department of

Housing and Urban Development, (1999). 196Richard Williams, The Effect of GSEs,

CRA, and Institutional Characteristics on

Home Mortgage Lending to Underserved

Markets,’’ Research Study submitted to the

Office of Policy Development and Research,

Department of Housing and Urban

Development, (1999). 197 Joseph Gyourko and Dapeng Hu. The

Spatial Distribution of Secondary Market

Purchases in Support of Affordable Lending,

Research Study submitted to the Office of

Policy Development and Research,

Department of Housing and Urban

Development, (1999). 198Bradford Case and Kevin Gillen. Studies

of Mortgage Purchases by Fannie Mae and

Freddie Mac: Spatial Variation in GSE

Mortgage Purchase Activity. Research Study

submitted to the Office of Policy

Development and Research, Department of

Housing and Urban Development, (1999).

199The coefficient for geographic targeting

was significant and negative in 19 MSAs,

significant and positive in another eight, and

not significant in the remaining 17 MSAs. 200The coefficient for the highest minorityconcentration category (census tracts with

greater than 50% minority population) was

significantly negative in 21 MSAs, but

significantly positive in 10 MSAs and not

significantly different from zero in the

remaining 13. 201Samuel L. Myers, Jr. The Effects of

Government-Sponsored Enterprise Secondary

Market Decisions on Racial Disparities in

Loan Rejection Rates. Research Study

submitted to the Office of Policy

Development and Research, Department of

Housing and Urban Development, (1999). 202 Variables from the GSE Public Use Data

Base include the income and gender of the

borrower, the gender and race of the

coborrower, first-time homebuyer, and loan

amount. Variables from Census 1990 include

the following information for the census tract

in which the property is located: percent of

owner-occupied houses, average size of

household, average number of persons per

owner-occupied house, average number of

persons per renter-occupied unit, percentage

of white, black, Asian, American Indian, and

other minority households, average poverty

rate, median monthly rent, median house

value, percent of persons 65 or older, percent

of persons under 18, and percent of femaleheaded households. Variables from HMDA

include reason for denial, whether or not

loan is sold to GSE, type of loan

(conventional), type of agency, and

origination year. 203The unconditional probability that a

loan will not be sold, P(NS), to a GSE is

computed using Bayes’ rule. It is based on

the conditional probability that a loan is sold

to GSEs given that it was originated, P(SO),

and the probability that a loan is originated

which are obtained using HMDA data. The

unconditional probability that a loan will be

sold to a GSE can not be obtained from either

the HMDA data which does not include

details of which loans were sent for review

and which were declined by the secondary

purchaser—or from the HUD-GSE data,

which only includes approved loans.

However, we know from Bayes’ rule that

P (S¦ O) = P (S) P (O ¦ S)

P (O)

where S mean that the loan was sold and

O means that the loan was originated and

where all loan sold by the lender must have

been originated such that P(OS)=1. We can

obtain a measure of the unconditional

probability that a loan will not be sold from

P (NS) = 1 P (S) = 1 P (S − − ¦ O) P (O).

204Calvin Bradford, The Patterns of GSE

Participation in Minority and Racially

Changing Markets Reviewed from the Context

of the Levels of distress Associated with High

Levels of FHA Lending, Research Study

submitted to the Office of Policy

Development and Research, Department of

Housing and Urban Development (2000). 205 David M. Harrison, Wayne R. Archer,

David C. Ling, and Marc T. Smith, Mitigating

Information Externalities in Mortgage

Markets: The Role of Government Sponsored

Enterprises, Research Study submitted to the

Office of Policy Development and Research,

Department of Housing and Urban

Development (2000). 206 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). 207 In following up on the Urban Institute

study, HUD began in February 2000 a review

of Fannie Mae’s and Freddie Mac’s

automated underwriting systems. 208Standard guidelines refer to guidelines

not associated with affordable lending

programs. 209Temkin, et al. (1999), p. 4. 210Temkin, et al. (1999), p. 5. 211Temkin, et al. (1999), p. 28. 212Senate Report 102–282, (May 15, 1992),

p. 35. 213Table A.7a(A.7b) considers GSE

purchases during 1997, 1998, and 1999 (1998

and 1999) of conventional mortgages that

were originated during 1997 (1998). HUD’s

methodology for deriving the market

estimates is explained in Appendix D. B&C

loans have been excluded from the market

estimates in Table A.7. 214Two caveats about the data in Table A.7

should be mentioned here. First, the various

market totals for underserved areas are

probably understated due to the model’s

underestimation of mortgage activity in nonmetropolitan underserved counties and of

manufactured housing originations in nonmetropolitan areas. Second, as discussed in

Appendix D, some uncertainty exists around

the adjustment for B&C single-family owner

loans. 215Table A.7a shows that multifamily

represented 19 percent of total units financed

during 1997 (obtained by dividing 1,393,677

multifamily units by 7,306,950 ‘‘Total

Market’’ units). Increasing the single-familyowner number in Table A.7 by 732,182 to

account for excluded B&C mortgages

increases the ‘‘Total Market’’ number to

8,039,132 which is consistent with the

percent multifamily share reported in the

text. See Appendix D for discussion of the

B&C market. 216A similar imbalance is evident with

regard to figures on the stock of mortgage

debt published by the Federal Reserve Board.

Within the single-family mortgage market the

GSEs held loans or guarantees with an

unpaid principal balance (UPB) of $1.5

trillion, comprising 36 percent of $4.0 trillion

in outstanding single-family mortgage debt as

of the end of 1997. At the end of 1997, the

GSEs direct holdings and guarantees of $41.4

billion represented 13.7 percent of $301

billion in multifamily mortgage debt

outstanding. (Federal Reserve Bulletin, June

1998, A 35.) 217The 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).

65144 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

218A jumbo mortgage is one for which the

loan amount exceeds the maximum principal

amount for mortgages purchased by the

enterprises—$240,000 for mortgages on 1-

unit properties in 1999, with limits that are

50 percent higher in Alaska, Hawaii, Guam,

and the Virgin Islands. 219Office of Federal Housing Enterprise

Oversight, 1998 Report to Congress, (June 15,

1998), Figure 9, p. 32; and unpublished

OFHEO estimates for 1998. 220Mortgage originations for 1997 were

reported in the Department of Housing and

Urban Development, HUD Survey of

Mortgage Lending Activity: Fourth Quarter/

Annual 1997, (September 24, 1998). 221The underwriting guidelines published

by the two GSEs are similar in most aspects.

And since November 30, 1992, Fannie Mae

and Freddie Mac have provided lenders the

same Uniform Underwriting and Transmittal

Summary (Fannie Mae Form 1008/Freddie

Mac Form 1077), which is used by

originators to collect certain mortgage

information that they need for data entry

when mortgages are sold to either GSE. 222Freddie Mac stock was not publicly

traded until after the passage of the Financial

Institutions Reform, Recovery and

Enforcement Act of 1989 (FIRREA), thus it is

not possible to calculate a 10-year annualized

rate of return. 223 Fortune, (April 17, 2000), pp. F–1, F–2. 224Business Week, (March 27, 2000), p.

197. 225U.S. Department of Housing and Urban

Development. Rental Housing Assistance—

The Worsening Crisis: A Report to Congress

on Worst Case Housing Needs. (March 2000). 226Standard & Poor’s DRI, The U.S.

Economy. (June 2000), p. 56. 227See Drew Schneider and James Follain,

‘‘A New Initiative in the Federal Housing

Administration’s Office of Multifamily

Housing Programs: An Assessment of Small

Projects Processing,’’ Cityscape: A Journal of

Policy Development and Research 4(1),

(1998), pp. 43–58. 228Senate Report 102–282, (May 15, 1992),

p. 36. 229 ‘‘Final Report of Standard & Poor’s to

the Office of Federal Housing Enterprise

Oversight (OFHEO),’’ (February 3, 1997), p.

10.230However, the Department’s goals for the

GSEs have been set so that they will be

feasible even under less favorable conditions

in the housing market. 231Another area where stepped-up GSE

involvement could benefit low- and

moderate-income families is lending for the

rehabilitation of properties, which is

especially needed in our urban areas. The

GSEs have made some efforts in this complex

area, but the benefits of stepped-up roles by

the GSE could be sizable.

Appendix B—Departmental

Considerations to Establish the Central

Cities, Rural Areas, and Other

Underserved Areas Goal

A. Introduction and Response to Comments

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 ‘‘Geographically Targeted 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

Geographically Targeted 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 Geographically

Targeted Goal for both metropolitan areas

and nonmetropolitan areas and then

discusses HUD’s response to the public

comments raised in this appendix. 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 Geographically Targeted Goal (the

third factor) and Sections E–G report the

Secretary’s findings for the remaining factors.

Section H summarizes the Secretary’s

rationale for setting the level for the

Geographically Targeted Goal.

2. HUD’s Geographically Targeted Goal

HUD’s definition of the geographic areas

targeted by this goal is basically the same as

that used during 1996–99. It is divided into

a metropolitan component and a

nonmetropolitan component.

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.

The definition includes 20,326 of the

43,232 census tracts (47 percent) in

metropolitan areas, which include 44 percent

of the metropolitan population.1 The tracts

included in this definition suffer from poor

mortgage access and distressed

socioeconomic conditions. The average

mortgage denial rate in these tracts is 19.4

percent, almost twice the denial rate in

excluded tracts. The tracts include 73 percent

of the number of poor persons in

metropolitan areas.

This definition is based on studies of

mortgage lending and mortgage credit flows

conducted by academic researchers,

community groups, the GSEs, HUD and other

government agencies. While more research

must be done before mortgage access for

different types of people and neighborhoods

is fully understood, one finding from the

existing research literature stands out—highminority 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 measuring access

to mortgage credit.

Nonmetropolitan Areas. This rule provides

that in nonmetropolitan areas mortgage

purchases that finance properties that are

located in counties will count toward the

Geographically Targeted Goal 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.

The nonmetropolitan definition has been

expanded slightly by adding criterion (b)

under part (2) of this definition—as a result,

14 counties in Texas, Mississippi, Arizona,

Arkansas, Georgia, and Louisiana that were

previously classified as served areas have

now been reclassified as underserved

counties.

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, this rule uses

a more inclusive, county-based definition of

underservedness in rural areas. HUD’s

definition includes 1,511 of the 2,305

counties (66 percent) in nonmetropolitan

areas and accounts for 54 percent of the

nonmetropolitan population and 67 percent

of the nonmetropolitan poverty population.

Goal Levels. The Geographically Targeted

Goal is 31 percent of eligible units financed

for calendar years 2001–03. HUD estimates

that the mortgage market in areas included in

the Geographically Targeted Goal accounts

for 29–32 percent of the total number of

newly-mortgaged dwelling units. HUD’s

analysis indicates that 27.0 percent of Fannie

Mae’s 1998 purchases and 26.8 percent of its

1999 purchases financed dwelling units

located in these areas. The corresponding

performance for Freddie Mac was 26.1

percent in 1998 and 27.5 percent in 1999.

3. Response to Comments

This section briefly reviews the main

comments on the analyses reported in this

appendix. First, both GSEs, but particularly

Freddie Mac, were concerned that the

Underserved Areas Goal was set too high.

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65145

Second, HUD received varying responses on

changing the underserved areas definition to

adopt an ‘‘enhanced’’ definition that would

lower the income threshold for the census

tract definition to 80 percent and raise the

minority threshold to 50 percent. Finally,

HUD received a range of comments on

switching the non-metropolitan underserved

areas definition from a county-based to a

tract-based approach. With respect to the

latter two issues, HUD has decided to wait

until year 2000 Census data are available,

which will allow for an up-to-date

comprehensive analysis of these issues.

a. The Level of the Underserved Areas Goal

Fannie Mae supported the increase in

affordable housing goals, which includes

raising the underserved areas goal from its

current level of 24 percent to 31 percent.

Freddie Mac stated that ‘‘the Underserved

Areas Goal proposed by the Department is

unreasonably high’’ and recommended that

the goal level be reduced from 31 percent to

30 percent. Freddie Mac stated further that

‘‘setting the Underserved Areas Goal at 31

percent for those three years [2001–03]

amounts to a significantly larger stretch than

for the other two goals and makes it

significantly less feasible under a variety of

economic conditions’’. Freddie Mac based its

conclusion on a number of factors, such as

the fact that this goal is set closer to the

upper end of HUD’s market range (29–32

percent), as compared with the Low-Mod and

Special Affordable Goals; Freddie Mac

concluded that consistency with the other

two goals would call for a 30 percent

Underserved Areas Goal. In addition, Freddie

Mac stated that HUD’s market range is

overestimated and does not fully account for

adverse economic changes. According to

Freddie Mac, HUD’s overestimation of the

underserved areas market is due to HUD’s

overestimation of the rental property share of

the mortgage market; to a bias in HMDA data

that leads to the underserved areas portion of

the owner market being overstated; and to

HUD’s underestimation of the subprime

portion of the single-family market.

HUD’s Response. HUD does not agree with

Freddie Mac’s recommendation that the

Underserved Areas Goal should be lowered

below the proposed level. Several factors

must be considered when evaluating Freddie

Mac’s analysis and recommendations. First,

HUD disagrees with Freddie Mac’s

conclusion that the Department’s

methodology overstates the rental portion of

the market. HUD’s analysis of this issue is

discussed in Sections B and C of Appendix

D. By relying on HMDA data, Freddie Mac

(as well as the Freddie Mac-funded study by

PriceWaterhouseCoopers) significantly

underestimates the multifamily share of the

mortgage market, which leads to its

erroneous conclusions about the size of the

underserved areas market.

Second, HUD has set its range of market

estimates for this goal at a rather conservative

level. As discussed in Section G of Appendix

D, the underserved areas portion of the

market (without B&C loans) averaged 33

percent between 1995 and 1998—somewhat

higher than the top end of HUD’s 29–32

percent market range. As shown in Table

D.19 of Appendix D, the underserved areas

share of the owner market could fall from its

1995–98 average of 33 percent to 24 percent

before the overall market estimate would fall

to 30 percent, and to below 22 percent before

the overall market estimate would fall below

29 percent. As mentioned in HUD’s response

to the ‘‘volatility’’ issue (see Section B of

Appendix D), the Secretary can re-examine

the feasibility of the housing goals if a

recession or other economic conditions cause

a substantial decline in the mortgage market

in underserved areas.

Third, HUD excluded the B&C portion of

the subprime market when determining its

market range (29–32 percent) for underserved

areas. As explained in Section G of Appendix

D, the estimated increase in the market share

due to the county-based definition in nonmetropolitan areas more than offsets the

estimated reduction in market share due to

the exclusion of B&C loans. (This offsetting

pattern can be seen in Table D.15 of

Appendix D for the years 1995–98.) But due

to inadequate mortgage market data for nonmetropolitan areas, HUD was unable to fully

include the effects of underserved counties in

its market range for the Underserved Areas

Goal. Thus, the 29–32 percent range is a

conservative market estimate. HUD continues

to explore other data bases to improve its

estimates of the mortgage market in rural

underserved counties.

Finally, it should be noted that the rental

sectors that the GSEs have traditionally

experienced the most difficulty penetrating

are less important for the Underserved Areas

Goal than for the Low-Mod and Special

Affordable Goals. The latter two goals rely

more heavily on the GSEs’ single-family

rental and multifamily purchases than the

Underserved Areas Goal. For example,

special affordable loans amounted to one half

of the rental units financed by the GSEs

during 1998, versus only 10.6 percent of the

owner units, yielding a rental-to-owner ratio

of 4.7. On the other hand, units in

underserved areas amounted to 43.1 percent

of the rental units financed, versus 23.4

percent of the owner units, yielding a much

lower rental-to-owner ratio of 1.8.

b. Changes in the Underserved Areas

Definition for Metropolitan Areas

Neither Fannie Mae nor Freddie Mac

supported changing the underserved areas

definition in metropolitan areas. With regard

to the enhanced option, the GSEs advocated

against reducing the number of census tracts

that qualified for goal based on 1990 Census

data, since these tracts might qualify under

the updated 2000 Census data. Both GSEs

believe that HUD should not change the

current definition until the updated

information for demographics and housing

stock composition of census tracts is

available from the 2000 census data.

In addition to the GSEs’ views, a number

of comments both supporting and opposing

the enhanced definition were received.

Advocates for the enhanced definition

supported changing the tract income ratio

from 90 percent to 80 percent to coincide

with the definition under the Community

Reinvestment Act (CRA). This change would

make the GSEs’ housing goals and CRA

mutually supportive and would use a

standard already employed by banks.

Comments against the enhanced definition

fell into two categories: some commenters

did not support decreasing the number of

census tracts that qualify as underserved

areas, while others did not support using the

greater of local or national median income in

computing the tract income ratio.

No general support from the GSEs or other

commenters was found for increasing the

minimum minority composition of

underserved census tracts from 30 percent to

50 percent. One commenter indicated that

this change would disproportionately impact

the Hispanic population, though no data was

presented to support this claim.

HUD’s Response. HUD is not changing the

definition of underserved metropolitan areas

in this final rule, but the Department reserves

the right to reexamine this definition

following the release of the 2000 Census data.

The Department acknowledges that the 2000

Census will impact the designation of census

tracts that are currently targeted as

underserved areas. Many changes have

occurred in the last decade that impact the

various factors which make up the

underserved areas definition. Any changes in

the underserved area definition based on the

1990 Census data would not provide a

complete assessment of outcomes.

c. Changes to the Underserved Areas

Definition for Non-metropolitan Areas

Fannie Mae and Freddie Mac agreed that

the current county-based definition for nonmetropolitan areas should be retained. Both

GSEs believe, as also indicated in their

comments on the 1995 rule, that rural

lenders’ business is centered around

counties, rather than census tracts. They cite

the lack of data for rural areas as sufficient

cause to maintain the status quo, since the

information void makes it difficult to judge

the impact of any change in the definition.

Some commenters agreed with the GSEs,

while others did not. One set of commenters

including America’s Community Bankers and

the Independent Community Bankers of

America agreed with the GSEs regarding

retention of the county-based definition. The

Housing Assistance Council supported

changing the underserved areas definition to

a more targeted, census tract-based

definition.

Other recommendations for defining rural

underserved areas were received. The

Wisconsin Rural Development Center and the

Fair Lending Coalition of Milwaukee

proposed looking at the minimum income

ratio based on county, tract, or block group.

A few commenters proposed using poverty

levels as a criteria for targeting underserved

counties.

HUD’s Response. HUD recognizes the

broad nature of the current definition of rural

underserved areas. As explained in the

proposed rule, one shortcoming of this goal

in non metropolitan counties is that it does

not target the GSEs’ purchases very well—for

example, the GSEs’ mortgage purchases in

rural underserved areas have a higher share

of borrowers with income above county

median income than their purchases in urban

underserved areas. However, due to the lack

of data on mortgage originations in nonmetropolitan areas, it is difficult to precisely

identify rural underserved areas. The

65146 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

Department acknowledges that the 2000

Census will impact the designation of

counties that are currently targeted as

underserved. Before changing the definition

for underserved non-metropolitan areas, it

would be prudent to wait for new data on

area demographics. HUD will re-examine this

issue when data from the 2000 Census are

available.

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 mortgage funding. Section B.1

provides an overview of the problem of

unequal access to mortgage funding in the

nation’s housing finance system, 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 Geographically

Targeted Goal in metropolitan areas. The

most thorough studies available provide

strong evidence that in metropolitan areas

low income and high minority census tracts

are underserved by the mortgage market.

Three main points are made in this section:

• There is evidence of racial disparities in

both the housing and mortgage markets.

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 definition.

Studies conclude that characteristics of the

applicant and the neighborhood where the

property is located are the major

determinants of mortgage denials and

origination rates. Once these characteristics

are accounted for, other influences, such as

location in an OMB-designated central city,

play only a minor role in explaining

disparities in mortgage lending.2

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. 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. A quick look at mortgage

denial rates reported by the 1998 HMDA data

reveals that minority denial rates were higher

than those for white loan applicants. For

lower-income borrowers, the conventional

denial rate for African Americans was 1.9

times the denial rate for white borrowers,

while for higher-income borrowers, the

denial rate for African Americans was 2.5

times the rate for white borrowers. Similarly,

the FHA denial rate for lower-income African

Americans was 1.7 times the denial rates for

lower-income white borrowers and twice as

high for higher-income African Americans as

for whites with similar incomes.

Several analytical studies, some of which

are reviewed later in this section, show that

these differentials in denial rates are not fully

accounted for by differences in credit risk.

Perhaps the most publicized example is a

study by the Federal Reserve Bank of Boston,

described in more detail below, which found

that differential denial rates were most

prevalent among marginal applicants.3

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.

The Urban Institute conducted a case study

of lenders’ origination processes.4 The

research team and lenders believed

origination processes to be race-blind. A

review of the HMDA data revealed that

origination outcomes were different for

whites, black, and Hispanics—where lenders

denied a small proportion of minority

applicants, they denied an even smaller

proportion of white applications. This may

result from the lender’s staff making greater

efforts to qualify marginal white applicants

compared with marginal black and Hispanic

applicants.

In addition to discrimination in the

lending market, substantial evidence exists of

discrimination in the housing market. The

1991 Housing Discrimination Study

sponsored by HUD found that minority home

buyers encounter some form of

discrimination about half the time when they

visit a rental or sales agent to ask about

advertised housing.5 The incidence of

discrimination was higher for African

Americans than for Hispanics and for

homebuyers than for renters. For renters, the

incidence of discrimination was 46 percent

for Hispanics and 53 percent for African

Americans. The incidence among buyers was

56 percent for Hispanics and 59 percent for

African Americans.

While discrimination is rarely overt,

minorities are more often told the unit of

interest is unavailable, shown fewer

properties, offered less attractive terms,

offered less financing assistance, or provided

less information than similarly situated nonminority homeseekers. Some evidence

indicates that properties in minority and

racially-diverse neighborhoods are marketed

differently from those in White

neighborhoods. Houses for sale in non-White

neighborhoods are rarely advertised in

metropolitan newspapers, open houses are

rarely held, and listing real estate agents are

less often associated with a multiple listing

service.6

Discrimination, while not the only cause,

contributes to the pervasive level of

segregation that persists between African

Americans and Whites in our urban areas.

Because minorities tend to live in segregated

neighborhoods, their difficulty in obtaining

mortgage credit has a concentrated effect on

the viability of their neighborhoods. 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

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

from existing data. To provide the reasoning

behind the Department’s definition of

underserved areas, this section first uses

1998 HMDA data to examine geographic

variation in mortgage denial rates, and then

it reviews three sets of studies that support

HUD’s definition. These include (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 review of the economics literature

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.

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65147

a. HMDA Data on Mortgage Originations and

Denial Rates

Home Mortgage Disclosure Act (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, it is a rich data base

for analyzing mortgage activity in urban

neighborhoods. HUD’s analysis using HMDA

data for 1998 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.1 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 1998 the denial rate

for census tracts that are over 90 percent

minority (26.6 percent) was 2.5 times that for

census tracts with less than 10 percent

minority (10.4 percent).

• Census tracts with lower incomes have

higher denial rates and lower origination

rates than higher income tracts. For example,

in 1998 mortgage denial rates declined from

26.8 percent to 7.4 percent as tract income

increased from less than 20 percent of area

median income to more than 150 percent of

area median income.7 Similar patterns arose

in HUD’s analysis of 1993 and 1994 HMDA

data (see Appendix B of the 1995 rule).

BILLING CODE 4210–27–P

65148 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

BILLING CODE 4210–27–C

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65149

Table B.2 illustrates the interaction

between tract minority composition and tract

income by aggregating the data in Table B.1

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 7.9

percent and an origination rate of 19.6 loans

per 100 owner occupants in 1998. The highminority (over 50 percent), low-income

(under 90 percent of area median) group had

a denial rate of 24.0 percent and an

origination rate of only 8.5 loans per 100

owner occupants. The other groupings fall

between these two extremes.

The advantages of HUD’s underserved area

definition can be seen by examining the

minority-income combinations highlighted in

Table B.2. 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 1998

underserved areas had almost twice the

average denial rate of served areas (19.4

percent versus 10.3 percent) and less than

two-thirds the average origination rate per

100 owner occupants (10.8 versus 17.5).

HUD’s definition does not include highincome (over 120 percent of area median)

census tracts even if they meet the minority

threshold. The mortgage denial rate (13.3

percent) for high-income tracts with a

minority share of population over 30 percent

is much less than the denial rate (19.4

percent) in underserved areas as defined by

HUD, and only slightly above the average

(10.3 percent) for all served areas.

b. Federal Reserve Bank Studies

The analysis of denial rates in the above

section suggests that HUD’s definition is a

good proxy for identifying areas experiencing

credit problems. However, an important

question is the degree to which variations in

denial rates reflect lender bias against certain

kinds of neighborhoods and borrowers versus

the degree to which they reflect the credit

quality of potential borrowers (as indicated

by applicants’ available assets, credit rating,

employment history, etc.). Some studies of

credit disparities have attempted to control

for credit risk factors that might influence a

lender’s decision to approve a loan. Without

fully accounting for the creditworthiness of

the borrower, racial differences in denial

rates cannot be attributed to lender bias.

The best example of accounting for credit

risk is the study by researchers at the Federal

Reserve Bank of Boston, which analyzed

mortgage denial rates.8 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. 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 highly-qualified applicants of all

races were approved, differential treatment

was observed among borrowers with more

marginal qualifications.9

A subsequent reassessment and refinement

of the data used by the Federal Reserve Bank

of Boston confirmed the findings of that

study.10 William C. Hunter of the Federal

Reserve Bank of Chicago 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.

The two Fed studies concluded that the

effect of borrower race on mortgage rejections

persists even after controlling for legitimate

determinants of lenders’ credit decisions.

Thus, they imply that variations in mortgage

denial rates, such as those given in Table B.2,

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 11 and

by underwriting standards that have

disparate effects on minority and lowerincome borrowers and their neighborhoods.12

c. 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 1980’s and

early 1990’s, a number of studies using

HMDA data (such as that reported in Tables

B.1 and B.2) 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.13

However, such analyses were criticized

because they did not distinguish between

demand, risk, and supply effects 14—that is,

they did not determine whether loan volume

was low because families in high-minority

and low-income areas were unable to afford

home ownership 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.15 16

Recent statistical studies have sought to

test the redlining hypothesis by more

completely controlling for differences in

neighborhood risk and demand. The first two

studies reviewed below are good examples of

the more recent literature. 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. However, as explained below,

these studies cannot reach definitive

conclusions about redlining because

segregation in our inner cities makes it

difficult to distinguish the impacts of

geographic redlining from the effects of

individual discrimination.

Additional studies related to redlining and

the credit problems facing low- income and

minority neighborhoods are also

summarized. Particularly important are

studies that 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 low-income 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.

Holmes and Horvitz Study. Andrew

Holmes and Paul Horvitz used 1988–1991

HMDA data to examine variations in

conventional mortgage originations across

census tracts in Houston. Their singleequation regression model included as

explanatory variables the economic viability

of the loan, characteristics of properties in

and residents of the tract (e.g., house value,

income, age distribution and education

level), measures of demand (e.g., recent

movers into the tract and change in owneroccupied units between 1980 and 1990), and

measures of credit risk (defaults on

government-insured loans and change in

tract house values between 1980 and 1990).

To test the existence of racial redlining, the

model also included as explanatory variables

the percentages of African American and

Hispanic residents in the tract and the

increase in the tract’s minority percentage

between 1980 and 1990. 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 Study. Michael Schill

and Susan Wachter posited that the

probability that a lender will accept a

specific mortgage application depends on

characteristics of the individual loan

application 17 and characteristics of the

neighborhood where the property

collateralizing the loan is located. Schill and

Wachter included neighborhood risk proxies

that are likely to affect the future value of the

properties,18 and they included the

percentage of the tract population comprised

of African Americans and Hispanics in order

to test for the existence of racial

discrepancies in lending patterns across

census tracts.

Testing their model for conventional

mortgages in Philadelphia and Boston, Schill

and Wachter found that the applicant race

variables—whether the applicant was African

65150 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

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). In an initial analysis that

excluded the neighborhood risk variables

from the model, the percentage of the census

tract that was African American also showed

a significant and negative coefficient, a result

that is consistent with redlining. However,

when the neighborhood risk proxies were

included in the model along with the

individual loan variables, the percentage of

the census tract that was African American

became insignificant. Thus, similar 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.’’ 19

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.20 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.21 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.22

Geoffrey Tootell has authored two papers

on neighborhood redlining based on the

mortgage rejection data from the Boston Fed

study.23 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.24

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 at both lender redlining and the role

of private mortgage insurance (PMI) in

neighborhood lending.25 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. A

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.26 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.27 While this

effect was not found in high-minority tracts,

he concludes that ‘‘informational returns to

scale’’ contribute to disparities in the

availability of mortgage credit between lowminority and high-minority 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.28

Robert Avery, Patricia Beeson, and Mark

Sniderman found significant evidence of

economies associated with the scale of

operation of individual lenders in a

neighborhood.29 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.

d. 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. This section reports findings

from several analyses by HUD and 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.

Socioeconomic Characteristics. The

targeted nature of HUD’s definition can be

seen from the data presented in Table B.3,

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65151

which show that families living in

underserved areas experience much more

economic and social distress than families

living in served areas. For example, the

poverty rate in underserved census tracts is

20.1 percent, or almost four times the poverty

rate (5.8 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 in

underserved areas (11.5 percent) than in

served areas (4.3 percent).

The majority of units in served areas are

owner-occupied, while the majority of units

in underserved areas are renter-occupied.

BILLING CODE 4210–27–P

65152 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

BILLING CODE 4210–27–C

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65153

Credit Characteristics. Tables B.1 and B.2

documented the relatively high denial rates

and low mortgage origination rates in

underserved areas as defined by HUD. This

section extends that analysis by comparing

underserved and served areas within central

cities and suburbs. Figure B.1 shows that

HUD’s definition targets central city

neighborhoods that are experiencing

problems obtaining mortgage credit. The 19.6

percent denial rate in these neighborhoods in

1998 was nearly twice the 10.6 percent

denial rate in the remaining areas of central

cities. A broad, inclusive definition of

‘‘central city’’ that includes all areas of all

OMB-designated central cities would include

these ‘‘remaining’’ portions of cities. Figure

B.1 shows that these areas, which account for

approximately 43 percent of the population

in OMB-designated central cities, appear to

be well served by the mortgage market. As a

whole, they are not experiencing problems

obtaining mortgage credit.30

HUD’s definition also targets underserved

census tracts in the suburbs as well as in

central cities—for example, the average

denial rate in underserved suburban areas

(19.2 percent) is more than twice that in the

remaining served areas of the suburbs (10.1

percent). Low-income and high-minority

suburban tracts appear to have credit

problems similar to their central city

counterparts. These suburban tracts, which

account for 40 percent of the suburban

population, are encompassed by the

definition of other underserved areas.

As explained in the Preamble, HUD asked

for public comment on two options that

would tighten the targeting of the

underserved areas definition and reduce the

number of qualifying census tracts. After

examining the comments the Department has

decided to wait until the release of the 2000

Census Bureau data. In addition to providing

updated information on neighborhoods, the

2000 Census Bureau will incorporate changes

adopted by the Metropolitan Area Standards

Review Committee that will impact the

boundaries of current metropolitan areas.31

Shear, Berkovec, Dougherty, and Nothaft

Study. William Shear, James Berkovec, Ann

Dougherty, and Frank Nothaft conducted an

analysis of mortgage flows and application

acceptance rates in 32 metropolitan areas that

supports a targeted definition of underserved

areas.32 They 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; 33 and (b)

once census tract influences were accounted

for, central city location had only a minimal

effect on credit flows. Shear, Berkovec,

Dougherty, and Nothaft 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. They explain the implications

of their results for measuring underserved

areas as follows:

While it is not at all clear how we might

rigorously define, let alone measure, what it

means to be underserved, it is clear that there

are important housing-related problems

associated with certain location

characteristics, and it is possible that, in the

second or third best world in which we live,

mortgage markets might be useful in helping

to solve some of these problems. We then

might use these data to help single out

important areas or at least eliminate some

bad choices. * * * The regression results

indicate that income and minority status are

better indicators of areas with special needs

than central city location.34

Avery, Beeson, and Sniderman Study.

Robert Avery, Patricia Beeson, and Mark

Sniderman of the Federal Reserve Bank of

Cleveland presented a paper specifically

addressing the issue of underserved areas in

the context of the GSE legislation.35 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. They sought to isolate the

differences that stem from the characteristics

of the neighborhood itself rather than the

characteristics of the individuals that apply

for loans in the neighborhood or lenders that

happen to serve them. Similar to the studies

of redlining reviewed in the previous section,

Avery, Beeson and Sniderman hypothesized

that variations in mortgage application and

denial rates would be a function of several

risk variables such as the income of the

applicant and changes in neighborhood

house values; they tested for independent

racial effects by adding to their model the

applicant’s race and the racial composition of

the census tract. Econometric techniques

were used to separate individual applicant

effects from neighborhood effects.

Based on their empirical work, Avery,

Beeson and Sniderman reached the following

conclusions:

• 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.36 For white applicants, on the

other hand, denial rates were significantly

higher in minority tracts.37 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, Avery et al. noted that

since minorities tend to live in segregated

communities, a policy of targeting minority

neighborhoods may be warranted.

Other findings were:

• 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.

• There was little difference in overall

denial rates between central cities and

suburbs, once individual applicant and

census tract characteristics were controlled

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.

e. Conclusions from HUD’s Analysis and 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. HUD’s analysis of

HMDA data shows that 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

our 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,

thus, the need continues for further research

on the underlying determinants of geographic

disparities in mortgage lending.38

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’s analysis shows that both credit and

socioeconomic problems are highly

concentrated in underserved areas within

central cities and suburbs. The remaining,

high-income portions of central cities and

suburbs appear to be well served by the

mortgage market.

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. HUD believes, however, that the

economics literature is consistent with a

targeted rather than a broad approach for

defining underserved areas.

65154 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

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

Because of the absence of HMDA data for

rural areas, the analysis for metropolitan

underserved areas cannot be carried over to

non-metropolitan 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. But this definition parallels that used

in metropolitan areas—specifically, a

nonmetro 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 nonmetro or

national nonmetro 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 nonmetro or

national nonmetro median income. For

nonmetro areas the median income

component of the underserved areas

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 ‘‘enhanced income’’—the greater of state

nonmetro income and national nonmetro

income. This is based on HUD’s analysis of

1990 census data, which indicated that

comparing county nonmetro income only to

state nonmetro income would lead to the

exclusion of many lower-income lowminority counties from the definition,

especially in Appalachia. 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 county-wide

definition of rural underserved areas could

give the GSEs an incentive to purchase

mortgages in the ‘‘better served’’ portions of

underserved counties which may face few, if

any, barriers to accessing mortgage credit in

rural areas. This issue is discussed in more

detail in the proposed Rule.

The demographic characteristics of served

and underserved counties are first presented

in this section. Next, a literature review of

recent studies provides an overview of rural

mortgage markets, GSE activity, and the

growing demand for manufactured housing

in rural housing markets. It also discusses

characteristics of rural housing markets that

lead to higher interest rates and mortgage

access problems and makes some policy

recommendations for addressing market

inefficiencies.

1. Demographics

As discussed, majorities of rural

households and rural counties fall under the

definition of underserved areas. As shown in

Table B.4, rural underserved counties have

higher unemployment, poverty rates,

minority shares of households, and

homeownership rates than rural served

counties. The poverty rate in underserved

rural counties (21.2 percent) is nearly twice

that in served rural counties (12.2 percent).

Joblessness is more common, with average

unemployment rates of 8.3 percent in

underserved counties and 5.9 percent in

served counties. Minorities make up 20.8

percent of the residents in underserved

counties and 7.4 percent in served counties.

Homeownership is slightly higher in

underserved counties (72.4 percent) than in

served counties (70.8 percent).

BILLING CODE 4210–27–P

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65155

BILLING CODE 4210–27–C

65156 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

Some differences exist between metro and

nonmetro underserved areas. The definition

is somewhat more inclusive in nonmetro

areas—the majority of the nonmetro

population lives in underserved counties,

while the majority of the metropolitan

population lives in served areas. The

majority of units in underserved

metropolitan areas are occupied by renters,

while the majority of units in underserved

rural counties are occupied by owners. But

poverty and unemployment rates are higher

in underserved areas than in served areas in

both nonmetropolitan and metropolitan

areas.

2. Literature Review

Research related to housing and mortgage

finance issues in rural areas is reviewed in

this section. It finds that lack of competition

between rural lenders and lack of

participation in secondary mortgage markets

may contribute to higher interest rates and

lower mortgage availability in rural areas.

The mortgages purchased by the GSEs on

properties in underserved counties are not

particularly focused on lower-income

borrowers and first-time homebuyers, which

suggests that additional research needs to be

conducted to target areas in nonmetropolitan

areas which experience difficulty accessing

mortgage credit. The role of manufactured

housing in providing affordable housing in

rural areas is also discussed.

Mikesell Study (1998).39 A study by Jim

Mikesell provides an overview of mortgage

lending in rural areas. It finds that home

loans in rural areas have higher costs, which

can be attributed to at least three factors that

characterize rural mortgage markets. First,

the fixed cost associated with rural lending

may be higher as a result of the smaller loan

size and remoteness of many rural areas.

Second, there are fewer mortgage lenders in

rural areas competing for business, which

may account for higher interest rates. Third,

the secondary mortgage market is not as well

developed as in metropolitan areas.

Higher interest rates for rural mortgages are

documented by the Federal Housing Finance

Board’s monthly survey of conventional

home purchase mortgages. On average,

relative to rates on mortgages in urban areas,

rates on mortgages in rural areas in 1997

were 8 basis points (bp) higher on 30-year

fixed rate mortgages (FRMs), 18 bp higher for

15-year FRMs, 38 bp higher for adjustablerate mortgages (ARMs), and 52 bp higher for

nonstandard loans.40 The higher rates in

rural areas translate into differences in

monthly payments of $3 to $16 for a

$100,000 mortgage.

Mikesell finds that property location and

small loan size are two factors that make

lending more costly in rural areas. Borrower

characteristics, such as income, assets, and

credit history, and lender characteristics,

such as ownership, size, and location, might

influence loan pricing, but the influence of

these factors could not be tested due to lack

of data.

Rural-based lenders are fewer and originate

a smaller volume of loans than their urban

counterparts. These factors contribute to less

competition between rural lenders and a less

efficient housing finance market, which

result in higher costs for rural borrowers.

Rural lenders are less likely than urban

lenders to participate in the secondary

mortgage market. As a result, rural borrowers

do not receive the benefits associated with

the secondary market—the increased

competition between lenders, the greater

potential supply of mortgage financing, and

the alignment of financing costs more closely

with those in urban markets.

Some obstacles for rural lenders

participating in the secondary market are that

borrower characteristics and remote

properties may not conform to the secondary

market’s underwriting standards. Rural

households may have their borrowing

capacity reduced by loan qualification

standards which discount income that varies

widely from year to year and income from

self-employment held for less than several

years. Rural properties may have one or more

of the following characteristics which

preclude a mortgage from being purchased by

the GSEs: excessive distance to a firehouse,

unacceptable water or sewer facilities,

location on a less-than-all-weather road, and

dated plumbing or electrical systems.

Mikesell concludes that increased

participation by rural lenders in the

secondary mortgage market would bring

down lending costs and offset some of the

higher costs characteristic of rural lending,

and that HUD’s goals for the GSEs could

encourage such increased participation.

MacDonald Study.41 This study

investigates variations in GSE market shares

among a sample of 426 non-metropolitan

counties in eight census divisions.

Conventional conforming mortgage

originations are estimated using residential

sales data, adjusted to exclude nonconforming mortgages. Multivariate analysis

is used to investigate whether the GSE

market share differs significantly by location,

after controlling for the economic,

demographic, housing stock, and credit

market differences among counties that could

affect use of the secondary markets by

lenders.42

MacDonald has four main findings

regarding mortgage financing and the GSEs’

purchases in rural mortgage markets. First,

smaller, poorer and less rapidly growing nonmetro areas have less access to mortgage

credit than larger, wealthier and more rapidly

growing areas. Second, the mortgages that are

originated in the former areas are seldom

purchased by the GSEs. Third, higher-income

borrowers are more likely, and first-time

homebuyers are less likely, to be served by

the GSEs in underserved areas than in served

areas. This suggests that the GSEs are not

reaching out to marginal borrowers in

underserved nonmetropolitan areas. Finally,

the GSEs serve a smaller proportion of the

low-income market in rural areas than do

depository institutions. This finding is

consistent with studies of the GSEs’

affordable lending performance in

metropolitan areas.

With regard to the GSEs’ underwriting

guidelines MacDonald makes two points.

First, the GSEs’ purchase guidelines may

adversely affect non-metro areas where many

borrowers are seasonally-or self-employed

and where houses pose appraisal problems.

Second, MacDonald speculates that mortgage

originators in nonmetropolitan areas may

interpret guidelines too conservatively, or

may not try to qualify non-traditional

borrowers for mortgages.

MacDonald also echoes the findings of

Mikesell that the existence and extent of

mortgage lending problems are difficult to

identify in many rural areas because of the

lack of comprehensive mortgage lending

data. Problems that have been identified

include the lack of market competition

among small, conservative lending

institutions typical in rural and nonmetropolitan areas; consolidation and other

changes in the financial services industry,

which may have different consequences in

rural areas than in urban areas; lack of access

to government housing finance programs in

more rural locations; and weak development

of secondary market sources of funds in rural

areas, exacerbating liquidity problems.

MacDonald discusses briefly the

importance of low-cost homeownership

alternatives in rural areas. One alternative is

manufactured (mobile) housing. In general,

manufactured housing is less costly to

construct than site-built housing.

Manufactured housing makes up more than

25 percent of the housing stock in rural

counties in the South and Mountain states.

MacDonald concludes that the lower

participation of the GSEs in underserved

areas compared with served areas may result

from additional risk components for some

borrowers and from lack of sophistication by

the lenders that serve small non-metro

markets. In smaller and poorer counties, low

volumes of loan sales to the GSEs may be a

result of lower incomes and smaller

populations. These counties may not have

sufficient loan-generating activity to justify

mortgage originators pursuing secondary

market outlets.

The Role of Manufactured Housing.43 The

Joint Center for Housing Studies at Harvard

University conducted a comprehensive study

of the importance of manufactured housing

as an affordable housing choice in rural

communities. In all segments of the housing

market, but especially in rural areas and

among low-income households,

manufactured housing is growing. Based on

the American Housing Survey, in 1985, 61

percent of the manufactured housing stock

was located in rural areas, compared with 70

percent in 1993. Between 1985 and 1993,

manufactured housing increased over 2.2

percent annually while all other housing

increased 0.7 percent per year. In 1993, 6.0

percent (or 6 million) of households lived in

manufactured housing.

Since the 1970’s, the face of manufactured

housing has changed. Once a highly mobile

form of recreational housing in this country,

today manufactured housing provides basic

quality, year-round housing for millions of

American households. Most earlier units

were placed in mobile home parks or on

leased parcels of land. Today an increasing

number of units are owned by households

that also own the land on which the

manufactured home is located.

Manufactured housing’s appeal lies in its

affordability. The low purchase price,

downpayments, and monthly cash costs of

manufactured housing provide households

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65157

who are priced out of the conventional

housing market a means of becoming

homeowners. The occupants of manufactured

housing on average are younger, have less

income, have less education and are more

often white than occupants of single-family

detached homes. This type of housing is

often found in areas with persistent poverty,

retirement destinations, areas for recreation

and vacations, and commuting counties.

The manufactured housing industry is well

positioned for continued growth. The

affordability of manufacturing housing is

increasingly attractive to the growing ranks of

low-income households. Manufactured

housing is becoming more popular among

first-time homebuyers and the elderly, both

of which are growing segments of the

housing market. The migration of people to

the South, where manufactured housing is

already highly accepted, and to metropolitan

fringes will further increase the demand for

this type of housing.44

D. Factor 3: Previous Performance and Effort

of the GSEs in Connection With the Central

Cities, Rural Areas and Other Underserved

Areas Goal

As discussed in Sections B and C, HUD has

structured the Geographically Targeted Goal

to increase mortgage credit to areas

underserved by the mortgage markets. This

section looks at the GSEs’ past performance

to determine the impact the Geographically

Targeted Goal is having on borrowers and

neighborhoods, with particular emphasis on

underserved areas. Section D.1 reports the

past performance of each GSE with regard to

the Geographically Targeted 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.

Section D.3 concludes this section with an

analysis of the GSEs’ purchases in rural

(nonmetropolitan) areas.

1. GSE Performance on the Geographically

Targeted Goal

This section discusses each GSE’s

performance under the Geographically

Targeted Goal over the 1993–99 period. The

data presented here are ‘‘official results’’ i.e.,

they are based on HUD’s in-depth analysis of

the loan-level data submitted annually to the

Department, subject and the counting

provisions contained in Subpart B of HUD’s

December 1, 1995 Regulation of Fannie Mae

and Freddie Mac. As explained below, in

some cases these ‘‘official results’’ differ to

some degree from goal performance reported

by the GSEs in their Annual Housing

Activities Reports to the Department.

HUD’s goals specified that in 1996 at least

21 percent of the number of each GSE’s units

eligible to count toward the Geographically

Targeted Goal should qualify as

geographically targeted, and at least 24

percent should qualify in 1997 and 1998.

Actual performance, based on HUD analysis

of GSE loan-level data, was as follows:

1996 1997 1998 1999

Fannie Mae:

Units Eligible to Count Toward Goal ........................................................ 1,891,896 1,765,347 3,546,302 2,956,155

Geographically Targeted Units ................................................................. 532,434 508,746 958,233 791,593

Percent Geographically Targeted ............................................................. 28.1 28.8 27.0 26.8

Freddie Mac:

Units Eligible to Count Toward Goal ........................................................ 1,325,900 1,180,517 2,658,556 2,245,087

Geographically Targeted Units ................................................................. 331,495 310,572 693,748 618,385

Percent Geographically Targeted ............................................................. 25.0 26.3 26.1 27.5

Thus, Fannie Mae and Freddie Mac

surpassed the goals in 1996 by 7.1 percentage

points and 4.0 percentage points,

respectively. And both GSEs surpassed the

1997–99 goals by at least 2 percentage points

in each of these three years.

Fannie Mae’s performance on the

Geographically Targeted Goal jumped

sharply in just two years, from 23.6 percent

in 1993 to 31.9 percent in 1995, before tailing

off to 28.1 percent in 1996. As indicated, it

then rose slightly to 28.8 percent in 1997,

before tailing off to 27.0 percent in 1998 and

26.8 percent in 1999.45 Freddie Mac has

shown more steady gains in performance on

the Geographically Targeted Goal, from 21.3

percent in 1993 to 24.2 percent in 1994, 25.0

percent in 1995–96, just over 26 percent in

1997–98, and 27.5 percent in 1999.46

Fannie Mae’s performance on the

Geographically Targeted Goal has surpassed

Freddie Mac’s in every year from 1993

through 1998. However, Freddie Mac’s 1999

performance represented a 26 percent

increase over the 1993 level, exceeding the

14 percent increase for Fannie Mae. As a

result, Freddie Mac’s performance in 1999

(27.5 percent) was 103 percent of Fannie

Mae’s geographically targeted share last year

(26.8 percent)—the only year in which

Freddie Mac’s performance on this goal has

exceeded Fannie Mae’s performance. The

main reason why Freddie Mac moved past

Fannie Mae in performance on the

Geographically Targeted Goal last year is that

the geographically-targeted share of Freddie

Mac’s total single-family mortgage purchases

rose from 24.5 percent in 1998 to 26.7

percent in 1999, exceeding the corresponding

increase for Fannie Mae, from 24.8 percent in

1998 to 25.5 percent in 1999. A second

reason why Freddie Mac surpassed Fannie

Mae in performance on this goal last year is

that multifamily properties are ‘‘goal-rich’’-

that is, they are more likely to be in

underserved areas than single-family units,

and the multifamily share of purchases

eligible for this goal rose slightly for Freddie

Mac, from 8.3 percent in 1998 to 8.5 percent

in 1999, but fell somewhat for Fannie Mae,

from 10.4 percent in 1998 to 9.8 percent in

1999.

2. GSEs’ Mortgage Purchases in

Metropolitan Neighborhoods

As shown in Table B.5, metropolitan areas

accounted for about 85 percent of total GSE

purchases under the Geographically Targeted

Goal in 1998 and 1999. 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 that of portfolio lenders and 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.

In subsection 2.b., the characteristics of the

GSEs’ purchases within underserved areas

are compared with those for their purchases

in served areas.

BILLING CODE 4210–27–P

65158 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

BILLING CODE 4210–27–C

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65159

a. Comparisons With the Primary Market

Overview and Main Conclusions. Tables

A.3 and A.4a in Appendix A provided

information on the GSEs’ funding of home

purchase loans for properties located in

underserved neighborhoods for the years

1993 to 1998. 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. While both

GSEs improved their performance over the

1993–1998 period, they lagged the

conventional conforming market in providing

affordable loans to underserved

neighborhoods. As discussed in Appendix A,

the two GSEs showed very different patterns

of lending—Freddie Mac was much less

likely than Fannie Mae to fund home loans

in underserved neighborhoods through 1998.

The percentage of Freddie Mac’s purchases

financing properties in underserved census

tracts was substantially less than the

percentage of total market originations in

these tracts; furthermore, by 1998 Freddie

Mac had not made progress closing the gap

with the primary market. Fannie Mae, on the

other hand, was much closer to 1998 market

levels in its funding of underserved areas.

The GSE data for 1999 show a shift in these

patterns—during 1999, Freddie Mac

surpassed Fannie Mae in funding mortgages

in underserved neighborhoods.

Freddie Mac—1993–1998. While Freddie

Mac lagged Fannie Mae, portfolio lenders,

and the overall conforming market in

providing home loans to underserved

neighborhoods during the 1993–1998 period,

it pulled ahead of Fannie Mae during 1999

in purchasing mortgages for properties

located in urban underserved areas

(discussed below). Over the 1993–1998

period, underserved census tracts accounted

for 19.7 percent of Freddie Mac’s singlefamily home mortgages, compared with 22.9

percent of Fannie Mae’s purchases, 26.3

percent of loans originated and held in

portfolio by depository lenders, and 24.5

percent of the overall conforming primary

market. If the analysis is restricted to the

1996–98 period during which the current

housing goals have been in effect, the data

continue to show that Freddie Mac lagged the

market in funding underserved

neighborhoods (see Table A.3 in Appendix

A). In 1998, underserved census tracts

accounted for 20.0 percent of Freddie Mac’s

purchases and 24.6 percent of loans

originated in the conforming home purchase

market, yielding a ‘‘Freddie Mac-to-market’’

ratio of only 0.81 (i.e. 20.0 divided by 24.6).

Fannie Mae—1993–1998. Over the longer

1993–98 period and the more recent 1996–98

period, Fannie Mae has lagged the market

and portfolio lenders in funding properties in

underserved areas, but to a much smaller

degree than Freddie Mac. During the 1996–

98 period, underserved tracts accounted for

22.9 percent of Fannie Mae’s purchases,

compared with 25.8 percent of loans retained

in portfolio by depositories and with 24.9

percent of home loans originated in the

conventional conforming market. Fannie

Mae’s performance is much closer to the

market than Freddie Mac’s performance, as

can be seen by the ‘‘Fannie Mae-to-market’’

ratio of 0.92 for the 1996–98 period (i.e. 22.9

divided by 24.9).Fannie Mae’s performance

improved during 1997, due mainly to Fannie

Mae’s increased purchases during 1997 of

prior-year mortgages in underserved

neighborhoods. Overall, Fannie Mae’s

purchases of home loans in underserved

areas increased from 22.3 percent in 1996 to

23.5 percent in 1997. The underserved area

percentage for Fannie Mae’s purchases of

newly-originated mortgages was actually

lower in 1997 (20.8 percent) than in 1996

(21.9 percent). This decline was offset by the

fact that a particularly high percentage (30.1

percent) of Fannie Mae’s 1997 purchases of

prior-year mortgages was for properties in

underserved areas. Thus, Fannie Mae

improved its overall performance in 1997 by

supplementing its purchases of newlyoriginated mortgages with purchases of prioryear mortgages targeted to underserved

neighborhoods. As shown in Table A.4a in

Appendix A, Fannie Mae continued this

strategy in 1998, but not in 1999. The annual

data in Table A.4a show the progress that

Fannie Mae has made in closing the gap

between its performance and that of the

overall market. 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-to-market’’

ratio of 0.82. By 1998, underserved areas

accounted for 22.9 percent of Fannie Mae’s

purchases and 24.6 percent of market

originations, for a higher ‘‘Fannie Mae-tomarket’’ ratio of 0.93. Freddie Mac, on the

other hand, fell further behind the market

during this period. In 1992, Freddie Mac had

a slightly higher underserved area percentage

(18.6 percent) than Fannie Mae (18.3

percent). However, Freddie Mac’s

underserved area percentage had only

increased to 20.0 percent by 1998 (versus

22.9 percent for Fannie Mae). Thus, the

‘‘Freddie Mac-to-market’’ ratio fell from 0.84

in 1992 to 0.81 in 1998.

1999 GSE Purchases. In 1999, Freddie

Mac’s funding of both home purchase loans

and total (combined home purchase and

refinance) loans in underserved

neighborhoods improved to the point that it

surpassed Fannie Mae’s performance. In

1999, underserved areas accounted for 21.2

percent of Freddie Mac’s purchases of home

purchase loans in metropolitan areas—a

figure slightly higher than the 20.6 percent

for Fannie Mae. With respect to combined

home purchase and refinance loans, Freddie

Mac’s underserved areas percentage in

metropolitan areas jumped by 2.6 percentage

points, from 20.9 percent in 1998 to 23.5

percent in 1999, while the corresponding

percentage for Fannie Mae increased by only

0.6 percentage point, from 21.2 percent in

1998 to 21.8 percent in 1999.

Down Payment Characteristics. Table B.6

reports the down payment and borrower

income characteristics of mortgages that the

GSEs purchased in underserved areas during

1999. Two points stand out. First, loans on

properties in underserved areas were more

likely to have a high loan-to-value ratio than

loans on properties in served areas.

Specifically, about 15.4 percent of loans in

underserved areas had a down payment less

than ten percent, compared with 13.4 percent

of all loans purchased by the GSEs. Second,

loans to low-income borrowers in

underserved areas were typically high down

payment loans. Approximately 70 percent of

the GSE-purchased loans to very low-income

borrowers living in underserved areas had a

down payment more than 20 percent.

BILLING CODE 4210–27–P

65160 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65161

BILLING CODE 4210–27–C

b. Characteristics of GSEs’ Purchases of

Mortgages on Properties in Metropolitan

Underserved Areas

Several characteristics of loans purchased

by the GSEs in metropolitan underserved

areas are presented in Table B.7. As shown,

borrowers in underserved areas are more

likely than borrowers in served areas to be

first-time homebuyers, females, and older

than 40 or younger than 30. And, as

expected, they are more likely to have belowmedian income and to be members of

minority groups. For example, first-time

homebuyers make up 12.0 percent of the

GSEs’ mortgage purchases in underserved

areas and 10.4 percent of their business in

served areas. In underserved areas, 54.7

percent of borrowers had incomes below the

area median, compared with 35.9 percent of

borrowers in served areas.

Minorities’ share of the GSEs’ mortgage

purchases in underserved areas (30.1

percent) was nearly three times their share in

served areas (11.4 percent). And the pattern

was even more pronounced for African

Americans and Hispanics, who accounted for

20.9 percent of the GSEs’ business in

underserved areas, but only 5.5 percent of

their purchases in served areas.

65162 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

3. GSE Mortgage Purchases in

Nonmetropolitan Areas

Nonmetropolitan mortgage purchases made

up 13 percent of the GSEs’ total mortgage

purchases in 1999. Mortgages in underserved

counties made up 39 percent of the GSEs’

business in nonmetropolitan areas. 47

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 is provided by

the Home Mortgage Disclosure Act (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.8

compares borrower and loan characteristics

for the GSEs’ mortgage purchases in served

and underserved areas.

The GSEs are slightly less likely to

purchase loans for first-time homebuyers and

more likely to purchases mortgages for highincome borrowers in underserved than in

served counties. Mortgages to first-time

homebuyers accounted for 8.4 percent of the

GSEs’ 1999 mortgage purchases in served

counties, compared with 7.3 percent of their

purchases in underserved counties.

Surprisingly, borrowers in served counties

were more likely to have incomes below the

median than in underserved counties (37.9

percent, compared to 33.6 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.

BILLING CODE 4210–27–P

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65163

BILLING CODE 4210–27–C

There are similarities and differences

between the types of loans that Fannie Mae

and Freddie Mac purchase in served and

underserved counties. The GSEs are similar

in that they are more likely to purchase

refinance loans in underserved counties than

in served counties and that, in general,

mortgage purchases with loan-to-value ratios

above 80 percent are more likely to be in

underserved counties than in served

counties. The GSEs differ in that Freddie Mac

is more likely to purchase seasoned

mortgages in served than in underserved

counties, while the reverse is true for Fannie

Mae.

E. Factor 4: Size of the Conventional

Conforming Mortgage Market for

Underserved Areas

HUD estimates that underserved areas

account for 29–32 percent of the

conventional conforming mortgage market.

The analysis underlying this estimate is

detailed in Appendix D.

65164 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

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.

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

discussions with the Office of Federal

Housing Enterprise Oversight, HUD

concludes that the goals raise minimal, if

any, safety and soundness concerns.

H. Determination of the GeographicallyTargeted Areas Housing Goals

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 established at 31

percent of eligible units financed in each of

calendar years 2001–03. The 2001–03 goal

will remain in effect in subsequent years,

unless changed by the Secretary prior to that

time. The goal represents an increase over the

1996 goal of 21 percent and the 1997–2000

goal of 24 percent. However, it is

commensurate with the market share

estimates of 29–32 percent, presented in

Appendix D.

This section summarizes the Secretary’s

consideration of the six statutory factors that

led to the choice of these goals. It discusses

the Secretary’s rationale for defining these

geographically-targeted 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. Credit Needs in Metropolitan Areas

HUD’s analysis of HMDA data shows that

mortgage credit flows in metropolitan areas

are substantially lower in high-minority and

low-income neighborhoods and mortgage

denial rates are much higher for residents of

such neighborhoods. The economics

literature discusses the underlying causes of

these disparities in access to mortgage credit,

particularly as related to the roles of

discrimination, ‘‘redlining’’ of specific

neighborhoods, and the barriers posed by

underwriting guidelines to potential minority

and low-income borrowers. Studies reviewed

in Section B of this Appendix found that the

racial and income composition of

neighborhoods influence mortgage access

even after accounting for demand and risk

factors that may influence borrowers’

decisions to apply for loans and lenders’

decisions to make those loans. Therefore, the

Secretary concludes that high-minority and

low-income neighborhoods in metropolitan

areas are underserved by the mortgage

system.

2. Identifying Underserved Portions of

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: 48 application

denial rates and mortgage origination rates

per 100 owner-occupied units.49 Tables B.1

and B.2 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.50 Aggregating this data is

useful in order to examine denial and

origination rates for broader groupings of

census tracts:

Minority composition

(percent)

Denial rate

(percent)

Orig.

rate

Tract income

(percent)

Denial rate

(percent)

Orig.

rate

0–30 .................................................................. 11.4 16.4 Less than 90 .................................................... 19.8 10.7

30–50 ................................................................ 17.2 12.5 90–120 ............................................................. 13.0 15.5

50–100 .............................................................. 21.9 9.4 Greater than 120 .............................................. 8.3 19.2

Two points stand out from these data. 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.51 Second, census tracts

with lower incomes have higher denial rates

and lower origination rates than higher

income tracts. Tracts with income less than

or equal to 90 percent of area median income

have nearly 2.5 times the denial rate and

three-fourths the origination rate for tracts

with income over 120 percent of area median

income.

In 1995, HUD’s research determined that

‘‘underserved areas’’ could best be

characterized in metropolitan areas as census

tracts with minority population of at least 30

percent in 1990 and/or census tract median

income no greater than 90 percent of area

median income in 1990, excluding highminority high-income tracts. These cutoffs

produced sharp differentials in denial and

origination rates between underserved areas

and adequately served areas. For example,

the mortgage denial rate in underserved areas

(19.4 percent) was nearly twice that in

adequately served areas (10.3 percent) in

1999.

These minority population and income

thresholds apply in the suburbs as well as in

OMB-defined central cities. HUD’s research

has found that the average denial rate in

underserved suburban areas is almost twice

that in adequately served areas in the

suburbs. (See Figure B.1 in Section B of this

Appendix.) 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. And HUD’s definition,

which covers 57 percent of the central city

population and 33 percent of the suburban

population, is clearly preferable to a

definition which would count 100 percent of

central city residents and zero percent of

suburban residents as living in underserved

areas.

This definition of metropolitan

underserved areas includes 21,586 of the

46,904 census tracts in metropolitan areas,

covering 44 percent of the metropolitan

population. It includes 73 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 rental units comprise 52.4

percent of total units in underserved tracts,

versus 28.6 percent of total units in served

tracts. As shown in Table B.9, this definition

covers most of the population in the nation’s

most distressed central cities: Newark (99

percent), Detroit (96 percent), Hartford (97

percent), and Cleveland (90 percent). The

nation’s five largest cities also contain large

concentrations of their population in

underserved areas: New York (62 percent),

Los Angeles (69 percent), Chicago (77

percent), Houston (67 percent), and

Philadelphia (80 percent).

BILLING CODE 4210–27–P

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65165

65166 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65167

BILLING CODE 4210–27–C

65168 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

3. Identifying Underserved Portions of

Nonmetropolitan Areas

Recognizing the difficulty of defining rural

underserved areas and the need to encourage

GSE activity in such areas, HUD has chosen

a rather broad, county-based definition of

underservedness in rural areas. Specifically,

a nonmetropolitan county is underserved if

in 1990 (1) county median family income

was less than or equal to 95 percent of the

greater of state or national nonmetropolitan

income or (2) county median family income

was less than or equal to 120 percent of the

greater of state or national nonmetropolitan

income and county minority population was

at least 30 percent of total county population.

This definition includes 1,511 of the 2,305

counties in nonmetropolitan areas and covers

54 percent of the nonmetropolitan

population. The definition does target the

most disadvantaged rural counties—it

includes as underserved areas 67 percent of

the nonmetropolitan poor and 75 percent of

nonmetropolitan minorities. The average

poverty rate in underserved counties in 1990

was 21 percent, significantly greater than the

12 percent poverty rate in counties

designated as adequately served. The

definition also includes 84 percent of the

population that resides in remote counties

that are not adjacent to metropolitan areas

and have fewer than 2,500 residents in

towns.

4. Past Performance of the GSEs

The GSEs’ performance on the

geographically-targeted goal has improved

significantly in recent years, as shown in

Figure B.2. Fannie Mae’s performance, as

measure by HUD, increased sharply from

23.6 percent in 1993 to 31.9 percent in 1995,

dropped to 28.1 percent in 1996, rose to 28.8

percent in 1997, and then dropped to 27.0

percent in 1998 and 26.8 percent in 1999.

Freddie Mac’s performance, as measured by

HUD, rose from 21.8 percent in 1993 to 26.4

percent in 1995, followed by 25.0 percent in

1996, 26.3 percent in 1997, 26.1 percent in

1998, and 27.5 percent in 1999. Last year was

the only year in which Freddie Mac’s

performance on this goal has exceeded

Fannie Mae’s performance.

While both GSEs improved their

performance in underserved areas during the

past six years, they lagged the conforming

primary market in providing single-family

home loans to distressed neighborhoods. As

discussed in Section D, the GSEs show

different patterns of lending—through 1998

Freddie Mac was less likely than Fannie Mae

to purchase home loans on properties in lowincome and high-minority neighborhoods.

During the 1996–98 period, Freddie Mac

lagged Fannie Mae, portfolio lenders, and the

overall conforming market in providing

funds to underserved neighborhoods. As

shown in Figure B.3, underserved areas

accounted for 20.0 percent of Freddie Mac’s

1998 purchases of home loans, compared

with 22.9 percent of Fannie Mae’s purchases,

26.1 percent of home loans retained in

depositories’ portfolios, and 24.6 percent of

the overall conforming market. While

Freddie Mac did not make any progress

during the 1993–98 period in reducing the

gap between its performance and that of the

conventional conforming home purchase

market, Fannie Mae improved its funding in

underserved areas and closed the gap

between its performance and the singlefamily primary market in funding lowincome and high-minority neighborhoods.52

However, between 1998 and 1999, Freddie

Mac improved its purchases in underserved

areas so much that its performance surpassed

Fannie Mae’s performance. In 1999,

underserved areas accounted for 21.2 (23.5)

percent of Freddie Mac’s purchases of home

(total) loans, compared with 20.6 (21.8)

percent of Fannie Mae’s purchases of home

(total) loans.

HUD also conducted an analysis of the

share of the overall (single-family and

multifamily) conventional conforming

mortgage market accounted for by the GSEs.

As shown in Tables A.7a and A.7b of

Appendix A, the GSEs’ purchases

represented 40/55 percent of total dwelling

units financed during 1997/1998, but they

represented only 33/46 percent of the

dwelling units financed in underserved

neighborhoods. In other words, the GSEs

accounted for less than half of the singlefamily and multifamily units financed in

underserved areas. This suggests that there is

room for the GSEs to increase their purchases

in underserved neighborhoods.

5. Size of the Mortgage Market for

Geographically-Targeted Areas

As detailed in Appendix D, the market for

mortgages in geographically-targeted areas

accounts for 29 to 32 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 Geographically-Targeted Areas

Housing Goal for 2001–03

There are several reasons that the Secretary

is increasing the Geographically Targeted

Areas Goal. First, the present 24 percent goal

level for 1997–2000 and the GSEs’ recent

performance are below the estimated 29–32

percent of the primary mortgage market

accounted for by units in properties located

in geographically-targeted areas. Raising the

goal reflects the Secretary’s concern that the

GSEs close the remaining gap between their

performance and that of the primary

mortgage market.

Second, the single-family-owner mortgage

market in underserved areas has

demonstrated remarkable strength over the

past few years relative to the preceding

period. This market had only recently begun

to grow in 1993 and 1994, the latest period

for which data was available when the 1996–

99 goals were established in December 1995.

But the historically high underserved areas

share of the primary single-family mortgage

market attained in 1994 has been maintained

over the 1995–99 period. The three-average

of the underserved areas share of the singlefamily-owner mortgage market in

metropolitan areas was 22.2 percent for

1992–94, but 25.1 percent for 1995–98 and

24.1 percent for the 1992–98 period as a

whole.

Third, as discussed in detail in Appendix

A, there are several market segments that

would benefit from a greater secondary

market role by the GSEs; many of these

market segments are concentrated in

underserved areas. For example, one such

area is single-family rental dwellings. These

properties, containing 1–4 rental units, are an

important source of housing for families in

low-income and high-minority

neighborhoods. However, the GSEs’

purchases accounted for only 14/19 percent

of the single-family rental units financed in

underserved areas during 1997/1998. The

Secretary believes that the GSEs can do more

to play a leadership role in providing

financing for such properties. Examples of

other market segments in need of an

enhanced GSE role include small multifamily

properties, rehabilitation loans, seasoned

CRA loans, and manufactured housing.

Additional efforts by the GSEs in these

markets would benefit families living in

underserved areas.

Finally, a wide variety of quantitative and

qualitative indicators indicate that the GSEs’

have the financial strength to improve their

affordable lending performance. For example,

combined net income has risen steadily over

the last decade, from $677 million in 1987 to

$6.1 billion in 1999, an average growth rate

of 20 percent per year. This financial strength

provides the GSEs with the resources to lead

the industry in supporting mortgage lending

for properties located in geographicallytargeted areas.

Summary. Figure A.4 of Appendix A

summarizes many of the points made in this

section regarding opportunities for Fannie

Mae and Freddie Mac to improve their

overall performance on the GeographicallyTargeted Goal. The GSEs’ purchases provided

financing for 6,507,173 dwelling units, which

represented 55 percent of the 11,744,804

single-family and multifamily units that were

financed in the conventional conforming

market during 1998. However, in the

underserved areas part of the market, the

1,679,464 units that were financed by GSE

purchases represented only 46 percent of the

3,629,144 dwelling units that were financed

in the market in 1998. Thus, there appears to

be ample room for the GSEs to increase their

purchases in underserved areas. It is hoped

that expression of concern in the current

rulemaking will foster additional effort by

both GSEs to increase their purchases in

underserved areas.

7. Conclusions

Having considered the projected mortgage

market serving geographically-targeted areas,

economic, housing and demographic

conditions for 2001–03, and the GSEs’ recent

performance in purchasing mortgages on

properties in geographically-targeted areas,

the Secretary has determined that the annual

goal of 31 percent in calendar year 2001 and

the years following is feasible. Moreover, the

Secretary has considered the GSEs’ ability to

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65169

lead the industry as well as the GSEs’

financial condition. The Secretary has

determined that these goal levels are

necessary and appropriate.

Endnotes to Appendix B

1Tracts are excluded from the analysis if

median income is suppressed or there are no

owner-occupied 1–4 unit properties. There

are 2,033 such tracts. When reporting denial,

origination, and application rates, tracts are

excluded from the analysis if there are no

purchase or refinance applications. Tracts are

also excluded from the analysis 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 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. 2For the sake of brevity, in the remainder

of this appendix, the term ‘‘central city’’ is

used to mean ‘‘OMB-designated central city.’’ 3Alicia H. Munnell, Lynn Browne, James

McEneaney, and Geoffrey Tootell. 1996.

‘‘Mortgage Lending in Boston: Interpreting

HMDA Data,’’ American Economic Review,

86(1) March:25–54. 4 Mortgage Lending Discrimination: A

Review of Existing Evidence edited by

Margery A. Turner and Felicity Skidmore,

The Urban Institute: Washington, D.C., June

1999. 5Margery A. Turner, Raymond J. Struyk,

and John Yinger. Housing Discrimination

Study: Synthesis, Washington, D.C., U.S.

Department of Housing and Urban

Development: 1991. 6Margery A. Turner, ‘‘Discrimination in

Urban Housing Markets: Lessons from Fair

Housing Audits,’’ Housing Policy Debate,

Vol. 3, Issue 2, 1992, pp. 185–215. 7The denial rates in Table B.1 are for home

purchase mortgages. Denial rates are several

percentage points lower for refinance loans

than for purchase loans, but denial rates

follow the same pattern for both types of

loans: rising with minority concentration and

falling with increasing income. 8Alicia H. Munnell, Lynn E. Browne,

James McEneaney, and Geoffrey M. B.

Tootell, ‘‘Mortgage Lending in Boston:

Interpreting HMDA Data,’’ American

Economic Review, March 1996. 9A HUD study also found mortgage denial

rates for minorities to be higher in ten

metropolitan areas, even after controlling for

credit risk. In addition, the higher denial

rates observed in minority neighborhoods

were not purely a reflection of the higher

denial rates experienced by minorities.

Whites experienced higher denial rates in

some minority neighborhoods than in some

predominantly white neighborhoods. Ann B.

Schnare and Stuart A. Gabriel, ‘‘The Role of

FHA in the Provision of Credit to

Minorities,’’ ICF Incorporated, prepared for

the U.S. Department of Housing and Urban

Development, April 25, 1994. 10William C. Hunter, ‘‘The Cultural

Affinity Hypothesis and Mortgage Lending

Decisions,’’ WP–95–8, Federal Reserve Bank

of Chicago, 1995. 11Since 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. 12Traditional 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 frontend and back-end ratios applied to

applicants’ housing expenditures and other

on-going 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. 13These 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. 14For 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. 15Like early HMDA studies, an analysis of

deed transfer data in Boston found lower

rates of mortgage activity in minority

neighborhoods. The discrepancies held even

after controlling for income, house values

and other economic and non-racial factors

that might explain 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. 16Using 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 Science 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 Affairs, Volume 11, No. 3, 1989, pp.

201–223. 17 Individual loan characteristics include

loan size (economies of scale cause lenders

to prefer large loans to small loans) and all

individual borrower variables included in the

HMDA data (the applicant’s income, sex, and

race). 18Their neighborhood risk proxies include

median income and house value (inverse

indicators of risk), percent of households

receiving welfare, median age of houses,

homeownership rate (an inverse indicator),

vacancy rate, and the rent-to-value ratio (an

inverse indicator). A high rent-to-value ratio

suggests lower expectations of capital gains

on properties in the neighborhood. 19Schill 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. 20Fred 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. 21Samuel 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. 22For 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, 88(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. 23See 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. 24Tootell 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. 25Stephen L. Ross and Geoffrey M. B.

Tootell, ‘‘Redlining, the Community

Reinvestment Act, and Private Mortgage

Insurance’’, unpublished manuscript, March,

1999. 26Lang, William W. and Leonard I.

Nakamura, ‘‘A Model of Redlining,’’ Journal

65170 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

of Urban Economics, Volume 33, 1993, pp.

223–234. 27Calem, Paul S. ‘‘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. 28Ling, David C. and Susan M. Wachter,

‘‘Information Externalities and Home

Mortgage Underwriting,’’ Journal of Urban

Economics, Volume 44, 1998, pp. 317–332. 29Robert 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. 30The Preamble to the 1995 Rule provides

additional reasons why central city location

should not be used as a proxy for

underserved areas. 31Federal Register, October 20, 1999,

‘‘Office of Management and Budget:

Recommendations from the Metropolitan

Area Standards Review Committee to the

Office of Management and Budget

Concerning Changes to the Standards for

Defining Metropolitan Areas.’’ 32William 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. The

tract’s minority composition and central city

location were included to test if these

characteristics were associated with

underserved neighborhoods after controlling

for the demand and supply variables.

Examples of the demand and supply

variables at the census tract level include:

tract income relative to the area median

income, the increase in house values between

1980 and 1990, the percentage of units

boarded up, and the age distributions of

households and housing units. See also

Susan Wharton Gates, ‘‘Defining the

Underserved,’’ Secondary Mortgage Markets,

1994 Mortgage Market Review Issue, 1995,

pp. 34–48. 33For example, census tracts at 80 percent

of area median income were estimated to

have 8.6 originations per 100 owners as

compared with 10.8 originations for tracts

over 120 percent of area median income. 34Shear et al., p. 18. 35See Avery, et al. 36Avery et al. find very large unadjusted

differences in denial rates between white and

minority neighborhoods, and although the

gap is greatly reduced by controlling for

applicant characteristics (such as race and

income) and other census tract characteristics

(such as house price and income level), a

significant difference between white and

minority tracts remains (for purchase loans,

the denial rate difference falls from an

unadjusted level of 16.7 percent to 4.4

percent after controlling for applicant and

other census tract characteristics, and for

refinance loans, the denial rate difference

falls from 21.3 percent to 6.4 percent).

However, when between-MSA differences are

removed, the gap drops to 1.5 percent and 1.6

percent for purchase and refinance loans,

respectively. See Avery, et al., p. 16. 37Avery, et al., page 19, note that, other

things equal, a black applicant for a home

purchase loan is 3.7 percent more likely to

have his/her application denied in an allminority tract than in an all-white tract,

while a white applicant from an all-minority

tract would be 11.5 percent more likely to be

denied. 38Methodological 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. 39Mikesell, Jim. Can Federal Policy

Changes Improve the Performance of Rural

Mortgage Markets, Economic Research

Service, U.S. Department of Agriculture,

Issues in Agricultural and Rural Finance.

Agriculture Information Bulletin No. 724–12,

August 1998. 40Standard mortgage types are 30-year

fixed-rate mortgages, 15-year FRMs, and 30-

year adjustable rate mortgages (ARMs). These

are the ones most often traded in the

secondary markets. Nonstandard mortgages

generally have shorter terms than the

standard mortgages. 41MacDonald, Heather. Fannie Mae and

Freddie Mac in Rural Housing Markets: Does

Space Matter? Study funded as part of the

1997 GSE Small Grants by HUD’s Office of

Policy Development and Research. 42MacDonald constructs a county-level

mortgage market data in rural areas using

information collected by the Department of

Revenue for counties and states. Annual

Sales Ratio Studies conducted by many

states’ Department of Revenue provide the

number of sales for different property types.

This is done by using residential sales

recorded for property tax purposes. Other

county-level variables used to compare rural

counties are obtained from the 1990 Census

of Population and Housing and Bureaus of

labor Statistics. Data obtained from Census

included county populations, racial

composition, a variety of housing stock

characteristics like home ownership rates,

vacancy rates, proportion of owner-occupied

mobile homes, median housing value in

1990, median age of the housing stock,

proportion of units with complete plumbing,

and access to infrastructure, e.g., public roads

and sewage systems. Data collected from the

Bureau of Labor Statistics included

unemployment rates and residential building

permits. 43The Future of Manufactured Housing,

Harvard University Joint Center for Housing

Studies, February 1997. 44Though future demand for manufactured

housing is promising, the Joint Center notes

some continued obstacles to growth.

Challenges for the industry to overcome

include a lack of standardization of

installation procedures and product

guarantees, exclusionary zoning laws, and

certain provisions of the national building

code.

45The official figures on goal performance

shown above for Fannie Mae are identical

with the corresponding figures present by

Fannie Mae in its Annual Housing Activity

Report to HUD except for 1997 (HUDreported: 28.8 percent/Fannie Mae-reported:

30.0 percent) and 1999 (26.8 percent/26.7

percent), reflecting minor differences in the

application of counting rules. 46The official figures on goal performance

shown above for Freddie Mac are identical

with the corresponding figures presented by

Freddie Mac in its Annual Housing Activity

Reports to HUD except for 1999 (HUDreported: 27.5 percent/Freddie Mac-reported:

27.6 percent), reflecting minor differences in

the application of counting rules. 47Underserved 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 (38

percent) than in metropolitan areas (22

percent). 48HMDA provides little useful information

on rural areas. Therefore, the HMDA data

reported here apply only to metropolitan

areas. 49Analysis of application rates are not

reported here. Although application rates are

sometimes used as a measure of mortgage

demand, they provide no additional

information beyond that provided by looking

at both denial and origination rates. The

patterns observed for application rates are

still very similar to those observed for

origination rates. 50As shown in Table B.1, no sharp breaks

occur in the denial and origination rates

across the minority and income deciles—

mostly, the increments are somewhat similar

as one moves across the various deciles that

account for the major portions of mortgage

activity. 51The 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. 52Although this goal is targeted to lowerincome and high-minority areas, it does not

mean that GSE purchase activity in

underserved areas derives totally from lower

income or minority families. In 1999, abovemedian income households accounted for 50

percent of the mortgages that the GSEs

purchased in underserved areas. This

suggests that these areas are quite diverse.

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

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65171

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

The final rule provides that the Special

Affordable Housing Goal is 20 percent in

2001–2003. Of the total Special Affordable

Housing Goal for each year, each GSE must

purchase multifamily mortgages in an

amount at least equal to one percent of the

GSE’s combined (single-family and

multifamily) annual average mortgage

purchases over 1997–1999.

Approximately 23–26 percent of the

conventional conforming mortgage market in

2001–03 would qualify under the Special

Affordable Housing Goal as defined in the

final rule, as projected by HUD.

Units that count toward the goal: Subject

to further provisions discussed in the

Preamble to this final rule regarding seasoned

loans, units that count toward the Special

Affordable Housing Goal include units

occupied by low-income 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.

B. Summary and Response to Comments

1. Multifamily Subgoal Level

HUD’s proposed rule would have set the

multifamily subgoal at 0.9 percent of the

dollar volume of combined (single-family

and multifamily) 1998 mortgage purchases in

calendar year 2000, and 1.0 percent in each

of calendar years 2001–2003. This would

have implied the following thresholds for the

two GSEs:

2000

(in billions)

2001–2003

(in billions)

Fannie Mae $3.31 $3.68

Freddie

Mac ....... 2.46 2.73

Both GSEs opposed establishing the special

affordable multifamily subgoal as a

percentage of their 1998 transaction volume,

stating that 1998 was in some respects an

unusual year in the mortgage markets.

Instead, they both recommended that the

special affordable multifamily subgoal be

established as a percentage of a five-year

average of each GSEs’ transactions volume.

Freddie Mac commented further that HUD’s

proposed subgoal was ‘‘unreasonably high.’’

Many other commenters supported the

multifamily subgoal, although they

questioned whether 1998 was the appropriate

base year upon which to establish the

subgoal. Some commenters argued that the

proposed subgoal was too high, in light of an

expected decline in multifamily origination

volume. Others argued that the subgoal was

too low, based on the needs of very low- and

low-income families and families in rural

areas. Comments were received from some

who felt the subgoal should be percentagebased and move from year to year. Still other

commenters felt that the multifamily subgoal

should be eliminated, as it no longer

appeared to serve a purpose, particularly

since Freddie Mac had re-entered the

multifamily market.

From its inception, the multifamily subgoal

has been viewed as a means for expanding

and maintaining Freddie Mac’s presence in

the multifamily mortgage market. Both the

multifamily mortgage market and Freddie

Mac’s multifamily transactions volume have

grown significantly during the 1990s,

indicating both increased opportunity and

capacity to grow by Freddie Mac. While

Freddie Mac continues to lag behind Fannie

Mae somewhat in its multifamily volume, it

appears to be within reach of catching up

with its larger competitor with regard to the

multifamily proportion of total purchases. In

1999, Fannie Mae’s multifamily mortgage

purchases were 9.5 percent of its total

mortgage purchases and Freddie Mac’s

multifamily mortgage purchases were 8.3

percent of its total mortgage purchases.

Freddie Mac’s multifamily special

affordable transactions volume was $2.7

billion in 1998 and $2.3 billion in 1999,

showing that Freddie Mac does have the

capacity to generate significant multifamily

special affordable transactions volume in a

favorable market environment. At the same

time, however, the Department is mindful of

the fact that multifamily market conditions

experienced during 1998–1999 may not be

representative of future years. Because of

extensive multifamily refinancing during

1998–1999, in particular, in conjunction with

the widespread use of ‘‘lockout’’ provisions

which place significant limitations on

borrower’s right to refinance recently

originated loans, HUD expects conventional

multifamily origination volume in 2001–2003

to be somewhat lower than the levels reached

during 1998–1999. Based on partial-year

information collected by the Department on

GSE and CMBS multifamily transactions

volume during 2000, it appears that

origination volume will be somewhat lower

this year than in 1999. Taking into

consideration new information and data not

available at the time HUD published its

proposed GSE rule in March of 2000, the

Department has determined that a modest

reduction in multifamily special affordable

goal thresholds relative to those in the

proposed rule is reasonable and appropriate.

There is merit to the view that 1998 was

an unusual year in the mortgage markets.

HUD’s motivation in setting the subgoal

based on 1998 transactions volume was to

establish the subgoal in a fair and reasonable

manner, given the difference between the two

GSEs in size and capacity. HUD selected a

subgoal of one percent of 1998 transactions

volume in recognition of the increased

capacity of the GSEs to conduct multifamily

special affordable lending, as well as the

need to challenge the GSEs to maintain and

expand their commitment to this segment of

the market in a manner feasible and

consistent with safety and soundness. Now

that more recent data are available, it is

apparent that establishing the subgoal in a

manner taking 1999 mortgage volume into

consideration, along with that of 1997 and

1998, more accurately corresponds to the

relative size and respective capabilities of the

GSEs over the 2001–2003 goals period than

would a subgoal established on the basis of

1998 volume alone. Accordingly, the final

rule establishes the special affordable

multifamily subgoal at the respective average

of one percent of each GSEs’ combined

(single-family and multifamily) mortgage

purchases over 1997–1999, resulting in

subgoals somewhat lower than those in the

proposed rule, but with the advantages of (i)

being based on more recent and complete

information regarding the differential size

and resource capabilities of each GSE, and

(ii) taking into consideration new

information regarding multifamily

conventional origination volume. This

implies the following thresholds for the two

GSEs: 1

2001–2003

(in billions)

Fannie Mae ........................... $2.85

Freddie Mac .......................... $2.11

2. Multifamily Subgoal Alternatives

In the proposed rule, HUD identified three

alternative approaches for specifying

multifamily subgoals for the GSEs based on

a (i) minimum number of units; (ii) minimum

percentage of multifamily acquisition

volume; and (iii) minimum number of

mortgages acquired. While some of these

proposals did receive support from

commenters, HUD does not see any

compelling reason to alter the dollar-based

structure of the multifamily subgoal as

established in the 1995 rule, which can be

updated and adapted to the current market

environment by basing it upon recent

acquisition volume. It is noteworthy that the

Special Affordable Housing Goal, as a

percentage-of-business goal based on number

of units financed, combines elements of

options (i) and (iii). HUD’s decision to award

bonus points toward the housing goals for

GSE transactions involving small multifamily

properties with 5–50 units will achieve some

of the intended policy objectives associated

with option (iii).

3. Temporary Adjustment Factor

In the proposed rule, HUD noted that

Freddie Mac’s presence in the multifamily

market has lagged far behind that in singleVerDate 11<MAY>2000 18:02 Oct 30, 2000 Jkt 194001 PO 00000 Frm 00129 Fmt 4701 Sfmt 4700 E:\FR\FM\31OCR2.SGM pfrm08 PsN: 31OCR2
65172 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

family, in part because Freddie Mac ceased

purchasing multifamily mortgages for a

period of time in the early 1990s. Freddie

Mac’s direct holdings of multifamily

mortgages and guarantees outstanding as of

the end of 1999, $16.8 billion, are much

smaller than that Fannie Mae’s $47.4 billion,

not only in absolute terms, but also a

percentage of all mortgage holdings and

guarantees. Freddie Mac’s multifamily

holdings and guarantees are 2.1 percent of its

total, compared with 4.3 percent for Fannie

Mae.2 Freddie Mac’s smaller multifamily

portfolio relative to that of Fannie Mae has

meant fewer refinance opportunities from

within its portfolio, reducing anticipated

multifamily transactions volume.

Because of the importance of multifamily

mortgages to GSE performance on the Special

Affordable Housing Goal, Fannie Mae’s larger

multifamily portfolio confers a significant

advantage with regard to goals performance.

For example, in 1999, 56.0 percent of units

backing Fannie Mae’s multifamily

transactions met the special affordable goal,

representing 31.3 percent of units meeting

the special affordable goal, when multifamily

units represented only 9.5 percent of total

purchase volume. In contrast, only 13.4

percent of Fannie Mae’s single-family owneroccupied units met the special affordable

goal.3

In recognition of the implications for

housing goals performance of differences in

the relative size of multifamily portfolios

between the two GSEs, the Conference Report

on HUD’s appropriations for 2000 provides

the following guidance: ‘‘* * * the stretch

affordable housing efforts required of each of

Freddie Mac and Fannie Mae should be

equal, so that both enterprises are similarly

challenged in attaining the goals. This will

require the Secretary to recognize the present

composition of each enterprise’s overall

portfolio in order to ensure regulatory parity

in the application of regulatory guidelines

measuring goal compliance.’’ 4

In order to overcome any lingering effects

of Freddie Mac’s decision to leave the

multifamily market in the early 1990s, and to

provide an incentive to continue the rapid

expansion of its multifamily presence since

then, the Department proposed a ‘‘Temporary

Adjustment Factor’’ for Freddie Mac’s

multifamily mortgage purchases for purposes

of calculating performance on the Low- and

Moderate-Income Housing Goal and the

Special Affordable Housing Goal. In

determining Freddie Mac’s performance for

each of these two goals, each unit in a

property with more than 50 units meeting

one or both of these two housing goals would

be counted as 1.2 units in calculating the

numerator of the respective housing goal

percentage. The Temporary Adjustment

Factor will be limited to properties with

more than 50 units because of separate

provisions regarding multifamily properties

with 5–50 units.

In its comments, Freddie Mac supported

the idea of a temporary adjustment factor;

however, Freddie Mac recommended that it

be set at 1.35 instead of the 1.2 level

proposed by HUD. According to Freddie

Mac, the difference in size and age between

Freddie Mac’s and Fannie Mae’s multifamily

portfolios makes goal achievement easier for

Fannie Mae. Freddie Mac also recommended

that the temporary adjustment factor apply to

all three goals and opposed any phasing out

of the factor over the three-year goals period.

In the period since HUD’s interim housing

goals took effect in January 1993, Freddie

Mac’s multifamily transactions volume has

expanded rapidly, as noted above. Freddie

Mac’s 1999 multifamily transactions volume

was $7.6 billion, compared with only $191

million in 1993. HUD’s analysis indicates

that a Temporary Adjustment Factor of 1.2 is

sufficient to provide ‘‘regulatory parity’’

consistent with the direction provided by the

Conference Report addressing this issue. The

Department has, therefore, decided to

implement the temporary adjustment factor

as proposed in the proposed rule. The

Adjustment Factor of 1.2 will be applied to

the Low- and Moderate-Income and Special

Affordable Goals. The Temporary

Adjustment Factor would terminate

December 31, 2003. The Temporary

Adjustment Factor will not apply to Fannie

Mae.

4. Seasoned Mortgage Loan Purchases

‘‘Recycling’’ Requirement

Comments submitted in response to HUD’s

proposed rule regarding ‘‘recycling

requirements’’ pertaining to seasoned loans

are discussed in the Preamble, as are the

Department’s determinations regarding this

matter.

C. 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 1997,

the Census Bureau’s 1991 Residential

Finance Survey, the 1990 Census of

Population and Housing, Home Mortgage

Disclosure Act (HMDA) data for 1992

through 1998, and annual loan-level data

from the GSEs on their mortgage purchases

through 1999. 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 goal.

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.

a. GSE Performance Relative to the 1996–99

Goals

This section discusses each GSE’s

performance under the Special Affordable

Housing Goal over the 1993–99 period. The

data presented here are ‘‘official results’’—

i.e., they are based on HUD’s in-depth

analysis of the loan-level data submitted

annually to the Department 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 to the

Department by the GSEs in their Annual

Housing Activities Reports.

HUD’s goals specified that in 1996 at least

12 percent of the number of units eligible to

count toward the Special Affordable goal

should qualify as Special Affordable, and at

least 14 percent annually beginning in 1997.

The actual performance in 1996 through

1999, based on HUD’s analysis of loan-level

data submitted by the GSEs, is shown in

Table C.1 and Figure C.1. Fannie Mae

surpassed the goal by 3.4 percentage points

and 3.0 percentage points, respectively, in

1996 and 1997, while Freddie Mac surpassed

the goal by 2.0 and 1.2 percentage points. In

1998, Fannie Mae exceeded the goal by 0.3

percentage point, while Freddie Mac

exceeded the goal by 1.9 percentage points.

Both GSEs stepped up their performance

and attained their highest performance to

date in 1999, with Fannie Mae surpassing the

14 percent goal by 3.6 percentage points and

Freddie Mac surpassing the goal by 3.2

percentage points (Table C.1). After lagging

Freddie Mac on special affordable

performance in 1998, Fannie Mae surpassed

Freddie Mac last year.5 A major reason for

Fannie Mae’s record special affordable goal

performance in 1999 was the 15 percent

increase in the dollar volume of its special

affordable multifamily purchases; Freddie

Mac, on the other hand, experienced a 16

percent decline in such purchases between

1998 and 1999.6

BILLING CODE 4210–27–P

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65173

65174 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65175

65176 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65177

BILLING CODE 4210–27–C

65178 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

Table C.1 also includes, for comparison

purposes, comparable figures for 1993

through 1995, calculated according to the

counting conventions of the 1995 rule that

became applicable in 1996. Each GSE’s

performance in 1996 through 1999 exceeded

its performance in each of the three

preceding years.

The Fannie Mae figures presented above

are smaller than the corresponding figures

presented by Fannie Mae in its Annual

Housing Activity Reports to HUD by

approximately 2 percentage points in both

1996 and 1997, 1.3 percentage points in

1998, and 1.1 percentage points in 1999. The

difference largely reflects HUD-Fannie Mae

differences in application of counting rules

relating to counting of seasoned loans for

purposes of this goal. In particular, HUD’s

tabulations reflect inclusion of seasoned loan

purchases in the denominator in calculating

performance under the Special Affordable

goal, as discussed in Preamble section

II(B)(6)(c) on the Seasoned Mortgage Loan

Purchases ‘‘Recycling’’ Requirement. Freddie

Mac’s Annual Housing Activity Report

figures for this goal differ from the figures

presented above by 0.1 percentage point,

reflecting minor differences in application of

counting rules.

Since 1996 each GSE has been subject to

an annual subgoal for multifamily Special

Affordable mortgage purchases, as discussed

above, established as 0.8 percent of the dollar

volume of single-family and multifamily

mortgages purchased by the respective GSE

in 1994. Fannie Mae’s subgoal was $1.29

billion and Freddie Mac’s subgoal was $988

million for each year. Fannie Mae surpassed

the subgoal by $1.08 billion, $1.90 billion,

$2.24 billion, and $2.77 billion in 1996, 1997,

1998, and 1999, respectively, while Freddie

Mac exceeded the subgoal by $18 million,

$220 million, $1.70 billion, and $1.27 billion.

Table C.1 includes figures on subgoal

performance, and they are depicted

graphically in Figure C.2.

b. Characteristics of 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).

Increased reliance on multifamily housing

to meet goal. Tables C.2 and C.3 show that

both GSEs have increasingly relied on

multifamily housing units to meet the special

affordable goal since 1993. Fannie Mae’s

multifamily purchases represented 31.3

percent of all purchases qualifying for the

goal in 1999, compared with 28.1 percent in

1993. Freddie Mac’s multifamily purchases

represented 21.6 percent of all purchases

qualifying for the goal in 1999, compared to

5.5 percent in 1993. The trends for both GSEs

were steadily upward throughout the 1993–

97 period, with some decrease in multifamily

share of the special affordable purchases

since 1997.

The other two housing categories—singlefamily owner and single-family rental—both

exhibited downward trends for both GSEs. In

1999 Fannie Mae’s single-family owner units

qualifying for the goal represented 54.8

percent of all qualifying units, and Fannie

Mae’s single-family rental units were 13.9

percent of all qualifying units. In 1999

Freddie Mac’s single-family owner units

qualifying for the goal represented 62.0

percent of all qualifying units, and Freddie

Mac’s single-family rental units were 16.3

percent of all qualifying units.

Reliance on household income relative to

area income characteristics to meet goal.

Tables C.2 and C.3 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 85.2

percent of Fannie Mae’s units qualifying

under the goal in 1999, compared to 80.2

percent in 1993. For Freddie Mac, very-lowincome families accounted for 84.9 percent of

units qualifying under the goal in 1999 and

80.3 percent in 1993. In contrast, mortgage

purchases from low-income areas (shown in

the first and third columns in the tables)

accounted for 32.0 percent of Fannie Mae’s

units qualifying under the goal in 1999,

compared to 36.8 percent in 1993. The

corresponding percentages for Freddie Mac

were 33.7 percent in 1999 and 36.3 percent

in 1993. 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.

c. GSEs’ Performance Relative to Market

Section E in Appendix A used HMDA data

and GSE loan-level data for home purchase

mortgages on single-family owner-occupied

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 were three main

findings. First, both GSEs lag depositories

and the overall market in providing mortgage

funds for very low-income and other special

affordable borrowers. Second, the

performance of Freddie Mac through 1998

was particularly weak compared to Fannie

Mae, the depositories, and the overall market.

For example, between 1996 and 1998, special

affordable borrowers accounted for 9.8

percent of the home loans purchased by

Freddie Mac, 11.9 percent of Fannie Mae’s

purchases, 16.7 percent of home loans

originated and retained by depositories, and

15.3 percent of all home loans originated in

the conventional conforming market (see

Table A.3 in Appendix A). While Freddie

Mac improved its performance, it had not

closed the gap between its performance and

that of the overall market. In 1992, special

affordable loans accounted for 6.5 percent of

Freddie Mac’s purchases and 10.4 percent of

market originations, for a ‘‘Freddie-Mac-tomarket’’ ratio of 0.63. By 1998, that ratio had

increased only to 0.73 (11.3 percent versus

15.5 percent). Third, in 1999, Freddie Mac

matched Fannie Mae in purchasing special

affordable home loans. Special affordable

loans accounted for 12.5 percent of Freddie

Mac’s 1999 home purchase mortgages, and

for 12.3 percent of Fannie Mae’s purchases.

With respect to the GSEs’ total (combined

home purchase and refinance) loans, Freddie

Mac’s performance in 1999 surpassed Fannie

Mae’s performance. The special affordable

category accounted for 13.3 percent of

Freddie Mac’s 1999 purchases, compared

with 12.3 percent of Fannie Mae’s purchases.

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 have accounted for 25

percent of all special affordable owner and

rental units that were financed in the

conventional conforming market during

1997. The GSEs’ 25-percent share of the

special affordable market was three-fifths of

their 40-percent share of the overall market.

Even in the owner market, where the GSEs

account for 50 percent of the market, their

share of the special affordable market was

only 36 percent. Similar patterns prevailed in

1998. This analysis suggests that the GSEs are

not leading the single-family market in

purchasing loans that qualify for the Special

Affordable Goal. There is room for the GSEs

to improve their performance in purchasing

affordable loans at the lower-income end of

the market.

3. National Housing Needs of Low-Income

Families in Low-Income Areas and VeryLow-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 rule. Table C.4 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 1995 were much more

frequent for the lowest-income groups.7

Incidence of problems is shown for

households in the income range covered by

the special affordable goal, as well as for

higher income households.

BILLING CODE 4210–27–P

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65179

BILLING CODE 4210–27–C

65180 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

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 (31.5 percent

of renter households and 23.8 percent of

owner households). In contrast, 3.5 percent

of renter households and 7.1 percent of

owner households with incomes above 60

percent of area median income, up to 80

percent of area median income, had priority

problems. For more than two-thirds of the

very low-income renter families with worst

case problems, the only problem was

affordability—they did not have problems

with housing adequacy or crowding.

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.5 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.2 generally, and in

Section D.3 with respect to multifamily

housing.

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 low- and

moderate-income loans and (b) the financial

safety and soundness implications of the

housing goals. Based on this economic

analysis and discussions with the Office of

Federal Housing Enterprise Oversight, HUD

concludes that the housing goals in this final

rule raise minimal, if any, safety and

soundness concerns.

D. Determination of the 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.

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.

2. GSE Performance and the Market

a. GSEs’ Single-Family Performance

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. The

bulk of the GSEs’ low- and moderate-income

mortgage purchases are for the higher-income

portion of this category. The lowest-income

borrowers account for approximately onefourth of each GSE’s below-median income

purchases of owner-occupied mortgages.

b. Single-Family Market Comparisons in

Metropolitan Areas

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 both GSEs lag

depositories and the overall market in

providing mortgage funds for very lowincome and other special affordable

borrowers. Figure C.3 illustrates these

findings. In 1998, special affordable

borrowers accounted for 11.3 percent of the

home loans purchased by Freddie Mac, 13.2

percent of Fannie Mae’s purchases, 17.7

percent of home loans originated and

retained by depositories, and 15.5 percent of

all home loans originated in the conventional

conforming market. Section C also noted that

Freddie Mac improved its performance, but

it had not made much progress in closing the

gap between its performance and that of the

overall market. In 1999, however, Freddie

Mac’s funding of special affordable loans

improved to the point that it matched Fannie

Mae’s performance with respect to purchases

of home loans (12.5 percent and 12.3 percent,

respectively) and it surpassed Fannie Mae’s

performance with respect to purchases of

total combined home purchases and

refinance loans (13.3 percent and 12.3

percent, respectively).

BILLING CODE 4210–27–P

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65181

BILLING CODE 4210–27–C

c. Overall Market Comparisons

Section C compared the GSEs’ role in the

overall market with their role in the special

affordable market. The GSEs’ purchases have

provided financing for 2,948,112 dwelling

units, which represented 40 percent of the

7,306,950 single-family and multifamily

units that were financed in the conventional

conforming market during 1997. However, in

the special affordable part of the market, the

519,371 units that were financed by GSE

65182 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

purchases represented only 25 percent of the

2,105,508 dwelling units that were financed

in the market. A similar pattern prevailed in

1998. Thus, there appears to ample room for

the GSEs to improve their performance in the

special affordable market.

3. Reasons for Increasing the Special

Affordable Housing Goal

The reasons the Secretary is increasing the

Special Affordable Goal are essentially the

same as those given in Section H.4 of

Appendix A for the Low- and ModerateIncome Goal. Although that discussion will

not be repeated here, the main considerations

are the following: Freddie Mac’s re-entry into

the multifamily market; the underlying

strength of the primary mortgage market for

lower-income families; the need for the GSEs

to improve their purchases of mortgages for

lower-income families and their

communities; the existence of several lowincome market segments that would benefit

from more active efforts by the GSEs; and the

substantial profits and financial capacity of

Fannie Mae and Freddie Mac. The

Department’s analysis shows that the GSEs

are not leading the market in purchasing

loans that qualify for the Special Affordable

Goal. There are also plenty of opportunities

for the GSEs to improve their performance in

purchasing special affordable loans. The

GSEs’ accounted for only 25 percent of the

special affordable market in 1997—a figure

substantially below their 40-percent share of

the overall market. Similarly, the GSEs

accounted for only 33 percent of the special

affordable market in 1998, compared with

their 55-percent share of the overall market

during that heavy refinance year.

4. Multifamily Purchases—Further Analysis

As noted previously, the multifamily sector

is especially important in the establishment

of the special affordable housing goals for

Fannie Mae and Freddie Mac because of the

relatively high percentage of multifamily

units meeting the special affordable goal as

compared with single-family. For example, in

1999, 56.0 percent of units backing Fannie

Mae’s multifamily transactions met the

special affordable goal, representing 31.3

percent of units meeting the special

affordable goal, when multifamily units

represented only 9.5 percent of total

purchase volume.8

Significant new developments in the

multifamily mortgage market have occurred

since the publication of the December 1995

rule, most notably the increased rate of debt

securitization via Commercial Mortgage

Backed Securities (CMBS) and a higher level

of equity securitization by Real Estate

Investment Trusts (REITs). Fannie Mae has

played a role in establishing underwriting

standards that have been widely emulated in

the growth of the CMBS market. Freddie Mac

has contributed to the growth and stability of

the CMBS sector by acting as an investor.

Increased securitization of debt and equity

interests in multifamily property present the

GSEs with new challenges as well as new

opportunities. The GSEs are currently

experiencing a higher degree of secondary

market competition than they did in 1995. At

the same time, recent volatility in the CMBS

market underlines the need for an ongoing

GSE presence in the multifamily secondary

market. The potential for an increased GSE

presence is enhanced by virtue of the fact

that an increasing proportion of multifamily

mortgages are originated to secondary market

standards.

Despite the expanded presence of the GSEs

in the multifamily mortgage market and the

rapid growth in multifamily securitization by

means of CMBS, increased secondary market

liquidity does not appear to have benefited

all segments of the market equally. Small

properties with 5–50 units appear to have

been adversely affected by excessive

borrowing costs as described in Appendix A.

Another market segment that appears

experiencing difficulty in obtaining mortgage

credit consists of multifamily properties with

significant rehabilitation needs. Properties

that are more than 10 years old are typically

classified as ‘‘C’’ or ‘‘D’’ properties, and are

considered less attractive than newer

properties by many lenders and investors.

Context. As discussed above, in the 1995

Final Rule, the multifamily subgoal for the

1996–1999 period was set at 0.8 percent of

the dollar value of each GSEs’ respective

1994 origination volume, or $998 million for

Freddie Mac and $1.29 billion for Fannie

Mae. Freddie Mac exceeded the goal by a

narrow margin in 1996 and more comfortably

in 1997–1999. Fannie Mae has exceeded the

goal by a wide margin in all four years.

The experience of the 1996–1999 period

suggests the following preliminary findings

regarding the multifamily special affordable

subgoal:

• The goal has contributed toward a

significantly increased presence by Freddie

Mac in the multifamily market.

• The current goal is out of date, as it is

based on market conditions in 1993–94. The

goal has remained at a fixed level, despite

significant growth in the multifamily market

and in the GSEs’ administrative capabilities

with regard to multifamily.

As mentioned previously, HUD’s final rule

establishes the multifamily subgoal at the

respective average of one percent of each

GSEs’ combined mortgage purchases over

1997–1999. This implies the following

thresholds for the two GSEs:

2001–2003

(in billions)

Fannie Mae ....................... $2.85

Freddie Mac ...................... 2.11

A multifamily subgoal for 2001–2003 set at

one percent of each GSEs’ combined

mortgage purchases over 1997–1999 will

sustain and likely increase the efforts of the

GSEs in the multifamily mortgage market,

with particular emphasis upon the special

affordable segment.

5. Conclusion

HUD has determined that the Special

Affordable Housing Goal in this final 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 20

percent in 2001–2003 is both necessary and

achievable. HUD has also determined that a

multifamily special affordable subgoal for

2001–2003 set at one percent of the average

of each GSE’s respective dollar volume of

combined (single-family and multifamily)

1997–1999 mortgage purchases in is both

necessary and achievable.

Endnotes to Appendix C

1HUD has determined that the total dollar

volume of the GSEs’ combined (single and

multifamily) mortgage purchases by Fannie

Mae was $165.3 billion in 1997, $367.6

billion 1998, and $323.0 in 1999. Freddie

Mac’s corresponding acquisition volume was

$117.7 billion in 1997, $273.2 billion in

1998, and $240.7 billion in 1999. 2 Federal Reserve Bulletin, June 2000, A 35. 3Source: HUD analysis of GSE loan-level

data. 4U.S. House of Representatives,

Congressional Record. (October 13, 1999), p.

H10014. 5 It should be noted that in all years,

Fannie Mae’s performance on the special

affordable goal under HUD scoring lags

performance as reported by Fannie Mae,

because of differences pertaining to the

‘‘recycling’’ of proceeds from the sales of

portfolios of special affordable loans. 6Total dollar volume of multifamily

purchases moved in the opposite direction

from special affordable multifamily volume

last year—total volume fell by 25 percent for

Fannie Mae (from $12.50 billion in 1998 to

$9.39 billion in 1999), but rose by 16 percent

for Freddie Mac (from $6.58 billion in 1998

to $7.62 billion in 1999); special affordable

multifamily volume rose by 15 percent for

Fannie Mae (from $3.53 billion in 1998 to

$4.06 billion in 1999), but fell by 16 percent

for Freddie Mac (from $2.69 billion in 1998

to $2.26 billion in 1999). 7Tabulations of the 1995 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. 8Source: HUD analysis of GSE loan-level

data.

Appendix D—Estimating the Size of the

Conventional Conforming Market for

Each Housing Goal

A. Introduction

1. Overview of Appendix D

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, the remainder of

Section A summarizes the main components

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65183

of HUD’s market-share model and identifies

those parameters that have a large effect on

the relative market shares. With this material

as background, Section B provides an

overview of the GSEs’ main comments on,

and criticisms of, HUD’s market share

methodology, as well HUD’s response to

those comments and criticisms. More

detailed analyses of selected comments by

the GSEs are provided throughout this

appendix. 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 single-family 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

Geographically-Targeted (Underserved Areas)

Goal, and the Special Affordable Housing

Goal, respectively.1

In developing this 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. Data on

the multifamily mortgage market from HUD’s

Property Owners and Managers’ Survey

(POMS), not available at the time 1995 GSE

final rule was published, is utilized here.

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.

In a 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.2 Blackley and

Follain correctly note that the challenge lies

in getting accurate estimates of the model’s

parameters. As noted later, both GSEs

reached the same conclusion in their

comments on the proposed rule.

This appendix reviews in some detail

HUD’s efforts to combine information from

several mortgage market data bases to obtain

reasonable values for the model’s parameters.

Numerous sensitivity analyses are performed

in order to arrive at a set of reasonable market

estimates.

The single-family market analysis in this

appendix is based heavily on HMDA data for

the years 1992 to 1998. The HMDA data for

1999 were not released until August 2000,

which did not give HUD enough time to

incorporate that data into the analyses

reported in the Appendices. It should also be

noted that the discussion sometimes focuses

on the year 1997, as 1997 represents a more

typical mortgage market than the heavy

refinancing year of 1998.

2. Overview of HUD’s Market Share

Methodology 3

a. 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. 4 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

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.5 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).

b. 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:

(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); 6

(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).7

(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. 8 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.

BILLING CODE 4210–27–P

65184 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

BILLING CODE 4210–27–C

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 Geographically-Targeted Goal 9

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.

c. 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), Property Owners

and Managers Survey (POMS) and the

Census Bureau’s 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 1995 rule-making process and as

noted in Section B below, an issue that the

GSEs focussed on in their comments on this

year’s proposed rule. In 1997, HMDA

reported about $20.0 billion in multifamily

originations while the SMLA reported more

than double that amount ($47.9 billion).

Because most renters qualify under the Lowand 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 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 G–

H do.

Goal Percentages. To derive the goal

percentages for each property type, HUD

relied heavily on HMDA, AHS, and POMS

data. For single-family owner originations,

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65185

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. The

POMS data, which were not available during

the 1995 rule-making process, are used below

to examine the rents of newly-mortgaged

rental properties; thus, the POMS data

supplements the AHS data. 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.

d. Conclusions

HUD is using the same basic methodology

for estimating market shares that it used

during 1995. 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 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.

During the 1995 rule-making process, HUD

contracted with the Urban Institute to

comment on the reasonableness of its market

share approach and to conduct analyses

related to specific comments received from

the public about its market share

methodology. Several findings from the

Urban Institute reports are discussed

throughout this appendix. Since 1995, HUD

has continued to examine the reliability of

data sources about mortgage activity. HUD’s

Office of Policy Development and Research

has published several studies concerning the

reliability of HMDA data. 10 In addition, since

1995, HUD has gathered additional

information regarding the mortgages for

multifamily and single-family rental

properties through the Property Owners and

Managers Survey (POMS). 11 Findings

regarding the magnitude of multifamily

originations, as well as the rent and

affordability characteristics of mortgages

backing both single-family and multifamily

rental properties have been made by

combining data from POMS with that from

internal Census Bureau files from the 1995

American Housing Survey-National Sample.

The results of these more recent analyses will

be presented in the following sections.

B. Comments on HUD’s Market Share

Methodology

1. Overall Issues

Both Fannie Mae and Freddie Mac stated

that HUD’s market share model (outlined in

Section A above) 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 Proposed 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.12

Similarly, Fannie Mae stated that ‘‘* * *

HUD has developed a reasonable model for

assessing the size of the affordable housing

market’’. 13

However, both GSEs provided extensive

criticisms of HUD’s implementation of its

market methodology. Their major comments

fall into two general areas. First, the GSEs

expressed concern about HUD’s assumptions

and use of specific data elements both in

constructing the distribution of property

shares among single-family owner, singlefamily rental, and multifamily properties and

in estimating the goals-qualifying shares for

each property type. The GSEs contended that

HUD chose assumptions and data sources

that result in an overstatement of the market

estimate for each of the housing goals. In

particular, the GSEs claimed that HUD

overstated the importance of rental properties

(both single-family and multifamily) in its

market model and overstated the low-mod,

special affordable, and underserved areas

shares of the single-family owner market.

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

A.2 above).

While HUD recognizes that existing

mortgage market data bases vary in terms of

comprehensiveness and quality, HUD

believes that the GSEs have exaggerated the

inadequacies of available mortgage market

data, such as HMDA-reported data on the

borrower income and census tract

characteristics of mortgages for single-family

owner properties. In addition, as explained

below and demonstrated throughout this

appendix, 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 goalsqualifying 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.

Second, both GSEs argued that HUD’s

market estimates depended heavily on a

continuation of recent conditions of

economic expansion and low interest rates.

According to the GSEs, HUD’s range of

market estimates did not include periods of

adverse economic and affordability

conditions such as those which existed in the

early 1990s. HUD believes that the range for

the market shares should be broad enough to

reflect the likely volatility in the mortgage

market over the three-year period (2001–03)

in which the new housing goals will be in

effect. As explained below and demonstrated

throughout this appendix, HUD’s range of

market estimates for each of the housing

goals is reasonable because it allows for

economic and interest rate conditions

significantly more adverse than have existed

in the mid-to-late 1990s. As HUD stated in

its 1995 final GSE rule, policy should not

necessarily be based on market estimates that

include the worst possible economic

scenarios.

To support their contentions, the GSEs

made extensive criticisms of the

inadequacies of the major mortgage market

data bases (such as HMDA and the American

Housing Survey), offering in their place

findings from market share and simulation

models they had developed. Fannie Mae

focused many of its comments on the

inadequacy of the single-family-owner data

reported by HMDA, arguing that significant

portions of HMDA data are not relevant for

calculating the market standard for

evaluating GSE performance in the

conventional conforming market. Fannie

Mae’s comments on this topic are discussed

and critiqued by HUD in Appendix A of this

final rule. Freddie Mac focused many of its

comments on the size of the rental portion of

the mortgage market, concluding that HUD

had overestimated that portion of the market.

Both Fannie Mae and Freddie Mac

commented extensively on the need for the

market estimates to reflect the significant

volatility that exists in the single-family and

multifamily mortgage markets. In this regard,

the GSEs relied heavily on a Freddie-Macfunded study by PriceWaterhouseCoopers

(PWC), entitled ‘‘The Impact of Economic

Conditions on the Size and the Composition

of the Affordable Housing Market’’ (dated

April 5, 2000). Because the GSEs’ comments

(especially those of Freddie Mac) draw

heavily upon the PWC study, the next section

reports and critiques its main findings. This

analysis of the PWC report also incorporates

related GSE comments where appropriate.

Following that, other major issues raised by

the GSEs about HUD’s market estimates will

be examined.

The discussion in the remainder of this

section assumes readers are familiar with the

market methodology and related concepts

developed in later sections of the appendix.

There is no attempt in this section to fully

develop the various concepts. Rather, the

purpose of this section is to provide, in one

place, HUD’s insights and comments on the

more important issues raised by the GSEs in

their comments and by

PriceWaterhouseCoopers in its report. It

should be noted that the GSEs’ comments are

also discussed throughout the development

of the market share methodology in this

appendix.

65186 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

2. PriceWaterhouseCoopers (PWC) Study

The main purpose of the PWC study was

to address how the business cycle affects the

affordability of mortgages originated in the

conventional conforming mortgage market.

Based on its analysis of the 1990–98

mortgage market, PWC concluded that (a)

changing economic conditions can quickly

impact the low-and moderate-income portion

of the mortgage market; (b) the highly

affordable economic conditions that have

existed since 1995 are not likely to persist in

the future; and (c) it is difficult to project

affordable lending levels accurately. PWC

argues that HUD’s basing its market shares on

the recent past may lead to unrealistic

housing goals.

HUD’s review of the PWC study found that

it included several interesting analyses and

insights about economic volatility. For

example, its regression analyses of the

multifamily and affordable lending shares of

the market highlight the impacts that shifts

in economic conditions can have on these

sectors of the market, as well as the difficulty

in modeling changes in market conditions.

The PWC document also included a useful

critique of existing mortgage market data

bases. In the event of a severe economic

downturn, the PWC study will serve as an

interesting reference document for

policymakers and mortgage market analysts

concerned about the implications of the

business cycle for affordable lending.

In relation to the policy discussion

surrounding the GSE housing goals, however,

the PWC document contains significant

shortcomings. A major shortcoming is that

the PWC document underestimates the size

of the multifamily mortgage market by

relying heavily on multifamily originations

reported in HMDA. While HMDA is for many

purposes a preeminent data source on singlefamily lending, it has been widely

discredited as a multifamily data source due

to severe underreporting of loan originations.

Indeed, HMDA has been rejected as

inadequate in published work by highly

regarded independent researchers, as well as

by Fannie Mae in its comments submitted in

response to HUD’s proposed rule.

Another major shortcoming of the PWC

report is an error in calculating the size of the

single-family conventional conforming

market. The discussion of single-family

lending in the PWC document initially

appears to contradict HUD’s analysis in

Appendix D of the proposed rule, but this is

mainly because HUD’s analysis is based upon

the conforming conventional mortgage

market, whereas PWC effectively includes

FHA loans and loans above the conforming

loan limit in portions of their analysis of the

1980–98 mortgage market. For example, in

1998, PWC estimates the size of the singlefamily mortgage market at $1.5 trillion. This

is identical to the widely used estimate by

the Mortgage Bankers Association (MBA) for

the entire single-family mortgage market that

year, including jumbo and FHA loans.14

Because the GSEs are prohibited from

purchasing loans above the conforming limit,

and because HUD is directed by statute to

focus on the conventional market in setting

the housing goals, it is necessary to restrict

analyses of the mortgage market to the

conventional conforming market if they are

to be used in connection with the housing

goals. Because of these statutory

considerations, PWC’s calculations (which

effectively include mortgages outside the

conventional conforming market) cannot be

relied upon for policymaking purposes.

PWC’s error (overstating single-family

originations), combined with their

underestimating multifamily originations

(see above), leads PWC to substantially

underestimate the multifamily share of the

conventional conforming mortgage market,

which further leads them to substantially

underestimate the low- and moderate-income

share of the market.

The PWC study focuses on the low-mod

share of the mortgage market during the

1990s. PWC claims that the low-mod share of

the market ranged from 35 percent to 56

percent during the 1990s, with a mean of 46

percent. These figures are contrasted with

HUD’s 50–55 percent projection of the lowmod market for the years 2001–03. The

following are observations about this and

other findings in the PWC report.

• PWC begins its analysis by estimating

the low-mod share of the existing mortgage

market and then applying its results to an

analysis of the low-mod share of the market

for newly-originated mortgages. In the top

portion of its Table 2, PWC assumes the lowmod share of the existing housing stock is 50

percent. In fact, it can be shown empirically

that the actual proportion is 56.8 percent

based on data from AHS and the Property

Owners and Managers Survey (POMS).15

PWC then proceeds to compound this error.

Based on the mistaken assumption that 50

percent of the housing stock is occupied by

low- and moderate-income households, PWC

infers that the low-mod share of the stock of

mortgaged owner-occupied properties is 31

percent. Empirically, however, the correct

figure is 37 percent, based on AHS data.

• Based on HUD’s best estimates of the

multifamily market, the multifamily mix

averaged 16–17 percent for 1991–1998, not

8.7 percent as estimated by PWC.16 PWC’s

multifamily mix is unrealistically low

because of their reliance on a flawed, HMDAbased methodology which underestimates

the size of the conventional multifamily

origination market, and because they used

techniques for estimating the size of the

single-family mortgage market equivalent in

several years to including FHA and jumbo

single-family loans. Inclusion of loans

outside the conventional conforming market

is inappropriate for purposes of setting the

housing goals, as discussed above.

• Although Fannie Mae relies on the PWC

study, Fannie Mae’s multifamily market

estimates are higher than PWC’s—for

example, Fannie Mae’s $35–$40 billion

multifamily origination estimate for 1997

leads to a multifamily mix of 16–18 percent

(versus 11 percent for PWC) and its $40–$45

billion estimate for 1998 leads to a 11–12

percent multifamily mix (versus 7.3 percent

for PWC).

• In calculating the multifamily share of

housing units financed each year (the

‘‘multifamily mix’’) PWC compounds the

problems associated with its unrealistically

low figure for multifamily originations by

utilizing estimates for single-family

origination volume far exceeding realistic

figures for the conventional conforming

segment of the single-family mortgage

market. When HUD implemented PWC’s

HMDA-based procedure for calculating the

size of the multifamily market, it derived an

average multifamily mix of 11.6 percent for

1991–1998, well above the PWC figure of 8.7

percent.

• Results of PWC simulations are

contradicted by historical evidence. For

example, PWC simulates a refinance boom

and under one scenario projects that the lowmod share of the market would fall to 40

percent. However, during the 1998 refinance

wave, the low-mod share of the market was

54 percent, and even GSE performance

exceeded 45 percent, suggesting that PWC

overestimates the effect of a refinance boom

on the low-mod share.

Mainly for the above reasons, PWC

substantially underestimates the size of the

low-mod market during the 1990s. Using

realistic estimates of the multifamily market

outlined in Section C, HUD derives an

average low-mod share of 52 percent during

the 1990s, substantially higher than the 46

percent average advocated by PWC.

The remainder of the section summarizes

the main comments of Fannie Mae and

Freddie Mac on HUD’s market share

methodology. Because the GSEs relied

heavily on the PWC study or a similar

analysis, the points in this section will apply

to their comments as well.

3. Volatility of the Mortgage Market

Based on the PWC study and their own

analyses, both GSEs contended that HUD had

not adequately considered the impact that

changes in the national economy could have

on the size of the conventional conforming

mortgage market. The GSEs commented that

HUD based its market estimates on the

unusually favorable economic and housing

market conditions that have existed since

1995. Fannie Mae stated that HUD’s analysis

overstates the size of the market because it

‘‘does not reflect the potential effects of a

broader range of plausible economic

scenarios’’. Freddie Mac recommended that

‘‘the market estimates in the Final Rule be

revised to reflect the large impact of

economic conditions on the very-low, lowand moderate-income, and underserved

areas’ shares of the market’’. As noted earlier,

both GSEs relied on the PWC study which

concluded that ‘‘interest rate movements and

changes in the rate of economic growth are

statistically significant determinants of the

low- and moderate-income share of the

conventional conforming mortgage market by

affecting both the multifamily share of

aggregate lending and the affordability

composition of single-family lending’’. (PWC,

page iv).

As explained in Appendix A and Section

F of this appendix, HUD understands that the

current levels of interest rates, home prices,

borrower incomes, alternative rental costs,

and consumer confidence, as well as

expectations about their future levels, play a

role in determining whether homeownership

is feasible or desirable for any particular

household. HUD is also aware that the

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65187

mortgage market is very dynamic and

susceptible to significant changes in

conditions that would affect the overall level

of affordable lending to lower-income

families. HUD agrees that forecasting all

these factors for upcoming years to obtain a

picture of the future climate for the mortgage

market is difficult.

In response to concerns expressed about

the volatility of the mortgage markets over

time, HUD has estimated a range of market

shares for each of the housing goals—50–55

percent of the Low-Mod Goal, 23–26 percent

for the Special Affordable Goal, and 29–32

percent for the Underserved Areas Goal—that

reflect economic environments significantly

more adverse than those which existed

during the period between 1995 and 1998,

when the Low-Mod Goal averaged 56.5

percent, the Special Affordable Goal, 28.1

percent, and the Underserved Areas Goal,

33.0 percent.

HUD conducted detailed sensitivity

analyses for each of the housing goals to

reflect affordability conditions that are less

conducive to lower-income homeownership

than those that existed during the mid- to

late-1990s. The following examples drawn

from Sections F and H of this appendix may

be helpful in clarifying this issue:

• The low-mod percentage for singlefamily home purchase loans can fall to as low

as 34 percent—or four-fifths of its 1995–98

average of over 42 percent—before the

projected low- and moderate-income share of

the overall market would fall below 50

percent.

• Similarly, the underserved areas

percentage for owner loans can fall to as low

as 22 percent—also about four-fifths of its

1995–98 average of almost 27 percent—

before the projected underserved areas share

of the overall market would fall below 29

percent.

HUD also conducted additional sensitivity

analyses by examining recession and

refinance scenarios and varying other key

assumptions, such as the size of the

multifamily market. These sensitivity

analyses, presented in this appendix, show

that HUD’s market estimates cover a range of

mortgage market and affordability conditions

and provide a sound basis for setting housing

goals for the years 2001–03.

HUD recognizes that under certain

extremely adverse circumstances, the goalsqualifying market shares could fall below its

estimates. The PWC study and the GSEs

presented estimates based on a hypothetical

economic slowdown accompanied by low

affordability conditions that fall below the

range of HUD’s estimates. Fannie Mae, for

example, included mortgage originations

falling to as low as $771 billion and as high

as $1,706 billion in its ‘‘likely single family

mortgage market volume ranges’’ for the year

2001. However, as HUD stated in its 1995

GSE rule, setting goals so that they can be

met even under the worst of circumstances

is unreasonable. If macroeconomic

conditions change dramatically, then the

levels of the goals can be revised to reflect

the changed conditions. As discussed below

in Section F, FHEFSSA and HUD recognize

that conditions could change in ways that

would require revised expectations. Thus,

HUD is given the statutory discretion to

revise the goals if the need arises. If a GSE

fails to meet a housing goal, HUD has the

authority to determine that the goal was not

feasible, and not take further action.

4. Size of the Multifamily Market

Section C contains a detailed discussion of

the size of the conventional multifamily

origination market, summarizing findings

from a variety of sources regarding the size

of the conventional multifamily mortgage

market, measured in terms of dollars, units,

and as a share of total conventional

conforming annual mortgage origination

volume, a key factor influencing the share of

the overall market comprised of units

meeting each of the housing goals. This

section considers a number of alternative

data sources providing evidence on

conventional multifamily origination volume

over a number of years, in some cases the

entire 1990–1999 period. The approaches

considered here include the HUD Survey of

Mortgage Lending Activity (SMLA); Home

Mortgage Disclosure Act data (HMDA); and a

projection model developed by the Urban

Institute based on data from the 1991

Residential Finance Survey (RFS). A new

methodology, developed by HUD for

purposes of this analysis, is discussed, as are

estimates submitted by Fannie Mae and

Freddie Mac on their comments on the

proposed rule. Estimates for 1990 from the

RFS and for 1995 from the Property Owners

and Managers Survey (POMS) are also

discussed.

Based on the likely range of annual

conventional multifamily origination

volume, multifamily units represent an

average of 16–17 percent of units financed

each year during the 1990s.17 HUD’s

estimated multifamily market shares exceed

estimates prepared by PWC (averaging 8.7

percent for 1991–1998) for two reasons, as

mentioned previously. One is that PWC’s

adjusted HMDA methodology does not

adequately correct for underreporting in

HMDA, resulting in unrealistically low

estimates of the size of the conventional

multifamily origination market. Another

reason that PWC’s estimated multifamily

market shares are low is that a number of

their calculations appear to include FHA and

jumbo loans in estimating the number of

single-family units financed each year, as

discussed above. HUD’s market share

calculations, in contrast, are based on the

multifamily share of conventional

conforming mortgage loans originated each

year.

The multifamily share of the conforming

conventional market (or ‘‘multifamily mix’’)

derived from this discussion of multifamily

origination volume is utilized below as part

of HUD’s analysis of the share of units

financed each year meeting each of the

housing goals. For purposes of that analysis,

a multifamily mix of 16.5 percent is

reasonable, based upon the analysis and

discussion below. However, a 15 percent

market share can be utilized as an alternative

market share estimate corresponding to a

somewhat less favorable environment for

multifamily lending. While somewhat low

from an historical standpoint, a 15 percent

mix more readily accommodates the

possibility of a recession or heavy refinance

year than would baseline assumptions based

more strictly on historical data. In order to

more fully consider the effects of an even

more adverse market environments, an

alternative multifamily mix assumptions of

13.5 is also considered, as well as a number

of others.

5. Size of the Single-Family Rental Market

Both GSEs argued that the single-family (1–

4) investor portion of the single-family

mortgage market should be eight percent or

less of total single-family originations, based

on HMDA data. In both 1995 and in the

proposed rule, HUD considered three

scenarios for investor mortgages when

estimating the housing goals—a baseline

model that assumed 10 percent, a lower

scenario that assumed 8 percent, and a higher

scenario that assumed 12 percent. HUD’s

base case of 10 percent is well below the 17.3

percent reported by the 1991 Residential

Finance Survey (which is considered

accurate but unfortunately is out-of-date) and

above the 7–8 percent estimates provided by

HMDA over the past few years. In 1995,

research by Urban Institute researchers

concluded that the HMDA estimates were too

low (although the GSEs raise concerns about

this research in their comments). HUD has

decided to stay with its baseline 10 percent

estimate but it acknowledges that due to

limited data there is some uncertainty about

the investor share of the single-family

market, which will be clarified when the next

Residential Finance Survey is released in a

couple of years. Sensitivity analyses indicate

that reducing the investor share from 10

percent to 8 percent would reduce the lowmod market share by 1.05 percent, the

special affordable share by 0.90 percent, and

the underserved areas share by 0.36 percent.

6. Relevant Market for Single-Family Owner

Market

Both GSEs provided numerous comments

concerning the types of mortgages that HUD

should exclude from the definition of the

single-family owner market when HUD is

calculating the market shares for each

housing goal. The GSEs comments and

HUD’s response to them are discussed in

Section A of Appendix A. As noted there,

HUD believes that the risky, B&C portion of

the subprime market should be excluded

from the market definition for each of the

housing goals. HUD includes the A-minus

portion of the subprime market in its market

estimates. This appendix explains HUD’s

method for making this adjustment to the

overall market estimates.

As explained in Appendix A, HUD

disagrees with most of the other adjustments

proposed by the GSEs. Excluding important

segments of the lower-income mortgage

market as the GSEs recommend would distort

HUD’s estimates of the goals-qualifying

shares of the conventional conforming

market.

7. Shortcomings of Various Mortgage Market

Data Bases

Major mortgage market data bases such as

HMDA and the American Housing Survey

(AHS) are used to implement HUD’s market

65188 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

methodology. In their comments, Fannie Mae

and Freddie Mac, as well as PWC, each

provided a useful critique of the various

mortgage data bases. Based on its analysis,

Freddie Mac concluded that HUD should

revise its market share estimates to reflect

‘‘the lack of reliable data’’. Similarly, Fannie

Mae concluded that ‘‘HUD analysis

overstates the size of the market because it

relies on unreliable data sources. * * *’’.

Fannie Mae further states that ‘‘* * * HUD

has chosen to extrapolate from several

disparate data sources in ways that inflate the

Department’s estimate of the market size for

each of the goals’’. PWC, as well as the GSEs,

expressed concern that mortgage market data

bases had not improved since 1995, when

HUD issued its last GSE rule on the housing

goals.

Examples of problems noted by the GSEs

include: limited variables (such as LTV ratio)

and bias in HMDA data; inability of HMDA

to identify important segments of the market

(such as subprime lenders); underreporting of

multifamily mortgages in HMDA and general

unreliable reporting of rental mortgages in

other data bases; underreporting of income in

the AHS; and the fact that some important

mortgage market data bases such as the 1991

Residential Mortgage Finance Survey are

simply out of date. Both GSEs expressed

particularly strong criticism of HUD’s use of

data on the rental market, that is, estimates

of the proportion of 1-to 4-unit rental

properties and of annual multifamily

origination volume.

HUD agrees that a comprehensive source of

information on mortgage markets is not

available. However, HUD considered and

analyzed a number of data sources for the

purpose of estimating market size, because

no single source could provide all the data

elements needed. In these appendices, HUD

has carefully defined the range of uncertainty

associated with each of these data sources,

has pulled together estimates of important

market parameters from independent

sources, and has conducted sensitivity

analyses to show the effects of various

assumptions. In fact, Freddie Mac noted that

‘‘We [Freddie Mac] support the Department’s

approach for addressing the empirical

challenges of setting the goals by examining

several different data sets, using alternative

methodologies, and conducting sensitivity

analysis.’’

While HUD recognizes the shortcomings of

the various data and the inability to derive

precise point estimates of various market

parameters, HUD, however, does not believe

that these limitations call for expanding the

range of the market estimates, as suggested by

the GSEs. One purpose of this appendix is to

demonstrate that careful consideration of

independent data sources can lead to reliable

ranges of estimates for the goals-qualifying

shares of the mortgage market. It should also

be emphasized that while there are some

problems with existing mortgage market data,

there is a wealth of information on important

components of the market. HMDA provides

wide coverage of the single-family owner

market in metropolitan areas, yielding

important information on the borrower

income and census tract (underserved area)

characteristics of that market. The AHS

provides excellent information on the

affordability characteristics of the singlefamily rental and multifamily housing stock.

As explained in Section F of this appendix,

POMS data confirm that the rent affordability

data based on the AHS stock provide reliable

estimates of the rent characteristics of newlymortgaged dwelling units in the rental stock.

HUD’s specific responses to the GSEs’

comments on data are included throughout

these appendices. For example, see

subsection B.4 above and Section C of this

appendix for a discussion of the multifamily

data; as explained there, HUD concludes that

Freddie Mac and PWC, in particular,

underestimate the size of the multifamily

market. Issues related to single-family rental

data are discussed in B.5 above and in

Section D to this appendix. Appendix A

provides a complete discussion of the singlefamily owner data reported in HMDA. As

noted in Section A of Appendix A, HUD

disagrees with the GSEs in terms of the

seriousness of the bias problem in HMDA

data. It should also be mentioned that HUD

does not rely heavily on some of the data

bases that the GSEs criticize. For example,

Freddie Mac argues that the AHS

underreports borrower income; but HUD

relies on HMDA data for the borrower

income characteristics of home purchase and

refinance markets. According to the out-ofdate RFS data, investor mortgages account for

17 percent of the single-family mortgage

market the RFS; as explained in above,

HUD’s baseline model uses 10 percent, with

sensitivity analyses at 8 percent and 12

percent.

8. Miscellaneous Comments

There are several specific comments of the

GSEs that should be mentioned and clarified.

In many cases, these comments relate to the

broad issues that have already been

discussed in this section. However, because

of their technical nature, it was decided to

discuss them in this separate section rather

than including them in the above discussion.

• On page 17 of its Appendix III, Freddie

Mac states that HUD assumed the investor

share of single-family mortgages was 10.7

percent; in fact, HUD’s baseline model

assumed 10 percent.

• On page 22 of its Appendix III, Freddie

Mac states that because HMDA does not

identify subprime and manufactured housing

loans, the proposed rule does not adjust for

these loans originated by prime lenders. As

this appendix explains, HUD’s market

estimates for the three housing goals are

adjusted for all loans originated in the B&C

portion of the subprime market.

• On page 23 of its Appendix III, Freddie

Mac states that HUD does not compare

HMDA and GSE data with the same precision

as Berkovec and Zorn because HUD has

included HMDA-reported non-metropolitan

loans, which are poorly reported by HMDA.

Freddie Mac is incorrect. HUD’s analysis in

Table A.4a is based on HMDA and GSE data

for only metropolitan areas. In addition, HUD

does not include GSE purchases of FHA

loans in Table A.4a, as suggested by Freddie

Mac.

• On page 1 of its Appendix III, Freddie

Mac states that HUD’s market projections

‘‘effectively are based on an analysis of

mortgage lending patterns since 1995.’’

Freddie Mac is incorrect, as explained in B.3

above and throughout this appendix. For

example, as reported in Table D.15 below,

the low-mod share of the conventional

conforming market has averaged over 56

percent since 1995; this compares with

HUD’s projection of 50–55 percent for this

market.

• On page 6 of its Appendix III, Freddie

Mac states that HMDA accurately reports

multifamily originations for commercial

banks. HUD’s analysis concurs with that of

other researchers that HMDA significantly

underreports multifamily originations by

commercial banks. For example, Crews,

Dunsky and Follain (1995) conclude that

‘‘HMDA surely underestimates lending by

both mortgage bankers and commercial

banks.’’ 18

• On pages 20–21, Freddie Mac uses the

AHS and POMS to estimate the distribution

of newly-mortgaged units by property type.

Based on this analysis, Freddie Mac

estimates that multifamily units represented

10.6 percent of newly financed dwelling

units over the 1993–95 period. Based on

HUD’s calculations, however, multifamily

units were 20.6 percent of conventional

conforming units financed during 1993–

1995. Freddie Mac may have underestimated

the number of rental units by excluding

observations with missing origination year,

and may have overestimated the number of

single-family units by including jumbo or

FHA loans.

• In its comments (page 30) about the lowmod goal, Freddie Mac states that ‘‘an

analysis limited to the exceptional economic

environment since 1995 would suggest a

narrow range centered at 50 percent * * *’’.

As explained in Section F of this appendix,

the low-mod goal averaged 56.5 percent

between 1995 and 1998.

• On pages 34 and 35 of its comments,

Fannie Mae states that HUD’s approach to

housing and economic conditions involves

‘‘point estimates’’. As this appendix makes

clear, HUD’s analysis is based on a range of

market estimates—not point estimates as

stated by Fannie Mae. Of course, the ‘‘likely

single-family mortgage market volume

ranges’’ chosen by Fannie Mae are not

necessarily the ones HUD would choose for

setting housing goals for the next three years.

Fannie Mae offers wide ranges in mortgage

market projections for the years 2001–03; for

example, $771 billion to $1,706 billion is its

projection for the year 2001.

• Fannie Mae states ‘‘HUD should provide

an explicit range of goals based upon

differing economic outlooks with reasonable

chances of occurring—ranging from modest

recession to a continued boom economy’’. As

demonstrated in Sections F–H, HUD’s market

ranges are reasonably set to include much

more adverse economic and affordability

conditions than have existed during the past

few years.

• On pages 66–67, Fannie Mae estimates a

market range of 48–51 percent for the LowMod Goal, 21–24 percent for the Special

Affordable Goal, and 24–28 percent for the

Underserved Areas Goals; the range covers a

recession scenario and a growth scenario and

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65189

adjusts for B&C loans. Fannie Mae states that

its market share analysis supports the

proposed higher levels for the new housing

goals but it also shows that the GSEs will

experience greater difficulty achieving the

new goals (and particularly the underserved

areas goal) than suggested by HUD’s market

share estimates. Fannie Mae assumes a lower

percentage of single-family and multifamily

rental properties than HUD, which is one

reason Fannie Mae obtains lower market

estimates than HUD. Fannie Mae assumes

that the goals-qualifying shares for the singlefamily owner market can fall to their 1993

levels when, for example, the underserved

areas share of the owner market equaled 20

percent. As explained in Section G, HUD’s

range of market estimates (29–32 percent) for

the underserved areas goal is consistent with

the underserved areas owner percentage for

the single-family market falling from its

average of 28 percent over the 1995–98

period to 22 percent. Fannie Mae’s assumes

an additional two percentage point decline in

its sensitivity analysis. It should also be

noted that while Fannie Mae adjusts for B&C

loans, it does not make the 1–2 percentage

point upward adjustment to incorporate the

effects of underserved counties in nonmetropolitan areas.

9. Conclusions

In considering the levels of the goals, HUD

carefully examined the comments 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, the basic methodology

employed by HUD is a reasonable and valid

approach to estimating market share and the

percentage range for each of the three market

share estimates do not need to be adjusted

from those reported in the proposed rule.

While a number of technical changes have

been made in response to the comments, the

approach for determining market size has not

been modified substantially. The detailed

evaluations show that the methodology, as

modified, produces reasonable estimates of

the market share for each goal. 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

This section derives projections of

conventional multifamily mortgage

origination volume.19

The multifamily sector is especially

important in the establishment of housing

goals for Fannie Mae and Freddie Mac

because multifamily properties are

overwhelmingly occupied by low- and

moderate-income families. For example, in

1999, 9.5 percent of units financed by Fannie

Mae were multifamily, but 95 percent of

those units met the Low- and ModerateIncome Goal, accounting for 20 percent of all

of Fannie Mae’s low- and moderate-income

purchases for that year.20 Multifamily

acquisitions are also of strategic significance

with regard to the Special Affordable Goal. In

1999, 43 percent of units backing Freddie

Mac’s multifamily acquisitions met the

Special Affordable Goal, representing 22

percent of units counted toward its Special

Affordable Goal, at a time when multifamily

units represented only 8.3 percent of total

annual purchase volume.21

This discussion is organized as follows:

Section 1 identifies and evaluates available

data resources regarding the dollar value of

conventional multifamily mortgage

origination during 1990–1999. Section 2

discusses loan amount per unit, a key

parameter in estimating the number of units

backing multifamily originations. Section 3

summarizes findings from a variety of

sources regarding the size of the conventional

multifamily mortgage market, measured in

terms of dollars, units, and as a share of total

conventional conforming annual mortgage

origination volume, a key factor influencing

the share of the overall market comprised of

units meeting each of the housing goals.

Inferences regarding the likely range and

‘‘baseline’’ estimates of annual multifamily

origination volume for 1990–1999 are drawn.

1. Multifamily Data Sources

This section considers a number of

alternative data sources providing evidence

on conventional multifamily origination

volume over a number of years, in some cases

the entire 1990–1999 period. The approaches

considered here include the HUD Survey of

Mortgage Lending Activity (SMLA); Home

Mortgage Disclosure Act data (HMDA); and a

projection model developed by the Urban

Institute based on data from the 1991

Residential Finance Survey (RFS). A new

methodology, developed by HUD for

purposes of this analysis, is discussed, as are

estimates submitted by Fannie Mae and

Freddie Mac in connection with the

Department’s GSE rulemaking efforts.

Estimates for 1990 from the RFS and for 1995

from the Property Owners and Managers

Survey (POMS) are also discussed.

a. Survey of Mortgage Lending Activity

(SMLA)

The data that enter into SMLA were

compiled by HUD until 1998 from source

materials generated in various ways from the

different institutional types of mortgage

lenders. Data on lending by savings

associations were collected for HUD by the

Office of Thrift Supervision; these data cover

all thrifts, not a sample. Mortgage company

and life insurance company data were

collected through sample surveys conducted

by the Mortgage Bankers Association of

America and the American Council of Life

Insurance, respectively. Data on commercial

banks and mutual savings banks were

collected through sample surveys conducted

by a number of different entities over the

years. Federal credit agencies such as the

U.S. Small Business Administration and

HUD non-FHA programs as well as State

credit agencies such as housing finance

agencies reported their data directly to HUD.

Local credit agency data are collected by

HUD staff from a publication that lists their

mortgage financing activities. The SMLA was

discontinued by HUD in 1998, and data are

available only through 1997.

Commercial bank data in the SMLA have

been questioned by a number of researchers.

Part of the problem arises from the possibility

of double-counting of originations by

mortgage banks in the American Bankers

Association (ABA) and Mortgage Bankers

Association (MBA) surveys conducted as part

of SMLA. Originations by mortgage banks

which are affiliated with commercial banks

may be counted in both surveys. A 1995

analysis prepared by Crews, Dunsky and

Follain found that, in 1993, the SMLA

conventional origination figure of $30 billion

was calculated on the basis of overstated

originations by commercial banks, but

understated lending volume by mortgage

banks, life insurance companies, and

individuals. Taking all of these factors into

consideration, as well as other evidence, they

conclude that actual 1993 origination volume

appears to be in the range of $25-$30

billion. 22

One solution to the double-counting

problem in SMLA is to remove the mortgage

bank subtotal from total origination volume.

The resulting figure may provide a more

accurate representation of conventional

multifamily lending volume. Table D.2

presents SMLA figures for 1990–1997,

including and excluding mortgage banks.

BILLING CODE 4210–27–P

65190 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

BILLING CODE 4210–27–C

b. Home Mortgage Disclosure Act (HMDA)

HMDA data are collected by lending

institutions and reported to their respective

regulators as required by law. HMDA was

enacted as a mechanism to permit the public

to determine locations of properties on which

local depository institutions make mortgage

loans, ‘‘to enable them to determine whether

depository institutions are filling their

obligations to serve the housing needs of the

communities and neighborhoods in which

they are located * * *’’ (12 U.S.C. 2801).

HMDA reporting requirements generally

apply to all depository lenders with more

than $29 million in total assets and which

have offices in Metropolitan Statistical Areas.

Reporting is generally required of other

mortgage lending institutions (e.g. mortgage

bankers) originating at least 100 home

purchase loans annually provided that home

purchase loan originations exceed 10 percent

of total loans. Reporting is required for all

loans closed in the name of the lending

institution and loans approved and later

acquired by the lending institution, including

multifamily loans. Thus, the HMDA data

base concentrates on lending by depository

institutions in metropolitan areas but, unlike

SMLA and RFS, it is not a sample survey; it

is intended to include loan-level data on all

loans made by the institutions that are

required to file reports.

A deficiency of the HMDA database is that

there is compelling evidence of significant

underreporting of multifamily mortgages. In

their 1995 analysis, Crews, Dunsky and

Follain conclude ‘‘We clearly demonstrate

that HMDA alone is not an accurate measure

of the total market. Our argument is based

upon two facts. First, HMDA was not

designed to cover multifamily lending by all

lenders; it focuses on lending done primarily

by commercial banks, thrifts, and large

mortgage bankers in metropolitan areas.

Second, HMDA surely underestimates

lending by both mortgage bankers and

commercial banks.’’ 23 In its comments

submitted in response to HUD’s proposed

rule, Fannie Mae observes that ‘‘HMDA is not

considered a reliable source of multifamily

mortgage originations because it provides an

incomplete view of non-depository

institution sources of loans.’’ 24

It does not appear that HMDA has

significantly improved its multifamily

coverage since the time of the 1995 Crews,

Dunsky and Follain analysis. For example, in

1998, HMDA reports approximately $1

billion in FHA multifamily origination

volume, compared with $2.5 billion reported

by FHA. The underreporting appears to be

even more serious with regard to GSE

acquisitions. The 1998 HMDA file reports

approximately $2 billion in Fannie Mae

multifamily transactions, compared with an

actual total of $12.5 billion. A sizeable

shortfall is also evident with regard to

Freddie Mac, with HMDA reporting 1998

transactions volume of $295 million,

compared with an actual figure of $6.6

billion.

In addition, the HMDA data base does not

cover a number of important categories of

multifamily lenders such as life insurance

companies and State housing finance

agencies, providing another reason that the

HMDA data understates the size of the

multifamily market.

One way to address the undercounting

problem in HMDA is to incorporate an

adjustment factor to correct for

underreporting, for example by multiplying

each year’s annual total by 1.25, as suggested

by PriceWaterhouseCoopers (PWC) in their

report prepared for Freddie Mac in

connection with HUD’s proposed rule.

However, this 1.25 correction factor is based

upon an estimate of underreporting of singlefamily loans in HMDA, and may be too small

to accurately capture the degree of

multifamily underreporting in HMDA,

judging from comparisons between actual

and HMDA-reported volume by the GSEs and

FHA cited above.

To the adjusted HMDA figure, PWC then

adds an estimate for originations by life

insurance companies by utilizing figures on

multifamily loan commitments published by

the American Council on Life Insurance

(ACLI), a trade group which conducts regular

surveys. Table D.3 shows annual

conventional multifamily origination volume

as reported in HMDA, as well as an adjusted

HMDA figure including a 1.25 correction

factor as well as the ACLI figure for loan

commitments in the last quarter of the

preceding year as well as the first three

quarters of each origination year. In

calculating annual totals, the absolute value

is taken of loan amounts reporting as

negative numbers. The table shows a sharp

drop in origination volume between 1990

and 1991, possibly associated with the

commercial real estate recession of the early

1990s. However, the implication that

multifamily mortgage lending has remained

20 percent below the 1990 level for the entire

remainder of the decade is inconsistent with

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65191

other data sources, and raises further

concerns regarding the accuracy and

reliability of HMDA as a multifamily data

source.

BILLING CODE 4210–27–P

BILLING CODE 4210–27–C

A difficulty with the adjustment factor

approach is that very little is known

regarding the degree of underreporting of

multifamily originations in HMDA. There is

no reason that the 20 percent underreporting

figure sometimes used in single-family

discussions of HMDA is applicable to

multifamily. Indeed, if the degree of

underreporting of FHA originations or GSE

acquisitions noted above is representative,

even the adjusted HMDA figures are likely to

significantly underreport the actual totals.

c. Urban Institute Statistical Model

In 1995, Urban Institute researchers

developed a model to project multifamily

origination volumes from 1992 forward,

based on data from the 1991 Survey of

Residential Finance.25 They applied a

statistical model of mortgage terminations

based on Freddie Mac’s experience from the

mid-1970s to around 1990. While mortgage

characteristics in 1990 are not wholly similar

to the characteristics of these historical

mortgages financed by Freddie Mac,

nevertheless the prepayment propensities of

contemporary mortgages may at least be

approximated by the prepayment experience

of these historical mortgages. The research

methodology took account of the influence of

interest rate fluctuations on prepayments of

the historical mortgages; the projections

assumed that prepayments are motivated

mainly by property sales.

Table D.4 shows annual projected

conventional multifamily origination volume

as reported in the Urban Institute model,

derived by subtracting actual FHA

origination volume from the overall projected

multifamily total each year, except in 2000,

when 1999 FHA originations are used as a

proxy for 2000 originations.

BILLING CODE 4210–27–P

65192 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

BILLING CODE 4210–27–C

d. New Methodology for Recent Years

In the context of (i) the discontinuation of

SMLA; (ii) evidence of significant

underreporting in HMDA; and (iii) increased

availability of data regarding purely private,

non-GSE securitization of commercial

mortgage loans, HUD has developed a new

methodology for the purpose of preparing a

lower-bound estimate for the minimum size

of the multifamily market. The following

sources are combined to calculate the

estimated size of the conventional

multifamily market in a way that is relatively

complete, but which avoids double-counting

and excludes seasoned loans:

(1) HMDA portfolio loans. This component

comprises conventional loans originated by

depositories and not sold, plus conventional

loans acquired by depositories but not sold,

less overlap between these two categories. In

principle, if a loan originated during the

current year is acquired by a depository, it

should show up as an origination. However,

due to underreporting, this is not always the

case. The procedure utilized here is to sum

conventional originations by depositories

and conventional acquisitions by

depositories, and then to utilize a matching

procedure to identify loans falling into both

categories, which are then subtracted.

(2) GSE purchases of current-year

acquisitions. A data series on GSE

multifamily transactions covering 1995–1999

that excludes non-GSE securities and

repurchased GSE securities is published by

OFHEO in their 2000 Report to Congress.

These exclusions are needed in order to

avoid double-counting. However, this figure

must be further adjusted to take into

consideration the fact that some of these

transactions involved seasoned purchases,

and a few involve government-insured

mortgages. In order to adjust the data for this

possibility, the OFHEO figures are reduced

by 33 percent, the figure derived by

calculating the proportion of seasoned and

FHA mortgages among the GSEs’ cash and

swap transactions during 1995–1999, using

GSE loan-level data provided to HUD. Any

loans sold by depositories to the GSEs would

be counted here, but not in the HMDA

component, which is restricted to loans kept

in portfolio by depositories.

(3) Commercial Mortgage Backed Security

multifamily loans. Commercial Mortgage

Alert, Hoboken NJ, publishes detailed,

transaction-level database that provides

information on transaction size and the

proportion of collateral comprised by

multifamily collateral for the entire 1990–

1999 period. Multifamily loan amounts at the

transaction level are derived by applying the

multifamily proportion to the transaction

amount. These transaction-level loan

amounts are then aggregated over all

transactions conducted during a calendar

year to derive an annual total. This data

series identifies securitizations by

depositories, government and insurance

companies; seasoned loans; GSE transactions;

and transactions involving foreign collateral,

all of which are in order to avoid doublecounting. Thus, loans included in this

component consist of nongovernment, nonGSE securitizations of recently-originated

mortgages by non-depository, non-life

insurance company institutions.

(4) Conventional originations by life

insurance companies. Source: American

Council on Life Insurance (ACLI) quarterly

data on multifamily loan commitments.

Annual originations estimated by combining

commitment in the last quarter of the

preceding year and the first three quarters of

the origination year.

(5) Conventional originations by private

pension funds; state and local retirement

funds; federal credit agencies; state and local

credit agencies. Source: SMLA (1990–1997).

Data not available for 1998 and subsequent

years.

This methodology is intended to generate

a lower-bound estimate for the annual size of

the conventional multifamily mortgage

origination market. A more accurate and

realistic estimate could be derived if

corrections for the following could be

generated:

(1) HMDA under-reporting. To the extent

that lenders do not report to HMDA, this data

source leads to downward bias in origination

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65193

volume attributable to the depository sector.

While the true extent of under-reporting is

unknown, a correction factor of 1.25 could be

employed.

(2) State and local credit agencies, state

and local retirement funds, noninsured

pension funds are not counted following

1997 because of the discontinuation of

SMLA.

(3) REITs, individuals. FRB data show

significant growth in multifamily mortgage

debt held by ‘‘individuals and others’’

including mortgage companies, real estate

investment trusts, state and local credit

agencies, state and local retirement funds,

noninsured pension funds, credit unions,

and finance companies. Estimates derived

using the above procedure do not include

any data on originations by individuals.

Some REIT activity is included to the extent

that REITs purchase CMBS included in the

CMBS database. However, circumstances

where REITs originate and hold mortgage

loans without securitizing them would not be

included.

(4) Pipeline effects. Conduit loans

originated during the current year but which

remain in securitization pipelines as of the

end of the year are not counted. However,

this is mitigated by inclusion of CMBS

transactions conducted during the calendar

year, which may include a small number of

loans originated late in the prior year.

Table D.5 illustrates annual estimated

conventional multifamily origination flow

utilizing this methodology. A shortcoming of

the methodology is that it shows a sharp, $10

billion increase in origination volume from

1995–1996 which does not appear on any of

the other data sources discussed above. This

discontinuity may, in part, reflect improved

data quality during the latter part of the

decade as increased CMBS transactions

volume has promoted greater market

transparency and more complete and

accurate public reporting with regard to this

market segment. It may therefore be

concluded that this methodology appears to

provide more reliable estimates for the latter

part of the decade, from 1996 forward, than

with regard to 1995 and earlier years.

BILLING CODE 4210–27–P

65194 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

e. Fannie Mae

Fannie Mae has developed a number of

estimates of the size of the conventional

multifamily mortgage market that it has

shared with the Department. In discussions

with HUD staff in connection with the

Department’s 1995 GSE final rule, Fannie

Mae estimated the size of the market in 1994

at $32.2 billion, and in 1995 at $33.7 billion.

In discussions with HUD staff in

connection with the 2000 proposed rule,

Fannie Mae provided estimates for 1997–

1999 based on a combination of data sources

including SMLA, HMDA, ACLI, Commercial

Mortgage Alert, and the Office of Thrift

Supervision. Fannie Mae’s estimates are

summarized in Table D.6.

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65195

f. Freddie Mac

In its comments submitted in response to

HUD’s proposed rule, Freddie Mac provided

estimates of the size of the conventional

multifamily market for 1995–1997. Some of

these estimates are derived from HMDA,

incorporating a 25 percent expansion factor

to adjust for underreporting, plus estimated

originations by life insurance companies,

pension funds, and government credit

agencies. Other estimates are derived by

combining HMDA with SMLA. Freddie Mac

derives an alternative estimate for 1995 using

the public-use version of the Property

Owners and Managers Survey (POMS). In

discussions with HUD staff in connection

with the 2000 proposed rule, Freddie Mac

staff provided an estimate of the 1998

conventional multifamily market of $40–$50

billion. Freddie Mac’s estimates are

summarized in Table D.7.

BILLING CODE 4210–27–C

g. Other Estimates

1990 Residential Finance Survey (RFS).

The 1990 Residential Finance Survey (RFS)

can be utilized to derive an estimate of the

size of the conventional multifamily market

in 1990. Because loans originated during

1989–1991 are grouped together during in the

public use version of the RFS, a combined

figure for loans originated over this time

period must be divided by 21⁄3 to derive

estimated 1990 conventional origination

volume of $37.4 billion.

HUD Property Owners and Managers

Survey (POMS). HUD’s analysis of data in the

HUD Property Owners and Managers Survey

(POMS) yields an estimated size of the 1995

multifamily origination market of

approximately $37 billion. Analysis of this

survey data is complicated by virtue of the

65196 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

fact that data on mortgage loan amount are

missing for a large number of properties,

requiring the imputation of missing values,

and also because the mortgage loan amount

is ‘‘topcoded’’ on some observations in order

to protect the privacy of respondents. Such

topcoding complicates the use of multiple

regression techniques for imputation of

missing values. In order to more effectively

utilize regression techniques, HUD staff and

contractors were sworn in as special

employees of the Census Bureau in order to

gain access to the internal Census file. The

regression specification with the greatest

explanatory power imputed missing loan

amounts on the basis of number of units,

region of the country, and a dummy variable

for large properties with more than 1,000

units. The use of this specification yielded an

estimated total multifamily market size of

$39.1 billion. After subtracting $2.3 billion in

FHA-insured originations, this yields $36.7

billion as the estimated size of the

conforming multifamily mortgage market in

1995. Details are provided in Table D.8.

BILLING CODE 4210–27–P

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65197

2. Loan Amount per Unit

Another issue regarding the multifamily

mortgage market concerns average loan

amount per unit. This ratio is used in

converting estimates of conventional

multifamily lending volume as measured in

dollars into a number of units financed. For

this purpose, the ratio of total UPB to total

units financed, rather than UPB on a

‘‘typical’’ multifamily unit, is the appropriate

measure, since the objective of this exercise

is to convert total UPB to total units financed.

For the purposes of estimating the number

of units financed in the conventional

multifamily market during 1993–1998,

publicly available GSE loan-level data appear

to generate reasonable loan amount per unit

figures. The public use version of the GSE

data do not provide a means for excluding

seasoned loans, which limits the usefulness

of the data for the purpose of analyzing

current-year originations, but this does not

appear to be a major shortcoming for the

purposes of this analysis.

The GSE loan-level data are not available

for 1990–1992. For this time period,

therefore, multifamily loan amount per unit

must be estimated utilizing an alternative

technique. The method utilized here is to

calculate the ratio of the average

conventional conforming single-family

mortgage to the average per-unit multifamily

mortgage loan amount over 1993–1998. 26

The resulting figure (3.57) is then applied to

average single-family loan amounts over

1990–1992 to derive estimated multifamily

per-unit loan amounts for this earlier time

period. The resulting annual multifamily perunit loan amount series for 1990–1998 is

applied in the following section of this

discussion to the estimated dollar volume of

conventional multifamily originations to

derive an estimate of annual origination

volume measured in dwelling units.

While HUD’s market share analysis for

purposes of this final rule does not rely on

assumptions regarding per-unit loan amounts

on a going-forward basis, further discussion

of the issue is warranted in light of comments

by Freddie Mac in response to the analysis

supporting HUD’s proposed rule. Freddie

Mac forecasts that per-unit loan amounts will

rise to $37,500 to $40,000 over 2000–2003.

This forecast is based in part upon a sudden

increase in GSE per-unit loan amounts from

approximately $31,000 in 1998 to more than

$35,000 in 1999. In reality, however, this

increase is almost entirely attributable to

Freddie Mac, which experienced an increase

in per-unit loan amount of more than $10,000

over 1998–1999, in contrast to Fannie Mae,

which experienced an increase of only about

$200 over this time period. (See Table D.9 for

details.)

65198 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

BILLING CODE 4210–27–C

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65199

Additional information regarding

multifamily loan amount per unit can be

derived from loan-level data on multifamily

mortgages contained in prospectus

disclosures. This data source yields an

average per-unit loan amount of

approximately $31,000 in both 1998 and

1999, based on $12.5 billion in 1998 non-GSE

multifamily transactions and $9.2 billion the

following year. Thus, the large increase in

loan-amount per unit in the GSE data for

1999 does not appear to be representative of

larger trends in the multifamily market.

Rather, it appears to reflect changes in

Freddie Mac’s business practices which may

or may not be evident in future years.27

3. Conventional Multifamily Origination

Volume, 1990–1999

Taken by itself, none of the data sources

appears to definitively answer the question of

the size of the market each year for the entire

time period, but taken together, the various

data sources can be compared and analyzed

in relation to each other in order to

determine a likely range of estimates. Table

D.10 brings together the various estimates

discussed here, and presents the results of

calculations of the multifamily share of the

conventional conforming mortgage market

derived using per-unit loan amounts

discussed above.28 As discussed below in

Section E, the multifamily share of units

financed in the conventional conforming

market (or ‘‘multifamily mix’’) is a key

determinant of the share of units meeting

each of the HUD housing goals.

BILLING CODE 4210–27–P

65200 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

In the 1991–1994 period, the SMLA can be

utilized to derive annual estimates of

multifamily origination volume after

removing originations by mortgage banks in

order to eliminate double-counting of lending

in the commercial bank and mortgage bank

surveys included in SMLA. The plausibility

of the revised SMLA estimates during this

time period is enhanced by their proximity

to other, independently derived figures. For

example, the 1992 revised SMLA estimate of

$23.5 billion is relatively close to the Urban

Institute (UI) estimate of $28.7 billion during

the period of time when the UI projection

model is presumably most reliable, since it

was based on the 1991 RFS, a relatively

recent data source during the early 1990s.

The 1994 revised SMLA estimate of $31.7

billion is relatively close to the Fannie Mae

estimate of $32.2 billion. It is not clear that

the ‘‘augmented’’ HMDA methodology

introduced by PWC adequately corrects for

undercounting. The likely range of estimates

for the 1991–1994 period therefore express a

range of uncertainty around the revised

SMLA figures.

In 1995, it appears likely that actual

origination volume lies somewhere between

the revised SMLA ($32.4 billion) and POMS

($36.7 billion) estimates. The Freddie Mac

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65201

POMS figure of $27 billion, based on the

public-use version of the POMS file, may be

affected adversely by topcoding, and for this

reason the HUD POMS estimate, derived

from internal Census data, may be considered

more reliable. The Fannie Mae estimate of

$33.7 billion lies approximately in the

middle of the reasonable range of $33-$35

billion for 1995. Freddie Mac’s HMDA-based

methodology, generating an estimate of $21

billion, appears to suffer from significant

undercounting as discussed above. Overall,

the Fannie Mae multifamily estimates

summarized here appear to reflect more

careful consideration of the various

components of the multifamily market, in

contrast to the mechanical application of a 25

percent correction factor to the HMDA data

by Freddie Mac, based on estimated singlefamily underreporting.

HUD’s new methodology can be utilized

for the years 1996 and later, in part because

the accuracy and completeness of CMBS data

expanded rapidly during this time period.

The new methodology estimate of $34.5

billion for 1996 is close to the revised SMLA

estimate of $33.3 billion. Based on these two

independent estimates, a likely range of $33–

37 billion is selected.

In 1997, the new methodology ($38.2

billion ) and the revised SMLA figure ($35.5

billion) diverge slightly, but remain relatively

close to each other, and to Fannie Mae’s

estimate of $35–40 billion, in comparison

with other methodological choices. In light of

these three, relatively consistent estimates, a

likely range of $36–40 billion is a reasonable

choice for 1997.

HUD’s new methodology generates a 1998

estimate of $52.9 billion, exceeding even

Freddie Mac’s estimate of $40–50 billion.

However, because of the careful avoidance of

double-counting in construction of this

methodology, it is difficult to see how

conventional multifamily volume could be

less than $52.9 billion. Indeed, because of the

discontinuation of the SMLA in 1998, the

$52.9 billion new methodology estimate does

not include originations by pension funds or

government credit agencies. Therefore, a

likely range of $52–55 billion appears

reasonable.

Table D.10 concludes with estimates for

1999 origination volume as well as

projections for 2000. The Federal Reserve

Board of Governors has published data

indicating that net multifamily borrowing in

1999 was $42.4 billion.29 Because net

multifamily borrowing includes only

increases in the stock of indebtedness, it

excludes refinance loans, which are a

significant component of the multifamily

origination market. Hence, the Federal

Reserve figure can be used as a lower bound

for 1999 origination volume. Consequently, it

would appear reasonable to reject the Fannie

Mae figure of $37–$41 billion for 1999 as

unrealistically low. Because it is based on

data regarding the multifamily mortgage

market from 1991, the UI figure of $48.8

billion may not be valid. Of the four 1999

estimates reported in Table D.10, the $44.5

billion HUD figure appears to be the most

reliable. Because this figure excludes several

important conventional lending categories,

such as pension and retirement funds and

state and federal agencies, it would appear to

be on the low side of the likely range. Based

on information on origination volume

represented by these omitted categories in

the years prior to discontinuation of the

SMLA, a likely range of $45–$48 billion for

1999 may be derived.

Multifamily Mix During the 1990s. Based

on the likely range of annual conventional

multifamily origination volume, multifamily

units represent an average of 16–17 percent

of units financed each year during the

1990s.30 HUD’s estimated multifamily market

shares exceed estimates prepared by PWC

(averaging 8.7 percent for 1991–1998) for two

reasons.31 One is that PWC’s adjusted HMDA

methodology does not adequately correct for

underreporting in HMDA, resulting in

unrealistically low estimates of the size of the

conventional multifamily origination market.

Another reason that PWC’s estimated

multifamily market shares are low is that a

number of their calculations appear to

include FHA and jumbo loans in estimating

the number of single-family units financed

each year. For example, in 1998, PWC

estimates the size of the single-family

mortgage market at $1.5 trillion. This is

identical to the widely-used estimate by the

Mortgage Bankers Association (MBA) for the

entire single-family mortgage market that

year, including jumbo and FHA loans, as

discussed previously. HUD’s market share

calculations, in contrast, are based on the

multifamily share of conventional

conforming mortgage loans originated each

year.

The multifamily share of the conforming

conventional market (or ‘‘multifamily mix’’)

derived from this discussion of multifamily

origination volume is utilized below as part

of HUD’s analysis of the share of units

financed each year meeting each of the

housing goals. For purposes of that analysis,

a multifamily mix of 16.5 percent is

reasonable, since it corresponds most closely

to the midpoint of the likely range of

estimates in Table D.10. However, a 15

percent market share can be utilized as an

alternative market share estimate

corresponding to a somewhat less favorable

environment for multifamily lending. While

somewhat low from an historical standpoint,

a 15 percent mix more readily accommodates

the possibility of a recession or heavy

refinance year than would baseline

assumptions based more strictly on historical

data. In order to more fully consider the

effects of an even more adverse market

environments, an alternative multifamily mix

assumption of 13.5 is also considered, as well

as a number of others.

D. Single-Family Owner and Rental

Mortgage Market Shares

1. Available Data

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 RFS and HMDA are the data sources

for estimating the relative size of the singlefamily rental market. The RFS, provides

mortgage origination estimates for each of the

three single-family property types but it is

quite dated, as it includes mortgages

originated between 1987 and 1991. HMDA

divides newly-originated single-family

mortgages into two property types:32

(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 mortgages

from these data sources are provided in Table

D.11a. (Table D.11b will be discussed below.)

Because HMDA combines the first two

categories (SF–O and SF 2–4), the

comparisons between the data bases must

necessarily focus on the SF investor category.

According to 1997 (1998) HMDA data,

investors account for 9.4 (9.0 percent)

percent of home purchase loans and 7.4

percent (5.5 percent) of refinance loans.33

Assuming a 35 percent refinance rate per

HUD’s projection model, the 1997 (1998)

HMDA data are consistent with an investor

share of 8.7 (7.8) percent. The RFS estimate

of 17.3 percent is approximately twice the

HMDA estimates. In their comments, the

GSEs argued that the HMDA-reported SF

investor share of approximately 8 percent

should be used by HUD. In its 1995 rule as

well as in this year’s proposed rule, HUD’s

baseline model assumed a 10 percent share

for the SF investor group; alternative models

assuming 8 percent and 12 percent were also

considered. As discussed below, HUD’s

baseline projection of 10 percent is probably

quite conservative; however, given the

uncertainty around the data, it is difficult to

draw firm conclusions about the size of the

single-family investor market, which

necessitates the sensitivity analysis that HUD

conducts.

2. Analysis of Investor Market Share

Blackley and Follain

During the 1995 rule-making, 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. 34 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.

65202 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

Blackley and Follain also noted that the

fact that investor loans prepay at a faster rate

than other single-family loans suggests that

the investor share of single-family mortgage

originations should be higher not lower than

the investor share of the single-family

housing stock. In comments, Freddie Mac

questions this part of Follain and Blackely’s

analysis.

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 oneunit properties. Blackley and Follain’s

analysis of this issue suggests lowering the

investor share from 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. 35 In their 1996

paper, they conclude that 12 percent is a

reasonable estimate of the investor share of

single-family mortgage originations. 36

Blackley and Follain caution that uncertainty

exists around this estimate because of

inadequate data.

3. 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.

Thus, it is necessary to compare unit-based

distributions of the single-family mortgage

market under the alternative estimates

discussed so far. The mortgage-based

distributions given in Table D.11a were

adjusted in two ways. First, the owneroccupied HMDA data were disaggregated

between SF–O and SF 2–4 mortgages by

assuming that SF 2–4 mortgages account for

2.0 percent of all single-family mortgages;

according to RFS data, SF 2–4 mortgages

represent 2.3 percent of all single-family

mortgages so the 2.0 percent assumption may

be slightly conservative. Second, the

resulting mortgage-based distributions were

shifted to unit-based distributions by

applying the following unit-per-mortgage

assumptions: 2.25 units per SF 2–4 property

and 1.35 units per SF investor property. Both

figures were derived from the 1991 RFS.37

Based on these calculations, the percentage

distribution of newly-mortgaged single

family dwelling units was derived for each of

the various estimates of the investor share of

single-family mortgages (discussed earlier

and reported in Table D.11a). The results are

presented in Table D.11b. Three points

should be made about these data. First, notice

that the ‘‘SF-Rental’’ row highlights the share

of the single-family mortgage market

accounted for by all rental units.

BILLING CODE 4210–27–P

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65203

BILLING CODE 4210–27–C

Second, notice that the rental categories

represent a larger share of the unit-based

market than they did of the mortgage-based

market reported earlier. This, of course,

follows directly from applying the loan-perunit expansion factors.

Third, notice that the rental share under

HMDA’s unit-based distribution is again

about one-half of the rental share under the

RFS’s distribution. The rental share in HUD’s

1995 rule and this year’s proposed rule is

slightly larger than that reported by HMDA.

The rental share in the ‘‘Blackley-Follain’’

alternative is slightly above that in HUD’s

1995 rule. Rental units account for 15.1

percent of all newly financed single-family

units under HUD’s baseline model, compared

with 13.5 (12.4) percent under a model based

on 1997 (1998) HMDA data.

4. Conclusions

This section has reviewed data and

analyses related to determining the rental

share of the single-family mortgage market.

There are two main conclusions:

(1) While there is uncertainty concerning

the relative size of this market, the

projections made by HUD in 1995 appear

reasonable and, therefore, will serve as the

baseline assumption in the HUD’s market

share model for this year’s final rule.

(2) HMDA likely underestimates the singlefamily rental mortgage market. Thus, this

part of the HMDA data are not considered

reliable enough to use in computing the

market shares for the housing goals. Various

sensitivity analyses of the market shares for

single-family rental properties are conducted

in Sections F, G, and H. These sensitivity

analyses will include the GSEs’

recommended model that assumes investors

65204 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

account for 8 percent of all single-family

mortgages. These sensitivity analyses will

show the effects on the overall market

estimates of the different projections about

the size of the single-family rental market.

The upcoming RFS based on the year 2000

Census will help clarify issues related to the

investor share of the single-family mortgage

market. At that time, HUD will reconsider its

estimates of the investor share of the

mortgage market.

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.38 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$=CONF%*CONV%*SFORIG$

Where

CONV%=conforming mortgage originations

(measured in dollars) as a percent of

conventional single-family originations;

estimated to be 87%.39

CONF%=conventional mortgage

originations as a percent of total

mortgage originations; forecasted to 78%

by industry and GSEs.40

SFORIG$=dollar volume of single-family

one-to-four unit mortgages; $950 billion

is used here as a starting assumption to

reflect market conditions during the

years 2001–2003.41 Alternative

assumptions will be examined later.42

Substituting these values into (1) yields an

estimate for the conventional conforming

market (CCSFM$) of $645 billion.

Second, the number of conventional

conforming single-family mortgages

(CCSFM#) is derived as follows:

(2) CCSFM#=CCSFM$/SFLOAN$

Where SFLOAN$=the average conventional

conforming mortgage amount for singlefamily properties; estimated to be

$110,000.43 Substituting this value into

(2) yields an estimate of 5.9 million

mortgages.

Third, the total number of single-family

mortgages is divided among the three singlefamily property types. Using the 88/2/10

percentage distribution for single-family

mortgages (see Section D), the following

results are obtained:

(3a) SF–OM#=.88*CCSFM#

=number of owner-occupied, one-unit

mortgages

=5.2 million.

(3b) SF–2–4M#=.02*CCSFM#

=number of owner-occupied, two-to-four

unit mortgages

=.1 million.

(3c) SF–INVM# =.10*CCSFM#

=number of one-to-four unit investor

mortgages

=.6 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

=5.3 million.

(4b) SF 2–4=1.25*SF–2–4M#

=number of rental units in 2–4 properties

where a owner occupies one of the units

=.1 million.44

(4c) SF–INVESTOR=1.35*SF–INVM#

=number of single-family investor dwelling

units financed

=.8 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

= 6.2 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. Section C

concludes that 15.0 percent and 16.5

percent are reasonable projections for the

year 2001–03. The baseline model

assumes the more conservative of these

two multifamily mixes—15 percent.

Assuming a multifamily mix of 15 percent

and solving (5b) yields the following:

(5c) MF–UNITS = [0.15/0.85] * SF–UNITS

= 0.176 * SF–UNITS

= 1.1 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 =

7,308,558

(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.45

The projections used above in equations

(1)–(6) produce the following distributions of

financed units by property type:

% Share % Share

SF–O ............................................................................ 72.2 ...................................................................................... ........................

SF 2–4 .......................................................................... 2.0 SF–O ............................................................................ 46 72.2

SFINVESTOR ............................................................... 10.8 SF–RENTER ................................................................ 12.8

MF–UNITs .................................................................... 15.0 MF–UNITS ................................................................... 15.0

Total ...................................................................... 100.0 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 focus

on three multifamily mixes (13.5 percent,

15.0 percent, and 16.5 percent) but there will

also be sensitivity analysis of other

multifamily mix assumptions. Under a 16.5

percent multifamily mix’the average mix

during the 1990s—the newly-mortgaged unit

distribution would be 70.9 percent for SingleFamily Owner, 12.6 percent for SingleFamily Renter, and 16.5 percent for

Multifamily-Units. This distribution is

similar to the baseline distribution in HUD’s

1995 final rule and in this year’s proposed

rule. The analysis in sections F-H will focus

on goals-qualifying market shares for this

property distribution as well as the one

presented above for the more conservative

multifamily mix of 15 percent.

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65205

The appendix will assume the following

for the investor share of single-family

mortgages—8 percent, 10 percent, and 12

percent. The middle value (10 percent

investor share) is used in the above

calculations and will be considered the

‘‘baseline’’ projection throughout the

appendix. However, HUD recognizes the

uncertainty of projecting origination volume

in markets such as single-family investor

properties; therefore, the analysis in Sections

G–H will also consider market assumptions

other than the baseline assumptions.

Table D.12 reports the unit-based

distributions produced by HUD’s market

share model for different combinations of

these projections. The effects of the different

projections can best be seen by examining the

owner category which varies by 6.6

percentage points, from a low of 68.9 percent

(multifamily mix of 16.5 percent coupled

with an investor mortgage share of 12

percent) to a high of 75.5 percent

(multifamily mix of 13.5 percent coupled

with an investor mortgage share of 8 percent).

The owner share under the baseline

projections (15 percent mix and 10 percent

investor) is 72.2 percent, which is slightly

higher than the owner share (71.0 percent) in

the baseline projection of HUD’s 1995 rule

and this year’s proposed rule.

BILLING CODE 4210–27–P

BILLING CODE 4210–27–C

Comparison with the RFS. The Residential

Finance Survey is the only mortgage data

source that provides unit-based property

distributions directly comparable to those

reported in Table D.12. Based on RFS data for

1987 to 1991, HUD estimated that, of total

dwelling units in properties financed by

recently acquired conventional conforming

mortgages, 56.5 percent were owneroccupied units, 17.9 percent were singlefamily rental units, and 25.6 percent were

65206 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

multifamily rental units.47 Thus, the RFS

presents a much lower owner share than does

HUD’s model. This difference is due mainly

to the relatively high level of multifamily

originations (relative to single-family

originations) during the mid-to late-1980s,

which is the period covered by the RFS.48 As

noted earlier, the RFS based on the year 2000

census should clarify issues related to the

rental segment of the mortgage market.

F. Size of the Conventional Conforming

Mortgage Market Serving Low- and

Moderate-Income 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.12. This

section essentially accomplishes Steps 2 and

3 of the three-step procedure discussed in

Section A.2.b.

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 50–55

percent is a reasonable estimate of the

mortgage market’s low- and moderate-income

share for the years (2001–2003) when the

new goals will be in effect.

This rule establishes that the Low- and

Moderate-Income Goal at 50 percent of

eligible units financed in each of calendar

years 2001–2003.

HMDA data for 1999 was not released until

August 2000, thus it was not available at the

time this rule was prepared.

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.49 Table

D.13 gives the percentage of mortgages

originated for low- and moderate-income

families for the years 1992–1998. Data for

home purchase and refinance loans are

presented separately; the discussion will

focus on home purchase loans because they

typically account for the majority of all

single-family owner mortgages. For each

year, a low- and moderate-income percentage

is also reported for the conforming market

without loans originated by lenders that

primarily originate manufactured home loans

(discussed below) in metropolitan areas.

BILLING CODE 4210–27–P

BILLING CODE 4210–27–C

Table D.13 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.

Two trends in the income data should be

mentioned—one related to the market’s

funding of low- and moderate-income

families since the 1995 rule was written and

the other related to the different borrower

income distributions for refinance and home

purchase mortgages.

Low-Mod Market Share Since 1995. As

discussed in the 1995 rule, the percentage of

borrowers with less than area median income

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65207

increased significantly between 1992 and

1994. Mortgages to low-mod borrowers

increased from 34.4 percent of the home

purchase market in 1992 to 41.8 percent of

that market in 1994. Over the next four years

(1995–98), the low-mod share of the home

purchase market remained at a high level,

averaging about 42 percent, or almost 40

percent if manufactured loans are excluded

from the market totals. The share of the

market accounted for by very-low-income

borrowers followed a similar trend,

increasing from 8.7 percent in 1992 to 11.9

percent in 1994 and then remaining at a high

level through 1998. As discussed in

Appendix A, this jump in low-income

lending has been attributed to several factors,

including a favorable economy accompanied

by historically low interest rates; the entry

into the housing market of more diverse

groups including non-traditional households

(e.g., singles), immigrants, and minority

families seeking homeownership for the first

time; and affordable lending initiatives and

outreach efforts on the part of the mortgage

industry. Essentially, the affordable lending

market is much stronger than it appeared to

be when HUD wrote the 1995 rule. At that

time, there had been two years (1993 and

1994) of increasing affordable lending for

lower-income borrowers. The four additional

years of data for 1995–98 show more clearly

the underlying strength of this market.

It is recognized that lending patterns could

change with sharp changes in the economy.

However, the fact that there have been six

years (1993–98) of strong affordable lending

suggests the market may have 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 nontraditional borrowers, minorities, and

immigrants 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

low- and moderate-income share of mortgage

originations, there is evidence that the lowmod market might not return to the low

levels of the early 1990s.

Refinance Mortgages. HUD’s model for

determining the size of the low- and

moderate-income market assumes that lowmod borrowers will represent a smaller share

of refinance mortgages than they do of home

purchase mortgages. However, as shown in

Table D.4, 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. In 1998, another period

of high refinance activity, low- and moderateincome borrowers accounted for 39.7 percent

of refinance loans, versus 43.0 percent of

home purchase loans. But during the years

(1995–97) characterized by lower levels of

refinancing activity, the low-mod share for

refinance mortgages was about the same as

that for home purchase mortgages. In 1997,

the low-mod share of refinance mortgages

(45.0) was even higher than the low-mod

share of home loans (42.5 percent).

The projection model assumes that

refinancing will be 35 percent of the singlefamily mortgage market. However given the

volatility of refinance rates from year to year,

it is important to conduct sensitivity tests

using different refinance rates.

b. Manufactured Housing Loans

The mortgage market definition in this

appendix includes manufactured housing

loans,50 which have become an important

source of affordable housing and which the

GSEs have started to purchase. Because the

market estimates in HUD’s 1995 rule were

adjusted to exclude manufactured housing

loans, several tables in this appendix will

show how the goals-qualifying shares of the

single-family-owner market change

depending on the treatment of manufactured

housing loans. As explained later, the effect

of manufactured housing on HUD’s

metropolitan area market estimate for each of

the three housing goals is a modest one

percentage point

As discussed in Appendix A, the

manufactured housing market has been

increasing rapidly over the past few years, as

sales volume has increased from $4.7 billion

in 1991 to $15.3 billion in 1999. The

affordability of manufactured homes for

lower-income families is demonstrated by

their average price of $44,000 in 1999, a

fraction of the $196,000 for new homes and

$168,000 for existing 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.

Data on the incomes of purchasers of

manufactured homes is not readily available,

but HMDA data on home loans made by 22

lenders that primarily originate

manufactured home loans, discussed below,

indicate that: 51

• A very high percentage of these loans—

76 percent in 1998—would qualify for the

Low- and Moderate-Income Goal,

• A substantial percentage of these loans—

42 percent in 1998—would qualify for the

Special Affordable Goal, and

• Almost half of these loans—47 percent in

1998—would qualify for the Underserved

Areas Goal.

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.

To date the GSEs have played a minimal

role in the manufactured home loan market,

but both enterprises have expressed an

interest in expanding their roles.52 Except in

structured transactions, the GSEs do not

purchase manufactured housing loans under

their seller/servicer guidelines unless they

are real estate loans. That is, such homes

must have a permanent foundation and the

site must be either purchased as part of the

transaction or already owned by the

borrower. Industry trends toward more

homes on private lots and on concrete

foundations suggest that the percentage of

manufactured homes that would qualify as

real estate loans under GSE guidelines has

grown in the past few years. There has also

been a major shift from single-section homes

to multisection homes, which contain two or

three units which are joined together on site.

Although manufactured home loans cannot

be identified in the HMDA data, HUD staff

have identified 22 lenders that primarily

originate manufactured home loans and

likely account for most of these loans in the

HMDA data for metropolitan areas. In Table

D.13, the data presented under ‘‘Conforming

Market Without Manufactured Home Loans’’

excludes loans originated by manufactured

housing lenders, as well as loans less than

$15,000. The lenders include companies

such as Green Tree Financial; Vanderbilt

Mortgage; Deutsche Financial Capital;

Oakwood Acceptance Corporation; Allied

Acceptance Corporation; Belgravia Financial

Services; Ford Consumer Finance Company;

and the CIT Group.53

c. American Housing Survey Data

The American Housing Survey also reports

borrower income data similar to that reported

in Table D.3. The low- and moderate-income

market shares from the AHS are as follows:

1985 27.0%

1987 32.0%

1989 34.0%

1991 36.0%

1993 33.0% (38.7% home purchase and

28.6% refinance)

1995 40.0% (38.5% home purchase and

43.2% refinance)

According to the AHS, 38.5 percent of

those families surveyed during 1995 who had

recently purchased their homes, and who

obtained conventional mortgages below the

conforming loan limit, had incomes below

the area median; this compares with 39.3

percent based on 1995 HMDA data that

excludes manufactured homes (as the AHS

data do).

A longer-term perspective of the mortgage

market can be gained by examining income

data from the last six American Housing

Surveys. During the earlier period between

1987 and 1991, the low- and moderateincome share increased from 27 percent to 36

percent, and averaged 32.3 percent. After

remaining at a relatively low percentage (33.0

percent) during the heavy refinance year of

1993, the low- and moderate-income share

rebounded to 40.0 percent in 1995. As noted

earlier, this is about the same market share

reported by HMDA data for 1995.

The GSEs have raised issues concerning

underreporting of income in the AHS.54

Since HMDA data cover over 80 percent of

the single-family-owner mortgage market,

and the American Housing Survey represents

only a very small sample of this market, the

HMDA data will be the source of information

on the characteristics of single-family

property owners receiving mortgage

financing. As discussed next, the American

Housing Survey and the Property Owners

and Managers Survey will be relied on for

information about the rents and affordability

65208 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

of single-family and multifamily rental

properties.

2. Low- and Moderate-Income Percentage for

Renter Mortgages

The 1995 rule relied on the American

Housing Survey for a measure of the rent

affordability of the single-family rental stock

and the multifamily rental stock. As

explained below, the AHS provides rent

information for the stock of rental properties

rather than for the flow of mortgages

financing that stock. This section discusses a

new survey, the Property Owners and

Managers Survey (POMS), that provides

information on the flow of mortgages

financing rental properties. 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 presents AHS

data on the affordability of the rental housing

stock for the survey years between 1985 and

1997. The 1997 AHS shows 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 1989, 1991, 1993, and 1995 are

similar to the 1997 data.

BILLING CODE 4210–27–P

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65209

BILLING CODE 4210–27–C

65210 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

b. Property Owners and Managers Survey

(POMS)

During the 1995 rule-making, concern was

expressed about using data on rents from the

outstanding rental stock to proxy rents for

newly mortgaged rental units.55 At that time,

HUD conducted an analysis of this issue

using the Residential Finance Survey and

concluded that the existing stock was an

adequate proxy for the mortgage flow when

rent affordability is defined in terms of less

than 30 percent of area median income,

which is the affordability definition for the

Low- and Moderate-Income Goal. More

specifically, that analysis suggested that 85

percent of single-family rental units and 90

percent of multifamily units are reasonable

estimates for projecting the percentage of

financed units affordable at the low- and

moderate-income level.56 HUD has

investigated this 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

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 reported in

Table D.5 on the affordability of the rental

stock. Ninety-six (96) percent of single-family

rental properties with new mortgages

between 1993 and 1995 were affordable to

low- and moderate-income families, and 56

percent were affordable to very-low-income

families. The corresponding percentages for

newly-mortgaged multifamily properties are

96 percent and 51 percent, respectively.

Thus, these percentages for newly-mortgaged

properties from the POMS are similar to

those from the AHS for the rental stock. As

discussed in the next section, the baseline

projection from HUD’s market share model

assumes that 90 percent of newly-mortgaged,

single-family rental and multifamily units are

affordable to low- and moderate-income

families.

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 provides some

necessary background by comparing HUD’s

estimate made during the 1995 rule-making

process with actual experience between 1995

and 1998. Subsection 3.b presents new

estimates of the low-mod market while

Subsection 3.c reports the sensitivity of the

new estimates to changes in assumptions

about economic and mortgage market

conditions.

a. Comparison of Market Estimates With

Actual Performance

The market share estimates that HUD made

during 1995 can now be compared with

actual market shares for 1995 to 1998. This

discussion of the accuracy of HUD’s past

market estimates considers all three housing

goals, since the explanations for the

differences between the estimated and actual

market shares are common across the three

goals. HUD estimated the market for each

housing goal for 1995–98, and obtained the

results reported in Table D.15.57 B&C loans

are not included in the market estimates

reported in Table D.15. The discussion of

Table D.15 will proceed as follows. It will

first focus on the market estimates for 1995

to 1997 which are the most useful

comparisons with HUD’s market estimates

from the 1995 rule. The discussion will then

examine the market estimates for the heavy

refinance year of 1998. After that, HUD’s

method for adjusting the 1995–98 market

data to exclude B&C loans as well as the nonmetropolitan area adjusted market for the

Underserved Areas Goal will be explained.

(See Table D.15)

BILLING CODE 4210–27–P

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65211

BILLING CODE 4210–27–C

HUD’s market estimates in 1995 were 48–

52 percent for the Low- and ModerateIncome Goal, 20–23 percent for the Special

Affordable Goal, and 25–28 percent for the

Underserved Areas Goal. Thus, even the

upper bound figures for the market share

ranges in the 1995 rule proved to be low for

the 1995–97 period—for the low-mod

estimate, 52 percent versus 57–58 percent;

for the special affordable estimate, 23 versus

28–29 percent, and for the underserved areas

estimate, 28 percent versus 33–34 percent.

There are several factors explaining HUD’s

underestimate of the goals-qualifying market

shares. The 1995–97 mortgage markets

originated more affordable single-family

mortgages than anticipated, mainly due to

historically low interest rates and strong

economic expansion. In 1997, for instance,

almost 44 percent of all (home purchase and

refinance) single-family-owner mortgages

qualified for the Low- and Moderate-Income

Goal, 16 percent qualified for the Special

Affordable Goal, and 28 percent qualified for

the Underserved Areas Goal.58 HUD’s 1995

estimates anticipated smaller shares of new

mortgages being originated for low-income

families and in their neighborhoods.59 60

65212 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

The financing of multifamily properties

during 1995–97 was larger than anticipated.

HUD’s earlier estimates assumed a

multifamily share of 16 percent, which was

lower than the approximately 19 percent

multifamily share for the years 1995–97. The

underestimate for the multifamily share was

due both to a larger multifamily dollar

volume ($34 billion for 1995, $37 billion for

1996, and $38 billion for 1997) than

anticipated in the 1995 GSE rule ($30 billion)

and to lower per unit multifamily loan

amounts than assumed in HUD’s earlier

model.61

B&C Mortgages. As discussed in Appendix

A, the market for subprime mortgages has

experienced rapid growth over the past 2–3

years. Table D.15 provides goals-qualifying

market shares that exclude the B&C portion

of the subprime market. This section explains

how these ‘‘adjusted’’ market shares are

calculated from ‘‘unadjusted’’ market shares

that include B&C loans, using the year 1997

as an example. Comprehensive data for

measuring the size of the subprime market

are not available. However, estimates by

various industry observers suggest that the

subprime market could have accounted for as

much as 15 percent of all mortgages

originated during 1997, which would have

amounted to approximately $125 billion.62 In

terms of credit risk, this $125 billion includes

a wide range of mortgage types. ‘‘A-minus’’

loans, which represented at least half of the

subprime market in 1997, make up the least

risky category. 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). The

B&C loans experience much higher

delinquency rates than A-minus loans.63

The procedure for excluding B&C

mortgages from estimated ‘‘unadjusted’’

market shares for goals-qualifying loans in

1997 combined information from several

sources. First, the $125 billion estimate for

the subprime market was reduced by 20

percent to arrive at an estimate of $100

billion for subprime loans that were less than

the conforming loan limit of $214,600 in

1997. This figure was reduced by one-half to

arrive at an estimate of $50 billion for the

conforming B&C market; with an average

loan amount of $68,289 (obtained from

HMDA data, as discussed below), the $50

billion represented approximately 732,182

B&C loans originated during 1997 under the

conforming loan limit.

HMDA data was used to provide an

estimate of the portion of these 732,182 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

200 HMDA reporters that primarily originate

subprime loans. The goals-qualifying

percentages of the loans originated by these

subprime lenders in 1997 were as follows:

57.3 percent qualified for the Low- and

Moderate-Income Goal, 28.1 percent for the

Special Affordable Goal, and 44.7 percent for

the Underserved Areas Goal.64 Applying the

goals-qualifying percentages to the estimated

B&C market total of 732,182 gives the

following estimates of B&C loans that

qualified for each of the housing goals in

1997: Low- and Moderate Income (419,540),

Special Affordable (205,743), and

Underserved Areas (327,286).

Adjusting HUD’s model to exclude the B&C

market involves subtracting the above four

figures’ one for the overall B&C market and

three for B&C loans that qualify for each of

the three housing goals—from the

corresponding figures estimated by HUD for

the total single-family and multifamily

market inclusive of B&C loans. HUD’s model

estimates that 8,039,132 single-family and

multifamily units were financed during 1997;

of these, 4,620,828 (57.5 percent) qualified

for the Low- and Moderate-Income Goal,

2,311,251 (28.8 percent) for the Special

Affordable Goal, and 2,694,351 (33.5 percent)

for the Underserved Areas Goal. Deducting

the B&C market estimates produces the

following adjusted market estimates: a total

market of 7,306,950, of which 4,201,287 (57.5

percent) qualified for the Low- and ModerateIncome Goal, 2,105,508 (28.8 percent) for the

Special Affordable Goal, and 2,367,066 (32.4

percent) for the Underserved Areas Goal.

As seen, the low-mod market share

estimate exclusive of B&C loans (57.5

percent) is the same as the original market

estimate (57.5 percent) and the

corresponding special affordable market

estimate (28.8 percent) is also the same as the

original estimate. This occurs because the

B&C loans that were dropped from the

analysis had similar low-mod and special

affordable percentages as the overall (both

single-family and multifamily) market. For

example, the low-mod share of B&C loans

was projected to be 57.3 percent and HUD’s

market model projected the overall low-mod

share to be 57.5 percent. Thus, dropping B&C

loans from the market totals does not change

the overall low-mod share of the market.

The situation is different for the

Underserved Areas Goal. Underserved areas

account for 44.7 percent of the B&C loans,

which is a higher percentage than the

underserved area share of the overall market

(33.5 percent). Thus, dropping the B&C loans

leads to a reduction in the underserved areas

market share of 1.1 percentage points, from

33.5 percent to 32.4 percent.

Dropping B&C loans from HUD’s model

changes the mix between rental and owner

units in the final market estimate. Based on

assumptions about the size of the owner and

rental markets for 1997, HUD’s model

calculates that single-family-owner units

accounted for 70.2 percent of total units

financed during 1997. Dropping the B&C

owner loans, as described above, reduces the

owner percentage of the market by three

percentage points to 67.2 percent. Thus,

another way of explaining why the goalsqualifying market shares are not affected so

much by dropping B&C 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.

There are caveats that should be mentioned

concerning the above adjustments for the

B&C market for 1997. The adjustment for

B&C loans depends on several estimates

relating to the 1997 mortgage market, derived

from various sources. Different estimates of

the size of the B&C market in 1997 or the

goals-qualifying shares of the B&C market

could lead to different estimates of the goalsqualifying 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 A-minus and B&C loans, the

goals-qualifying percentages used here may

not be accurately measuring the goalsqualifying 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.65 Despite

these caveats, it also 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 below, 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.

Adjustment for Non-Metropolitan Areas.

The first set of 1995–98 market shares for

underserved areas is based on single-familyowner parameters for metropolitan areas. It is

necessary to adjust these market shares

upward by about 1.5 percentage points to

reflect the fact that underserved counties

account for a much larger portion of nonmetropolitan areas than underserved census

tracts do metropolitan areas. The method for

deriving the 1.5 percentage point adjustment

is explained in Section G.3 below, which

presents the projected 2001–03 market

estimates for the Underserved Areas Goal.

1998 Market Estimates. The high volume of

single-family mortgages in the heavy

refinance year of 1998 increased the share of

single-family-owner units to 73.1 percent,

compared with 68–70 percent for 1995 to

1997. This shift toward single-family loans,

combined with the higher level of singlefamily refinance activity in 1998, results in

market shares that are slightly smaller than

reported for 1995–97. The following

estimates are obtained: low-mod, 53.8

percent; special affordable, 25.8 percent; and

underserved areas, 30.9 percent.66 While

lower, these estimates remain higher than the

market estimates that HUD made in 1995 (see

earlier discussion for reasons).

b. Market Estimates

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 (2001–2003) when the

new housing goals will be in effect. Three

alternative sets of projections about property

shares and rental property low-and moderateVerDate 11<MAY>2000 18:02 Oct 30, 2000 Jkt 194001 PO 00000 Frm 00170 Fmt 4701 Sfmt 4700 E:\FR\FM\31OCR2.SGM pfrm08 PsN: 31OCR2
Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65213

income percentages are given in Table D.16.

Case 1 projections represent the baseline and

intermediate case; it assumes that investors

account for 10 percent of the single-family

mortgage market. Case 2 assumes a lower

investor share (8 percent) based on HMDA

data and slightly more conservative low-and

moderate-income percentages for singlefamily rental and multifamily properties (85

percent). Case 3 assumes a higher investor

share (12 percent) consistent with Follain

and Blackley’s suggestions.

BILLING CODE 4210–27–P

65214 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

BILLING CODE 4210–27–C

Because single-family-owner units account

for about 70 percent of all newly mortgaged

dwelling units, the low- and moderateincome percentage for owners is the most

important determinant of the total market

estimate.67 Thus, Table D.17 provides market

estimates for different low-mod percentages

for the owner market as well as for different

multifamily mix percentages—the 15.0

percent projection bracketed by 13.5 percent

and 16.5 percent. As discussed in Section C

of this appendix, 16.5 percent represents the

average multifamily share between 1991 and

1998, while 15 percent represents a slightly

more conservative baseline.

Several low-mod percentages of the owner

market are given in Table D.17 to account for

different perceptions about the low-mod

share 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 singlefamily-owner market on the goals-qualifying

share of the overall mortgage 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.

With respect to excluding B&C loans from

the market estimates, Table D.17 can be

interpreted in two ways. First, readers could

choose a home purchase low-mod percentage

(that is, one of the percentages in the first

column) that they believe is adjusted for B&C

loans and then obtain a rough estimate of the

overall low-mod estimate from the second to

fourth columns corresponding to different

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65215

multifamily mixes. For instance, if one

believes the appropriate home purchase

percentage adjusted for B&C loans (or

adjusted for any other exclusions that the

reader thinks are appropriate) is 39 percent,

then the low-mod market estimate is 52.4

percent assuming a multifamily mix of 15

percent. Second, readers could choose a

home purchase percentage directly from

HMDA data that is unadjusted for B&C loans

and then rely on HUD’s methodology

(described below) for excluding B&C loans

from the market estimates reported in Table

D.17. 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) due to excluding B&C owner

loans from the analysis. According to HUD’s

methodology, dropping B&C owner loans

would reduce the various low-mod market

estimates reported in Table D.17 by less than

half of a 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 share when B&C owner

loans are dropped from the market totals. For

this reason, the low-mod market estimates

reported in Table D.17 provide a reasonable

proxy for low-mod market estimates without

B&C loans. This issue is discussed in more

detail below.

As shown in Table D.17, the market

estimate is 53–56 percent if the owner

percentage is at or above 40 percent (slightly

less than its 1994–98 levels), and it is 52–53

percent if the owner percentage is 39 percent

(its 1993 level). If the low- and moderateincome percentage for owners fell from its

1997–98 level of 43 percent to 35 percent, the

overall market estimate would be

approximately 50 percent. Thus, 50 percent

is consistent with a rather significant decline

in the low-mod share of the single-family

home purchase market. Under the baseline

projection, the home purchase percentage

can fall as low as 34 percent—about fourfifths of the 1997–98 level—and the low- and

moderate-income market share would still be

49 percent.

The volume of multifamily activity is an

important determinant of the size of the lowand 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. As

discussed in Section E.2, the multifamily mix

assumption of 15 percent produces an overall

(both multifamily and single-family) rental

mix of 27.8 percent, which is about a

percentage point less than the overall rental

mix projection in HUD’s 1995 rule. Lowering

the multifamily mix to 13.5 produces the set

of overall low-mod market estimates that are

reported in the first column of Table D.17.

Compared with 15 percent, the 13.5 percent

mix assumption reduces the overall low-mod

market estimates by slightly over a half

percentage point. For example, when the

low-mod share of the owner market is 42

percent, the low-mod share of the overall

market is 54.6 percent assuming a 15 percent

multifamily mix but is 54.0 percent assuming

a 13.5 percent multifamily mix.68

The market estimates for Case 2 and Case

3 bracket those for Case 1. The smaller singlefamily rental market and lower low- and

moderate-income percentages for rental

properties result in the Case 2 estimates

being almost two percentage points below the

Case 1 estimates. Conversely, the higher

percentages under Case 3 result in estimates

of the low-mod market approximately three

percentage points higher than the baseline

estimates.

The various market estimates presented in

Table D.17 are not all equally likely. Most of

them equal or exceed 51 percent; in the

baseline model, estimates below 51 percent

would require the low-mod share of the

single-family owner market for home

purchase loans to drop to approximately 36

percent which would be over six percentage

points lower than the 1993–98 average for the

low-mod share of the home purchase market.

With a multifamily mix at 13.5 percent, the

low-mod share of the owner market can fall

to 36 percent before the average market share

falls below 50 percent.

The upper bound (56 percent) of the lowmod estimates reported in Table D.17 for the

baseline case is lower than the low-mod

share of the market between 1995 and 1997.

As reported above, HUD estimates that the

low-mod market share during this period was

about 57 percent. There are two reasons the

projected low-mod estimates are lower than

the 1995–97 experience. First, the projected

rental share of 28 percent is lower than the

rental share of 31 percent for the 1995–97

period; a smaller market share for rental units

lowers the low-mod market share. Second,

HUD’s projections assume that refinancing

borrowers will have higher incomes than

borrowers purchasing a home (explained

below). As Table D.14 shows, this was the

reverse of the situation between 1995 and

1997 when refinancing borrowers had higher

incomes than borrowers purchasing a

home. 69 This fact, along with the larger

single-family mix effect, resulted in the lowmod share of the market falling below the

1997 level of 57 percent.

B&C Loans. As discussed above, if one

assumes the home purchase percentages in

the first column of Table D.17 are unadjusted

for B&C loans, then the overall low-mod

market estimates must be adjusted to exclude

these loans. B&C loans can be deducted from

HUD’s low-mod market estimates using the

same procedure described earlier. But before

doing that, some additional comments about

how HUD’s projection model operates are in

order. HUD’s projection model assumes that

the low-mod share of refinance loans will be

three percentage points lower than the lowmod share of home purchase loans, even

though there have been years recently (1995–

97) when the low-mod share of refinance

loans has been as high or higher than that for

home purchase loans (see Table D.14).70

Since B&C loans are primarily refinance

loans, this assumption of a lower low-mod

share for refinance loans partially adjusts for

the effects of B&C loans, based on 1995–97

market conditions. For example, in Table

D.17, the low-mod home purchase percentage

of 43 percent, which reflects 1997 conditions,

is combined with a low-mod refinance

percentage of 40 percentage when, in fact, the

low-mod refinance percentage in 1997 was

45 percent. Thus, by taking the 1992–98

average low-mod differential between home

purchase and refinance loans, the projection

model deviates from 1995–97 conditions in

the single-family owner market.71

The effects of deducting the B&C loans

from the projection model can be illustrated

using the above example of a low-mod home

purchase percentage of 42 percent and a lowmod refinance percentage of 39 percent; as

Table D.17 shows, this translates into an

overall low-mod market share of 54.6

percent. It is assumed that the subprime

market accounts for 12 percent of all

mortgages originated, which would be $114

billion based on $827 billion for the

conventional market. This $114 billion

estimate for the subprime market is reduced

by 20 percent to arrive at $91 billion for

subprime loans that will be less than the

conforming loan limit. This figure is reduced

by one-half to arrive at approximately $46

billion for the conforming B&C market; with

an average loan amount of $82,022; the $46

billion represents 556,000 B&C loans

projected to be originated under the

conforming loan limit.72

Following the procedure discussed in

Section F.3.a, the low-mod share of the

market exclusive of B&C loans is estimated

to be 54.3 percent, which is only slightly

lower than the original estimate (54.6

percent).73 As noted earlier, this occurs

because the B&C loans that were dropped

from the analysis had similar low-mod

percentages as the overall (both single family

and multifamily) market (59.3 percent and

55.7 percent, respectively). The impact of

dropping B&C loans is larger when the

overall market share for low-mod loans is

smaller. As shown in Table D.17, a 38

percent low-mod share for single-family

owners is associated with an overall low-mod

share of 51.7 percent. In this case, dropping

B&C loans would reduce the low-mod market

share by 0.5 percentage point to 51.2 percent.

Still, dropping B&C loans from the market

totals does not change the overall low-mod

share of the market appreciably.

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 30.1 percent of

total units after dropping B&C loans

compared with 27.8 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.

Section F.3.a discussed several caveats

concerning the analysis of B&C loans. It is

not clear what types of loans (e.g., first versus

second mortgages) are included in the B&C

market estimates. There is only limited data

on the borrower characteristics of B&C loans

and the extent to which these loans are

included in HMDA is not clear. Still, the

analysis of Table D.17 and the above analysis

of the effects of dropping B&C loans from the

market suggest that 50–55 percent is a

reasonable range of estimates for the low- and

moderate-income market for the years 2001–

65216 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

2003. This range covers markets without B&C

loans 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.

c. Economic Conditions, Market Estimates,

and the Feasibility of the Low- and ModerateIncome Housing Goal

During the 1995 rule-making, there was a

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. As discussed in Section B of

this appendix, the GSEs expressed similar

concerns in their comments on this year’s

proposed rule. This section discusses 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 existed

during the 1993 to 1998 period.

Volatility of Market. The starting point for

HUD’s estimates of market share is the

projected $950 billion in single-family

originations. Shifts in economic activity

could obviously affect the degree to which

this projection is borne out. As noted earlier,

the Mortgage Bankers Association has

recently revised its forecasts of mortgage

originations numerous times in the face of

projected changes in market conditions.

Changing economic conditions can affect the

validity of HUD’s market estimates as well as

the feasibility of the GSEs’ accomplishing the

housing goals.

One only has to recall the volatile nature

of the mortgage market in the past few years

to appreciate the uncertainty around

projections of that market. Large swings in

refinancing, consumers switching between

adjustable-rate mortgages and fixed-rate

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 1998 and early 1999.

While interest rates have recently risen, they

continue to be moderate by historical

standards. A period of low-to-moderate

interest rates would sustain affordability

levels without causing the rush to refinance

seen earlier in 1993 and more recently in

1998. A high percentage of potential

refinancers have already done so, and are less

likely to do so again.

HUD conducted numerous sensitivity

analyses of the market shares. In the

projection model, increasing the singlefamily mortgage origination forecast while

holding the multifamily origination forecast

constant is equivalent to reducing the

multifamily mix. Increasing the single-family

projection by $100 billion, from $950 billion

to $1,050 billion, would reduce the market

share for the Low-and Moderate-Income Goal

by approximately 0.5 percentage point,

assuming the other baseline assumptions

remain unchanged.74 A $200 billion increase

would reduce the low-mod projected market

share by 0.9 percentage point.

HUD also examined potential changes in

the market shares under very different

macroeconomic environments, one assuming

a recession and one assuming a period of low

interest rates and heavy refinancing. The

recessionary environment was simulated

using Fannie Mae’s minimum projections of

single-family mortgage originations ($880

billion). The low- and moderate-income

share of the home purchase market was

reduced to 34 percent, or 8.5 percentage

points lower than its 1997 share.75 Under

these rather severe conditions, the overall

market share for the Low- and ModerateIncome Goal would decline to 50.4 percent.

If the low-mod share of the owner market

were reduced to 32 percent (for both home

purchase and refinance loans), the low-mod

share for the overall market would fall to 49.0

percent.

The heavy refinance environment was

simulated assuming that the single-family

origination market increased to $1,400

billion, which increases the owner share of

newly-mortgaged dwelling units from 72.2

percent under HUD’s baseline model to 73.2

percent. Refinances were assumed to account

for 60 percent of all single-family mortgage

originations. If low- and moderate-income

borrowers accounted for 40 percent of

borrowers purchasing a home but only 36

percent of refinancing borrowers, then the

market share for the Low- and ModerateIncome Goal would be 51.6 percent. If the

first two percentages were reduced to 39

percent and 32 percent, respectively, then the

market share for the Low- and ModerateIncome Goal would fall to 49.6 percent.

However, if the refinance market resembled

1998 conditions, the low-mod share would

be 54 percent, as reported earlier.

Finally, HUD simulated the specific

scenario based on the MBA’s most recent

market estimate of $912 billion and a

refinance rate of 22 percent. In this case,

assuming a low-mod home purchase

percentage of 40, the overall low-mod market

share was 53.4 percent, assuming a

multifamily mix of 15 percent; 52.8 percent,

assuming a multifamily mix of 13.5 percent;

and 54.1 percent, assuming a multifamily

mix of 16.5 percent.

Feasibility Determination. As stated in the

1995 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.76 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 the

1995 GSE rule, 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

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.

Affordability Conditions and Market

Estimates. The market share estimates rely on

1992–1998 HMDA data for the percentage of

low- and moderate-income borrowers. 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 first-time buyers and lowincome borrowers to enter the market during

the mid- and late-1990s. 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 discussed above,

the 50–55 percent range for the low-mod

market share covers economic and housing

market 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 34 percent, which is over nine

percentage points lower than its 1998 level

of about 43 percent, before the baseline

market share for the Low- and ModerateIncome Goal would fall to 49 percent.

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 50–55 percent is a reasonable

range of estimates of the mortgage market’s

low- and moderate-income share for each of

years 2001–2003. This range covers much

more adverse market conditions than have

existed recently, allows for different

assumptions about the multifamily market,

and excludes the effects of B&C loans. HUD

recognizes that shifts in economic conditions

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 or the

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65217

Geographically-Targeted Goal. The first two

sections focus on underserved census tracts

in metropolitan areas. 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.

This rule establishes that the Central Cities,

Rural Areas, and other Underserved Areas

Goal at 31 percent of eligible units financed

in each of calendar years 2001–2003.

1. Geographically-Targeted Goal Shares by

Property Type

For purposes of the GeographicallyTargeted 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.18 are the percentages of singlefamily-owner mortgages that financed

properties located in underserved census

tracts of metropolitan areas between 1992

and 1998. In 1997 and 1998, approximately

25 percent of home purchase loans financed

properties located in these areas; this

represents an increase from 22 percent in

1992 and 1993. In some years, refinance

loans are even more likely than home

purchase loans to finance properties located

in underserved census tracts. Between 1994

and 1997, 28.5 percent of refinance loans

were for properties in underserved areas,

compared to 25.1 percent of home purchase

loans.77 In the heavy refinance year of 1998,

underserved areas accounted for about 25

percent of both refinance and home purchase

loans.

BILLING CODE 4210–27–P

65218 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

BILLING CODE 4120–27–C

Since the 1995 rule was written, the singlefamily-owner market in underserved areas

has remained strong, similar to the low- and

moderate-income market discussed in

Section F. Over the past five years, the

underserved area share of the metropolitan

mortgage market has leveled off at 25–28

percent, considering both home purchase and

refinance loans. This is higher than the 23

percent average for the 1992–94 period,

which was the period that HUD was

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65219

considering when writing the 1995 rule. As

discussed earlier, economic conditions could

change and reduce the size of the

underserved areas market; however, that

market appears to have shifted to a higher

level over the past five years.

Renter Mortgages. The second and third

sets of numbers in Table D.18 are the

underserved area percentages for singlefamily rental mortgages and multifamily

mortgages, respectively. Based on HMDA

data for single-family, non-owner-occupied

(investor) loans, the underserved area share

of newly-mortgaged single-family rental units

has been in the 43–45 percent range over the

past five years. HMDA data also show that

about half of newly-mortgaged multifamily

rental units are located in underserved areas.

2. Market Estimates for Underserved Areas in

Metropolitan Areas

In the 1995 GSE rule, HUD estimated that

the market share for underserved areas would

be between 25 and 28 percent. This estimate

turned out to be below market experience, as

underserved areas accounted for

approximately 33–34 percent of all mortgages

originated in metropolitan areas between

1995 and 1997 and for 31 percent in 1998

(see Table D.15).78

Table D.19 reports HUD’s estimates of the

market share for underserved areas based on

the projection model discussed earlier.79 As

indicated in Table D.18, these overall market

estimates are based mainly on HMDAreported underserved area shares of owner

and rental properties in metropolitan areas.

As explained in Section F.3 below, the

estimated combined effect of dropping B&C

loans and of including non-metropolitan

areas is to increase the underserved area

market shares reported in Table D.19 by

approximately one-half percentage point.

BILLING CODE 4210–27–P

65220 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

BILLING CODE 4210–27–C

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.19 reports

market shares for different single-familyowner percentages ranging from 28 percent

(1997 HMDA) to 20 percent (1993 HMDA) to

18 percent. If the single-family-owner

percentage for underserved areas is at its

1994–98 HMDA average of 26 percent, the

market share estimate is over 31 percent. The

overall market share for underserved areas

peaks at 33 percent when the single-familyowner percentage is at its 1997 figure of 28

percent. Most of the estimated market shares

for the owner percentages that are slightly

below recent experience are in the 30 percent

range. In the baseline case, the single-familyowner percentage can go as low as 23

percent, which is over 3 percentage points

lower than the 1994–98 HMDA average, and

the estimated market share for underserved

areas remains over 29 percent.

Unlike the Low- and Moderate-Income

Goal, the market estimates differ only slightly

as one moves from Case 1 to Case 3 and from

a 13.5 percent mix to 16.5 percent mix. For

example, reducing the assumed multifamily

mix to 13.5 percent reduces the overall

market projection for underserved areas by

only about 0.3 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. Additional

sensitivity analyses were conducted as

described in Section F.3c.

For example, adding $100 ($200) billion to

the $950 billion single-family originations

would reduce the underserved area market

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65221

share by about 0.3 (0.5) percent, assuming

there were no other changes. The MBA

scenario combined with a single-family

owner underserved area percentage of 25

percent, would produce an overall market

share for underserved areas of 30.7 percent.

The recession scenario described in Section

F.3.c assumed that the underserved area

percentage for single-family-owner mortgages

was 21 percent or almost seven percentage

points lower than its 1997 value. In this case,

the overall market share for underserved

areas declines to 28.4 percent. In the

refinance scenarios, the underserved areas

market share was approximately 31 percent.

3. Adjustments: B&C Loans and the Rural

Underserved Area Market

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 44.7 percent,

which is much higher than the projected

percentage for the overall market (30–33

percent as indicated in Table D.19). Thus,

dropping B&C loans will reduce the overall

market estimates. Consider in Table D.19, the

case of a single-family-owner percentage of

26 percent, which yields an overall market

estimate for underserved areas of 31.4

percent. Dropping B&C loans from the

projection model reduces the underserved

areas market share by 1.1 percentage points

to 30.3.

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 the greater of

statewide or national non-metropolitan

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 1997–99, 36–38 percent of the

GSE’s total purchases in non-metropolitan

areas were in underserved counties while

25–27 percent of their purchases in

metropolitan areas were in underserved

census tracts. These figures suggest the

market share for 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. Over

the past few years, the non-metropolitan

portion of the Underserved Areas Goal has

contributed approximately 1.3 percentage

point to the GSEs performance, compared

with a goals-counting system that only

included metropolitan areas.

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

42 percent of all mortgages originated in nonmetropolitan areas during 1997 and 1998. By

contrast, underserved census tracts

accounted for approximately 25 percent of all

mortgages in metropolitan area.80 If this 17

point differential reflected actual market

conditions, then the underserved areas

market share estimated using metropolitan

area data should be increased by 1.9

percentage points to account for the effects of

underserved counties in non-metropolitan

areas.81 To be conservative, HUD used a 1.5

percentage adjustment in Table D.15 which

reported market estimates for the 1995–98

period.

The combined effects of the above analyses

on the underserved area market shares

presented in Table D.19 can now be

considered. First, deducting B&C loans from

the analysis reduces the market estimates

presented in Table D.19 by almost one

percentage point. Second, including nonmetropolitan counties in data for estimating

the underserved areas market share could

increase the market share estimates up to 2

percentage points. Therefore, the

combination of these two effects suggests that

the market estimates in Table D.19 should be

increased by up to one percentage point, with

one-half percentage point being a

conservative upward adjustment. At a

minimum, the various estimates presented in

Table D.19 are conservative estimates of the

underserved areas market excluding B&C

loans but including non-metropolitan

counties.82

The estimates presented in Table D.19 and

this section’s analysis of dropping B&C loans

and including non-metropolitan areas suggest

that 29–32 percent is a conservative range for

the market estimate for underserved areas

based on the projection model described

earlier. This range incorporates market

conditions that are more adverse than have

existed recently and it excludes B&C loans

from the market estimates. The estimate is

conservative because, due to lack of data, it

does not fully reflect the size of the mortgage

market in non-metropolitan underserved

counties.

4. Conclusions

Based on the above findings as well as

numerous sensitivity analyses, HUD

concludes that 29–32 percent is a

conservative estimate of mortgage market

originations that would qualify toward

achievement of the Geographically Targeted

Goal if purchased by a GSE. 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).38

HUD estimates that the special affordable

market is 23–26 percent of the conventional

conforming market.

HUD has determined that the annual goal

for mortgage purchases qualifying under the

Special Affordable Housing Goal shall be 20

percent of eligible units financed in each of

calendar years 2001–2003. This final rule

further provides that of the total mortgage

purchases counted toward the Special

Affordable Housing Goal, each GSE must

annually purchase multifamily mortgages in

an amount equal to at least 1.0 percent of the

dollar volume of combined (single-family

and multifamily) mortgage purchases over

1997 through 1999. This implies the

following thresholds for the two GSEs:

(In billions)

Fannie Mae ......................... $2.85

Freddie Mac ........................ 2.11

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 in a particular

year that are occupied by very-low-income

families or by low-income families living in

low-income areas. HUD has combined

mortgage information from HMDA, the

American Housing Survey, and the Property

Owners and Managers Survey in order to

estimate these special affordable shares.

a. Special Affordable Owner Percentages

The percentage of single-family-owners

that qualify for the Special Affordable Goal

is reported in Table D.20. That table also

reports data for the two components of the

Special Affordable Goal—very-low-income

65222 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

borrowers and low-income borrowers living

in low-income census tracts. HMDA data

show that special affordable borrowers

accounted for 15.3 percent of all conforming

home purchase loans between 1996 and

1998. The special affordable share of the

market has followed a pattern similar to that

discussed earlier for the low-mod share of the

market. The percentage of special affordable

borrowers increased significantly between

1992 and 1994, from 10.4 percent of the

conforming market to 12.6 percent in 1993,

and then to 14.1 percent in 1994. The

additional years since the 1995 rule was

written have seen the special affordable

market maintain itself at an even higher

level. Over the past four years (1995–98), the

special affordable share of the home loan

market has averaged 15.1 percent, or almost

13.0 percent if manufactured and small loans

are excluded from the market totals. 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

market has changed in fundamental ways

from the mortgage market of the early 1990s.

The effect of one factor, the growth in the

B&C loans, on the special affordable market

is discussed below in Section H.2.

BILLING CODE 4210–27–P

BILLING CODE 4210–27–C

b. Very-Low-Income Rental Percentages

Table D.14 in Section F reported the

percentages of the single-family rental and

multifamily stock affordable to very-lowincome families. According to the AHS, 59

percent of single-family units and 53 percent

of multifamily units were affordable to verylow-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.

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65223

Outstanding Housing Stock versus

Mortgage Flow. As discussed 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.84 HUD’s analysis of

POMS data suggests that it can—estimates

from POMS of the rent affordability of newlymortgaged rental properties are quite

consistent with the AHS data reported in

Table D.14 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 was 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. The baseline projection from

HUD’s market share model assumes that 50

percent of newly-mortgaged, single-family

rental units, and 47 percent of multifamily

units, are affordable to very-low-income

families.

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. 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.85 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.86

The baseline analysis below assumes 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.87

2. Size of the Special Affordable Market

During the 1995 rule making, HUD

estimated a market share for the Special

Affordable Goal of 20–23 percent. This

estimate turned out to be below market

experience, as the special affordable market

accounted for almost 29 percent of all

housing units financed in metropolitan areas

between 1995 and 1997 (see Table D.15). As

explained in Section F.3.a, there are several

explanations for HUD’s underestimate of the

1995–97 market. The financing of rental

properties during 1995–97 was larger than

anticipated. Another important reason for

HUD’s underestimate was not anticipating

the high percentage of single-family-owner

mortgages that would be originated for

special affordable borrowers. During the

1995–97 period, 15.4 percent of all (both

home purchase and refinance) single-familyowner mortgages financed properties for

special affordable borrowers; this compares

with 9.5 percent for the 1992–94 period

which was the basis for HUD’s earlier

analysis. The 1995–97 mortgage markets

originated more affordable single-family

mortgages than anticipated.88 Furthermore,

the special affordable market remained strong

during the heavy refinance year of 1998.

Almost 26 percent of all dwelling units

financed in 1998 qualified for the Special

Affordable Goal.

The size of the special affordable market

depends in large part on the size of the

multifamily market and on the special

affordable percentages of both owners and

renters. Table D.21 gives new market

estimates for different combinations of these

factors. As before, Case 2 is slightly more

conservative than the baseline projections

(Case 1) mentioned above. For instance, Case

2 assumes that only 6 percent of rental units

are affordable to low-income renters living in

low-income areas.

BILLING CODE 4210–27–P

65224 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

BILLING CODE 4210–27–C

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65225

When the special affordable share of the

single-family market for home mortgages is at

its 1994–98 level of 14–15 percent, the

special affordable market estimate is 26–27

percent under HUD’s projections. In fact, the

market estimates remain above 23 percent

even if the special affordable percentage for

home loans falls from its 15-percent-plus

level during 1996–1998 to as low as 10–11

percent, which is similar to the 1992 level.

Thus, a 23 percent market estimate allows for

the possibility that adverse economic

conditions could keep special affordable

families out of the housing market. On the

other hand, if the special affordable

percentage stays at its recent levels, the

market estimate is in the 26–27 percent

range.89

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 special

affordable percentage for B&C loans is 28.5

percent, which is not much higher than the

projected percentages for the overall market

given in Table D.21. Thus, dropping B&C

loans will not appreciably reduce the overall

market estimates. Consider in Table D.21, the

case of a single-family-owner percentage of

14 percent, which yields an overall market

estimate for Special Affordable Goal of 25.9

percent. Dropping B&C loans from the

projection model reduces the special

affordable market share by 0.2 percentage

points to 25.7. Thus, the market shares

reported in Table D.21 are reasonable

estimates of the size of the special affordable

market excluding B&C loans.

Based on the data presented in Table D.21

and the analysis of the effects of excluding

B&C loans from the market, a range of 23–

26 percent is a reasonable estimate of the

special affordable market. This range

includes market conditions that are much

more adverse than have recently existed.

Additional sensitivity analyses are provided

in the remainder of this section.

Additional Sensitivity Analyses. Assuming

that the special affordable share of the home

loan market is 13 percent, reducing the

multifamily mix from 15 percent to 12 (10)

percent would reduce the overall special

affordable market share from 25.2 percent to

24.0 (23.3) percent. In this case, increasing

the multifamily mix from 15 percent to 18

percent would increase the special affordable

market share from 25.2 percent to 26.4

percent.

As shown in Table D.21, the market

estimates under the more conservative Case

2 projections are approximately two

percentage points below those under the Case

1 projections. This is due mainly to Case 2’s

lower share of single-family investor

mortgages (8 percent versus 10 percent in

Case 1) and its lower affordability and lowincome-area percentages for rental housing

(e.g., 53 percent for single-family rental units

in Case 2 versus 58 percent in Case 1).

Increasing the single-family projection by

$100 billion, from $950 billion to $1,050

billion, would reduce the market share for

the Special Affordable Goal by approximately

0.4 percentage points, assuming the other

baseline assumptions remain unchanged.90 A

$200 billion increase would reduce the

special affordable market share by 0.8

percentage point.

A recession scenario and a heavy refinance

scenario were described during the

discussion of the Low- and Moderate-Income

Goal in Section F. The recession scenario

assumed that special affordable borrowers

would account for only 10 (9) percent of

newly-originated home loans. In this case,

the market share for the Special Affordable

Goal declines to 24.2 (23.5) percent. In the

heavy refinance scenario, the special

affordable percentage for refinancing

borrowers was assumed to be four percentage

points lower that the corresponding

percentage for borrowers purchasing a home.

In this case, the market share for the Special

Affordable Goal was typically in the 24–25

percent range, depending on assumptions

about the incomes of borrowers in the home

purchase market. As noted earlier, the special

affordable market share was approximately

26 percent during 1998, a period of heavy

refinance activity.

Finally, HUD simulated the specific

scenario based on the MBA’s most recent

market estimate of $912 billion and a

refinance rate of 22 percent. In this case,

assuming a special affordable home purchase

percentage of 14, the overall special

affordable market share was varied from 25.5

percent to 26.6 percent as the multifamily

mix of varied from 13.5 percent to 16.5

percent.

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

increase Fannie Mae’s performance as

follows: 0.5 percentage point in 1997 (from

16.5 to 12.0 percent); 0.29 percentage point

in 1998 (from 14.05 to 14.34 percent); and

0.42 percent point in 1999 (from 17.20 to

17.62 percent). The increase for Freddie Mac

has been lower (about 0.20 percentage point

in 1998 and 1999).

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 23–26 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 five years. In addition,

the estimate covers a range of projections

about the size of the multifamily market.

Endnotes to Appendix D

1Appendix D of the proposed rule also

included a Section I that examined the likely

impacts of the increase in FHA loans limits

on market originations for lower-income

families in the conventional market. That

analysis—which concluded that the market

impacts would likely be small given that

FHA attracts a different group of borrowers

than conventional lenders—is now included

in the Department’s Economic Analysis for

this final GSE rule. 2 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. 3Readers not interested in this overview

may want to proceed to Section B, which

summarizes HUD’s response to the GSEs’

comments on HUD’s market methodology. 4Sections 1332(b)(4), 1333(a)(2), and

1334(b)(4). 5So-called ‘‘jumbo’’ mortgages, greater

than $227,150 in 1998 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. 6The owner of the SF 2–4 property is

counted in (a). 7Property 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). 8The 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. 9This goal will be referred to as the

‘‘Underserved Areas Goal’’. 10See Randall M. Scheessele, HMDA

Coverage of the Mortgage Market, Housing

Finance Working Paper No. 7, Office of

Policy Development and Research,

Department of Housing and Urban

Development, July 1998; and 1998 HMDA

Highlights, Housing Finance Working Paper

No. HF–009, Office of Policy Development

and Research, Department of Housing and

Urban Development, October 1999. 11See William Segal, The Property Owners

and Managers Survey and the Multifamily

Housing Finance System, Housing Finance

Working Paper No. 10, Office of Policy

Development and Research, Department of

Housing and Urban Development, September

2000. 12See 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. 13See 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. 14PWC estimates of single-family mortgage

lending volume exceed the MBA figure for

65226 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

the entire single-family market (conventional,

conforming, jumbo, and government-insured)

in 1993. The PWC estimates exceed MBA

figures on all conventional lending volume,

including jumbo loans, in 1994, 1996 and

1997. In effect, therefore, the PWC estimates

of the single-family market include the jumbo

market in 1993, 1994, 1996, 1997, and 1998.

The PWC estimates are as large, or larger than

the entire single-family market in 1993 and

1998. The MBA figures are found at

www.mbaa.org/marketkdata. 15PWC does not offer any empirical

evidence in support of their claim that 50

percent of households have below median

family income. The main reason that more

than half of all households have incomes

below the median family income is that,

empirically, household incomes are

significantly lower than family incomes

(which serve as the basis for the local area

median income against which household

incomes are compared to determine

affordability status). Individuals are not

included in family income calculations, but

are included in household income

calculations, thus causing a family-based

median income to be larger than a

household-based median income. 16 1990 is excluded from this discussion

because of the unusually high multifamily

mix that year. 17These market share estimates are based

on the annual averages of the likely range of

multifamily origination volume expressed in

the last column of Table D.10 over 1991–

1998. 1990 is excluded from this calculation

because of the unusually high multifamily

mix that year. 18Amy D. Crews, Robert M. Dunsky, and

James R. Follain, ‘‘What We Know about

Multifamily Mortgage Originations,’’ report

for the U.S. Department of Housing and

Urban Development, October 1995, 20. 19Because they are not counted toward the

GSE housing goals (with the exception of a

relatively small risk-sharing program), FHA

mortgages are excluded from this analysis.

Other categories of mortgages, considering

the type of insurer, servicer, or holder, do not

tend to have mortgage characteristics that

appear to differ substantially from the

multifamily mortgages that are purchased by

Fannie Mae and Freddie Mac. There is thus

no particular basis for excluding them. 20Corresponding percentages for Freddie

Mac were 8.3 percent, 90 percent and 17

percent. 21Corresponding percentages for Fannie

Mae were 56 percent and 31 percent. 22Amy D. Crews, Robert M. Dunsky, and

James R. Follain, ‘‘What We Know about

Multifamily Mortgage Originations,’’ report

for the U.S. Department of Housing and

Urban Development, October 1995. 23Crews, Dunsky, and Follain, ibid., 20. 24Fannie Mae (2000), p. 58. 25Robert Dunsky, James R. Follain, and Jan

Ondrich, ‘‘An Alternative Methodology to

Estimate the Volume of Multifamily Mortgage

Originations,’’ report for the U.S. Department

of Housing and Urban Development, October

1995. 26Average single-family loan amounts are

from HMDA. Multifamily per-unit loan

amounts are from the loan-level GSE data, as

discussed above.

27 Increased per-unit loan amounts evident

in the 1999 Freddie Mac data could be

related to a higher level of activity in senior

housing. Freddie Mac reported an increase in

multifamily senior housing transactions from

$84 million in 1998 to $383 million in 1999.

See ‘‘Freddie Mac Posts Record Year in

Multifamily Financing, Nearly $7 Billion in

Originations, ‘‘ press release, February 8,

1999; and ‘‘Freddie Mac Posts Record Year in

Multifamily Financing, Nearly $8 Billion in

Total Funding In 1999,’’ press release,

February 14, 2000. Per-unit loan amounts on

some Freddie Mac seniors transactions

appear to exceed $100,000. See ‘‘Freddie Mac

Teams With Glaser Financial to Credit

Enhance $65 Million in Seniors Housing

Loans,’’ press release, July 13, 1999; and

‘‘Freddie Mac Closes Its Largest Seniors

Housing Transaction With $88 Million Deal

With GMAC and Sunrise Assisted Living,’’

press release, June 1, 1999. 28Assumptions regarding the single-family

mortgage market utilized in preparing the

market share estimates presented in Table

D.10 are discussed below in section F. 29Board of Governors of the Federal

Reserve System, Flow of Funds Accounts of

the United States, Federal Reserve Statistical

Release Z.1, June 9, 2000, p. 49. 30These market share estimates are based

on the annual averages of the likely range of

multifamily origination volume expressed in

the last column of Table 10 over 1991–1998.

1990 is excluded from this calculation

because of the unusually high multifamily

mix that year. 31Calculation based on

PriceWaterhouseCoopers, Ibid., p. 15. 32The data in Table D.11a ignore HMDA

loans with ‘‘non-applicable’’ for owner type. 33 Due to the higher share of refinance

mortgages during 1998, the overall singlefamily owner percentage reported by HMDA

for 1998 (93.2 percent) is larger than that

reported for 1997 (91.5 percent). 34 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. 35For example, they note that discussions

with some lenders suggest that because of

higher mortgage rates on investor properties,

some HMDA-reported owner-occupants may

in fact be ‘‘hidden’’ investors; however, it

would be difficult to quantify this effect.

They also note that some properties may

switch from owner to renter properties soon

after the mortgage is originated. While such

loans would be classified by HMDA as

owner-occupied at the time of mortgage

origination, they could be classified by the

RFS as rental mortgages. Again, it would be

difficult to quantify this effect given available

data. 36Blackley and Follain (1996), p. 20. 37The unit-per-mortgage data from the

1991 RFS match closely the GSE purchase

data for 1996 and 1997. Blackley and Follain

show that an adjustment for vacant investor

properties would raise the average units per

mortgage to 1.4; however, this increase is so

small that it has little effect on the overall

market estimates. 38The property distribution reported in

Table D.1 is an example of the market share

model. Thus, this section completes Step 1

of the three-step procedure outlined in

Section A.2.b. 39From MBA volume estimates, 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-period low of

81 percent in 1994. Calculated from ‘‘1–4

Family Mortgage Originations’’ tables (Table

1—Industry and Table 2—Conventional

Loans) from ‘‘MBA Mortgage and Market

Data,’’ at www.mbaa.org/marketdata/ as of

July 13, 2000. 40 Data provided by Fannie Mae show that

conforming loans have been about 78 percent

of total conventional loans over the past few

years. 41Single-family mortgage originations of

$950 billion were $266 billion higher than

the $834 billion in 1997, $520 billion less

than the record setting $1,470 billion in 1998

and $335 billion less than the $1,285 billion

in 1999. As discussed later, single-family

originations could differ from $950 billion

during the 2001–2003 period that the goals

will be in effect. As recent experience shows,

market projections often change. For

example, $950 billion is similar to recent

projections (made in June, 2000) by the

Mortgage Bankers Association (MBA) of $955

billion in 2000 and $903 billion in 2001. (See

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

June, 2000 Mortgage Finance Forecasts.)

However, MBA estimates for year 2000

volume have changed substantially over the

past year, dropping from $1,043 in June, 1999

to $955 billion more recently (see MBA

Mortgage Finance Forecasts table in Mortgage

Finance Review, Vol. 7, Issue No. 2, 1999 2nd

quarter, p. 2). Section F will report the effects

on the market estimates of alternative

estimates of single-family mortgage

originations. As also explained later, the

important concept for deriving the goalqualifying 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 singlefamily originations considered in isolation. 42The model also requires an estimated

refinance rate because purchase and

refinance loans have different shares of goalsqualifying units. Over the past year, the MBA

has estimated the year 2000 refinance rate to

be 16, 20, 30, and 38 percent for the total

market (expressed in dollar terms), with 16

percent the latest estimate. The MBA’s

current estimate of the year 2001 refinance

rate is very low 12 percent. The baseline

model uses a refinance rate of 35 percent for

conforming conventional loans, which is

consistent with an MBA-type estimate of 22

percent, since refinance rates are higher for

the number of conventional conforming loans

than for the total market expressed in dollar

terms. The 35 percent refinance assumption

(compared with the recent, lower MBA

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65227

projections) results in conservative estimates

of goals-qualifying units in the market, since

the low-mod share of refinance units in

HUD’s model is lower than the low-mod

share of home purchase units. Sensitivity

analyses for alternative refinance rates are

presented in Sections F–H. 43The average 1998 loan amount is

estimated at $104,656 for owner occupied

units using 1998 HMDA metro average loan

amounts for purchase and refinance loans,

and then weighting by an assumed 35

percent refinance rate. A small adjustment is

made to this figure for a small number of

two-to-four and investor properties (see

Section D above). This produces an average

loan size of $102,664 for 1998, which is then

inflated 3 percent a year for three years to

arrive at an estimated $110,000 average loan

size for 2001. 44Based on the RFS, there is an average of

2.25 housing units per mortgage for 2–4

properties. 1.25 is used here because one

(i.e., the owner occupant) of the 2.25 units is

allocated to the SF–O category. The RFS is

also the source of the 1.35 used in (4c). 45The 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. 46Owners of 2–4 properties account for 1.6

percentage points of the 88 percent for SF–

O.47Restricting the RFS analysis to 1991

resulted in only minor changes to the market

shares. 48 1990 conventional multifamily

origination volume in RFS can be estimated

at $37.4 billion, comparable to HUD’s

estimate of $36–$40 billion in 1997.

Conventional, conforming single-family

origination volume grew from $285 billion to

$581 billion over the same period. 1990

appears to have exhibited unusually high

multifamily origination volume, as discussed

earlier in Section C. 49As noted earlier, HMDA 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 owneroccupied 2–4 properties. This is not a

particular problem for this section’s analysis

of owner incomes. 50Actually, the goals-qualifying

percentages reported in this appendix

include only the effects of manufactured

houses in metropolitan areas, as HMDA does

not adequately cover non-metropolitan areas. 51Since 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. 52Freddie Mac, the Manufactured Housing

Institute and the Low Income Housing Fund

have formed an alliance to utilize

manufactured housing along with permanent

financing and secondary market involvement

to bring affordable, attractive housing to

underserved, low- and moderate-income

urban neighborhoods. Origination News.

(December 1998), p.18. 53Randall M. Scheessele had developed a

list of nine manufactured home lenders that

has been used by several researchers in

analyses of HMDA data prior to 1997.

Scheessele developed the expanded list of 21

manufactured home loan lenders in his

analysis of 1998 HMDA data. (See Randall M.

Scheessele, 1998 HMDA Highlights, op. cit.)

In these appendices, the number of

manufactured home loans deducted from the

market totals for the years 1993 to 1997 are

the same as reported by Scheessele (1999) in

his Table D.2b. 54See Appendix D of the 1995 rule for a

detailed discussion of the AHS data and

improvements that have been made to the

survey to better measure borrower incomes

and rent affordability. 55Some even argued that data based on the

recently completed stock would be a better

proxy for mortgage flows. In the case of the

Low- and Moderate-Income Goal, there is not

a large difference between the affordability

percentages for the recently constructed stock

and those for the outstanding stock of rental

properties. But this is not the case when

affordability is defined at the very-lowincome level. As shown in Table D.5, the

recently completed stock houses

substantially fewer very-low-income renters

than does the existing stock. Because this

issue is important for the Special Affordable

Goal, it will be further analyzed in Section

H when that goal is considered. 56 In 1999, 88.7 percent of GSE purchases

of single-family rental units and 93.1 percent

of their purchases of multifamily units

qualified under the Low- and ModerateIncome Goal, excluding the effects of missing

data. 57The goals-qualifying shares reported in

Table D.15 for 1995–98 are, of course,

estimates themselves; even though

information is available from HMDA and

other data sources for most of the important

model parameters, there are some areas

where information is limited, as discussed

throughout this appendix. 58The 1995–98 goals-qualifying

percentages for single-family mortgages are

based on HMDA data for all (both home

purchase and refinance) mortgages. Thus, the

implicit refinance rate is that reported by

HMDA for conventional conforming

mortgages. 59HUD had based its earlier projections

heavily on market trends between 1992 and

1994. During this period, low- and moderateincome borrowers accounted for only 38

percent of home purchase loans, which is

consistent with an overall market share for

the Low- and Moderate-Income Goal of 52

percent (see Table D.17 below), which was

HUD’s upper bound in the 1995 rule. Based

on the 1993 and 1994 mortgage markets,

HUD’s earlier estimates also assumed that

refinance mortgages would have smaller

shares of lower-income borrowers than home

purchase loans; the experience during the

1995–1997 period was the reverse, with

refinance loans having higher shares of

lower-income borrowers than home purchase

loans. For example, in 1997, 45 percent of

refinancing borrowers had less-than-areamedian incomes, compared with 42.5 percent

of borrowers purchasing a home. 60The 1995–97 estimates also include the

effects of small loans (less than $15,000) and

manufactured housing loans which increase

the market shares for metropolitan areas by

approximately one percentage point. For

example, assuming a constant mix of owner

and rental properties, excluding these loans

would reduce the goals-qualifying shares as

follows: the Low- and Moderate-Income Goal

by 1.4 percentage points, and the Special

Affordable Goal and Underserved Areas

Goals by one percentage point. However,

dropping manufactured housing from the

market totals would increase the rental share

of the market, which would tend to lower

these impact estimates. 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

this segment of the market would tend to

increase the goals-qualifying shares of the

overall market. Thus, the analyses of

manufactured housing reported above and

throughout the text pertain only to

manufactured housing loans in metropolitan

areas, as measured by loans originated by the

manufactured housing lenders identified by

Scheessele, op. cit. 61The accuracy of the single-family portion

of HUD’s model can be tested using HMDA

data. The number of single-family loans

reported to HMDA for the years 1995 to 1997

can be compared with the corresponding

number predicted by HUD’s model. Singlefamily loans reported to HMDA during 1995

were 79 percent of the number of loans

predicted by HUD’s model; comparable

percentages for 1996, 1997, and 1998 were 83

percent , 82 percent, and 88 percent,

respectively. Studies of the coverage of

HMDA data through 1996 conclude that

HMDA covers approximately 85 percent of

the conventional conforming market. (See

Randall M. Scheessele, HMDA Coverage of

the Mortgage Market, op. cit.) The fact that

the HMDA data account for lower

percentages of the single-family loans

predicted by HUD’s model suggests that

HUD’s model may be slightly overestimating

the number of single-family loans during the

1995–97 period. The only caveat to this

concerns manufactured housing in nonmetropolitan areas. The average loan amount

that HUD used in calculating the number of

units financed from mortgage origination

dollars did not include the effects of

manufactured housing in non-metropolitan

areas; thus, HUD’s average loan amount is too

high, which suggests that single-familyowner mortgages are underestimated.

(Similarly, the goals-qualifying percentages

in HUD’s model are based on metropolitan

area data and therefore do not include the

effects of manufactured housing in nonmetropolitan areas.) 62A 15 percent estimate for 1997 is

reported by Michelle C. Hamecs and Michael

Benedict, ‘‘Mortgage Market Developments’’,

in Housing Economics, National Association

of Home Builders, April 1998, pages 14–17.

Hamecs and Benedict draw their estimate

from a survey by Inside B&C Lending, an

industry publication. A 12 percent estimate

is reported in ‘‘Subprime Products:

Originators Still Say Subprime Is ‘Wanted

Dead or Alive’ ’’ in Secondary Marketing

Executive, August 1998, 34–38. Forest

Pafenberg reports that subprime mortgages

65228 Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations

accounted for 10 percent of the conventional

conforming market in 1997; see his article,

‘‘The Changing Face of Mortgage Lending:

The Subprime Market’’, Real Estate Outlook,

National Association of Realtors, March

1999, pages 6–7. Pafenberg draws his

estimate from Inside Mortgage Capital, which

used data from the Mortgage Information

Corporation. 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. 63Based on information from The Mortgage

Information Corporation, Pafenberg reports

the following serious delinquency rates

(either 90 days past due or in foreclosure) for

1997 by type of subprime loan: 2.97 percent

for A-minus; 6.31 percent for B; 9.10 percent

for C; and 17.69 percent for D. The D category

accounted for only 5 percent of subprime

loans and of course, is included in the ‘‘B&C’’

category referred to in this appendix. Also

see ‘‘Subprime Mortgage Delinquencies Inch

Higher, Prepayments Slow During Final

Months of 1998’’, Inside MBS & ABS: Inside

MBS & ABS, March 12, pages 8–11, where it

is reported that fixed-rate A-minus loans

have delinquency rates similar to high-LTV

(over 95 percent) conventional conforming

loans. 64Not surprisingly, the goals-qualifying

percentages for subprime lenders are much

higher than the percentages (43.6 percent,

16.3 percent, and 27.8 percent, respectively)

for the overall single-family conventional

conforming market in 1997. For further

analysis of subprime lenders, see Randall M.

Scheessele, 1998 HMDA Highlights, op. cit. 65 Dropping B&C loans in the manner

described in the text results in the goalsqualifying 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 non-B&C

loans. For instance, the low-mod shares of

the market reported in Table D.13

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. 66 In 1998, the ‘‘unadjusted’’ market shares

(i.e., inclusive of B&C loans) were as follows:

Low-Mod Goal (54.1 percent); Special

Affordable Goal (26.0 percent); and

Underserved Areas Goal (30.4 percent). The

1998 conforming B&C market is estimated to

be $61 billion, with an average loan amount

of $75,062 representing an estimated 812,662

B&C conforming loans. The 1998 goalsqualifying percentages (low-mod, 58.0

percent; special affordable, 28.5 percent; and

underserved areas, 44.7 percent) used to

‘‘proxy’’ the B&C market are similar to those

for 1995–97. As noted earlier, there is much

uncertainty about the size of the B&C market. 67The percentages in Table D.17 refer to

borrowers purchasing a home. In HUD’s

model, the low-mod share of refinancing

borrowers is assumed to be three percentage

points lower than the low-mod share of

borrowers purchasing a home; three

percentage points is the average differential

between 1992 and 1999. Thus, the market

share model with the 40 percent owner

percentage in Table D.17 assumes that 40

percent of home purchase loans and 37

percent of refinance loans are originated for

borrowers with low- and moderate-income. If

the same low-mod percentage were used for

both refinancing and home purchase

borrowers, the overall market share for the

Low- and Moderate-Income Goal would

increase by 0.7 of a percentage point. 68Assuming a 42 (40) percent low-mod

share of the owner market, the low-mod

share of the overall market increased from

52.5 (51.0) percent to 55.9 (54.5) percent as

the multifamily mix increased from 10

percent to 18 percent. 69On the other hand, in the heavy

refinance year of 1998, refinancing borrowers

had higher incomes than borrowers

purchasing a home. 70The three percentage point differential is

the average for the years 1992 to 1998 (see

Table D.14). 71Rather, this approach reflects 1998

market conditions when the low-mod

differential between home purchase and

refinance loans was approximately three

percentage points. 72The $82,022 is derived by adjusting the

1997 figure of $68,289 upward based on

recent growth in the average loan amount for

all loans. Also, it should be mentioned that

one recent industry report suggests that the

B&C part of the subprime market has fallen

to 37 percent. See ‘‘Retail Channel Surges in

the Troubled ‘‘98 Market’’ in Inside B&C

Lending, March 25, 1999, page 3. 73As before, 1998 HMDA data for 200

subprime lenders were used to provide an

estimate of 58.0 percent for the portion of the

B&C market that would qualify as low- and

moderate-income. Applying the 58.0

percentage to the estimated B&C market total

of 555,948 gives an estimate of 322,450 B&C

loans that would qualify for the Low- and

Moderate-Income Goal. Adjusting HUD’s

model to exclude the B&C market involves

subtracting the 555,948 B&C loans and the

322,450 B&C low-mod loans from the

corresponding figures estimated by HUD for

the total single-family and multifamily

market inclusive of B&C loans. HUD’s

projection model estimates that 7,308,558

single-family and multifamily units will be

financed and of these, 3,990,525 (54.6

percent as in Table D.17) will qualify for the

Low- and Moderate-Income Goal. Deducting

the B&C market estimates produces the

following adjusted market estimates: a total

market of 6,752,610 of which 3,668,074 (54.3

percent) will qualify for the Low- and

Moderate-Income Goal. 74This reduction in the low-mod share of

the mortgage market share occurs because the

multifamily mix is reduced from 15 percent

to 13.8 percent. (See Section F.3b for

additional sensitivity analyses of the

multifamily mix.) 75Refinance mortgages were assumed to

account for 15 percent of all single-family

originations; 31 percent of refinancing

borrowers were assumed to have less-thanarea-median incomes, which is 14 percentage

points below the 1997 level. A multifamily

mix of 17.3 percent was assumed during the

recession scenario. If the multifamily mix

were reduced to 15.2 percent in this

environment, the low-mod share would drop

to 47.9 percent. 76Section 1336(b)(3)(A). 77As shown in Table D.18, excluding loans

less than $15,000 and manufactured home

loans reduces the 1997 underserved area

percentage by 1.2 percentage points for all

single-family-owner loans from 27.8 to 26.6

percent. Dropping only small loans reduces

the underserved areas share of the

metropolitan market by 0.4 and dropping

manufactured loans (above $15,0000) reduces

the market by 0.8. 78The main reason for HUD’s

underestimate in 1995 was not anticipating

the high percentages of single-family-owner

mortgages that would be originated in

underserved areas. During the 1995–97

period, about 27 percent of single-familyowner mortgages financed properties in

underserved areas; this compares with 24

percent for the 1992–94 period which was

the basis for HUD’s earlier analysis. There are

other reasons the underserved area market

shares for 1995 to 1997 were higher than

HUD’s 25–28 percent estimate. Single-family

rental and multifamily mortgages originated

during this period were also more likely to

finance properties located in underserved

areas than assumed in HUD’s earlier model.

In 1997, 45 percent of single-family rental

mortgages and 48 percent of multifamily

mortgages financed properties in

underserved areas, both figures larger than

HUD’s assumptions (37.5 percent and 42.5

percent, respectively) in its earlier model.

Even in the heavy refinance year of 1998, the

underserved areas market share (31 percent)

was higher than projected by HUD during the

1995 rule-making process. 79Table D.19 presents estimates for the

same combinations of projections used to

analyze the Low- and Moderate-Income Goal.

Table D.16 in Section F.3 defines Cases 1, 2,

and 3; Case 1 (the baseline) projects a 42.5

percent share for single-family rentals and a

48 percent share for multifamily properties

while the more conservative Case 2 projects

40 percent and 46 percent, respectively. 80These data do not include loans

originated by lenders that specialize in

manufactured housing loans. 81Assuming that non-metropolitan areas

account for 15 percent of all single-familyowner mortgages and recalling that the

projected single-family-owner market for the

year 2001 accounts for 72.2 percent of newlymortgaged dwelling units, then the nonmetropolitan underserved area differential of

17 percent would raise the overall market

estimate by 1.9 percentage point—17

percentage points times 0.15 (nonmetropolitan area mortgage market share)

times 0.722 (single-family owner mortgage

Federal Register / Vol. 65, No. 211 / Tuesday, October 31, 2000 / Rules and Regulations 65229

market share). This calculation is the basis

for the 1.5 percentage point adjustments to

the 1995–98 underserved area market shares

reported earlier in Table D.15. 82 It is recognized that some may not view

all of the assumptions made to generate the

results in Table D.19 as conservative. The

term ‘‘conservative’’ is being use here to

reflect the fact that adjusting the data in

Table D.19 to include underserved nonmetropolitan counties would increase the

underserved areas market share more than

adjusting the same data to exclude B&C loans

would reduce it. 83There 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. 84Previous analysis of this issue has

focused on the relative merits of data from

the recently completed stock versus data

from the outstanding stock. The very-lowincome percentages are much lower for the

recently completed stock—for instance, the

averages across the five AHS surveys were 15

percent for recently completed multifamily

properties versus 46 percent for the

multifamily stock. But it seems obvious that

data from the recently completed stock

would underestimate the affordability of

newly-mortgaged units because they exclude

purchase and refinance transactions

involving older buildings, which generally

charge lower rents than newly constructed

buildings. Blackley and Follain concluded

that newly constructed properties did not

provide a satisfactory basis for estimating the

affordability of newly mortgaged properties.

See ‘‘A Critique of the Methodology Used to

Determine Affordable Housing Goals for the

Government Sponsored Enterprises.’’ 85Affordability was calculated as

discussed earlier in Section F, using AHS

monthly housing cost, monthly rent, number

of bedrooms, and MSA location fields. Lowincome 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 lowincome tracts and other tracts. POMS data on

year of mortgage origination were utilized to

restrict the sample to properties mortgaged

during 1993–1995. 86 During the 1995 rule-making process,

HUD examined the rental housing stock

located in low-income zones of 41

metropolitan areas surveyed as part of the

AHS between 1989 and 1993. While the lowincome 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. 87Therefore, combining the assumed verylow-income percentage of 50 percent (47

percent) for single-family rental (multifamily)

units with the assumed low-income-in-lowincome-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 is the

baseline Case 1 in Table D.6. 88The 28.8 percent estimate for 1997

excludes B&C loans but includes

manufactured housing and small loans while

HUD’s earlier 20–23 percent estimate

excluded the effects of these loans. Excluding

manufacturing housing and small loans from

the 1997 market would reduce the special

affordable share of 28.8 percent by a

percentage point. This can be approximated

by multiplying the single-family-owner

property share (0.702) for 1997 by the 1.4

percentage point differential between the

special affordable share of all (home

purchase and refinance) single-family-owner

mortgages in 1997 with manufactured and

small loans included (16.3 percent) and the

corresponding share with these loans

excluded (14.9 percent). This gives a

reduction of 0.98 percentage point. These

calculations overstate the actual reduction

because they do not include the effect of the

increase in the rental share of the market that

accompanies dropping manufactured

housing and small loans from the market

totals. 89The upper bound of 27 percent from

HUD’s baseline special affordable model is

obtained when the special affordable share of

home purchase loans is 15 percent, which

was the figure for 1997 (see Table D.20).

However, the upper bound of 27 percent is

below the 1997 estimate of the special

affordable market of almost 29 percent (see

Table D.15). There are several reasons for this

discrepancy. As mentioned earlier, the rental

share in HUD’s baseline projection model is

less than the rental share of the 1997 market.

In addition, HUD’s projection model assumes

that the special affordable share of refinance

mortgages will be 1.4 percentage points less

than the corresponding share for home

purchase loans (1.4 percent is the average

difference between 1992 and 1998). But in

1997, the special affordable share (17.6

percent) of refinance mortgages was larger

than the corresponding share (15.3 percent)

for home loans. 90This reduction in the special affordable

share of the mortgage market share occurs

because the multifamily mix is reduced from

15 percent to 13.8 percent. (See above for

additional sensitivity analyses of the

multifamily mix.)

[FR Doc. 00–27367 Filed 10–30–00; 8:45 am]

BILLING CODE 4210–27–P