1

Abstract

The HUD Property Owners and Managers Survey (POMS) can be utilized to

analyze a number of housing-finance related policy issues. This paper offers some findings

relevant to multifamily finance from preliminary analysis of POMS data, offered in the

spirit of illustrating potential uses of the data base. Adjustment techniques to correct for

the effects of data truncation are developed and are applied to derive estimates of number

of units per property, the size of the multifamily mortgage stock, and the magnitude of

annual mortgage origination volume. Mortgage origination volume for 1995 is estimated

using both a “hot-deck” and a regression-based imputation approach. Results from the

internal POMS file at the Census Bureau as well from the public-use version of the file are

included here. Advantages and shortcomings of POMS in relation to a number of other

multifamily data sources are noted, as are possible directions for future research.
2

The Property Owners and Managers Survey

and the Multifamily Housing Finance System1

Introduction

Discussion of policy issues in connection with the multifamily mortgage market in

the 1990s have been significantly shaped by enactment of Federal legislation in 1992

establishing affordable goals for Fannie Mae and Freddie Mac, the two principal

government sponsored enterprises (GSEs) in the mortgage markets.2 In recognition of the

substantial public benefits provided to the GSEs by federal sponsorship, HUD established

minimum standards for the acquisition of mortgage loans on properties (a) affordable to

low- and moderate (“low-mod”) income families; (b) located in geographic areas

underserved by the mortgage market; and (c) affordable to low-income families in lowincome areas and very-low-income families, which is referred to as the “special affordable”

housing goal in HUD’s regulations.3

In setting the housing goals, HUD is statutorily required to consider a number of

factors including the size of the conventional mortgage market relative to each of the

housing goals.4 The Department has found that in 1997, the GSEs represented acquired

or guaranteed mortgages representing approximately 39 percent of units mortgaged that

year. However, their transactions accounted for only 30 percent of conventionally

 

1 I would like to thank Fred Eggers, Ron Sepanik and Dav Vandenbroucke of HUD’s Office of

Policy Development and Research (PD&R) for their help interpreting the POMS data base.

Acknowledgment is also due Harold Bunce of PD&R for extensive comments on an earlier draft. Howard

Savage of the Housing and Household and Economic Statistics Division at the Bureau of the Census and

Jack Goodman of the National Multi Housing Council provided additional technical assistance. Ming

Chow and David Ni of Computer Based Systems, Inc. provided research assistance. Yongheng Deng

provided valuable comments on an earlier version of this paper presented at the January, 1998 meetings of

the American Real Estate and Urban Economics Association, as did other participants in the session.

Responsibility for any remaining errors is my own. The views expressed here are those of the author and

shold not be construed as representing those of the U.S. Department of Housing and Urban Development.

2

 The title of this legislation is the Federal Housing Enterprises Financial Safety and Soundness

Act of 1992 (FHEFSSA). For details see U.S. Department of Housing and Urban Development (1995).

Analysis of the multifamily activities of the GSEs since the goals were implemented is contained in Segal

and Szymanoski (1997).

3

 An interim rule covering 1993-1994 (and later extended to 1995) became effective January 1,

1993. A Final Rule that became effective January 1, 1996 was published in the Federal Register

December 1, 1995. A Proposed Rule covering 2000-2003 was published in the Federal Register March 8,

2000. See http://www.hud.gov/gse/.

4

 Other factors that HUD is required to consider in setting the low-mod goal are (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; (5) the

ability of the enterprises to lead the industry in making mortgage credit available for low- and moderateincome families; and (6) the need to maintain the sound financial condition of the enterprises. Similar

factors apply to the other housing goals.
3

financed units meeting the low-mod goal and 24 percent of units meeting the special

affordable goal.

GSE performance can be determined directly from loan-level data provided by

Fannie Mae and Freddie Mac to HUD. But their performance relative to the conventional

mortgage market can be measured only by measuring or estimating the various

components of that market, including both single-family and multifamily properties.

Because of the disproportionately large share of multifamily units qualifying for each of

the housing goals, the size of the conventional multifamily origination market is therefore

of critical significance in establishing a benchmark against which GSE performance can be

evaluated.

Multifamily loans represent a relatively small portion of the GSEs’ business

activities. For example, 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 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. In multifamily,

the overall market presence of the GSEs is more modest. At the end of 1997, the GSEs

direct holdings and guarantees were $41.4 billion, representing 13.8 percent of $301

billion in outstanding multifamily mortgage debt.5 Viewed from another perspective,

multifamily loans held in portfolio or guaranteed by the GSEs at the end of 1997 totaled

$41.4 billion, less than 3 percent of their single-family combined portfolio and guaranteed

holdings. In comparison, the outstanding stock of multifamily mortgages represent

approximately 8 percent of the overall stock of mortgage debt.6

However, because of the relatively high degree of affordability of rental properties,

the multifamily market contributes significantly to GSE transactions meeting the low-mod

and special affordable goals. In 1997, Fannie Mae’s multifamily purchases represented

13.4 percent of their total acquisition volume, measured in terms of dwelling units. Yet

these multifamily purchases comprised 26.7 percent of units qualifying for the Low- and

Moderate Income Housing Goal, and 44.4 percent of units meeting the Special Affordable

goal. Multifamily purchases were 8.2 percent of units backing Freddie Mac’s 1997

acquisitions, 18.8 percent of units meeting the Low- and Moderate Income Housing Goal,

and 31.4 percent of units qualifying for the Special Affordable Housing Goal.7 The

multifamily market therefore comprises a significant share of units meeting the low-mod

 

5

Federal Reserve Bulletin, June 1998, A 35. The comparable figure for year-end 1992, before the

interim housing goals took effect, was 10.5 percent. (Federal Reserve Bulletin, (December 1993), A 38.)

6

Federal Reserve Bulletin, (June 1998), A 35.

7

1997 Annual Housing Activity Reports, Table 1.
4

and special affordable goals for both GSEs, and the goals may have contributed to

increased emphasis by both GSEs on multifamily in the period since the Final Rule took

effect in 1995.8

The majority of units backing GSE multifamily transactions meet the low-mod

because the great majority of rental units are affordable to families at 100 percent of

median income, the standard upon which the goal is defined. For example, 33.3 percent of

units securing Freddie Mac’s 1997 one-family owner-occupied mortgage purchases met

the low-mod, compared with 95.9 percent of its multifamily transactions. Corresponding

figures for Fannie Mae were 33.8 percent and 85.2 percent.9 For this reason, multifamily

purchases represent a crucial component of the GSEs’ efforts in meeting the low-mod.

Because such a large proportion of multifamily units qualify for the low-mod and

for the special affordable 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 7.9 percent of Freddie Mac’s mortgage purchases

during 1996-1998, compared with 12.2 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.

In recognition of their importance to GSE performance relative to the housing

goals, issues regarding the size and structure of the larger multifamily market figured

prominently in the public discussion and policy analysis surrounding the HUD rulemaking

process during 1995. Ultimately, the levels at which HUD set the goals were based on

analyses of the share of the market represented by goal-qualifying mortgages. Estimation

of market parameters such as the average loan amount per unit proved especially difficult

in multifamily because of uneven data quality. The size of the multifamily market became

the object of controversy, with the GSEs arguing that $15 billion was a reasonable

estimate based on data from the Home Mortgage Disclosure Act (HMDA), while HUD

concluded that a $30 billion figure, drawn in part from the Survey of Mortgage Lending

Activity, was more accurate.10

 

8 William Segal and Edward J. Szymanoski. The Multifamily Secondary Mortgage Market: The

Role of Government-Sponsored Enterprises. Housing Finance Working Paper No. HF-002, Office of

Policy Development and Research, Department of Housing and Urban Development, (March 1997).

9 HUD analysis of GSE loan-level data. Affordability data are missing on 11.1 percent of units

backing Fannie Mae’s 1997 multifamily acquisitions, which may contribute to the disparity between

Fannie Mae and Freddie Mac regarding percentage of multifamily acquisitions contributing to the lowmod goal.

10 Analyses of multifamily issues connected with the GSE housing goals are contained in

Blackley and Follain (1995); Bogdon and Follain (1995); Crews, Dunsky, and Follain (1995a, b); Dunsky,

Follain and Ondrich (1995); and appendices to U.S. Department of Housing and Urban Development

(1995).
5

The HUD Property Owners and Managers Survey (POMS) represents a relatively

new data base, not available during the debate regarding the GSE housing goals, that can

be utilized to analyze a number of policy issues relevant to GSE performance in the

context of the broader multifamily mortgage market. This paper offers some findings

relevant to multifamily finance from preliminary analysis of POMS data, offered in the

spirit of illustrating potential uses of the data base. Advantages and shortcomings of

POMS in relation to a number of other multifamily data sources are noted, as are

directions for future research.

Previous analyses of POMS data include Goodman (1997a, 1997b); Harvard

University Joint Center for Housing Studies (1997); and U.S. Department of Housing and

Urban Development (1996, 1997); and Bogdon and Ling (1998). For POMS

documentation, see U.S. Bureau of the Census (n.d.) and U.S. Department of Housing

and Urban Development (1996b).

The paper begins with a summary of other data sources that have been drawn upon

in analyses of the multifamily mortgage market. Section II summarizes the characteristics

of the POMS sample. Findings regarding the average number of units per property are

presented in Section III. In Section IV, the analysis turns to the issue of mortgage loan

amount, utilizing a “hot deck” approach to imputate of missing values, ultimately for the

purpose of estimating the size of the conventional multifamily mortgage origination

market in 1995. This discussion is supplemented in Section V with a regression-based

imputation approach. Section VI discusses the reasonableness of estimates of 1995

origination volume by drawing on additional data sources and methodologies. Section VII

consists of a brief conclusion.

I. Other Multifamily Data Sources

A lack of reliable data has come to be recognized as one of several features of the

multifamily housing finance system that demarcates it from single-family finance. It does

not appear that there is a single data base that comprehensively and consistently describes

all segments of the multifamily mortgage market. Significant inconsistencies between data

sources are evident. Studies of the multifamily mortgage market, therefore, typically draw

upon a number of different data sources in order to piece together an understanding of the

overall market.

Home Mortgage Disclosure Act (HMDA). HMDA is designed to provide

complete loan level data that includes a number of fields specially relevant to multifamily

finance including loan amount at origination; type of lender (e.g., bank, thrift, mortgage

bank); property location by census tract; loan approval/denial; conventional/government

insured status; and loan purchase by Fannie Mae or Freddie Mac. Fields not covered in

HMDA include loan-to-value ratio; refinance/purchase status; number of units; and

affordability/rent information.
6

HMDA is intended to provide data on all multifamily mortgage originations in

metropolitan areas in the period beginning January, 1990. Loans backed by properties in

non-metropolitan areas are excluded.

The principal shortcoming of HMDA is that it suffers from a significant degree of

under-reporting, especially with regard to commercial banks and mortgage banks,

apparently because of confusion over reporting requirements. For example, HMDA

reports that FHA-insured mortgages, which are originated primarily by mortgage bankers,

totaled $383 million in 1995; FHA figures put this total at $1.7 billion. As much as 50

percent of Fannie Mae’s multifamily purchases are not reported in HMDA.11

Survey of Mortgage Lending Activity (SMLA). The SMLA is compiled by FHA’s

Office of the Comptroller. Instead of a loan-level format, data are presented in summary

tables providing annual origination volume by type of lender and by new/existing status of

property. Thrift data are taken from OTS call reports and are therefore considered

reliable. Survey data are utilized to estimate totals for other types of institutions, and are

considered less reliable.

Questions have been raised about SMLA origination volume figures for

commercial banks, reported at $19 billion for 1996. Many regard this figure as

substantially overstated, possibly because of changes in the lending industry since the

survey was designed 25 years ago, when commercial banks’ multifamily market share was

larger than today.12

GSE Public Use Data Base (PUDB). The PUDB consists of loan-level data on

each mortgage purchase by Fannie Mae and Freddie Mac starting January 1, 1993. The

source of these data are computer tapes collected by HUD from the GSEs as part of its

regulatory oversight of the two Enterprises. Data fields included on the PUDB

multifamily “Census Tract File” include mortgage loan amount (in ranges); census tract

location; and various tract characteristics. A separate “National File,” without census

tract identifiers, includes affordability data; tract characteristics; number of units; and loan

purpose (purchase/refinance/new construction/ rehabilitation). In order to protect the

proprietary interests of the GSEs, as determined by the HUD Secretary, the two files

cannot be merged.

The principal limitations of the PUDB are that (i) UPB is reported in ranges, rather

than as a continuous variable; (ii) key measures of credit risk such as debt coverage ratio

(DCR) and loan-to-value ratio (LTV) are missing; and (iii) only mortgages purchased by

the GSEs are included.

 

11Crews, Dunsky, and Follain 1995a.

12 Crews, Dunsky and Follain, 1995b, 3ff; and ICF (1994).
7

Residential Finance Survey (RFS). Conducted by the Census Bureau most

recently in 1990, the RFS provides detailed information on a sample of housing units

drawn from the decennial census. Unlike HMDA, SMLA, and the PUDB, therefore, the

RFS is used principally to measure the multifamily mortgage stock, rather than the flow of

new originations. RFS data fields including number of units; purchase price; property

value; mortgage loan amount; contract interest rate; and mortgage term are presented in

summary form in a published volume.13 As part of a study commissioned by HUD,

Bogdon and Follain (1996) were granted access to confidential loan-level records

maintained by the Census Bureau. These data were utilized to conduct a detailed analysis

of default risk in the multifamily mortgage stock with an emphasis on DCR, LTV, and

contract interest rate.

Multifamily Housing Institute. Efforts are underway at the Multifamily Housing

Institute to construct an “Apartment Performance and Trends DataSource” (“APT”)

which will provide loan performance data to assist in evaluating credit risk, thereby

facilitating securitization. Toward this end, the new database will provide loan level data

on approximately 200 data elements, approximately two-thirds of which represent stable

information that is not expected to change (e.g., UPB and LTV at origination, property

location); other, dynamic elements such as rents and loan performance will be periodically

updated. The Institute has collected data on approximately 24,00 properties and 3 million

apartment units as of this writing.14

II. POMS Sample

A nationwide sample of 16,268 housing units identified as rented or vacant-forrent in the 1993 American Housing Survey (AHS) National Sample was selected for the

HUD Property Owners and Managers Survey (POMS), conducted between November,

1995 and June, 1996. The original sample was reduced by 2,990 units that were

determined to be “out-of-scope” for a number of reasons such as ownership by a public

housing authority; occupancy by the property owner; change in tenancy status after the

1993 AHS-National survey; new construction; or for other reasons.15 The sample was

further reduced by another 5,009 units that were classified as “noninterviews.” The

POMS public use data base made available from the Census Bureau consists of two files

comprising 8,258 records, each corresponding to a single interview of a property owner or

manager.

Data fields reported in POMS include mortgage loan amount, contract interest

rate, estimated property value, purchase price, rents, operating costs, number of units, and

others with specific relevance to housing finance. The potential for linking records in

 

13 Bureau of the Census, 1990 Census of Housing, Residential Finance, 1990 CH-4-1.

14 Update, newsletter of the Multifamily Housing Institute, Fall 1997.

15 U.S. Census Bureau (n.d.).
8

POMS to those in the AHS16 offers the potential of combining POMS housing finance

data with property age and condition, tract location, and other variables considered

relevant to the pricing and availability of mortgage credit, permitting analysis of the

relation between credit risk and housing affordability.

Weighting. The POMS sample includes 4,256 multifamily properties, representing

15,029,001 units after applying Census-derived unit weights. By design, the sum of

POMS unit weights is close to the total of 15,137,000 multifamily units reported in the

1993 American Housing Survey.17

In addition to unit weights, the Census Bureau has also added property weights to

the POMS data file. The property weights were calculated by dividing the unit weight by

the number of units in a property, and then applying a noninterview adjustment factor18

and a ratio estimate factor.19 Summing property weights across the 4,256 multifamily

properties in the POMS sample yields a total universe of 518,840 multifamily rental

properties. This total compares with 622,201 multifamily rental properties reported in the

1991 Residential Finance Survey.20

 

16 Such links are not possible in the public use files, and require access to confidential Census

data.

17 This figure is the sum of renter-occupied and vacant-for-rent units located in structures with

five or more units reported in the 1993 AHS. Note that in AHS, the unit of analysis is the structure, while

in POMS it is the property. The following example illustrates the difference between the two approaches:

Consider three duplexes on a single parcel of land. A unit in such a property would be identified as

located within a two-unit structure in AHS, but in a 6-unit property in POMS.

18 The noninterview adjustment factor adjusted the weights of interviewed records to account for

records that could not be interviewed (noninterviews). POMS records were classified into noninterview

cells by using AHS sample information for geography and units-in-structure. The noninterview

adjustment factor was then computed separately for each cell.

19 If there are a sufficient number of housing units in a Census primary sampling unit (PSU,

comprised of counties or groups of counties and independent cities), the PSU is included in the AHS

sample with certainty, and is called a “self-representing PSU.” Other, “nonself-representing,” PSUs are

grouped into strata, and one PSU is selected to represent all PSUs in the stratum. In calculating POMS

property weights, ratio estimation is used to reduce the portion of the variance due to the sampling of

nonself-representing primary sampling units. These factors were identical to the first-stage ratio estimate

factors used for the POMS unit-level weighting.

20 The discrepancy between POMS and RFS total property weights arises in part because of a

difference in the unit of analysis, which in POMS is the property, in contrast to RFS, where the unit of

analysis is the mortgage, for mortgaged properties. According to Census Bureau staff, there are a

relatively large number of multifamily properties with more than one first mortgage, whether because the

property was built in stages or for other reasons. Recalculation of POMS property weights using an RFSbased control total (corrected for additions and deletions from the rental housing stock between 1990 and

1995) would overcome RFS/POMS discrepancies.
9

As illustrated below, the choice of unit versus property weights leads to widely

divergent population estimates based on POMS data. This, in itself, is not a deficiency of

the survey, but is a consequence of a salient institutional characteristic of the multifamily

housing stock: the typical multifamily unit is located in a relatively small property as

measured by the number of units; whereas the typical multifamily property is relatively

large property. Table 1, based on RFS data, illustrates the difference in the distribution of

units and of properties over unit size groups.

III. Number of Units per Property

The number of dwelling units in multifamily properties has become the object of

policy concern, in part because of evidence that financing for smaller properties is more

difficult.21 Using POMS to calculate average number of units per property illustrates a

potential use of the database as well as some of the complexities involved.

Multifamily Housing Stock. Average number of units per property can be

calculated both for the entire stock of multifamily properties as well as the flow of 1995

originations.22 The average for the stock is 27.3 units per property, which compares with

an estimate based on Residential Finance Survey (RFS) data of 25.0 in Bogdon and Follain

(1995).

 

21 U.S. Department of Housing and Urban Development 1994a; U.S. General Accounting Office

1993; National Mortgage News, August 4, 1997.

22 Because of subsequent refinances, 1995 originations represent the most complete finance data

of any origination year in the POMS sample.
10

Table 1

Distribution of number of properties and of dwelling units by unit size group

Residential Finance Survey

Multifamily properties (5+ units)

Unit

size group Properties Cum. % Units* Cum. %

5 - 9 343,844 55.3% 2,406,908 14.5%

10 - 19 126,698 75.6% 1,837,121 25.6%

20 - 29 46,477 83.1% 1,138,687 32.5%

30 - 39 23,313 86.8% 804,299 37.3%

40 - 49 16,681 89.5% 742,305 41.8%

50 - 74 20,407 92.8% 1,265,234 49.4%

75 - 99 10,574 94.5% 919,938 55.0%

100 - 149 13,675 96.7% 1,702,538 65.2%

150 - 199 7,623 97.9% 1,326,402 73.2%

200 - 299 7,711 99.2% 1,923,895 84.8%

300 - 499 4,104 99.8% 1,639,548 94.7%

500 - 999 944 100.0% 707,528 99.0%

1000+ 150 100.0% 168,750 100.0%

* Midpoint of unit size group multiplied by number of properties in group for all groups with <

1000 units. Average number of units in properties with 1000+ units estimated from POMS.

The effort involved in calculating this figure for the flow of 1995 originations is

complicated by the fact that mortgage origination year information is missing for nearly

half of the properties in the sample. The missing data problem regarding origination year,

as well as other finance-related fields such as mortgage loan amount at origination, is

correlated with property size: survey respondents on larger multifamily properties are

more often third-party managers who apparently do not know, or do not have the

authority to release, mortgage loan-related information.23 Mortgage origination year is

missing on 22.6 percent of 5 - 9 unit mortgaged properties and 52.3 percent of mortgaged

properties with 100 or more units. (Table 2, line 1d.) 24

Because of differences in the degree of data truncation between unit size

categories, the strategy utilized to estimate units per property begins by calculating

averages for each unit size category separately for 1995 reported observations and for

observations where origination year data are missing. These estimates are reported on

lines 2e and 2f (1995) and 1e and 1f (missing origination year) on Table 2. The weighted

 

23 On the incidence of third-party management by property size, see Goodman (1997b).

24 These unit size categories were selected because they are identical to those used by the Bureau

of the Census in calculating POMS weights. Note that the percentage of observations with missing

origination year information is slightly different if unit weights are used, as shown in line 1b.
11

number of units and of properties within each unit size category is found in lines 2a and 2c

(1995) and 1a and 1c (missing origination year).

As a first approximation, it would appear reasonable to assume that the percentage

of “missing origination year” mortgaged properties that are in fact 1995 originations

would be the same as the percentage of 1995 originations in the data for which origination

year is known, and a proportion share of missing origination year observations are also

assumed to be unmortgaged.

25 Thus it is assumed that 9.1 percent of 5 - 9 unit properties

with missing origination year are in fact 1995 originations (line 2d). The product of line

2d and line 1c then yields an imputation of 5,753 missing-origination-year 5 - 9 unit

properties to the 1995 origination year, using property weights to derive a population

estimate (line 3b). Similarly, the use of unit weights results in an estimate of 37,833

missing origination-year-units (line 3a) in 5 - 9 unit properties that were in fact mortgaged

during 1995 (lines 2b, 1a, and 3a).

Combining reported and imputed 1995 originations, applying property and unit

weights, and then calculating separate weighted averages over unit size groups results in

an estimate of approximately 201 units per property, averaging over units (line 5a), and 33

units per property if the average is taken over properties (5b). These widely divergent

estimates underscore the significance of differences between the distribution of units and

properties in the multifamily stock noted previously.26 It is evident from Table 1 that

properties with missing origination years are larger than properties with reported 1995

originations (lines 1e, 1f, 2e, and 2f, final column), an issue taken up in the following

section.

IV. Mortgage Loan Amount

The POMS data base includes a number of fields pertaining specifically to

mortgage indebtedness including amount of first mortgage when made; year first mortgage

made; mortgage insurance status; current interest rate; and mortgage purpose

(purchase/refinance). Information is also provided on junior financing. The use of these

data fields is complicated considerably by missing data, however. Thirty seven percent of

respondents did not know whether the property they owned or managed had been

mortgaged (Table 3); 72 percent of respondents who identified themselves as representing

mortgaged properties did not know the amount of first mortgage when originated (Table

4).

 

25 In 93 percent of unweighted missing origination year observations, a property manager or

other agent of the owner was the respondent. Thus the hypothesis can be rejected that missing origination

year observation year observations are associated with mortgage loans so highly seasoned that the owner

has forgotten the origination year.

26 Note that the 6:1 ratio between unit- and property based averages is approximately similar to

the proportion between estimates of the corresponding medians based on the RFS data in Table 1.
12

Lack of information regarding mortgage indebtedness is concentrated among thirdparty managers whose responsibilities evidently do not often require detailed knowledge

of project financing. Among respondents who identified themselves as the property

owner, only six percent did not know whether their property was mortgaged, compared

with 42 percent of property managers (Table 5). Twenty three percent of property

owners with mortgaged properties did not know the amount of their first mortgage when

originated, compared with 73 percent for property managers (Table 6).

Third-party management is highly correlated with property size. As shown in

Table 7, only 32 percent of respondents representing 5 - 9 unit properties were property

managers or other agents of the owner, compared with 90 percent for properties with 100

or more units. This finding suggests that lack of mortgage information is concentrated

among larger properties, a hypothesis which is confirmed by Tables 8 and 9. In properties

with 5 - 9 units, 71 percent of respondents knew whether the property was mortgaged,

compared with only 56 percent of respondents in properties with 100 or more units.

(Table 8.) Excluding properties identified as unmortgaged, 49 percent of respondents in

small, 5-9 unit properties knew the mortgage loan amount at origination, compared with

only 20 percent for large properties with 100 or more units. (Table 9.)
13
10
11

Table 2

Average number of units per property and total number of units by size groups, 1995 originations

Estimated from Property Owners and Managers Survey (POMS)

Unit size category

5 - 9 10 - 19 20 - 49 50 - 99 100+ All*

1 Missing origination year

a Units 463,279 518,746 811,749 925,424 3,929,186

b Units as % all orig. yrs.* 24.5% 35.3% 36.5% 4.6% 52.8%

c Properties 63,539 37,735 23,951 12,066 17,245

d Properties as % all orig. yrs.* 22.6% 35.1% 33.9% 45.4% 52.3%

e Average units per property (unit weights) 6.7 13.6 34.8 75.0 320.2 205.5

f Average units per property (property weights) 6.5 13.2 32.2 72.1 216.3 40.6

2 1995 reported originations

a Units 116,873 66,484 97,254 117,556 396,026

b Units as % all nonmissing orig. yrs.* 8.2% 7.0% 6.9% 11.0% 11.3%

c Properties 19,695 4,273 3,176 1,839 1,792

d Properties as % all nonmissing orig. yrs.* 9.1% 6.1% 6.8% 12.7% 11.4%

e Average units per property (unit weights) 6.7 13.7 32.2 69.1 327.3 179.5

f Average units per property (property weights) 6.3 13.7 30.6 65.8 220.3 25.9

3 Missing orig. year obs. imputed to 1995

a Units 37,833 36,340 55,909 101,344 442,703

b Properties 5,753 2,308 1,631 1,529 1,962

4 Reported and imputed 1995 observations

a Units 154,706 102,824 153,163 218,900 838,729

b Properties 25,448 6,580 4,807 3,368 3,754

a Units 6.7 13.7 33.2 71.8 323.6 200.7

b Properties 6.4 13.5 31.2 68.7 218.2 33.0

* Within unit size category.

** Reported and imputed

*** Reported and imputed; weighted average across unit size groups
12
12

Table 3

Mortgage status

Property Owners and Managers Survey

Multifamily properties (5+ units)

Unweighted data

 Frequency

Percent

Mortgaged 2006 47.6%

Unmortgaged 652 15.5%

Not reported 1557 36.9%

TOTAL 4215 100.0%

Table 4

Mortgage loan amount status

Property Owners and Managers Survey

Multifamily properties (5+ units)

Unweighted data

Properties identified as unmortgaged excluded

Frequency Percent

Reported 1041 28.4%

Not reported 2626 71.6%

TOTAL 3667 100.0%

Table 5

Mortgage status by respondent identity

Property Owners and Managers Survey

Multifamily properties (5+ units)

Unweighted data

Mortgaged Unmortgaged

 Not reported TOTAL

Property owner 64.8% 29.0% 6.3% 100.0%

Property manager 46.9% 11.6% 41.5% 100.0%

Other agent of owner 45.8% 13.9% 40.3% 100.0%
13

Table 6

Respondent identity by mortgage loan amount status

Property Owners and Managers Survey

Multifamily properties (5+ units)

Unweighted data

Reported Unmortgaged Not reported TOTAL

Property owner 53.9% 23.4% 22.7% 100.0%

Property manager 16.5% 10.1% 73.5% 100.0%

Other agent of owner 23.4% 11.1% 65.5% 100.0%

Table 7

Property size group by respondent identity

Property Owners and Managers Survey

Multifamily properties (5+ units)

Unweighted data

Property Property

 Other agent

Number of units

Owner

 Manager of

Owner

 TOTAL

5 - 9 67.8% 20.6% 11.6% 100.0%

10 - 19 51.9% 36.9% 11.1% 100.0%

20 - 49 39.2% 48.5% 12.3% 100.0%

50 - 99 18.5% 67.5% 14.1% 100.0%

100+ 9.2% 72.0% 18.9% 100.0%

Table 8

Property size group by mortgage status

Property Owners and Managers Survey

Multifamily properties (5+ units)

Unweighted data

Number of units Mortgaged

Unmortgaged

 Not reported

TOTAL

5 - 9 46.8% 34.1% 19.2% 100.0%

10 - 19 44.3% 26.5% 29.2% 100.0%

20 - 49 51.5% 17.7% 30.8% 100.0%

50 - 99 50.2% 11.0% 38.8% 100.0%

100+ 46.6% 8.9% 44.4% 100.0%
14

Table 9

Property size group by mortgage loan amount status

Property Owners and Managers Survey

Multifamily properties (5+ units)

Unweighted data

Properties identified as unmortgaged excluded

Number of units Reported Not reported TOTAL

5 - 9 48.8% 51.2% 100.0%

10 - 19 37.9% 62.1% 100.0%

20 - 49 37.4% 62.6% 100.0%

50 - 99 28.6% 71.4% 100.0%

100+ 20.3% 79.7% 100.0%

One method of estimating mortgage loan amounts for observations where this field

is missing is to utilize the relationship between mortgage loan amount and number of units

where both fields are populated to make inferences regarding observations where the

number of units, but not the mortgage loan amount, is known.27 In order to take account

of the possibility that this relation varies by property size, observations were divided into

the size groups utilized by the Bureau of the Census for purposes of calculating weights,

which are identical to those used in Tables 6-8 above. It is assumed that, within each

size group, the share of unmortgaged properties in the pool of properties where the

mortgage loan amounts is not reported is the same as the share of unmortgaged properties

in the pool where mortgage loan amount is reported.

Missing values for mortgage loan amounts were then calculated as the product of

(a) the mean mortgage loan amount per unit for observations within known mortgage loan

amounts, and (b) the number of units in the property, within the corresponding size group.

In order to take into account that some observations with missing values for the mortgage

loan amount were actually unmortgaged, however, this product was then multiplied by (c)

the proportion of properties with nonmissing mortgage loan amounts that are identified as

mortgaged, again within the corresponding size group. This approach of imputing missing

values using relationships observed between variables in cases where the relevant fields are

populated is sometimes described as “hot-decking.”

Formally, define

loan_amt = mortgage loan amount at origination

t_units = total number of units in property

unit_upb = loan_amt/t_units

m(unit_upb)|size group = mean value of unit_upb within t_units size

group* for loan_amt populated

prp_mrt = proportion of properties with nonmissing loan_amt that are

mortgaged within t_units size group*

 

27 Regarding imputation techniques for missing data, see Little and Rubin (1987); Rubin (1987);

and Schafer (1997).
15

est_upb = estimated value of mortgage loan amount

loan_amt missing

est_ upb = m(unit_upb)|size group * t_units

loan_amt populated:

est_ upb = loan_amt for,

* t_units size groups:

5 - 9 units

10 - 19 units

20 - 49 units

50 - 99 units

100+ units

The aggregate mortgage loan amount represented by the entire multifamily

housing stock originations can then be calculated as the sum over all size categories of the

product of (a) average loan amount per unit, over all mortgaged and unmortgaged

properties, incorporating estimated loan amounts for missing observations as specified

above, and applying POMS unit weights, within each size category, and (b) the

corresponding number of units in the population, calculated as the sum of POMS unit

weights, over all mortgaged and unmortgaged properties, for that size category. Estimated

aggregate loan amount within each size category is then summed across size categories to

derive an estimate of the multifamily mortgage stock.28

Table 10, which illustrates this procedure, shows considerable variation in

mortgage loan amount per unit as between size categories. Calculating this average over

all size categories, , including mortgaged and unmortgaged properties, and applying

POMS unit weights, yields an estimate of $16,589. If the analysis is restricted to

mortgaged properties, a figure of $23,779 is derived. It should be emphasized that

population estimates derived using POMS property weights would diverge significantly

from these figures.

 

28 Alternatively, this total could be calculated as the product of loan amounts on mortgaged

properties and the number of units in such properties. Because the latter quantity cannot be directly

inferred from POMS due to missing data, however, the method utilized in the text is the preferred

alternative.
16

Table 10

Estimation of multifamily mortgage stock

Property Owners and Managers Survey

Multifamily properties (5+ units)

Unit weights

(A) (B) (C)

Average loan Units in Aggregate

Number of units Amount per unit* Population** Loan amount***

5 - 9 14,780 1,894,444 $ 27,999,677,922

10 - 19 12,493 1,467,785 $ 18,337,596,263

20 - 49 14,399 2,223,786 $ 32,020,874,382

50 - 99 21,259 1,998,896 $ 42,493,769,846

100+ 17,256 7,444,090 $128,457,456,824

TOTAL 15,029,002 $249,309,375,237

* Incorporates adjusted estimates for observations with missing loan amount data.

** Calculated as sum of POMS unit weights

*** (A) x (B)

The estimated size of the multifamily mortgage stock derived here from POMS

data, $249 billion, is reasonably close to the actual year-end 1995 figure for the size of the

multifamily housing stock published in the Federal Reserve Bulletin of $288 billion. The

difference between these two estimates may be explained, in part, by the fact that the

Federal Reserve figure includes not only first mortgages, but junior liens as well.29 A more

refined calculation would take into account paydown of principal on mortgage loans

backing properties in the POMS sample.

Origination volume

POMS can also be utilized to estimate the annual flow of multifamily origination

volume. Data are most complete for 1995, the survey year, since many prior-year

originations would have been refinanced or paid down by the time the survey was

conducted.

An additional difficulty in calculating annual origination flows from POMS is that

the origination year is missing from a large percentage of observations, as noted

previously. It is again assumed that the actual weighted distributions as between

mortgaged/ unmortgaged, and, if mortgaged, of mortgage loan origination year, are

identical to observations for which mortgage status and origination year are known. The

total number of units in each size category that are reported as, or imputed to, 1995

originations (from Table 2) is then multiplied by the corresponding average mortgage loan

 

29 Steve Lumpkin, Board of Governors, Federal Reserve Bank.
17

amount per property for reported 1995 originations, to reach an estimate of 1995

origination volume of $37.2 billion as shown in Table 11.

V. Estimating Origination Volume Using Regression-Based Imputation

Another way to impute mortgage loan amount for observations where this field is

missing is to use regression-based techniques. In essence, this approach involves fitting a

regression equation for the purpose of identifying the relationship between mortgage loan

amount and specified independent variables for observations where all these fields are

populated. The regression equation can then applied to observations where mortgage loan

amount is missing in order to calculate “predicted” loan amount for these observations.

Table 11

Estimated mortgage loan amount by number of units, 1995 originations

Property Owners and Managers Survey

Multifamily properties (5+ units)

Unit weights

(A) (B) (C)

Average Loan Number of Total mortgage

Number of units Amount per unit* Units** Loan amount

5 - 9 $ 20,822 154,706 $ 3,221,335,127

10 - 19 $ 20,525 102,824 $ 2,110,407,223

20 - 49 $ 19,918 153,163 $ 3,050,729,260

50 - 99 $ 47,923 218,900 $10,490,372,852

100+ $ 21,794 838,729 $18,279,352,596

TOTAL 1,468,321 $37,152,197,058

* Reported 1995 originations.

** Actual and imputed 1995 originations, per Table 2.

An advantage of regression-based approaches is that they provide the analyst with

the opportunity to utilize information from other fields as well as units in property for the

purpose of exploiting observed relationships between these other fields and mortgage loan

amount to increase explanatory power. A disadvantage of regression-based techniques, at

least of the ordinary least squares approach used here, is that they may not be as flexible as

the “hot-decking” approach outlined above in their treatment of nonlinear relationships

between variables. By fitting a linear regression relationship, ordinary least squares may

impose linearity in cases where such an assumption may not be appropriate. To some

degree, this deficiency can be mitigated through the use of dummy variables, as discussed

below.

Regression analysis of the POMS data is complicated by virtue of the fact that

mortgage loan amount is “topcoded” on some observations in the public-use version of

the data in order to protect the confidentiality of respondents. Presumably, disclosure of
18

raw, untopcoded mortgage loan-amount in conjunction with other fields would, in some

instances, permit analysts to identify individual properties in the database. Consequently,

partly in order to overcome limitations imposed by topcoding, access to internal,

confidential Census bureau files was secured by applying for status, and being sworn in, as

a Special Employee of the Census Bureau.

The first step in the imputation process was to identify the properties that reported

to have been mortgaged in 1995 and where the mortgage loan amount was disclosed.

These 138 nonmissing cases were used to estimate a regression equation linking mortgage

loan amount and specified independent variables. A number of different regression

specifications were considered. (See Appendix for details.) The preferred specification

includes number of units in property; dummy variables to identify property location in the

Northeastern, Midwestern, and Southern Census regions, and a dummy for properties

with more than 1,000 units. The regional dummy variables were included to take account

of the wide variation in property values across distinct markets in different geographic

areas. The dummy for large properties was included in order to take account of an

observed nonlinearity in the relationship between mortgage loan amount and number of

units. This specification was chosen over others because it had the highest adjusted, R2,

approximately 0.69.

Table 12 summarizes regression results from the preferred specification. Number

of units in property is significant at a p-value of 0.0001, confirming the strength of the

relationship between number of units and mortgage loan amount. All of the dummy

variables are negative in sign, indicating lower mortgage loan amounts in the

Northeastern, Midwestern and Southern census regions relative to the Western region,

which was the control group. Coefficients are significant at the 5 percent level for the

Midwestern and Southern census region dummies. Finally, the dummy variable for

properties with more than 1,000 units has a large, significant coefficient with a p-value of

0.0001.

Table 12

Regression Analysis of Mortgage Loan Amount, 1995 Originations

Property Owners and Managers Survey

Multifamily properties (5+ units)

Unit weights

Parameter estimate T Prob > |T|

INTERCEPT 1477530 2.907 0.0043 Adj. R2= 0.6933

T_UNITS 18172 10.660 0.0001 F= 62.932

NE -323736 -0.459 0.6533 Prob>F= 0.0001

MW -1463703 -2.128 0.0352 DF= 132

S -2094596 -2.896 0.0044

BIG_PROP 25316150 6.613 0.0001
19

The regression equation was first applied for the purpose of estimating mortgage

loan amount for 97 properties in the sample identified as having been mortgaged in 1995,

but where the mortgage loan amount was missing.

The next steps were to estimate mortgage loan amount for observations where (i)

year of mortgage origination was missing, and it is unknown whether the property is

mortgaged; and (ii) year of mortgage origination was missing, but the property is known

to be mortgaged. Presumably, some portion of these observations represent 1995

originations as well.

The above regression equation was applied to 1,827 observations where year of

origination and mortgage loan amount were both missing in order to determine predicted

mortgage loan amount. In order to calculate the probability that a unit in a property with

missing origination year was in fact located in a property mortgaged in 1995, the percent

of units in properties mortgaged in 1995 was calculated as a proportion of the nonmissing

total, defined as the sum of all units in properties where mortgage origination year was

known plus all units in properties that were known to be unmortgaged.

Because it was observed that the 1995-origination-year percentage of units varied

with the number of units in property, this calculation was conducted separately for

properties with 5-9 units; 10-19 units; 20-49 units; 50-99 units, and 100 or more units.

Based on the results of these tabulations, for example, the probability that a unit in a 5-9

unit missing-data property was actually in a property mortgaged in 1995 was 8.2 percent.

This percentage was then applied to the loan amounts derived for the 5-9 unit missingdata cases using the regression equation. Corresponding adjustments were applied to

each unit size category. Table 13 shows adjustment factors by number of units in

property.

Table 13

1995 Originations as Percent of All Observations With Known Origination Year or Known to Be Unmortgaged

Property Owners and Managers Survey

Unit Weights

Units in Property 1995%

5-9 8.2%

10-19 7.0%

20-49 6.9%

50-99 11.0%

100+ 11.3%

The final step in the imputation procedure was to add information from 12

observations where year of mortgage origination was missing but where the property was

known to be mortgaged. Again, mortgage loan amount was estimated using the 1995

regression equation. In this case, however, a different adjustment factor was applied,

taking into consideration the fact that these properties were known to be mortgaged. The
20

percentage of units in properties mortgaged in 1995 was calculated as a proportion of all

units in mortgaged properties with known origination year, by unit size category. (Thus,

unmortgaged properties were excluded from the calculation.) Based on these results, it

was inferred that 13.2 percent of the loan amount imputed to 5-9 unit properties with

missing origination year, but known to be mortgaged, actually represented 1995

originations, with corresponding adjustments applied to each of the other unit size

categories. Table 14 shows adjustment factors by number of units in property.

Table 14

1995 Originations as Percent of All Observations With Known Origination Year

Property Owners and Managers Survey

Unit Weights

Units in Property 1995%

5-9 13.2%

10-19 10.8%

20-49 8.8%

50-99 13.0%

100+ 13.0%

When the imputation process was complete, POMS unit weights were applied and

adjusted loan amounts were summed across all observations. Combining reported and

imputed mortgage loan amounts using the above regression-based technique yields

estimated 1995 total multifamily origination volume of $39.1 billion. After removing FHAinsured loans totaling $2.3 billion, this method yields $36.7 billion as the estimated size of

the conventional multifamily mortgage market. It is noteworthy that properties

constructed during 1991-1995 were excluded from the POMS sample. For this reason,

POMS yields a conservative estimate of the size of the 1995 multifamily mortgage market.

VI. Other Estimation Methods

The reasonableness of origination volume estimates derived from the POMS data

can be evaluated by comparing them with results derived from other sources and

methodologies.

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.30 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

 

30 Dunsky, Follain, and Ondrich (1995).
21

interest rate fluctuations on prepayments of the historical mortgages; the projections

assumed that prepayments are motivated mainly by property sales. Forecast total

mortgage origination volume (including FHA) based on mortgages existing in 1991 were

$40.8 billion for 1995. After removing FHA-insured loans totaling $2.3 billion, this

method yields $38.5 billion as the estimated size of the conforming multifamily mortgage

market. The latter figure is closer to the $36.7 billion POMS regression imputation

estimate and the $37.9 billion SMLA figure than to the $12.8 billion HMDA number.

Table 15 summarizes the various estimates of the 1995 conventional multifamily

market discussed here. The POMS, Urban Institute, and SMLA figures are grouped

together relatively closely in the $37-$39 billion range, with the HMDA figure of $12.8

billion representing an outlier.

Table 15

Estimated 1995 Multifamily Origination Volume ($ billions)

Urban Institute $38.5

POMS $36.7

SMLA figure $37.9

HMDA $12.8

VII. Conclusion

The POMS data base provides a unique vantage point on the nation’s multifamily

housing stock with much unexplored potential. Because of the capability of linking

POMS records with AHS unit-level records, for example, new possibilities have been

established for the analysis of the availability and cost of mortgage credit by a range of

factors including property characteristics, borrower characteristics, and neighborhood

characteristics. Data truncation issues represent a significant challenge to the use of

POMS mortgage finance data, and afford opportunities to apply and compare a variety of

techniques for imputation of values to missing observations.
22

Appendix:

Effect of alternative regression specifications on estimated 1995 multifamily origination

volume

95 ORIGINATIONS

POMS Census internal file

MODEL 1 Parameter estimate T Prob > |T|

INTERCEPT 53840 0.144 0.8857 Adj. R2= 0.5582

T_UNITS 23086 13.195 0.0001 F= 174.097

Prob>F= 0.0001

DF= 136

est. '95 orig. flow = $ 35,806,944,774 (POMS unit weights)

MODEL 2 Parameter estimate T Prob > |T|

INTERCEPT 1369563 2.345 0.0205 Adj. R2= 0.5947

T_UNITS 23854 14.09 0.0001 F= 51.261

NE -731135 -0.888 0.3762 Prob>F= 0.0001

MW -2187034 -2.802 0.0058 DF= 133

S -2852969 -3.476 0.0007

est. '95 orig. flow = $ 31,134,729,255 (POMS unit weights)

MODEL 3 Parameter estimate T Prob > |T|

INTERCEPT 648086 1.957 0.0524 Adj. R2= 0.6758

T_UNITS 17115 9.956 0.0001 F= 143.761

BIG_PROP 27437492 7.093 0.0001 Prob>F= 0.0001

DF= 135

est. '95 orig. flow = $ 43,422,707,330 (POMS unit weights)

MODEL 4 Parameter estimate T Prob > |T|

INTERCEPT 1477530 2.907 0.0043 Adj. R2= 0.6933

T_UNITS 18172 10.660 0.0001 F= 62.932

NE -323736 -0.459 0.6533 Prob>F= 0.0001

MW -1463703 -2.128 0.0352 DF= 132

S -2094596 -2.896 0.0044

BIG_PROP 25316150 6.613 0.0001

est. '95 orig. flow = $ 39,064,009,211 (POMS unit weights)
23

REFERENCES

Blackley, Dixie M. and James R. Follain (1995). “A Critique of the Methodology Used to

Determine Affordable Housing Goals for the Government Sponsored Housing

Enterprises.” Report prepared for the U.S. Department of Housing and Urban

Development (October).

Bogdon, Amy S. and James R. Follain (1995). “Multifamily Housing: An Exploratory

Analysis Using the 1991 Residential Finance Survey.” Unpublished report prepared

for the U.S. Department of Housing and Urban Development (November 9).

______________ (1996). “An Estimation of Three Sets of Indicators of Financial Risk

Among Multifamily Properties.” Unpublished report prepared for the U.S.

Department of Housing and Urban Development (April 14).

Bodgon, Amy S. and David C. Ling, 1998. “The Effects of Property, Owner, Location,

and Tenant Characteristics on Multifamily Profitability,” Journal of Housing

Research 9(2), 285-316.

Crews, Amy D., Robert Dunsky, and James R. Follain (1995a). “What We Know About

Multifamily Mortgage Originations.” Report prepared for the U.S. Department of

Housing and Urban Development (October).

______________ (1995b). “Estimating the Volume of Multifamily Mortgage Originations

by Commercial Banks Using the Survey of Mortgage Lending Activity and the

Home Mortgage Disclosure Act Data.” Report prepared for the U.S. Department

of Housing and Urban Development (October).

Dunsky, Robert, James R. Follain, and Jan Ondrich (1995). “An Alternative Methodology

to Estimate the Volume of Multifamily Mortgage Originations.” Report prepared

for the U.S. Department of Housing and Urban Development (October).

Goodman, Jack (1997a). “Highlights from HUD’s New Survey of Property Owners and

Managers.” Research Notes, National Multi-Housing Council (February).

______________ (1997b). “Third Party Management of Large Apartment Communities.”

Research Notes, National Multi-Housing Council (May).

Harvard University Joint Center for Housing Studies (1997). The State of the Nation’s

Housing 1997.

Little, Roderick J.A. and Donald B. Rubin (1987). Statistical Analysis With Missing Data.

(New York: Wiley).
24

Rubin, Donald B. (1987). Multiple Imputation for Nonresponse in Surveys. (New York:

Wiley).

Schafer, Joseph L. (1997). Analysis of Incomplete Multivariate Data. (London:

Chapman & Hall).

Segal, William and Edward J. Szymanoski (1997). “The Multifamily Secondary Mortgage

Market: The Role of Government-Sponsored Enterprises.” U.S. Department of

Housing and Urban Development, Office of Policy Development and Research,

Housing Finance Working Paper Series No. HF-002 (March).

U.S. Bureau of the Census (n.d.) “Property Owners and Managers Survey Source and

Accuracy Statement,”

http:///www.census.gov/hhes/www/housing/poms/source.html.

U.S. Department of Housing and Urban Development (1994). “Business Strategic Plan

Appendices,” FHA Office of Multifamily Housing Programs (November).

______________ (1995). The Secretary of HUD’s Regulation of the Federal National

Mortgage Association (Fannie Mae) and the Federal Home Loan Mortgage

Corporation (Freddie Mac), Final Rule, 60 Fed. Reg. 61646, (December 1).

______________ (1996a). “Surveying the Owners and Managers of Rental Housing,”

U.S. Housing Market Conditions, Office of Policy Research and Development

(November).

______________ (1996b). Property Owners and Managers Survey, Office of Policy

Development and Research (December 6).

______________ (1997). “The Providers of Affordable Housing,” U.S. Housing Market

Conditions, Office of Policy Development and Research (February).

U.S. General Accounting Office (1993). “Expanding Capital for Affordable Multifamily

Housing.” GAO/RCED-94-3 (October).
12

Housing Finance

W O R K I N G P A P E R S E R I E S

HF-010 The Property Owners and Managers Survey and the Multifamily Housing

Finance System, by William Segal, September 2000.

The HUD Property Owners and Managers Survey (POMS) can be utilized to analyze a

number of policy issues relating to financing for rental properties. In this paper,

adjustment techniques to correct for the effects of data truncation are developed and are

applied to derive estimates of number of units per property, the size of the multifamily

mortgage stock, and the magnitude of annual mortgage origination volume, a critical

parameter for benchmarking the performance of Fannie Mae and Freddie Mac. Mortgage

origination volume for 1995 is estimated using both a “hot-deck” and a regression-based

imputation approach. Results from the internal POMS file at the Census Bureau as well

from the public-use version of the file are included here. Advantages and shortcomings of

POMS in relation to a number of other multifamily data sources are noted, as are possible

directions for future research.

HF-009 1998 HMDA Highlights, by Randall M. Scheessele, October 1999.

This paper describes home purchase and refinance mortgage market trends at the national

level using HMDA data on mortgage denials and originations from 1998 and earlier. An

important contribution of the paper is the recognition of manufactured home and subprime

lenders that report to HMDA and their effect on mortgage market trends. The paper

provides a list of 21 lenders that specialize in manufactured home lending and 200 lenders

that specialize in subprime lending.

The paper finds that manufacture home loan applications and their increasing denial rates

were the primary reason for the increasing conventional denial rat since 1993. The paper

also finds that conventional prime home purchase lending to minority and lower-income

borrowers increased substantially between 1993 and 1994 but growth in lending to these

groups since 1994 was attributable to growth in FHA, manufactured home, and subprime

lending.

For more information about any of the Housing Finance Working Paper Series, please contact the authors by

calling (202) 401-0388 or (202) 708-1455 (TTY) or by writing to the author(s) at: U.S. Department of Housing

and Urban Development, Office of Policy Development and Research, 451 7th Street, SW, Washington, DC

20410.

Additional information on housing finance topics also may be found by visiting the following web sites:

http://www.huduser.org and http://www.rtk.net.
13

HF-008 Do FHA Multifamily Mortgage Insurance Programs Provide Affordable

Housing and Serve Underserved Areas? An Analysis of FHA’s Fiscal Year

1997 Book of Business and Comparison with the GSEs, by Edward J.

Szymanoski and Susan J. Donahue, October 1999.

This paper analyzes the rent affordability of about 67,500 unassisted multifamily units,

which were insured by FHA during Fiscal Year 1997, and the proportion of these units

located in underserved areas. In addition, the paper also compares FHA’s 1997

multifamily loans purchased by Fannie Mae and Freddie Mac (the government-sponsored

enterprises, or GSEs) in regard to rent affordability and proportion of units located in

underserved areas. The analysis shows that FHA is providing a substantial amount of

modest cost rental housing and serving underserved areas with its unassisted multifamily

mortgage insurance programs. About 95 percent of the FHA units in this study (including

new construction and existing housing) were affordable at 100 percent of area median

income, and over 40 percent were affordable at 60 percent of area median income. About

40 percent of the FHA units in the study were located in underserved areas. In drawing

comparison between FHA and the GSEs, the paper first notes the differences as well as

similarities between the multifamily programs of these respective agencies- for example,

FHA offers higher loan-to-value ratios, lower debt service coverage ratios, and longer

fixed-rate mortgage terms than do the GSEs. These underwriting differences

notwithstanding, FHA’s affordability and underserved area percentages for FY 1997 were

very similar to those of comparable Fannie Mae and Freddie Mac mortgage purchases.

HF-007 HMDA Coverage of the Mortgage Market, by Randall M. Scheessele, July

1998.

This paper examines the coverage of HMDA data by taking advantage of loan-level data

reported to HUD on mortgages insured by FHA and mortgages purchased by the GSEs.

The FHA and GSE data bases provide an accurate standard against which HMDA data on

FHA and GSE loans can be measured. The results of this paper provide background for

using HMDA data to estimate the market share of loans for FHA and the GSEs by

reporting HMDA coverage rates for FHA originations and GSE acquisitions of mortgages

for 1993 through 1996. The paper finds that HMDA data under-reports GSE acquisitions

mainly because a few large lenders fail to correctly report the sale of a significant number

of their loans to the GSEs. Notwithstanding coverage issues, HMDA data continues to be

the most comprehensive data base for measuring primary and secondary mortgage market

activity.

HF-006 Characteristics of Mortgages Purchased by Fannie Mae and Freddie Mac:

1996-97 Update, by Paul B. Manchester, August 1998.

This paper (an update of HF-003) examines the mortgages purchased by Fannie Mae and

Freddie Mac, the two major Government-Sponsored Enterprises (GSEs) in the

conventional secondary mortgage market. The analyses focus on detailed borrower,

locational, and loan characteristics of such mortgages in the 1996-97 period. In general,

the report is based on the loan-level data that the GSEs submit annually to the Department.

The paper finds that the GSEs generally increased their performance on the goals
14

established by HUD in 1995 and that they surpassed all of their 1996-97 goals, with

Fannie Mae’s performance exceeding Freddie Mac’s performance on each of the goals in

both years.

HF-005 The GSEs’ Funding of Affordable Loans: 1996 Update, by Harold L. Bunce

and Randall M. Scheessele, July 1998.

This paper (an update of HF-001) examines the borrower and neighborhood characteristics

of (GSEs) in the conventional secondary mortgage market. The analysis is based on Home

Mortgage Disclosure Act (HMDA) data on home purchase loans originated in

metropolitan areas between 1992 and 1996. The GSEs' mortgage purchases are compared

to all mortgages originated in the conventional conforming loan market, including

originations retained in portfolio by banks and thrift institutions. The paper finds that

there continues to be room for further increases in purchases of affordable loans by Fannie

Mae and, especially, Freddie Mac.

HF-004 The GSEs’ Purchases of Single-Family Rental Property Mortgages, by

Theresa R. DiVenti, March 1998.

This paper examines the single-family rental mortgages purchased by Fannie Mae and

Freddie Mac, the two major Government-Sponsored Enterprises (GSEs) in the

conventional secondary mortgage market. These properties are the “mom and pop shops”

of the rental market, meaning they are small and largely individually owned and managed.

To date there has been little research on this segment of the rental market. This analysis

looks at neighborhood, affordability, borrower, and financial characteristics of the GSEs’

mortgage purchases. The study finds that, while single-family rental properties are a small

portion of the GSEs’ overall business, they are a large and important segment of the rental

stock for lower income families.

HF-003 Characteristics of Mortgages Purchased by Fannie Mae and Freddie Mac,

1993-95, by Paul B. Manchester, Sue George Neal, and Harold L. Bunce, March

1998.

This paper examines the mortgages purchased by Fannie Mae and Freddie Mac, the two

major Government-Sponsored Enterprises (GSEs) in the conventional secondary mortgage

market. The analyses focus on detailed borrower, locational, and loan characteristics of

such mortgages in the “1993-95 transition period,” established by Congress in the Federal

Housing Enterprises Financial Safety and Soundness Act of 1992. In general, the report is

based on the loan-level data that the GSEs submit annually to the Department. The paper

finds that although there were significant increases between 1993 and 1995 in the GSEs’

funding of loans for groups traditionally underserved by the mortgage market, their

support is generally less than that provided by portfolio lenders.

HF-002 The Multifamily Secondary Mortgage Market: The Role of GovernmentSponsored Enterprises, by William Segal and Edward J. Szymanoski, March

1997.
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This paper examines the performance of Fannie Mae and Freddie Mac in enhancing the

liquidity and efficiency of the affordable segment of the multifamily mortgage market. The

paper focuses specifically on the period since 1993, when HUD established affordable

housing goals for these two Government-Sponsored Enterprises (GSEs). A private

secondary mortgage market has developed to address the finance needs of higher end

properties; yet a comparable market for mortgages on properties affordable to lowerincome families lags in development. Placed within a wider market context, it is found that

the GSEs have been cautious in their affordable multifamily transactions. It is concluded

that the GSEs have the potential to do more to enhance the affordable segment of the

multifamily mortgage market.

HF-001 The GSEs’ Funding of Affordable Loans, by Harold L. Bunce and Randall M.

Scheessele, December 1996.

This paper examines the borrower and neighborhood characteristics of mortgages

purchased by Fannie Mae and Freddie Mac, the two major Government-Sponsored

Enterprises (GSEs) in the conventional secondary mortgage market. The GSEs' mortgage

purchases are compared to all mortgages originated in the conventional conforming loan

market, including originations retained in portfolio by banks and thrift institutions. The

analysis is based on Home Mortgage Disclosure Act (HMDA) data on home purchase

loans originated in metropolitan areas between 1992 and 1995. The paper finds that there

is room for further increases in purchases of affordable loans by Fannie Mae and,

especially, Freddie Mac.