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The Potential and Limitations of Mortgage Innovation in Fostering Homeownership in the United States

David Listokin, Elvin K. Wyly, Brian Schmitt, and Ioan Voicu

ABSTRACT

Homeownership is widely regarded as the foundation of neighborhood stability and long-termwealth accumulation for American families. In turn, home-purchase mortgage lending hasbecome a central policy instrument in efforts to broaden access to homeownership amongunderserved populations.

This study presents a nationwide empirical analysis of the potential and limitations of mortgageinnovation to increase homeownership among underserved populations. We examine the finan-cial and underwriting criteria of a typology of mortgage products, and we develop syntheticunderwriting models calibrated with 1993–95 Survey of Income and Program Participation datato account for all direct purchase costs (including itemized components of closing costs anddown payments). Our analysis provides comprehensive estimates of a large, untapped market,but even the most aggressive mortgage market innovations can play only a limited role in effortsto deliver the material benefits of homeownership to underserved populations.

INTRODUCTION

Homeownership is widely regarded as the foundation of neighborhood stability andlong-term wealth accumulation for American families. Although two-thirds of thehouseholds in the United States have achieved homeownership, the rate is much lowerfor racial and ethnic minority and low- to moderate-income (LMI)1 populations, recentimmigrants, and others referred to as traditionally underserved populations. Recentscholarly research, community activism, and regulatory intervention have focused onways of expanding homeownership opportunities, especially for traditionally under-served populations. However, the limitations of available data and methods have, un-til recently, made it difficult to estimate how many renters could become homeownersif mortgage underwriting requirements were changed. The purpose of this study,conducted by the Center for Urban Policy Research (CUPR), Rutgers University, is tomeasure the effects of more affordable and flexible home financing (mortgage inno-

1 LMI is often defined in terms of a percentage of median income. For example, as used here “low income” refersto a household of four earning less than 50 percent of the median income, and “moderate income” refers to ahousehold of four earning between 50 percent and 80 percent of the median income.

© Fannie Mae Foundation 2002. All Rights Reserved. 1

The Potential and Limitations of Mortgage Innovation

vation) on access to homeownership by renters, especially members of traditionallyunderserved populations.2

The study is organized into the following sections:

1. Background2. Literature Review3. Simulation Framework and Models4. Model Calibration and Analysis5. Results: Homeownership Affordability6. Explanation of the Findings7. Policy Considerations8. Summary: Achievements and Limitations of Mortgage Innovation9. Evaluation of Simulation Results

10. Technical Appendices

2 David Listokin, Elvin K. Wyly, Brian Schmitt, and Ioan Voicu

Fannie Mae Foundation Research Report

2 Researchers have considered various aspects of the underserved market. Carr et al. (1994a), for example, ana-lyzed the spatial distribution of loans acquired by Fannie Mae and Freddie Mac in order to define underservedand served mortgage markets. (See also Can and Megbolugbe 1995; Carr et al. 1994b.)

BACKGROUND: HOMEOWNERSHIP IN THE UNITED STATES AND

STRATEGIES TO ASSIST THE TRADITIONALLY UNDERSERVED

Homeownership in the United States has long been touted as a preferred form ofhousing tenure, one that offers numerous advantages over renting a home. Allegedbenefits of owning a home include equity accumulation, greater involvement in neigh-borhood activities, greater sense of civic responsibility, improved educational andother achievements by the owner’s children, and other socioeconomic gains (Bunce etal. 1996; Dreier 1996; Green and White 1994; Rohe and Stegman 1994a, 1994b; Roheand Stewart 1995; Rossi and Weber 1996; Scanlon 1996; Shear, Wachter, and Weicher1988). Given this array of supposed benefits, it is not surprising that homeownershipis associated with realizing the “American Dream” (U.S. Department of Housing andUrban Development [HUD] 1995a).

Some researchers have criticized a historical bias by policy makers that has favoredproperty ownership (Krueckeberg 1999). Although the empirical evidence that home-ownership benefits both owners and society is supported, it is far from unequivocal(Rohe and Stegman 1994a, 1994b; Rossi and Weber 1996). Further, some studies in-dicate that many households rent their homes by choice. Varady and Lipman (1994)and Goodman (1999) have identified lifestyle renters (e.g., many elderly people andhouseholds with adult interests and schedules), who rent their homes because of theflexibility, mobility, reduced portfolio risk, locational benefits (e.g., apartments in adult-oriented downtown areas), and other advantages renting offers. There are also transi-tory renters, for whom renting for a selected period of time is preferable to owning ahome (e.g., households temporarily relocated because of a job or for a personal rea-son). Thus, the benefits of homeownership may be overstated, or the benefits maynot always be verifiable. Indeed, homeownership may not be for everyone; further-more, an individual’s preference for homeownership or renting may depend on severalfactors, including stage of life.

While homeownership may not be a universal good, it nonetheless is deeply ingrainedas the American Dream. To realize that dream, billions of dollars in federal tax incen-tives have been extended to homeowners. Tax and other societal support for home-ownership has prompted a dramatic shift to this form of tenure. In 1980, the home-ownership rate was almost 66 percent. It then declined to 64 percent in 1990, causingmuch consternation. Yet homeownership has since rallied to reach a recent historichigh; as of the first quarter of 2000, 67.1 percent of all American households werehomeowners. The most current (2000) State of the Nation’s Housing reports that inthe last five years alone, the number of owner households grew by 6.9 million (JointCenter for Housing Studies 2000, 17).

Realizing the goal of homeownership is harder for some than for others (Gyourko,Linneman, and Wachter 1996). Since housing is such an expensive consumption item,its purchase is heavily influenced by one’s financial position (Gyourko and Linneman1993). Thus, homeownership understandably correlates with income. As of the firstquarter of 2000, 82 percent of all U.S. households with a family income higher than

Background: Homeownership in the United States 3


the median were homeowners, compared with only 52 percent of households with afamily income lower than the median (U.S. Bureau of the Census 2000).

Homeownership rates also vary by race and ethnicity, with racial and ethnic minoritiesmuch less likely than the rest of the population to be homeowners (Long and Caudill1992; Wachter and Megbolugbe 1992). As of the first quarter of 2000, the white (non-Hispanic) homeownership rate in the United States was 73.4 percent—much higherthan the rate of 47.4 percent for African-American households and 45.7 percent forHispanic households (U.S. Bureau of the Census 2000). According to the U.S. Bureauof the Census, since 1985, the white (non-Hispanic) homeownership rate has beenbetween 25 and 30 percentage points higher than the African-American or Hispanichomeownership rate (U.S. Bureau of the Census 2000).

These racial and ethnic disparities are the result of a complex set of economic, histor-ical, institutional, and other (e.g., demographic) factors (Gyourko and Linneman 1997).Historically, the nation’s housing finance system was designed to predominantly servethe needs of the housing construction industry and white middle-class nuclear familiesseeking to escape the crowded cities of the interwar period for new opportunities inthe suburbs (Hayden 1984; Jackson 1985; Wright 1981). The housing finance systemhas changed dramatically in recent years, but economic barriers—minorities’ reducedincome and wealth, and lower levels of intergenerational wealth transfers and upwardclass mobility—continue to suppress the homeownership rate of African Americansand Hispanics (Gyourko, Linneman, and Wachter 1997). Moreover, there is wide-spread evidence that racial discrimination persists in various forms in the housingand mortgage markets (Yinger 1995, 1998). The Boston Federal Reserve study pro-vided the most conservative and rigorous measurement of discrimination because itinvolved the analysis of household financial characteristics actually considered byunderwriters. Even after controlling for income, wealth, credit history, and all otherfactors considered in the loan-underwriting decision, African Americans were approx-imately 60 percent more likely to be denied mortgage credit than were identicallyqualified non-Hispanic whites (Munnell et al. 1996). The Boston Federal Reservestudy generated a torrent of criticism that focused on issues of omitted-variable bias,data-coding errors, econometric specification, and model fit (Horne 1993; Liebowitzand Day 1992; Schill and Wachter 1993; Zandi 1993). Nevertheless, several reanalysesof the same data yielded similar results (Carr and Megbolugbe 1993), and the BostonFederal Reserve researchers convincingly defended their conclusions in a point-by-point response to their critics (Browne and Tootell 1995). In sum, debate persists onthe question of discrimination, and there is little prospect for widespread consensus.While there is universal agreement on disparities in mortgage market outcomes, thequestion of discrimination remains.

Our study does not address mortgage market discrimination. We start with the ob-served differences in homeownership attainment and, holding aside its causality, lookto ways, through mortgage innovation, to increase the homeownership rate, especiallyamong minority, LMI, immigrant, and similar households. The U.S. National Home-ownership Strategy, while attempting to help all Americans, underscores a “specialresponsibility and an important opportunity to target underserved populations and



communities,” including minorities (HUD 1996, 1). Because financing is key to realiz-ing homeownership, there are growing efforts to improve traditionally underservedpopulations’ access to credit. Given the multiple barriers faced by minorities and LMIpopulations in securing credit, the response by the public and private sectors has beenmultifaceted. Approaches have included attempts to address differential treatmentdiscrimination and differential impact discrimination as well as efforts to relax mort-gage borrowing constraints.

Attempts to eliminate discrimination involve strengthened enforcement of existinglaws (Federal Reserve Bank of Boston 1993; Federal Reserve System 1996). The FairHousing Act makes it unlawful for any person who engages in the business of mak-ing or purchasing residential real estate loans, or in the selling, brokering, or apprais-ing of residential real estate property, to discriminate based on race, color, nationalorigin, or religion. The Equal Credit Opportunity Act prohibits lending discriminationbased on the sex, marital or family status, disability, age, race, color, national origin,or public assistance status of the borrower (Federal Reserve Bank of Boston 1993,26–27). Although most analysts believe that blatant discrimination is no longer wide-spread in mortgage financing, there is credible evidence that subtle forms of discrimi-nation do persist (Holloway 1998; Hunter and Walker 1996; Yinger 1995, 1998). More-over, the mortgage transaction is only one part of a complex set of institutional andsocial processes involved in housing markets, and various types of discrimination inother housing transactions and in other markets can affect minority access to home-ownership (Yinger 1995). Segmentation of minorities into unstable or lower-wage jobs,for example, historically has placed minorities at a disadvantage when underwritingguidelines have required applicants to demonstrate long-term employment or a singlesource of income. Furthermore, minority home seekers sometimes experience discrim-ination in their encounters with home sales agents (Turner 1992; HUD 1991). As aconsequence, efforts to eliminate discrimination in mortgage financing must be coor-dinated with a broad-based assault on discrimination in other markets and in thehousing search process (Listokin and Wyly 1998; Vartanian et al. 1995; Yinger 1995).

There have also been efforts to expand the availability of more affordable and flexiblemortgages. The Community Reinvestment Act (CRA) provides a major incentive. En-acted in 1977 and since amended, CRA requires financial institutions to meet theircommunity’s need for credit in low- and moderate-income neighborhoods, consistentwith safe and sound operation of the institution. CRA applies to depository lendinginstitutions with offices in a metropolitan area and with assets above a specific thresh-old. These lenders are required to prepare CRA statements that define their marketcommunity and list the types of credit offered (Fishbein 1992).

Fannie Mae and Freddie Mac, the government-sponsored enterprises (GSEs) that arethe preeminent forces in the nation’s secondary mortgage market, have also beencalled upon to broaden access to mortgage credit and homeownership. The 1992 Fed-eral Housing Enterprises Financial Safety and Soundness Act (FHEFSSA) mandatedthat the GSEs increase their acquisition of primary-market loans made to lower-income borrowers and to areas unserved by private mortgage credit institutions(Carr et al. 1994a, 1994b). Spurred in part by the FHEFSSA mandate, Fannie Mae



announced a trillion-dollar commitment in 1994 to help 10 million families—particu-larly those most in need—buy homes of their own (Fannie Mae 1997, 2). Freddie Mac,the other dominant secondary-market institution, has pursued similar initiatives.

The result has been a wider variety of innovative mortgage products. The GSEs haveintroduced a new generation of affordable, flexible, and targeted mortgages, therebyfundamentally altering the terms upon which mortgage credit was offered in theUnited States from the 1960s through the 1980s (Gyourko, Linneman, and Wachter1997; Linneman and Wachter 1989; Listokin and Wyly 1998). Moreover, these second-ary-market innovations have proceeded in tandem with shifts in the primary markets:depository institutions, spurred by the threat of CRA challenges and the lure of sig-nificant profit potential in underserved markets, have pioneered flexible mortgageproducts (Listokin and Wyly 1998; Listokin et al. 2000; Schwartz 1998). For years, de-positories held these products in portfolios when their underwriting guidelines ex-ceeded benchmarks set by the GSEs. Current shifts in government policy, GSE ac-quisition criteria, and the primary market have fostered greater integration of capitaland lending markets.

These changes in lending herald what we refer to as mortgage innovation. To betterappreciate the paradigm shift in financing introduced by mortgage innovation, it isinstructive to organize the evolving mortgage products into a typology:

1. Historical Mortgages—standard mortgages as they existed through the two decadesbefore 1990.

2. GSE Standard Mortgages—loans conforming to the current (1990 and subsequent)standard mortgage guidelines of the GSEs.

3. GSE Affordable Mortgages—more affordable and flexible current loans developedby the GSEs (e.g., the Fannie Mae Community Home Buyer’s Program™ [CHBP]and Freddie Mac’s Affordable Gold™ [AG]) that share characteristics such as highloan-to-value ratios (LTVs; value of property) and credit flexibility. Emerging GSEAffordable Mortgages represent further innovations in GSE products that haverecently become available.

4. Portfolio Affordable Mortgages—more affordable and flexible current mortgageinstruments that are held in portfolios by individual lenders. These loans exceedthe parameters of the GSE Affordable Mortgages.

5. Governmental Affordable Mortgages3—publicly backed loans, such as mortgagesinsured by the Federal Housing Administration (FHA; e.g., Section 203), that areoften used by lower-income and minority borrowers.



3 This study includes the Governmental Affordable Mortgages as contemporary products (i.e., nonhistorical)even though Federal Housing Administration loans have been offered for many decades.

This study examines the potential and limitations of mortgage innovation in fosteringhomeownership by current renters, especially those who are members of traditionallyunderserved populations. While we recognize that homeownership is not for every-one (e.g., lifestyle renters) and that it would be counterproductive to bring the mar-ginally qualified to homeownership if they would shortly thereafter fail, it is instruc-tive to establish the baseline potential of renters to realize homeownership. To thisend, we estimate the ability of specific mortgage instruments, grouped according tothe mortgage typology described above, to expand homeownership. For example, weexamine how many renters can become homeowners through the application of theCHBP, AG, and other GSE Affordable Mortgages. The study estimates the number ofrenter families who would be served by the various loan products—that is, the num-ber of renters who would qualify for a home purchase loan given the confluence ofthe families’ financial resources and the terms of the respective mortgages. Rentersnot realizing homeownership from the alternative loan products are referred to asthe unserved. We apply a mortgage simulation—a modeled mortgage underwriting—to determine who is served and who is unserved.

As noted, the analysis focuses on renter families, defined here as related individualsresiding together or persons living alone. Our information on families comes from the1993 Survey of Income and Program Participation (SIPP). We use the seventh-wavepanel of the 1993 survey, which reports information for 1995. Based on our definitionof family and wave seven of the 1993 SIPP, our data set contains 25,762,939 renterfamilies in the United States. Our estimate varies from the American Housing Sur-vey (AHS)4 estimate for the total number of renters (34,150,000) for several reasons.First, we counted renter families while AHS counted renter households. Second, theAHS estimate of 34,150,000 renters is the total number of renter households in 1995.Our estimate is for families who were renters in 1993 and continued to be renters in1995. Thus, anyone who owned a home in 1993 but rented one in 1995, or who enteredthe SIPP after 1993, or who was under 18 in wave one, would not be counted, for ourpurposes, as a renter in 1993.

The analysis of the served compared with the unserved is made for all renters as agroup and for all renters by race/ethnicity. Our race/ethnicity categories are non-Hispanic white, non-Hispanic black, non-Hispanic other, and Hispanic. These race/ethnicity groups include both native-born Americans and immigrants. Immigrantsinclude all people not born in the United States, regardless of when they arrived. Inaddition, we present numbers for recent immigrants, defined as renter families whose



4 The AHS figure is taken from the 1995 American Housing Survey, table 2–1, Introductory Characteristics—Occupied Units (HUD 1997, 42).

householder entered the United States after 1984.5 This last category is a subset ofall renters and is not differentiated by race/ethnicity.6

The analysis is applied at the national level because there is insufficient sample sizeto differentiate mortgage simulations at a more microgeographic level (e.g., metro-politan statistical areas [MSAs]). For example, we cannot with statistical confidencesay that x percent of the unserved are in the Washington, D.C., MSA compared withy percent in the Atlanta MSA. Yet while we do not report separate results at the MSAlevel, we do incorporate important geographic distinctions in our analysis. In gauginghomeownership, we incorporate differences in housing prices and renter incomesacross the four census regions and nine census divisions.7 We also incorporate arealdistinctions in property tax rates, closing costs, and other home-buying expenses foreight major and geographically dispersed MSAs: Atlanta; Chicago; Houston; Los Ange-les; Miami; New York City; Philadelphia; and Washington, D.C. These area variations,however, are internal to the calculations and, as noted in our findings, are reportedon only an aggregate national basis.

We also compare the served and unserved renters by income category, and, in this re-gard, divide renters into four relative income groups: low, moderate, middle, andupper. Low-income families earn between 0 percent and 49 percent of median familyincome, moderate-income families 50 percent to 79 percent; middle-income families80 percent to 119 percent, and upper-income families at least 120 percent. The respec-tive percentages of median income that define each income group are for a family offour, and adjustments are applied for smaller and larger family sizes.8

Our approach both builds from and extends prior literature on mortgage simulationand other relevant studies (Bogdon and Can 1997; Gyourko and Tracy 1999; Herbert1995; Linneman and Megbolugbe 1992).



5 The SIPP uses the following categories to define periods of immigration: (1) 1911–59, (2) 1960–64, (3) 1965–69,(4) 1970–74, (5) 1975–79, (6) 1980–81, (7) 1982–84, and (8) 1985–93. Our recent immigrant groups are thoseindicating response (8) above.

6 Non-Hispanic white, non-Hispanic black, Hispanic, and non-Hispanic other renter families are discrete. Com-bined, they sum to all renter families. Recent immigrant families, those entering the United States after 1984,overlap with the previously listed race/ethnic groups (e.g., non-Hispanic whites and non-Hispanic blacks).

7 The census areas are the Northeast Region (New England and Middle Atlantic Divisions), Midwest Region(East North Central and West North Central Divisions), South Region (South Atlantic, East South Central, andWest South Central Divisions), and West Region (Mountain and Pacific Divisions).

6 For example, for a family of two, the four member–based median family income is reduced by 20 percent; for afamily of seven, the median is increased by 24 percent.

LITERATURE REVIEW

In a recent paper, Calhoun and Stark spoke of “synthetic loan underwriting simula-tions as studies determining the number of households that would qualify to pur-chase a home under various mortgage assumptions” (1997, 3–4). The most relevantsynthetic underwriting research has been produced by the National Association ofHome Builders (NAHB 1997), the National Association of Realtors (NAR 1998), Sav-age (1997, 1999), Savage and Fronczek (1993), and Calhoun and Stark (1997). Anotherrelevant study by Galster et al. (1996) examined the potential size of the homeown-ership market. Each of these studies is discussed below and summarized in table 1.(See appendix A for a more detailed description.)

Prior Studies

Industry Estimates of Housing Affordability

The intent of the NAHB and NAR work is to develop indices—the NAHB’s HousingOpportunity Index (HOI) and the NAR’s Housing Affordability Index (HAI)—of rela-tive housing affordability over time and by place. The higher the HOI and HAI values,the greater the housing affordability. The HOI and HAI tap a variety of data sourcesand include numerous underwriting considerations. However, each measure has limi-tations. The HAI considers the affordability of only median-priced housing (the HOIconsiders the distribution of home prices), and both the HAI and HOI limit their af-fordability analysis to median-income families.9 Our research design seeks a morediverse range of potential housing selection (i.e., not just the median-priced home) anda wider pool of would-be home buyers (i.e., not just median-income families). Further,the HOI and HAI approaches were never intended to be comprehensive financial un-derwriting models. The HAI considers only principal and interest (PI) payments aspart of the housing expense–to-income (front-end) ratio. It omits property taxes (T),property insurance (I), and mortgage insurance (MI), all of which are included intypical front-end ratios used by underwriters.10 Typical mortgage qualification pro-cesses also consider the combination of housing and other debt as a share of income—commonly known as the back-end ratio. Neither the HAI nor the HOI factors in theback-end ratio.

These indices have other limitations. Research has shown that limited assets constrainrenters’ ability to purchase a home because they lack sufficient resources for the downpayment, closing costs, and other related outlays (Duca and Rosenthal 1994; Linne-man and Wachter 1989). Yet neither the HAI nor the HOI considers renters’ assets:

Literature Review 9


9 One HAI variation, for first-time home buyers, targets units priced at 85 percent of the median and uses themedian income of families with heads of household aged 25 to 44.

1o The HOI compensates, in part, for this by applying a front-end maximum ratio, 25 percent, that is lower thanthe typical maximum allowed (28 percent).



Tabl

e 1.

Syn

thet

ic U

nd

erw

riti

ng

Stu

die

s an

d H

om

eow

ner

ship

Aff

ord

abili

ty In

dic

es

NA

HB

Hou

sing

NA

RH

ousi

ng A

fford

abili

ty I

ndex

(H

AI)

Sav

age

(199

7, 1

999)

Opp

ortu

nity

FR

M,a

AR

M,b

and

Firs

t-tim

eS

avag

e an

dC

alho

un a

ndC

hara

cter

istic

Inde

x (H

OI)

Com

posi

te H

AIs

Hom

e-B

uyer

HA

IFr

oncz

ek (

1993

)S

tark

(19

97)

Targ

et h

ome

buye

r(s)

Med

ian-

inco

me

fam

ilyM

edia

n-in

com

e fa

mily

25-

to 4

4-ye

ar-o

ld

All

owne

rs a

nd r

ente

rs

All

rent

ers

diffe

rent

iate

dre

nter

sdi

ffere

ntia

ted

by

by r

ace

and

othe

r in

com

e, r

ace,

and

ch

arac

teris

tics

othe

r ch

arac

teris

tics

Targ

et h

ousi

ng u

nit(

s)N

ew a

nd e

xist

ing

hom

e M

edia

n-pr

iced

exi

stin

g 85

% o

f med

ian-

Crit

erio

n ho

mes

—

Max

imum

-affo

rdab

lesa

les

hom

e so

ldpr

iced

exi

stin

g in

clud

ing

low

-pric

ed

hom

es:5

0th

hom

e so

ld(1

0th

perc

entil

e),

perc

entil

e co

mpa

red

mod

estly

pric

ed (

25th

w

ith ta

rget

-pric

ed

perc

entil

e), a

nd

hom

es s

ugge

sted

m

edia

n-pr

iced

hom

es

by c

onsu

mpt

ion

of

(50t

h pe

rcen

tile—

and

sim

ilarly

situ

ated

m

axim

um-a

fford

able

ow

ners

ho

mes

Geo

grap

hic

area

(s)

200

larg

est

Uni

ted

Sta

tes/

Uni

ted

Sta

tes/

Uni

ted

Sta

tes/

Uni

ted

Sta

tes/

cens

us

met

ropo

litan

are

asce

nsus

reg

ions

cens

us r

egio

nsce

nsus

reg

ions

/div

isio

nsre

gion

s/di

visi

ons

Fin

anci

al Q

ualif

icat

ion

Con

side

ratio

ns1.

Mor

tgag

e ty

peF

RM

-AR

M C

ompo

site

Var

ies:

FR

M, A

RM

, F

RM

-AR

M

FR

M

FR

Man

d F

RM

-VR

Mc

Com

posi

te

Com

posi

te2.

Mor

tgag

e LT

V90

%80

%90

%95

% (

FH

A—

95%

–97%

)80

%–1

00%

(ra

nge)

3.D

own

paym

ent

10%

20%

10%

5% (

FH

A—

3%–5

%)

20%

–0%

(ra

nge)

4.H

ome-

buye

r as

sets

No

No

No

Yes

Yes

5.H

ousi

ng e

xpen

ses

a.P

rinci

pal a

nd

Yes

Yes

Yes

Yes

Yes

inte

rest

b.P

rope

rty

taxe

sYe

sN

oN

oYe

sIn

dire

ctly

c.H

ome

insu

ranc

eYe

sN

oN

oYe

sIn

dire

ctly

d.M

ortg

age

No

Not

app

licab

led

Yes

Yes

Indi

rect

lyin

sura

nce

6.M

axim

um

25%

25%

25%

28%

(F

HA

—29

%)

28%

–38%

(ra

nge)

perc

enta

ge o

f in

com

e al

low

ed

for

hous

ing

expe

nses

7.E

xist

ing

debt

N

oN

oN

oYe

sYe

s

Literature Review 11


Tabl

e 1.

Syn

thet

ic U

nd

erw

riti

ng

Stu

die

s an

d H

om

eow

ner

ship

Aff

ord

abili

ty In

dic

es (

cont

inue

d)

NA

HB

Hou

sing

NA

RH

ousi

ng A

fford

abili

ty I

ndex

(H

AI)

Sav

age

(199

7, 1

999)

Opp

ortu

nity

FR

M,a

AR

M,b

and

Firs

t-tim

eS

avag

e an

dC

alho

un a

ndC

hara

cter

istic

Inde

x (H

OI)

Com

posi

te H

AIs

Hom

e-B

uyer

HA

IFr

oncz

ek (

1993

)S

tark

(19

97)

8.M

axim

um

Not

con

side

red

Not

con

side

red

Not

con

side

red

36%

(F

HA

—41

%)

33%

–43%

(ra

nge)

pe

rcen

tage

of

inco

me

allo

wed

fo

r ho

usin

g ex

pens

es a

nd

debt

9.C

redi

t rec

ord/

othe

r N

oN

oN

oN

oN

oun

derw

ritin

g va

riabl

es

10.C

losi

ng c

osts

N

oN

oN

oYe

sN

oco

nsid

ered

11.M

ajor

dat

a E

xper

ian,

Cen

sus

Cen

sus

Cen

sus

Cen

sus,

SIP

P,

NS

FH

,eot

hers

sour

ces

man

y ot

hers

aF

ixed

-rat

e m

ortg

age.

bA

djus

tabl

e-ra

te m

ortg

age.

cV

aria

ble-

rate

mor

tgag

e.d

Mor

tgag

e in

sura

nce

is n

ot r

equi

red

for

loan

s w

ith a

t le

ast

a 20

per

cent

dow

n pa

ymen

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Each merely assumes that renters will be able to come up with a 20 percent downpayment and closing and other costs (HAI) or a 10 percent down payment and closingand other costs (HOI, as well as the first-time home buyer under HAI). Althoughdifficulty in establishing a credit record and a blemished credit experience also mayconstrain a renter’s ability to purchase a home, especially among minority and LMIhouseholds (Listokin and Wyly 1998), this underwriting factor—admittedly difficultto obtain—is not considered in the HAI and HOI.

In short, the HOI and HAI serve the role they were designed for: they are roughgauges of relative affordability over time and by place. In the current investigation,we seek a more refined synthetic underwriting approach that will inform us, for ex-ample, about the share of African-American, white, and Hispanic families that canafford a home using a GSE Affordable Mortgage compared with the share of thesefamilies that can afford a home using a Portfolio Affordable Mortgage.

Savage and Fronczek’s Estimates

A more expansive analysis is offered by Savage (1997, 1999) and Savage and Fronczek(1993) in their Who Can Afford to Buy a House periodic series. The authors analyzeaffordability for both owners and renters. They examine these groups using a richdatabase, the SIPP. Housing affordability is gauged for a range of six alternativelypriced units, termed criterion homes by Savage and Fronczek. The criterion homesinclude units priced at various ranges: the median, 25th percentile (modestly pricedhouse), 10th percentile (low-priced house), new housing, and so on. Savage and Fron-czek specify a criterion home and then determine its affordability on the basis of apotential buyer’s financial profile (e.g., income, debt, assets). They also calculate themaximum home affordable based on the applicant’s financial characteristics.

Savage and Fronczek’s financial model is comprehensive. Two alternative mortgageinstruments—conventional and FHA—are factored with an assumed fixed interestrate and a 30-year term. All of the components of the front-end ratio—PI, T, I, MI—are calibrated. Existing debt is considered as well, thereby permitting calculation ofa would-be buyer’s total debt obligations and back-end ratio. These front-end andback-end costs are compared with the maximum front-end and back-end ratios allow-ed in conventional mortgages (28 percent/36 percent) and FHA mortgages (29 per-cent/41 percent). Similarly, Savage and Fronczek match the down payment require-ments for conventional and FHA mortgages against the actual assets available tovarious individuals and households. Closing costs are also compared with the would-bebuyer’s available assets. In addition, Savage and Fronczek assemble data for keyvariables (e.g., income, assets, debt) from the SIPP.

Much valuable information is contained in the Savage and Fronczek analysis, andtheir contribution is critically important to the field. However, their research haslimitations. Credit record—an important underwriting consideration, especially forthose at the economic margin—is not considered. In practice, however, “lenders placegreat emphasis on credit scores as the first step in determining whether the house-



hold can qualify for an A [credit] mortgage” (Caplin et al. 1997, 2.1). Additionally,although dual mortgage instruments—conventional and FHA—are incorporated bySavage and Fronczek, there is a much richer mosaic of loan products with respect tovarying LTVs, front-end and back-end ratios, credit acceptability, and the like.

The use of criterion-priced homes is also limiting. Although the use of six criterionhomes is preferable to, say, the HAI’s single median-priced property, the appropriatelinkage between these levels of housing consumption and various household typesand incomes (Follain 1999) remains unclear. For example, what is the “appropriatehome” for a middle-aged household earning twice the median income? Should theassociation here be to the median-priced criterion home, a new house, or an upscaleunit? A single-person household with an annual income of $20,000 is not likely toseek the same type of house as a household that has the same income but consists ofa married couple with two children. Should the “appropriate home” be the same foryoung white, African-American, and Hispanic renters? Should it be the same in allmetropolitan areas?

Calhoun and Stark’s Approach

This discussion points to the many influences that bear on specifying the referencehouse in any housing affordability analysis—that is, the appropriate pairing of agiven household to a given housing unit. In their paper titled “Credit Quality andHousing Affordability of Renter Households,” Calhoun and Stark (1997) describe an-other approach to specifying the reference house price. These authors also use a data-base that heretofore has not been tapped in determining housing affordability—theNational Survey of Families and Households (NSFH). Calhoun and Stark apply multi-variate regression to the NSFH data to specify reference house prices that can bereasonably paired with different renters based on the observed housing consumptionbehavior of similarly situated owners. Thus, a better-educated, higher-income renterliving in a more affluent area would be linked with a more expensive reference housethan would a less educated, lower-earning renter residing in a less affluent location.In both instances, African-American renters would be paired with less expensive unitsthan would their white counterparts—reflecting African Americans’ historically moremodest housing consumption (Galster, Aron, and Reeder 1999).

Next, Calhoun and Stark calculate the maximum-priced home affordable to eachrenter household. This is accomplished by linking a household’s financial resourcesto mortgage terms. The authors assume a 30-year fixed-rate mortgage with a rangeof terms; they do not use the specific requirements of actual mortgage products. Cal-houn and Stark incorporate affordability results with mortgage LTVs ranging from80 percent to 100 percent (20 percent to 0 percent down payments), front-end ratiosfrom 28 percent to 38 percent, back-end ratios from 33 percent to 43 percent, and in-terest rates from 7 percent to 13 percent. The confluence of these items and the renterhousehold’s financial resources produces the maximum-priced housing unit affordableto each renter. Calhoun and Stark then compare the target-priced home for each rent-er household—that is, the one based on historically observed consumption by compa-



rable owners—with the maximum-priced unit affordable to the renter household. Ifthe maximum-priced unit is more costly than the target-priced unit, the household isable to afford homeownership.

Calhoun and Stark have advanced the discussion on the subject, and our researchmodel is grounded, in part, on their conceptual framework. Still, there are somelimitations to their analysis. They did not factor closing costs in their financial calcu-lations, even though closing costs are not inconsequential outlays: depending on loca-tion, closing costs can amount to 3 percent to 4 percent of the cost of the house (Caplinet al. 1997). There is also a far-from-transparent identification by Calhoun and Starkof the housing costs included in the front-end ratio. As noted earlier, these housingcosts encompass principal and interest, taxes, property insurance, and mortgage in-surance. Ideally, these would be individually expensed—a procedure included bySavage and Fronczek. Calhoun and Stark did not take this approach; instead, theycharge directly only the principal and interest expenses, and the other outlays areindirectly subsumed in the range of the front-end ratios that they factor.

Galster’s Model

Galster and colleagues suggest an alternative approach for specifying the reference-price house in their 1996 study entitled Estimating the Number, Characteristics, andRisk Profile of Potential Homeowners. This important analysis developed a modelthat predicted the likelihood of renter households moving into homeownership duringan 18-month period. In formulating that model, the basis was the observed behaviorof white suburban renters—those thought to be the least constrained in buying ahome. The renter-to-homeowner transition of the white suburban subgroup was thenapplied to all households to gauge the potential overall homeowner market. Galsterand colleagues project that just over 600,000 renter households (2 percent) wouldbecome homeowners over an 18-month period if the underwriting practices found inwhite suburban areas were employed uniformly across the nation.

This is significantly different from the model applied by Calhoun and Stark. Theseresearchers paired a household with a reference-price house based on the observedconsumption patterns of owners in the same racial/ethnic group. Thus, if minoritiesin the past bought lower-priced homes, then minorities are paired with the same ref-erence-price house in the affordability analysis. In contrast, Galster and colleaguesmodel potential behavior on the basis of the consumption patterns of the least-con-strained population—white suburbanites who entered the homeownership marketin the favorable economic and housing market conditions of the mid-1990s. Galsterand colleagues thus pair minority renters with the higher-priced housing sought bywhite renters in the suburbs and reveal the size of the unserved market in a scenariofree of all discrimination.11



11 Other researchers have grappled with the issue of pairing certain types of housing in terms of price, amenities,location, and so on with various categories of households (e.g., minority versus majority; higher- versus lower-

Context of the Current Investigation and Prior Literature

It is useful at this point to place our current investigation in the context of relevantpast literature and research:

1. Because the purpose of this study is to identify who is served and who is notserved by mortgage innovation, we need to synthetically model the mortgageprocess as closely as possible. For this reason, the HOI and HAI approaches do notsuffice (they were not intended to perform such a duty), but the work of others ismore relevant.

2. We model our synthetic underwriting financial calculations on the Who Can Affordto Buy a House approach, the most comprehensive synthetic macroscale under-writing research done to date (Savage 1997, 1999; Savage and Fronczek 1993).Savage and Fronczek itemize the housing expenditures of the front-end ratio (PI,T, I, MI); include debt, so that back-end ratio considerations can be incorporated;factor in closing costs; and in other ways approximate the realities of mortgagequalification. We also use the same database (the SIPP) as that used by Savageand Fronczek.

3. We build upon the Savage and Fronczek financial calculations by applying thesynthetic underwriting for a menu of mortgages organized by the typology of loans(GSE Standard, GSE Affordable, Portfolio Affordable, Governmental Affordable,and Historical Mortgages). By comparison, Savage and Fronczek incorporated onlytwo mortgage types: conventional and FHA. In this regard, our work functionallyresembles the wide range of mortgage terms incorporated by Calhoun and Stark(1997); however, we target specific mortgage products, whereas Calhoun and Starkdid not.

4. We further augment the Savage and Fronczek (and Calhoun and Stark) analysesby incorporating credit record information into the synthetic underwriting. Sinceour credit data and credit score assignment have severe limitations, our credit un-derwriting must be regarded as an exploratory effort to be refined in the future.(The results of the pilot credit underwriting are reported in appendix B.)

5. Savage and Fronczek measure housing affordability with respect to the house-hold’s ability to purchase a benchmark (i.e., criterion) home and through dollarvalues (e.g., a mean- or median-priced affordable home under different mortgageproducts). We follow Savage and Fronczek in this regard and use both a bench-mark approach and a dollar measure of home-buying capacity.



income). For instance, Linneman and Wachter (1989, 391) related the housing services a family desires to pur-chase (with that capitalized value designated as V*) as a function of the family’s income (labeled as I) and a vec-tor of preference variables labeled X, with that overall relationship expressed as V* = V(I, X; b), where b is avector of parameters.

6. Although our financial analysis is an enhanced version of the basic Savage andFronczek approach, we depart somewhat from these authors with respect to thebenchmark housing used in analyzing affordability. Savage and Fronczek examinethe affordability of criterion homes. As there is an inherent attraction to using thecriterion homes as a benchmark, we present estimates for the criterion homes atthe metropolitan median, as well as estimates for modestly priced homes (25thpercentile) and low-priced homes (10th percentile).12 Yet, as discussed earlier, suchan arbitrary price-point approach does not consider the appropriate-shelter frameof reference for any given family. We therefore also link a renter family to a hous-ing unit based on observed consumption, as introduced by Calhoun and Stark andothers (e.g., Linneman and Wachter 1989). Our basic consumption model identifiesthe price of the housing unit each renter family would seek if its behavior conform-ed to the behavior of comparable renters who previously moved to homeowner-ship. The unit with a price calibrated in this way is termed the target house.

As noted, there can be various target houses. Galster and colleagues (1996) use asa reference the housing consumption of the least constrained group—white renterswho recently attained homeownership. Calhoun and Stark present affordabilityanalyses based on the consumption of all recent buyers. Galster and colleagues’standard benchmarks are appropriate in a world unconfounded by historicalracial and other barriers, whereas Calhoun and Stark orient their benchmarks tothe world as it exists.

In the current investigation, we benchmark the target house according to the ap-proach used by Calhoun and Stark—that is, the analysis is based on the consump-tion of all renters who recently moved to homeownership. We note, however, thattarget prices would be higher were the Galster and colleagues approach used.



12 While Savage and Fronczek examine six different criterion homes, our investigation will use three that span themore modestly priced housing spectrum.

SIMULATION FRAMEWORK AND MODELS

Our overall simulation framework and the data used to calibrate its component mod-els are summarized in figure 1. The framework comprises three major models. TheHousing Consumption Model estimates the price of the housing unit that each renterfamily would seek if its behavior conformed to that of all first-time home buyers enter-ing the market in the mid-1990s. The Housing Consumption Model yields a target orreference unit of housing consumption for each renter family. To provide comparabilitywith Savage and Fronczek, criterion-priced homes are considered here as well. Thetarget or criterion home thus provides the reference or benchmarked unit of housingconsumption. This is followed by the Mortgage Model, in which alternative mortgageinstruments are used to estimate the maximum purchase price for which each renterfamily could qualify based on its financial characteristics. The Mortgage Model alsoprovides other measures of home-buying capacity for each mortgage product, such asthe aggregate value of housing that could be purchased using the product. Given thatcredit is a key underwriting consideration, a pilot credit submodel, admittedly withmany limitations, informs the Mortgage Model. Finally, the Affordability Analysismodel compares a family’s reference house prices with the maximum-priced house forwhich it could qualify and provides other measures of affordability.

The overall simulation framework provides a measure of reference home-buying ca-pacity because it establishes a benchmark (e.g., individually calibrated target orprice-point criterion home) and then determines whether that referenced housing unitcan be afforded, given the confluence of the renters’ financial profile and the mortgageterms. There is much to be gained from such an approach; by establishing a bench-mark of housing consumption, we gain a point of reference by which we can gaugeachievement. If the renter can afford the reference home with a given mortgage prod-uct, the renter is served; if not, the renter is unserved. Thus, the loan product may bejudged in terms of its capacity to allow the renter to achieve the housing benchmark.

While there is much logic to the establishment of a reference point for measuringhousing affordability, such a reference point does not fully convey the ability of agiven mortgage to expand purchasing power, because purchasing capacity below thebenchmark is noted but not quantified. Assume, for instance, that a reference pur-chase price is $100,000 and that mortgage product A offers a given renter $50,000in purchasing power while mortgage product B conveys $75,000. If one simply notesthat both products fail to achieve the $100,000 benchmark, product B’s greater capac-ity to expand purchasing power is unrecognized.13 If, however, we incorporate a dollarhome-buying capacity, then the second product’s greater potential can be measured.

Consequently, in addition to the reference home-buying capacity, our AffordabilityAnalysis includes absolute home-buying capacity. The latter provides monetized mea-sures of the purchasing power afforded by the respective mortgage products. These

Simulation Framework and Models 17


13 Calhoun and Stark (1997) measure the relative level of achievement through the use of ratios. For more detail,see appendix A.



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measures include the total home-purchasing power of current renters under the dif-ferent mortgage instruments as well as the mean/median value of house prices afford-able to renters using the respective products.

In measuring the absolute home-buying capacity, we stipulate a threshold of consump-tion related to the “lumpy” and expensive consumption of housing; that is, we establisha minimum level of buying power required for inclusion in the absolute home-buyingcapacity measure. In this study, we stipulate the low-priced house as the minimumthreshold. If the low-priced house is, for example, priced at $40,000, then buying powerof $40,000 and up with no ceiling (unless otherwise indicated) would count toward theabsolute home-buying capacity.

The absolute home-buying capacity builds off the Mortgage Model. As noted, thatmodel estimates the maximum purchase price for which each renter could qualify,based on his or her financial characteristics. A maximum purchase price estimate thatexceeds the stipulated threshold equals the renter’s total absolute home-buying capac-ity. The aggregate of each of these renter calculations constitutes the total absolutehome-buying capacity. Mean/median values can be similarly derived.

In sum, the overall research framework comprises the Housing Consumption Model,the Mortgage Model, and the Affordability Analysis, with the latter incorporating boththe reference and the absolute home-buying capacities. The paired results of theHousing Consumption Model and the Mortgage Model provide the reference home-buying capacity. The Mortgage Model alone is the basis for the absolute home-buyingcapacity.

Ideally, an affordability analysis would account for the full array of circumstancesconfronted by different kinds of families attempting to locate and purchase homes.Unfortunately, few existing data sources provide sufficient information or sample sizeto permit such a comprehensive analysis, and researchers must therefore evaluatetradeoffs between spatial, temporal, and thematic coverage.14 In our judgment, theSIPP, released by the U.S. Census Bureau (U.S. Bureau of the Census 1996), providesthe most balanced compromise among these many tradeoffs. In appendix C, we pre-sent detailed evaluations of the SIPP and several alternative information sources.For the bulk of the analysis, the SIPP constitutes our core database, and we minimizethe use of imputed information from other sources. Almost all of the Housing Con-sumption Model (i.e., derivation of the target-priced homes) is derived from the SIPP,

Simulation Framework and Models 19


14 For example, the microdata samples from the decennial census of population and housing provide the largestpossible sample of individuals and families, and are the only source of information that includes rigorous spatialsampling protocols to illuminate local variations in population characteristics within metropolitan areas (U.S.Bureau of the Census 1993). Yet the Public Use Microdata Sample (PUMS) files are now a decade old; they aredesigned only for cross-sectional analyses, and the census long form includes no detailed questions regardingassets, employment history, or other factors that are crucial in the underwriting decision. In our initial researchdesign we considered the possibility of assigning to PUMS more recent estimates of population characteristicsfrom the Current Population Survey and other sources, and adding variables from other databases. That approach,however, would require the adoption of many unrealistic assumptions, and there is currently no reliable meansof cross-validating the accuracy of imputed data from an independent source.

and most of the Mortgage Model (with the exception of the credit submodel) is basedon renter financial data from the SIPP. In the following sections, we calibrate andapply the Housing Consumption Model and the Mortgage Model and describe ingreater detail their inclusive data sets.



MODEL CALIBRATION AND ANALYSIS

Housing Consumption Model

Model Framework and Data

The Housing Consumption Model builds on the work of Galster et al. (1996), Calhounand Stark (1997), Linneman and Wachter (1989), Gyourko, Linneman, and Wachter(1997), Jones (1995), and other literature. The development of details on this model isdescribed in a separate paper (Wyly et al. 1999). In brief, after deciding to enter thehomeownership market, a family (as earlier defined) chooses a desired level of hous-ing consumption that is influenced by income (I), wealth (W), demographic and lifecourse factors (X), and regional housing market conditions (R). These factors relateas follows:

H = f (I, W, X, R; β), (1)

where f is commonly modeled in a linear regression framework. In our model, the de-pendent variable is defined as the natural log of house value, and we refer to H asthe target house price that captures the various needs, financial resources, and hous-ing market circumstances of different families.

We predict the expected housing consumption of current renters, H, on the basis ofobserved choices of renter families that purchased homes during the recovery of themid-1990s. The analysis involves three main procedures. First, we use the first andseventh waves of the 1993 SIPP files to identify those families that moved from rent-ing to homeownership between 1993 and mid-1995. (The 1993 SIPP contained a finalsample of 4,565 renter families, of which 456 moved from renting to owning duringthe first and seventh waves of the survey.) Second, we calibrate a regression modelthat relates reported house prices to a vector of social, demographic, and local hous-ing market characteristics; the sample used consists of all families that moved fromrenting to owning a home (the Calhoun and Stark [1997] approach). Finally, the co-efficients from this model are applied to the remaining renters in the core SIPP dataset, providing an estimate of expected housing consumption for all renters in the sam-ple. Together with criterion homes (the Savage [1997, 1999] approach), these target-priced homes are then used as the reference homes for each renter family.

The Housing Consumption Model, which develops the target-priced homes, certainlyhas limitations. On theoretical grounds, one may question the assumption that ob-served choices made by recent buyers can be used to describe the behavior of currentrenters if they were to move into homeownership. On methodological grounds, thetechnique relies on a fairly small sample of recent home buyers. Nevertheless, thetarget house price methodology provides a valuable alternative to arbitrary affordabil-ity thresholds. As with the simulations developed by Galster et al. (1996), our methodis an explicit attempt to use the behavior of recent home buyers as a critical bench-mark by which to measure the effects of alternative mortgage market innovations.

Model Calibration and Analysis 21


Housing Consumption Model Results

Table 2 defines the variables for the Housing Consumption Model and Table 3 presentsresults of ordinary least-squares estimates of equation (1). It estimates the level ofhousing consumption that would be expected of current renters if they made the samechoices as similar families who moved into homeownership during the mid-1990s.Demographic, regional, and financial variables are comparable to those used in sim-ilar studies of housing consumption and tenure choice (Calhoun and Stark 1997;Galster et al. 1996; Linneman and Wachter 1989; Quercia, McCarthy, and Wachter1998). Diagnostics suggest a fairly robust model fit, in line with similar studies.15

Variations in housing consumption reflect differences in individual preferences andneeds, family financial resources, and regional and local housing market conditions.White-collar workers typically seek out more expensive homes than do blue-collarworkers, although this difference is not statistically significant. House values riseamong families with higher incomes and assets, and for people with higher levels ofeducation. Model results imply an income elasticity of approximately 20 percent withrespect to house price: in other words, a 10 percent increase in family income amongrecent home buyers leads to a house purchase price increase of approximately 2 per-cent. By contrast, the elasticity measure for family assets is remarkably low: a 10percent increase in assets translates to only 0.1 percent capitalized into higher homeprices. Variations in family assets may be more important in the tenure decision thanin the desired amount of entry-level housing consumption once the decision is madeto purchase a home. Higher assets may simply be used to reduce mortgage debt or tomaintain liquidity.

Regional housing market variations correlate with wide contrasts in housing consump-tion. Not surprisingly, house prices rise in more populated metropolitan areas, withcosts at the peak of the urban hierarchy running 49 percent above prices in nonmetro-politan areas. Regional variations inscribe a clear bicoastal pattern, with prices inNew England and on the West Coast standing 50 percent above those in the UpperMidwest. Even after controlling for all other factors in the model, however, a premiumis still apparent in the distinctive housing markets of Washington, D.C. (29 percent),and New York (36 percent).

Racial and ethnic differences in house prices are not significant, although the relevantparameter estimates are in the hypothesized direction. This result may be related tothe relatively small number of minority home buyers (70) in the sample. But it is alsoconsistent with Gyourko, Linneman, and Wachter’s (1996) finding that racial differ-ences in homeownership are not significant among families with sufficient resourcesto meet underwriting and down payment requirements. Gyourko, Linneman, andWachter (1996) also find, however, that racial differences in homeownership are sig-nificant among families constrained by low assets.



15 The adjusted multiple coefficients of determination (0.59 and 0.60) are comparable to Calhoun and Stark’s(1997) value of 0.45 and Quercia, McCarthy, and Wachter’s (1998) value of 0.48.



Table 2. Variable Definitions for Housing Consumption Model

All Renters MovingFull Sample into Homeownership

Standard StandardVariable Name Mean Deviation Mean Deviation

Dependent VariableLHVALUE Natural log of reported house value 11.5 0.475

Financial/human capital characteristicsLFINC Natural log of family income 9.58 1.44 10.3 0.696LASSETS Natural log of family assets 3.10 4.14 5.19 4.41ED Years of education 12.2 2.92 13.3 2.05SELF Self-employed (dummy) 0.0465 0.0526WHTC1 White-collar occupation, worked 0.337 0.550

entire month (dummy)WHTC2 White-collar occupation, worked 0.00480 0.00438

on and off this month (dummy)BLUC1 Blue-collar occupation, worked 0.272 0.270

entire month (dummy)BLUC2 Blue-collar occupation, worked 0.0138 0.00877

on and off this month (dummy)

Demographic characteristicsA15_24a Age 15–24 (dummy) 0.124 0.127A25_34a Age 25–34 (dummy) 0.329 0.515A35_44a Age 35–44 (dummy) 0.219 0.193A45_54a Age 45–54 (dummy) 0.121 0.107A55_64a Age 55–64 (dummy) 0.706 0.0395MARST Married (dummy) 0.314 0.491CHILD Number of children under age 0.743 1.21 0.691 1.06

18 in familyCHILD*A35_44 Interaction term between 0.119 0.0965

CHILD and A35_44CHILD*A45_54 Interaction term between 0.0380 0.0417

CHILD and A45_54

Metropolitan/regional housing market dummiesSMALLb MSA population < 500,000 0.0398 0.0395MEDIUMb MSA population 500,001–1,500,000 0.154 0.158LARGEb MSA population 1,500,001–4,000,000 0.191 0.228V_LARGEb MSA population > 4,000,001 0.284 0.173SO_ESCc East South Central 0.0422 0.0614SO_WSCc West South Central 0.102 0.103SO_SOAc South Atlantic 0.151 0.210WEST_MTNc West (Mountain) 0.0410 0.0548WEST_CSTc West (Coast) 0.203 0.158NE_NEWc New England 0.0570 0.0395NE_MATc Mid Atlantic 0.173 0.101MW_ENCc East North Central 0.161 0.169DC Washington, D.C., MSA 0.0153 0.0263NY New York MSA 0.0979 0.0373

Race/ethnicity (reference category is non-Hispanic whites)BLACK Black reference person 0.139 0.0614HISPAN Hispanic reference person 0.129 0.0614OTHER Reference person of other race 0.0419 0.0263

Our results suggest that minorities’ level of housing consumption is not appreciablydifferent from that of whites with similar financial and demographic characteristics.Nevertheless, this finding flies in the face of widespread empirical evidence and the-oretical expectation that predict reduced consumption among minorities. For someanalysts, these reductions signify racial differences in tastes or preferences, yet a morecompelling case can be made for the effects of housing market processes that limitaccess to high-value suburban submarkets (Adams 1987; Badcock 1994; Gyourko,Linneman, and Wachter 1996; Ladd 1998). Either way, for the purpose of assessingmortgage-related criteria, it is reasonable to assume that the relative effects of con-sumer preference and housing market discrimination will continue to exist in the shortterm. Accordingly, in our subsequent analyses we use a model specification (tables 2and 3) that includes negative race/ethnicity coefficients. This approach reduces pre-dicted housing consumption among minorities, thereby narrowing the gap betweenthese families’ financial resources and the cost of homes deemed suitable for theirneeds.

The model allows us to predict desired housing consumption, which we term the tar-get house. We assign or impute a target house price for each of the 4,10916 remainingrenter families in the 1993 SIPP. To give a sense of that calculation, we report overallresults. Based on the observed experience of first-time buyers who purchased a homebetween 1993 and 1995, the Housing Consumption Model suggests a median target-priced house (in 1995 dollars) of $85,210 and a mean price of $92,781 (table 4). Thismedian estimate stands at 75 percent of the median price of all existing single-familyhomes sold nationwide in 1995 ($112,900) (U.S. Bureau of the Census 1996, 717).This gap reflects the higher purchase prices among trade-up home buyers, whoseaccumulated equity permits larger down payments and corresponding leveraging ofincome to purchase more expensive homes.



16 The 4,109 remaining renter families is the starting figure of 4,565 renter families in the 1993 SIPP less the 456who moved from renting to owning between the first and seventh waves of the survey.

Table 2. Variable Definitions for Housing Consumption Model (continued)

All Renters MovingFull Sample into Homeownership

Standard StandardVariable Name Mean Deviation Mean Deviation

Sample size 4,509 .456

Source: Authors’ analysis of the first and seventh waves of the 1993 SIPP.a Reference category is age 65 and over.b Reference category is nonmetropolitan areas.c Reference category is Midwest, West North Central.

Criterion-Priced Homes

In addition to the target homes based on the Housing Consumption Model, we con-sider as a benchmark criterion-priced housing. This follows the Savage and Fronczekapproach, and we use three criterion-priced homes: median-priced, modestly priced



Table 3. Housing Consumption Model for Identifying a Target-Priced House:All Renters Moving into Homeownership

Variable Name Parameter Estimate Standard Error T Value

Intercept 9.07 0.306 29.6**

LFINC 0.197 0.0281 6.99**LASSETS 0.0124 0.00378 3.28**ED 0.0149 0.00754 1.98*SELF 0.249 0.0675 3.69***WHTC1 0.0838 0.0482 1.74WHTC2 0.308 0.223 1.38BLUC1 –0.0841 0.048 –1.75BLUC2 –0.0166 0.160 –0.310

A15_24 –0.394 0.127 –2.32*A25_34 –0.202 0.120 –1.68A35_44 –0.189 0.125 –1.51A45_54 –0.369 0.128 –2.87**A55_64 –0.317 0.135 –2.34*MARST –0.0135 0.0351 –0.386CHILD –0.0697 0.0177 –3.94***CHILD*A35_44 0.138 0.0724 1.91CHILD*A45_54 0.382 0.0945 4.04***

SMALL 0.177 0.0783 2.26*MEDIUM 0.193 0.0454 4.24***LARGE 0.165 0.0419 3.94***V_LARGE 0.401 0.0544 7.37***SO_ESC 0.0896 0.0763 1.17SO_WSC 0.0230 0.0672 0.343SO_SOA 0.211 0.0597 3.53***WEST_MTN 0.219 0.0787 2.79**WEST_CST 0.407 0.0655 6.21***NE_NEW 0.405 0.0881 4.60***NE_MAT 0.176 0.0760 2.32*MW_ENC 0.130 0.0609 2.13*DC 0.262 0.102 2.58*NY 0.307 0.102 3.07**

BLACK –0.0536 0.0625 –0.858HISPAN –0.0881 0.0675 –1.30OTHER –0.0530 0.0911 –0.585

Source: Authors’ analysis of the first and seventh waves of the 1993 SIPP.Note: The dependent variable is the natural log of reported house value. Model F ratio: 20.34 (p ≤ 0.0001). AdjustedR2: 0.590. *p ≤ 0.05. **p ≤ 0.01. ***p ≤ 0.001.

(25th percentile), and low-priced (10th percentile). National criterion home pricesappear in table 5.

The national modestly priced and low-priced criterion homes are, as expected, con-siderably less expensive than the national Housing Consumption Model–ascribedtarget price of current renters ($85,210 median), assuming these renters made thesame choices as similar families who moved into homeownership. After all, rentersbuying homes typically want more than modest- or low-priced units. The nationalmedian-priced criterion home ($84,000) is just slightly less expensive than the con-sumption-ascribed target figure ($85,210 median). Prices of the criterion homes varyconsiderably by census region and division, as shown in table 4. The array of target



Table 4. Target-Priced and Reference-Priced Houses Used in the Mortgage Simulation Analysis

Reference-Priced Housec (in $)Target-Priced Median Modest LowHousea (in $) (50th (25th (10th

Region/Area Meanb Medianb Percentile) Percentile) Percentile)

Nation 92,781 85,210 84,000 58,500 42,500

Census DivisionNortheast

New England Metro 103,065 99,880 137,500 103,500 75,000 New England Nonmetro 85,435 82,095 109,000 84,000 62,000 Middle Atlantic Metro 119,264 111,188 106,000 65,500 42,500 Middle Atlantic Nonmetro 69,081 73,351 79,000 56,000 40,000

MidwestEast North Central Metro 84,000 82,548 78,500 55,500 38,000 East North Central Nonmetro 62,136 62,221 62,000 43,000 29,000 West North Central 64,818 62,593 73,500 53,000 34,500 West North Central Nonmetro 60,280 58,046 52,000 35,000 22,000

SouthSouth Atlantic Metro 84,835 81,908 84,000 58,500 42,500 South Atlantic Nonmetro 68,064 68,887 68,000 46,000 30,000 East South Central Metro 69,605 71,440 65,500 49,000 32,000 East South Central Nonmetro 58,520 56,748 56,000 40,000 28,000 West South Central Metro 67,299 64,821 67,500 46,500 32,000 West South Central Nonmetro 54,005 53,765 53,000 33,000 17,000

WestMountain Metro 81,973 81,048 85,000 66,000 47,500 Mountain Nonmetro 72,439 76,485 71,000 52,000 35,000 Pacific Metro 117,768 111,605 164,500 108,500 68,500 Pacific Nonmetro 82,140 80,146 101,000 71,000 52,000

a This is the 1995 house value predicted using the Housing Consumption Model.b These statistics are computed for the sample of renter families that did not make the transition to homeownership

during 1993–95.c Reference-priced house estimates by census region are based on Savage’s study (1997). For each reference-

priced house, the national statistics represent the weighted median (based on SIPP family weights) of the regionalestimates; to facilitate comparison with the national median target-priced house, the statistics are computed for thesample of renter families that did not make the transition to homeownership during 1993–95.

and criterion-priced homes indicated in table 4 are the benchmarks to which weapply the Mortgage Model.

Mortgage Model

Mortgage Model Framework

Because a central theme of this study is the potential of mortgage innovation to ex-pand homeownership affordability, it is essential that details of the range and charac-teristics of alternative mortgage products be explained. Mortgages are often dividedinto two categories: governmental (those granted by the government or those havinggovernment backing, such as FHA insurance) and conventional (those that do nothave a public connection). The current investigation briefly examines GovernmentalAffordable Mortgages, most notably FHA loans insured under the Section 203 pro-gram. It also considers several conventional loan instruments. To better understandconventional mortgages, it is instructive to differentiate between three broad cate-gories of mortgages available since approximately 1990 (table 6).

The first is the Standard Mortgage, which is the current basic instrument of homefinance in the United States. The characteristics of this mortgage are heavily influ-enced by the GSEs that dominate the secondary market—Fannie Mae and FreddieMac. As such, we refer to this loan as the GSE Standard Mortgage.

Although it is the envy of other nations, the Standard Mortgage is beyond the finan-cial reach of many disadvantaged Americans. Over the last decade, a more affordableproduct has evolved. Its basic parameters have been set by Fannie Mae and FreddieMac, since primary-market lenders’ willingness to offer new products often dependson the GSEs’ willingness to buy the affordable loans on the secondary market. TheGSEs use different programmatic terms for these loans, for example, the FannieMae CHBP, CHBP with 3/2 Option, and Fannie 97, or Freddie Mac’s AG, AG with3/2 Option, and AG 97. To reflect their function—to reach the financially less advan-taged—and parentage, we refer to these loans as GSE Affordable Mortgages. At theleading edge of this affordable group are evolving products that we term EmergingGSE Affordable Mortgages. Examples are Fannie Mae’s Flex 97 and Freddie Mac’sCommunity Gold. (Because this subset was in a state of flux when we conducted ourresearch in 1998–99 and has evolved further since then, we will focus the followingdiscussion on the more established GSE Affordable Mortgages.)



Table 5. National Criterion Home Prices

National WeightedCriterion-Priced Home Median Price

Median-priced (50th percentile) $84,000Modestly priced (25th percentile) $58,500Low priced (10th percentile) $42,500



Tabl

e 6.

Lo

an F

inan

cial

Ch

arac

teri

stic

s by

Mo

rtg

age

Typ

e

GS

E

GS

EP

ortfo

lio

Mor

tgag

e C

hara

cter

istic

His

toric

al M

ortg

age

Sta

ndar

d M

ortg

age

Affo

rdab

le M

ortg

age

Affo

rdab

le M

ortg

age

Pu

rpo

seS

erve

all

borr

ower

s/ar

eas

Ser

ve a

ll bo

rrow

ers/

area

sS

erve

low

- to

mod

erat

e-

Ser

ve lo

w-

to m

oder

ate

inco

me

and

unde

rser

ved

inco

me

and

unde

rser

ved

area

sar

eas

Ap

plic

atio

nP

urch

ase

or r

efin

ance

Pur

chas

e or

ref

inan

ceP

urch

ase

or r

efin

ance

—P

rimar

ily p

urch

ase

with

exc

eptio

nsa

Targ

eted

inco

me

No

inco

me

limits

No

inco

me

limits

Max

imum

100

% o

f are

a M

axim

um 8

0%–1

00%

of

med

ian

inco

me—

with

ar

ea m

edia

n in

com

e—w

ith

exce

ptio

nsb

exce

ptio

nsc

Inve

sto

r G

SE

GS

EG

SE

Por

tfolio

Deb

t rat

ios:

fron

t/bac

k-en

d25

%–2

8%/3

3%–3

6%28

%/3

6%33

%/4

0%e

Max

imum

35%

/42%

;(g

uid

elin

e m

axim

um

d)

maj

ority

at 3

3%/3

8%

Min

imu

m d

own

pay

men

t10

% w

ith m

ortg

age

5%fw

ith m

ortg

age

3%–5

%g

3% o

r le

ssin

sura

nce

insu

ranc

e20

% w

ithou

t20

% w

ithou

t m

ortg

age

insu

ranc

em

ortg

age

insu

ranc

e

Min

imu

m b

orr

ower

10%

5%3%

–5%

hM

inim

al—

1%–2

% o

r le

ss

con

trib

uti

on

(“sw

eat e

quity

”m

ay b

e al

low

ed)

Max

imu

m L

TV

90

% w

ith 1

0% d

own

95%

with

5%

dow

n 95

%–9

7%97

% o

r hi

gher

paym

ent

paym

ent

80%

with

20%

dow

n 80

% w

ith 2

0% d

own

paym

ent

paym

ent

Pri

vate

mor

tgag

e in

sura

nce

Req

uire

d fo

r LT

Vs

over

90%

Req

uire

d fo

r LT

Vs

over

80%

Req

uire

dM

ay o

r m

ay n

ot b

e re

quire

d

Inte

rest

rat

eM

arke

tM

arke

tM

arke

tO

ccas

iona

lly 0

.5 p

oint

sbe

low

mar

ket

Res

erve

s2

mon

ths

requ

ired

2 m

onth

s re

quire

dTy

pica

lly 1

mon

th

Freq

uent

ly n

ot r

equi

red

requ

ired—

with

ex

cept

ions

i

Co

un

selin

gN

ot r

equi

red

Not

req

uire

dR

equi

red—

with

R

equi

red

exce

ptio

ns

aT

he G

SE

Affo

rdab

le M

ortg

ages

are

ofte

n fo

rmal

ly r

efer

red

to a

s “c

omm

unity

lend

ing

prod

ucts

.”A

few

of t

he G

SE

Affo

rdab

le M

ortg

ages

(e.

g., F

anni

e 97

,A

fford

able

Gol

d 97

) ar

e lim

ited

to p

urch

ases

(w

ith e

xcep

tions

);m

ost

of t

he o

ther

GS

E A

fford

able

Mor

tgag

es c

an b

e us

ed fo

r ei

ther

pur

chas

es o

r re

fi-na

ncin

g.S

ee t

able

s D

.1 a

nd D

.2.

bIn

com

e m

ay e

xcee

d th

e m

edia

n in

hig

h-co

st a

reas

and

in t

arge

ted

loca

tions

(e.

g.,

high

-min

ority

cen

sus

trac

ts,

cent

ral-c

ity lo

catio

ns).



Tabl

e 6.

Lo

an F

inan

cial

Ch

arac

teri

stic

s by

Mo

rtg

age

Typ

e(c

ontin

ued)

cV

arie

s by

pro

gram

, w

ith m

axim

um in

com

es r

angi

ng f

rom

rou

ghly

50

perc

ent

to 1

50 p

erce

nt o

f ar

ea m

edia

n in

com

e.d

The

se a

re t

he “

norm

al”

guid

elin

e m

axim

ums

that

can

be

exce

eded

with

com

pens

atin

g fa

ctor

s.e

The

max

imum

gui

delin

e fr

ont-

end

ratio

in t

he F

anni

e M

ae a

fford

able

s is

33

perc

ent;

Fred

die

Mac

has

no

max

imum

fro

nt-e

nd r

atio

.The

max

imum

bac

k-en

d ra

tio is

38

perc

ent

for

Fann

ie M

ae’s

CH

BP

and

36

perc

ent

for

Fann

ie 9

7;Fr

eddi

e M

ac a

llow

s a

40 p

erce

nt g

uide

line

max

imum

.See

tab

les

D.1

and

D.2

.f

The

min

imum

bor

row

er c

ontr

ibut

ion

is 5

per

cent

(w

ith o

r w

ithou

t m

ortg

age

insu

ranc

e).A

min

imum

20

perc

ent

dow

n pa

ymen

t is

req

uire

d w

hen

mor

t-ga

ge in

sura

nce

is n

ot p

lace

d on

a lo

an;h

owev

er,

the

borr

ower

nee

d on

ly m

ake

a 5

perc

ent

cont

ribut

ion

from

his

or

her

own

fund

s.T

he b

alan

ce o

ffu

nds

may

be

in t

he fo

rm o

f a

gift.

gT

he m

inim

um d

own

paym

ent

is 3

per

cent

for

Fann

ie 9

7 an

d A

fford

able

Gol

d 97

;the

min

imum

dow

n pa

ymen

t is

5 p

erce

nt fo

r C

HB

P, C

HB

P w

ith 3

/2O

ptio

n, A

fford

able

Gol

d 3/

2, a

nd A

fford

able

Gol

d 5.

hT

he m

inim

um b

orro

wer

con

trib

utio

n is

3 p

erce

nt fo

r Fa

nnie

97,

CH

BP

with

3/2

Opt

ion,

Affo

rdab

le G

old

97,

and

Affo

rdab

le G

old

3/2.

It is

5 p

erce

nt fo

rC

HB

P a

nd A

fford

able

Gol

d 5.

Whe

re t

he m

inim

um b

orro

wer

con

trib

utio

n is

3 p

erce

nt,

the

addi

tiona

l 2 p

erce

nt c

an c

ome

from

gift

s, g

rant

s, a

nd s

ubsi

-di

zed

seco

nd m

ortg

ages

(co

mm

unity

sec

onds

).i

One

mon

th r

eser

ve is

req

uire

d by

Fan

nie

97,

Affo

rdab

le G

old

97,

and

Affo

rdab

le G

old

3/2.

One

mon

th is

rec

omm

ende

d bu

t no

t re

quire

d fo

r A

fford

able

Gol

d 5.

Res

erve

s ar

e no

t re

quire

d fo

r C

HB

P o

r C

HB

P w

ith 3

/2 O

ptio

n.

Portfolio Affordable Mortgages are loans kept in-house by a lender for its investmentpurposes, as opposed to loans sold to outside investors.17 There is nothing inherent ina portfolio loan that differentiates it from a standard GSE issue. In fact, banks maykeep loans in their portfolio for a while and then sell them on the secondary market.In practice, however, when dealing with less advantaged borrowers, the loans kept inportfolio tend to have somewhat more flexible financial and underwriting terms thanother loans do. These in-house loans are typically kept in portfolio because they ex-ceed the financial and underwriting parameters set by the GSEs.

To provide a perspective on recent advances in mortgage underwriting, we also referto the Historical Mortgage—the standard conventional mortgage that dominated themarket during the two decades before 1990.

The various mortgage typologies and the subsumed loan products differ in their fi-nancial characteristics and in their borrower and property underwriting criteria. Fi-nancial characteristics include such fundamental considerations as the loan’s interestrate, the LTV, and the front-end and back-end ratios. Borrower and property under-writing criteria consider the credit, employment, and income record of the would-bemortgagor; the condition and attractiveness of the property to be mortgaged (as wellas that of the surrounding neighborhood); and the assets acceptable to close the trans-action (table 7). Although all of the above items are commonly referred to as “under-writing” collectively, for the purposes of this discussion (and in tables 6 and 7), thefinancial characteristics and borrower and property criteria will be considered sepa-rately.

The Historical Mortgage required a minimum 10 percent down payment, thus allow-ing a maximum 90 percent LTV. The front-end ratio had a 25 percent to 28 percentrange, and the back-end ratio had a 33 percent to 36 percent range. Additional finan-cial characteristics are summarized in table 6.

The current version of this loan—the GSE Standard Mortgage—is more liberal. Itallows a maximum 95 percent LTV. The maximum guideline ratios18 are 28 percentfor the front-end ratio and 36 percent for the back-end ratio. For those who are ableto afford it, the GSE Standard Mortgage is a reliable vehicle for purchasing or refi-nancing a home.

The mortgage parameters described above exclude many financially disadvantagedhome seekers. The GSE Affordable Mortgages were created to address that gap andto serve LMI borrowers (including minorities and others not well served by the Stan-



17 The mortgage parameter of what is salable to the secondary market is a moving target. Thus, the GSEs arebeginning to buy portions of affordable loans that we have labeled as portfolio loans. Freddie Mac, for example,has informed the research team that it is purchasing some Bank of America products (e.g., Zero Down, CreditFlex) that we have labeled portfolio loans.

18 The GSE front- and back-end ratios are “guidelines” that a lender uses to qualify a borrower. If the ratios exceedthe “guidelines” of the program, a lender, with compensating factors documented, can make the loan with higherratios.



Tabl

e 7.

Bo

rro

wer

an

d P

rop

erty

Un

der

wri

tin

g C

rite

ria

by M

ort

gag

e Ty

pe

Und

erw

ritin

gG

SE

Sta

ndar

d M

ortg

age

and

Crit

eria

His

toric

al M

ortg

age

GS

EA

fford

able

Mor

tgag

eP

ortfo

lio A

fford

able

Mor

tgag

e

I.C

red

ith

isto

ry

II.Em

ploy

men

t/in

com

eh

isto

ry

a.S

tron

g fo

rmal

cre

dit

reco

rd is

requ

ired.

b.S

tron

g cr

edit

bure

au r

epor

t is

requ

ired.

c.A

pplic

ant

mus

t ha

ve h

ad n

o le

gal

actio

n (i.

e.,

judg

men

t, ba

nkru

ptcy

, or

fore

clos

ure)

with

in p

ast

7 ye

ars.

a.A

pplic

ant

mus

t do

cum

ent

sam

eoc

cupa

tion

and

empl

oyee

his

tory

for

at le

ast

2 ye

ars.

b.O

vera

ge (

e.g.

, bo

nuse

s) a

nd s

ea-

sona

l inc

ome

incl

uded

onl

y if

con-

sist

ent

over

a 2

-yea

r pe

riod

and

expe

cted

to

cont

inue

at

the

sam

ele

vel o

r in

crea

se in

the

fut

ure.

c.A

ll th

ird-p

arty

–pro

vide

d in

com

ein

clud

ed if

the

re is

a h

isto

ry o

f su

chpa

ymen

ts a

nd if

inco

me

is e

xpec

ted

to c

ontin

ue in

the

fut

ure

(no

men

tion

of s

peci

al t

reat

men

t, e.

g.,

gros

s up

of n

onta

xabl

e in

com

e).

d.Ta

x be

nefit

s of

hom

eow

ners

hip

are

not

incl

uded

as

inco

me.

e.“B

oard

er”

inco

me

is n

ot in

clud

ed(r

enta

l inc

ome

is in

clud

ed).

f.In

add

ition

to

the

prim

ary

appl

ican

t’sin

com

e, o

nly

the

inco

me

of c

omor

t-ga

gors

is in

clud

ed.

a.C

redi

t ca

n be

est

ablis

hed

thro

ugh

alte

rnat

e m

eans

.B

lem

ishe

d cr

edit

is n

ot s

uffic

ient

grou

nds

for

deni

al:

•O

ccas

iona

l lap

ses

acce

pted

•E

xten

uatin

g ci

rcum

stan

ces

acce

pted

b.T

here

is f

lexi

ble

trea

tmen

t of

cre

dit

scor

ing.

c.Le

gal a

ctio

ns a

t lea

st 2

yea

rs o

ld c

anbe

dis

coun

ted

with

ree

stab

lishe

d pa

y-m

ent h

isto

ry a

nd n

o la

te p

aym

ents

.

a.A

pplic

ant

mus

t do

cum

ent

inco

me

(not

nec

essa

rily

job/

empl

oyer

) st

a-bi

lity

for

2 ye

ars.

b.S

imila

r to

His

toric

al M

ortg

age:

over

-tim

e an

d bo

nuse

s ar

e av

erag

edov

er a

2-y

ear

perio

d.

c.A

ll th

ird-p

arty

–pro

vide

d in

com

e is

coun

ted;

nont

axab

le in

com

e is

gros

sed

up.(

Som

etim

es S

ocia

lS

ecur

ity in

com

e al

so is

gro

ssed

up.

)

d.Ta

x be

nefit

s of

hom

eow

ners

hip

are

not

incl

uded

as

inco

me.

e.B

oard

er in

com

e is

incl

uded

as

a co

m-

pens

atin

g fa

ctor

for

high

er q

ualif

ying

ratio

s fo

r G

SE

Affo

rdab

le M

ortg

age

but

not

GS

E S

tand

ard

Mor

tgag

e.f.

In a

dditi

on t

o th

e pr

imar

y ap

plic

ant’s

inco

me,

onl

y th

e in

com

e of

com

ort-

gago

rs is

incl

uded

.

a.T

his

mor

tgag

e ha

s es

sent

ially

the

sam

e pa

ram

eter

s as

GS

E A

fford

able

,w

ith s

omew

hat

grea

ter

flexi

bilit

y.E

xam

ples

:•

Med

ical

act

ions

dis

coun

ted

•G

reat

er r

ecep

tivity

to

exte

nuat

ing

circ

umst

ance

sb.

Cre

dit

scor

ing

may

be

som

ewha

tde

emph

asiz

ed.a

c.Le

gal a

ctio

ns m

ay b

e di

scou

nted

afte

r 1

to 2

yea

rs if

act

ion

isex

plai

ned

(e.g

., ex

tenu

atin

g ci

rcum

-st

ance

s) a

nd c

lean

rep

aym

ent

reco

rd h

as b

een

mai

ntai

ned.

a.A

pplic

ant

mus

t do

cum

ent

inco

me

(not

nec

essa

rily

job/

empl

oyer

) st

a-bi

lity

for

1 to

2 y

ears

.b.

Sim

ilar

to H

isto

rical

Mor

tgag

e:ov

er-

time

and

bonu

ses

are

aver

aged

over

a 2

-yea

r pe

riod.

c.A

ll th

ird-p

arty

–pro

vide

d in

com

e is

coun

ted;

nont

axab

le in

com

e is

gros

sed

up.(

Som

etim

es S

ocia

lS

ecur

ity in

com

e al

so is

gro

ssed

up.

)

d.Ta

x be

nefit

s of

hom

eow

ners

hip

may

be in

clud

ed a

s in

com

e.e.

Boa

rder

inco

me

is in

clud

ed a

s a

com

-pe

nsat

ing

fact

or fo

r hi

gher

qua

lifyi

ngra

tios

for

GS

E A

fford

able

Mor

tgag

ebu

t no

t G

SE

Sta

ndar

d M

ortg

age.

f.T

he in

com

e of

all

adul

t ho

useh

old

mem

bers

(ev

en n

on-c

omor

tgag

ors)

may

be

coun

ted.



Tabl

e 7.

Bo

rro

wer

an

d P

rop

erty

Un

der

wri

tin

g C

rite

ria

by M

ort

gag

e Ty

pe

(con

tinue

d)

Und

erw

ritin

gG

SE

Sta

ndar

d M

ortg

age

and

Crit

eria

His

toric

al M

ortg

age

GS

EA

fford

able

Mor

tgag

eP

ortfo

lio A

fford

able

Mor

tgag

e

II.Em

ploy

men

t/in

com

eh

isto

ry(c

on

tinu

ed)

III.A

sset

veri

fica

tio

n

IV.P

rop

erty

—n

eig

hb

or-

ho

od

stan

dar

ds/

app

rais

al

g.S

tric

t ve

rific

atio

n of

all

inco

me

com

-po

nent

s is

req

uire

d.

a.A

sset

his

tory

mus

t be

ver

ifiab

le(c

ash

on-h

and

not

acce

pted

).

b.B

orro

wer

mus

t pr

ovid

e al

l fun

ds fo

rdo

wn

paym

ent

and

clos

ing

cost

s.

c.O

nly

gifts

fro

m fa

mily

mem

bers

are

allo

wed

;mon

ies

iden

tifie

d as

gift

sm

ust

be v

erifi

ed a

s su

ch (

e.g.

, le

tter

from

fam

ily m

embe

r sp

ecify

ing

that

the

tran

sfer

is a

bsol

ute

and

repa

y-m

ent

is n

ot r

equi

red)

.d.

Col

late

raliz

ed lo

an (

on p

erso

nal

prop

erty

, fo

r in

stan

ce)

is a

ccep

tabl

eas

an

asse

t w

ith s

tric

t ve

rific

atio

n.

a.N

eigh

borh

ood

char

acte

ristic

s co

unt

in a

ppra

isal

(po

sitiv

e an

d ne

gativ

e).

b.P

rope

rty

econ

omic

and

fun

ctio

nal

obso

lesc

ence

neg

ativ

ely

affe

cts

valu

atio

n.

c.“C

omps

”(c

ompa

rabl

e sa

les)

are

cons

ider

ed a

nd a

re r

estr

icte

d by

dist

ance

(fr

om s

ubje

ct p

rope

rty)

and

time

(sal

es w

ithin

the

last

6 m

onth

s).

g.Ve

rific

atio

n of

all

inco

me

com

pone

nts

is r

equi

red;

verb

al c

onfir

mat

ion

may

suffi

ce.b

a.A

sset

his

tory

mus

t be

verif

iabl

e;fo

rG

SE

Affo

rdab

le M

ortg

age,

cas

h on

-ha

nd is

acc

epta

ble

unde

r sp

ecifi

csi

tuat

ions

.b.

Bor

row

er is

not

req

uire

d to

pro

vide

all f

unds

for

dow

n pa

ymen

t and

clo

s-in

g co

sts;

a po

rtio

n m

ay b

e pr

ovid

edby

out

side

sou

rces

.

c.G

ifts

from

fam

ily m

embe

rs a

re a

llow

-ed

(af

ter

the

borr

ower

con

trib

utio

n is

satis

fied)

;pub

lic g

rant

s ar

e ac

cept

-ab

le b

ut tr

ansf

ers

from

the

selle

r an

dth

e re

alto

r ar

e re

stric

ted.

d.C

olla

tera

lized

loan

s ar

e m

ore

read

ilyac

cept

able

.

a.P

ositi

ve a

spec

ts o

f a n

eigh

borh

ood

(e.g

., re

cent

reh

abili

tatio

n or

new

cons

truc

tion)

are

str

esse

d.b.

Obs

oles

cenc

e di

scou

nt is

dee

mph

a-si

zed

by r

ecog

nitio

n of

a w

ider

ran

geof

acc

epta

ble

stan

dard

s.

c.G

reat

er r

ange

of c

omps

is a

ccep

ted.

g.T

he r

equi

rem

ents

are

ess

entia

lly t

hesa

me

as t

hose

for

GS

E A

fford

able

Mor

tgag

e, w

ith m

inor

exc

eptio

ns.

a.A

sset

s no

t al

way

s ve

rifie

d an

d ca

shon

-han

d is

fre

quen

tly a

ccep

ted.

b.R

equi

rem

ents

are

gen

eral

ly t

hesa

me

as t

hose

for

GS

E A

fford

able

,bu

t P

ortfo

lio A

fford

able

may

allo

wm

ost

or a

ll of

the

dow

n pa

ymen

t to

com

e fr

om o

utsi

de s

ourc

es.

c.P

ortfo

lio A

fford

able

allo

ws

mor

e gi

ftso

urce

s th

an G

SE

Affo

rdab

le a

llow

s(e

.g.,

tran

sfer

s fr

om t

he s

elle

r or

the

real

tor

are

perm

issi

ble)

.

d.C

olla

tera

lized

loan

s ar

e m

ore

read

ilyac

cept

able

.

a.P

ositi

ve a

spec

ts o

f a n

eigh

borh

ood

(e.g

., re

cent

reh

abili

tatio

n or

new

cons

truc

tion)

are

str

esse

d.b.

Sam

e as

GS

E A

fford

able

:ofte

n th

ego

od c

ondi

tion

of a

pro

pert

y is

stre

ssed

and

a h

ome

insp

ectio

n is

requ

ired

to a

void

a s

ituat

ion

whe

re a

prop

erty

nee

ds m

ajor

rep

airs

ear

lyon

.c.

Gre

ater

ran

ge o

f com

ps is

acc

epte

d.



Tabl

e 7.

Bo

rrow

er a

nd

Pro

per

ty U

nd

erw

riti

ng

Cri

teri

a by

Mo

rtg

age

Typ

e (c

ontin

ued)

Und

erw

ritin

gG

SE

Sta

ndar

d M

ortg

age

and

Crit

eria

His

toric

al M

ortg

age

GS

EA

fford

able

Mor

tgag

eP

ortfo

lio A

fford

able

Mor

tgag

e

aT

hat

is n

ot t

he c

ase

in t

he t

wo

Ban

k of

Am

eric

a m

ortg

ages

con

side

red

here

.b

Alte

rnat

ive

docu

men

tatio

n of

inco

me

is a

ccep

tabl

e;th

e bo

rrow

er m

ay p

rovi

de o

rigin

al p

ayst

ub(s

) an

d In

tern

al R

even

ue S

ervi

ce o

r W

-2 fo

rms

for

the

prev

ious

tw

o ye

ars,

and

the

lend

er m

ay v

erify

cur

rent

em

ploy

men

t by

tel

epho

ne.

V.O

ther

a.C

ontin

gent

liab

ility

deb

t (e

.g.,

cosi

gned

loan

) is

incl

uded

in t

otal

debt

(ba

ck-e

nd)

ratio

.

b.P

oole

d as

set f

unds

(i.e

., th

ose

draw

nfr

om fo

rmal

/info

rmal

alte

rnat

ive

sav-

ings

acc

ount

s, s

uch

as s

ous-

sous

)m

ay n

ot b

e us

ed a

s as

sets

, ev

en if

the

fund

s dr

awn

are

not

repa

yabl

e.

a.G

SE

gui

delin

es r

equi

re th

e le

nder

tove

rify

a hi

stor

y of

doc

umen

ted

pay-

men

ts o

n a

debt

by

the

prim

ary

oblig

or a

nd a

scer

tain

that

a h

isto

ryof

del

inqu

ent p

aym

ents

doe

s no

tex

ist f

or th

at d

ebt.

Whe

n th

is c

anno

tbe

don

e, th

e le

nder

incl

udes

the

cont

inge

nt li

abili

ty a

s lo

ng-t

erm

deb

tw

hen

calc

ulat

ing

the

borr

ower

’squ

alify

ing

ratio

s.b.

Poo

led

asse

t fun

ds m

ay b

e us

ed fo

rbo

th G

SE

Sta

ndar

d an

d A

fford

able

Mor

tgag

es a

nd a

re s

ubje

ct to

gui

de-

lines

est

ablis

hed

for

cash

on-

hand

.

a.S

ame

as G

SE

Affo

rdab

le:c

ontin

gent

liabi

lity

debt

may

be

excl

uded

fro

mth

e ba

ck-e

nd r

atio

if t

he p

rimar

y bo

rrow

er h

as a

con

sist

ent

paym

ent

hist

ory.

b.P

oole

d as

set

fund

s ar

e m

ore

read

ilyac

cept

able

.



dard Mortgage). Several variations of this type are covered in tables D.1 and D.2 inappendix D (e.g., Fannie Mae’s Fannie 97 and Freddie Mac’s Affordable Gold 5™);their general characteristics are summarized in table 6.

The focus of these affordable loans is to help less advantaged people achieve home-ownership; accordingly, the GSE Affordable Mortgages are sometimes limited to homepurchases,19 and the loans are typically targeted to people earning no more than thearea median income. The GSE Standard Mortgage has no such income limitation—one just needs to be able to afford it. The GSE Affordable Mortgage is not subsidizedwith a below-market interest rate; a market rate is charged. However, the standardfinancial requirements are significantly modified. Because LMI mortgagors have verymodest assets, only a minimum down payment is required—3 percent to 5 percent ofthe purchase price. Not all of the down payment has to come from the borrower; 2percent of the 5 percent is sometimes allowed to come from gifts or grants. The lowdown payments are mirrored by very high LTVs (as high as 97 percent). Becausethese ratios are so high, private mortgage insurance (PMI) is always required. More-over, since closing costs can also be a hurdle to homeownership, GSE Affordable Mort-gages allow payment of these costs to come from sources other than the borrower (forexample, a grant or a subsidized loan from a nonprofit group).20 Because the mort-gagor’s income is constrained, higher front-end and back-end ratios of 33 percent and40 percent, respectively, are allowed.21

Some lenders offer terms that are even more liberal than those offered by the GSEs.These terms are listed under the Portfolio Affordable Mortgage column in table 6. Toillustrate, both of the more liberal GSE Affordable Mortgages—Fannie Mae’s Fannie97 and Freddie Mac’s AG 97—require a 3 percent minimum borrower contribution.In contrast, one portfolio mortgage, Bank of America’s Neighborhood Advantage ZeroDown™, requires no down payment. Portfolio Affordable Mortgage allowances wereintroduced in recognition of the minimal assets available to many traditionally un-derserved families for the down payment and closing costs. The resources of thesefamilies are so insubstantial that even the greatly reduced GSE Affordable Mortgagerequirements (e.g., a 3 percent down payment) can prove too great an obstacle. To en-hance affordability, some portfolio mortgages have rescinded PMI requirements, evenon high LTV loans. For example, this was the policy of some lenders participating inthe Delaware Valley Mortgage Plan (DVMP). (We refer to no-PMI portfolio productsreflective of the DVMP experience as a Portfolio Composite.)

A further level of affordability is offered by underwriting flexibility. Historically, manyunderwriting standards concerning credit history, employment history, asset verifica-tion, and so on were quite strict. These standards are detailed in table 7 under His-

19 This is the case for Fannie Mae’s Fannie 97 and Freddie Mac’s AG 97. Fannie 97 is also available for no-cashout rate/term refinances of existing Fannie 97 mortgages.

20 This depends on the specific GSE Affordable Mortgage, as detailed in tables 6 and 7.

21 Freddie Mac’s GSE Affordable Mortgages have no maximum front-end ratio.



torical Mortgage. Since 1990, however, underwriting requirements have been mademore flexible for both the GSE Standard Mortgages and the GSE Affordable Mort-gages. In fact, they currently have nearly indistinguishable underwriting standards.22

Still, the portfolio loans of some institutions venture even further, incorporating ad-ditional provisions for mortgage-qualification flexibility.

The changes in underwriting are summarized in table 7 under five sections: (1) cred-it history; (2) employment/income history; (3) asset verification; (4) property—neigh-borhood standards/appraisal; and (5) other. For example, the credit bar for the His-torical Mortgage was high. Institutions typically demanded a strong credit bureaureport. Credit misdeeds were treated harshly. It was difficult to obtain a mortgage ifa bankruptcy, foreclosure, or other legal action had taken place within the past sevenyears.

The current GSE Standard and GSE Affordable Mortgages have changed the creditworld, however. Recognizing that some people may not have obtained formal credit inthe past, the GSE Standard and GSE Affordable Mortgages allow credit to be estab-lished through alternative means. Also realizing that credit misdeeds may occur, es-pecially among lower-income families and those new to using credit, the GSE Stan-dard and GSE Affordable Mortgages are more tolerant of occasional credit lapses, aslong as a pattern is not indicated and the lapses are due to extenuating circumstances(e.g., illness, unemployment linked to downsizing, a spouse’s death).

Credit underwriting for portfolio mortgages is a “nuanced change” from GSE policy.It may mean greater tolerance for payment lapses common among the less advan-taged, such as medical collection or missed winter utility bills. Portfolio lending mayalso pay less heed to credit scoring. The GSEs encourage lenders to use credit scoringas one of the numerous components of risk assessment, and they mention Fair, Isaac& Company (FICO) specifically (Fannie Mae 1995, 2).23 The GSEs have referred to abroad parameter or guideline FICO score of approximately 620 for their standardproducts. At the same time, the GSEs encourage lenders to factor the FICO scorealong with compensating factors (e.g., a lower mortgage LTV) and extenuating cir-cumstances (Fannie Mae 1995, 1997; Freddie Mac 1995, 1996). The portfolio lenderssometimes accept applicants with scores lower than the comfort range of the GSEs(e.g., the Portfolio Composite accepted FICO scores in the mid-500s). Each portfolioprogram, however, has its own requirements. For example, the Bank of AmericaNeighborhood Advantage Zero Down mortgage grants a 100 percent LTV mortgagebut has a guideline FICO score of 660, whereas a sister Bank of America Neighbor-hood Advantage Credit Flex portfolio product has somewhat lower FICO guidelinesbut also a lower LTV.

22 There are very few technical differentiations. For instance, boarder income is included as a compensating factorfor higher qualifying ratios for GSE Affordable Mortgages but not for GSE Standard Mortgages.

23 After the research for this report was completed, Fannie Mae announced that it was replacing credit scores withspecific credit characteristics in the most recent release of its automated underwriting system (Fannie Mae 2000).



Current underwriting with respect to employment, income, and assets has been sig-nificantly liberalized compared with the Historical Mortgage. This is true for the cur-rent GSE Standard Mortgages, the GSE Affordable Mortgages, and the Portfolio Af-fordable Mortgages (see table 7 for a comparison of the underwriting criteria for eachmortgage type).

It is important to note that elements of each mortgage product are subject to change.For example, the Emerging GSE Affordable category shares numerous characteristicswith the Portfolio Affordable category (e.g., very high front-end and back-end ratios).The Government Affordable Mortgage also has evolved. Loans guaranteed by theU.S. Department of Veterans Affairs, important in the earlier post–World War II era,are no longer significant today. The primary unsubsidized Government AffordableMortgage loans insured under the FHA 203(b) program have also been changing. Be-fore 1997, FHA 203(b) mortgages, termed FHA 203(b)–prior, had a maximum LTV ofabout 96 percent24 on purchase amounts up to $125,000. Since 1997, the FHA 203(b)mortgage, termed FHA 203(b)–current, has increased the LTV to about 98 percent25

on purchase amounts up to $125,000. Although the FHA 203(b)–prior allowed non-recurring closing costs to be financed, that option ended with the FHA 203(b)–cur-rent. Both forms of the FHA 203(b) mortgage have been relatively lenient on creditunderwriting.

To summarize, our mortgage framework comprises the types and products listed intable 8.

Mortgage Model Data

The next step in the Mortgage Model is to obtain appropriate data so that the finan-cial and underwriting facets of the alternative mortgages can be applied to the renterfamilies. While data are not available for every component cited in the preceding sec-tion, much information can be obtained from various sources, and we can calibratethe more critical financial and underwriting elements. This process is described indetail in appendices B and D; we summarize the major points below.

Some of the mortgage data elements are not family specific. These include the mort-gage interest rate, property taxes, property insurance, mortgage insurance, and clos-ing costs, all of which are determined in as specific a fashion as possible, as noted inappendix D.

24 The exact maximum LTV was 97 percent of the first $25,000; 95 percent of the amount between $25,001 and$125,000; and 90 percent of the amount over $125,000.

25 The exact maximum LTV is 98.75 percent on the first $50,000; 97.65 percent of the amount between $50,001and $125,000; and 97.15 percent of the amount over $125,000.

For instance, it is important to carefully specify closing costs, as these are quite signif-icant, often exceeding the amount of the down payment in this era of very high LTVloans. Closing cost information for eight major metropolitan areas in different regionsof the country were obtained from Countrywide Home Loans (1998), one of the nation’slargest lenders. From these actual transactions, we derived closing costs as a percent-age of the housing unit price. These costs varied from 3 percent to 4 percent of thehousing unit price in the Atlanta and Los Angeles metropolitan areas to 5 percent to6 percent in the New York and Philadelphia metropolitan areas. Closing costs weredifferent for several reasons. Certain areas—for example, New York and Philadel-phia—had very high mortgage taxes, while other locations had no mortgage taxes ormuch lower mortgage levies. Prepaid property taxes also varied; these payments werelow in the Atlanta and Los Angeles metropolitan areas and high in the Northeastlocations. Interestingly, there was variation in the price of items that one would havethought would be uniformly priced commodity items. For instance, title insuranceamounted to 0.6 percent of the home’s value in Miami, compared with 0.2 percent inAtlanta. There were also specific area idiosyncrasies. For example, the cost of hazardinsurance was three times higher in Miami than in the other metropolitan areas—aresult of the tremendous insurance company losses suffered in the wake of HurricaneAndrew.



Table 8. Mortgage Types and Products in Report Tables

Mortgage Types/Products Detailed in Tables

Historical Mortgage 6, 7, D.6

GSE Standard Mortgage D.1, D.2, D.6(Fannie Mae and Freddie Mac)

GSE Affordable MortgagesFannie Mae

CHBP D.1, D.6CHBP, 3/2 Option D.1, D.6Fannie 97 D.1, D.6

Freddie MacAG D.2, D.6AG, 3/2 Option D.2, D.6AG 97 D.2, D.6

Emerging GSE Affordable MortgagesFannie Mae

Flex 97 D.3, D.6Freddie Mac

Community Gold D.3, D.6

Portfolio Affordable MortgagesBank of America Zero Down D.4, D.6Bank of America Credit Flex D.4, D.6Portfolio Composite D.6

Government Affordable MortgagesFHA 203(b)–prior D.5, D.6FHA 203(b)–current D.5, D.6

The Mortgage Model also calibrates family characteristics that influence mortgagequalification: (1) income, (2) debt, (3) assets, (4) credit record, and (5) other items (e.g.,employment stability). There are various sources of data for these items, as explainedin appendix C. Our choice of which source to use is governed by the demands of thecurrent investigation. Since our objective is to determine the degree of housing af-fordability ensuing from the alternative mortgages, the data sought must correlatewith each of the mortgage’s financial and underwriting characteristics. For instance,the GSE Standard, GSE Affordable, and Portfolio Affordable Mortgages “gross up”nontaxable sources of income and count boarder income; the Historical Mortgagedoes not.

These specific mortgage-driven data needs are then compared with the range of po-tential available data sources on families and households, and an appropriate sourceis selected (table 9). For example, income information found in the SIPP is the richestof all the sources. The SIPP disaggregates income into 38 categories, including “room-er and boarder” income as well as nontaxable sources (e.g., child support, disability),which allows for a more accurate mortgage qualification. The SIPP is also the pre-ferred source for asset, debt, and other characteristics, as explained in appendix C.Furthermore, the SIPP has the advantage of consistency because the ConsumptionModel is based on the SIPP.

Appendices C and D describe in detail how each SIPP variable is treated to developthe data for the Mortgage Model. For instance, debt is reported in the SIPP on a totalbasis, and this has to be translated into a periodic debt-repayment amount for under-writing the back-end ratio. Similarly, as the alternative mortgage products treat cat-egories of income differently, we adjust the various income components in the SIPPaccordingly. Thus, Social Security is not grossed up under the Historical Mortgage,but it is under today’s more innovative mortgage products. The Historical Mortgagewould discount public support, but today’s mortgages fully count that source. Further,the Historical Mortgage would penalize the borrower with occupational instability,income instability, or both, in effect discounting income in these situations. Currentmortgages are much more lenient in this regard. We therefore adjust income to re-flect these mortgage traits, as detailed in appendix D.

The results of these adjustments provide the asset, debt, and income statistics reportedin table 10 for all renters and for renter subgroups (non-Hispanic white; non-Hispanicblack; Hispanic; non-Hispanic other; and recent immigrant).

A number of characteristics are significant. Renter income is modest, especially forthe traditionally underserved, as noted in table 11 based on 1993 SIPP data.

The modest renter incomes above would not always be fully credited by underwriters.Thus, since the Historical Mortgage would discount or exclude such income compo-nents (reported in the SIPP) as Aid to Families with Dependent Children, child sup-port, and alimony, which are drawn upon by some renters, the median mortgage-available family income (or adjusted family income) for all renters and for white, black,and Hispanic renters under the Historical Mortgage would be $15,957, $17,879,





Tabl

e 9.

Su

mm

ary

An

alys

is o

f D

ata

So

urc

es C

on

sid

ered

fo

r th

e H

ou

sin

g A

ffo

rdab

ility

an

d M

ort

gag

e Q

ual

ific

atio

n R

esea

rch

Mos

tM

ortg

age

Und

erw

ritin

g C

riter

ia

Sou

rce

Rec

ent Y

ears

Inco

me

Ass

ets

Liab

ilitie

sC

redi

tE

mpl

oym

ent

Sou

rce:

See

app

endi

x C

(ta

bles

C.1

, C

.2,

and

C.3

).N

ote:

Sha

ded

boxe

s ar

e so

urce

s ch

osen

in t

he a

naly

sis.

Co

nsu

mer

Exp

end

itu

reS

urv

ey

Su

rvey

of

Inco

me

and

Pro

gra

m

Par

tici

pat

ion

Pan

el S

tud

y o

fIn

com

eD

ynam

ics

(PS

ID)

Su

rvey

of

Co

nsu

mer

Fin

ance

Nat

ion

al

Su

rvey

of

Fam

ilies

an

dH

ou

seh

old

s

Pu

blic

Use

Mic

rod

ata

Sam

ple

Am

eric

anH

ou

sin

g

Su

rvey

1990

–95

1991

–93,

199

6

1990

–96

1991

, 19

93,

and

1995

1992

–94

(wav

e2

data

)

1990

Nat

iona

l sam

ple

is 1

999

(oth

ers

vary

by

MS

A)

Non

e

Non

e

•Q

uest

ions

on

fisca

l str

ess

byye

ar•

Ava

ilabl

e in

onl

y19

96 s

urve

yN

one

•O

rdin

al s

cale

•

Que

stio

n on

over

due

pay-

men

ts:t

otal

inpa

ymen

ts t

hat

wer

e 2

mon

ths

over

due

Non

e

Non

e

•In

terv

al s

cale

•

11 s

ourc

es•

Bef

ore

and

afte

rta

xes

•In

terv

al s

cale

•38

sou

rces

•B

y in

divi

dual

s in

hou

seho

ld

•In

terv

al s

cale

•

8 so

urce

s•

By

indi

vidu

als

inho

useh

old

•O

rdin

al s

cale

•5

cate

gorie

s•

Ove

rsam

ples

wea

lthy

hous

e-ho

lds

•O

rdin

al s

cale

•7

sour

ces

•In

terv

al s

cale

•8

sour

ces

•O

rdin

al s

cale

•9

sour

ces

•In

terv

al s

cale

•7

sour

ces

•A

ggre

gate

est

i-m

ates

not

rob

ust

•In

terv

al s

cale

•

15 f

inan

cial

ass

etca

tego

ries

•5

nonf

inan

cial

asse

t ca

tego

ries

•A

ggre

gate

ass

ets

avai

labl

e •

PS

ID c

onta

ins

asu

pple

men

t w

itha

few

inte

rval

estim

ates

of

asse

ts•

Ord

inal

sca

le•

10 c

ateg

orie

s fo

rfin

anci

al a

sset

s;5

for

nonf

inan

cial

asse

ts•

Ord

inal

sca

le

•5

sour

ces

Non

e

Non

e

•In

terv

al s

cale

•M

onth

ly p

ay-

men

ts o

n ou

t-st

andi

ng d

ebt

•In

terv

al s

cale

•

Ans

wer

s on

3ca

tego

ries

in19

93 S

urve

y(w

ave

7)

•P

SID

con

tain

s a

supp

lem

ent

with

a fe

w in

terv

ales

timat

es o

f lia

bilit

ies

•O

rdin

al s

cale

•6

cate

gorie

s

•O

rdin

al s

cale

•M

onth

ly p

ay-

men

ts a

nd t

otal

outs

tand

ing

•5

sour

ces

Non

e

Non

e

•12

mon

ths

•A

vera

ge h

ours

wor

ked

•48

+ m

onth

s•

Ave

rage

hou

rsw

orke

d•

Occ

upat

ion

and

indu

stry

•S

elf-

empl

oym

ent

•Le

ngth

of

time

with

cur

rent

empl

oyer

•C

urre

nt e

mpl

oy-

men

t st

atus

•E

mpl

oym

ent

hist

ory

1987

–94

•O

ccup

atio

n

•12

mon

ths

•A

vera

ge h

ours

wor

ked

Non

e



Tabl

e 10

.Fin

anci

al a

nd

Dem

og

rap

hic

Pro

file

s o

f R

ente

r G

rou

ps

(in

Do

llars

,exc

ept

N R

ow

s an

d H

ou

seh

old

Siz

e,Fa

mily

Siz

e,an

d F

amily

Wei

gh

t C

olu

mn

s)

Nom

inal

Adj

uste

dA

djus

ted

Nom

inal

Adj

uste

dA

djus

ted

Targ

etH

ouse

hold

Hou

seho

ldH

ouse

hold

Fam

ilyFa

mily

Fam

ilyH

ouse

hold

Fam

ilyFa

mily

Ren

ter

Gro

upa

Ass

ets

Deb

tH

ouse

Inco

me

Inco

meb

Inco

mec

Inco

me

Inco

meb

Inco

mec

Siz

eS

ize

Wei

ght

All M

ean

7,90

4 3,

609

92,7

81

27,3

98

23,1

78

26,2

00

24,1

17

19,9

33

22,9

28

2.50

2.24

6,95

9 M

edia

n30

0 45

85

,210

21

,600

18

,580

20

,857

18

,786

15

,957

18

,000

2.

002.

005,

933

Std

.Dev

iatio

nd38

,900

8,

250

41,2

88

22,6

65

24,9

02

22,7

65

20,6

31

23,1

00

20,7

57

1.61

1.59

2,08

0 M

inim

um

0 0

7,52

9 0

–123

,764

–2

1,11

2 0

–123

,764

–2

1,11

2 1.

001.

0077

2 M

axim

um71

2,87

7 21

1,50

0 32

7,59

6 20

8,09

5 19

2,11

1 19

2,11

1 20

8,09

5 19

2,11

1 19

2,11

1 13

.00

13.0

027

,728

N

(w

eigh

ted)

25,8

27,5

2625

,827

,526

25,8

27,5

2625

,827

,526

25

,827

,526

25

,827

,526

25

,827

,526

25,8

27,5

2625

,827

,526

25,8

27,5

2625

,827

,526

25,8

27,5

26N

(un

wei

ghte

d)4,

120

4,12

0 4,

109

4,12

0 4,

120

4,12

0 4,

120

4,12

0 4,

120

4,12

0 4,

120

4,12

0

Whi

te (

non-

His

pani

c)M

ean

11,3

68

4,24

7 96

,649

30

,196

25

,912

29

,107

26

,243

21

,987

25

,153

2.

181.

936,

358

Med

ian

649

500

87,7

99

24,2

40

21,1

98

23,3

62

20,5

56

17,8

79

19,9

92

2.00

1.00

5,77

5 S

td.D

evia

tiond

45,5

65

9,06

5 42

,906

23

,955

26

,102

23

,872

21

,489

23

,878

21

,442

1.

411.

391,

703

Min

imum

0

0 8,

286

0 –1

16,6

47

–20,

449

0 –1

16,6

47

–20,

449

1.00

1.00

772

Max

imum

712,

877

211,

500

327,

596

208,

095

192,

111

192,

111

208,

095

192,

111

192,

111

13.0

013

.00

27,7

28

N (

wei

ghte

d)16

,235

,457

16,2

35,4

5716

,183

,879

16,2

35,4

5716

,235

,457

16

,235

,457

16

,235

,457

16,2

35,4

5716

,235

,457

16,2

35,4

5716

,235

,457

16,2

35,4

57N

(un

wei

ghte

d)2,

769

2,76

9 2,

760

2,76

9 2,

769

2,76

9 2,

769

2,76

9 2,

769

2,76

9 2,

769

2,76

9

Bla

ck (

non-

His

pani

c)M

ean

1,60

1 2,

443

83,4

83

20,9

17

17,0

48

19,5

62

19,4

71

15,6

66

18,1

54

2.69

2.50

8,44

7 M

edia

n0

0 73

,205

15

,402

12

,342

14

,319

13

,770

11

,220

12

,993

2.

002.

006,

824

Std

.Dev

iatio

nd9,

165

5,95

5 36

,600

18

,533

20

,356

18

,742

18

,149

20

,083

18

,354

1.

691.

662,

850

Min

imum

0

0 8,

704

0 –7

5,08

0 –1

5,01

1 0

–75,

080

–15,

011

1.00

1.00

2,43

1 M

axim

um11

4,99

9 76

,000

24

5,29

8 15

7,80

6 15

7,80

6 15

7,80

6 15

7,80

6 15

7,80

6 15

7,80

6 12

.00

12.0

022

,863

N

(w

eigh

ted)

4,47

1,07

0 4,

471,

070

4,46

4,52

5 4,

471,

070

4,47

1,07

0 4,

471,

070

4,47

1,07

0 4,

471,

070

4,47

1,07

0 4,

471,

070

4,47

1,07

0 4,

471,

070

N (

unw

eigh

ted)

609

609

608

609

609

609

609

609

609

609

609

609

His

pani

cM

ean

2,00

0 2,

308

86,8

00

23,0

26

18,7

25

21,6

77

20,5

46

16,2

65

19,2

01

3.38

3.06

7,75

8 M

edia

n0

0 82

,812

18

,266

15

,010

17

,283

15

,314

12

,844

14

,178

3.

003.

006,

745

Std

.Dev

iatio

nd23

,148

5,

613

33,6

17

17,4

24

21,4

99

18,3

13

16,8

19

21,0

81

17,7

04

1.89

1.88

2,17

3 M

inim

um

0 0

8,48

5 0

–123

,764

–2

1,11

2 0

–123

,764

–2

1,11

2 1.

001.

001,

995

Max

imum

524,

999

59,5

00

221,

505

119,

196

119,

196

119,

196

119,

196

119,

196

119,

196

11.0

011

.00

22,9

42

N (

wei

ghte

d)3,

986,

596

3,98

6,59

6 3,

980,

131

3,98

6,59

6 3,

986,

596

3,98

6,59

6 3,

986,

596

3,98

6,59

6 3,

986,

596

3,98

6,59

6 3,

986,

596

3,98

6,59

6 N

(un

wei

ghte

d)56

2 56

2 56

1 56

2 56

2 56

2 56

2 56

2 56

2 56

2 56

2 56

2

Oth

er r

ace

(non

-His

pani

c)M

ean

3,92

7 3,

647

95,1

68

28,2

54

23,8

49

26,6

51

24,5

38

20,2

43

22,9

89

3.25

2.88

6,88

2 M

edia

n28

2 0

89,5

48

22,6

55

19,0

94

22,2

66

17,5

89

14,1

65

16,4

25

3.00

3.00

5,94

2 S

td.D

evia

tiond

17,4

93

7,60

2 44

,494

21

,650

23

,124

22

,093

21

,087

22

,531

21

,445

1.

751.

781,

917

Min

imum

0

0 7,

529

180

–35,

845

–4,8

72

0 –3

5,84

5 –4

,872

1.

001.

002,

941

Max

imum

214,

000

59,0

00

237,

383

100,

296

100,

152

100,

152

100,

296

100,

152

100,

152

10.0

010

.00

23,5

97

N (

wei

ghte

d)1,

134,

403

1,13

4,40

3 1,

134,

403

1,13

4,40

3 1,

134,

403

1,13

4,40

3 1,

134,

403

1,13

4,40

3 1,

134,

403

1,13

4,40

3 1,

134,

403

1,13

4,40

3 N

(un

wei

ghte

d)18

0 18

0 18

0 18

0 18

0 18

0 18

0 18

0 18

0 18

0 18

0 18

0



Tabl

e 10

.Fin

anci

al a

nd

Dem

og

rap

hic

Pro

file

s o

f R

ente

r G

rou

ps

(in

Do

llars

,exc

ept

N R

ows

and

Ho

use

ho

ld S

ize,

Fam

ily S

ize,

and

Fam

ily W

eig

ht

Co

lum

ns)

(con

tinue

d)

Nom

inal

Adj

uste

dA

djus

ted

Nom

inal

Adj

uste

dA

djus

ted

Targ

etH

ouse

hold

Hou

seho

ldH

ouse

hold

Fam

ilyFa

mily

Fam

ilyH

ouse

hold

Fam

ilyFa

mily

Ren

ter

Gro

upa

Ass

ets

Deb

tH

ouse

Inco

me

Inco

meb

Inco

mec

Inco

me

Inco

meb

Inco

mec

Siz

eS

ize

Wei

ght

Rec

ent i

mm

igra

ntM

ean

9,07

6 2,

696

86,0

15

26,7

57

22,7

21

25,3

38

21,8

36

17,8

00

20,4

18

2.48

2.

18

6,95

9 M

edia

n25

0 31

4 77

,938

21

,843

20

,739

22

,794

16

,710

14

,690

16

,200

2.

00

1.00

6,

075

Std

.Dev

iatio

nd52

,306

4,

142

37,2

75

20,9

03

20,8

65

20,0

28

17,6

60

18,4

43

16,6

55

1.54

1.

62

1,98

6 M

inim

um

0 0

8,79

4 0

–21,

716

–4,3

86

0 –3

8,60

2 –4

,386

1.

00

1.00

2,

296

Max

imum

514,

100

17,0

00

212,

301

116,

826

102,

297

113,

872

115,

845

93,7

56

93,7

56

7.00

7.

00

14,1

93

N (

wei

ghte

d)70

4,03

5 70

4,03

5 70

4,03

5 70

4,03

5 70

4,03

5 70

4,03

5 70

4,03

5 70

4,03

5 70

4,03

5 70

4,03

5 70

4,03

5 70

4,03

5 N

(unw

eigh

ted)

111

111

111

111

111

111

111

111

111

111

111

111

Sou

rce:

All

stat

istic

s ar

e co

mpu

ted

usin

g th

e w

ave

7 da

ta o

f S

IPP

199

3.a

Non

-His

pani

c w

hite

, no

n-H

ispa

nic

blac

k, H

ispa

nic,

and

oth

er n

on-H

ispa

nic

rent

er fa

mili

es a

re d

iscr

ete.

Com

bine

d, t

hey

sum

to

all r

ente

r fa

mili

es.R

ecen

tim

mig

rant

fam

ilies

, th

ose

ente

ring

the

Uni

ted

Sta

tes

afte

r 19

84,

over

lap

with

the

pre

viou

sly

liste

d ra

cial

/eth

nic

grou

ps (

e.g.

, no

n-H

ispa

nic

whi

tes

and

blac

ks).

Fin

ally

, if

not

othe

rwis

e no

ted,

whi

tes,

bla

cks,

and

oth

er g

roup

s ar

e no

n-H

ispa

nic.

bA

djus

ted

for

stab

ility

and

oth

er c

hara

cter

istic

s ac

cord

ing

to t

he H

isto

rical

Mor

tgag

e st

anda

rds

(see

app

endi

x D

).c

Adj

uste

d fo

r st

abili

ty a

ccor

ding

to

curr

ent

mor

tgag

e in

stru

men

ts’s

tand

ards

(se

e ap

pend

ix D

).d

Sta

ndar

d de

viat

ion—

unw

eigh

ted

valu

es.

$11,220, and $12,844, respectively. These mortgage-available incomes are approxi-mately 20 percent less than the gross (unadjusted, or what we term nominal) renterincomes (table 10).

Renters are not heavily indebted. The median debt of all renters and white, black, andHispanic renters, as derived from the SIPP, is $45, $500, $0, and $0, respectively.Assets, however, are of a trace magnitude. The median assets of all renters and white,black, and Hispanic renters are $300, $649, $0, and $0, respectively.

Also of note in table 10 is the financial profile of recent immigrants. With medianassets, debt, and nominal family income of $250, $314, and $16,710, respectively (meanvalues of $9,076, $2,696, and $21,836, respectively), recent immigrant renters areconsiderably more advantaged than their black and Hispanic counterparts. Recentimmigrant assets, debt, and income, however, approach but fall short of the respectiveresources available to white renters.

In short, the SIPP allows calibration of assets, debt, and income—critical input intothe Mortgage Model. That leaves credit as a final key mortgage underwriting element.Unfortunately, the publicly available sources that contain data on renters have eitherno information on credit (that is true of the SIPP), or they have such rough data (e.g.,the Panel Study of Income Dynamics reports whether a household has experienced“financial distress”) that the information is not very useful (see appendix C). More-over, we specifically seek credit data that are actually used by underwriters; increas-ingly, that includes FICO scores. Appendix B explains how FICO scores were imputedto the renters in the SIPP.

After assembling or estimating renter financial characteristics and factoring thesedata according to the standards used by the alternative mortgage products, the Mort-gage Model calculates the maximum-priced affordable home. This is done for each ofthe renter families in the SIPP. Because of the number of families we are attemptingto qualify and the 15 individual mortgage products considered, the Mortgage Modeluses a software program to facilitate the calculations. The mortgage simulation soft-ware is described in appendix D.

To summarize, the Affordability Analysis encompasses two measures of housingaffordability:



Table 11. Median Family Income (1995)

All FamiliesRenter (Renters and

Families ($) Homeowners) ($)

All 18,786 28,710Non-Hispanic whites 20,556 30,600Non-Hispanic blacks 13,770 19,515Hispanics 15,314 19,791

Note: The figures shown are the unadjusted family incomes. See table 10 forfurther details.

1. Reference home-buying capacity is the ability of current renters to afford eitherthe target-priced home (derived from the all-renter consumption model) or thecriterion home (median priced, modestly priced [25th percentile], and low priced[10th percentile]).

2. Absolute home-buying capacity is the dollar purchasing power of renters (mea-sured in the aggregate and by considering mean/median purchasing ability).

We anticipate significant variations in the home-buying capacities for our typologyof mortgage products.

• Historical Mortgage. With comparatively rigid down payment minimums and debtratios and other stringent financial and underwriting requirements, this type ofmortgage is expected to yield the lowest home-buying capacity.

• GSE Standard Mortgage. We expect that the GSE Standard Mortgage’s higherLTVs and front-end and back-end debt ratios (compared with those of the Histor-ical Mortgage) will result in a corresponding increase in home-buying capacity.Given the similar financial characteristics of Fannie Mae and Freddie Mac GSEStandard products, we expect no significant differences in the performance ofthese products.

• GSE Affordable Mortgages and Emerging GSE Affordable Mortgages. The relaxedfinancial characteristics of this group (e.g., high debt ratios, LTVs approaching 100percent, or both) should increase affordability in comparison to the GSE StandardMortgages. It is also reasonable to expect greater variation in affordability amongthese products because they incorporate varying debt, LTV, and other criticalparameters.

• Portfolio Affordable Mortgages. We have selected portfolio products that are espe-cially aggressive and innovative; therefore, we expect these instruments to achievea particularly high level of home-buying capacity.

• Governmental Affordable Mortgages. Because the FHA’s 203(b) loan has tradition-ally been used to expand homeownership opportunities, we expect it to achieve areasonably high level of home-buying capacity. The prior and current FHA 203(b)products have some distinctive loan terms; we therefore expect some differencesin affordability between these versions.

Given the very limited financial resources of renters, it will be a challenge for eventhe most aggressive mortgage products to bring large numbers of renters to home-ownership. Thus, we anticipate a large share of renters to remain unserved. We antic-ipate that more whites and recent immigrant renters, given these groups’ greaterrelative financial resources, than black and Hispanic renters will be served.



When renters are categorized according to income group (low, moderate, middle, andupper), we anticipate that the number of middle- and upper-income renters servedwill be greater than the number of low- and moderate-income renters served.

In the body of this report, we present our affordability results without consideringFICO scores imputed by the credit submodel. We apply FICO information in appen-dix B. Because credit is an additional underwriting hurdle, we expect the number ofpeople served by the respective mortgage instruments to decrease when the FICOresults are applied.



RESULTS: HOMEOWNERSHIP AFFORDABILITY

The results of the affordability analysis are summarized in table 12 and detailedbelow.

Absolute Home-Buying Capacity

We begin with the absolute home-buying capacity measures, which provide a “first-cut” estimate of the potential mortgage market. Consider first the case in which allcurrent renter families apply for a mortgage and are evaluated on the basis of His-torical Mortgage underwriting standards. Our models suggest that the HistoricalMortgage provides approximately $314 billion in aggregate national home-purchasingpower (table 12). This figure is simply the sum total of the prices of homes above thestipulated threshold (i.e., a low-priced house) for which renters can qualify with thisloan product. Thus, it includes some renters who are able to afford very expensivehomes as well as a few renters unable to qualify for anything more than a low-pricedhouse. Moreover, this figure does not consider variations in credit history or differ-ences in preference for homeownership among different demographic groups. Never-theless, the estimate of $314 billion may be interpreted as a baseline against whichother mortgage instruments may be measured.

The past decade has brought dramatic integration in America’s housing and capitalmarkets. The Historical Mortgage has been replaced with standardized products con-forming to the GSEs’ purchasing guidelines. Our models suggest that the advent anddiffusion of the GSE Standard Mortgage boosts total purchasing capacity by approx-imately $74 billion, to $388 billion. Two factors have contributed to the increase inpurchasing power: first, relaxed debt ratios and higher LTVs allow renters to qualifyfor larger loans; second, many families that would be disqualified by historical crite-ria from purchasing even a low-priced house can now afford such a unit with the GSEStandard Mortgage guidelines.

The GSE Affordable Mortgages (when the 3/2 Option is not effected) increase aggre-gate home-buying capacity to a range between $445 billion and $518 billion, depend-ing on the individual product. GSE Affordable Mortgages from Fannie Mae provideaggregate home-buying capacity in the $445 billion to $477 billion range, and FreddieMac GSE Affordable Mortgages yield higher aggregate home-buying capacity—inthe $516 billion to $518 billion range. The Freddie Mac GSE Affordable products pro-vide greater home-buying capacity because they have no front-end ratio; Fannie MaeGSE Affordable loans have a 33 percent front-end ratio. Additionally, the Freddie Macback-end ratios are higher than those allowed by Fannie Mae in the GSE Affordablecategory (39 percent compared with 36 percent to 38 percent).

For both the Fannie Mae and the Freddie Mac GSE Affordable Mortgages, home-buying capacity is the same for the “base” affordable product (CHBP, AG) as for the3/2 variation because we do not assume here that the 2 percent down payment is

Results: Homeownership Affordability 45




Tabl

e 12

.Im

pac

t o

f A

lter

nat

ive

Mo

rtg

age

Inst

rum

ents

:A

bso

lute

an

d R

efer

ence

Ho

me-

Bu

yin

g C

apac

ity

for

All

Ren

ters

,Nat

ion

wid

e

Ref

eren

ce H

ome-

Buy

ing

Cap

acity

(Per

cent

age

of R

ente

rs W

ho C

ould

Abs

olut

e H

ome-

Buy

ing

Cap

acity

Affo

rd th

e In

dica

ted

Hou

se)

Tota

l Nat

iona

lA

vera

ge/M

edia

n50

th25

th10

thH

ome-

Buy

ing

Cap

acity

aH

ome-

Buy

ing

Cap

acity

bTa

rget

Per

cent

ileP

erce

ntile

Per

cent

ileLo

an N

ame

(in $

Bill

ions

)M

ean(

$)M

edia

n($)

Hou

seH

ouse

Hou

seH

ouse

His

toric

al M

ortg

age

314.

312

,199

0 3.

85.

17.

510

.0

GS

E S

tand

ard

Mor

tgag

e (F

anni

e M

ae

387.

715

,049

0 5.

06.

39.

212

.1an

d Fr

eddi

e M

ac)

GS

E A

fford

able

Mor

tgag

esc

Fann

ie M

ae

CH

BP

;CH

BP,

3/2

Opt

ion

(NA

)47

7.3

18,5

260

6.4

7.8

11.0

14.3

CH

BP,

3/2

Opt

ion

(A)

538.

420

,898

0 7.

28.

812

.316

.2Fa

nnie

97

445.

017

,273

0 5.

97.

110

.614

.1Fr

eddi

e M

acA

G;A

G, 3

/2 O

ptio

n (N

A)

516.

120

,032

0 7.

08.

411

.414

.9A

G, 3

/2 O

ptio

n (A

)58

3.7

22,6

580

7.9

9.4

12.9

16.9

AG

97

518.

020

,108

0 7.

08.

511

.514

.8

Em

ergi

ng G

SE

Affo

rdab

le M

ortg

ages

Fann

ie M

aeFl

ex 9

748

7.5

18,9

240

6.7

8.1

11.3

14.3

Fred

die

Mac

Com

mun

ity G

old

539.

520

,940

0 7.

58.

711

.814

.9

Por

tfolio

Affo

rdab

le M

ortg

ages

d

Ban

k of

Am

eric

a Ze

ro D

own

511.

419

,850

0 7.

18.

511

.714

.9B

ank

of A

mer

ica

Cre

dit F

lex

546.

421

,208

0 7.

78.

811

.914

.9P

ortfo

lio C

ompo

site

e58

4.1

22,6

740

8.0

9.4

13.0

17.0

Gov

ernm

ent A

fford

able

Mor

tgag

esFH

A 2

03(b

)–pr

iorf

601.

323

,341

0 7.

99.

414

.220

.1FH

A 2

03(b

)–cu

rren

tg56

2.4

21,8

320

7.8

9.2

13.0

17.3

Sou

rce:

Aut

hors

’ana

lysi

s of

199

3 S

IPP

data

.a

The

agg

rega

te v

alue

of

hous

es t

hat

coul

d be

pur

chas

ed b

y cu

rren

t re

nter

s ab

ove

a lo

w-p

riced

hou

se t

hres

hold

.b

For

all

curr

ent

rent

ers,

not

just

ren

ters

who

can

affo

rd a

t le

ast

a lo

w-p

riced

hou

se.

cA

= 3

/2 O

ptio

n is

act

ivat

ed (

i.e.,

2 pe

rcen

t ou

tsid

e fu

ndin

g is

fort

hcom

ing)

;NA

= 3

/2 O

ptio

n is

not

act

ivat

ed (

i.e.,

2 pe

rcen

t ou

tsid

e fu

ndin

g is

not

fort

h-co

min

g).

dT

hese

are

sel

ecte

d as

exa

mpl

es o

f th

e m

any

port

folio

mor

tgag

es c

urre

ntly

offe

red.

eIn

corp

orat

es t

he m

ortg

age

char

acte

ristic

s of

a c

ompo

site

of

port

folio

pro

duct

s.f

Inco

rpor

ated

FH

A(b

) te

rms

befo

re 1

997.

gIn

corp

orat

ed F

HA

(b)

term

s fr

om 1

997

onw

ard.

provided by a source other than the buyer.26 For both the Fannie Mae and FreddieMac Affordables, the “97” product (Fannie 97 and AG 97) has a somewhat lower (orequivalent) home-buying capacity than the base product; although the 97 product hasa higher LTV (97 percent compared with 95 percent), this is somewhat offset by itsother requirements (e.g., more costly mortgage insurance [because the LTV is higher],more demanding reserves).

The Emerging GSE Affordable Mortgages incrementally increase home-buying capac-ity from the GSE Affordable baseline. Fannie Mae’s Flex 97 realizes approximately$488 billion in home-buying capacity (compared with $445 billion to $477 billion withthe Fannie Mae Affordable Mortgages). Freddie Mac’s Community Gold reaches ap-proximately $540 billion in home-buying capacity (compared with $516 billion to $518billion with the Freddie Mac Affordable Mortgages). For both GSEs, the gain isachieved because the Emerging GSE Affordable Mortgages have more aggressive fea-tures (e.g., higher debt ratios, higher LTVs), albeit with some offsets (e.g., higher re-quired reserves, higher mortgage insurance costs).

The portfolio products studied here achieve significant aggregate home-buying capac-ity in the $511 billion to $584 billion range—a function of their high LTVs and fairlyaggressive front-end and back-end ratios. Of the two Bank of America portfolio prod-ucts examined here, Zero Down and Credit Flex, the latter realizes greater home-buying capacity: Although Credit Flex has a lower LTV than does Zero Down (97 per-cent compared with 100 percent), it has lower mortgage insurance requirements (a“payback” from its lower LTV), no front-end ratio (recalling the Freddie Mac approach),and a higher back-end ratio (43 percent compared with 41 percent).

The Portfolio Composite product has the highest home-buying capacity ($584 billion)considered thus far because its parameters extend so far beyond the “normal” market(e.g., the product does not require mortgage insurance on a very high LTV and allowsvery high front-end and back-end ratios) that it is essentially a subsidized loan. (TheDVMP-inspired Portfolio Composite Mortgage was so costly to its originators that itultimately was rescinded. That recalls the 45 percent back-end ratio allowed by lendersin the Atlanta Mortgage consortium, which also was ultimately dropped.)

The Governmental Affordable Mortgages realize very high home-buying capacity inthe $562 billion to $601 billion range—a reflection of their high LTVs and high front-end and back-end ratios. Of the two FHA 203(b) products, the “prior” product hassomewhat greater home-buying prowess than does the “current” product—perhapsbecause the former permits a portion of the closing costs to be financed.

Other measures in parallel denote varying housing affordability by mortgage type(table 12). Under the Historical Mortgage, the mean home affordable, including



26 In the Policy Considerations section, we examine the impacts assuming that the 2 percent down payment isprovided by a source other than the buyer. Results with the 3/2 Option activated are also shown in all of the ref-erenced tables.

those with no home-buying capacity, is $12,199.27 The mean affordable increases to$15,049 with the GSE Standard Mortgage; to a range between $17,273 and $20,940with the GSE Affordable Mortgages (without the 3/2 Option activated), including theEmerging GSE Affordable Mortgages group; to a range between $19,850 and $22,674for the Portfolio Affordable Mortgages; and to a range between $21,832 and $23,341for the Governmental Affordable Mortgages.

Table 13 disaggregates the absolute home-buying capacity by subgroups of renter fam-ilies: whites, blacks, Hispanics, recent immigrants, and others. (Here and elsewherein this study, the referenced white, black, and other groups are non-Hispanic.) Forwhite renter families, absolute home-buying capacity increased from approximately$285 billion under the Historical Mortgage to approximately $347 billion under theGSE Standard Mortgage, and to approximately $505 billion for the Portfolio Com-posite. For black renter families, total purchasing capacity under these three mort-gage products increased from approximately $11 billion to $17 billion and then to$36 billion. This progression means that mortgage innovation increases the totalpurchase power of whites by about 77 percent; blacks, starting from a smaller base,realize a 227 percent gain in purchasing power. Hispanic families, like blacks, simi-larly experience a quantum increase of total purchasing power (a 139 percent gain)from mortgage innovation. The pattern for recent immigrant families resemblesthat described for whites.

Mortgage innovation is thus dramatically expanding the total purchasing capacity oftraditionally underserved groups, most notably blacks and Hispanics. Yet because thelatter groups have fewer financial resources (e.g., lower income and assets), thesegroups capture a disproportionately small fraction of total buying capacity relative totheir share of the total population. We can express that relationship in the form of aratio (table 14). For example, black renter families make up 17.3 percent of all renterfamilies (4,464,525 of 25,762,939). Under the Historical Mortgage, black renter fam-ilies capture 3.6 percent of the total home-buying capacity ($11.356 billion of $314.291billion)—representing a 0.21 ratio (3.6 percent divided by 17.3 percent) of their dollarhome-buying capacity relative to their population representation. Greater home-buyingaccess for blacks in the form of rising ratios is afforded by today’s more aggressivemortgage products; however, even with these, the ratios never exceed 0.40. Hispanicfamilies fare similarly. Their ratio, starting at 0.20 for the Historical Mortgage, riseswith today’s more aggressive loan products, yet it never exceeds 0.30. (For both blacksand Hispanics, the highest ratios are realized by the FHA 203(b)–prior product.)

In contrast, white renter families capture a disproportionately large share of the totalpurchasing capacity relative to their population representation—their ratios are sig-nificantly above 1.00 (table 14). The ratio for whites is 1.45 for the Historical Mort-gage and drops slightly to no lower than approximately 1.34 under today’s more ag-gressive products. Recent immigrant families, realizing relative total purchasing capac-ity that is nearly identical to their proportional share of the renter population, have



27 That is, renters who have a home-buying capacity of zero are included in the calculation of the mean.

ratios near 1.00. For example, under the GSE Standard Mortgage, recent immigrantfamilies realize $10.724 billion of the total $387.695 billion purchasing capacity—or2.8 percent—a capture rate that is nearly identical to their 2.7 percent share of therenter population.

This does not presume that there should be full equality in home-buying prowess (i.e.,that the ratios should always be 1.0). Under our political and economic system, thereis far from equal access to a broad array of goods and services. The point of the aboveexercise is merely to gauge the degree of relative home-buying capacity; the fact thatthe ratios are for the most part far from 1.0 points to considerable unevenness. Wealso learn that mortgage innovation is reducing some of the disparity, albeit by aminor measure.

In sum, analysis of absolute home-buying capacity reveals much about the ability ofmortgage innovation to expand homeownership. Yet because it is an aggregate mea-sure that is unlinked to a benchmark, it obscures certain aspects of affordability. Con-sider, for example, how the aggregate capacity measure treats two hypothetical fami-lies. Renter A is able to qualify for a home purchase of $200,000 under the HistoricalMortgage but might be able to secure a $300,000 unit under the more liberal GSEStandard guidelines. By contrast, renter B might be unable to purchase anything



Table 13. Absolute Home-Buying Capacity for Renter Groupsa

by Mortgage Instrument, Nationwide (in $ Millions)

White Black OtherFamilies Families Families Recent

All (Non- (Non- Hispanic (Non- ImmigrantLoan Name Families Hispanic) Hispanic) Families Hispanic) Families

1.Historical 314,291 285,347 11,356 9,631 7,957 9,0592.GSE Standard 387,695 347,340 16,702 13,422 10,231 10,7243.Fannie Mae CHBP; CHBP 3/2 (NA)b 477,289 421,454 24,602 16,604 14,629 13,2434.Fannie Mae CHBP 3/2 (A)c 538,396 466,417 32,271 20,639 19,069 14,6645.Fannie Mae 97 445,006 391,387 24,016 15,822 13,781 12,5786.Freddie Mac AG 5; AG 3/2 (NA)b 516,087 455,364 26,427 18,680 15,616 14,4977.Freddie Mac AG 3/2 (A)c 583,749 505,947 34,754 22,764 20,284 16,4248.Freddie Mac AG 97 518,046 457,728 26,054 18,783 15,481 14,7479.Fannie Mae Flex 97 487,533 430,821 24,941 17,048 14,723 13,607

10.Freddie Mac Community Gold 539,486 475,988 27,567 19,784 16,147 15,22911.Bank of America Zero Down 511,393 446,771 27,186 19,492 17,943 14,54912.Bank of America Credit Flex 546,372 482,359 27,681 19,963 16,369 15,41813.Portfolio Composite 584,144 505,042 35,649 23,053 20,400 16,44614.FHA 203(b)–prior 601,325 507,648 41,687 27,887 24,103 15,79215.FHA 203(b)–current 562,449 480,712 36,183 23,335 22,219 14,920

Weighted sample size 25,762,939 16,183,879 4,464,525 3,980,131 1,134,403 704,035

Source: Authors’ analysis of 1993 SIPP data.a Non-Hispanic white, non-Hispanic black, Hispanic, and other non-Hispanic renter families are discrete. Combined,

they sum to all renter families. Recent immigrant families—those entering the United States after 1984—overlapwith the previously listed racial/ethnic groups (e.g., non-Hispanic whites and blacks). Finally, if not otherwise noted,whites, blacks, and other groups are non-Hispanic.

b NA = 3/2 Option is not activated (i.e., 2 percent outside funding is not forthcoming).c A = 3/2 Option is activated (i.e., 2 percent outside funding is forthcoming).

under the Historical Mortgage but might secure a home of $100,000 under the GSEStandard. The aggregate home-buying measure treats both of these families equal-ly—each inducing an increase of $100,000 in total purchasing power. Clearly, however,affordability means very different things to each of these families, and it is arguablymuch more important to expand homeownership opportunities than to boost the



Table 14. Relative Home-Buying Capacity for Renter Groupsa

by Mortgage Instrument, NationwideRecent

All White Black Hispanic Other ImmigrantLoan Name Families Families Families Families Families Families

Percentage of Families 100.0 62.9 17.3 15.4 4.4 2.7

Percentage of home-buying capacity by mortgage instrument1.Historical 100.0 90.8 3.6 3.1 2.5 2.92.GSE Standard 100.0 89.6 4.3 3.5 2.6 2.83.Fannie Mae CHBP; CHBP 3/2 (NA)b 100.0 88.2 5.2 3.5 3.1 2.84.Fannie Mae CHBP 3/2 (A)c 100.0 86.7 6.0 3.8 3.5 2.75.Fannie Mae 97 100.0 88.0 5.4 3.6 3.1 2.86.Freddie Mac AG 5; AG 3/2 (NA)b 100.0 88.3 5.1 3.6 3.0 2.87.Freddie Mac AG 3/2 (A)c 100.0 86.7 6.0 3.9 3.5 2.88.Freddie Mac AG 97 100.0 88.4 5.0 3.6 3.0 2.89.Fannie Mae Flex 97 100.0 88.4 5.1 3.5 3.0 2.8

10.Freddie Mac Community Gold 100.0 88.2 5.1 3.7 3.0 2.811.Bank of America Zero Down 100.0 87.4 5.3 3.8 3.5 2.812.Bank of America Credit Flex 100.0 88.2 5.1 3.7 3.0 2.813.Portfolio Composite 100.0 86.5 6.1 3.9 3.5 2.814.FHA 203(b)–prior 100.0 84.5 6.9 4.6 4.0 2.615.FHA 203(b)–current 100.0 85.5 6.4 4.1 4.0 2.7

Relative ratiod by mortgage instrument1.Historical 1.00 1.45 0.21 0.20 0.57 1.052.GSE Standard 1.00 1.43 0.25 0.22 0.60 1.013.Fannie Mae CHBP; CHBP 3/2 (NA)b 1.00 1.41 0.30 0.23 0.70 1.024.Fannie Mae CHBP 3/2 (A)c 1.00 1.38 0.35 0.25 0.80 1.005.Fannie Mae 97 1.00 1.40 0.31 0.23 0.70 1.036.Freddie Mac AG 5; AG 3/2 (NA)b 1.00 1.40 0.30 0.23 0.69 1.037.Freddie Mac AG 3/2 (A)c 1.00 1.38 0.34 0.25 0.79 1.038.Freddie Mac AG 97 1.00 1.41 0.29 0.23 0.68 1.049.Fannie Mae Flex 97 1.00 1.41 0.30 0.23 0.69 1.02

10.Freddie Mac Community Gold 1.00 1.40 0.29 0.24 0.68 1.0311.Bank of America Zero Down 1.00 1.39 0.31 0.25 0.80 1.0412.Bank of America Credit Flex 1.00 1.41 0.29 0.24 0.68 1.0313.Portfolio Composite 1.00 1.38 0.35 0.26 0.79 1.0314.FHA 203(b)–prior 1.00 1.34 0.40 0.30 0.91 0.9615.FHA 203(b)–current 1.00 1.36 0.37 0.27 0.90 0.97


they sum to all renter families. Recent immigrant families, those entering the United States after 1984, overlap withthe previously listed racial/ethnic groups (e.g., non-Hispanic whites and blacks). Finally, if not otherwise noted, whites,blacks, and other groups are non-Hispanic.

b NA = 3/2 Option is not activated (i.e., 2 percent outside funding is not forthcoming).c A = 3/2 Option is activated (i.e., 2 percent outside funding is forthcoming).d Equals group’s share of home-buying capacity divided by group’s share of all renter families.

housing consumption of middle- or higher-income buyers. Similarly, any gain abovea stipulated threshold (i.e., the low-cost houses) is counted—even a $1 advantage inthe ability to secure a housing asset. Thus, the absolute aggregate home-buying capac-ity must be viewed as a largely theoretical measure of the home-buying and business-expanding potential of mortgage product innovation. Despite these caveats, the aggre-gate home-buying measure powerfully communicates the dollar value of the housingasset reach of different mortgage instruments, and we find that there are multi-bil-lion-dollar differences in this aggregate reach across the different mortgages. Furtherinsight in this regard is afforded in considering the reference home-buying capacity.

Reference Home-Buying Capacity

Both the effectiveness and the shortfalls of the mortgage instruments in expandinghomeownership are apparent when we examine the percentage of renters able tosecure the various reference-priced homes. These figures are summarized in tables15 through 18 for the four reference-priced homes: the target house and the threecriterion units (homes priced at the 50th [median-priced], 25th [modestly priced], and10th [low-priced] percentiles). Only 3.8 percent of all renter families (approximately0.986 million) can afford the target house under the Historical Mortgage. With today’sinnovative mortgage products, the percentage of renters able to afford the targethouse increases to the following levels: 5.0 percent (approximately 1.288 million renterfamilies) under the GSE Standard Mortgage; 5.9 percent to 7.0 percent (approximately1.518 million to 1.804 million renters) under the GSE Affordable Mortgages;28 6.7percent to 7.5 percent (approximately 1.718 million to 1.932 million renters) underthe Emerging GSE Affordable Mortgages; 7.1 percent to 8.0 percent with the Portfo-lio Affordable Mortgages (approximately 1.830 million to 2.049 million renters); and7.9 percent (approximately 2.028 million renters) for the highest-achieving Govern-mental Affordable Mortgage (FHA 203(b)–prior). There is a similar continuum bymortgage product—but at a higher share affording—when the reference home is thelow-priced unit. Ten percent of all renters (approximately 2.580 million) can affordthe low-priced home under the Historical Mortgage. This percentage rises to the fol-lowing levels under the current more innovative mortgage products: 12.1 percent (ap-proximately 3.106 million renters) under the GSE Standard Mortgage; 14.1 percentto 14.9 percent (approximately 3.635 to 3.842 million renters) under the GSE Afford-able Mortgages,29 including the Emerging GSE Affordable Group; 14.9 percent to 17.0percent (approximately 3.828 million to 4.373 million renters) under the Portfolio Af-fordable Mortgages; and 20.1 percent (5.168 million renters) for the best-performingGovernmental Affordable Mortgage (FHA 203(b)–prior). (As observed in the preced-ing section, the FHA 203(b)–prior again slightly outperforms the FHA 203(b)–cur-rent with respect to the reference home-buying capacity.)



28 Without the 3/2 Option activated.




Tabl

e 15

.Ref

eren

ce H

om

e-B

uyi

ng

Cap

acit

y by

Mo

rtg

age

Inst

rum

ent

and

Ren

ter

Gro

up

,aN

atio

nw

ide:

Targ

et H

ou

se (

Nu

mb

er a

nd

Per

cen

tag

e o

f R

ente

rs W

ho

Co

uld

Aff

ord

th

e In

dic

ated

Ho

use

)

Rec

ent

His

pani

cIm

mig

rant

All

Fam

ilies

Whi

te F

amili

esB

lack

Fam

ilies

Fam

ilies

Oth

er F

amili

esFa

mili

es

Loan

Nam

eN

umbe

r%

Num

ber

%N

umbe

r%

Num

ber

%N

umbe

r%

Num

ber

%

1.H

isto

rical

986,

175

3.8

887,

739

5.5

43,1

13

1.0

16,9

28

0.4

38,3

97

3.4

32,4

81

4.6

2.G

SE

Sta

ndar

d1,

288,

186

5.0

1,13

8,26

6 7.

066

,321

1.

539

,384

1.

044

,215

3.

932

,481

4.

63.

Fann

ie M

ae C

HB

P;C

HB

P 3

/2 (

NA

)b1,

655,

784

6.4

1,44

5,20

48.

995

,041

2.

160

,199

1.

555

,343

4.

945

,747

6.

54.

Fann

ie M

ae C

HB

P 3

/2 (

A)c

1,86

0,78

0 7.

21,

621,

625

10.0

115,

810

2.6

68,0

60

1.7

55,3

48

4.9

45,7

47

6.5

5.Fa

nnie

Mae

97

1,51

7,66

9 5.

91,

317,

934

8.1

89,6

66

2.0

60,1

99

1.5

49,8

82

4.4

32,4

81

4.6

6.Fr

eddi

e M

ac A

G 5

;AG

3/2

(N

A)b

1,80

3,89

5 7.

01,

570,

079

9.7

95,0

41

2.1

72,5

82

1.8

66,1

95

5.8

45,7

47

6.5

7.Fr

eddi

e M

ac A

G 3

/2 (

A)c

2,04

5,19

1 7.

91,

782,

816

11.0

115,

810

2.6

80,4

38

2.0

66,1

92

5.8

45,7

47

6.5

8.Fr

eddi

e M

ac A

G 9

71,

793,

796

7.0

1,57

2,26

49.

795

,041

2.

165

,975

1.

760

,527

5.

345

,747

6.

59.

Fann

ie M

ae F

lex

971,

717,

512

6.7

1,49

9,27

59.

395

,041

2.

160

,199

1.

563

,005

5.

645

,747

6.

510

.Fre

ddie

Mac

Com

unity

Gol

d1,

932,

272

7.5

1,68

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4,96

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465

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776

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611

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k of

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eric

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ro D

own

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943

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12.B

ank

of A

mer

ica

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dit F

lex

1,96

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722,

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10.6

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13.P

ortfo

lio C

ompo

site

2,04

8,78

1 8.

01,

785,

274

11.0

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73,8

37

1.9

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56

6.5

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47

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14.F

HA

203

(b)–

prio

r2,

028,

033

7.9

1,73

6,20

710

.713

3,22

1 3.

074

,986

1.

983

,651

7.

445

,747

6.

515

.FH

A 2

03(b

)–cu

rren

t1,

997,

478

7.8

1,72

3,58

310

.712

5,05

1 2.

868

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1.

780

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7.

145

,747

6.

5

Wei

ghte

d sa

mpl

e si

ze25

,762

,939

100

.016

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,879

100.

04,

464,

525

100.

03,

980,

131

100.

01,

134,

403

100.

070

4,03

5 10

0.0

Sou

rce:

Aut

hors

’ana

lysi

s of

199

3 S

IPP

data

.a

Non

-His

pani

c w

hite

, non

-His

pani

c bl

ack,

His

pani

c, a

nd o

ther

non

-His

pani

c re

nter

fam

ilies

are

dis

cret

e.C

ombi

ned,

they

sum

to a

ll re

nter

fam

ilies

.Rec

ent

imm

igra

nt fa

mili

es,

thos

e en

terin

g th

e U

nite

d S

tate

s af

ter

1984

, ov

erla

p w

ith t

he p

revi

ousl

y lis

ted

raci

al/e

thni

c gr

oups

(e.

g.,

non-

His

pani

c w

hite

s an

dbl

acks

).F

inal

ly,

if no

t ot

herw

ise

note

d, w

hite

s, b

lack

s, a

nd o

ther

gro

ups

are

non-

His

pani

c.b

NA

= 3

/2 O

ptio

n is

not

act

ivat

ed (

i.e.,

2 pe

rcen

t ou

tsid

e fu

ndin

g is

not

fort

hcom

ing)

.c

A =

3/2

Opt

ion

is a

ctiv

ated

(i.e

., 2

perc

ent

outs

ide

fund

ing

is fo

rthc

omin

g).



Tabl

e 16

.Ref

eren

ce H

om

e-B

uyi

ng

Cap

acit

y by

Mo

rtg

age

Inst

rum

ent

and

Ren

ter

Gro

up

,aN

atio

nw

ide:

50th

Per

cen

tile

(M

edia

n-P

rice

d)

Ho

use

(N

um

ber

an

d P

erce

nta

ge

of

Ren

ters

Wh

o C

ou

ld A

ffo

rd t

he

Ind

icat

ed H

ou

se)

Rec

ent

His

pani

cIm

mig

rant

All

Fam

ilies

Whi

te F

amili

esB

lack

Fam

ilies

Fam

ilies

Oth

er F

amili

esFa

mili

es

Loan

Nam

eN

umbe

r%

Num

ber

%N

umbe

r%

Num

ber

%N

umbe

r%

Num

ber

%

1.H

isto

rical

1,31

4,43

6 5.

11,

204,

814

7.4

59,6

62

1.3

30,5

44

0.8

19,4

17

1.7

38,0

82

5.4

2.G

SE

Sta

ndar

d1,

631,

007

6.3

1,49

5,35

1 9.

271

,036

1.

639

,384

1.

025

,235

2.

244

,904

6.

43.

Fann

ie M

ae C

HB

P;C

HB

P 3

/2 (

NA

)b2,

021,

824

7.8

1,80

8,06

3 11

.210

5,27

3 2.

460

,458

1.

548

,076

4.

251

,348

7.

34.

Fann

ie M

ae C

HB

P 3

/2 (

A)c

2,27

4,32

6 8.

82,

010,

361

12.4

130,

364

2.9

73,5

93

1.8

59,9

31

5.3

51,3

48

7.3

5.Fa

nnie

Mae

97

1,83

3,29

1 7.

11,

633,

763

10.1

114,

515

2.6

59,8

61

1.5

25,2

40

2.2

44,9

04

6.4

6.Fr

eddi

e M

ac A

G 5

;AG

3/2

(N

A)b

2,15

5,61

1 8.

41,

923,

940

11.9

115,

229

2.6

60,4

58

1.5

56,0

51

4.9

59,3

25

8.4

7.Fr

eddi

e M

ac A

G 3

/2 (

A)c

2,41

3,93

6 9.

42,

126,

400

13.1

140,

320

3.1

73,5

93

1.8

73,6

57

6.5

59,3

25

8.4

8.Fr

eddi

e M

ac A

G 9

72,

180,

704

8.5

1,93

8,34

3 12

.012

0,54

2 2.

765

,752

1.

756

,051

4.

959

,325

8.

49.

Fann

ie M

ae F

lex

972,

082,

882

8.1

1,84

7,87

5 11

.412

1,16

7 2.

765

,752

1.

748

,076

4.

251

,348

7.

310

.Fre

ddie

Mac

Com

unity

Gol

d2,

243,

952

8.7

1,98

9,64

6 12

.312

5,45

3 2.

867

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1.

761

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5.

459

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8.

411

.Ban

k of

Am

eric

a Ze

ro D

own

2,20

2,34

5 8.

51,

966,

180

12.1

114,

515

2.6

73,5

93

1.8

48,0

76

4.2

60,8

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8.6

12.B

ank

of A

mer

ica

Cre

dit F

lex

2,27

0,01

9 8.

82,

021,

017

12.5

120,

130

2.7

67,0

79

1.7

61,7

93

5.4

59,3

25

8.4

13.P

ortfo

lio C

ompo

site

2,43

4,29

4 9.

42,

139,

630

13.2

145,

212

3.3

81,5

40

2.0

67,9

12

6.0

59,3

25

8.4

14.F

HA

203

(b)–

prio

r2,

411,

669

9.4

2,11

2,64

4 13

.115

3,66

9 3.

481

,553

2.

063

,776

5.

660

,889

8.

615

.FH

A 2

03(b

)–cu

rren

t2,

365,

476

9.2

2,07

2,18

4 12

.813

9,11

5 3.

181

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2.

072

,602

6.

460

,889

8.

6

Wei

ghte

d sa

mpl

e si

ze25

,762

,939

100.

016

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100.

04,

464,

525

100.

03,

980,

131

100.

01,

134,

403

100.

070

4,03

510

0.0

Sou

rce:

Aut

hors

’ana

lysi

s of

199

3 S

IPP

data

.a

Non

-His

pani

c w

hite

, non

-His

pani

c bl

ack,

His

pani

c, a

nd o

ther

non

-His

pani

c re

nter

fam

ilies

are

dis

cret

e.C

ombi

ned,

they

sum

to a

ll re

nter

fam

ilies

.Rec

ent

imm

igra

nt fa

mili

es,

thos

e en

terin

g th

e U

nite

d S

tate

s af

ter

1984

, ov

erla

p w

ith t

he p

revi

ousl

y lis

ted

raci

al/e

thni

c gr

oups

(e.

g.,

non-

His

pani

c w

hite

s an

dbl

acks

).F

inal

ly,

if no

t ot

herw

ise

note

d, w

hite

s, b

lack

s, a

nd o

ther

gro

ups

are

non-

His

pani

c.b

NA

= 3

/2 O

ptio

n is

not

act

ivat

ed (

i.e.,

2 pe

rcen

t ou

tsid

e fu

ndin

g is

not

fort

hcom

ing)

.c

A =

3/2

Opt

ion

is a

ctiv

ated

(i.e

., 2

perc

ent

outs

ide

fund

ing

is fo

rthc

omin

g).



Tabl

e 17

.Ref

eren

ce H

om

e-B

uyi

ng

Cap

acit

y by

Mo

rtg

age

Inst

rum

ent

and

Ren

ter

Gro

up

,aN

atio

nw

ide:

25th

Per

cen

tile

(M

od

estl

y P

rice

d)

Ho

use

(N

um

ber

an

d P

erce

nta

ge

of

Ren

ters

Wh

o C

ou

ld A

ffo

rd t

he

Ind

icat

ed H

ou

se)

Rec

ent

His

pani

cIm

mig

rant

All

Fam

ilies

Whi

te F

amili

esB

lack

Fam

ilies

Fam

ilies

Oth

er F

amili

esFa

mili

es

Loan

Nam

eN

umbe

r%

Num

ber

%N

umbe

r%

Num

ber

%N

umbe

r%

Num

ber

%

1.H

isto

rical

1,92

6,93

9 7.

51,

742,

420

10.8

71,0

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1.6

51,6

90

1.3

61,7

93

5.4

52,8

81

7.5

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SE

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ndar

d2,

381,

156

9.2

2,11

9,39

9 13

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9,60

0 2.

772

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1.

869

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6.

159

,325

8.

43.

Fann

ie M

ae C

HB

P;C

HB

P 3

/2 (

NA

)b2,

835,

727

11.0

2,51

6,10

8 15

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5,23

1 3.

392

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2.

381

,314

7.

264

,229

9.

14.

Fann

ie M

ae C

HB

P 3

/2 (

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3,17

5,28

2 12

.32,

761,

617

17.1

193,

225

4.3

106,

389

2.7

113,

860

10.0

83,4

70

11.9

5.Fa

nnie

Mae

97

2,72

1,33

9 10

.62,

388,

741

14.8

145,

231

3.3

100,

021

2.5

87,4

40

7.7

68,8

69

9.8

6.Fr

eddi

e M

ac A

G 5

;AG

3/2

(N

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2,94

5,99

2 11

.42,

620,

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16.2

145,

231

3.3

99,3

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2.5

81,3

14

7.2

64,2

29

9.1

7.Fr

eddi

e M

ac A

G 3

/2 (

A)c

3,32

3,16

1 12

.92,

891,

897

17.9

202,

154

4.5

115,

185

2.9

113,

860

10.0

83,4

70

11.9

8.Fr

eddi

e M

ac A

G 9

72,

962,

996

11.5

2,62

7,45

3 16

.214

1,65

9 3.

210

6,38

9 2.

787

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7.

764

,229

9.

19.

Fann

ie M

ae F

lex

972,

915,

592

11.3

2,57

1,45

7 15

.915

0,14

2 3.

410

6,38

9 2.

787

,440

7.

764

,229

9.

110

.Fre

ddie

Mac

Com

unity

Gol

d3,

037,

966

11.8

2,68

6,03

8 16

.615

5,32

1 3.

511

5,18

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981

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7.

264

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9.

111

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k of

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eric

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ro D

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3,02

0,44

7 11

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651,

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16.4

146,

883

3.3

106,

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2.7

115,

607

10.2

73,7

69

10.5

12.B

ank

of A

mer

ica

Cre

dit F

lex

3,05

8,50

8 11

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706,

465

16.7

155,

321

3.5

115,

170

2.9

81,5

51

7.2

64,2

26

9.1

13.P

ortfo

lio C

ompo

site

3,34

6,09

9 13

.02,

906,

418

18.0

210,

651

4.7

115,

170

2.9

113,

860

10.0

83,4

73

11.9

14.F

HA

203

(b)–

prio

r3,

654,

988

14.2

3,07

3,48

1 19

.028

4,25

6 6.

415

0,36

9 3.

814

6,89

4 12

.987

,244

12

.415

.FH

A 2

03(b

)–cu

rren

t3,

351,

243

13.0

2,87

7,49

4 17

.823

0,54

8 5.

212

2,34

9 3.

112

0,89

3 10

.783

,632

11

.9

Wei

ghte

d sa

mpl

e si

ze25

,762

,939

100

.016

,183

,879

100

.04,

464,

525

100.

03,

980,

131

100.

01,

134,

403

100.

070

4,03

5 10

0.0

Sou

rce:

Aut

hors

’ana

lysi

s of

199

3 S

IPP

data

.a

Non

-His

pani

c w

hite

, non

-His

pani

c bl

ack,

His

pani

c, a

nd o

ther

non

-His

pani

c re

nter

fam

ilies

are

dis

cret

e.C

ombi

ned,

they

sum

to a

ll re

nter

fam

ilies

.Rec

ent

imm

igra

nt fa

mili

es,

thos

e en

terin

g th

e U

nite

d S

tate

s af

ter

1984

, ov

erla

p w

ith t

he p

revi

ousl

y lis

ted

raci

al/e

thni

c gr

oups

(e.

g.,

non-

His

pani

c w

hite

s an

dbl

acks

).F

inal

ly,

if no

t ot

herw

ise

note

d, w

hite

s, b

lack

s, a

nd o

ther

gro

ups

are

non-

His

pani

c.b

NA

= 3

/2 O

ptio

n is

not

act

ivat

ed (

i.e.,

2 pe

rcen

t ou

tsid

e fu

ndin

g is

not

fort

hcom

ing)

.c

A =

3/2

Opt

ion

is a

ctiv

ated

(i.e

., 2

perc

ent

outs

ide

fund

ing

is fo

rthc

omin

g).



Tabl

e 18

.Ref

eren

ce H

om

e-B

uyi

ng

Cap

acit

y by

Mo

rtg

age

Inst

rum

ent

and

Ren

ter

Gro

up

,aN

atio

nw

ide:

10th

Per

cen

tile

(L

ow

-Pri

ced

) H

ou

se (

Nu

mb

er a

nd

Per

cen

tag

e o

f R

ente

rs W

ho

Co

uld

Aff

ord

th

e In

dic

ated

Ho

use

)

Rec

ent

His

pani

cIm

mig

rant

All

Fam

ilies

Whi

te F

amili

esB

lack

Fam

ilies

Fam

ilies

Oth

er F

amili

esFa

mili

es

Loan

Nam

eN

umbe

r%

Num

ber

%N

umbe

r%

Num

ber

%N

umbe

r%

Num

ber

%

1.H

isto

rical

2,58

0,00

6 10

.02,

296,

689

14.2

128,

499

2.9

79,0

90

2.0

75,7

29

6.7

59,3

25

8.4

2.G

SE

Sta

ndar

d3,

105,

523

12.1

2,73

5,40

8 16

.915

9,59

4 3.

611

5,17

0 2.

995

,352

8.

468

,825

9.

83.

Fann

ie M

ae C

HB

P;C

HB

P 3

/2 (

NA

)b3,

692,

602

14.3

3,20

4,73

2 19

.822

8,36

0 5.

112

5,29

5 3.

113

4,24

5 11

.893

,616

13

.34.

Fann

ie M

ae C

HB

P 3

/2 (

A)c

4,18

0,03

7 16

.23,

545,

564

21.9

298,

186

6.7

161,

991

4.1

174,

426

15.4

107,

091

15.2

5.Fa

nnie

Mae

97

3,63

5,15

1 14

.13,

141,

453

19.4

236,

843

5.3

122,

110

3.1

134,

699

11.9

97,9

38

13.9

6.Fr

eddi

e M

ac A

G 5

;AG

3/2

(N

A)b

3,84

1,51

2 14

.93,

312,

840

20.5

246,

040

5.5

148,

339

3.7

134,

245

11.8

99,4

87

14.1

7.Fr

eddi

e M

ac A

G 3

/2 (

A)c

4,34

3,88

9 16

.93,

671,

313

22.7

310,

284

7.0

187,

982

4.7

174,

426

15.4

120,

981

17.2

8.Fr

eddi

e M

ac A

G 9

73,

804,

928

14.8

3,29

5,84

7 20

.422

9,52

1 5.

114

5,15

5 3.

613

4,24

5 11

.899

,487

14

.19.

Fann

ie M

ae F

lex

973,

694,

663

14.3

3,21

5,25

1 19

.922

3,04

8 5.

012

2,11

0 3.

113

4,24

5 11

.893

,616

13

.310

.Fre

ddie

Mac

Com

unity

Gol

d3,

828,

115

14.9

3,29

9,89

3 20

.424

2,20

0 5.

415

1,84

2 3.

813

4,24

5 11

.899

,487

14

.111

.Ban

k of

Am

eric

a Ze

ro D

own

3,82

8,63

0 14

.93,

290,

183

20.3

231,

753

5.2

145,

036

3.6

161,

698

14.3

111,

498

15.8

12.B

ank

of A

mer

ica

Cre

dit F

lex

3,83

8,07

6 14

.93,

309,

805

20.5

242,

185

5.4

151,

846

3.8

134,

240

11.8

99,4

87

14.1

13.P

ortfo

lio C

ompo

site

4,37

3,12

4 17

.03,

687,

791

22.8

322,

936

7.2

187,

974

4.7

174,

422

15.4

120,

983

17.2

14.F

HA

203

(b)–

prio

r5,

168,

303

20.1

4,25

6,03

7 26

.342

1,09

4 9.

426

9,69

4 6.

822

1,50

4 19

.513

9,92

0 19

.915

.FH

A 2

03(b

)–cu

rren

t4,

467,

036

17.3

3,72

1,48

3 23

.034

2,83

1 7.

720

5,09

6 5.

219

7,73

8 17

.412

0,28

4 17

.1

Wei

ghte

d sa

mpl

e si

ze25

,762

,939

100

.016

,183

,879

100

.04,

464,

525

100.

03,

980,

131

100.

01,

134,

403

100.

070

4,03

5 10

0.0

Sou

rce:

Aut

hors

’ana

lysi

s of

199

3 S

IPP

data

.a

Non

-His

pani

c w

hite

, non

-His

pani

c bl

ack,

His

pani

c, a

nd o

ther

non

-His

pani

c re

nter

fam

ilies

are

dis

cret

e.C

ombi

ned,

they

sum

to a

ll re

nter

fam

ilies

.Rec

ent

imm

igra

nt fa

mili

es,

thos

e en

terin

g th

e U

nite

d S

tate

s af

ter

1984

, ov

erla

p w

ith t

he p

revi

ousl

y lis

ted

raci

al/e

thni

c gr

oups

(e.

g.,

non-

His

pani

c w

hite

s an

dbl

acks

).F

inal

ly,

if no

t ot

herw

ise

note

d, w

hite

s, b

lack

s, a

nd o

ther

gro

ups

are

non-

His

pani

c.b

NA

= 3

/2 O

ptio

n is

not

act

ivat

ed (

i.e.,

2 pe

rcen

t ou

tsid

e fu

ndin

g is

not

fort

hcom

ing)

.c

A =

3/2

Opt

ion

is a

ctiv

ated

(i.e

., 2

perc

ent

outs

ide

fund

ing

is fo

rthc

omin

g).

We further detail home-buying capacity by population subgroup. Tables 15 through18 identify the number, type, and percentage of various categories of renter familieswho can afford the range of reference-priced units under the respective mortgageproducts. For instance, under the Historical Mortgage, approximately 1.314 millionrenter families (5.1 percent of the total 25.8 million renter families), including approx-imately 0.060 million black renters (1.3 percent of the total black renter families), canafford the median-priced house (table 16). With the application of the GSE StandardMortgage, approximately 1.631 million renter families (6.3 percent of the total), in-cluding approximately 0.071 million black renters (1.6 percent of the total), are ableto afford the median-priced house. Similar small increments of gain are achieved withthe more liberal mortgage instruments—the GSE Affordable, Governmental Afford-able, and Portfolio products. Thus, the most aggressive of the products consideredhere brings just over 2.4 million renter families (9.4 percent of the total), includingapproximately 0.154 million black renter families (3.4 percent of the total), to home-ownership at the median-priced reference point (table 16). Greater numbers of fami-lies are served at the less expensive reference-priced levels. For example, under theHistorical Mortgage, approximately 0.128 million black renters (2.9 percent of thetotal) can secure the low-priced house (table 18). That low-priced homeownership at-tainment reaches approximately 0.421 million black renter families (9.4 percent ofthe total) with the FHA 203(b)–prior mortgage.

A similar result is observed for Hispanic renter families (tables 15 through 18). Theirability to purchase a reference-priced home increases from the Historical Mortgageas the more liberal loan instruments are applied. For any given mortgage, equal orgreater numbers of Hispanic families qualify for homeownership at the less expensivereference prices. In general, the percentage of Hispanic families able to secure a homefor any given mortgage and reference-priced home combination is in order of magni-tude similar to or slightly less than that of black renters. For example, 0.4 percent ofHispanic renter families can afford the target-priced house with the Historical Mort-gage (compared with 1.0 percent of black renters); that share rises very slightly toabout 2 percent of Hispanic renters affording the target house (compared with approx-imately 3 percent of black renters) with the GSE Affordable and other more aggres-sive products. A maximum of about 7 percent of Hispanic30 renters can purchase alow-priced house with the most aggressive Governmental Affordable Mortgages—similar to but somewhat lower than the roughly 10 percent of black renters who canafford the low-priced home with these same most aggressive loans. Recent immigrantrenter families fare better, however, and come closest to the homeownership capabil-ity of white renter families. This last finding is consistent with the observed vigoroushomeownership gains among immigrants in recent years (Joint Center for HousingStudies 1998).



30 Hispanic renters’ home-buying capacity is similar but somewhat lower than that of their black counterparts,despite Hispanic renters having on average higher incomes and assets (table 10). We hypothesize that the His-panic home-buying capacity may be less than that of blacks for various reasons. First, the Hispanics’ target-pricedhomes are more expensive (table 10). Second, Hispanics, relative to blacks, are disproportionately concentrated inregions (e.g., California) with higher-priced criterion homes (e.g., the mean modestly priced house for Hispanicsis $77,049 compared with $61,802 for blacks).

We further break down reference home-buying capacity in table 19. This table countsonly the purchasing capacity of renter families specifically oriented to the target price.For example, if family A can afford a $200,000 house, only the purchase capacity ofthe target-priced house (about $90,000) would be counted in table 19. If all currentrenters were to seek a level of housing consumption similar to that attained by demo-graphically similar families who actually did purchase houses between 1993 and 1995,the Historical Mortgage would yield a total purchasing power of approximately $122billion. All of the mortgage market innovations allow dramatic increases in this im-pact: the GSE Standard guidelines yield a total impact of approximately $144 billion;the GSE Affordable Mortgages31 (including the Emerging GSE Affordable Mortgagegroup) realize a maximum of $166 billion in buying power; the Portfolio AffordableMortgages realize a maximum of $183 billion; and the Governmental Affordable Mort-gages realize a maximum of $233 billion in buying power (table 19).

We can extend the type of calculation just noted to further express the shortfall inrenters’ ability to become owners. We can do this by calculating the difference be-tween the price of the four reference homes (target, median-priced, modestly priced,and low-priced) and the value that each renter can afford (capped at the four refer-ence points)32 under the various mortgage instruments. This difference is termed thereference gap. The reference gap figures are shown in table 20.

Consider first the gap between (1) the sum total of houses for which renters can quali-fy based on each of the 15 mortgage instruments, and (2) the sum total of the targethouse prices for each renter. This affordability gap represents the shortfall betweenmortgage market innovations and the kinds of homes that were purchased by first-time buyers in the mid-1990s. Clearly, this gap is most pronounced for the HistoricalMortgage. Were all of the nation’s renter families to make the same choices made bydemographically similar families who purchased homes between 1993 and 1995,total housing demand would be in the $2.2 trillion to $2.3 trillion range (about 25.8million renter families, each seeking a target-priced home of about $90,000). However,the Historical Mortgage allows only a very low percentage of all renters to buy thetarget house, and the most they can afford is a fraction of the target house price (re-call that the mean uncapped home-buying capacity with the Historical Mortgage is$12,199 and the median is $0). The gap between what renters can afford with theHistorical Mortgage and the price of that house, summed for all renters, or the targethouse reference gap, is $2.2 trillion (table 20). Today’s more aggressive mortgagesreduce this gap somewhat, with the most flexible products considered here (e.g., theFlex 97, Community Gold, and Portfolio Composite mortgages) narrowing the gap toabout $2.0 trillion. Nevertheless, these staggering figures illustrate the enormousbarriers to homeownership and housing affordability among the nation’s renters. Evenvery aggressive mortgage market innovations are unable to address the low incomeand negligible savings of many renters.




32 The absolute home-buying capacity shown in tables 12 and 13 did not cap the affordability.

One way to reduce that gap is to ratchet down the reference home from the higher-amenity target-priced unit to a less expensive home—in our current example, themodestly priced and low-priced houses. Table 20 shows that lowering housing expec-tation reduces the housing affordability gap by hundreds of billions of dollars butstill leaves a yawning chasm in the trillion-dollar range, with the exact amountvarying by product. For example, the reference gap on the low-priced house for theFreddie Mac Community Gold Mortgage stands at about $1.02 trillion.

Thus, while any gains are welcome, a large gap remains. Even very liberal mortgageproducts leave the vast majority of renters unserved at any level. Every mortgageproduct leaves at least 21 million renter families—or about 80 percent of the total—unable to enter homeownership at even the low-priced threshold. They are unservedas defined in this study. Less advantaged subgroups (e.g., blacks, Hispanics) fare evenworse.



Table 19. Impact of Alternative Mortgage Instruments:Reference Home-Buying Capacity for All Renters, Nationwide with Target-Price Cap

Total National Average/Median

Home-Buying Home-Buying

Capacitya Capacityb

Loan Name (in $ Billions) Mean ($) Median ($)

Historical Mortgage 122.0 4,736 0

GSE Standard Mortgage (Fannie Mae and Freddie Mac) 144.1 5,594 0


CHBP; CHBP, 3/2 Option (NA)c 161.7 6,278 0 CHBP, 3/2 Option (A)d 182.3 7,076 0 Fannie 97 165.7 6,430 0

Freddie MacAG; AG, 3/2 Option (NA)c 161.5 6,270 0AG, 3/2 Option (A)d 178.1 6,914 0 AG 97 160.7 6,238 0


Flex 97 158.5 6,153 0 Freddie Mac

Community Gold 151.7 5,888 0

Portfolio Affordable MortgagesBank of America Zero Down 161.9 6,285 0 Bank of America Credit Flex 150.0 5,824 0 Portfolio Composite 182.9 7,100 0

Governmental Affordable MortgagesFHA 203(b)–prior 233.3 9,054 0 FHA 203(b)–current 193.7 7,519 0

Source: Authors’ analysis of 1993 SIPP data.a The aggregate value of houses that could be purchased by current renters above a low-priced house threshold.b For all current renters, not just just renters who can afford at least a low-priced house.c NA = 3/2 Option is not activated (i.e., 2 percent outside funding is not forthcoming).d A = 3/2 Option is activated (i.e., 2 percent outside funding is forthcoming).

Therefore, the crucial differences are in how the mortgages incrementally, albeit oftenmarginally, increase affordability for the renter population. Under the Historical Mort-gage, only 1.0 million to 2.6 million renter families of approximately 25.8 million totalrenter families could afford the various reference-priced homes. Each of the alterna-tive mortgages improves these figures slightly.



Table 20. Reference Gap for All Renters, Nationwide by Mortgage Instrument

Reference Gapa (in $ Billions)

50th 25th 10thTarget Percentile Percentile Percentile

Loan Name House House House House

Historical Mortgage 2,159.5 2,406.8 1,615.8 1,077.5

GSE Standard Mortgage (Fannie Mae 2,103.3 2,359.8 1,580.8 1,052.9 and Freddie Mac)


CHBP; CHBP, 3/2 Option (NA)b 2,048.4 2,311.1 1,544.1 1,028.0 CHBP, 3/2 Option (A)c 2,006.1 2,272.2 1,513.4 1,005.8 Fannie 97 2,059.6 2,319.5 1,548.4 1,029.5

Freddie MacAG; AG, 3/2 Option (NA)b 2,031.7 2,297.3 1,533.7 1,020.3 AG, 3/2 Option (A)c 1,988.0 2,257.3 1,502.5 997.9 AG 97 2,033.2 2,298.7 1,535.4 1,021.2


Flex 97 2,044.4 2,308.1 1,542.7 1,027.3 Freddie Mac

Community Gold 2,026.6 2,293.9 1,532.8 1,019.9

Portfolio Affordable MortgagesBank of America Zero Down 2,026.9 2,291.7 1,530.7 1,019.6 Bank of America Credit Flex 2,024.6 2,292.4 1,532.0 1,019.4 Portfolio Composite 1,984.9 2,254.5 1,500.5 996.2

Governmental Affordable MortgagesFHA 203(b)–prior 1,941.8 2,209.2 1,458.8 964.7 FHA 203(b)–current 1,981.7 2,248.8 1,494.1 991.3

Source: Authors’ analysis of 1993 SIPP data.Note: The reference gap is largest for the 50th percentile reference category, even though the national median targethouse price exceeds the weighted median of the regional 50th percentile house prices. This somewhat counterintu-itive finding reflects the distribution of individual families according to their respective target house prices and geographicregions. The national medians for the 50th percentile and target house prices cannot be used to infer the underlyingdistribution of values that yield the aggregate reference gaps.a The difference between the price of the four reference homes (target-, median-, modestly, and low-priced) and the

value that each renter can afford (capped at the four reference points) summed across all renter families is the refer-ence gap.

b NA = 3/2 Option is not activated (i.e., 2 percent outside funding is not forthcoming).c A = 3/2 Option is activated (i.e., 2 percent outside funding is forthcoming).

Absolute and Reference Home-Buying Capacity by Family Income Level

Similar results are obtained when the analysis is applied by income level. Renters aresorted into four income categories: low income (0 percent to 49 percent of the medi-an family income); moderate income (50 percent to 79 percent of the median familyincome); middle income (80 percent to 119 percent of the median family income);and upper income (at least 120 percent of the median family income). To maintainsufficient sample size, we do not cross-tabulate by both income and race/ethnicity.

The mortgage simulation results by income category are detailed in appendix E andare summarized below.

1. As expected, home-buying capacity is positively correlated with income. For exam-ple, of the total $388 billion in absolute home-buying capacity under the GSEStandard Mortgage, the low-, moderate-, middle-, and upper-income renter fami-lies realize very different purchasing power—$2.8 billion, $9.8 billion, $28.3, and$346.8 billion, respectively (tables E.1 through E.4).

2. Homeownership affordability also tracks income in the sense that an increasingshare of renters is served at higher income levels. For example, 0.4 percent oflow-income renters can afford a modestly priced house with the GSE StandardMortgage, compared with 17.2 percent of their upper-income counterparts.

3. For any given income group, mortgage innovation increases homeownership af-fordability, but the gains are slightest for the least advantaged, who are simply,for the most part, too poor to purchase irregardless of mortgage product or refer-ence home price. For example, only 0.4 percent of low-income renters can afford tobuy a low-priced house with the Historical Mortgage. That rises, but barely so, toabout 0.9 percent with the most aggressive loan products. For moderate-incomerenters, the gain in the low-priced home-buying capacity rises from 2.5 percentof these renters, using the Historical Mortgage, to about 7 percent with the mostaggressive mortgage products considered in this study.

Once a threshold of home-buying capacity is reached, the gains from mortgageinnovation become more marked. For example, 6.6 percent of middle-income fam-ilies can afford the low-priced house with the Historical Mortgage. This percent-age doubles to 13.4 percent with mortgage innovation.

4. Stratifying by income reaffirms the overall finding of the mortgage simulationthat there is a tremendous challenge in bringing renters to homeownership. Forall income categories, the overwhelming share of renters remain unserved. Only15.4 percent of upper-income renters can afford the target-priced house with themost aggressive mortgage product. Even when the housing benchmark is ratch-eted down to a low-priced unit, only 35.7 percent of the upper-income renters areserved with the most liberal mortgage.



Comparison with Previous Research

Our findings are similar to those of prior researchers. As we have conceptually mod-eled much of our work on and used the same database (SIPP) as Savage (1997, 1999),it is appropriate to compare our results to Savage’s findings. Our results differ slightlyfrom those of Savage because of differences in the following:

1. SIPP panels. In Who Can Afford to Buy a House in 1993 (1997) Savage combinedresults from wave four of the 1992 SIPP and wave seven of the 1991 SIPP (inter-views conducted between February and May 1993). We were able to access a laterpanel of the SIPP; we include results from the 1993 panel. In Who Can Afford toBuy a House in 1995 (Savage 1999), which was released after we had completedthe major portion of our investigation, Savage uses results from wave seven of the1993 SIPP.

2. Criterion homes. In Savage’s study (1997), the criterion-priced homes were basedon 1993 house values, differentiated by location (e.g., census regions and divisions).We incorporated these 1993 values in our investigation. Savage’s more recent WhoCan Afford to Buy a House in 1995 (Savage 1999) considers 1995 house values.

3. Interest rates. The earlier Savage study (1997) used a 7.55 percent conventionalmortgage contract interest rate; the rate is 8.79 percent in his latest study (1999).Our study was conducted during a time period between the two Savage studies,and, therefore, the rate we applied was 8.05 percent.

4. Closing costs. Savage (1997, 1999) calculates closing costs at about 3 percent; weestimate closing expenses at approximately 4 percent.

5. Income used for underwriting. We adjust income based on underwriting consider-ations such as income stability (table 10). This has the effect of reducing the in-come counted by underwriters for mortgage-qualifying purposes. (Note that theadjusted incomes in table 10 are almost always lower than the nominal income.)Savage did not make such an adjustment.

6. Definition of “family.” The definition of “family” used in this study is broader thanthat used by Savage. He defines a family as a group of two or more related personswho reside together, and he treats a person living alone as an “unrelated individ-ual.” In contrast, we include related individuals as well as a person living alone asa “family.” That, along with the timing difference noted above, results in differencesin the number of families counted; we counted 25.8 million families, comparedwith 21.4 million counted by Savage in his earlier study (1997) and 20.7 millioncounted in his later study (1999).

Given the above differences, we do not expect our results to mirror those of Savage.Nonetheless, given the overall commonality of database and approach, we expect ourinvestigations to yield similar outcomes. This order of magnitude parallelism is shownwhen we examine homeownership affordability for all renters (table 21).



The overall similarity of outcomes is also evident when homeownership affordabilityof a modestly priced house is compared by renter category (e.g., race and Hispanicorigin; table 22).

We also find overall equivalent results when homeownership affordability by pricelevel is compared (see table 23). For example, Savage (1999) found that 69.9 percentof all renters could afford a house priced at less than $20,000 with an FHA mort-gage. Our figure is 69.1 percent. In short, Savage’s findings—that relatively few cur-rent renters (about 1 in 10) can afford even a modestly priced house, and that fewerthan 1 in 30 black or Hispanic renter families can realize modestly priced homeown-ership—are essentially reaffirmed in our mortgage simulations.



Table 21. Percentage of All Renter Families Who Can Afford Variously Priced HousesUsing a GSE Standard Mortgage

House Price Savage (1997) Savage (1999) Listokin et al.

Target priced NA NA 5.0Median priced 8.6 6.7 6.3Modestly priced 11.7 9.9 9.2Low priced 15.1 12.8 12.1

NA = not applicable.

Table 22. Percentage of Renter Families by Type Who Can Afford a Modestly Priced HouseUsing a GSE Standard Mortgage

Renter Type Savage (1997) Savage (1999) Listokin et al.

All 11.7 9.9 9.2White (non-Hispanic) 14.2 14.4 13.1Black (non-Hispanic) 3.3 3.2 2.7Hispanic 4.1 3.3 1.8



Tabl

e 23

.Co

mp

aris

on

of

Lis

toki

n e

t al

.’s a

nd

Sav

age’

s (1

999)

Aff

ord

abili

ty R

esu

lts

(Nu

mb

er a

nd

Per

cen

tag

e o

f R

ente

rs W

ho

Co

uld

Aff

ord

th

e In

dic

ated

Ho

use

Pri

ce)

Con

vent

iona

l Mor

tgag

eF

HA

Mor

tgag

e

List

okin

et a

l.*S

avag

e (1

999)

List

okin

et a

l.aS

avag

e (1

999)

Num

ber

Num

ber

Num

ber

Num

ber

Hou

se-P

rice

Cat

egor

y ($

)(in

Tho

usan

ds)

%(in

Tho

usan

ds)

%(in

Tho

usan

ds)

%(in

Tho

usan

ds)

%

Less

than

20,

000

21,3

08

82.7

17

,345

81.0

17,8

00

69.1

14

,977

69.9

20,0

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EXPLANATION OF THE FINDINGS: WHY ARE RENTERS LIMITED

IN THEIR ABILITY TO REALIZE HOMEOWNERSHIP?

Why are so many renters, especially minority renters and LMI renters, unserved inthe mortgage market? Recall that in the current study lender bias is not a barrierbecause every renter is treated fairly; that is, all renters are treated solely on thebasis of their financial resources. This, however, is just the issue. Renters in general,and minority and LMI families in particular, have very modest financial resources,and this condition limits their home-buying capacity.

These financial limitations were noted earlier. To recap, renters as a group had a me-dian family income of only $18,786—about two-thirds of the $28,710 median incomeof all families (renters and homeowners) as reported in the SIPP. Black and Hispanicrenters trailed their majority counterparts considerably, with median family incomesof $13,770 and $15,314, respectively (table 10).

Disparities are further apparent when assets and debts are considered (table 10).White renters have mean assets of $11,368, and recent immigrants have mean assetsof $9,076; black and Hispanic renters have fractions of these resources, with meanassets of $1,601 and $2,000, respectively. Indeed, all renters have very limited assets,as indicated by the fact that the median level of assets for every subgroup consideredhere is about $500 or less.

With such a trace level of assets, even a 100 percent LTV mortgage will not facilitatehomeownership because of the resources required to meet substantial closing costs.Given the combination of modest income and assets, it is no wonder that home-buyingcapacity is a distant dream for many renter families, particularly minority families(Haurin, Hendershott, and Wachter 1997).

Which of the dual hurdles to homeownership is more significant—modest income ormodest assets? One way of addressing that issue is to first calculate the maximum-priced housing unit that could be purchased by traditionally underserved rentersgiven their actual characteristics. Next, one determines how much more these renterscould afford were they endowed, in turn, with the higher income and then the largerassets of their more advantaged white counterparts. We effect such an analysis below,focusing on a comparison of non-Hispanic white and black renters. The analysis ap-plies the GSE Standard Mortgage and the financial-demographic profiles of rentergroups detailed in table 10.

A black renter with an adjusted family income of $18,154, $1,601 in assets, and anaverage of $2,443 of debt could afford only a $50 maximum-priced house with a GSEStandard Mortgage. By contrast, the white renter counterpart, with an adjusted fam-ily income of $25,153, assets of $11,368, and debt of $4,247, could afford a $60,750maximum-priced home. The $50 versus $60,750 home-buying capacities reflect thevastly different existing financial conditions of white and black renters.

Explanation of the Findings 65


What if the black renter could hypothetically draw on, in turn, the enhanced incomeand assets of his or her white renter counterpart? Interestingly, tapping into the whiterenter’s greater income alone while retaining other existing financial conditions (e.g.,assets, debt) would not boost our example black renter’s home-buying capacity above$50. Income has no effect since the black renter’s assets are so limited. In contrast, ifthe black renter could retain the profile group’s average existing income and debt butdraw on the white renter’s greater assets, he or she would dramatically increase home-buying capacity from $50 to $52,650. The thousandfold increase from $50 to $52,650shows how important assets are to a renter’s ability to buy. Yet the fact that the blackrenter, even with the white renter’s assigned assets, can afford only a $52,650 house,as opposed to the $60,750 house affordable to the white renter, shows that otherfinancial constraints (e.g., lower income) play a role, albeit a less significant one, inthe home-buying equation.

Using the different mortgage instruments, we can more specifically trace the linksbetween the above financial attributes, the demands of purchasing various reference-priced homes, and constraints on homeownership. Table 24 attempts such an analysisby applying a GSE Standard Mortgage to a modestly priced (25th percentile) home.We begin with this combination because it is the one previously considered by Savage(1997, 1999).

Given the limited assets of renters, one would anticipate that a down payment con-straint—insufficient cash resources to cover the down payment and closing costs—would be a major hurdle to homeownership. Modest renter income also suggests anincome constraint—insufficient income to cover the monthly mortgage costs. Becauserenters are both income constrained and asset constrained, we anticipate a reasonablyhigh incidence of dual down payment and income constraints.

These anticipated findings are borne out in table 24. We find that 28 percent of allrenters who cannot afford the modestly priced home are constrained only by downpayment costs and that an additional 68 percent are dually constrained by the downpayment and income. Only 4 percent of all renters are limited solely by income. Thesefindings resemble those of Savage. The constraints by renter subgroup are furtherdetailed in table 24.

We began the constraints analysis with the modestly priced house/GSE Standardcombination because that was the scenario considered by Savage and we wished tocompare our findings with his. Yet renters aspire to their “dream house”—that is,our target-priced house. What are the homeownership constraints when the targetunit is the benchmark? These findings are shown in table 25 for two combinations:the target-priced house with a GSE Standard Mortgage and the target-priced housewith a CHBP mortgage. As anticipated, the number and percentage of renters ableto purchase the target house increase with the more liberal financing terms offeredby the CHBP. This comports with the findings traced earlier in this study. We can also



observe the constraints to affordability. A constraint by income alone is relatively rare.A down payment constraint alone or a dual down payment and income constraintpresents a greater hurdle. The same is true when the constraints to purchasing themodestly priced house are examined (see table 26).

Explanation of the Findings 67


Table 24. Constraints on Renter Group’sa Ability to Buy a Modestly Priced Home Using a GSE Standard Mortgage

Savage Listokin et al.

(1999) RecentAll All White Black Hispanic Other Immigrant

Constraint Families Families Families Families Families Families Families

Down paymentb 27.9 28.2 31.5 26.3 19.6 23.2 25.5Income 2.1 4.4 6.6 0.8 1.2 2.0 3.2Down payment and income 69.9 67.5 62.0 72.9 79.2 74.8 71.3Totalc 100.0 100.0 100.0 100.0 100.0 100.0 100.0

Source: Savage 1999 and authors’ analysis of 1993 SIPP data.Note: The table categorizes those who cannot afford the 25th percentile home in their census region according toconstraint. There are three reasons why a household might not be able to afford the home: (1) insufficient income tocover the monthly mortgage costs; (2) insufficient cash resources to cover the down payment and closing costs; and(3) too much debt. Savage (1997) conflates down payment and excessive debt into a single category (insufficientdown payment), and we mostly follow that approach in reproducing his results.

To determine if a household has too little income to buy the reference home, we took the monthly housing expendi-ture (monthly income times the front-end ratio for the given mortgage instrument) and determined the net presentvalue (NPV) of that payment over 30 years (using an interest rate of 8.05 percent for all instruments). If the NPV wasless than 95 percent of the value of the reference house, we determined that the household was income constrained.This calculation was performed without regard to the creditworthiness of the household or the availability of cash tocover the down payment and closing costs.

To determine if a household had insufficient cash to cover the down payment and closing costs, we took the family’sassets and subtracted the down payment (5 percent times the value of the house as given by the maximum LTV ratio)and the estimated closing costs for the housing market. This differs slightly from Savage’s analysis in that our closingcosts are estimated based on the particulars of the housing market (at least for the eight MSAs for which we researchedlocal closing costs.) Cash available for a down payment was estimated without regard to a family’s debts. For instance,if a family had $5,000 in assets and $2,500 in debt, we used $5,000 (and not assets less debt) as the cash availablefor down payment.

a Non-Hispanic white, non-Hispanic black, Hispanic, and other non-Hispanic renter families are discrete. Combined,they sum to all renter families. Recent immigrant families, those entering the United States after 1984, overlap withthe previously listed racial/ethnic groups (e.g., non-Hispanic whites and blacks). Finally, if not otherwise noted, whites,blacks, and other groups are non-Hispanic.

b Savage also includes those constrained by excessive debt in this category.c Figures may not add to indicated totals because of rounding.



Table 25. Comparison of the GSE Standard Mortgage with the Fannie Mae CHBP with Two Policy Options, Target-Priced House

(Number and Percentage of Renter Families Who Can Afford a Target-Priced House with the Indicated Mortgage)

CHBP with $100Monthly Housing CHBP with

Fannie Mae Income $5,000 DownAffordability of a Target House GSE Standard CHBP Supplement Payment Voucher

Can afford to buy, number (%) 1,288,186 (5.0) 1,655,782 (6.4) 1,862,248 (7.2) 3,538,367 (13.7)

Constrained by income only, 1,487,092 (5.8) 1,399,314 (5.4) 1,192,850 (4.6) 5,177,856 (20.1)number (%)

Constrained by down payment 3,989,196 (15.5) 5,041,003 (19.6) 6,648,642 (25.8) 3,277,084 (12.7)only, number (%)

Constrained by income and 18,998,464 (73.7) 17,666,839 (68.6) 16,059,070 (62.3) 13,769,630 (53.4)down payment, number (%)

Total, number (%) 25,762,939 (100) 25,762,939 (100) 25,762,939 (100) 25,762,939 (100)Sample size, number 4,109 4,109 4,109 4,109

NPV of policy option 858.5a 9,413b

(in $ millions)

Note: Figures may not add to indicated totals because of rounding.a The NPV of the income supplement assumes a supplement of $100 per month for 48 months at a 6.0 percent dis-

count rate and that the supplement is applied to only the additional qualifying applicants.b The NPV of the down payment voucher assumes that a lump sum payment of $5,000 is given to only the additional

qualifying applicants.

Table 26. Comparison of the GSE Standard Mortgage with the Fannie Mae CHBP with Two Policy Options, Modestly Priced House

(Number and Percentage of Renter Families Who Can Afford a Modestly Priced House with the Indicated Mortgage)

CHBP with $100Monthly Housing CHBP with

Affordability of a Fannie Mae Income $5,000 DownModestly Priced House GSE Standard CHBP Supplement Payment Voucher

Can afford to buy, number (%) 2,381,156 (9.2) 2,835,612 (11.0) 3,057,030 (11.9) 7,560,159 (29.3)

Constrained by income only, 1,027,692 (4.0) 895,107 (3.5) 673,649 (2.6) 5,339,348 (20.7)number, (%)

Constrained by down payment 6,582,168 (25.5) 7,486,227 (29.1) 9,418,673 (36.6) 2,971,919 (11.5)only, number (%)

Constrained by income and 15,771,923 (61.2) 14,545,992 (56.5) 12,613,535 (49.0) 9,891,513 (38.4)down payment, number (%)

Total, number (%) 25,762,939 (100) 25,762,939 (100) 25,762,939 (100) 25,762,939 (100)Sample size, number 4,109 4,109 4,109 4,109

NPV of policy option 920.7a 23,623b

(in $ millions)

Note: Figures may not add to indicated totals because of rounding.a The NPV of the income supplement assumes a supplement of $100 per month for 48 months at a 6.0 percent dis-

count rate and that the supplement is applied to only the additional qualifying applicants.b The NPV of the down payment voucher assumes that a lump sum payment of $5,000 is given to only the additional

qualifying applicants.

POLICY CONSIDERATIONS

This section examines various policies to enhance the capacity of renters to achievehomeownership. In doing so, we recognize that some researchers assert that there isalready too much support for, and subsidization of, homeownership (Krueckeberg1999). Further, even those who are generally supportive of homeownership may verywell propose that this tenure is not suitable for everyone and that by emphasizinghomeownership we may very well not pay enough attention to other pressing hous-ing needs, such as the need for new rental housing and maintenance of the existingrental housing stock. One could also argue that the government’s job in the marketshould be limited to guarding against discrimination as opposed to equalizing the con-sumption of homeownership. We recognize these very valid points. For the sake ofdiscussion, however, we shall proceed under the assumption that homeownership isan important good deserving of public support.

A critical strategy to foster homeownership is to address renters’ financial constraints(Eller and Fraser 1995; Engelhardt and Mayer 1994). Because so many renters areconstrained by down payment costs, whether solely or in conjunction with income con-straints, one reasonable intervention would be to supplement the assets available torenters (Mayer and Engelhardt 1996). This could take the form of a voucher to beapplied for down payment and closing costs. An income supplement, say a voucher topay for mortgage costs, would be a way of compensating for renters’ modest incomes.The cost of both of these programs would depend on the magnitude and duration ofthe supplements.

Table 25 poses hypothetical examples of both of these approaches to addressing finan-cial constraints to homeownership. One is a $5,000 down payment voucher to be usedfor down payment or other up-front costs (e.g., closing expenses). The other is a hous-ing payment supplement of $100 per month, or $1,200 yearly. It is anticipated thatthis voucher would be in force for four years, amounting to $4,800 in cumulative pay-ments. Although the voucher would cease after four years, it is anticipated that therenter’s income would rise sufficiently over this period to compensate for the end ofthe voucher supplement. (The same reasoning underlies the concept of the adjustablerate mortgage, where the initial rate is pegged below the market rate.)

The different approaches address different constraints. As shown in table 25, the downpayment voucher has the biggest effect on reducing the share of renters who are con-strained by down payment costs with respect to purchasing the target-priced house;the income voucher targets the income constrained. Since down payment constraintsloom so large as a barrier to renters, it is not surprising that the down payment vouch-er dramatically enhances home-buying capacity. Approximately 1.7 million renterfamilies (6.4 percent of all renters) can afford the target-priced house with the CHBPmortgage. When the down payment voucher is added to the CHBP financing, 3.5million renter families (13.7 percent of all renters) can afford target-priced home-ownership. The increase in affordability is much lower with the income voucher/CHBPcombination, which brings about 1.9 million renter families, or approximately 7.2percent of the total, to homeownership.

Policy Considerations 69


Not surprisingly, these outcomes have different costs. The down payment voucher isa potent tool. Yet its cost in net present value terms—approximately $9.4 billion—far exceeds the approximate $0.86 billion expenditure of the income voucher. Thisdisparity in cost is due to two factors. First, the down payment voucher moves manymore renters into homeownership than the income voucher does. Second, the formeris an up-front payment, making it more expensive in time-value terms than the lat-ter, which is spread over four years.

Similar results are observed when the policy options are applied to the modestlypriced house (table 26). Approximately 2.8 million renters (11.0 percent of the total)can afford this home with the CHBP mortgage alone. That number increases to ap-proximately 3.1 million renters (11.9 percent of the total) with the income supplementand 7.6 million renters (29.3 percent of the total) with the down payment assistance.Clearly, supplementing assets is a more powerful way to enable renters to afford themodestly priced house. However, in this instance, the present value of the asset sup-plement, $23.6 billion, is more than 25 times greater than the present value cost ofthe income supplement ($0.9 billion).

In practice, an asset supplement approach underlies the design of the 3/2 Option inthe GSE Affordable Mortgages. Thus far, we have discussed the affordability outcomesusing Fannie Mae’s CHBP and Freddie Mac’s AG without consideration of each pro-gram’s 3/2 Option (i.e., 3 percent minimum borrower contribution to the down pay-ment; 2 percent additional amount allowed from gifts and grants). We maintainedthis restriction because we wanted to determine the renters’ homeownership poten-tial based solely on their own resources. What happens if the 2 percent gift or grantis considered? The gift or grant is, in effect, an asset supplement and should increasethe attainment of homeownership. We simulate the impact of the 3/2 Option in tables12 through 20. This option increases the absolute and the reference home-buyingcapacities. For instance, while 11.4 percent of all renter families (2.945 million) canafford the modestly priced house with an AG mortgage without the 3/2 Option acti-vated, this proportion rises to 12.9 percent (3.323 million renter families) with the3/2 Option in force (table 17).

Given that an asset transfer can boost homeownership, it is important to measurethe aggregate asset gap. Notwithstanding renters’ income constraints, what is thetotal amount of assistance needed to address the shortfall in resources required forthe down payment and closing costs? We calculate this shortfall, which we term theasset gap, by mortgage type for the four reference-priced houses (table 27). The assetgap for the target house and GSE Standard Mortgage is approximately $182 billion.This figure decreases to approximately $105 billion when the same mortgage is usedbut applied to the low-priced house. The asset gap for the target house changes whendifferent mortgages are applied: the asset gap is approximately $287 billion with theHistorical Mortgage; $182 billion with the GSE Standard Mortgage; as low as $121billion with the GSE Affordable Mortgages33 (including the Emerging GSE Affordable



33 With the 3/2 Option activated.

loans); and less than $90 billion for the very aggressive Portfolio Affordable and Gov-ernmental Affordable Mortgages. The above amounts are the resources needed toaddress the asset shortfall. Even with that supplement, renters could remain incomeconstrained, yet meeting the shortfall in assets would go a long way toward enablingrenters to become homeowners.

In short, although mortgage innovation expands homeownership potential, otherstrategies, such as income and asset supplements, are needed to address the financialconstraints faced by the vast majority of renters. In fact, there is a larger universe ofpolicy options that we illustrate for the modestly priced house and the target-pricedhouse in tables 28 and 29, respectively. Each of these tables starts with a baseline: thepercentage of renters who can afford the reference house with the GSE StandardMortgage. Each table then presents four generic approaches to improving the home-ownership potential. The first, mortgage term adjustment, relaxes the mortgage terms



Table 27. Asset Gap by Mortgage Type and Reference-Priced House

Asset Gapa (in $ Billions)

50th 25th 10thTarget Percentile Percentile Percentile

Loan Name House House House House

Historical Mortgage 287.4 319.4 221.7 155.1

GSE Standard Mortgage (Fannie Mae 182.3 201.6 144.1 105.2 and Freddie Mac)


CHBP; CHBP, 3/2 Option (NA)b 161.5 181.2 124.5 86.3 CHBP, 3/2 Option (A)c 121.2 135.4 94.3 66.7 Fannie 97 131.7 145.9 104.2 76.0

Freddie MacAG; AG, 3/2 Option (NA)b 161.5 181.2 124.5 86.3 AG, 3/2 Option (A)c 121.2 135.4 94.3 66.7 AG 97 134.9 149.0 107.2 78.8


Flex 97 133.7 147.9 106.1 77.8 Freddie Mac

Community Gold 136.1 150.2 108.3 79.9

Portfolio Affordable MortgagesBank of America Zero Down 87.3 93.2 73.4 60.0 Bank of America Credit Flex 136.5 150.6 108.7 80.3 Portfolio Composite 121.4 135.7 94.5 66.8

Governmental Affordable MortgagesFHA 203(b)–prior 87.5 102.9 65.9 41.2 FHA 203(b)–current 103.3 115.0 80.9 58.0

Source: Authors’ analysis of 1993 SIPP data.a Notwithstanding income constraints, the total amount of assets needed to address the shortfall in resources required

to buy the respective reference-priced houses.b NA = 3/2 Option is not activated (i.e., 2 percent outside funding is not forthcoming).c A = 3/2 Option is activated (i.e., 2 percent outside funding is forthcoming).

by (1) reducing the interest rate from the market level, (2) reducing the required downpayment, and/or (3) reducing the PMI requirement.

The second generic approach, transaction-carrying cost adjustment, reduces thetransaction-carrying cost of homeownership by allowing for (1) closing-cost savings,(2) property tax savings, and/or (3) hazard insurance savings. In all instances, the



Table 28. Effects of Policy Options by Renter Group:a Percentage of RentersNationwide Who Can Afford a Modestly Priced House

RecentAll White Black Hispanic Other Immigrant

Renters Renters Renters Renters Renters RentersBaseline/Option (%) (%) (%) (%) (%) (%)

BaselineModestly priced house, 9.2 13.1 2.7 1.8 6.1 8.4

GSE Standard Mortgage

OptionsI. Mortgage term adjustment

A. Interest rate reductionb

5% 10.0 14.3 2.7 2.1 6.1 9.12.5% 10.6 15.1 2.7 2.1 6.7 9.90% 11.2 16.0 3.0 2.1 7.3 10.6

B. Down payment reduction3% down 9.8 13.9 2.8 2.0 6.1 8.40% down 11.3 15.6 4.0 2.4 10.2 11.2

C. Reduced PMIc

0% 9.4 13.3 2.7 2.0 6.1 9.1II. Transaction-carrying cost adjustment

A. Closing-cost savingsLowest closing costs 9.6 13.6 2.8 2.0 6.6 8.4

B. Property tax savingsLowest property tax 9.4 13.3 2.7 1.8 6.1 8.4

C. Reduced hazard insuranceLowest insurance 9.3 13.1 2.7 2.0 6.1 8.4

III. House price adjustment–10% 9.9 14.1 2.7 2.0 6.1 9.1

IV. Borrower financial supplementA. Annual income supplement

+$1,000 9.9 14.2 2.7 2.0 6.1 9.1+$5,000 12.2 17.3 3.3 2.6 8.0 11.4+$10,000 13.1 18.6 3.5 3.0 8.0 11.4

B. Asset supplement+$1,000 9.9 13.8 3.0 2.3 6.7 8.4+$5,000 16.2 21.6 8.5 3.8 12.9 16.5+$10,000 35.6 41.8 29.8 20.1 24.7 33.2



b Current contract interest rate is 8.05 percent.c For periodic or recurring (not up-front) PMI payment.

savings are based on the lowest closing costs, property taxes, and hazard insuranceexpenses in the eight metropolitan areas for which we obtained such data.

The third generic area of adjustment, housing price, simply reduces the cost of thehousing unit. This is an extension of the reference-priced gradation considered inthis study.



Table 29. Effects of Policy Options by Renter Group:a Percentage of RentersNationwide Who Can Afford a Target-Priced House



BaselineTarget-priced house, 5.0 7.0 1.5 1.0 3.9 4.6




5% 5.9 8.3 1.6 1.1 4.4 5.62.5% 6.9 9.8 1.7 1.3 6.0 7.50% 7.6 10.8 1.7 1.4 6.6 8.3


C. Reduced PMIc

0% 5.2 7.4 1.5 1.0 4.4 4.6II. Transaction-carrying cost adjustment

A. Closing-cost savingsLowest closing costs 5.4 7.5 1.6 1.3 4.4 4.6

B. Property tax savingsLowest property tax 5.3 7.5 1.5 1.0 3.9 4.6

C. Reduced hazard insuranceLowest insurance 5.0 7.1 1.5 1.0 4.4 4.6

III. House price adjustment–10% 5.7 8.1 1.6 1.0 4.4 5.6


+$1,000 5.5 7.8 1.5 1.0 4.4 4.6+$5,000 8.2 11.6 1.7 1.7 7.3 9.0+$10,000 9.9 14.0 2.3 2.1 7.9 10.5





The fourth approach, borrower financial supplement, supplements the borrower’s fi-nances by adding income or adding assets. In the former option, we assume a varyingannual income supplement of $1,000, $5,000, or $10,000. In the latter option, we fac-tor a one-time asset transfer of $1,000, $5,000, or $10,000.

The improvements in homeownership potential from the respective baselines are in-dicated by the rising percentage of renters who can afford the modestly priced andtarget-priced homes. In all instances, the greatest gains are realized by the borrowerfinancial supplement approach. That is followed by the mortgage term adjustmentapproach. A more modest improvement results from adjusting the transaction-carry-ing costs and the house price.

To illustrate, 9.2 percent of all renters can afford a modestly priced house with theGSE Standard Mortgage. That share rises to 12.2 percent with a $5,000 income sup-plement; 16.2 percentage with a $5,000 asset supplement; and 11.2 percent with amortgage contract interest rate reduction from 8.05 percent to 0 percent (table 28).The baseline share of black renters who can afford a modestly priced home with theGSE Standard Mortgage is 2.7 percent. The only way to bring that share up appre-ciably is through an asset supplement. A $10,000 annual income supplement boostsblack renters’ ability to realize modestly priced homeownership by about 1 percent(from 2.7 to 3.5 percent); however, a $10,000 asset supplement would allow 29.8 per-cent of black renters to purchase the modestly priced home. This would essentiallybring black renters to parity with all renters, although they would still trail theirwhite counterparts.

In a similar vein, the affordability rate for Hispanics for a modestly priced home canbe increased to 20.1 percent (from a baseline share of 1.8 percent) with a $10,000asset supplement. Other options have a measurable but less significant impact onHispanic renters. For instance, a $10,000 income supplement allows only 3.0 percentof Hispanic renters to purchase the modestly priced unit. The impact of the variousoptions on the affordability rate follows a similar pattern for recent immigrants. The$10,000 asset transfer would bring approximately 33 percent of recent immigrants tohomeownership; a $5,000 asset supplement to this group would have half that impact(16.5 percent would be able to afford the modestly priced house). Finally, note thatthe results are similar when the options are applied to a target-priced house. A$10,000 income supplement would allow approximately 10 percent of all renters topurchase a target-priced home (doubling the 5 percent reach of the unsubsidized GSEStandard Mortgage); a $10,000 asset supplement would allow approximately 20 per-cent of all renters to realize target-priced homeownership (table 29).

We also can calculate how the policy options can affect absolute home-buying capac-ity (table 30). Using the GSE Standard Mortgage as the baseline, all renters haveapproximately $388 billion in aggregate purchasing power. With the lowest closingcosts, the absolute home-buying capacity is boosted by $25 billion (to approximately$413 billion). A $10,000 income supplement to each renter increases buying power toapproximately $631 billion; a $10,000 asset supplement boosts the purchasing abilityby about $890 billion (to approximately $1.280 trillion). The same $10,000 asset sup-



plement to each black renter would increase purchasing power about ninefold, from$17 billion (baseline) to $150 billion (table 30).

Tables 28 through 30 show the impact of item-by-item changes on homeownership af-fordability. Combining the items will magnify the increase in affordability and pur-chasing power. The three tables thus provide an informative matrix for policy consid-eration, but they do not exhaust the universe of possible changes. For example, we



Table 30. Effects of Policy Options by Renter Group:a

Absolute Home-Buying Capacity, Nationwide (in $ Millions)Recent

All White Black Hispanic Other ImmigrantRenters Renters Renters Renters Renters Renters

Baseline/Option (%) (%) (%) (%) (%) (%)

BaselineGSE Standard Mortgage 387,695 347,340 16,702 13,422 10,231 10,724



5% 435,440 390,564 18,604 14,534 11,739 12,078 2.5% 487,135 435,904 21,015 17,030 13,186 13,057 0% 546,219 488,388 23,578 19,008 15,245 14,230

B. Down payment reduction3% down 414,703 370,026 19,289 14,872 10,516 11,929 0% down 472,154 409,383 27,865 17,960 16,945 12,908

C. Reduced PMIc

0% 395,112 354,105 16,935 13,629 10,442 10,879 II. Transaction-carrying cost adjustment

A. Closing-cost savingsLowest closing costs 413,291 369,075 19,018 14,377 10,821 11,420

B. Property tax savingsLowest property tax 408,611 366,486 17,232 14,335 10,559 11,663

C. Reduced hazard insuranceLowest insurance 390,833 350,143 16,802 13,581 10,307 10,790

III. House price adjustment–10% 437,327 391,979 18,847 14,914 11,587 12,616


+$1,000 416,130 372,711 17,734 14,584 11,101 11,875 +$5,000 528,221 473,407 22,750 18,037 14,027 14,203 +$10,000 630,669 566,109 25,818 21,790 16,952 16,376

B. Asset supplement+$1,000 409,772 366,447 18,251 14,444 10,630 11,748 +$5,000 712,743 578,016 65,976 43,441 25,310 19,592 +$10,000 1,280,300 942,716 149,667 138,432 49,499 34,775




observe from our mortgage simulations that products with a back-end ratio but nofront-end ratio have greater potential to expand homeownership. Therefore, a policythat eliminates the front-end ratio (while keeping the back-end ratio) would enablemore renters to buy a house.

There are numerous policy options to foster homeownership that go beyond the mort-gage innovations and resource supplements considered thus far. A HUD (1995b) studylisted 100 actions to further the National Homeownership Strategy, and other litera-ture has considered a range of policies (DiPasquale 1990). For example, homeowner-ship counseling can guide households in repairing credit problems and assemblingassets to finance down payments and closing costs. Savings vehicles, such as indi-vidual development accounts, can be part of a process that helps accumulate assetsfor homeownership. Outreach is also important so that the traditionally underservedavail themselves of the more flexible mortgage instruments and do not fall victim topredatory home lending practices.

Assessments of policy options must also consider costs. The net present value (NPV)expense of the respective options is a critical consideration. Of the different policiespresented in tables 28 through 30, the most potent in terms of enhancing the absoluteand relative home-buying capacities were the income and asset supplements, and,therefore, we should consider their costs in NPV terms. In making this calculation, weassume that the $1,000, $5,000, and $10,000 asset supplements are one-time infusionsand that the $1,000, $5,000, or $10,000 income supplements are given annually andare maintained for varying periods of time, from 4 to 12 years, depending on theamount (a shorter duration for the lesser amounts and a longer period for the largersupplements).34 After the 4 to 12 years, it is assumed that the income supplementwould cease but that the renter’s income would rise sufficiently over this period tocompensate for the termination of the outside assistance.

Table 31 shows the NPV costs of the $1,000, $5,000, and $10,000 income and assetsupplements, based on the assumptions described above. The costs vary by the indi-cated home price and mortgage combinations for several reasons, including the factthat the number of renters able to realize homeownership varies with each option.

In short, supplementing income and, especially, assets can dramatically enhancehome-buying capacity. Both options are expensive, and the more potent asset sup-plement (i.e., $5,000 and $10,000) is especially expensive. Policy makers must makedecisions about the trade-offs. Providing a $10,000 asset supplement would raise theshare of renters able to afford a modestly priced house with a GSE Standard Mort-



34 We make the following assumptions: the $1,000 annual income supplement is provided for 4 years; the $5,000annual income supplement is provided for 8 years; and the $10,000 annual income supplement is provided for12 years. We assume an 8-year period for the $5,000 income supplement, as opposed to the 4-year period usedwhen we previously calculated the effect of a $5,000 voucher with the CHBP (tables 25 and 26) because here theincome supplements are added specifically to the earnings of traditionally underserved renters who have mod-est incomes. Consequently, it is reasonable to assume that it will take these renters a considerably longer periodof time to increase their incomes by the indicated amounts (e.g., $5,000) through their own resources.

gage from 9.2 percent to 35.6 percent. The cost of this intervention, however—a stag-gering $68 billion—must be balanced against the economic and noneconomic benefits.These benefits include the interest on larger mortgages paid to investors, the increaseddemand for real estate and home improvement services, and a variety of alleged soci-ological benefits associated with stable neighborhoods in which owners have a long-term stake. Scholars and policy makers remain deeply divided on the question ofwhether public policy should attempt to equalize homeownership. We would simplypoint out that the economic and sociological benefits are routinely invoked to justifythe current subsidy of middle-class homeownership. Our calculations simply indicatean explicit price tag if a decision is made to expand these benefits to all renters, espe-cially the underserved.

Many other costs need to be considered; for example, the innovative mortgage productshave long-term loan performance implications (Calem and Wachter 1996; Quercia andStegman 1992). Although FHA qualifies many people for homeownership because ofits generous mortgage terms, its delinquency rate has historically been much higherthan that of more restrictive conventional mortgages (Berkovec et al. 1997). Innova-tive mortgage products also carry greater risk because they allow increasing sharesof income to be applied for housing purposes. A high apportionment of income forhousing may not leave much leeway to meet household medical and other unexpectedexpenses. A high housing debt ratio may also be imperiled by future changes, suchas rising utility costs or a downturn in the economy that raises unemployment ratesand creates other adverse effects. For these and other reasons, the innovative mort-gage products may encounter higher delinquency rates than those encountered byconventional mortgages—which is surely a cost. Fully quantifying the costs of themany possible policy options, however, is beyond the scope of the current investigation.

In considering policy options, it is also instructive to consider the potential and costsof bringing to homeownership only those renters who are most “suitable” for or “ori-



Table 31. NPV Cost of Income and Asset Supplement Policy Options(Applied to All Renters)

Modestly Priced Target HouseHouse with a with a

GSE Standard GSE StandardMortgagea Mortgageb

Policy Option ($ Millions) ($ Millions)

Annual income supplement+$1,000 154 866+$5,000 2,980 3,881+$10,000 7,008 6,987

Asset supplement+$1,000 164 109+$5,000 8,939 4,366+$10,000 67,920 37,124

a See table 28 for details of the effects of each policy option.b See table 29 for details of the effects of each policy option.



ented” to that tenure (hereinafter referred to as homeowner-oriented renters). To thispoint in the analysis, we have looked at the ability and costs of all renters to realizehomeownership because that is an instructive benchmark. In fact, homeownership isnot sought by all renters; there are lifestyle renters and others who rent by choice(Goodman 1999; Varady and Lipman 1994). In addition, homeownership is an expen-sive good that is often viewed as far beyond the means of the most financially con-strained. Removing the lifestyle and the very poor renters from the all-renters groupyields as a remainder the homeowner-oriented renters.

It is difficult to precisely demarcate the homeowner-oriented target population. Varadyand Lipman (1994), Goodman (1999), and others have observed that both the youngand the old often prefer to rent. Varady’s lifestyle and elderly life-cycle renters, for ex-ample, had average ages of 55.5 and 60.8, respectively. Researchers also note thatlifestyle renters are frequently the most affluent of renters; they have the means tobuy but opt to rent. Goodman’s lifestyle market renters had a mean income of almost$65,000. Renters drawn to reviving urban locations in the New York metropolitan areaoften have six-figure incomes (Roseland Property Co. 1999). In addition, although thereis little consensus as to who is too poor to buy, it is not unrealistic to assume a $15,000to $20,000 income as the realistic earnings floor for achieving homeownership.

Based on these considerations, we demarcate the homeowner-oriented renters asthose between 25 and 55 years of age with family earnings of at least $15,000 butless than $80,000. We recognize that this delineation is rough at best, but it likelydifferentiates on an order-of-magnitude basis those renters who are more desirous of,and more suitable for, homeownership. What then is the home-buying capacity ofthe homeowner-oriented renters?

We shall not repeat the entire simulation for the homeowner-oriented group, but wecan obtain a glimpse of the results in tables 32 and 33. Comparing the data on thehomeowner-oriented group (table 32) with the data on the all-renters group (table 12)indicates that, overall, a higher share of the homeowner-oriented renters can purchasethe reference-priced homes. The increase is due to the deletion of renters earningbelow $15,000. The gain is slight, however, because the homeowner-oriented grouphas also dropped renters with earnings above $80,000.

The policy option results (table 33) indicate that for homeowner-oriented renters, asfor all renters (table 28), the most dramatic gains in homeownership potential areachieved through income and asset supplements, especially the latter. For example,3.8 percent of black homeowner-oriented renters can afford a modestly priced housewith a GSE Standard Mortgage. With the assistance of the $5,000 and $10,000 assetsupplement, the percentage of black homeowner-oriented renters who can afford amodestly priced home increases to 15.5 percent and 64.6 percent, respectively. Asnoted before, however, the NPV costs for these policy options are quite high. Table 34shows the NPV costs for the income and asset supplement policy options where ap-plied to the homeowner-oriented renters group.



Tabl

e 32

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t o

f A

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:A

bso

lute

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apac

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meo

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ted

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ters

,Nat

ion

wid

e

Ref

eren

ce H

ome-

Buy

ing

Cap

acity

(Per

cent

age

of R

ente

rs W

ho C

ould

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e H

ome-

Buy

ing

Cap

acity

Affo

rd th

e In

dica

ted

Hou

se)

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l Nat

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ome-

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acity

aH

ome-

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acity

bTa

rget

Per

cent

ileP

erce

ntile

Per

cent

ileLo

an N

ame

(in $

Bill

ions

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ean(

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edia

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ouse

Hou

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toric

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FHA

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3 S

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s ab

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a lo

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se t

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hold

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ers,

not

just

ren

ters

who

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ast

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A =

3/2

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(i.e

., 2

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ide

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/2 O

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fort

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.



In considering policies to further homeownership, we must also acknowledge some ofthe attendant risks and limitations. As noted earlier, the new homeowners may bevulnerable to worsening economic conditions that may affect their mortgage-payingability. To illustrate, we simulated the effect of a $2,000 downward “shift” in financesof the renters realizing homeownership via mortgage innovation. That “shift” couldconsist of income dropping by $2,000 or nonhousing expenses rising by that sameamount (e.g., for medical expenses)—two scenarios that could readily occur. In both

Table 33. Effects of Policy Options by Renter Group:a Percentage of Homeowner-OrientedRenters Nationwide Who Can Afford a Modestly Priced House



BaselineModestly priced house, 10.2 13.2 3.8 3.1 10.7 10.9




5% 11.0 14.3 3.8 3.7 10.7 10.92.5% 11.5 15.1 3.8 3.7 12.0 10.90% 11.8 15.3 4.3 3.7 13.3 10.9


C. Reduced PMI (renewal)0% 10.3 13.3 3.8 3.4 10.7 10.9

II. Transaction-carrying cost adjustmentA. Closing-cost savings

Lowest closing costs 10.8 14.0 3.8 3.4 10.7 10.9B. Property tax savings

Lowest property tax 10.3 13.4 3.8 3.1 10.7 10.9C. Reduced hazard insurance

Lowest insurance 10.2 13.2 3.8 3.4 10.7 10.9III. House price adjustment

–10% 10.7 14.0 3.8 3.4 10.7 10.9IV. Borrower financial supplement

A. Annual income supplement+$1,000 10.7 13.9 3.8 3.4 10.7 10.9+$5,000 11.9 15.4 4.0 3.7 13.3 10.9+$10,000 12.4 16.1 4.0 4.0 13.3 10.9




b Current contract interest rate is 8.05 percent.



situations, there would be $2,000 less available to pay for the homeownership-relatedoutlays of principal, interest, taxes, and insurance (PITI). We calculated what shareof renters who qualify for homeownership would become unable to meet the annualPITI mortgage commitment (i.e., become delinquent) with the $2,000 annual familyfinancial change. For example, for renters in the United States able to purchase amodestly priced home with a GSE Standard Mortgage, a $2,000 change in their fi-nances would result in a 9.3 percent delinquency in their ability to meet PITI costs;for renters able to buy a target-priced home with a GSE Standard Mortgage, a $2,000financial shift would result in their being 17.2 percent delinquent in covering PITIobligations. In an economic downturn, a $2,000 change in fortune is very likely andcarries with it the risk of new homeowners becoming delinquent.

Another type of “risk” is that mortgage innovation may expand homeownership oppor-tunities but not in locations with the better school systems and other sought-afterneighborhood traits that support future house-price appreciation. In many Americanmetropolitan areas, that often means homeownership in newer suburbs as opposedto inner-ring suburbs or inner-city locations. The 1999 State of the Nation’s Housingreports that “large and growing shares of both minority and low-income householdsare buying homes in the ‘suburbs’ ” (Joint Center for Housing Studies 1999, 18). None-theless, we still do not have a clear picture of the geography of housing opportunityoffered by mortgage innovation.

To further our knowledge in that regard, we applied the mortgage simulation in atest application. For illustrative purposes, we considered one of the more potent inno-vative mortgages, the Freddie Mac AG with 3/2 Option, and simulated the maximumhome-buying capacity with this product in the South Atlantic region. The medianhouse affordable to all renters in the South Atlantic region with the AG with 3/2Option falls just short of $109,000 in 1996. If we further assume that a sufficientnumber of homes would be offered for sale in this price range in all areas, then it ispossible to map a best-case scenario for the “geography of opportunity” (Galster and

Table 34. NPV Cost of Income-Asset Supplement PolicyOptions Applied to Homeowner-Oriented Renters

Modestly PricedHouse with a

GSE StandardMortgage*

Strategy ($ Millions)

Annual income supplement+$1,000 48+$5,000 742+$10,000 1,740

Asset supplement+$1,000 132+$5,000 6,698+$10,000 53,923

* See table 33 for details of the effects of each policy option.

Killen 1995) associated with mortgage market intervention. We apply this geographi-cal analysis in the Atlanta, metropolitan area, which is contained within the SouthAtlantic region. Results are shown in figure 2. Not surprisingly, flexible underwritingopens vast opportunities for ownership in Atlanta’s central city neighborhoods witholder, low-priced housing stock. Mortgage innovation also affords opportunities inAtlanta’s suburbs, yet it affords little access in the vibrant growth corridors of upper-middle-class Atlanta’s suburban housing submarkets located north and northeast of



Figure 2. Geographical Variations in Housing Affordability with Flexible Underwriting

Source: Based on synthetic underwriting model for AG with 3/2 Option and Housing Consumption Model derivedfrom 1993 SIPP. Median house value for renter families able to afford a home at least as expensive as the 10th per-centile home in the South Atlantic Region, adjusted for inflation, was $108,667 in 1996. This value was divided bytract median house value in 1996 (estimated by the Atlanta Regional Commission) to obtain the values shown onthe map.

0 10 Miles

21%–34.9%35%–49.9%50%–74.9%75%–99.9%100%–199.9%200% or more

Maximum affordable houseas a percentage of tract

median home value, 1996

Atlanta City Boundary

Central BusinessDistrict



Figure 2. Geographical Variations in Housing Affordability with Flexible Underwriting (continued)

Less than 10 years10–19.920–29.930–39.940–44.945–49.950–57

Median Housing Age, 1996

Less than 15.0%15.0%–24.9%25.0%–34.9%35.0%–49.9%50.0%–88.0%

Poverty Rate, 1990

Source: Claritas™, Inc. (Real EstateSolutions series, 1996–97 Release.

Source: U.S. Bureau of the Census, 1990Census of Population and Housing.

the city. Again, we emphasize that figure 2 presents a best-case scenario, and limita-tions are no doubt far more severe for LMI families searching for available homes inlower price ranges. Efforts to funnel mortgage credit to underserved borrowers orneighborhoods, therefore, may support inner-city revitalization if pursued on a suffi-cient scale, but these efforts may fall short in broadening access to the well-fundedschool districts, expanding employment, and healthy price appreciation of favoredsuburban markets.



SUMMARY: THE ACHIEVEMENTS AND LIMITATIONS

OF MORTGAGE INNOVATION

Our simulations show the following:

1.Compared with their predecessors, current mortgage instruments are much morepotent in furthering homeownership. Total national absolute home-buying capac-ity, which was approximately $314 billion under the Historical Mortgage,35 hasincreased to a range of $500 billion to $600 billion under today’s more liberal mort-gages (table 12). Affordability also has increased. Under the Historical Mortgage,only 3.8 percent of renter families (1.0 million) could afford to purchase their“dream house” (i.e., the target-priced house). With today’s more aggressive mort-gage instruments, approximately 8 percent (2.0 million) of renter families can doso (tables 12 and 15). Expressed another way, the delta, or gain, in home-buyingcapacity from the Historical Mortgage to today’s more liberal mortgage products isabout $300 billion. When measured against the historical baseline, approximately1 million more renters can potentially buy the target house with the currentlyavailable mortgage instruments considered here. (Appendix F reports all of therespective delta gains from mortgage innovation.)

2.The more liberal mortgage instruments offer incrementally rising home-buyingopportunities. The national home-buying capacity is about $388 billion for the GSEStandard Mortgage. This absolute figure increases to a range of $445 billion to$584 billion for the GSE Affordable Mortgages,36 including the Emerging category,and it is in the $511 billion to $601 billion range for the Portfolio Affordable andGovernmental Affordable Mortgages (table 12).

3.The FHA loans are some of the most potent mortgage products offered. Becausethese products have, in fact, been offered for several decades, FHA must be cred-ited as a trailblazer of contemporary mortgage innovation.

4.Major hurdles remain. Even the most aggressive mortgage (FHA included) allowsonly 2.0 million renter families to realize target-priced homeownership, leavingapproximately 24 million renters unserved at the target level (table 15).

5.Lowering housing expectations helps, but a large gap remains. Even the mostliberal mortgage products considered in this report leave the vast majority ofrenter families unserved at any level. The most aggressive product leaves at least

Summary 85


35 The mortgage simulation can be extended to give further historical perspective. For example, the 1999 FannieMae Foundation Housing Conference selected FHA as one of the most significant housing achievements of thiscentury. This is borne out by the Mortgage Model, which indicates that pre-FHA (i.e., before 1934) loan instru-ments would allow approximately $220 billion of national home-buying capacity among today’s renters. Thatpre-FHA capacity is much lower than the roughly $600 billion capacity afforded by the ultimately adopted FHAmortgage (i.e., FHA 203(b)–prior).

36 With the 3/2 Option activated.

21 million renter families—or about 80 percent of the total—unable to enter home-ownership at even the low-priced threshold. Less advantaged subgroups (e.g.,blacks, Hispanics) fare even worse (table 18).

6.The reference gap—the difference between the price of the four reference houses(target priced, median priced, modestly priced, and low priced) and the value thateach renter can afford (capped at the four reference points)—further attests to thebenefits and limitations of mortgage innovation. For example, the reference gapwith the Historical Mortgage is approximately $2.2 trillion for the target-pricedhouse. The shortfall drops noticeably with contemporary mortgage products; how-ever, even with the most flexible products analyzed in this report (i.e., Flex 97,Community Gold), the reference gap for the target-priced house remains approx-imately $2.0 trillion (table 20).

7.Although many renters cannot shift their tenure status solely through mortgageinnovation, the incremental gains provided by innovative mortgage products areimportant, especially among minority populations. Absolute home-buying capaci-ty for black and Hispanic renters, about $11 billion and $10 billion, respectively,under the Historical Mortgage, increases to approximately $42 billion and $28billion, respectively, under today’s more liberal loan products considered in thisreport (table 13). Under the Historical Mortgage, the ability of blacks and Hispan-ics to afford a modestly priced house was barely measurable (i.e., approximately2 percent of these renter groups); the most aggressive mortgage products consid-ered here would allow approximately 4 percent to 6 percent of these minoritygroups to achieve homeownership of the modestly priced house. Recent immigrantrenters also gain from mortgage innovation (table 17).

8.The challenge of realizing homeownership is daunting, given renters’ scant finan-cial resources. Renters generally face both income and asset constraints, with thelatter posing a major challenge. Traditionally underserved renters in particularare financially constrained. Black and Hispanic renters have an average familyincome of less than $20,000 and average assets of about $2,000 (table 10). Nearlyall of the renters in these groups, therefore, lack the resources to buy a target-priced home at a cost of almost $100,000.

9.The outlook is somewhat brighter when we focus on the home-buying capacity ofhomeowner-oriented renters only rather than the home-buying capacity of allrenters. Without financial supplements, however, the gains for this latter groupare slight. For example, approximately 10 percent of the homeowner-orientedrenters can afford to purchase the target-priced house with today’s more liberalmortgage products (a slight increase from 8 percent for all renters).

10. If we accept it as an important societal goal, expanding homeownership will re-quire layered interventions. These include furthering mortgage innovation alongthe continuum sketched here (e.g., lowering down payment costs, raising front-endand back-end ratios); reducing the price of housing (analogous to lowering the“housing bar” from the target-priced house to the lower-cost benchmarks); reduc-



ing transaction costs and carrying costs; and offering various asset and incomesubsidies. The most potent intervention is the provision of income and, especially,asset supplements. These supplements are especially potent when focused on thehomeowner-oriented renters. A $10,000 asset infusion could potentially allowalmost 60 percent of all homeowner-oriented renters (and 40 percent of Hispanicand 65 percent of black homeowner-oriented renters) to realize modestly pricedhomeownership (table 33). The income and asset supplements, especially the latter,can be very expensive (e.g., the $10,000 asset supplement for the homeowner-oriented renters costs about $54 billion). All of these interventions are costly andproblematic.

In attempting to expand homeownership opportunities through mortgage innovation,we must recognize some of the attendant risks. Traditionally underserved householdsthat attain homeownership may be challenged to meet their mortgage obligations inan economic downturn. We also must work to expand the geography of housing oppor-tunity so that mortgage innovation broadens access to favored suburban markets.

These pessimistic conclusions from the mortgage simulation must be tempered, how-ever, with substantial real-world progress in expanding homeownership. We contrastsimulation results with observed real-world progress in the next section.

Summary 87


EVALUATION OF SIMULATION RESULTS

Our simulations indicate that relatively few renter families and hardly any minorityrenters can realize homeownership. That finding parallels very closely the conclusionsreached by Savage (1997, 1999). It is important to understand the limitations of suchan analysis, especially in light of empirical evidence of vigorous gains in homeowner-ship, particularly among the traditionally underserved.

There are approximately 35 million renter households in the United States, includingabout 12.6 million black households. Our mortgage simulations suggest that, evenwithout factoring credit underwriting, only 9.2 percent of all renter households and2.7 percent of black renter households37 could afford the modestly priced house38

(table 17). That would translate into about 3.2 million total and 0.35 million blackrenter households having the capacity to become homeowners.

The above figures are “stock” rather than “annual flow” figures; that is, they are basedon an application of the mortgage simulation model to all renters and represent thestock of renters who might qualify for homeownership at a given point in time. Wewould expect the stock figures to exceed that of the flow of renters actually movinginto homeownership in any given year.

In reality, actual net flow numbers on homeownership far exceed our estimated stockfigures. Between 1993 and the first quarter of 1999, there was a net increase of 7.8million homeowners in the United States (Joint Center for Housing Studies 1999).Over that six-year period, there was a net gain of 1.2 million black homeowners. Thesenet flows are multiples of our simulation estimate of the number of renters who couldpotentially afford modestly priced houses: 3.2 million total renters and 0.35 millionblack renters.

The same incongruity applies to Savage’s study. In his latest research (1999), he findsthat 9.9 percent of all renters and 3.2 percent of black renters could afford to buy amodestly priced house. Even if we assume, as above, that all renters, as well as allblack renters, bought only modestly priced units (an improbable scenario), Savage’spercentages would indicate that approximately 3.5 million total renter householdsand 0.4 million black renter households could realize homeownership. This is a farcry from the net addition of 7.8 million total homeowners and 1.2 million black home-owners in the last few years.

Further, in both our research and Savage’s research, credit underwriting was notperformed. In other words, the mortgage simulations represent a best-case scenario.In a real-world scenario, credit surely is considered and would dampen affordability.We obtain a glimpse of that in appendix B, where we incorporate a credit underwrit-ing submodel. Application of the credit underwriting submodel indicates that the num-

Evaluation of Simulation Results 89


37 This applies in a gross fashion our family-based simulations to households.

38 This is with the GSE Standard Mortgage, which is likely to be the most common mortgage product.

ber of black renters able to achieve homeownership (375,000, averaging our resultsand Savage’s results) would be considerably lower in a real-world credit-scoring envi-ronment. However, actual figures show that there was a net increase of 1.2 millionblack homeowners from 1993 to 1999.

Other statistics also suggest more progress in real-world homeownership than thatsuggested by our simulation results. Home Mortgage Disclosure Act (HMDA) statis-tics, for example, show the changes indicated in table 35 in the recipients of homepurchase loans. While the HMDA data include home purchase loans made to existingas well as new homeowners, the above gains in home purchase loans made to blacksand Hispanics contradict the results of our simulation and Savage’s study, which in-dicate that very low numbers of black and Hispanic renters can purchase a home. Tocomport the above HMDA data with our data, one would have to assume that almostall of the minorities receiving a home purchase loan were move-up minority homebuyers, as opposed to first-time purchasers, and that is unlikely.

Longitudinal analysis of the SIPP also poses incongruities. Of the 4,565 total familiesin the 1993 SIPP that we examined, 4,109 remained renters and 456 (4,565 – 4,109)became owners over the course of the panel interviews. This tenure change allows aretroactive assessment. Based on the mortgage simulation model, we can predict themaximum home affordable to the 456 families as of the time they were still renters.This, after all, is exactly what we do for the 4,109 renter families who remainedrenters. However, for the 456 families that changed tenure, we can compare our pre-dicted maximum home-buying capacity with the price of the home actually bought.One would not expect that the actual purchase values would mirror the modeledprices because some fluctuation around the latter is to be expected (e.g., renters mayelect to buy a house that is priced at less than their absolute maximum home-buy-ing capacity). However, within that fluctuation, we should not find many instanceswhere families are consuming much more than what our mortgage simulation indi-cates is the ceiling.

In fact, the retroactive assessment summarized in table 36 shows that 93 percent ofthe 456 families are buying houses that are priced higher than the affordable levels



Table 35. Home Purchase Loans by Recipient Group,1990 and 1997

MortgageRecipient Group 1990 1997

Whites 1,733,981 2,997,069Blacks 94,624 257,233Hispanics 100,022 254,382Other* 87,496 133,123Total 2,016,123 3,641,807

Source: HMDA statistics for 1990 and 1997 were provided by theWoodstock Institute (1999).* Asian and Native American.

forecast by the mortgage simulation model. The gap between the modeled purchasingpower and the actual home resource consumed is quite large. We find that almost 88percent of the 456 families purchased a home that is at least 50 percent more expen-sive than the price we model as affordable. This gap must give considerable pause tomortgage simulation research that is based on the SIPP.

These disparate statistics suggest that renters—especially traditionally underservedrenters—have a home-purchasing capacity that is greater than that suggested bySIPP-based mortgage simulations. That does not mean that the SIPP-based simula-tions are wrong but rather that we must understand the limitations of the exercise.

There are numerous reasons why observed homeownership gains may exceed whatwe and Savage predict. First, SIPP data may understate the level of resources avail-able to renters; respondents may underreport informal income or the “hidden wealth”associated with intergenerational or other wealth transfers (Gale and Scholz 1994;Gyourko, Linneman, and Wachter 1997). The mortgage industry is recognizing thisunderreporting phenomenon. For example, one Bank of America portfolio product,



Table 36. Comparison of the Price of the Actual Home Purchasedwith the Simulated Purchasing Capacity ofSIPP Renters Who Became Homeowners*

Price of Percentage ofPurchased House as SIPP Rentersa Percentage of the Who BecamePurchasing Capacity Homeowners

More expensive>50 87.7440–50 0.4130–40 1.1520–30 1.0210–20 0.930–10 1.77

Subtotal 93.03

Less expensive(–10)–0 0.92(–20)–(–10) 1.44(–30)–(–20) 0.79(–40)–(–30) 0.49(–50)–(–40) 1.53<(–50) 1.80

Subtotal 6.97

Total 100.00

Note: Figures may not add to indicated totals because ofrounding.* The 1993 SIPP contained a sample of 4,565 renter families,

of which 456 moved from renting to owning between the firstand seventh waves of the survey. Simulated purchasingcapacity was computed using the assets in wave 1 (beforethe house was bought) and income in wave 7.

Zero Flex™, will allow up to $600 a month in undocumented income. However, whenwe conduct simulations based on the income statistics in the SIPP, we are applying avery stringent standard. The simulations might be improved by obtaining guidancefrom lenders, community groups, and others who deal with renter populations (espe-cially the traditionally underserved) on the “hidden” financial resources that may beavailable to renters from relatives and other sources.

We must also recognize that the SIPP renter data, even if fully accurate, representsa fixed point in time and that renters electing to become homeowners can bootstrapthemselves financially. For example, a head of a household can take a second job, anonemployed spouse can enter the labor force, or discretionary spending can be re-duced. Given the low LTVs of today’s mortgages and, therefore, the tremendouslylessened down payment requirements, the actions just noted can quickly yield resultsfor renters.

Researchers are increasingly recognizing the endogenous relationship of savings andthe decision to purchase (Haurin, Hendershott, and Wachter 1997). Thus, renters mayvery well raise their savings rate just before buying a home. Traditionally underservedrenters, many of whom have the income but not the down payment to buy a house,may simply delay saving until they are ready to choose homeownership.

Earlier, we examined the tremendous homeownership-enhancing capacity of a $5,000public grant to renters. We observed that the multi-billion-dollar cost of this grantmade it unlikely to be widely adopted. But if renters could access $5,000 (e.g., fromrelatives), or if they could save this sum, the results of the SIPP-based mortgage sim-ulation would be dramatically altered.

Thus, we must be careful in the conclusions drawn from an SIPP-derived model. Sim-ilar cautions would apply to others doing similar work with other data sources. If weapply the simulation model with the data as given, we are likely to underestimaterenters’ true home-buying capacity. The results we, Savage, and others have obtainedare informative; however, these results also convey gross orders of magnitude or rel-ative order of accomplishment across mortgage instruments as opposed to literal po-tential market penetrations. For example, we find that 1.0 percent of black renterfamilies (43,113) could have afforded a target-priced home with a Historical Mortgage.The figures increase with today’s loan products: 1.5 percent of black renter families(66,321) can afford the target-priced house with a GSE Standard Mortgage; 2.1 per-cent of black renter families (95,041) can be served with the CHBP mortgages; and2.1 percent of black renter families (95,041) can be served with the AG mortgages.Product managers at the GSEs should not take these figures literally as representingmaximum market captures. Instead, the lessons are that the GSE Affordable productsoffer a slight gain in affordability over the Historical Mortgage, and that the FreddieMac mortgages offer a slight gain in affordability relative to their Fannie Mae equiv-alents. (The delta gains from mortgage innovation are detailed in appendix F.) Finally,the simulations indicate that although commendable progress has been made fromthe historical baseline, much remains to be done to expand homeownership opportu-nities to traditionally underserved populations.



Refining the results of the mortgage simulations will require better data on and un-derstanding of renters. We need more insight into the relationship between savingsand the decision to purchase. Access to credit score data is especially critical for im-proving the simulations (see appendix B). In addition, the simulations do not considerhousing availability, which is another component of the affordability equation worthyof future study.

Finally, even with enhanced data, the mortgage simulations report only the mathe-matical calculation of home-buying capacity based on the confluence of the renter’sfinancial profile and the loan terms. As the mortgage industry changes the latter,home-buying capacity expands. This study documents the significant gain providedby mortgage innovation as of the late 1990s. Yet mortgage innovation is only a firststep to a more comprehensive approach to expanding homeownership.

The more encompassing approach adds other components to the very real contribu-tions of mortgage innovation. Subsidies from the public sector, foundations, interme-diaries, and other sources help bridge the financial gap. Partnerships of varioustypes—such as lenders joining forces with other for-profit entities, government, andthe nonprofit sector—are also commonly used to address the multiple barriers tohomeownership that arise at different stages of the home-buying process. To reachthe underserved, for example, a lender will typically join forces with a church or neigh-borhood group. Counseling to address credit issues and education of prospective homebuyers often involve similar alliances. Ensuring that traditionally underserved house-holds remain successful borrowers over the long term also often requires a joint effortby the lender and a nonprofit counselor.

A companion study to the current investigation presents detailed case studies of thecomprehensive approach described above (Listokin et al. 2000). One example is theLittle Haiti Housing Association (LHHA), which is bringing homeownership to one ofthe most disadvantaged populations—very low income Haitian immigrants living inan impoverished neighborhood (“Little Haiti”) in Miami, Florida. LHHA is currentlyqualifying households earning approximately $18,000 to $20,000 to purchase homescosting about $80,000. That is roughly the price of the median-priced home in thecensus division (South Atlantic) that contains Florida, and the Haitians’ $18,000 to$20,000 average income comports very closely with that of the average black renter.Applying mortgage innovation alone, mortgage simulations find that few black renterscould afford a median-priced house. Yet LHHA is making homeownership affordableto the residents of Little Haiti. How LHHA accomplishes that feat and addresses themany barriers to attracting, qualifying, and retaining disadvantaged homeowners isdetailed in the companion study. We offer a synopsis below.

To bring the Haitians to homeownership, LHHA engages in a broad array of activities(Harder 1998). Outreach is important, for few Haitians believe they can afford a homeand many fear dealing with banks. (These apprehensions are widespread among im-migrant populations [Ratner 1997].) Because a large number of Haitians have blem-ished credit records, LHHA works on addressing credit issues. Furthermore, LHHA



helps Haitian renters overcome the tremendous financial barriers to homeownership(the underpinning of our and Savage’s findings) through the following:

1. A low 5 percent down payment by the purchaser

2. A market-rate, no points, modest-size first mortgage granted by a lender

3. A large, soft second mortgage (i.e., with minimal repayment requirements) fund-ed by Miami-Dade County or the federal government

4. A modest-size, soft third mortgage from the Federal Home Loan Bank’s Afford-able Housing Program

The 5 percent down payment typically amounts to about $4,000. LHHA notes thatHaitian households often do not have these funds immediately available. These house-holds, however, garner the down payment from relatives’ gifts, informal lending ar-rangements (e.g., “sous-sous”), and, most frequently, savings. Counselors at LHHArecount numerous instances of Haitian households earning as little as $15,000 to$20,000 a year who manage to save $3,000 to $5,000 of their income. Haitians livedvery frugally, often growing food on empty lots or forgoing a car purchase. The Haitianexperience more generally points to the ability of renters to save once they decide tobecome homeowners. In other words, the low assets of renters might be a transitoryrather than a permanent condition.

The 5 percent down payment from the LHHA home buyers still leaves about $75,000in financing to be secured on the $80,000 homes currently being sold. A $75,000 “hard”(i.e., fully payable) mortgage would be beyond the reach of the Haitian renters. Inresponse, LHHA assembles substantial “soft” financing so that the loan repaymentcan be sustained. Thus, of the $75,000 total, approximately $25,000 would be carriedas a hard first mortgage and the remaining $50,000 would be partially repaid.

In sum, LHHA clientele benefit from mortgage innovation. The Haitian home buyersare making small down payments—5 percent of the house value. Such a small invest-ment would be impermissible under the Historical Mortgage. In addition, LHHAfinancing permits the buyer to fold the closing costs into the home purchase price—another strategy not allowed by the Historical Mortgage. Haitian home buyers arealso gaining from the underwriting reforms of mortgage innovation. The HistoricalMortgages’ underwriting requirements of a strong formal credit record, steady docu-mented employment at the same job/occupation, strict asset verification, and a middle-income “neighborhood standard” would disqualify almost all Haitians attempting toobtain a home loan in Little Haiti.

Mortgage innovation alone would not enable the Haitians to become homeowners.They need LHHA’s outreach to assuage deep apprehensions about dealing with insti-tutional lenders. LHHA’s intensive homeownership and credit counseling is also aprerequisite and helps Haitians become “bankable.” The layered subsidies packaged byLHHA are essential to the strategy, enabling very low income Haitians to purchase



an $80,000 home. We calculate a subsidy of about $30,000 for each LHHA unit (Lis-tokin et al. 1999). In addition, the intensive postpurchase support provided by LHHAcontributes to the zero mortgage delinquency rate observed thus far.

All of the above LHHA activities are examples of what we term the comprehensivepartnership strategy. That strategy, coupled with mortgage innovation, is contributingto the impressive recent gains in homeownership in the United States.



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