Credit Risk, Credit Scoring, and the Performance of Home Mortgages

Credit Risk, Credit Scoring, and

the Performance of Home Mortgages

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

and Glenn B. Canner, of the Board’s Division of

Research and Statistics, wrote this article. Jon

Matson provided research assistance.

Institutions involved in lending, including mortgage

lending, carefully assess credit risk, which is the

possibility that borrowers will fail to pay their loan

obligations as scheduled. The judgments of these

institutions affect the incidence of delinquency and

default, two important factors inﬂuencing proﬁtabil-

ity. To assess credit risk, lenders gather information

on a range of factors, including the current and past

ﬁnancial circumstances of the prospective borrower

and the nature and value of the property serving as

loan collateral. The precision with which credit risk

can be evaluated affects not only the proﬁtability of

loans that are originated but also the extent to which

applications for mortgages that would have been

proﬁtable are rejected. For these reasons, lenders con-

tinually search for better ways to assess credit risk.

This article examines the ways institutions

involved in mortgage lending assess credit risk and

how credit risk relates to loan performance.

The

discussion focuses mainly on the role of credit risk

assessment in the approval process rather than on its

effects on pricing. Although the market for home

purchase loans is characterized by some pricing of

credit risk (acceptance of below-standard risk quality

in exchange for a higher interest rate or higher fees),

mortgage applicants in general are either accepted or

rejected on the basis of whether they meet a lender’s

underwriting standards. The article draws on the

extensive literature that examines the performance of

home mortgages and the way that performance relates

to borrower, loan, and property characteristics.

An increasingly prominent tool used to facilitate

the assessment of credit risk in mortgage lending is

credit scoring based on credit history and other perti-

nent data, and the article presents new information

about the distribution of credit scores across popula-

tion groups and the way credit scores relate to the

performance of loans. In addition, the article takes a

special look at the performance of loans that were

made through nontraditional underwriting practices

and through ‘‘affordable’’ home lending programs.

DELINQUENCY AND DEFAULT

Delinquency occurs when a borrower fails to make a

scheduled payment on a loan. Since loan payments

are typically due monthly, the lending industry cus-

tomarily categorizes delinquent loans as either 30,

60, 90, or 120 or more days late depending on the

length of time the oldest unpaid loan payment has

been overdue.

Default occurs, technically, at the same time as

delinquency; that is, a loan is in default as soon as the

borrower misses a scheduled payment. In this article,

however, we reserve the term ‘‘default’’ for any of

the following four situations:

• A lender has been forced to foreclose on a mort-

gage to gain title to the property securing the loan.

• The borrower chooses to give the lender title to

the property ‘‘in lieu of foreclosure.’’

• The borrower sells the home and makes less than

full payment on the mortgage obligation.

• The lender agrees to renegotiate or modify the

terms of the loan and forgives some or all of the

delinquent principal and interest payments. Loan

modiﬁcations may take many forms including a

change in the interest rate on the loan, an extension of

the length of the loan, and an adjustment of the

principal balance due.

Because practices differ in the lending industry, not

all of the above situations are consistently recorded

as defaults by lenders. Moreover, the length of the

foreclosure process may vary considerably, affecting

1. Institutions that originate mortgages do not necessarily bear the

credit risk of the loans; the risk is often borne, at least in part, by a

mortgage insurer or by an institution that purchases mortgages. A

previous article in the Federal Reserve Bulletin assessed which insti-

tutions bear the risks of mortgage lending by examining the distribu-

tion of home loans originated in 1994 across the various institutions

participating in the mortgage market. See Glenn B. Canner and Wayne

Passmore, ‘‘Credit Risk and the Provision of Mortgages to Lower-

Income and Minority Homebuyers,’’ Federal Reserve Bulletin, vol. 81

(November 1995), pp. 989–1016.

the measured default rate. For these reasons, analyses

of default experiences can be difﬁcult and are often

based on only a subset of actual defaults. Delinquen-

cies, on the other hand, are recorded contemporane-

ously and generally on a more consistent basis.

Therefore, delinquency data may provide a good

source of information for analysis, particularly for

evaluating the performance of newly originated loans.

and for identifying underperforming loans that

require greater attention.

The number of borrowers who become delinquent

on their loans is much greater than the number of

actual defaults. In some cases, delinquency results

from a temporary disruption in income or an unex-

pected expense, such as might arise from a medical

emergency. Many of these borrowers are able to

catch up on missed payments (and any associated late

payment fees) once their ﬁnancial circumstances

improve. In other cases, lenders work with borrowers

to establish a repayment plan to bring payments back

on schedule.

Delinquencies, particularly serious ones, are often

resolved when the borrower sells the property and

uses the proceeds to pay off the loan. Even when the

proceeds of the sale are insufﬁcient to fully repay the

mortgage obligation, the lender may accept a partial

payment to avoid foreclosure. Foreclosure is usually

a costly process. Lenders face a variety of expenses,

including interest accrued from the time of delin-

quency through foreclosure; legal expenses; costs to

maintain the property; expenses associated with the

sale of the property; and the loss that arises if the

foreclosed property sells for less than the outstanding

balance on the loan. Because foreclosure is so costly

to lenders, they may encourage delinquent borrowers

to sell their homes and avoid foreclosure even if the

proceeds of the sale would not cover the entire

amount owed on the loan.

This alternative is attrac-

tive to many borrowers because having a foreclosure

recorded on their credit histories is particularly

derogatory and will usually be a signiﬁcant hindrance

in their future efforts to obtain credit.

Because default is costly, the interest rates lenders

charge incorporate a risk premium. To the extent that

the causes of default are not well understood, lenders

may charge a higher average price for mortgage

credit to reﬂect this uncertainty. Alternatively, lend-

ers may respond to this uncertainty by restricting

credit to only the most creditworthy borrowers. By

better distinguishing between applicants that are

likely to perform well on their loans from those that

are less likely to do so, lenders can ensure wider

availability of mortgages to borrowers at prices that

better reﬂect underlying risks.

Default also imposes great costs both on the bor-

rowers involved in the process and on society in

general. For borrowers, default ordinarily results in a

lower credit rating and reduced access to credit in the

future, a loss of assets, and the costs of ﬁnding and

moving to a new home. When geographically con-

centrated, defaults can also have a pronounced social

effect because they lower local property values,

reduce the incentives to invest in and maintain the

homes in the affected neighborhoods, increase the

risk of lending in those neighborhoods, and thus

reduce the availability of credit there.

THEORETICAL AND EMPIRICAL DETERMINANTS

CREDIT RISK

Gaining a greater understanding of the factors that

determine mortgage loan delinquency and default has

been an objective of mortgage lenders, policy mak-

ers, and academics for decades. A better understand-

ing of these relationships holds the promise that

lenders can more accurately gauge the credit risk

posed by different applicants and increase the safety

and proﬁtability of mortgage lending.

An extensive literature regarding the theoretical

and empirical determinants of mortgage credit risk

has developed over the past three decades.

This

literature emphasizes the important roles of equity in

the home and vulnerability to so-called triggering

events in determining the incidence of delinquency

and default. These studies have enhanced our under-

standing of the determinants of credit risk and have

established a better foundation for consistent and

effective mortgage lending.

Theoretical Determinants

of Mortgage Loan Performance

Most models of mortgage loan performance empha-

size the role of the borrower’s equity in the home in

the decision to default. So long as the market value of

2. For an assessment of the factors that inﬂuence the length of time

lenders are willing to allow mortgage loans to remain delinquent

before foreclosing, see Thomas M. Springer and Neil G. Waller, ‘‘A

New Look at Forbearance,’’ Mortgage Banking, December 1995,

pp. 81–84. For a discussion of the reduced losses to lenders associated

with alternatives to foreclosure, see John Bancroft, ‘‘Freddie Mac

Pushes Alternatives to Foreclosures,’’ Real Estate Finance Today,

November 6, 1995, pp. 12 and 18.

3. See Roberto G. Quercia and Michael A. Stegman, ‘‘Residential

Mortgage Default: A Review of the Literature,’’ Journal of Housing

Research, vol. 3, no. 1 (1993), pp. 341–79.

622 Federal Reserve Bulletin July 1996

the home (after accounting for sales expenses and

related costs) exceeds the market value of the mort-

gage, the borrower has a ﬁnancial incentive to sell the

property to extract the equity rather than default.

‘‘Option-based’’ theories provide a framework for

understanding the relationship between equity and

loan performance; these theories view the amount of

equity accumulated in the property as the key deter-

minant of whether a borrower will default. Within

this framework, mortgage default is viewed as a put

option, in which the borrower has the right (option)

to transfer ownership of (put) the home to the lender

(through foreclosure or voluntarily) to retire the out-

standing balance on the loan. Borrowers will be

increasingly likely to exercise this option the further

the market value of the house falls below the value of

the mortgage. However, because of high transaction

and other costs (for example, moving expenses and

damage to the borrower’s credit rating resulting from

default), few borrowers would be expected to exer-

cise this option ‘‘ruthlessly’’ (that is, default as soon

as equity falls below zero).

Option-based theories of loan performance identify

a number of equity-related factors likely to inﬂuence

default rates. Included among these are the initial

loan-to-value ratio (the ratio of the loan amount to the

value of the property), which determines the amount

of equity at the time of loan origination; current and

expected future rates of home price appreciation,

which determine the direction, speed, and size of

changes in equity levels; the age of the loan, because

equity accumulates as payments on a mortgage

reduce the amount owed; and the term of the mort-

gage, because loans of shorter duration are amortized

more quickly. In addition, current mortgage interest

rates (relative to the rate on an outstanding loan)

inﬂuence the likelihood of default by affecting the

value of the mortgage to a borrower. For example, a

mortgage interest rate below current market levels is

a disincentive for the borrower to default because a

new mortgage would carry a higher rate.

While option-based theories emphasize the role of

equity in the home in determining loan performance,

other theories of loan performance additionally

emphasize the ﬁnancial footing of borrowers and

their corresponding vulnerability to signiﬁcant

adverse changes in their ﬁnancial or personal circum-

stances, referred to as ‘‘triggering events.’’ In this

view, both negative equity and a triggering event

would be associated with most defaults. A triggering

event alone would not ordinarily cause a default

when a borrower has equity in a home; rather, the

borrower would sell the property and fully repay the

loan to keep the equity (net of transactions costs) and

avoid the adverse consequences of a default. On the

other hand, in the absence of a triggering event, a

borrower would not be expected to exercise the

default option ruthlessly because of the large (trans-

action and reputation) costs the borrower would bear.

A default, in this latter case, would occur only if, in

the owner’s view, the property’s value had declined

signiﬁcantly and prospects for its near-term recovery

were poor.

Analysts who emphasize the role of triggering

events focus on adversities such as reductions in

income brought about by a period of unemployment.

Other events that may lead to repayment problems

include bouts of illness, which may result in both

large expenses and a disruption in income, and

changes in family circumstances, particularly divorce.

Measures of the borrower’s vulnerability to such

events include ratios of monthly debt payment to

income; the level of ﬁnancial reserves available to the

borrower; measures of earnings stability, such as the

borrower’s employment history; and the borrower’s

credit history, which in part reﬂects the borrower’s

ability and willingness to manage debt payments in

the face of changing circumstances.

Option-based and triggering-event theories suggest

different relationships between delinquency and

default. In the options-based view, delinquency

occurs only as a precursor to default and would be

evident only among borrowers with substantial nega-

tive equity. Triggering-event theories view delinquen-

cies as related to an event and not necessarily to the

borrower’s level of equity. In this view, delinquen-

cies are not explicitly linked to default but can lead to

default if the triggering event is sufﬁciently severe

and the borrower has substantial negative equity in

the home.

Empirical Evidence on the Determinants

of Mortgage Loan Performance

Empirical investigations have found that both equity

and adverse changes in borrowers’ circumstances are

related to mortgage loan performance, as predicted

by theory. Studies consistently ﬁnd that the level of

equity (whether proxied by the loan-to-value ratio at

4. The value of the mortgage is not determined solely by the

principal balance owed. It also depends on the relationship between

the rate of interest on the loan and the current market rate for

mortgages of similar duration.

5. In some states, lenders have the statutory right to seek deﬁciency

judgments against a borrower to try to recover losses incurred as a

consequence of default. Such statutory provisions tend to reduce the

ruthless exercise of the default option. In many instances, however,

borrowers do not have other assets available to cure deﬁciencies.

Credit Risk, Credit Scoring, and the Performance of Home Mortgages 623

the time of origination or by a contemporaneous

measure of the ratio) is closely related to both the

likelihood of default and the size of the loss in the

event of default.

A recent analysis of the performance of nearly

425,000 loans originated over the 1975–83 period

illustrates these relationships. The analysis found that

conventional mortgages with loan-to-value ratios at

origination in the range of 91 percent to 95 percent

default more than twice as frequently as loans with

loan-to-value ratios in the range of 81 percent to

90 percent and more than ﬁve times as often as loans

with loan-to-value ratios in the range of 71 percent to

80 percent (table 1). Loss severity (that is, loss to the

lender measured as a proportion of the original loan

balance) is about 40 percent higher for loans with

original loan-to-value ratios in the range of 91 per-

cent to 95 percent than it is with loans with loan-to-

value ratios in the range of 81 percent to 90 percent.

Additional evidence regarding the relationship

between loan-to-value ratios at time of origination

and mortgage default is provided in an analysis con-

ducted by Duff & Phelps Credit Rating Company.

They found that among thirty-year ﬁxed rate mort-

gages, those with a 90 percent loan-to-value ratio are

230 percent more likely to default than loans with an

80 percent loan-to-value ratio and that loans with a

95 percent loan-to-value ratio are 350 percent more

likely to default than a loan with an 80 percent

loan-to-value ratio.

Research also ﬁnds that the likelihood of default is

positively related to loan-to-value ratios among

single-family loans insured by the Federal Housing

Administration (FHA). The default rate among FHA-

insured loans with down payments of 3 percent or

less is approximately twice as high as the rate among

those with down payments of 10 percent to 15 per-

cent, and ﬁve times as high as the rate among loans

with down payments of 25 percent or more.

While research suggests that negative equity is a

necessary condition for default, it also suggests that

negative equity is not a sufﬁcient condition (most

loans with negative equity do not default).

In line

with the triggering-event explanations, measures of a

borrower’s ability to pay also explain default and

delinquency, although delinquency relationships are

less well documented. Default rates have been found

to decrease generally with increases in levels of

wealth and liquid assets. Further, default likelihoods

are closely linked to measures of income stability.

Default rates are generally higher for the self-

employed and for those with higher percentages of

nonsalary income and lower for those with longer

employment tenures. Perhaps surprisingly, after con-

trolling for other factors, the initial ratio of debt

payment to income has been found to be, at best, only

weakly related to the likelihood of default.

Although a borrower’s credit history may play an

important role in determining mortgage loan perform-

ance, few published studies have been able to incor-

porate such information in their analyses. Relevant

credit history data are often difﬁcult to obtain and

hard to quantify. The available evidence, however,

indicates that loans made to borrowers with ﬂawed

credit histories (those who have had difﬁculties meet-

ing scheduled payments on past loans) default or

6. See Robert Van Order and Peter Zorn, ‘‘Income, Location, and

Default: Some Implications for Community Lending,’’ paper pre-

sented at the Conference on Housing and Economics, Ohio State

University, Columbus, July 1995. Further, a number of studies have

found that neighborhood and property conditions, which ultimately

affect property values and thus equity, are signiﬁcant factors for

mortgage performance. See, for example, James R. Barth, Joseph J.

Cordes, and Anthony M.J. Yezer, ‘‘Financial Institution Regulations,

Redlining, and Mortgage Markets,’’ in The Regulation of Financial

Institutions, Conference Series 21, Federal Reserve Bank of Boston

(April 1980), pp. 101–43.

7. ‘‘The State of the Private Mortgage Insurance Industry,’’ Special

Report, Duff & Phelps Credit Rating Company, December 1995.

8. See ‘‘An Actuarial Review of the Federal Housing Administra-

tion’s Mutual Mortgage Insurance Fund,’’ prepared by Price

Waterhouse for the U.S. Department of Housing and Urban Develop-

ment, June 6, 1990, p. 12.

9. See Robert Van Order and Ann B. Schnare, ‘‘Finding Common

Ground,’’ Secondary Mortgage Markets, vol. 11 (Winter 1994),

pp. 15–19.

10. See Quercia and Stegman, ‘‘Residential Mortgage Default’’;

and James A. Berkovec, Glenn B. Canner, Stuart A. Gabriel, and

Timothy H. Hannan, ‘‘Race, Redlining, and Residential Mortgage

Loan Performance,’’ Journal of Real Estate Finance and Economics,

vol. 9 (November 1993), pp. 263–94; and Van Order and Zorn,

‘‘Income, Location, and Default.’’

1. Proportion of selected mortgages that defaulted

by year-end 1992 and resulting severity of loss,

by selected loan-to-value ratio ranges

Percent

Performance measure

Loan-to-value ratio (percent)

All

10–70 71–80 81–90 91–95

Proportion defaulted .. . .24 1.11 2.74 6.20 2.16

Average loss severity . . 22.3 29.2 34.4 47.9 39.2

Note. Mortgages were originated during the 1975–83 period and purchased

by Freddie Mac. Defaulted loans are those on which Freddie Mac acquired the

property through foreclosure. Loan-to-value ratio is the original loan amount

divided by the value of the property at origination. Loss severity is the total loss

before mortgage insurance payouts (if any) resulting from foreclosure (including

interest and transaction costs) divided by the mortgage balance.

Source. Robert Van Order and Peter Zorn, ‘‘Income, Location and Default:

Some Implications for Community Lending,’’ paper presented at the Conference

on Housing and Economics, Ohio State University, Columbus, July 1995.

624 Federal Reserve Bulletin July 1996

become delinquent more often than loans made to

borrowers with good credit histories.

The relation-

ship between credit history and loan performance is

discussed further in the section on credit scoring.

On balance, defaults likely occur as a result of a

combination of factors. Almost uniformly, studies

indicate that the level of equity is a robust predictor

of default. Studies also demonstrate a signiﬁcant rela-

tionship between mortgage performance and mea-

sures of vulnerability to triggering events.

MORTGAGE UNDERWRITING AND

RISK MITIGATION

Institutions that bear the credit risk of mortgage lend-

ing mitigate that risk by screening borrowers and

by sharing risk with others. Screening of prospective

borrowers is accomplished primarily through the

underwriting process, whereby information needed to

assess credit risk is collected, veriﬁed, and evaluated.

Risk-sharing may take a number of forms. First,

and most important, lenders share the risk of default

with the borrower by requiring a down payment and

establishing a schedule of payments that will fully

amortize the loan over a set period of time. Second,

lenders often share the credit risk of a loan with

either a private mortgage insurer or a government

agency such as the FHA or the Department of Veter-

ans Affairs (VA). Finally, lenders may sell a loan to

another party under arrangements that partly or fully

transfer the credit risk. The institutions that share or

assume the risk of lending do not solely rely on the

screening done by mortgage originators but also

make independent assessments.

The Underwriting Practices

of Mortgage Lenders

Lenders pursue different business strategies, and their

underwriting practices and standards reﬂect those

strategies. Some lenders choose to underwrite mort-

gages more strictly and thus limit their exposure to

losses. Others accept more credit risk but also price

for this risk, attempting to recoup higher expected

losses by charging higher fees or interest rates on

riskier mortgages. Still others may choose to spe-

cialize in ﬁnancing certain types of properties or

borrowers.

In assessing credit risk, lenders consider the size of

the proposed down payment and the value of the

collateral as determined by a property appraisal,

which together determine the loan-to-value ratio.

Lenders also evaluate the capacity of the prospective

borrower to meet scheduled debt payments and to

provide the initial funds required to close the loan. In

so doing, lenders rely on many of the same factors

that researchers have found to be important predic-

tors of loan performance, including borrower sources

of income; employment history (such as measures of

employment stability and prospects for income

growth); ratios of debt payment to income; and asset

holdings, particularly the amount of liquid assets

available to meet down-payment, closing cost, and

cash reserve requirements.

In addition, lenders evaluate the credit history of

prospective borrowers as an indicator of their ﬁnan-

cial stability, ability to manage credit, and willing-

ness to make timely payments. Credit histories are

often complex and consist of many items, including

the number and age of credit accounts of different

types, the number of recent inquiries to the credit ﬁle,

account activity patterns, the incidence and severity

of payment problems, and the length of time since

any payment problems occurred.

Some applicants fall well within the underwriting

guidelines established by lenders, whereas others fall

far below the standards. The decision to either

approve or deny loan requests from such applicants is

generally straightforward. Frequently, however, the

decision is less clear-cut. For example, an applicant

may fail to meet one of many established underwrit-

ing guidelines, such as a satisfactory record of pay-

ments on past debts.

Lending policies generally allow for ﬂexibility in

implementation so that applicants may offset weak-

ness in one factor with strength in others. For exam-

ple, even if an applicant’s ratio of debt payment to

income exceeds a lender’s established guidelines, the

11. See, for example, Wilson Thompson, ‘‘A Model of FHA’s

Origination Process and How it Relates to Default and Non-Default,’’

Working Paper, Department of Housing and Urban Development

(1980); and Gordon H. Steinbach, ‘‘Ready to Make the Grade,’’

Mortgage Banking (June 1995), pp. 36–42.

12. Most lenders require borrowers to have cash reserves sufﬁcient

to cover two months of mortgage payments (including principal,

interest, and tax and insurance escrows) at the time of closing. This

reserve may provide a cushion should the borrower suffer a temporary

ﬁnancial setback, and it is a signal to the lender that the borrower has

the discipline to accumulate savings.

13. For example, a study of mortgage lending in Boston found that

more than 80 percent of the applicants for home purchase loans

appeared either to have a weakness in their credit histories or to fail to

meet some other underwriting standard. See Alicia H. Munnell, Lynn

E. Browne, James McEneaney, and Geoffrey M.B. Tootell, ‘‘Mort-

gage Lending in Boston: Interpreting HMDA Data,’’ American Eco-

nomic Review, vol. 86 (March 1996), pp. 25–53.

Credit Risk, Credit Scoring, and the Performance of Home Mortgages 625

lender may approve the loan if the applicant exhibits

very stable income and an excellent credit history.

Similarly, a lender might consider a large down pay-

ment to be a compensating factor offsetting weakness

in some other area. Lenders will generally weigh all

the factors and in some cases seek additional informa-

tion in attempting to make a more precise evaluation

of credit risk.

Risk Sharing

Originators of mortgage loans typically share or

transfer risk by requiring borrowers to purchase mort-

gage insurance or by selling mortgages to secondary-

market institutions. For most mortgages, all or a

signiﬁcant portion of the credit risk is borne by a

party other than the originator of the loan. For

instance, credit risk was either shared or transferred

on nearly three-fourths of all the home purchase

loans originated in 1994.

Mortgage lenders generally require a down pay-

ment of at least 20 percent of the appraised value of a

home, unless the mortgage is backed by a type of

insurance, paid for by the borrower, known as mort-

gage guarantee insurance. Mortgage insurance for

low-down-payment loans is available from the fed-

eral government, primarily through programs admin-

istered by the FHA and the VA and from private

mortgage insurance (PMI) companies.

When a loan is backed by mortgage insurance,

much of the credit risk is transferred to the insurer.

Should the borrower default, the insurer will reim-

burse the lender for the losses resulting from default,

up to certain limits. Mortgage insurers, like loan

originators, establish underwriting standards that

determine which loans they will insure and how

much credit risk they will bear. Lenders may encour-

age applicants seeking mortgages with low down

payments and those posing higher risks to apply for

government-backed loans rather than conventional

loans backed by PMI because the greater depth of

insurance coverage provided by the government on

such loans affords the lender greater protection in the

event of default.

Secondary-market institutions buy and sell billions

of dollars of mortgages and securities backed by

mortgages each year. Secondary-market institutions

promulgate the underwriting guidelines that loans

must meet to be eligible for purchase or securitiza-

tion. Three government-sponsored enterprises (GSEs)

dominate secondary-market activity—the Federal

National Mortgage Association (Fannie Mae), the

Federal Home Loan Mortgage Corporation (Freddie

Mac), and the Government National Mortgage Asso-

ciation (Ginnie Mae). Fannie Mae and Freddie Mac

mainly buy conventional mortgages, holding some in

portfolio and converting others into securities that are

sold to investors. Ginnie Mae does not purchase

loans but guarantees the timely payment of interest

and principal for privately issued securities backed

by mortgages insured by the FHA or VA. Various

non-GSE institutions, including commercial banks,

savings associations, insurance companies, and pen-

sion funds are also active purchasers of mortgages.

Mortgage insurers and secondary-market institu-

tions generally consider the same set of factors origi-

nators review when assessing credit risk. The under-

writing standards applied, however, will differ across

institutions in accordance with their various business

strategies and tolerance for risk. Private mortgage

insurers, for example, while backing loans with high

loan-to-value ratios, generally require borrowers to

make larger down payments and pay a larger share of

the closing costs than do the FHA and VA.

Sometimes mortgage originators do not share

credit risk with other institutions. Unlike mortgage

insurers and secondary-market institutions, which are

generally remote from borrowers, institutions that

both originate and bear the credit risk of mortgages

(known as portfolio lenders) are typically located in

the communities where they extend credit and have

numerous other ﬁnancial relationships with their

communities. For these reasons, portfolio lenders

may have better information about local economic

conditions and the risks posed by individual borrow-

ers, which, in turn, may enable them to better mea-

sure and mitigate the risks associated with mortgage

lending. With better information to gauge credit risk,

portfolio lenders may be able to proﬁtably originate

some loans that do not meet the underwriting stan-

dards established by secondary-market institutions

and PMI companies.

CREDIT SCORING AND THE

MORTGAGE LENDING PROCESS

Mortgage lending institutions establish guidelines for

underwriters to follow when evaluating applications

14. See Canner and Passmore, ‘‘Credit Risk and the Provision of

Mortgages,’’ p. 998.

15. See Glenn B. Canner, Wayne Passmore, and Monisha Mittal,

‘‘Private Mortgage Insurance,’’ Federal Reserve Bulletin, vol. 80

(October 1994), pp. 883–99.

626 Federal Reserve Bulletin July 1996

for credit, but they also rely heavily on the experi-

ence and judgment of underwriters when assessing

credit risk. Relying on subjective analysis has some

important limitations, however. Loan ofﬁcers differ in

their experience and in their views regarding the

relationships between risk and speciﬁc credit charac-

teristics of applicants. Consequently, an institution

cannot be sure that its underwriters are approving all

applications that have risk proﬁles consistent with the

objectives of the institution. In addition, because of

the numerous and often complex factors mortgage

underwriters need to consider, subjective underwrit-

ing is time-consuming and costly.

To facilitate the mortgage underwriting process,

reduce costs, and promote consistency, ‘‘credit scor-

ing’’ models have been developed that numerically

weigh or ‘‘score’’ some or all of the factors consid-

ered in the underwriting process and provide an indi-

cation of the relative risk posed by each application.

In principle, a well-constructed credit scoring system

holds the promise of increasing the speed, accuracy,

and consistency of the credit evaluation process while

reducing costs. Thus, credit scoring can reduce risk

by helping lenders weed out applicants posing exces-

sive risk and can also increase the volume of loans by

better identifying creditworthy applicants.

Generically, scoring is a process that uses recorded

information about individuals and their loan requests

to predict, in a quantiﬁable and consistent manner,

their future performance regarding debt repayment.

Scores represent the estimated relationship between

information obtained from credit bureau reports or

loan applications and the likelihood of poor loan

performance, most often measured as delinquency or

default (see box ‘‘Developing a Credit History Scor-

ing System’’).

Scoring has been used to assess applications for

motor vehicle loans, credit cards, and other types

of consumer credit for decades.

Technological

advances in information processing and risk analysis

combined with competitive pressures to process

applications more quickly and efﬁciently are pushing

the lending industry to incorporate scoring in the

mortgage underwriting process.

Mortgage lenders ordinarily consider two kinds of

scores: those that are based primarily on the credit

histories of individuals and those that weigh credit

history as well as the other factors considered in the

underwriting process. The former will be referred to

here as ‘‘credit history’’ scores and the latter as

‘‘application’’ scores. Because they reﬂect the wide

range of factors considered in the evaluation of credit

risk, application scores are more comprehensive than

credit history scores. The credit history score is, then,

a single element to be weighed along with the other

factors in determining the total application score.

Credit History Scores

The difﬁculties in assessing the often complex infor-

mation about individuals’ past and current experience

with credit has helped motivate the adoption of scor-

ing methods for interpreting credit history. A credit

history score represents the estimated relationship

between information on the credit histories of indi-

viduals contained in credit bureau reports and the

likelihood of poor loan performance. In credit history

scoring systems, prospective applicants receive a

numerical score based on their individual credit his-

tory information; the score reﬂects the historic perfor-

mance of loans extended to individuals with similar

characteristics. Individuals with identical credit

scores may have received them for different reasons,

but within the context of the credit scoring index,

they are assessed to have equal likelihoods of the

predicted behavior, that is, they are considered to

pose the same credit risk.

Credit history scores can supplement or even

replace the traditional subjective assessment of credit

history with a quantitative measure summarizing the

pertinent information in an applicant’s credit report.

Adding a statistically derived measure of the credit

risk associated with a given credit history may allow

underwriters to better and more quickly assess the

strengths and weaknesses of applications.

Each of the three national credit bureaus, Equifax,

TRW, and Trans Union, make available credit history

scores—developed by Fair, Isaac and Company, Inc.

(FICO)—based on information contained in each of

the credit bureau’s ﬁles. These generic credit history

scores—the Equifax Beacon, the TRW-FICO, and

the Trans Union Empirica scores—are made avail-

able to help lenders assess risk on a wide variety of

loans. In addition, credit history scores tailored to the

mortgage market (mortgage credit history scores) are

now available; these scores are speciﬁcally designed

to assess the credit history risk of mortgage loans.

16. See Robert A. Eisenbeis, ‘‘Problems in Applying Discriminant

Analysis in Credit Scoring Models,’’ Board of Governors of the

Federal Reserve System, Staff Economic Studies (1977); and Edward

M. Lewis, An Introduction to Credit Scoring (San Rafael, Calif.:

Athena Press, 1990).

17. See ‘‘Equifax, Inc. Develops Mortgage Credit Scoring Sys-

tem,’’ National Mortgage News, June 13, 1994, p. 25. A number of

‘‘custom’’ credit history scoring models have been developed for

speciﬁc lenders to assess credit risk for speciﬁc loan products.

Credit Risk, Credit Scoring, and the Performance of Home Mortgages 627

Recent events have ensured that credit history

scores will be used much more often in the mort-

gage lending process than they have been in the

past. Most prominently, letters issued by Fannie

Mae and Freddie Mac in 1995 strongly encourage

the thousands of lenders from whom they purchase

loans to consider the Beacon, TRW-FICO, and

Empirica credit history scores in their loan

underwriting.

Application Scores

Based on all information relevant to a loan applica-

tion, application scores are most often used to deter-

18. See Fannie Mae Letter LL09-95 to all Fannie Mae lenders from

Robert J. Engelstad, ‘‘Measuring Credit Risk: Borrower Credit Scores

and Lender Proﬁles,’’ October 24, 1995; and Freddie Mac Industry

Letter from Michael K. Stamper, ‘‘The Predictive Power of Selected

Credit Scores,’’ July 11, 1995. As an alternative, Freddie Mac and

Fannie Mae recommend that, when underwriting loans, lenders con-

sider credit history scores that are calculated to predict bankruptcy.

The generic bankruptcy scores are the Equifax Delinquency Alert

System, Trans Union’s Delphi score, and the TRW-MDS score. Also

see Marshall Taylor, ‘‘Secondary Markets Explain Credit Scores,’’

Real Estate Finance Today, April 1, 1996, p. 16.

Developing a Credit History Scoring System

Developing a credit history scoring system requires infor-

mation about the experiences of individuals with credit.

Information is ordinarily drawn from credit account ﬁles

maintained by credit bureaus and sometimes from records

maintained by lending institutions. The credit account ﬁles

of individuals are segregated into groups based on measures

of loan performance. Ordinarily, the credit account ﬁles are

segregated into two distinct categories: those in which debts

have not been paid as scheduled as of a speciﬁed date or

during a speciﬁed time period (referred to here as ‘‘bad’’

accounts) and the rest (‘‘good’’ accounts). Bad credit

accounts can be deﬁned in various ways depending on the

severity of observed credit difﬁculties. For example, bad

accounts might include any ﬁle with at least one thirty-day

delinquency within the past year, or they may be limited to

accounts that have had more serious delinquencies.

Having sorted the ﬁles according to performance as of a

speciﬁed date or during a speciﬁed period, the analyst then

focuses on information in the credit ﬁles from a preceding

time period that might have predicted the performance

outcome. Detailed information drawn from each credit ﬁle

is then recorded for statistical analysis. The selection of

speciﬁc items is often based on discussions with loan under-

writers plus a preliminary (bivariate) statistical analysis of

the relationship between individual credit factors and loan

performance. The information recorded pertains primarily

to the individual’s experience with credit.

The analyst then uses multivariate statistical analysis of

the recorded information to identify which set of character-

istics is most useful in identifying borrowers who are likely

to meet their scheduled payments and those who are not.

The statistical analysis provides weights (or scores) for each

factor, ranking its relative importance in predicting into

which group an individual will fall. Applying these weights

to the characteristics of individual accounts yields a total

score for each individual. Most credit scoring systems that

1. Federal law prohibits lenders from considering certain factors such as

gender, race, or ethnicity in making credit decisions. Consequently, these

factors are not used in constructing credit scoring models, and age and

marital status can be considered only under certain circumstances.

are widely used have adopted a scale with a range of scores

between 300 and 900, with higher scores corresponding to

lower credit risk.

Both the good accounts and the bad accounts will have

ﬁles with a wide range of scores. However, if the credit

scoring system is predictive of performance, good accounts

will have the highest percentage of high scores and bad

accounts likewise will have the highest percentage of low

scores. The predictive power or performance of a scoring

model is measurable, and the developer of the model looks

for the combination of attributes of the borrower’s credit

history that will maximize the score’s predictive power.

The distribution of total scores for individuals falling into

the good or bad categories can be described graphically (see

diagram). As shown, the good accounts tend to cluster

around a higher average score than do the bad accounts. To

operate a scoring system for credit underwriting, a lender

must select a cutoff score (such as 620) that can be used to

distinguish acceptable from unacceptable risks. Regardless

of the cutoff score selected, some customers with bad scores

will be offered credit because of offsetting factors, and

some customers with good scores will be denied credit, also

because of offsetting factors.

Distribution of credit scores of good and bad accounts

Percentage of accounts

Cutoff score

Bad accounts

620 Credit score

Good accounts

628 Federal Reserve Bulletin July 1996

mine which credit requests are clearly acceptable

under established underwriting guidelines and which

need further review. The use of application scores

differs among the participants in the mortgage mar-

ket: Loan originators generally use application scores

to identify applications eligible for streamlined

underwriting; secondary-market institutions use them

to facilitate loan purchases; and PMI companies

use them to help screen applications for mortgage

insurance.

As a screen for streamlined underwriting, a thresh-

old score corresponding to low credit risk is estab-

lished by the lender. Applicants with scores within

the low-risk range generally would be eligible for a

streamlined review that focuses primarily on veriﬁca-

tion of reported information and evaluation of the

collateral. Streamlined underwriting allows those

making credit decisions to reduce costs by enabling

underwriters to spend less time on the low-risk appli-

cations and more time on those applications that

involve more complexity and potential risk.

Impor-

tantly, streamlined underwriting also beneﬁts many

customers by shortening the amount of time between

the date of application and the credit decision.

Secondary-market institutions also use application

scores. Freddie Mac and Fannie Mae, for instance,

have developed application scoring systems that

indicate to the lender whether a prospective loan is

clearly eligible for sale to these institutions or

whether the lender will need to show that compensat-

ing factors exist that make the loan an acceptable

credit risk.

Private mortgage insurance companies use applica-

tion scoring systems to quickly identify those pro-

spective loans that clearly meet the underwriting

standards of the insurer. Loan applications that fail

the automated screen are reviewed by an underwriter

to determine whether compensating factors are

present that would make the loan insurable. Mort-

gage Guarantee Insurance Corporation (MGIC), for

example, reports that about 30 percent of the applica-

tions they receive for mortgage insurance are

approved through their automated application sys-

tem; the remaining applications are referred to under-

writers for closer review.

Most credit history and application scoring sys-

tems are proprietary, and the speciﬁc factors used and

the risk weights assigned to these factors in establish-

ing scores are not generally available to the public.

As a consequence, scoring systems have a ‘‘black

box’’ aspect to them. Nonetheless, most scoring sys-

tems share a number of elements. For example, most

credit history scoring systems consider records of

bankruptcy, current and historic ninety-day delin-

quencies, and the number of credit lines. Most

mortgage application scoring systems additionally

consider factors such as the loan-to-value ratio, the

ratio of debt payment to income, and measures of

employment stability. However, the risk weights

assigned to these factors vary from system to system.

Other Uses of Credit Scoring

Credit history scores and application scores have uses

other than in the loan underwriting process. To moni-

tor the quality of their portfolio and to determine the

appropriate level of reserves to set aside for losses,

lenders may periodically obtain credit scores for bor-

rowers with outstanding loans. Similarly, institutions

can use credit scores to evaluate the quality and value

of mortgages they are considering for sale. For exam-

ple, credit scores can help identify the credit risk of

seasoned loans and help determine the appropriate

grade (risk) pool into which individual loans should

be placed for sale to the secondary market.

Lenders may use credit scores to differentiate risk

categories of loans for pricing decisions. Rather than

reject higher-risk loans for origination or purchase,

the lender may decide to price the risk by requiring

an interest rate premium on those loans with higher

predicted probabilities of default. The use of credit

scores can also help with the collection and loss

mitigation process by, for example, allowing lenders

to concentrate staff resources on borrowers whose

credit scores indicate greater risk of delinquency.

Finally, lenders can use credit scores to facilitate

strategic planning decisions. For instance, lenders

concerned about possible attrition in their loan port-

folio due to competition for reﬁnancings may offer a

new loan to those current borrowers whose credit

scores indicate that they would be most attractive to

potential competitors.

Limitations of Scoring

Although credit scoring can reduce costs and bring

more consistency to the underwriting process, its

reliability depends upon the accuracy, completeness,

and timeliness of the information used to generate the

19. See, for example, Janet Sonntag, ‘‘The Debate Over Credit

Scoring,’’ Mortgage Banking (November 1995), pp. 46–52.

20. The automated underwriting systems developed by Freddie

Mac and Fannie Mae are known respectively as ‘‘Loan Prospector’’

and ‘‘Desktop Underwriter.’’

21. See Jim Kunkel, ‘‘The Risks of Mortgage Automation,’’ Mort-

gage Banking (December 1995), pp. 45–57.

Credit Risk, Credit Scoring, and the Performance of Home Mortgages 629

scores. For example, credit scores based on erroneous

or seriously incomplete credit report information are

not likely to accurately measure the risk posed by an

individual applicant and may lead to unwarranted

actions on an application (see box ‘‘How To Obtain

Your Credit Report and What To Do To Correct

Errors in the Report’’).

Also, concerns have been expressed that credit

scores may not accurately gauge the creditworthiness

of individuals whose experiences differ substantially

from those on whom the index is based. If the base-

line population used to generate the scoring index is

not sufﬁciently diverse, then scores may lack predic-

tive power for the underrepresented segments of the

overall population. For example, rent, utility, and

other nonstandard payment histories, which are often

considered important for low-income populations, are

frequently left out of scoring models. Thus, scores for

these populations may not reliably assess individual

risk.

Another set of concerns surrounds the use of credit

scores more generally in the underwriting process.

Lenders relying too heavily on scores might not give

adequate consideration to special circumstances, such

as a recent illness, that might mitigate a low score.

Further, scores may lack predictive power if the

underlying model used to generate the scores does

not reﬂect current relationships between risk charac-

teristics and measures of loan performance. Builders

of credit scoring models report that model perfor-

mance deteriorates over time. Thus, periodic valida-

tion may be necessary to ensure that scoring models

retain their accuracy.

Credit scoring and its application to mortgage mar-

kets are evolving. Credit history scores, for example,

traditionally have been based on the payment perfor-

mance of a cross-section of consumers who have

used credit, not all of whom have incurred mortgage

debt. But consumer behavior with respect to mort-

gage debt may differ from behavior with respect to

consumer debt. Consumers facing ﬁnancial difﬁcul-

ties may, for instance, choose to pay their mortgage

obligations ﬁrst and postpone payments on other

debts. For this reason, one might expect that a credit

scoring model developed speciﬁcally for the mort-

gage market would provide more accurate predic-

tions of future mortgage payment performance than a

generic credit history score, even before the borrower

has obtained a mortgage.

The development of models for credit history

scores and application scores based on the payment

performance of mortgage holders has historically

been hampered by incomplete information about

which consumers have mortgages and about other

characteristics of these consumers. Also, many indi-

vidual lenders have made too few mortgages to

develop a sound mortgage credit scoring model.

Recently, however, developers of scoring models

have integrated information from several sources to

develop both mortgage credit history scores and

mortgage application scores.

How To Obtain Your Credit Report and

What To Do To Correct Errors in the Report

In 1970 the Congress enacted the Fair Credit Reporting

Act (FCRA) to give consumers speciﬁc rights in dealing

with credit bureaus. The FCRA requires credit bureaus

to furnish a correct and complete consumer credit report

to businesses or persons to use in evaluating consumer

applications for credit, insurance, a job, or other legiti-

mate business need in connection with a transaction

involving the consumer.

Consumers can obtain a copy of their credit ﬁle from

a credit bureau. A reasonable fee may be charged for the

report. If a consumer has been denied credit, insurance,

or employment because of information that was supplied

by a credit bureau, the FCRA requires that the recipient

of the report give the consumer the name and address of

the credit bureau that supplied the information. The

consumer then has the right to obtain the report free of

charge if requested within thirty days of receiving a

notice of denial. Reports can be requested by phone at

the following numbers: Equifax—1-800-685-1111;

Trans Union—1-800-916-8800; and TRW—1-800-682-

7654.

Consumers have the right to dispute the information

in their credit ﬁles if they believe that their credit reports

contain errors or are incomplete. When a credit bureau

receives a complaint of this nature, it must investigate

and record the current status of the disputed items within

a reasonable period of time. If the credit bureau cannot

verify a disputed item, it must delete it from the ﬁle. The

credit bureau is required to correct any information

conﬁrmed to be erroneous and to add any information

that has been omitted.

If the credit bureau’s investigation does not resolve a

dispute, the consumer may ﬁle a brief statement explain-

ing the nature of the dispute. The credit bureau must

include this statement in the report each time it is sent

out.

The Federal Trade Commission is the federal agency

that enforces the FCRA. Questions or complaints related

to a credit report may be directed to the Correspondence

Branch of the Federal Trade Commission, Washington,

DC 20580. Free copies of publications discussing credit

issues are available from Public Reference at the same

address.

630 Federal Reserve Bulletin July 1996

CREDIT HISTORY SCORES AND

MORTGAGE PERFORMANCE

Relatively little information about the relationship

between credit history scores and mortgage loan per-

formance is publicly available. However, recently

obtained proprietary information (courtesy of Equifax

Credit Information Services, Inc., one of the three

large national repositories of credit information)

relates credit scores to loan performance for a large

sample of mortgage loans. The sample contains virtu-

ally all of the mortgages that were outstanding and

whose payments were current as of September 1994

at three of the largest lenders in the country. The

sample is not, however, necessarily representative of

the pool of borrowers nationwide; these lenders do

not, for example, participate in all markets, nor do

they offer all types of mortgages. To ensure conﬁden-

tiality, no information was included in the data that

could be used to identify individuals or ﬁnancial

institutions.

The data for each loan include a mortgage credit

history score, ‘‘The Mortgage Score’’ (TMS), devel-

oped by Equifax Mortgage Services and generated as

of September 1994.

TMS was developed by

Equifax on the basis of the credit records of mortgag-

ors and the payment performance on their mortgage

accounts. The data also include measures of the

performance of each loan over the subsequent twelve

months (to September 1995); the date the loan

was originated; the loan type (conventional or

government-insured and whether the interest rate

on the loan was ﬁxed or variable); the ZIP code of

the property securing the loan; and characteristics of

the loan such as loan size and loan-to-value ratio at

the time of origination. All loans in the sample were

current in their mortgage payments as of Septem-

ber 1994, the date the TMS was determined. For our

analysis, loans with payments at least thirty days late

at any point during the performance period (Septem-

ber 1994 through September 1995) are deﬁned as

delinquent.

For loans originated within the year preceding

September 1994, the TMS reasonably approximates

the credit history score that could have been used in

underwriting the loan. These loans, then, allow an

examination of the relationship between credit his-

tory scores at the time of origination and near-term

loan performance. For more seasoned (older) loans,

the TMS as of September 1994 does not necessarily

reﬂect the borrower’s credit record at the time the

loan was originated. Therefore, the sample relation-

ship between the TMS and loan performance does

not necessarily reﬂect the predictive value of credit

history scores at the time of loan origination. How-

ever, the older loans in the sample can be used to

demonstrate how lenders can use credit scores to help

monitor or evaluate the credit risk of seasoned loan

portfolios.

To analyze these relationships, we separated loans

into three types (conventional ﬁxed rate, conven-

tional adjustable rate, and government-backed) and

two ‘‘seasoning’’ categories (newly originated and

seasoned) and then sorted them into three credit score

ranges—low, medium, and high—based on their

TMS scores (which, again, are mortgage credit his-

tory scores). Newly originated loans are those issued

after September 1993; seasoned loans are those that

were originated between January 1990 and Septem-

ber 1993. The three ranges of TMS scores correspond

to the speciﬁc ranges identiﬁed in the Fannie Mae

and Freddie Mac letters to mortgage lenders on the

use of the generic credit history scores (the Beacon,

TRW-FICO, and Empirica scores) in underwriting

loans.

TMS scores in the low range correspond to generic

credit history scores that Freddie Mac has identiﬁed

as showing ‘‘a strong indication that the borrower

does not show sufﬁcient willingness to repay as

agreed’’ (generic credit history scores below 621).

TMS scores in the medium range correspond to

generic scores about which Freddie Mac has sufﬁ-

cient concern to require a more detailed evaluation of

the credit history ﬁle (generic credit history scores in

the 621–660 range). TMS scores in the high range

correspond to generic scores in a range at which,

unless additional credit history risks are identiﬁed,

‘‘the borrower’s willingness to pay as agreed is con-

ﬁrmed’’ (generic credit history scores above 660).

The distributions of mortgage loans by credit score

range for the three types of loans sorted by seasoning

status, and the delinquency rate within each range,

are shown in table 2. The vast majority of both newly

originated and seasoned loans have credit scores in

the high range. For example, more than 90 percent of

conventional ﬁxed rate mortgages have credit scores

22. The Mortgage Score and TMS are service marks of Equifax

Mortgage Services.

23. See note 18. The scales of the generic credit history scores and

of the TMS differ. Using the Equifax data on individuals scored with

both a generic credit history score and the TMS score, we set cutoffs

for the TMS score at a level designed to capture the same percentages

of borrowers in the low, medium, and high ranges as were implied by

the cutoffs of the generic credit history scores identiﬁed in the Freddie

Mac and Fannie Mae letters.

Credit Risk, Credit Scoring, and the Performance of Home Mortgages 631

in the high range. Relative to conventional ﬁxed rate

mortgages, a larger proportion of conventional adjust-

able rate mortgages and an even larger proportion of

government-backed loans have low credit scores. For

each type of loan, the proportion of seasoned loans

with low scores is larger than that of newly originated

loans.

Delinquency rates are low for each loan type

regardless of seasoning status. The highest overall

rate of delinquency, that for government-backed

seasoned loans, is only 4.0 percent (table 2). These

delinquency rates should be viewed in the context of

several considerations that bias the results in opposite

directions. On one hand, the rate is for delinquencies

arising at any time over a twelve-month period and

thus overstates the likelihood of a loan being delin-

quent at any point in time. On the other hand, eco-

nomic conditions over this particular twelve-month

period were relatively favorable, and all loans had to

have been current in their payments at the beginning

of the performance period. These latter factors tend

to reduce measured delinquency rates.

The data indicate that TMS scores are a predictor

of loan performance. For each loan type, regardless

of seasoning status, borrowers with low scores have

substantially higher delinquency rates than those with

medium or high scores. For example, the delinquency

rate for newly originated government-backed loans

2. Mortgage loans, grouped by seasoning status, type, and payment status and distributed by credit score

Percent

Loan

Credit score range Memo: Number of sample loans

Low Medium High All Total Delinquent

Newly originated

Conventional ﬁxed rate .......................... 1.5 4.9 93.6 100 109,433 417

Delinquencies in score range

As percentage of all delinquent loans of this

type and seasoning ..................... 17.3 21.8 60.9 100 . . . . . .

Memo: As percentage of all loans of this

type and seasoning in score range ....... 4.4 1.7 .2 .4 . . . . . .

Conventional adjustable rate ...................... 3.8 8.3 87.8 100 24,075 119

Delinquencies in score range

As percentage of all delinquent loans of this

type and seasoning ..................... 18.5 24.4 57.1 100 . . . . . .

Memo: As percentage of all loans of this

type and seasoning in score range ....... 2.4 1.4 .3 .5 . . . . . .

Government-backed ﬁxed rate .................... 12.8 16.7 70.5 100 36,596 985

Delinquencies in score range

As percentage of all delinquent loans of this

type and seasoning ..................... 52.0 25.2 22.8 100 . . . . . .

Memo: As percentage of all loans of this

type and seasoning in score range ....... 10.9 4.0 .9 2.7 . . . . . .

Seasoned

Conventional ﬁxed rate .......................... 2.1 4.9 93.0 100 257,741 1,909

Delinquencies in score range

As percentage of all delinquent loans of this

type and seasoning ..................... 32.4 19.6 48.0 100 . . . . . .

Memo: As percentage of all loans of this

type and seasoning in score range ....... 11.4 2.9 .4 .7 . . . . . .

Conventional adjustable rate ...................... 7.6 10.7 81.8 100 125,384 2,423

Delinquencies in score range

As percentage of all delinquent loans of this

type and seasoning ..................... 42.5 21.7 35.8 100 . . . . . .

Memo: As percentage of all loans of this

type and seasoning in score range ....... 10.9 3.9 .8 1.9 . . . . . .

Government-backed ﬁxed rate .................... 13.7 15.5 70.9 100 67,913 2,786

Delinquencies in score range

As percentage of all delinquent loans of this

type and seasoning ..................... 59.9 19.4 20.7 100 . . . . . .

Memo: As percentage of all loans of this

type and seasoning in score range ....... 18.0 5.1 1.2 4.1 . . . . . .

Note. Newly originated loans were originated during the October 1993–

June 1994 period. Seasoned loans were originated during the January 1990–

September 1993 period.

The credit score is The Mortgage Score (TMS; service mark of Equifax

Mortgage Services), a mortgage credit history score derived from a model based

exclusively on the credit records of households with mortgages and their

payment performance on mortgage loans. The credit score for each loan was

calculated at the end of the third quarter of 1994.

Score ranges have been structured to roughly approximate the generic credit

bureau score ranges used by Freddie Mac for evaluating whether an application

for a mortgage meets its underwriting guidelines. The ranges for The Mortgage

Score correspond to generic credit bureau scores (Beacon, TRW-FICO,

Empirica) as follows: low = less than 621, medium = 621–660, and high = more

than 660.

Delinquent accounts are those on which a payment was at least thirty days

past due at any time during the period from September 30, 1994, through

September 30, 1995.

. . . Not applicable.

Source. Equifax Credit Information Services, Inc.

632 Federal Reserve Bulletin July 1996

with low TMS scores is 10.9 percent, compared with

4.0 percent for those with medium scores and 0.9 per-

cent for those with high scores.

The relationship between credit scores and delin-

quency rates is further evidenced by the distribution

of delinquent borrowers across credit score ranges for

each type of loan. These distributions show that

delinquent borrowers disproportionately have scores

in the low range. Borrowers with low credit scores

accounted for only 1.5 percent of all newly originated

conventional ﬁxed rate loans but for 17 percent of

those that became delinquent (table 2, memo item).

This relationship holds for other product types and

seasoned loans as well. For example, borrowers

with low credit scores accounted for 2.1 percent of

all seasoned conventional ﬁxed rate mortgages, but

they accounted for 32 percent of those that became

delinquent.

The data, however, also consistently show that

most borrowers with credit scores in the low range

are not delinquent. For example, in the case of newly

originated conventional ﬁxed rate loans, only 4.4 per-

cent of borrowers with low credit scores became

delinquent over the performance period. Thus, while

delinquent borrowers disproportionately have low

scores, most borrowers with low scores are not

delinquent.

Distinct differences exist in delinquency rates

across loan types and seasoning status. Within each

credit score range and loan type, seasoned loans have

higher delinquency rates than newly originated loans

have.

For example, the delinquency rate for newly

originated conventional adjustable rate mortgages

with low credit scores is 2.4 percent, but the rate for

seasoned conventional adjustable rate loans with low

scores is 10.9 percent. Controlling for score and

seasoning, government-backed loans have the highest

rates of delinquency, a result consistent with data on

relative delinquency rates from other sources.

Detailed information on the distribution of TMS

scores by loan performance, type of loan, and

mortgage and location characteristics for newly origi-

nated loans is presented in tables 3, 4, and 5. In

general, loans with lower loan-to-value ratios and

loans on properties located in areas with higher rela-

tive incomes and higher relative home values have

higher mean and median TMS scores and a lower

percentage of borrowers with low and medium scores

than other loans. These relationships hold for delin-

quent loans as well as for loans that were paid on

schedule. For example, for newly originated conven-

tional ﬁxed rate mortgage loans (table 3 and chart 1),

the mean TMS score for paid-as-scheduled loans

with loan-to-value ratios less than 81 percent is

50 points higher than the mean score for those with

loan-to-value ratios of more than 90 percent. Simi-

larly, 94.5 percent of the loans with loan-to-value

ratios of less than 81 percent are in the high credit

score range, compared with 84.6 percent for those

with loan-to-value ratios of more than 90 percent.

For each loan type, the mean and median TMS

scores for delinquent loans are 100 to 150 points

lower than the mean and median scores for those that

were paid on schedule, and these differences are

statistically signiﬁcant. Similarly, the percentage of

borrowers in the low credit score range is at least four

to ﬁve times higher for delinquent loans than for

loans that were paid as scheduled. These relation-

ships hold across all subcategories of loans.

Additional information relating credit history

scores to mortgage loan performance was provided

by Freddie Mac (table 6). These data pertain to loans

for single-family owner-occupied properties pur-

chased by Freddie Mac in the ﬁrst six months of

1994. Performance is measured by whether the loan

had entered into foreclosure by the end of 1995.

Foreclosure rates for different categories of loans are

expressed relative to the rate for borrowers with

loan-to-value ratios of 80 percent or less and high

credit history scores, which was set to 1.

Foreclosure rates are substantially higher for bor-

rowers with low credit scores as well as for those

with high loan-to-value ratios (table 6). Moreover,

borrowers with low credit scores perform worse

within each loan-to-value ratio category. The foreclo-

sure rate is particularly high for borrowers with both

low credit scores and high loan-to-value ratios—

almost 50 times higher than that for borrowers with

both high credit scores and low loan-to-value ratios.

This ﬁnding, that loan performance deteriorates sig-

niﬁcantly when risks are high for multiple factors

(‘‘layering of risk’’), is discussed at length later in

this article.

The relationship between borrower income and

loan performance appears to be slight. Within each

credit score and loan-to-value ratio category, borrow-

ers with income below 80 percent of area median

24. This result is consistent with other research, which indicates

that delinquency rates increase as loans age, at least for the ﬁrst few

years after origination. See, for example, chart 1 in The Market Pulse,

Mortgage Information Corporation (vol. 1, January 1996), p. 1.

25. See Mortgage Bankers Association National Delinquency

Survey.

26. The credit score ranges are comparable to those used in tables 2

through 5.

Credit Risk, Credit Scoring, and the Performance of Home Mortgages 633

income have somewhat higher foreclosure rates than

average, and those with incomes above 120 percent

of area median income have somewhat lower foreclo-

sure rates than average. Credit score and, to a lesser

extent, loan-to-value ratio appear to be much stronger

predictors of foreclosure rates than income.

3. Newly originated conventional ﬁxed rate mortgage loans, grouped by payment performance and characteristic and distributed

by credit score

Percent except as noted

Performance of mortgage

and characteristic

Credit score

Total

Mean

Median

Low Medium High

Percent

of charac-

teristic

Memo:

Percent

of score

range

Percent

of charac-

teristic

Memo:

Percent

of score

range

Percent

of charac-

teristic

Memo:

Percent

of score

range

Percent

of charac-

teristic

Memo:

Percent

of all

loans

Paid as Scheduled

Mortgage characteristic

Loan-to-value ratio (percent)

Less than 81 ............... 845 865 1.2 72.9 4.3 77.6 94.5 87.1 100 86.5

81to90 .................. 818 840 2.6 19.8 7.1 16.2 90.3 10.5 100 10.9

More than 90 ............. 794 811 4.0 7.3 11.4 6.2 84.6 2.4 100 2.6

All

.................... 841 861 1.4 100 4.8 100 93.8 100 100 100

Loan size (dollars)

Less than 100,000 ......... 836 859 1.9 47.5 5.7 41.9 92.4 35.0 100 35.5

100,000–200,000 .......... 839 859 1.4 33.8 5.0 35.0 93.6 33.8 100 33.9

More than 200,000 ........ 847 866 .9 18.7 3.6 23.1 95.5 31.2 100 30.6

All ..................... 841 861 1.4 100 4.8 100 93.7 100 100 100

Location characteristic

ZIP code median income

(percentage of area median

income)

Less than 80 .............. 823 846 2.3 9.0 7.7 9.0 90.1 5.5 100 5.7

80to120 ................. 837 857 1.6 52.2 5.2 50.6 93.2 46.7 100 47.0

More than 120 ............ 847 867 1.2 38.8 4.1 40.4 94.7 47.8 100 47.3

All ..................... 841 861 1.4 100 4.8 100 93.7 100 100 100

Home values (percentage

of area median home value)

Less than 80 .............. 826 847 2.2 20.7 6.6 18.8 91.3 13.4 100 13.8

80to120 ................. 836 856 1.6 27.9 5.2 26.5 93.1 24.2 100 24.4

More than 120 ............ 846 866 1.2 51.4 4.3 54.7 94.5 62.3 100 61.8

All ..................... 841 861 1.4 100 4.8 100 93.7 100 100 100

Delinquent

Mortgage characteristic

Loan-to-value ratio (percent)

Less than 81 .............. 734 740 11.9 63.8 22.9 78.0 65.2 79.8 100 77.1

81to90 .................. 697 707 21.3 27.6 22.7 18.7 56.0 16.6 100 18.7

More than 90 ............. 699 744 29.4 8.6 17.6 3.3 52.9 3.6 100 4.2

All

.................... 720 730 14.4 100 22.6 100 62.9 100 100 100

Loan size (dollars)

Less than 100,000 ......... 692 686 25.1 59.7 24.6 46.2 50.3 33.9 100 41.0

100,000–200,000 .......... 720 730 14.7 29.2 23.8 37.4 61.5 34.6 100 34.3

More than 200,000 ........ 766 781 7.8 11.1 14.6 16.5 77.7 31.5 100 24.7

All ..................... 720 730 17.3 100 21.8 100 60.9 100 100 100

Location characteristic

ZIP code median income

(percentage of area median

income)

Less than 80 .............. 724 738 17.4 11.1 19.6 9.9 63.0 11.4 100 11.0

80to120 ................. 707 712 22.0 69.4 21.6 53.8 56.4 50.4 100 54.4

More than 120 ............ 739 753 9.7 19.4 22.9 36.3 67.4 38.2 100 34.5

All ..................... 720 730 17.3 100 21.8 100 60.9 100 100 100

Home value (percentage of

area median home value)

Less than 80 .............. 687 677 28.9 38.9 22.7 24.2 48.5 18.5 100 23.3

80to120 ................. 721 730 14.9 25.0 24.8 33.0 60.3 28.7 100 29.0

More than 120 ............ 735 743 13.1 36.1 19.6 42.9 67.3 52.8 100 47.7

All ..................... 720 730 17.3 100 21.8 100 60.9 100 100 100

Note. Loans were originated during the October 1993–June 1994 period.

For deﬁnitions of credit score, score range, and delinquency, see note to table 2.

1. Excluding loans with no reported ratio.

2. Median family income of ZIP code in which the property is located

relative to median family income of the property’s metropolitan statistical area

(MSA) or, if location is not in an MSA, relative to median family income of all

non-MSA portions of the state.

3. Value of the property relative to the median value of owner-occupied

homes in the property’s MSA or, if location is not in an MSA, relative to the

median value of owner-occupied homes in all non-MSA portions of the state.

4. Values of The Mortgage Score. The sample Mortgage Score range is 325

to 991.

Source. Equifax Credit Information Services, Inc.

634 Federal Reserve Bulletin July 1996

The performance patterns by credit score and

loan-to-value ratio are very similar for borrowers at

all income levels. For example, among borrowers

with high incomes, those with low credit scores and

high loan-to-value ratios still have a foreclosure rate

almost 50 times higher than those with high credit

scores and low loan-to-value ratios.

These performance data reﬂect foreclosures during

only the ﬁrst eighteen to twenty-four months after

origination. Typically, most foreclosures occur more

than two years after origination. Analysts at Freddie

Mac, however, believe that the pattern of relative

foreclosure rates presented in table 6 will hold as

these loans season.

4. Newly originated conventional adjustable rate mortgage loans, grouped by payment performance and characteristic and

distributed by credit score

Percent except as noted

Performance of mortgage

and characteristic

Credit score

Total

Mean Median

Low Medium High

Percent

of charac-

teristic

Memo:

Percent

of score

range

Percent

of charac-

teristic

Memo:

Percent

of score

range

Percent

of charac-

teristic

Memo:

Percent

of score

range

Percent

of charac-

teristic

Memo:

Percent

of all

loans

Paid as Scheduled

Mortgage characteristic

Loan-to-value ratio (percent)

Less than 81 ............... 817 842 3.8 86.8 8.2 85.5 88.1 86.8 100 86.7

81to90 .................. 801 821 3.7 12.6 8.8 13.6 87.5 12.6 100 12.7

More than 90 ............. 770 782 3.4 .6 12.8 1.0 83.9 .6 100 .6

All ..................... 815 839 3.8 100 8.3 100 88.0 100 100 100

Loan size (dollars)

Less than 100,000 ......... 814 840 4.4 44.2 8.3 37.7 87.3 37.5 100 37.8

100,000–200,000 .......... 812 836 3.9 39.1 8.7 39.9 87.5 37.8 100 38.0

More than 200,000 ........ 819 840 2.6 16.8 7.6 22.4 89.8 24.7 100 24.2

All ..................... 815 839 3.8 100 8.3 100 88.0 100 100 100

Location characteristic

ZIP code median income

(percentage of area median

income)

Less than 80 .............. 788 811 5.5 13.4 12.6 13.9 81.9 8.5 100 9.1

80to120 ................. 811 836 4.2 52.9 8.6 50.1 87.2 47.5 100 47.9

More than 120 ............ 824 847 2.9 33.6 6.9 36.0 90.1 44.1 100 43.0

All ..................... 815 839 3.8 100 8.3 100 88.0 100 100 100

Home values (percentage

of area median home value)

Less than 80 .............. 802 828 5.1 38.1 9.9 33.6 84.9 26.9 100 27.9

80to120 ................. 812 835 3.7 24.3 8.4 25.1 87.9 24.7 100 24.7

More than 120 ............ 824 848 3.0 37.6 7.2 41.3 89.8 48.4 100 47.4

All ..................... 815 839 3.8 100 8.3 100 88.0 100 100 100

Delinquent

Mortgage characteristic

Loan-to-value ratio (percent)

Less than 81 .............. 713 723 14.9 63.6 22.3 72.4 62.8 90.8 100 81.0

81to90 .................. 678 638 30.0 27.3 40.0 27.6 30.0 9.2 100 17.2

More than 90 ............. 576 576 100 9.1 0 0 0 0 100 1.7

All ..................... 710 718 19.0 100 25.0 100 56.0 100 100 100

Loan size (dollars)

Less than 100,000 ......... 683 661 24.1 31.8 27.6 27.6 48.3 20.6 100 24.4

100,000–200,000 .......... 700 694 14.6 31.8 33.3 55.2 52.1 36.8 100 40.3

More than 200,000 ........ 739 764 19.0 36.4 11.9 17.2 69.0 42.6 100 35.3

All ..................... 710 718 18.5 100 24.4 100 57.1 100 100 100

Location characteristic

ZIP code median income

(percentage of area median

income)

Less than 80 .............. 631 653 43.8 31.8 6.3 3.4 50.0 11.8 100 13.4

80to120 ................. 720 729 15.4 45.4 27.7 62.1 56.9 54.4 100 54.6

More than 120 ............ 725 718 13.2 22.7 26.3 34.5 60.5 33.8 100 31.9

All ..................... 710 718 18.5 100 24.4 100 57.1 100 100 100

Home value (percentage

of area median home value)

Less than 80 .............. 706 707 17.6 27.3 23.5 27.6 58.8 29.4 100 28.6

80to120 ................. 688 678 12.8 22.7 38.5 51.7 48.7 27.9 100 32.8

More than 120 ............ 731 764 23.9 50.0 13.0 20.7 63.0 42.6 100 38.7

All ..................... 710 718 18.5 100 24.4 100 57.1 100 100 100

Note. See notes to table 3.

Source. Equifax Credit Information Services, Inc.

Credit Risk, Credit Scoring, and the Performance of Home Mortgages 635

THE DISTRIBUTION OF SCORES

ACROSS THE

POPULATION

Little information is publicly available about how

credit histories vary across population groups. As a

summary measure of the credit histories of individ-

uals, credit history scores provide a convenient way

to compare different segments of the population with

respect to their credit history proﬁles. Such compari-

sons offer a rough and partial guide to the willingness

of lenders to extend credit to different categories of

households, since credit history is only one element

lenders consider in the evaluation of a mortgage

application. Even applicants with low scores may

qualify for a mortgage if they have compensating

factors such as a low loan-to-value ratio.

Proprietary information on the credit history

scores, mortgage status, and ZIP code location of

individuals and households was obtained from

Equifax. The information is based on a nationally

representative sample and includes the Equifax TMS

scores for 3.4 million individuals and the 2.5 million

households they comprise.

Households were classi-

27. The sample was drawn by sorting the country’s roughly 29,000

residential ZIP codes into strata deﬁned by Census region, center-city/

suburban/rural location, and median household income. A stratiﬁed

nationally representative sample of 994 ZIP codes was drawn from

these strata. TMS scores (computed in the same way as those dis-

5. Newly originated government-backed ﬁxed rate mortgage loans, grouped by payment performance and characteristic and

distributed by credit score

Percent except as noted

Performance of mortgage

and characteristic

Credit score

Total

Mean Median

Low Medium High

Percent

of charac-

teristic

Memo:

Percent

of score

range

Percent

of charac-

teristic

Memo:

Percent

of score

range

Percent

of charac-

teristic

Memo:

Percent

of score

range

Percent

of charac-

teristic

Memo:

Percent

of all

loans

Paid as Scheduled

Mortgage characteristic

Loan size (dollars)

Less than 100,000 ......... 752 767 12.6 71.4 17.1 68.9 70.2 64.9 100 66.4

More than 100,000 ........ 762 780 9.9 28.6 15.2 31.1 74.8 35.1 100 33.6

All ..................... 756 772 11.7 100 16.5 100 71.8 100 100 100

Location characteristic

ZIP code median income

(percentage of area median

income)

Less than 80 .............. 728 733 14.9 12.9 22.6 13.9 62.5 8.8 100 10.2

80to120 ................. 754 770 11.9 65.6 16.5 64.6 71.6 64.5 100 64.6

More than 120 ............ 770 792 10.0 21.5 14.1 21.5 76.0 26.7 100 25.2

All ..................... 756 772 11.7 100 16.5 100 71.8 100 100 100

Home values (percentage

of area median home value)

Less than 80 .............. 751 766 12.2 37.6 18.0 39.2 69.8 35.0 100 36.0

80to120 ................. 756 772 12.0 39.7 15.7 36.8 72.3 39.0 100 38.7

More than 120 ............ 763 780 10.5 22.8 15.6 24.0 73.8 26.1 100 25.4

All ..................... 756 772 11.7 100 16.5 100 71.8 100 100 100

Delinquent

Mortgage characteristic

Loan size (dollars)

Less than 100,000 ......... 604 592 54.5 68.9 25.0 65.3 20.5 59.1 100 65.8

More than 100,000 ........ 622 610 47.2 31.1 25.5 34.7 27.3 40.9 100 34.2

All ..................... 610 598 52.0 100 25.2 100 22.8 100 100 100

Location characteristic

ZIP code median income

(percentage of area median

income)

Less than 80 .............. 596 593 54.5 16.6 29.5 18.5 16.0 11.1 100 15.8

80to120 ................. 606 592 54.5 68.4 22.9 59.3 22.6 64.4 100 65.2

More than 120 ............ 635 626 41.2 15.0 29.4 22.2 29.4 24.4 100 19.0

All ..................... 610 598 52.0 100 25.2 100 22.8 100 100 100

Home value (percentage

of area median home value)

Less than 80 .............. 604 591 54.0 45.3 25.6 44.4 20.5 39.1 100 43.7

80to120 ................. 614 606 48.9 34.2 27.1 39.1 24.0 38.2 100 36.3

More than 120 ............ 616 597 53.3 20.5 20.8 16.5 25.9 22.7 100 20.0

All ..................... 610 598 52.0 100 25.2 100 22.8 100 100 100

Note. See notes to table 3.

Source. Equifax Credit Information Services, Inc.

636 Federal Reserve Bulletin July 1996

ﬁed according to whether or not they appeared to

have an outstanding mortgage loan. Other than the

TMS score and mortgage status, no information was

provided about the characteristics of the individuals.

However, because the ZIP code of the individual’s

residence is known, it is possible to classify individ-

uals by the characteristics of these locations.

We have calculated the distributions of three differ-

ent population groups—individuals, households, and

households identiﬁed as having mortgages—across

the same TMS score ranges used in the previous

section for various classiﬁcations of ZIP code. For all

three population groups, the distributions of TMS

scores are similar across different categories of ZIP

code, although some absolute differences exist

(table 7). For example, households with mortgages

tend to have fewer low scores and tend to live in

areas with higher relative median family incomes and

median home values. For all categories, more than

half, and in most cases more than two-thirds, of

sample households or individuals have TMS scores

in the high range. For these households, TMS scores

fall within the acceptable range for mortgage

qualiﬁcation.

About 20 percent of individuals, 23 percent of

households, and 15 percent of households with mort-

gages have low TMS scores and thus may have

problems qualifying for a mortgage on the basis of

their credit histories (table 7). These proportions do

not vary much across urban/suburban/rural classiﬁca-

tions but do vary substantially by median income and

cussed in the previous section) were obtained for all individuals with

credit ﬁles in Equifax’s off-line credit marketing database showing

addresses in the sample ZIP codes.

Credit reports showing the same address were considered to be

from the same household, and the low-score report (if two reports

were involved) or the middle-score report (if three or more reports

were involved) was chosen to represent the household. These ﬁgures

understate the number of households with more than one adult. A

possible explanation is that many couples obtain credit in only one

person’s name.

1. Mean mortgage scores of selected, newly originated, conventional ﬁxed rate loans, by payment status and characteristics

of loan and locality

700

800

700

800

Mean mortgage score

700

800

700

800

Mean mortgage score

Loan-to-value ratio Loan size, dollars

On time

Delinquent

Less than

81%

81–90% More than

90%

Less than

100,000

100,000–

200,000

More than

200,000

Borrower income, percent of area median Home value, percent of area median

Less than

80%

80–120% More than

120%

Less than

80%

80–120% More than

120%

Note. For deﬁnitions and source, see notes to tables 2 and 3.

Credit Risk, Credit Scoring, and the Performance of Home Mortgages 637

home value of ZIP codes and by Census region. For

example, about 33 percent of the households living in

ZIP codes with median family incomes in the lowest

range have low scores, compared with only 17 per-

cent of households living in ZIP codes with median

family incomes in the highest income range.

The extent of the variation in TMS scores by Cen-

sus region is somewhat surprising. Although some of

the variation by region is explained by differences in

economic factors such as income and unemployment

rates (additional analysis not shown), much of the

variation is unexplained.

Information on the distribution, across score

ranges, of households identiﬁed as having mortgages

is potentially useful for forecasting the ability of

mortgage holders to reﬁnance their outstanding

mortgage loans. As noted, 15 percent of all the

households with mortgages have low TMS scores

and thus may have difﬁculty reﬁnancing.

Again,

the proportion with low scores varies substantially by

area income and home value and region. Almost

one-fourth of households with mortgages in ZIP

codes with lower incomes or lower home values fall

in the low-score range and may have difﬁculty

reﬁnancing.

THE PERFORMANCE OF LOANS

AFFORDABLE HOME LOAN PROGRAMS

In recent years mortgage originators, secondary mort-

gage market institutions (Fannie Mae and Freddie

Mac in particular), and PMI companies have initiated

a wide variety of affordable home loan programs

intended to beneﬁt low- and moderate-income and

minority households and neighborhoods (see box

‘‘The Elements of an Affordable Home Loan Pro-

gram’’).

These initiatives supplement a variety of

long-standing government-sponsored programs, par-

ticularly those of the FHA and state and local housing

authorities. In many cases, the reach of private-sector

programs has been extended through public–private

partnerships.

Analysis of data gathered under the Home Mort-

gage Disclosure Act (HMDA) for the period 1992–94

suggests that affordable home loan programs may be

having an effect in metropolitan statistical areas

(MSAs), as conventional mortgage lending to low-

and moderate-income borrowers has increased at a

substantially faster rate than lending to other groups

(table 8). From 1992 to 1993 and from 1993 to 1994,

the number of conventional home purchase loans

extended to low- and moderate-income borrowers

(incomes below 80 percent of the MSA median)

increased 38 percent and 27 percent respectively.

Over these same two years, lending to upper-income

borrowers (incomes above 120 percent of the MSA

median) rose more slowly, increasing only 8 percent

and then 13 percent.

A combination of factors may have given rise to

this pattern of lending. In some cases, lenders may

be responding to newly perceived proﬁt opportunities

in underserved market niches. Some depository

institutions may also be seeking to build an out-

standing record of community reinvestment in order

to enhance their compliance with the Community

28. This ﬁnding should be viewed with some caution. The percent-

age of sample households identiﬁed as having mortgages is lower than

the proportion estimated from other data sources. If the sample

households identiﬁed as having mortgages have a different credit

score distribution than mortgage holders overall, then the sample

statistics may be biased.

29. See ‘‘Affordable Mortgage Program Study,’’ Consumer Bank-

ers Association, annual reports 1993–95. For a review of the afford-

able lending initiatives sponsored by Fannie Mae and Freddie Mac,

see the brochures ‘‘Opening Doors with Fannie Mae’s Community

Lending Products,’’ Fannie Mae, 1995, and ‘‘Expanding the Dream,’’

Freddie Mac, 1995.

6. Relative foreclosure rates for selected categories of

mortgage loans, by credit score range

Index

Loan-to-value ratio

and borrower income

Low Medium High

All loans

Borrower income

(percentage of area median income)

Less than 80 ...................... 36.8 13.9 2.2

80to120 ......................... 35.3 10.2 1.7

120 or more ....................... 31.1 8.9 1.1

All ............................. 33.9 10.3 1.5

Loan-to-value ratio less than

81 percent

Borrower income

(percentage of area median income)

Less than 80 ...................... 32.0 11.0 1.8

80to120 ......................... 29.0 7.4 1.1

120 or more ....................... 22.0 6.7 .7

All ............................. 26.9 7.9 1.0

Loan-to-value ratio 81 percent

or more

Borrower income

(percentage of area median income)

Less than 80 ...................... 51.4 23.0 4.4

80to120 ......................... 47.4 15.8 3.6

120 or more ....................... 46.7 12.9 2.8

All ............................. 47.6 15.3 3.3

Note. The loans are for single-family owner-occupied properties and were

purchased by Freddie Mac in the ﬁrst six months of 1994. Index of foreclosure

rate covers loans foreclosed by December 31, 1995; the index sets the average

foreclosure rate equal to 1 for loans with borrower generic credit bureau scores

of more than 660 and loan-to-value ratios of less than 81 percent.

The credit score ranges correspond to generic credit bureau scores as follows:

low = less than 621, medium = 621–660, and high = more than 660.

Area median income is the median family income of the property’s MSA or,

if location is not in an MSA, the median family income of the property’s county.

Borrower income is as of the time of loan origination.

Source. Freddie Mac.

638 Federal Reserve Bulletin July 1996

Reinvestment Act (CRA).

More generally, ﬁnancial

institutions may have determined that increased

lending to a targeted area would serve their long-run

interest in community stability. Finally, relatively

larger numbers of low- and moderate-income house-

holds may have been seeking to purchase homes

during this period because the affordability of hous-

ing improved to levels not seen since the 1960s.

Since affordable home lending initiatives typically

involve the application of ﬂexible underwriting stan-

dards, questions have been raised about whether the

payment performance, and ultimately the proﬁtabil-

ity, of these loans is substantially different from that

of traditionally underwritten loans. Analyses of these

issues have tended to focus on measures of payment

performance such as delinquency rates or, more

rarely, the incidence of default. Little information is

available about the cost of other aspects of affordable

lending programs, such as enhanced servicing, home-

buyer education, and various forms of direct subsi-

dies (for example, waivers of some or all closing

costs), that also affect the proﬁtability of these pro-

grams. Similarly, little is known about possible

increases in revenue that may result from a high-

volume affordable lending program. For example,

providing mortgages to lower-income households

may lead to other credit- or deposit-related relation-

ships that may be proﬁtable for the lender.

Evidence from Roundtable Discussions

Until recently, most of the available information on

the performance of affordable home lending pro-

grams had been anecdotal. For example, in round-

table discussions held with lenders in preparing the

Federal Reserve’s 1993 ‘‘Report to the Congress on

Community Development Lending by Depository

Institutions,’’ the participants generally held the view

that the costs of originating and servicing loans made

under affordable home loan programs were greater

than those incurred on other housing loans but that

delinquency and default experience to that time had

not been worse. Statistical analysis undertaken for

that report did not ﬁnd any notable relationship

between bank proﬁtability and the level of lower

income mortgage lending activity.

The roundtable participants suggested that the

increased risks associated with allowing more ﬂex-

ible underwriting can be mitigated in various ways.

Some lenders, by drawing on their specialized knowl-

edge of local market conditions, familiarity with bor-

rowers, and greater experience with affordable home

lending, may be able to reduce the risks of applying

ﬂexible underwriting guidelines. By integrating care-

30. The Community Reinvestment Act of 1977 is intended to

encourage commercial banks and savings associations to help meet

the credit needs of the local communities in which they are chartered,

including low- and moderate-income neighborhoods, in a manner

consistent with safe and sound operations. For a review of different

perspectives on the CRA, see Glenn B. Canner and Wayne Passmore,

‘‘Home Purchase Lending in Low-Income Neighborhoods and to

Low-Income Borrowers,’’ Federal Reserve Bulletin, vol. 81 (Febru-

ary 1995), pp. 71–103.

31. Statistical analysis of bank proﬁtability and affordable home

lending was based on data from the 1992 HMDA reports and from

Call Reports of commercial banks and thrift institutions. See Board of

Governors of the Federal Reserve System, ‘‘Report to the Congress on

Community Development Lending by Depository Institutions’’ (Board

of Governors, 1993).

The Elements of an Affordable

Home Loan Program

The details vary widely, but affordable home loan pro-

grams generally involve four distinct elements: targeted

groups, special marketing, the application of ﬂexible

underwriting standards, and the use of risk mitigation

activities. Targeted groups are usually deﬁned with eligi-

bility criteria tied to borrower or neighborhood income,

loan-to-value ratios, location, homebuyer status (for

example, ﬁrst-time homebuyers), and other factors.

Most important among these criteria are the income

eligibility restrictions, which normally require a prospec-

tive borrower to have a low or moderate income or to

purchase a home in a low- or moderate-income neigh-

borhood. Special marketing activities commonly include

homebuyer education seminars and outreach to religious

and community organizations active in targeted neigh-

borhoods. Flexible underwriting policies usually have

the following characteristics: low-down-payment

requirements; higher acceptable ratios of debt payment

to income; the use of alternative credit history informa-

tion such as records of payments for rent and utilities;

ﬂexible employment standards; and reduced cash reserve

requirements. In addition, many lenders offer reduced

interest rates, waive private mortgage insurance require-

ments, or reduce or waive points or fees associated with

originating the loan.

To reduce the potential for higher losses on these

ﬂexibly underwritten loans, lenders customarily require

the borrower to complete a homebuyer education pro-

gram and to undergo credit counseling when needed.

Lenders also use enhanced servicing techniques on these

loans, contacting borrowers by phone, for example, as

soon as they are thirty-days delinquent to determine the

cause of the delinquency and to establish a plan to

rectify the situation.

Credit Risk, Credit Scoring, and the Performance of Home Mortgages 639

7. Individuals and households, grouped by ZIP code characteristic and distributed by credit score range

Percent

Characteristic of ZIP code

Low Medium High Total

Percent

of charac-

teristic

Memo:

Percent

of score

range

Percent

of charac-

teristic

Memo:

Percent

of score

range

Percent

of charac-

teristic

Memo:

Percent

of score

range

Percent

of charac-

teristic

Memo:

Percent

of score

range

Individuals

Median income of ZIP code

(percentage of area median income)

Less than 80 ................................. 29.5 21.0 14.2 17.4 56.3 11.9 100 14.4

80to120 ................................... 19.8 65.2 11.8 66.6 68.4 67.2 100 66.7

More than 120 ............................... 14.9 13.8 10.0 16.0 75.1 20.8 100 18.8

All ......................................... 20.3 100 11.8 100 67.9 100 100 100

Median home value of ZIP code

(percentage of area median home value)

Less than 80 ................................. 27.4 34.3 13.9 29.7 58.7 21.9 100 25.3

80to120 ................................... 19.6 51.7 11.5 52.2 68.9 54.1 100 53.4

More than 120 ............................... 13.4 14.1 10.0 18.0 76.6 24.0 100 21.3

All ......................................... 20.3 100 11.8 100 67.9 100 100 100

Urbanization of ZIP code

Urban ....................................... 22.0 37.5 12.0 35.0 66.0 33.5 100 34.5

Suburban ................................... 18.9 44.0 11.4 45.4 69.7 48.4 100 47.2

Rural ....................................... 20.4 18.5 12.6 19.6 67.0 18.1 100 18.4

All ......................................... 20.3 100 11.8 100 67.9 100 100 100

Census region of ZIP code

Northeast

New England ............................. 17.8 5.4 11.2 5.8 71.0 6.5 100 6.2

Middle Atlantic ........................... 17.9 14.6 11.0 15.4 71.1 17.3 100 16.5

Midwest

East North Central ........................ 15.5 9.4 10.8 11.2 73.6 13.2 100 12.2

West North Central ........................ 16.5 5.3 10.4 5.8 73.1 7.0 100 6.5

South

South Atlantic ............................ 21.7 23.7 11.5 21.5 66.8 21.7 100 22.1

East South Central ........................ 25.5 5.1 13.8 4.8 60.7 3.6 100 4.1

West South Central ........................ 27.8 13.9 13.6 11.7 58.6 8.8 100 10.1

West

Mountain ................................. 20.4 6.3 12.5 6.7 67.1 6.2 100 6.3

Paciﬁc .................................... 20.6 16.2 12.7 17.1 66.7 15.7 100 15.9

All ......................................... 20.3 100 11.8 100 67.9 100 100 100

All households

Median income of ZIP code

(percentage of area median income)

Less than 80 ................................. 32.8 21.4 14.9 17.6 52.3 12.2 100 15.0

80to120 ................................... 22.4 65.0 12.7 66.6 64.9 67.2 100 66.6

More than 120 ............................... 17.0 13.6 11.0 15.9 72.1 20.6 100 18.4

All ......................................... 22.9 100 12.7 100 64.4 100 100 100

Median home value of ZIP code

(percentage of area median home value)

Less than 80 ................................. 30.7 34.3 14.7 29.6 54.6 21.8 100 25.6

80to120 ................................... 22.3 51.3 12.5 51.6 65.2 53.3 100 52.7

More than 120 ............................... 15.2 14.4 11.0 18.7 73.8 24.9 100 21.7

All ......................................... 22.9 100 12.7 100 64.4 100 100 100

Urbanization of ZIP code

Urban ....................................... 24.7 38.4 12.8 35.9 62.5 34.6 100 35.7

Suburban ................................... 21.5 43.0 12.3 44.5 66.2 47.2 100 45.9

Rural ....................................... 23.1 18.6 13.5 19.6 63.4 18.1 100 18.4

All ......................................... 22.9 100 12.7 100 64.4 100 100 100

Census region of ZIP code

Northeast

New England ............................. 20.3 5.3 12.3 5.8 67.3 6.3 100 6.0

Middle Atlantic ........................... 20.2 14.4 11.9 15.4 67.9 17.3 100 16.4

Midwest

East North Central ........................ 17.9 9.3 11.8 11.1 70.2 13.0 100 11.9

West North Central ........................ 18.9 5.3 11.4 5.8 69.8 7.0 100 6.5

South

South Atlantic ............................ 24.5 23.9 12.4 21.8 63.1 22.0 100 22.4

East South Central ........................ 28.6 5.1 14.7 4.8 56.7 3.6 100 4.1

West South Central ........................ 31.1 13.8 14.2 11.4 54.7 8.6 100 10.2

West

Mountain ................................. 23.1 6.4 13.5 6.7 63.4 6.3 100 6.4

Paciﬁc .................................... 23.2 16.3 13.6 17.3 63.2 15.9 100 16.1

All ......................................... 22.9 100 12.7 100 64.4 100 100 100

640 Federal Reserve Bulletin July 1996

fully designed homebuyer education efforts and credit

counseling services into their affordable lending pro-

grams, lenders may be able to screen out relatively

high-risk applicants and better prepare ﬁrst-time

homebuyers for the responsibilities of homeowner-

ship. In addition, by adopting an enhanced servicing

program for affordable home loan products that

includes postpurchase contact and counseling and, if

necessary, early delinquency intervention, lenders

may be able to help avoid some potential defaults.

Experiences of Secondary-Market Institutions

and Private Mortgage Insurers

Additional evidence has begun to accumulate about

the performance of loans extended under affordable

home loan programs and purchased by secondary-

market institutions or insured by private mortgage

insurance companies. For the most part, the evidence

pertains to delinquency rates, because the loans

examined are too recent in origin to permit a compre-

hensive evaluation of default and loss experience. In

what follows, it should be emphasized that the vast

majority of borrowers relying on affordable home

loan products are current on their mortgage pay-

ments. However, even relatively small delinquency

and default rates may make a program unproﬁtable.

Analyzing delinquencies and defaults can highlight

speciﬁc variables in the program that might be modi-

ﬁed to screen out particularly bad risks and enhance

program proﬁtability.

Freddie Mac has been following the performance

of the affordable home loans it purchases under its

7. Continued

Percent

Characteristic of ZIP code

Low Medium High Total

Percent

of charac-

teristic

Memo:

Percent

of score

range

Percent

of charac-

teristic

Memo:

Percent

of score

range

Percent

of charac-

teristic

Memo:

Percent

of score

range

Percent

of charac-

teristic

Memo:

Percent

of score

range

Households with mortgages

Median income of ZIP code

(percentage of area median income)

Less than 80 ................................. 22.6 14.5 14.5 12.8 62.9 8.2 100 9.6

80to120 ................................... 15.1 64.2 11.0 64.3 73.9 63.3 100 63.6

More than 120 ............................... 11.9 21.3 9.3 22.9 78.8 28.5 100 26.8

All ......................................... 15.0 100 10.9 100 74.1 100 100 100

Median home value of ZIP code

(percentage of area median home value)

Less than 80 ................................. 22.2 29.6 14.2 25.9 63.6 17.1 100 19.9

80to120 ................................... 14.9 55.5 10.8 55.2 74.3 55.9 100 55.8

More than 120 ............................... 9.2 14.9 8.5 18.9 82.4 27.0 100 24.3

All ......................................... 15.0 100 10.9 100 74.1 100 100 100

Urbanization of ZIP code

Urban ....................................... 15.0 32.2 11.1 32.7 73.9 32.0 100 32.1

Suburban ................................... 15.1 57.4 10.7 56.0 74.2 57.1 100 57.0

Rural ....................................... 14.4 10.4 11.3 11.3 74.3 10.9 100 10.9

All ......................................... 15.0 100 10.9 100 74.1 100 100 100

Census region of ZIP code

Northeast

New England ............................. 13.6 6.2 10.7 6.7 75.7 7.0 100 6.9

Middle Atlantic ........................... 14.1 13.7 10.1 13.5 75.8 14.9 100 14.6

Midwest

East North Central ........................ 11.4 8.7 10.1 10.5 78.5 12.0 100 11.3

West North Central ........................ 12.1 4.1 9.2 4.3 78.6 5.4 100 5.1

South

South Atlantic ............................ 15.6 26.5 10.4 24.3 73.9 25.3 100 25.4

East South Central ........................ 17.9 2.8 12.0 2.6 70.2 2.2 100 2.4

West South Central ........................ 19.8 11.3 11.9 9.3 68.3 7.8 100 8.5

West

Mountain ................................. 15.3 7.5 11.7 7.9 73.0 7.3 100 7.4

Paciﬁc .................................... 15.5 19.2 12.2 20.8 72.3 18.1 100 18.5

All ......................................... 15.0 100 10.9 100 74.1 100 100 100

Note. The credit score is The Mortgage Score (TMS), of Equifax Mortgage

Services. For deﬁnition of TMS and of the credit score ranges, see note to

table 2; see also text note 27.

1. Median family income in ZIP code in which the property is located

relative to median family income in the property’s MSA or, if location is not

in an MSA, relative to median family income in the non-MSA portion of the

state.

2. Median value of owner-occupied homes in ZIP code in which the property

is located relative to median value of owner-occupied homes in the property’s

MSA or, if location is not in an MSA, relative to median value of owner-

occupied homes in non-MSA portion of state.

3. See map of Census Bureau regions and divisions, inside front cover, U.S.

Department of Commerce, Statistical Abstract of the United States: 1994,

Bureau of the Census (Government Printing Ofﬁce, 1994).

Source. Equifax Credit Information Services, Inc.

Credit Risk, Credit Scoring, and the Performance of Home Mortgages 641

‘‘Affordable Gold’’ program, which was established

to promote lending to low- and moderate-income

households.

Freddie Mac reports that the sixty-day

delinquency rate on these loans has been higher than

on a ‘‘peer group’’ of traditionally underwritten mort-

gages, controlling for the loan-to-value ratio, the date

of loan origination, region of the country, and type of

property.

Among those Affordable Gold loans origi-

nated in 1994 for which borrowers were allowed to

meet part of the minimum down-payment require-

ments with funds provided by a third party, the delin-

quency rate through February 1996 has been about

4 times higher than that for the peer group of tradi-

tionally underwritten loans. Other Affordable Gold

loans originated in 1994 show a delinquency rate

about 50 percent higher than that for the peer group.

To help enhance the effectiveness of its Affordable

Gold home loan program, Freddie Mac offers lenders

a tool, titled the ‘‘Gold Measure Worksheet,’’ that can

assist loan underwriters in their efforts to accurately

assess the risk associated with combining various

ﬂexibilities in underwriting affordable home loans

(see box ‘‘Freddie Mac’s Gold Measure Worksheet’’).

32. Most of the loans extended to low- and moderate-income

households that are purchased by Freddie Mac (and Fannie Mae)

qualify under standard underwriting guidelines. Loans in the Afford-

able Gold program are generally underwritten using nonstandard

criteria. Fannie Mae has a similar program, the ‘‘Community Home

Buyers Program.’’

The performance of loans made to low- and moderate-income

households using standard underwriting guidelines may be different

from that of Affordable Gold loans. As shown in table 6 for loans

underwritten with standard guidelines, borrower income is not

strongly related to foreclosure rates.

33. See comments by Leland Brendsel in Snigdha Prakash,

‘‘Freddie Sounds a Delinquency Alarm on Popular Lower-Income

Mortgage,’’ American Banker, July 21, 1995, pp. 1 and 8.

8. Increase in number of conventional home purchase loans

for lenders reporting under HMDA, by selected character-

istics of borrowers, 1992–94

Percent

Borrower characteristic 1992–93 1993–94

Memo:

Number of

loans

in 1994

All ............................. 16.5 17.9 2,795,162

Race or ethnic group

American Indian/Alaskan Native . 7.3 23.8 10,691

Asian/Paciﬁc Islander ............ 6.5 18.6 93,319

Black ........................... 35.8 54.7 125,796

Hispanic ........................ 25.4 42.0 129,695

White ........................... 17.5 15.7 2,281,450

Other ........................... 64.1 61.3 18,984

Joint (white/minority) ............ 17.8 37.0 60,763

Income (percentage of MSA

median)

Less than 80 ..................... 38.4 27.0 516,824

80–99 ........................... 21.4 19.1 295,734

100–120 ........................ 16.2 15.7 285,044

More than 120 ................... 8.2 12.5 1,069,305

Income less than 80 percent

of MSA median

American Indian/Alaskan Native . 22.1 32.0 2,125

Asian/Paciﬁc Islander ............ 28.6 29.3 16,865

Black ........................... 67.7 62.8 39,666

Hispanic ........................ 49.5 67.9 38,213

White ........................... 36.4 19.8 391,535

Total

........................ 38.4 27.0 516,824

Note. As of 1993, a large number of additional independent mortgage

companies became covered by the Home Mortgage Disclosure Act (HMDA). To

provide the most appropriate year-over-year comparisons, the lending activity of

these newly covered ﬁrms was excluded from 1993 volume estimates.

1. MSA median is the median family income of the metropolitan statistical

area in which the property related to the loan is located (table includes only

properties in MSAs).

2. Includes loans for which race is unknown or categorized as ‘‘other’’ or

‘‘joint.’’

Source. Federal Financial Institutions Examination Council.

Freddie Mac’s Gold Measure Worksheet

Freddie Mac says that its Gold Measure Worksheet is a

tool ‘‘designed to assist management and underwriters in

their efforts to accurately assess the risk associated with

combining various underwriting ﬂexibilities,’’ and

thereby it helps the lender determine whether a loan will

be acceptable for sale to Freddie Mac under its Afford-

able Gold program.

The worksheet (facing page) identiﬁes borrower and

loan characteristics related to credit risk and assigns a

speciﬁc number of points (referred to as risk units, or

RUs) to each characteristic. The sum of the risk units

provides a summary measure of the risk associated with

a given loan. The applicant’s credit history is one ele-

ment considered and is evaluated by using a credit his-

tory score obtained from a credit bureau or by measuring

the individual components of the credit history ﬁle.

According to Freddie Mac, the Gold Measure Work-

sheet is intended to complement, rather than replace, the

judgment of underwriters. As indicated in the worksheet

instructions, it should be used in conjunction with

Freddie Mac’s booklet Discover Gold Through Expand-

ing Markets ‘‘to identify compensating factors and risk

offsets.’’ This booklet provides case studies illustrating

the ﬂexibility lenders have in applying Freddie Mac’s

underwriting guidelines.

Freddie Mac speciﬁes the following guidelines for

evaluating the summary score derived from the Gold

Measure Worksheet:

• A score of 15 or less (or up to 18 with comprehen-

sive borrower prepurchase and postpurchase homeown-

ership education) is acceptable to Freddie Mac, provided

no other risk is apparent from the review of borrower

eligibility, property appraisal, potential fraud, or data

integrity issues.

• A score between 16 and 25 is acceptable only with

documented offsets not captured on the Gold Measure

Worksheet.

• A score greater than 25 requires that the transaction

be further evaluated. Generally, Freddie Mac has found

that loans with RUs greater than 25 are not acceptable

for purchase without sufﬁcient compensating factors.

642 Federal Reserve Bulletin July 1996

Credit Risk, Credit Scoring, and the Performance of Home Mortgages 643

Freddie Mac ﬁnds that the ‘‘Gold Measure score’’

(the application score computed using the Gold

Measure Worksheet) is a strong predictor of loan

performance and that the Gold Measure Worksheet

provides a useful guide to making sound affordable

housing loans. For example, among the Affordable

Gold loans originated in 1994, the delinquency rate

for those with scores (at origination) in the ‘‘high

risk’’ range was 5.6 times higher than the overall

delinquency rate for the peer group.

Those with

scores in the ‘‘medium risk’’ range had a delinquency

rate 1.4 times higher than the peer group, while those

with scores in the ‘‘low risk’’ range had a delin-

quency rate only 0.6 times as high as the peer group.

Private mortgage insurance companies play an

important role in affordable home lending programs

because lenders and secondary-market institutions

often require borrowers under the programs to obtain

such insurance. Like the secondary-market institu-

tions, the PMI companies have been closely monitor-

ing the performance of the loans they insure that were

extended under affordable home lending programs.

Mortgage Guarantee Insurance Corporation (MGIC)

was the ﬁrst PMI company to provide a detailed

analysis of the performance of such loans. MGIC’s

analysis found that the delinquency rate on such

loans has been higher than on the other loans it

insures, controlling for loan-to-value ratios.

To better understand the factors that may be con-

tributing to the elevated delinquency rates, MGIC

focused on the effect of underwriting ﬂexibility pro-

vided in four areas: (1) funds for down payment

provided by a third party, (2) credit history, (3) allow-

able ratios of debt payment to income, and (4) avail-

able cash reserves after closing. MGIC found that,

among the affordable home program loans insured in

1992 and 1993, providing ﬂexibility in these four

areas was associated with the following results:

(1) Borrowers who covered a 3 percent down pay-

ment themselves and had a third party provide an

additional 2 percent (so-called 3/2 option loans) had a

delinquency rate twice as high as borrowers who

provided the entire 5 percent down payment.

(2) Borrowers with ‘‘adverse’’ credit histories had

delinquency rates four times higher than borrowers

with excellent credit histories, and borrowers with no

credit history had delinquency rates eight times

higher.

(3) Borrowers with ratios of debt payment to

income exceeding the traditional guideline levels had

a delinquency rate 60 percent higher than those with

ratios at or below the traditional guideline levels.

(4) Borrowers with less than two months of cash

reserves at closing had a delinquency rate 40 percent

higher than those with at least two months of cash

reserves.

To learn more about the relationship between

underwriting ﬂexibility and payment performance,

MGIC also reviewed its claim rate experience on all

loans (including those not originated under affordable

home lending programs) it had insured on properties

in the Midwest region from 1985 through 1990.

MGIC found that claim rates are substantially higher

when several criteria that qualify borrowers are

jointly eased in order to qualify an applicant for

credit, a practice referred to as layering of underwrit-

ing ﬂexibilities.

GE Capital Mortgage Insurance Corporation

(GEMICO) reports a delinquency experience with

loans made under affordable home loan programs

that it has insured that is similar to MGIC’s experi-

ence. Like MGIC, GEMICO investigated the results

of allowing borrowers to qualify for credit with lay-

ered ﬂexibilities. The baseline for comparison was

the delinquency rate for all GEMICO-insured loans

written under affordable home lending programs that

have a loan-to-value ratio of at least 95 percent and

that were originated over the 1992–94 period (labeled

100 percent in chart 2). Loan performance was

measured at the end of 1995. As illustrated, when

underwriting ﬂexibilities were layered to qualify an

applicant for credit, payment performance deterio-

rated markedly. For example, for those loans in which

borrowers’ cash reserves covered less than one month

of mortgage payments (the customary minimum is

two months), the delinquency rate was 32 percent

higher than the baseline rate. Among these low-cash-

reserve loans, delinquency rates soared to nearly

2.5 times the baseline rate when the seller contrib-

uted some of the funds needed to meet down-payment

or closing cost requirements.

The GEMICO analysis found that delinquency

rates on loans extended to borrowers with ‘‘good’’

credit histories have been lower than the baseline.

Conversely, delinquency rates have been particularly

high among loans in which the borrowers had mar-

ginal credit histories, high ratios of debt payment to

income, and no cash reserves.

34. For the analysis presented here, ‘‘high risk’’ loans are those that

have Gold Measure application scores above 25, ‘‘medium risk’’ loans

are those with scores between 16 and 25, and ‘‘low risk’’ are those

with scores below 16 (see box ‘‘Freddie Mac’s Gold Measure

Worksheet’’).

35. Steinbach, ‘‘Ready to Make the Grade.’’

36. A subsequent study updated this analysis to cover loans origi-

nated from 1986 through 1991 (Larry Pierzchalski, ‘‘Guarding Against

Risk,’’ Mortgage Banking, June 1996, pp. 38–45).

644 Federal Reserve Bulletin July 1996

A third large mortgage insurance company, United

Guaranty Corporation, reports that among the loans it

insures, delinquency rates on loans from affordable

home lending programs (of various types) exceed

those on traditionally underwritten loans with the

same loan-to-value ratio and year of origination

(chart 3).

Among the affordable home loans that it

has insured, those extended under the 3/2 option

program have the highest delinquency rate. Like the

other PMI companies, United Guaranty also indicates

that it is too soon to determine whether the elevated

delinquency rates on loans originated under afford-

able home lending programs will ultimately result in

elevated claim rates and higher losses.

The PMI industry has generally not attempted to

explicitly price the portion of the risk on loans made

under affordable home lending programs that exceeds

the risk on standard loans with the same loan-to-

37. Like the other PMI companies, United Guaranty also reports

that loans underwritten using multiple ﬂexibilities have substantially

higher delinquency rates than other loans.

2. Effect on the performance of mortgages originated under affordable home loan programs when underwriting ﬂexibilities are

combined to qualify the borrower, by risk factor

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

Relative delinquency rate

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

Relative delinquency rate

Credit history

. . . with high

housing debt

. . . with high

housing debt

and no

reserves

Good Marginal

Total debt (payment as percent of income)

. . . with seller

contribution

. . . with seller

contribution

and no

reserves

Less

than 39

More than 43

Seller contribution (percent)

. . . with marginal

credit

Less than 1 At least 3

Housing debt (payment as percent of income)

. . . with seller

contribution

. . . with seller

contribution

and no

reserves

Less

than 34

More than 38

Cash reserves (months)

. . . with seller

contribution

At least 3 Less than 1

Note. The delinquency rates are those relative to the average rate, set

to 1, for a reference group of mortgages. The reference group consists of

all mortgages insured by GE Capital and originated under affordable

home loan programs during the 1992–94 period with loan-to-value ratios

of at least 95 percent.

Delinquent loans are those on which a scheduled payment was 60 to

90 days past due at the end of 1995.

Cash reserves is the amount of ready cash that the borrower will have

available, after purchasing the home, to cover monthly debt payments,

real estate taxes, and homeowner’s insurance premiums should the

borrower’s income be interrupted.

Seller contribution is the amount of money provided by the seller to

cover the borrower’s obligations at the time of loan origination,

expressed as a percentage of the loan amount.

Credit history: ‘‘Good’’ refers to borrowers who, at the time of loan

origination, had no debt payments overdue sixty or more days, no

multiple thirty-day delinquencies, and no outstanding judgments or

collections. ‘‘Marginal’’ refers to all other borrowers.

Source. GE Capital Mortgage Corporation.

Credit Risk, Credit Scoring, and the Performance of Home Mortgages 645

value ratio.

But anticipating that greater lender ﬂex-

ibility on such loans would entail some additional

risk, insurers have employed various techniques to

mitigate credit risk, such as requiring that borrowers

receive some form of homebuyer education. Insurers

are now instructing lenders to tighten their proce-

dures, emphasizing that they should use the ﬂexibili-

ties in the underwriting guidelines judiciously and

that layering risk factors to qualify applicants for

credit is inappropriate unless the applicants have

offsetting strengths. Insurers have further emphasized

to underwriters that borrowers with marginal credit

histories also are at greater risk of default;

insurers

therefore have tried to clarify for lenders the circum-

stances under which applicants with marginal credit

histories would be considered creditworthy. The PMI

companies have expressed conﬁdence that tightening

procedures, along with improved homebuyer educa-

tion programs and enhanced servicing, will reduce

the risks of offering ﬂexible underwriting standards

to levels more in line with their current pricing

structure.

Experiences of Primary-Market Lending

Institutions

While secondary-market institutions and the PMI

companies have had quite similar experiences with

affordable home lending, individual banks and sav-

ings institutions that originate mortgages report much

more varied experiences with such loans. The pro-

grams of the depository institutions vary greatly in

their target populations and details of operation. Insti-

tutions also differ in their loan servicing practices,

which may affect the proportion of loans that move

from initial delinquency into more serious delin-

quency and foreclosure. Consequently, generalizing

about the experiences with loans made under afford-

able home loan programs by the large number of

individual creditors that offer them is difﬁcult.

Moreover, assessing the performance of affordable

home loan portfolios is often complicated or pre-

cluded by a lack of adequate performance data on the

loans. Most are relatively new and focused on rela-

tively small geographic areas. Equally important,

without information on the performance of tradition-

ally underwritten loans that were originated, for

example, during the same time period and within the

same geographic area, the effect of individual under-

writing ﬂexibilities cannot be established.

Information from individual lenders reveals the

varied nature of their experiences. NatWest, a large

bank in the Middle Atlantic region, found that the

delinquency rate was roughly 25 percent lower for

the loans it made under affordable home lending

programs than for its conventional loans made over

the same period and in the same area; the bank

attributes this record in part to enhanced counseling

efforts. Bank of America also reports a 25 percent

38. Recently, however, United Guaranty announced that it will

raise the insurance premium for its 95 percent loan-to-value ratio

loans in which 2 percentage points of the funds are provided by a third

party (that is, 3/2 option loans); the premium will rise to the level

required of 97 percent loan-to-value ratio loans, which have exhibited

elevated delinquency rates comparable to those on 3/2 option loans.

39. An analysis of delinquent loans made under affordable home

loan programs insured by United Guaranty found, for example, that

53 percent have one or more major credit payment problems listed in

their credit bureau reports.

40. Homebuyer education programs have varied considerably,

ranging from the rudimentary to a series of in-depth classes. Industry

representatives continue to believe that a well-designed program can

signiﬁcantly help borrowers prepare for the responsibilities of home-

ownership (‘‘Affordable Housing—An Interview With MGIC’s

Gordon H. Steinbach,’’ Creative Interfaces, Chevy Chase, Md.,

March–April 1996, p. 2).

In line with that objective, Fannie Mae has organized the American

Homeowner Education and Counseling Institute, whose purpose is to

help enlarge the pool of ﬁrst-time homebuyers through the develop-

ment of a high-quality, standardized education and counseling pro-

gram. The institute is being ﬁnanced initially by Fannie Mae, Freddie

Mac, and several lenders and industry associations (Edward Kulkosky,

‘‘Fannie Institute’s Goal: Informing Both Lenders and Potential Bor-

rowers,’’ American Banker, June 5, 1996, p. 8).

3. Relative delinquency rates of selected, privately insured,

affordable home mortgages, by year of origination and

type of loan

1992 1993 1994 1995

Relative delinquency rate

Affordable

95% LTV

with

3/2 option

97% LTV

Note. The delinquency rates shown are those relative to the rate on standard

95 percent loan-to-value (LTV) ratio loans, for which the rate was set to 1.

Delinquencies are payments reported by lenders as being at least thirty days

past due.

In this chart, the affordable loan category comprises loans designated by the

lender as affordable home loans, loans sold to a state or local housing ﬁnance

agency, and 97 percent loan-to-value ratio loans. In loans with the 3/2 option,

the borrower made a 3 percent down payment and a third party supplied a

2 percent down payment.

Source. United Guaranty Residential Insurance Co.

646 Federal Reserve Bulletin July 1996

lower delinquency rate for its affordable home loans

relative to its traditionally underwritten loans. They

attribute this relatively favorable performance to the

careful application of underwriting ﬂexibilities based

on their many years of experience with affordable

home lending.

In contrast, other banks have found that delin-

quency rates on loans extended under affordable

home programs have exceeded those on traditionally

underwritten loans having comparable loan-to-value

ratios. Moreover, like the secondary-market institu-

tions, these banks have had higher delinquency rates

on loans involving multiple ﬂexibilities.

Participants in the NeighborWorks network—

regional lending consortiums organized by the Neigh-

borhood Reinvestment Corporation (NRC)—have

also had a variety of experiences with the loans they

have originated under affordable home lending pro-

grams. For some NeighborWorks programs, the rate

for delinquencies lasting sixty days or longer is close

to or below the industry average, while the rate is

higher for other NeighborWorks programs. NRC

views homebuyer education, both prepurchase and

postpurchase, to be an essential element of successful

affordable home lending programs.

Geographic Concentration of Defaults

Not addressed in most analyses of affordable home

lending programs is the question of whether delin-

quencies and defaults of loans in such programs tend

to be geographically concentrated. Many affordable

lending programs target speciﬁc neighborhoods or

involve criteria that tend to focus the geographic

reach of these programs. Consequently, the portfolio

of affordable home program loans would tend to be

less geographically diverse than the portfolio of tradi-

tionally underwritten loans. From a social perspec-

tive, this issue may be important because geographic

concentrations of foreclosed properties can have

adverse effects on neighborhood stability.

Little is known about the degree of geographic

concentration of defaults in affordable lending pro-

grams. One recent study, however, has investigated

this issue using information from a single lender on

the performance of loans underwritten under an

affordable home loan program in Philadelphia.

The

study found that more than two-thirds of the loans

that were delinquent at least ninety days were located

in Census tracts where only one-third of the bank’s

affordable home loans had been extended. The

study’s preliminary analysis suggests that geographic

factors, such as area unemployment rates, are impor-

tant in predicting these delinquencies. In addition,

the borrower’s credit history, as summarized by a

credit history score, is also a strong predictor of loan

delinquency. Two factors may have mitigated the

adverse effects of concentration: Tracts with high

delinquency rates are dispersed across the city, and

the lender typically works with seriously delinquent

borrowers, providing a period of forbearance to help

them resume payments and avoid foreclosure.

SUMMARY

To measure credit risk, lenders gather information

about prospective borrowers and the collateral they

offer and then assess this information in light of

experience gained from extending credit in the past.

Historically, lenders have relied heavily on the sub-

jective judgment of underwriters in assessing credit

risk.

To facilitate the underwriting process, reduce costs,

and promote consistency, lenders have brought credit

scoring into the process. In some uses, credit scores

are based exclusively on credit bureau records and, as

such, provide a summary measure of the relative

credit risk posed by individuals with differing credit

histories. In other uses, credit scores are based on a

wider range of information and are used to evaluate

the overall credit risk posed by an applicant, provid-

ing a summary measure that lenders can use to gauge

the acceptability of an application.

The data consistently show that credit scores are

useful in gauging the relative levels of risk posed

by both prospective mortgage borrowers and those

with existing mortgages. Although the absolute levels

of delinquency and default are low in all score

41. George Knight and Catherine A. Smith, ‘‘Death Knell or False

Alarm? Assessing the Risks in Lending,’’ Stone Soup, Fall 1995,

pp. 4–7.

42. Concern about the adverse neighborhood consequences of geo-

graphic concentrations of defaults in the FHA lending program are

longstanding. Historically, the economic deterioration of many inner-

city neighborhoods has been linked to the level of FHA lending in

these communities and the relatively high rate of foreclosure and

property abandonment associated with this lending program. See

Calvin Bradford and Anne B. Schlay, ‘‘Assessing a Can Opener:

Economic Theory’s Failure to Explain Discrimination in FHA Lend-

ing Markets,’’ CityScape, U.S. Department of Housing and Urban

Development, March 1996, pp. 77–88.

43. See Paul S. Calem and Susan M. Wachter, ‘‘Performance of

Mortgages in a Community Reinvestment Portfolio: Implications for

Flexible Lending Initiatives,’’ paper presented at the American Real

Estate and Urban Economics Association meetings, San Francisco,

January 1996.

Credit Risk, Credit Scoring, and the Performance of Home Mortgages 647

categories, the proportion of problem loans increases

as credit scores decrease. That relationship puts the

focus of business concern on the prospective and

existing borrowers with low scores because even

small increases in the rate of default may mean the

difference between proﬁt and loss.

Analysis of the distribution of borrowers across

credit history score ranges suggests that most house-

holds have relatively high scores, regardless of the

income or home value characteristics of the areas in

which they reside. However, relatively more of those

who reside in lower-income locations or in locations

with lower home values have lower scores.

For many institutions in the mortgage market,

evaluating and managing the risks of lending to non-

traditional borrowers and the risks of allowing greater

ﬂexibility in underwriting are relatively new experi-

ences. Carefully evaluating the experiences to date

provides important insights.

Available information suggests that most borrow-

ers with loans made under affordable home loan

programs have made their payments on time. Prob-

lems to date appear to have been concentrated among

loans in which underwriting ﬂexibilities have been

layered and loans in which third-party down-payment

assistance has been allowed.

Lenders and mortgage insurers have responded by

tightening their procedures, emphasizing to under-

writers that the ﬂexibilities provided in underwriting

guidelines need to be used judiciously and that appro-

priate compensating factors are needed to offset the

risks associated with lending outside traditional

guidelines. Market participants generally agree that,

to be viable, affordable home lending programs must

be accompanied by effective risk mitigation activi-

ties, including homebuyer education programs and

enhanced loan servicing. Affordable lending pro-

grams are evolving and, as experience is gained,

lenders are likely to ﬁnd ways to expand homebuying

opportunities without accepting undue risks.

648 Federal Reserve Bulletin July 1996