a

GAO

United States General Accounting Office

Report to the Chairwoman, 

Subcommittee on Housing and 

Community Opportunity, Committee on 

Financial Services, House of 

Representatives

July 2002 MORTGAGE 

FINANCING

Changes in the 

Performance of 

FHA-Insured Loans

GAO-02-773
Page i GAO-02-773 Mortgage Financing

Contents

Letter 1

Results in Brief 2

Background 4

Early Performance of FHA Loans Originated during the Late

1990s Has Declined Slightly 8

Program- and Market-Related Changes that Could Explain 

Higher Foreclosure Rates 22

Performance of Recent Loans Suggests that FHA’s Portfolio 

May Be Riskier than Previously Estimated 30

Agency Comments and Our Evaluation 34

Appendixes

Appendix I: Scope and Methodology 36

Appendix II: Models Used to Forecast Defaults and Prepayments for 

FHA-Insured Mortgages 37

Data and Sample Selection 38

Specification of the Model 39

Estimation Results 51

Appendix III: Data for Figures Used in This Report 60

Appendix IV: Comments from the Department of Housing and Urban 

Development 66

Tables Table 1: Description of FHA’s Loss Mitigation Tools Available

to Lenders 29

Table 2: Variable Names and Descriptions 47

Table 3: Means of Predictor Variables 49

Table 4: Coefficients from Foreclosure Equations and 

Summary Statistics 53

Table 5: Coefficients from Prepayment Equations and 

Summary Statistics 55

Table 6: National 4-Year Cumulative Foreclosure Rates for

All FHA Loans Originated during Fiscal Years

1990–1998 (Figure 1) 60

Table 7: National 4-Year Cumulative Foreclosure Rates for 

Long-Term, Fixed Rate Loans Originated during

Fiscal Years 1990–1998 (Figure 2) 60
Contents

Page ii GAO-02-773 Mortgage Financing

Table 8: National 4-Year Cumulative Foreclosure Rates for

FHA Fixed- and Adjustable Rate Mortgage Loans 

Originated during Fiscal Years 1990–1998 (Figure 3) 61

Table 9: Adjustable Rate Mortgages as Share of All FHA Loans 

Originated during Fiscal Years 1990–1998 (Figure 4) 62

Table 10: Share of FHA Long-Term, Fixed-Rate Loans Originated

in Selected States during Fiscal Years 

1990–1998 (Figure 5) 62

Table 11: National 4-Year Cumulative Foreclosure Rates for 

FHA Long-Term, Fixed-Rate Loans Originated in 

Selected States during Fiscal Years 1990–1998 

(Figure 6) 63

Table 12: Share of FHA Adjustable Rate Mortgages Originated in 

Selected States during Fiscal Years 1990–1998

(Figure 7) 63

Table 13: National 4-Year Cumulative Foreclosure Rates for FHA 

Adjustable Rate Mortgages Originated in Selected States 

during Fiscal Years 1990–1998 (Figure 8) 64

Table 14: Distribution of LTV Categories for FHA Loans Originated 

during Fiscal Years 1990–1998 (Figure 9) 64

Table 15: National 4-Year Cumulative Foreclosure Rates for 

Selected LTV Classes of Long-Term, Fixed-Rate 

Mortgages Originated during Fiscal Years 1990–1998 

(Figure 10) 65

Table 16: Actual and Forecasted Cumulative Foreclosure Rates for 

FHA Loans Insured during Fiscal Years 1996–2001, as of 

September 30, 2001 (Figure 11) 65

Figures Figure 1: National 4-Year Cumulative Foreclosure Rates

for All FHA Loans Originated during Fiscal Years 

1990–1998 9

Figure 2: National 4-Year Cumulative Foreclosure Rates for 

All FHA Loans Originated during Fiscal Years 

1980–1998 10

Figure 3: National 4-Year Cumulative Foreclosure Rates for 

All FHA Loans Originated during Fiscal Years

1990–1998, by Loan Type 11

Figure 4: Adjustable Rate Mortgages as Share of All FHA 

Loans Originated during Fiscal Years 1990–1998 12

Figure 5: Share in Selected States of FHA Long-Term, 

Fixed-Rate Loans Originated during Fiscal Years

1990–1998 14
Contents

Page iii GAO-02-773 Mortgage Financing

Figure 6: National 4-Year Cumulative Foreclosure Rates in 

Selected States for FHA Long-Term, Fixed-Rate Loans 

Originated during Fiscal Years 1990–1998 16

Figure 7: Share of FHA Adjustable Rate Mortgages, in Selected 

States, Originated during Fiscal Years 1990–1998 18

Figure 8: National 4-Year Cumulative Foreclosure Rates in 

Selected States for FHA Adjustable Rate Mortgages 

Originated during Fiscal Years 1990–1998 19

Figure 9: Share of FHA Loans within Various LTV Categories for 

Loans Originated during Fiscal Years 1990–1998 20

Figure 10: National 4-Year Cumulative Foreclosure Rates for 

Selected LTV Classes of Long-Term, Fixed-Rate FHA 

Mortgages Originated during Fiscal Years 1990–1998 21

Figure 11: Actual and Forecasted Cumulative Foreclosure

Rates for FHA Loans Insured during Fiscal Years 

1996–2001, as of September 30, 2001 32

Figure 12: Cumulative Foreclosure Rates by Book of 

Business for 30-Year, Fixed-Rate, Nonrefinanced 

Mortgages, Actual and Predicted, Fiscal Years 

1975–1995 58

Figure 13: Cumulative Prepayment Rates by Book of 

Business for 30-Year, Fixed-Rate, Nonrefinanced 

Mortgages, Actual and Predicted, Fiscal Years 

1975–1995 59

Abbreviations

ARM Adjustable rate mortgage

Fannie Mae Federal National Mortgage Association

FHA Federal Housing Administration

Freddie Mac Federal Home Loan Mortgage Corporation

HUD Department of Housing and Urban Development

LTV Loan-to-value
Page 1 GAO-02-773 Mortgage Financing

United States General Accounting Office

Washington, D.C. 20548

A

July 10, 2002 Letr

The Honorable Marge Roukema

Chairwoman, Subcommittee on Housing

and Community Opportunity

Committee on Financial Services

House of Representatives

Dear Madam Chairwoman:

The Department of Housing and Urban Development (HUD), through its 

Federal Housing Administration (FHA), provides insurance for private 

lenders against losses on home mortgages. The insurance program is 

supported by the Mutual Mortgage Insurance Fund (Fund). To help place 

the Fund on a financially sound basis, the Congress enacted legislation in 

November 1990 that required the Secretary of HUD to, among other things, 

take steps to ensure that the Fund achieve and maintain an economic value 

of at least 2 percent of the Fund’s insurance-in-force.1

 In February 2001 we 

reported that a 2 percent capital ratio appeared sufficient to withstand 

moderately severe economic downturns that could lead to worse-thanexpected loan performance.2

 However, we cautioned against concluding 

that the Fund could withstand the specified economic scenarios regardless 

of the future activities of FHA or the market. Specifically, we noted that our 

estimates and those of others are valid only under a certain set of 

conditions, including that recently insured FHA loans respond to economic 

conditions similarly to the response of those insured in the more distant 

past. At the end of fiscal year 2001, loans originated in the most recent 4 

fiscal years accounted for about 70 percent of FHA’s portfolio.

Concerned about reported increases in FHA’s default and foreclosure rates, 

you asked that we assess the performance of loans made in recent years 

and the implications for the Fund of any worsening loan performance. To 

address your concerns, we (1) describe how the early performance of FHA 

loans originated in recent years differs from the performance of loans 

1

The economic value of the Fund is the sum of existing capital resources plus the net 

present value of future cash flows.

2

These included scenarios that are based on recent regional experiences and on the 1981 

through1982 national recession. See U.S. General Accounting Office, Mortgage Financing: 

FHA’s Fund Has Grown, but Options for Drawing on the Fund Have Uncertain Outcomes, 

GAO-01-460 (Washington, D.C.: Feb. 28, 2001).
Page 2 GAO-02-773 Mortgage Financing

originated earlier; (2) describe changes in FHA’s program or the 

conventional mortgage market that could explain recent loan performance; 

and (3) assess whether the overall riskiness of FHA’s portfolio is greater 

than we previously estimated and assess the impact that any increased 

riskiness might have on the ability of the Fund to withstand worse-thanexpected loan performance.

To meet these objectives, we used data provided by FHA to compare 

foreclosure rates for FHA-insured loans over time by the type of loan, the 

location of the property, and the amount of the loan as a percentage of the 

property’s value (loan-to-value ratio). We reviewed FHA guidance, trade 

literature, and publicly available information to identify changes in the FHA 

and conventional mortgage market that could explain any differences in 

loan performance for recently originated loans. Finally, using the model 

that we developed for our prior report and basing it on the experience of 

FHA loans insured from fiscal years 1975 through 1995, we also compared 

the estimated and actual foreclosure rates through 2001 of loans insured 

from fiscal years 1996 through 2001. Appendix I provides a more detailed 

description of our scope and methodology. Appendix II provides a 

technical description of the model we used to assess estimated and actual 

loan performance.

We conducted our work from July 2001 through June 2002, in accordance 

with generally accepted government auditing standards.

Results in Brief Although FHA loans made in recent years have experienced somewhat 

higher foreclosure rates than loans made in the years immediately 

preceding them, recent loans are performing much better than loans made 

in the 1980s. Specifically, FHA loans made during the 1990s had lower 

cumulative foreclosures by the fourth year after origination than similarly 

aged loans made during the 1980s. However, foreclosure rates were 

somewhat higher for loans originated during the latter 1990s than they 

were earlier in the decade. Specifically, through their fourth year, loans 

insured during fiscal years 1990 through 1994 had an average cumulative 

foreclosure rate of 2.23 percent, while loans originated later in the decade 

had an average foreclosure rate of 2.93 percent. Foreclosure rates were 

even higher for adjustable rate mortgages and mortgages on properties 

located in California. Specifically, between 1990 and 1994 the 4-year 

cumulative foreclosure rate for adjustable rate mortgages, which nearly 

doubled in volume during the 1990s, averaged 2.53 percent, as compared 

with a 3.90 percent average 4-year cumulative foreclosure rate for 
Page 3 GAO-02-773 Mortgage Financing

adjustable rate mortgages originated between 1995 and 1998. California, 

which accounted for 15 percent of the dollar value of all single-family loans 

FHA insured during the 1990s, had an average foreclosure rate of 6.41 

percent for both fixed rate and adjustable rate mortgages. In comparison, 

the 4-year cumulative foreclosure rate for FHA loans insured during the 

1990s outside of California averaged 1.97 percent. Part of the increase in 

the overall foreclosure rate during the 1990s is attributable to the 

increasing number of loans with higher loan-to-value ratios. However, 

regardless of the loan-to-value ratio of a loan, foreclosure rates generally 

were higher for loans made later in the decade. 

Although economic factors such as house price appreciation are key 

determinants of mortgage foreclosure, changes in underwriting 

requirements as well as changes in the conventional mortgage market may 

partly explain the higher foreclosure rates experienced later in the 1990s. 

Since 1995 there have been numerous changes to FHA’s underwriting 

procedures, designed mainly to increase homeownership opportunities. 

Generally, these changes have allowed more borrowers who may not have 

met previous underwriting standards to qualify for loans, or have increased 

the loan amounts for which these borrowers qualify. In addition, since 1995 

private mortgage insurers have been more likely to insure loans with low 

down payments for borrowers whom the private insurers identified as 

being relatively low risk. As a result of both types of changes, the risk 

associated with FHA’s loan portfolio may have increased since 1995. FHA 

also took steps to tighten underwriting and to mitigate losses from 

foreclosures. Because of data limitations, we were unable to directly 

estimate the effect of changes in FHA underwriting and the conventional 

mortgage market on loan performance. Specifically, the data that FHA 

collects at the individual loan level on items such as credit scores and debtto-income ratios, which would allow such an analysis, have not been 

collected for a sufficient number of years or are not sufficiently detailed to 

permit their inclusion in a model that estimates the impact of economic 

variables on loan performance.
Page 4 GAO-02-773 Mortgage Financing

Although more years of loan performance are necessary to make a 

definitive judgment, our analysis suggests that factors not fully captured in 

the model we used for our February 2001 report may be affecting the 

performance of recent FHA loans and causing the overall riskiness of FHA’s 

portfolio to be somewhat greater than we previously estimated. These 

factors could include the changes in underwriting and in the conventional 

mortgage market described above. In particular, we found that foreclosure 

rates through the end of fiscal year 2001, for books of business insured 

after fiscal year 1995, are greater than what would be anticipated from a 

model based on the performance of loans insured from 1975 through 1995.3

 

Thus the Fund may be somewhat less able to withstand worse-thanexpected loan performance resulting from adverse economic conditions. 

We continue to urge caution in concluding that the Fund can withstand 

specified economic scenarios regardless of how recently insured loans 

respond to economic conditions. 

We presented a draft of this report to officials from HUD for their review 

and comment. They provided written comments that are reprinted in 

appendix IV. Generally, HUD officials agreed with the findings of the report 

and commented that the underwriting changes made in 1995 allowed FHA 

to be successful in its mission of increasing homeownership opportunities 

for underserved groups.

Background FHA was established in 1934 under the National Housing Act (P.L. 73-479) 

to broaden homeownership, shore up and protect lending institutions, and 

stimulate employment in the building industry. FHA insures private lenders 

against losses on mortgages that finance purchases of properties with one 

to four housing units. Many FHA-insured loans are made to low-income, 

minority, and first-time homebuyers. 

3

A book of business represents all loans insured during a given year.
Page 5 GAO-02-773 Mortgage Financing

Generally, lenders require borrowers to purchase mortgage insurance when 

the value of the mortgage is large relative to the price of the house. FHA 

provides most of its single-family insurance through a program supported 

by the Mutual Mortgage Insurance Fund. The economic value of the Fund, 

which consists of the sum of existing capital resources plus the net present 

value of future cash flows, depends on the relative size of cash outflows 

and inflows over time. Cash flows out of the Fund from payments 

associated with claims on foreclosed properties, refunds of up-front 

premiums on mortgages that are prepaid, and administrative expenses for 

management of the program. To cover these outflows, FHA deposits cash 

inflows—up-front and annual insurance premiums from participating 

homebuyers and the net proceeds from the sale of foreclosed properties—

into the Fund. If the Fund were to be exhausted, the U.S. Treasury would 

have to cover lenders’ claims and administrative costs directly. The Fund 

remained relatively healthy from its inception until the 1980s, when losses 

were substantial, primarily because of high foreclosure rates in regions 

experiencing economic stress, particularly the oil-producing states in the 

West South Central section of the United States.4

 These losses prompted 

the reforms that were first enacted in November 1990 as part of the 

Omnibus Budget Reconciliation Act of 1990 (P.L. 101-508). The reforms, 

designed to place the Fund on an actuarially sound basis, required the 

Secretary of HUD to, among other things, take steps to ensure that the 

Fund attained a capital ratio of 2 percent of the insurance-in-force by 

November 2000 and to maintain or exceed that ratio at all times thereafter.5

As a result of the 1990 housing reforms, the Fund must meet not only the 

minimum capital ratio requirement but also operational goals before the 

Secretary of HUD can take certain actions that might reduce the value of 

the Fund. These operational goals include meeting the mortgage credit 

needs of certain homebuyers while maintaining an adequate capital ratio, 

minimizing risk, and avoiding adverse selection. However, the legislation 

does not define what constitutes adequate capital or specify the economic 

conditions that the Fund should withstand.

4

The West South Central region comprises Arkansas, Louisiana, Oklahoma, and Texas.

5

The Act defined the capital ratio as the ratio of the Fund’s capital, or economic net worth, 

to its unamortized insurance-in-force. However, the Act defined unamortized insurance-inforce as the remaining obligations on outstanding mortgages—a definition generally 

understood to apply to amortized insurance-in-force. FHA has calculated the 2 percent 

capital ratio using unamortized insurance-in-force as it is generally understood—which is 

the initial amount of mortgages. 
Page 6 GAO-02-773 Mortgage Financing

The 1990 reforms also required that an independent contractor conduct an 

annual actuarial review of the Fund. These reviews have shown that during 

the 1990s the estimated value of the Fund grew substantially. At the end of 

fiscal year 1995, the Fund attained an estimated economic value that 

slightly exceeded the amount required for a 2 percent capital ratio. Since 

that time, the estimated economic value of the Fund continued to grow and 

always exceeded the amount required for a 2 percent capital ratio. In the 

most recent actuarial review, Deloitte & Touche estimated the Fund’s 

economic value at about $18.5 billion at the end of fiscal year 2001. This 

represents about 3.75 percent of the Fund’s insurance-in-force.

In February 2001 we reported that the Fund had an economic value of $15.8 

billion at the end of fiscal year 1999. This estimate implied a capital ratio of 

3.20 percent of the unamortized insurance-in-force. The relatively large 

economic value and high capital ratio reported for the Fund reflected the 

strong economic conditions that prevailed during most of the 1990s, the 

good economic performance that was expected for the future, and the 

increased insurance premiums put in place in 1990.

In our February 2001 report we also reported that, given the economic 

value of the Fund and the state of the economy at the end of fiscal year 

1999, a 2 percent capital ratio appeared sufficient to withstand moderately 

severe economic scenarios that could lead to worse-than-expected loan 

performance. These scenarios were based upon recent regional 

experiences and the national recession that occurred in 1981 and 1982. 

Specifically, we found that such conditions would not cause the economic 

value of the Fund at the end of fiscal year 1999 to decline by more than 2 

percent of the Fund’s insurance-in-force. Although a 2 percent capital ratio 

also appeared sufficient to allow the Fund to withstand some more severe 

scenarios, we found that three of the most severe scenarios we tested 

would cause the economic value of the Fund to decline by more than 2 

percent of the Fund’s insurance-in-force.6

 These results suggest that the 

existing capital ratio was more than sufficient to protect the Fund from 

many worse-than-expected loan performance scenarios. However, we 

cautioned that factors not fully captured in our economic models could 

6

These scenarios included (1) a scenario in which the entire nation experiences a downturn 

similar to the one New England experienced during the late 1980s and early 1990s, (2) a 

scenario in which FHA experiences foreclosure rates similar to those it experienced in the 

late 1980s, and (3) a scenario in which 35.6 percent or more of FHA loans experience 

foreclosure rates similar to those experienced by FHA in the West South Central portion of 

the United States in the late 1980s.
Page 7 GAO-02-773 Mortgage Financing

affect the Fund’s ability to withstand worse-than-expected experiences 

over time. These factors include recent changes in FHA’s insurance 

program and the conventional mortgage market that could affect the 

likelihood of poor loan performance and the ability of the Fund to 

withstand that performance.

In deciding whether to approve a loan, lenders rely upon underwriting 

standards set by FHA or the private sector. FHA’s underwriting guidelines 

require lenders to establish that prospective borrowers have the ability and 

willingness to repay a mortgage. In order to establish a borrower’s 

willingness and ability to pay, these guidelines require lenders to evaluate 

four major elements: qualifying ratios and compensating factors; stability 

and adequacy of income; credit history; and funds to close. 

In recent years, private mortgage insurers and conventional lenders have 

begun to offer alternatives to borrowers who want to make small or no 

down payments.7

 Private lenders have also begun to use automated 

underwriting as a means to better target low-risk borrowers for 

conventional mortgages. Automated underwriting relies on the statistical 

analysis of hundreds of thousands of mortgage loans that have been 

originated over the past decade to determine the key attributes of the 

borrower’s credit history, the property characteristics, and the terms of the 

mortgage note that affect loan performance. The results of this analysis are 

arrayed numerically in what is known as a “mortgage score.” A mortgage 

score is used as an indicator of the foreclosure or loss risk to the lender. 

7

Conventional mortgage lenders, by offering second mortgages of up to 23 percent of the 

value of the house, sometimes allow borrowers to borrow more than the value of the house 

without obtaining mortgage insurance.
Page 8 GAO-02-773 Mortgage Financing

Early Performance of 

FHA Loans Originated 

during the Late 1990s 

Has Declined Slightly 

During their early years, FHA loans insured from fiscal year 1995 through 

fiscal year 1998 have shown somewhat higher cumulative foreclosure rates 

than FHA loans insured from fiscal year 1990 through fiscal year 1994, but 

these rates are well below comparable rates for FHA loans insured in the 

1980s. To better understand how foreclosure rates might vary, we 

compared the rates for different types of loans—fixed-rate and adjustable 

rate mortgages (ARMs)—locations of properties, and loan-to-value (LTV) 

ratios. For loans made in recent years, FHA has been experiencing 

particularly high foreclosure rates for ARMs and mortgages on properties 

located in California. One measure of the initial risk of a loan, its LTV, can 

partly explain the difference over time in foreclosure rates. That is, FHA 

insured relatively more loans with high LTVs later in the decade than it 

insured earlier in the decade. However, the same pattern of higher 

foreclosure rates in the later 1990s exists even after differences in LTV are 

taken into account.8

Foreclosure Rates Are 

Somewhat Higher for FHA 

Loans Made Later in the 

1990s, but Do Not Approach 

the Levels for Loans Made in 

the Previous Decade

We compared the four-year cumulative foreclosure rates across books of 

business to measure the performance of FHA’s insured loans.9

 As shown in 

figure 1, the 4-year cumulative foreclosure rate for FHA-insured loans was 

generally higher for loans originated later in the 1990s than for loans 

originated earlier in that decade.10 Through their fourth year, loans 

originated during fiscal years 1990 through 1994 had an average cumulative 

foreclosure rate of 2.23 percent, while loans originated during fiscal years 

1995 through 1998 had an average cumulative foreclosure rate of 2.93 

percent.

8

Later in this report we discuss in some detail the potential impact that both changes in 

FHA’s program and competition from conventional lenders may have on foreclosure rates 

for FHA-insured loans, and on the riskiness of FHA’s portfolio.

9

We selected a 4-year cumulative foreclosure rate because it best balanced the competing 

goals of having the greatest number of recent observations and the greatest number of years 

of experience. We also examined a 3-year cumulative foreclosure rate across books of 

business originated between 1990 and 1999 and found a similar pattern in foreclosure rates. 

Therefore, we concluded that a 4-year cumulative claim rate was a reasonable indicator of 

loan performance. 

10These figures represent the original loan amount of the foreclosed loans for which FHA 

paid a claim during the first 4 years of the life of these mortgages as a percentage of the total 

value of mortgages originated in that year. 
Page 9 GAO-02-773 Mortgage Financing

Figure 1: National 4-Year Cumulative Foreclosure Rates for All FHA Loans 

Originated during Fiscal Years 1990–1998

Note: Data for all figures are in appendix III.

Source: GAO analysis of FHA data.

Although the 4-year cumulative foreclosure rates for loans that FHA 

insured in the later part of the 1990s were higher than that for loans that 

FHA insured earlier in that decade, those rates were still well below the 

high levels experienced for loans that FHA insured in the early- to mid1980s, as shown in figure 2. The 4-year cumulative foreclosure rates for 

FHA loans originated between 1981 and 1985, a period of high interest and 

unemployment rates and low house price appreciation rates, ranged 

between 5 and 10 percent, while the rates for loans originated during the 

1990s, when economic conditions were better, have consistently been 

below 3.5 percent.
Page 10 GAO-02-773 Mortgage Financing

Figure 2: National 4-Year Cumulative Foreclosure Rates for All FHA Loans Originated during Fiscal Years 1980–1998

Source: GAO analysis of FHA data.

FHA Foreclosure Rates 

Have Been Particularly High 

for Adjustable Rate 

Mortgages

Since fiscal year 1993, FHA has experienced higher 4-year cumulative 

foreclosure rates for ARMs than it has for long-term (generally 30-year) 

fixed-rate mortgages, as shown in figure 3. In addition, between 1990 and 

1994 the 4-year cumulative foreclosure rate for ARMs averaged 2.53 

percent, as compared with a 3.90 percent average 4-year cumulative 

foreclosure rate for ARMs originated between 1995 and 1998. These higher 

foreclosures have occurred even though mortgage interest rates have been 

generally stable or declining during this period. 
Page 11 GAO-02-773 Mortgage Financing

Figure 3: National 4-Year Cumulative Foreclosure Rates for All FHA Loans 

Originated during Fiscal Years 1990–1998, by Loan Type

Source: GAO analysis of FHA data.

In the early 1990s, when ARMs were performing better than fixed-rate 

mortgages, the performance of ARMs had relatively little impact on the 

overall performance of loans FHA insured because FHA insured relatively 

few ARMs. However, as shown in figure 4, later in the decade ARMs 

represented a greater share of the loans that FHA insured, so their 

performance became a more important factor affecting the overall 

performance of FHA loans. FHA is studying its ARM program and has 

contracted with a private consulting firm to examine the program’s design 

and performance.
Page 12 GAO-02-773 Mortgage Financing

Figure 4: Adjustable Rate Mortgages as Share of All FHA Loans Originated during 

Fiscal Years 1990–1998

Source: GAO analysis of FHA data.

FHA Foreclosure Rates 

Have Been Particularly High 

in California 

FHA insured a greater dollar value of loans in the 1990s in California than 

in any other state. Among the states in which FHA does the largest share of 

its business, 4-year cumulative foreclosure rates for both long-term, fixedrate mortgages and ARMs were typically highest in California. California, 

which accounted for 15 percent of the dollar value of all single-family loans 

that FHA insured during the 1990s, had an average foreclosure rate of 

6.41 percent for both fixed rate and ARMs. In comparison, the 4-year 

cumulative foreclosure rate for FHA loans insured during the 1990s outside 

of California averaged 1.97 percent. According to FHA, the poor 

performance of FHA loans originated in California was attributable to poor 

economic conditions that existed during the early- to mid-1990s, coupled 
Page 13 GAO-02-773 Mortgage Financing

with the practice of combining FHA’s interest-rate buy-down program with 

an ARM to qualify borrowers in California’s high-priced housing market.11

The five states with the greatest dollar value of long-term fixed-rate 

mortgages insured by FHA during the 1990s were California, Texas, Florida, 

New York, and Illinois. Loans insured in these states made up about onethird of FHA’s business for this loan type from fiscal year 1990 through 

fiscal year 1998, with California alone accounting for about 13 percent, as 

shown in figure 5. As a result, the performance of loans insured in 

California can significantly affect the overall performance of FHA’s 

portfolio of loans of this type.

11Buy downs allow sellers to pay a nominal amount to lower (or buy down) the homebuyer’s 

interest rate for the first year. With lower first-year payments, buyers can more easily qualify 

for a mortgage for which they otherwise would have been ineligible. According to FHA, 

some homebuyers, when faced with a large increase in mortgage payments after the buy 

down period, had a greater likelihood of defaulting on their mortgages. 
Page 14 GAO-02-773 Mortgage Financing

Figure 5: Share in Selected States of FHA Long-Term, Fixed-Rate Loans Originated 

during Fiscal Years 1990–1998

Source: GAO analysis of FHA data.
Page 15 GAO-02-773 Mortgage Financing

For long-term fixed-rate mortgages that FHA insured in California from 

fiscal year 1990 through fiscal year 1998, the 4-year cumulative foreclosure 

rates averaged about 5.6 percent. As shown in figure 6, Florida, Texas, and 

New York also had relatively high 4-year foreclosure rates during the early 

1990s. And Florida experienced relatively high 4-year cumulative 

foreclosure rates again from 1995 through 1998. For states that were not 

among the five states with the greatest share of fixed-rate mortgages, the 4-

year cumulative foreclosure rates for the same type of loan over the same 

period averaged less than 2 percent.
Page 16 GAO-02-773 Mortgage Financing

Figure 6: National 4-Year Cumulative Foreclosure Rates in Selected States for FHA 

Long-Term, Fixed-Rate Loans Originated during Fiscal Years 1990–1998
Page 17 GAO-02-773 Mortgage Financing

Source: GAO analysis of FHA data.

The four states with the highest dollar value of ARMs insured by FHA 

during the 1990s were California, Illinois, Maryland, and Colorado. Loans 

insured in these states made up about 42 percent of FHA’s business for this 

loan type, with California alone accounting for about 21 percent, as shown 

in figure 7. As a result, the performance of ARMs insured in California can 

significantly affect the overall performance of FHA’s portfolio of loans of 

this type. 
Page 18 GAO-02-773 Mortgage Financing

Figure 7: Share of FHA Adjustable Rate Mortgages, in Selected States, Originated 

during Fiscal Years 1990–1998

Source: GAO analysis of FHA data.

As shown in figure 8, the 4-year cumulative foreclosure rates for ARMs that 

FHA insured in California were consistently higher than the rates for any of 

the other three states with the largest dollar volume of ARMs insured by 

FHA, as well as the average rate for the remaining 46 states and the District 

of Columbia combined. In fact, for ARMs that FHA insured in California in 

fiscal years 1995 and 1996, the 4-year cumulative foreclosure rate was 

about 10 percent, more than twice as high as the rate for any of the other 

three states with the highest dollar volume of loans or for the remaining 46 

states and the District of Columbia combined.
Page 19 GAO-02-773 Mortgage Financing

Figure 8: National 4-Year Cumulative Foreclosure Rates in Selected States for FHA Adjustable Rate Mortgages Originated 

during Fiscal Years 1990–1998

Source: GAO analysis of FHA data.

Difference in LTV Ratios 

Can Explain Part but Not All 

of the Difference in 

Foreclosure Rates

Although differences in the share of FHA-insured loans with high LTVs 

(above 95 percent) may be a factor accounting for part of the difference in 

cumulative foreclosure rates between more recent loans and loans insured 

earlier in the 1990s, the same pattern exists even when differences in LTV 

are taken into account. As shown in figure 9, the share of FHA-insured 

loans with LTVs of 95 percent or more was higher later in the 1990s.12

12For this analysis and the one that follows, we do not include loans for which the recorded 

LTV is zero. 
Page 20 GAO-02-773 Mortgage Financing

Figure 9: Share of FHA Loans within Various LTV Categories for Loans Originated during Fiscal Years 1990–1998

Note: Excludes loans whose LTV equals zero.

Source: GAO analysis of FHA data.

Generally, as shown in figure 10, higher LTV ratios, which measure 

borrowers’ initial equity in their homes, are associated with higher 

foreclosure rates.13 However, figure 10 also shows that the same general 

pattern over time for the 4-year cumulative foreclosure rates that was 

shown in figure 1 continues to exist even when the loans are divided into

13In previous modeling work we also found that even when the effects of other factors are 

taken into account, higher LTVs are associated with a greater likelihood of foreclosure.
Page 21 GAO-02-773 Mortgage Financing

categories by LTV.14 Thus, differences in LTV alone cannot account for the 

observed differences in foreclosure rates.

Figure 10: National 4-Year Cumulative Foreclosure Rates for Selected LTV Classes of Long-Term, Fixed-Rate FHA Mortgages 

Originated during Fiscal Years 1990–1998

Note: Excludes loans whose LTV equals zero. These loans showed a similar pattern of foreclosure 

rates. 

Source: GAO analysis of FHA data.

Finally, we also considered whether the differences in foreclosures rates 

could be explained by differences in prepayment rates. Higher prepayment 

rates might be associated with lower foreclosure rates: if a higher 

14For the purpose of this analysis, we grouped FHA loans into four categories by LTV: LTV 

greater than or equal to 97 percent; LTV at least 95 percent but less than 97 percent; LTV at 

least 90 percent but less than 95 percent; and LTV greater than zero but less than 90 percent.
Page 22 GAO-02-773 Mortgage Financing

percentage of loans in a book of business are prepaid, then only a smaller 

share of the original book of business might be subject to foreclosure. 

However, we found that during the 1990s, prepayment rates showed the 

same pattern across the years as foreclosure rates and, if anything, were 

generally higher when foreclosure rates were higher, suggesting that less 

frequent prepayment was not a factor explaining higher foreclosure rates in 

the late 1990s. 

Program- and MarketRelated Changes that 

Could Explain Higher 

Foreclosure Rates 

Although economic factors such as house-price-appreciation rates are key 

determinants of mortgage foreclosure, a number of program- and marketrelated changes occurring since 1995 could also affect the performance of 

recently insured FHA loans. Specifically, in 1995 FHA made a number of 

changes in its single-family insurance program that allow borrowers who 

otherwise might not have qualified for home loans to obtain FHA-insured 

loans. These changes also allow qualified borrowers to increase the 

amount of loan for which they can qualify. According to HUD, these 

underwriting changes were designed to expand homeownership 

opportunities by eliminating unnecessary barriers to potential homebuyers. 

The proportion of FHA purchase-mortgages made to first-time homebuyers 

increased from 65 percent in 1994 to 78 percent at the end of March 2002 

and the proportion of FHA purchase-mortgages made to minority 

homebuyers increased from 25 percent to 42 percent. At the same time, 

there has been increased competition from private mortgage insurers 

offering mortgages with low down payments to borrowers identified as 

relatively low risk. The combination of changes in FHA’s program and the 

increased competition in the marketplace may partly explain the higher 

foreclosure rates of FHA loans originated since fiscal year 1995. FHA has 

since made changes that may reduce the likelihood of mortgage default, 

including requiring that, when qualifying an FHA borrower for an ARM, the 

lender use the ARM’s second year mortgage rate rather than the first-year 

rate. In addition, FHA has implemented a new loss-mitigation program.15

Because certain data that FHA collects on individual loans have not been 

collected for a sufficient number of years or in sufficient detail, we were 

15Loss mitigation refers to steps taken by the mortgage lender to avoid foreclosure. In 

November 1996 FHA implemented a new loss mitigation program that included a range of 

options that helped homeowners to either retain their homes or dispose of them in ways 

that reduced the costs of foreclosure for both the homeowners and FHA.
Page 23 GAO-02-773 Mortgage Financing

unable to estimate the effect of changes in FHA’s program and competition 

from conventional lenders on FHA loan performance. 

Changes in FHA’s 

Underwriting Guidelines 

Could Have Resulted in 

Higher Foreclosure Rates 

FHA issued revised underwriting guidelines in fiscal year 1995 that, 

according to HUD, represented significant underwriting changes that 

would enhance the homebuying opportunities for a substantial number of 

American families.16 These underwriting changes made it easier for 

borrowers to qualify for loans and allowed borrowers to qualify for higher 

loan amounts. However, the changes may also have increased the 

likelihood of foreclosure. The loans approved with more liberal 

underwriting standards might, over time, perform worse relative to existing 

economic conditions than those approved with the previous standards. The 

revised standards decreased what is included as borrowers’ debts and 

expanded the definition of what can be included as borrowers’ effective 

income when lenders calculate qualifying ratios.17 In addition, the new 

underwriting standards expanded the list of compensating factors that 

could be considered in qualifying a borrower, and they relaxed the 

standards for evaluating a borrower’s credit history. 

FHA Has Changed How It 

Defines Long-Term Debt

The underwriting changes that FHA implemented in 1995 can decrease the 

amount of debt that lenders consider in calculating one of the qualifying 

ratios, the debt-to-income ratio, which is a measure of the borrower’s 

ability to pay debt obligations. This change results in some borrowers 

having a lower debt-to-income ratio than they would otherwise have, and it 

increases the mortgage amount for which these borrowers can qualify. For 

example, childcare expenses were considered a recurring monthly debt in 

16In 1994, FHA established an Underwriting Working Group to review FHA’s underwriting 

guidelines and recommend changes and modifications that would eliminate unnecessary 

barriers to homeownership; provide the flexibility to underwrite creditworthy 

nontraditional and underserved borrowers; and, clarify certain underwriting requirements 

so that they are not applied in a discriminatory manner. The group’s recommendations 

formed the basis for underwriting changes made in fiscal year 1995. 

17FHA uses two qualifying ratios to determine whether a borrower will be able to meet the 

expenses involved in homeownership. The payment-to-income ratio (not to exceed 29 

percent) examines a borrower’s expected monthly housing expenses as a percentage of 

monthly income; the debt-to-income ratio (not to exceed 41 percent) looks at a borrower’s 

expected monthly housing expenses plus long-term debt as a percentage of monthly 

income. Both ratios can be exceeded if significant compensating factors exist. 

Compensating factors are conditions related to the borrower that may be used in justifying 

approval of a mortgage with qualifying ratios exceeding FHA benchmark guidelines.
Page 24 GAO-02-773 Mortgage Financing

the debt-to-income ratio prior to 1995, but FHA no longer requires that 

these expenses be considered when calculating the debt-to-income ratio. 

Another change affecting the debt-to-income ratio is that only debts 

extending 10 months or more are now included in the ratio; previously, 

FHA required all debts extending 6 months or more to be included. As a 

result of this change, borrowers can have short-term debts that might affect 

their ability to meet their mortgage payments, but these debts would not be 

included in the debt-to-income ratio. However, FHA does encourage 

lenders to consider all of a borrower’s obligations and the borrower’s 

ability to make mortgage payments immediately following closing.

FHA Has Changed How It 

Defines Effective Income

The 1995 changes not only decreased the amount of debt considered in the 

debt-to-income ratio; they also increased the amount of income 

consideredincreasing the number of borrowers considered able to meet a 

particular level of mortgage payments. When calculating a borrower’s 

effective income, lenders consider the anticipated amount of income and 

the likelihood of its continuance. Certain types of income that were 

previously considered too unstable to be counted toward effective income 

are now acceptable in qualifying a borrower. For example, FHA previously 

required income to be expected to continue for 5 years in order for it to be 

considered as effective income. Now income expected to continue for 3 

years can be used in qualifying a borrower. Similarly, FHA now counts 

income from overtime and bonuses toward effective income, as long as this 

income is expected to continue. Before 1995, FHA required that such 

income be earned for 2 years before counting it toward effective income. 

FHA Uses Additional 

Compensating Factors to Qualify 

Borrowers

If borrowers do not meet the qualifying ratio guidelines for a loan of a given 

size, lenders may still approve them for an FHA-insured mortgage of that 

size. FHA’s 1995 revised handbook on underwriting standards adds several 

possible compensating factors or circumstances that lenders may consider 

when determining whether a borrower is capable of handling the mortgage 

debt. For example, lenders may consider food stamps or other public 

benefits that a borrower receives as a compensating factor increasing the 

borrower’s ability to pay the mortgage. These types of benefits are not 

included as effective income, but FHA believes that receiving food stamps 

or other public benefits positively affects the borrower’s ability to pay the 

mortgage. Lenders may also consider as a compensating factor a 

borrower’s demonstrated history of being able to pay housing expenses 

equal to or greater than the proposed housing expense. In FHA’s revised 

handbook, the section on compensating factors now states, “If the 

borrower over the past 12 to 24 months has met his or her housing 
Page 25 GAO-02-773 Mortgage Financing

obligation as well as other debts, there should be little reason to doubt the 

borrower’s ability to continue to do so despite having ratios in excess of 

those prescribed.”

FHA Has Changed How It 

Evaluates Borrowers’ Past Credit 

History

In addition to changes affecting borrowers’ qualifying ratios, the 1995 

underwriting changes affected how FHA lenders are supposed to evaluate 

credit history to determine a borrower’s willingness and ability to handle a 

mortgage. As with qualifying ratios and compensating factors, FHA relies 

on the lender’s judgment and interpretation to determine prospective 

borrowers’ creditworthiness. The 1995 underwriting changes affected FHA 

guidelines regarding unpaid federal liens as well as credit and credit 

reports. Specifically, before 1995, borrowers were ineligible for an FHAinsured mortgage if they were delinquent on any federal debt or had any 

federal liens, including taxes, placed on their property. Following the 1995 

changes, borrowers may qualify for a loan even if federal tax liens remain 

unpaid. FHA guidelines stipulate that a borrower may be eligible as long as 

the lien holder subordinates the tax lien to the FHA-insured mortgage. If 

the borrower is in a payment plan to repay liens, lenders may also approve 

the mortgage if the borrower meets the qualifying ratios calculated with 

these payments. Finally, FHA expanded the options available to lenders to 

evaluate a borrower’s credit history. The previous guidance on developing 

credit histories mentions only rent and utilities as nontraditional sources of 

credit history. Lenders can now elect to use a nontraditional mortgage 

credit report developed by a credit reporting agency if no other credit 

history exists.18 Lenders may also develop a credit history by considering a 

borrower’s payment history for rental housing and utilities, insurance, 

childcare, school tuition, payments on credit accounts with local stores, or 

uninsured medical bills.19 In general, FHA advises lenders that an individual 

with no late housing or installment debt payments should be considered as 

having an acceptable credit history.

18A nontraditional credit report is designed to assess the credit history for borrowers 

without the credit references normally appearing on a traditional credit report. In 

developing a nontraditional credit report, credit agencies are to consider only the type of 

credit that requires periodic payments, such as payments for rental housing, utilities, 

telephone and cable service, insurance payments, school tuition, and medical bills. 

19Since 1992, a borrower’s lack of credit history cannot be used as a basis for rejecting a loan 

application. At that time, FHA began requiring lenders to use an alternate method of 

verifying credit (or establishing an alternative credit history) for borrowers with no credit 

history by documenting rent and utility payments. 
Page 26 GAO-02-773 Mortgage Financing

Increased Competition and 

Changes in the 

Conventional Mortgage 

Market Could Have 

Resulted in Higher FHA 

Foreclosure Rates

Increased competition and recent changes in the conventional mortgage 

market could also have resulted in FHA’s insuring relatively more loans that 

carry greater risk. Homebuyers’ demand for FHA-insured loans depends, in 

part, on the alternatives available to them. In recent years, FHA’s 

competitors in the mortgage insurance market—private mortgage insurers 

and conventional mortgage lenders—have increasingly offered products 

that compete with FHA’s for those homebuyers who are borrowing more 

than 95 percent of the value of their home. In addition, automated 

underwriting systems and credit-scoring analytic software such as those 

introduced by the Federal National Mortgage Association (Fannie Mae) and 

the Federal Home Loan Mortgage Corporation (Freddie Mac) in 1996 are 

believed to be able to more effectively distinguish low-risk loans for 

expedited processing. The improvement of conventional lenders’ ability to 

identify low-risk borrowers might increase the risk profile of FHA’s 

portfolio as lower-risk borrowers choose conventional financing with 

private mortgage insurance, which is often less expensive. In addition, by 

lowering the required down payment, conventional mortgage lenders and 

private mortgage insurers may have attracted some borrowers who might 

otherwise have insured their mortgages with FHA. If, by selectively offering 

these low down payment loans to better risk borrowers, conventional 

mortgage lenders and private mortgage insurers were able to attract FHA’s 

lower-risk borrowers, recent FHA loans with down payments of less than 5 

percent may be more risky on average than they have been historically. 

FHA is taking some action to more effectively compete with the 

conventional market. For example, FHA is attempting to implement an 

automated underwriting system that could enhance the ability of lenders 

underwriting FHA-insured mortgages to distinguish better credit risks from 

poorer ones. Although this effort is likely to increase the speed with which 

lenders process FHA-insured loans, it may not improve the risk profile of 

FHA borrowers unless lenders can lower the price of insurance for better 

credit risks.

FHA Has Taken Steps to 

Improve the Quality of Its 

Underwriting

Since 1996, FHA has revised and tightened some guidelines, specifically in 

underwriting ARMs, identifying sources of cash reserves and requiring 

more documentation from lenders. These steps should reduce the riskiness 

of loans that FHA insures. In a 1997 letter to lenders, FHA expressed 

concern about the quality of the underwriting of ARMs, particularly when a 

buy down is used, and reminded lenders that the first-year mortgageinterest rate must be used when qualifying the borrower (rather than the 

lower rate after the buy down). FHA also stipulated that lenders should 
Page 27 GAO-02-773 Mortgage Financing

consider a borrower’s ability to absorb increased payments after buy down 

periods. FHA also emphasized that lenders should rarely exceed FHA’s 

qualifying ratio guidelines in the case of ARMs. In 1998, seeing that 

borrowers were still experiencing trouble handling increased payments 

after the buy down period, FHA required borrowers to be qualified at the 

anticipated second-year interest rate, or the interest rate they would 

experience after the buy down expired, and it prohibited any form of 

temporary interest-rate buy down on ARMs. These changes will likely 

reduce the riskiness of ARMs in future books of business.

FHA has also required stricter documentation from lenders on the use of 

compensating factors and gift letters in mortgage approvals. In a June 10, 

1997, letter to lenders, FHA expressed concern about an increased number 

of loans with qualifying ratios above FHA’s guidelines for which the lender 

gave no indication of the compensating factors used to justify approval of 

the loans. FHA emphasized in this letter that lenders are required to clearly 

indicate which compensating factor justified the approval of a mortgage 

and to provide their rationale for approving mortgages above the qualifying 

ratios. Similarly, in an effort to ensure that any gift funds a borrower has 

come from a legitimate source, FHA has advised lenders of the specific 

information that gift letters should contain and the precise process for 

verifying the donor or source of the gift funds.

In 2000, FHA also tightened its guidelines on what types of assets can be 

considered as cash reserves. Although cash reserves are not required, 

lenders use cash reserves to assess the riskiness of loans. FHA noticed that 

in some cases lenders considered questionable assets as cash reserves. For 

example, lenders were overvaluing assets or including assets such as 

401(k)s or IRAs that were not easily converted into cash. As a result, FHA 

strengthened its policy and required lenders to judge the liquidity of a 

borrower’s assets when considering a borrower’s cash reserves. The new 

policy requires lenders, when considering an asset’s value, to account for 

any applicable taxes or withdrawal penalties that borrowers may incur in 

converting the asset to cash.
Page 28 GAO-02-773 Mortgage Financing

FHA Has Implemented a 

New Loss Mitigation 

Program that Could Reduce 

Foreclosures and 

Foreclosure Losses 

In 1996 Congress passed legislation directing FHA to terminate its SingleFamily Mortgage Assignment Program.20 FHA ceased accepting assignment 

applications for this program on April 26, 1996. The same legislation 

authorized FHA to implement a new program that included a range of loss 

mitigation tools designed to help borrowers either retain their home’s or to 

dispose of their property in ways that lessen the cost of foreclosure for 

both the borrowers and FHA. Specifically, the loss mitigation program 

provides a number of options for reducing losses, including special 

forbearance, loan modification, partial claim, pre-foreclosure sale, and 

deed-in-lieu-of-foreclosure (see table 1 for an explanation of these options). 

To encourage lenders to engage in loss mitigation, FHA offers incentive 

payments to lenders for completing each loss mitigation workout. In 

addition, lenders face a variety of financial penalties for failing to engage in 

loss mitigation. FHA’s loss mitigation program went into effect on 

November 12, 1996; however, use was initially fairly low, with only 6,764 

loss mitigation cases realized in fiscal year 1997, as lenders began to 

implement the new approach. HUD experienced substantial growth in loss 

mitigation claims over the next 4 fiscal years, with total claims reaching 

25,027 in fiscal year 1999 and 53,389 in fiscal year 2001. The three loss 

mitigation tools designed to allow borrowers to remain in their 

homesspecial forbearance, loan modification, and partial claimrealized 

the largest increase in use. In contrast, the use of deed-in-lieu-offoreclosure and pre-foreclosure sale, options resulting in insurance claims 

against the Fund, declined.21

20Until April 26, 1996, lenders servicing FHA loans were required to either recommend that 

HUD accept assignment of FHA-insured mortgage notes that had become 90 days delinquent 

or initiate foreclosure proceedings. If loans were accepted for assignment, HUD paid an 

insurance claim to the servicer and became the holder and servicer of the loan. 

21Incentive claims for taking loss mitigation actions are paid to the mortgage lender or 

servicer for the three home retention tools—special forbearance, loan modification, and 

partial claim. However, for deed-in-lieu and pre-foreclosure sales, the loan is terminated and 

an insurance claim, similar to that paid when an FHA loan is foreclosed, is paid to the 

mortgage lender or servicer. 
Page 29 GAO-02-773 Mortgage Financing

Table 1: Description of FHA’s Loss Mitigation Tools Available to Lenders

Source: An Assessment of FHA’s Single-Family Mortgage Insurance Loss Mitigation Program: Final 

Report, Abt Associates Inc., November 30, 2000. 

Existing Data Preclude a 

Full Assessment of the 

Impact of FHA Program and 

Conventional Mortgage 

Market Changes on 

Mortgage Default Rates 

Existing FHA data are not adequate to assess the impact of both FHA 

program changes and the changes in the conventional mortgage market on 

FHA default rates. Adequately assessing the impact of those changes would 

require detailed data on information used during loan underwriting to 

qualify individual borrowers. Such data on qualifying ratios, use of 

compensating factors, credit scores, and sources and amount of income 

would allow FHA to assess how factors key to determining the quality of its 

underwriting have changed over time. In addition, these data could be used 

in a more comprehensive analysis of the relationship among FHA 

foreclosures and FHA program design, the housing market, and economic 

conditions. Some of the data required for that type of assessment and 

analysis are not collected by FHA, while other data elements have not been 

collected for a sufficient number of years to permit modeling the impact of 

underwriting changes on loan performance. 

Since 1993, FHA has collected data on items such as payment-to-income 

and debt-to-income ratios, monthly effective income, and total monthly 

debt payments. However, FHA has not collected more detailed information 

on individual components of income and debt, such as overtime, bonus 

income, alimony and childcare payments, or length of terms for installment 

debt. Nor does FHA collect information on the use by lenders of 

compensating factors in qualifying borrowers for FHA insurance. These 

Loss mitigation tool Type of action taken by lender

Special forbearance The use of a long-term repayment plan that may provide for reduced or suspended payments 

when there is a reasonable likelihood that the borrower can resume normal payments.

Loan modification A permanent change in the term, interest rate, or loan type of a mortgage to accommodate 

inclusion of the accumulated delinquency. The new monthly payment may be higher or lower 

than the existing payment.

Partial claim Provides for funds to be advanced from the Fund to repay past amounts due on the mortgage 

for a borrower. To be eligible for this option, a borrower must have long-term financial stability to 

support the mortgage debt but lack the resources to cure the delinquency.

Pre-foreclosure sale When the borrower is unable or unwilling to maintain ownership and the market value of the 

property is less than the level of debt, this option allows the borrower to sell the property and 

apply the proceeds to retire the debt. 

Deed-in-lieu-of- foreclosure If a pre-foreclosure sale is not feasible, the borrower may deed the property to HUD to avoid 

foreclosure.
Page 30 GAO-02-773 Mortgage Financing

data would be required, for example, to analyze the impact on loan 

performance of underwriting changes that FHA implemented in 1995. 

One of the most important measures of a borrower’s credit risk is the 

borrower’s credit score. Lenders began using credit scores to assess a 

borrower’s likelihood of default in the mid-1990s. In March 1998, FHA 

approved Freddie Mac’s automated underwriting system for use by lenders 

in making FHA-insured loans and began collecting data on borrower credit 

scores for those loans underwritten using the system. Similarly, in August 

1999 FHA approved the use of Fannie Mae’s and PMI Mortgage Servicers’ 

automated underwriting systems, and it currently collects credit scores on 

loans underwritten using these systems. According to HUD officials, FHA 

plans to begin collecting credit score data on all FHA-insured loans 

underwritten through either automated underwriting systems or 

conventional methods.

Finally, because of the newness of FHA’s loss mitigation program and the 

several years required for a loan delinquency to be completely resolved, it 

is difficult to measure the impact that loss mitigation activities will 

ultimately have on the performance of FHA loans. As recently as 2000, 

substantial revisions to the program were made that could improve the 

program’s effectiveness according to Abt Associates Inc.22 A recent audit of 

the program by HUD’s Office of Inspector General noted the large increase 

in usage of loss mitigation strategies and concluded that the program is 

reducing foreclosures and keeping families in their homes. 

Performance of Recent 

Loans Suggests that 

FHA’s Portfolio May Be 

Riskier than Previously 

Estimated

The overall riskiness of FHA loans made in recent years appears to be 

greater than we had estimated in our February 2001 report on the Mutual 

Mortgage Insurance Fund, reducing to some extent the ability of the Fund 

to withstand worse-than-expected loan performance.23 Although more 

years of loan performance are necessary to make a definitive judgment, 

factors not accounted for in the models that we used for that report appear 

to be affecting the performance of loans insured after 1995 and causing the 

22See An Assessment of FHA’s Single-Family Mortgage Insurance Loss Mitigation 

Program: Final Report, Abt Associates Inc., November 30, 2000.

23See U.S. General Accounting Office, Mortgage Financing: FHA’s Fund Has Grown, but 

Options for Drawing on the Fund Have Uncertain Outcomes, GAO-01-460 (Washington, 

D.C.: Feb. 28, 2001).
Page 31 GAO-02-773 Mortgage Financing

overall riskiness of FHA’s portfolio to be greater than we previously 

estimated. In that report we based our estimate of the economic value of 

the Fund (as of the end of fiscal year 1999), in part, on econometric models 

that we developed and used to forecast future foreclosures and 

prepayments for FHA-insured loans based on the historical experience of 

loans dating back to 1975. However, a large share of the loans in FHA’s 

portfolio at that time were originated in fiscal years 1998 and 1999, and 

therefore there was little direct evidence of how those loans would 

perform. As a result, at the time that we released that estimate we 

cautioned that recent changes in FHA’s insurance program and the 

conventional mortgage market, such as those discussed in the previous 

section, could be causing recent loans to perform differently, even under 

the same economic conditions, from earlier loans.

To estimate the potential impact of these changes, we first used our 

previous model to develop estimates of the relationship between, on the 

one hand, the probability of foreclosure and prepayment and, on the other 

hand, key explanatory factors such as borrower equity and unemployment 

for loans insured between fiscal years 1975 and 1995.24 On the basis of 

these estimates and of the actual values beyond 1995 for key economic 

variables, such as interest and unemployment rates and the rate of house 

price appreciation, we forecasted the performance (both foreclosures and 

prepayments) of loans that FHA insured from fiscal year 1996 through 

fiscal year 2001. We then compared those forecasts with the actual 

experience of those loans. (See app. II for a full discussion of our 

methodology.) As is shown in figure 11, for each year’s book of business, 

we found that cumulative foreclosure rates through the end of fiscal year 

2001 exceeded our forecasted levels.25 For example, for the book of 

business with the longest experience, loans insured in 1996, we forecasted 

that the cumulative foreclosure rate through the end of fiscal year 2001 

would be 3.44 percent, but the actual foreclosure rate was 5.81 percent. 

These results suggest that some factors other than those accounted for in 

24For our previous work, we used data on loans insured through fiscal year 1999.

25These figures represent the total value of unpaid balances on loans for which FHA paid 

claims as a percentage of the total value of mortgages originated.
Page 32 GAO-02-773 Mortgage Financing

the model may be causing loans insured after 1995 to perform worse 

thanwould be expected based on the historical experience of older loans.26

Figure 11: Actual and Forecasted Cumulative Foreclosure Rates for FHA Loans 

Insured during Fiscal Years 1996–2001, as of September 30, 2001

Note: The number of years of data varies by book of business. For example, there are up to 6 years of 

data on the performance of loans originated in 1996, while there is only 1 year of data for loans 

originated in 2001. Thus, the foreclosure rates for loans originated in 1996 represent 6-year cumulative 

foreclosure rates, while the foreclosure rates for loans originated in 2001 represent 1-year cumulative 

foreclosure rates.

Source: GAO analysis of FHA data.

The fact that cumulative foreclosures for recent FHA-insured loans have 

been greater than what would be anticipated from a model based on the 

performance of loans insured from fiscal year 1975 through fiscal year 1995 

26We view this evidence as merely suggestive because we have only a few years of 

experience with these loans, particularly those insured after 1997. After more years have 

passed, the evidence on the performance of loans insured after 1995 will be more 

conclusive.
Page 33 GAO-02-773 Mortgage Financing

suggests that the caution we expressed in our 2001 report about the effect 

of recent changes in FHA’s insurance program and the conventional 

mortgage market on the ability of the Fund to withstand future economic 

downturns is still warranted. In particular, the performance of loans 

insured in fiscal years 1998 and 1999, which represented about one-third of 

FHA’s loan portfolio at the end of 1999, could be worse than what we 

previously forecasted. In turn, lower performance by these loans could 

affect the economic value of the Fund and its ability to withstand future 

economic downturns. 

To assess the extent of this effect, we would need to know the extent to 

which the performance of loans insured in fiscal years 1998 and 1999 has 

been and will be worse than what we forecasted in developing our previous 

estimate of the economic value of the Fund. Because loans insured in fiscal 

years 1998 and 1999 have not completely passed through the peak years for 

foreclosures,27 these loans’ foreclosures to date provide only a limited 

indication of their long-term performance. We do, however, have a better 

indication of the long-term performance of loans insured in fiscal years 

1996 and 1997 because they are older loans with more years of experience. 

The experience of these loans suggests that changes that are not accounted 

for in our models are causing these books of business to have higher 

foreclosure rates than would be anticipated from a model based on the 

performance of earlier loans. If loans insured in fiscal years 1998 and 1999 

are affected by changes that are not accounted for in our models in the 

same way that loans insured in fiscal years 1996 and 1997 appear to be 

affected, then the 1998 and 1999 loans will continue to have higher 

cumulative foreclosure rates than we estimated. Higher foreclosure rates, 

in turn, imply a lower economic value of the Fund, which is generally 

estimated as a baseline value under an expected set of economic 

conditions. With a lower baseline economic value of the Fund under 

expected economic conditions, the Fund would be less able to withstand 

adverse economic conditions. 

To better understand the reasons for the increased risk of recently 

originated FHA loans would require additional data on factors that might 

explain loan performance—including qualifying ratios and credit scores. 

Even if these historical data were available today, it is too soon to estimate 

with confidence the impact that recent changes will ultimately have on 

27The peak foreclosure years for a book of business are generally the third through seventh 

years of the loans.
Page 34 GAO-02-773 Mortgage Financing

recently insured loans because many of these loans have not yet reached 

the peak years when foreclosures usually occur. Recently insured loans 

represent the majority of FHA’s portfolio. The impact of underwriting 

changes and changes in the conventional mortgage market on the riskiness 

of the portfolio is not fully understood. Understanding this risk will give a 

better basis for determining whether the Fund has an adequate capital 

ratio, and also whether program changes are in order to adjust that level of 

risk. 

Agency Comments and 

Our Evaluation

We obtained written comments on a draft of this report from HUD officials. 

The written comments are presented in appendix IV. Generally HUD agreed 

with the report’s findings that the underwriting changes made in 1995 likely 

increased the riskiness of FHA loans insured after that year. HUD 

commented that fiscal year 1995 was the first year in which FHA exceeded 

the 2 percent capital ratio mandated by the National Affordable Housing 

Act of 1990. According to HUD, by making the 1995 underwriting changes 

FHA modestly increased the risk characteristics of FHA loans and, by 

doing so, allowed FHA to achieve its mission of increasing homeownership 

opportunities for underserved groups. HUD also provided information, 

which has been incorporated into the final report as appropriate, on the 

change in homeownership rates among underserved groups since 1994. 

As agreed with your offices, unless you publicly release its contents earlier, 

we plan no further distribution of this report until 30 days after its issuance 

date. At that time, we will send copies of this report to the Ranking 

Minority Member of the House Subcommittee on Housing and Community 

Opportunity and other interested members of Congress and congressional 

committees. We will also send copies to the HUD Secretary and make 

copies available to others upon request. In addition, the report will be 

available at no charge on the GAO Web site at http://www.gao.gov.
Page 35 GAO-02-773 Mortgage Financing

Please contact me or Mathew J. Scire at (202) 512-6794, or Jay Cherlow at 

(202) 512-4918, if you or your staff have any questions concerning this 

report. Key contributors to this report were Jill Johnson, DuEwa Kamara, 

Mitch Rachlis, Mark Stover, and Pat Valentine.

Sincerely yours,

Richard J. Hillman

Director, Financial Markets and

Community Investment
Page 36 GAO-02-773 Mortgage Financing

Appendix I

Scope and Methodology AAppenednxidexiIs

We initiated this review to determine (1) how the early performance of FHA 

loans originated in recent years has differed from loans originated in earlier 

years; (2) how changes in FHA’s program and the conventional mortgage 

market might explain recent loan performance; and (3) if there is evidence 

that factors affecting the performance of recent FHA loans may be causing 

the overall riskiness of FHA’s portfolio to be greater than what we 

previously estimated, and if so what effect this might have on the ability of 

the Fund to withstand future economic downturns. 

To address these objectives, we obtained and analyzed data on loans 

insured by FHA from 1990 through 1998 by year of origination; by loan type 

(fixed interest rates versus adjustable interest rates); by loan-to-value ratio; 

and by location of the property, for selected states that held the greatest 

share of FHA-insured loans. We compared the foreclosure rates for the first 

4 years of these loans. We selected a 4-year cumulative foreclosure rate as a 

basis for comparing books of business because it best balanced the 

competing goals of having the greatest number of observations and the 

greatest number of years of foreclosure experience.28 We also interviewed 

HUD officials and reviewed HUD mortgagee letters, trade literature, and 

publicly available information on the conventional mortgage market. 

Finally, using the model that we developed for our prior report and basing 

it on the experience of FHA loans insured from fiscal years 1975 through 

1995, we also compared the estimated and actual foreclosure rates through 

2001 of loans insured from fiscal years 1996 through 2001. 

We worked closely with HUD officials and discussed the interpretation of 

HUD’s data. Although we did not independently verify the accuracy of the 

data, we did perform internal checks to determine (1) the extent to which 

the data fields were coded; and (2) the reasonableness of the values 

contained in the data fields. We checked the mean, median, mode, 

skewness, and high and low values for each of the variables used. 

We conducted our review in Washington, D.C., between July 2001 and June 

2002 in accordance with generally accepted government auditing 

standards.

28We also examined 3 years of foreclosure experience and found that the relative foreclosure 

rates for each book of business exhibited similar patterns.
Page 37 GAO-02-773 Mortgage Financing

Appendix II

Models Used to Forecast Defaults and 

Prepayments for FHA-Insured Mortgages ApendxiI

For an earlier report,29 we built econometric and cash flow models to 

estimate the economic value of FHA’s Mutual Mortgage Insurance Fund 

(Fund) as of the end of fiscal year 1999. In that report, we acknowledged 

that factors not fully captured in our models could affect the future 

performance of loans in FHA’s portfolio and, therefore, the ability of the 

Fund to withstand worse-than-expected economic conditions. In particular, 

we suggested that these factors could include changes in FHA’s insurance 

program and the conventional insurance market. For our current report we 

sought to assess whether there is evidence that factors not captured in our 

previous model may be causing the overall riskiness of FHA’s portfolio to 

be greater than we previously estimated and, if so, would that have a 

substantial effect on the ability of the Fund to withstand future economic 

downturns. In this appendix, we describe how we conducted that 

assessment.

Our basic approach was to (1) reestimate the econometric models built for 

our previous report using the same specifications as before and data on 

loans insured by FHA in all 50 states and the District of Columbia, but 

excluding U.S. territories, from 1975 through 1995 (in the previous report, 

we used data on loans originated through 1999); (2) use the estimated 

coefficients and actual values of our explanatory variables during the 

forecasted period to forecast foreclosures and prepayments through fiscal 

year 2001 for loans insured from fiscal year 1996 through fiscal year 2001; 

and (3) compare the forecasted and actual foreclosures and prepayments 

for these loans during that time. A finding that our foreclosure model fit the 

data well for loans insured from 1975 through 1995, but consistently 

underestimated foreclosure rates for post-1995 loans, would suggest that 

there had been a structural change in the post-1995 period not captured in 

our models that might cause the future performance of FHA-insured loans 

to be worse than we estimated for our previous report. 

Our econometric models used observations on loan years—that is, 

information on the characteristics and status of an insured loan during 

each year of its life—to estimate conditional foreclosure and prepayment 

probabilities.30 These probabilities were estimated using observed patterns 

of prepayments and foreclosures in a large set of FHA-insured loans. More 

29See U. S. General Accounting Office, Mortgage Financing: FHA’s Fund Has Grown, but 

Options for Drawing on the Fund Have Uncertain Outcomes, GAO-01-460 (Feb. 2001).

30These probabilities are conditional, because they are subject to the condition that the loan 

has remained active until a given year.
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Page 38 GAO-02-773 Mortgage Financing

specifically, our models used logistic equations to estimate the logarithm of 

the odds ratio,31 from which the probability of a loan’s payment (or a loan’s 

prepayment) in a given year could be calculated. These equations were 

expressed as a function of interest and unemployment rates, the borrower’s 

equity (computed using a house’s price and current and contract interest 

rates as well as a loan’s duration), the loan-to-value (LTV) ratio, the loan’s 

size, the geographic location of the house, and the number of years that the 

loan had been active. The results of the logistic regressions were used to 

estimate the probabilities of a loan being foreclosed or prepaid in each 

year.

We prepared separate estimates for fixed-rate mortgages, adjustable rate 

mortgages (ARMs), and investor loans. The fixed-rate mortgages with 

terms of 25 years or more (long-term loans) were divided between those 

that were refinanced and those that were purchase money mortgages 

(mortgages associated with home purchase). Separate estimates were 

prepared for each group of long-term loans. Similarly, investor loans were 

divided between mortgages that were refinanced and the loans that were 

purchase money mortgages. We prepared separate estimates for each 

group of investor loans (refinanced and purchase money mortgages). A 

separate analysis was also prepared for loans with terms that were less 

than 25 years (short-term loans). 

A complete description of our models, the data that we used, and the 

results that we obtained is presented in detail in the following sections. In 

particular, this appendix describes (1) the sample data that we used; (2) our 

model specification and the independent variables in the regression 

models; and (3) the model results. 

Data and Sample 

Selection

For our analysis, we selected from FHA’s computerized files a 10 percent 

sample of records of mortgages insured by FHA from fiscal years 1975 

through 1995 (1,046,916 loans). From the FHA records, we obtained 

information on the initial characteristics of each loan, such as the year of 

31If P is the probability that an event will occur, the “odds ratio” is defined as P/(1-P). The 

logistic transformation is the natural logarithm of the odds ratio, or ln[P/(1-P)], of which 

the logistic regression provides an estimate. See G.S. Maddala, Limited Dependent 

Variables and Qualitative Variables in Econometrics (Cambridge: Cambridge University 

Press, 1983). Also see John H. Aldrich and Forrest D. Nelson, Linear Probability, Logit, and 

Probit Models (SAGE Publications: Beverly Hills, London, and New York, 1984), pp. 41–44. 
Appendix II

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Page 39 GAO-02-773 Mortgage Financing

the loan’s origination and the state in which the loan originated; LTV ratio; 

loan amount; and contract interest rates. 

To describe macroeconomic conditions at the national and state levels, we 

obtained data at the national level on quarterly interest rates for 30-year 

fixed-rate mortgages on existing housing, and at the state level on annual 

civilian unemployment rates from DRI-WEFA.32 We also used state level 

data from DRI-WEFA on median house prices to compute house price 

appreciation rates by state. To adjust nominal loan amounts for inflation, 

we used data from the 2000 Economic Report of the President on the 

implicit price deflator for personal consumption expenditures. 

Specification of the 

Model

People buy houses for consumption and investment purposes. Normally, 

people do not plan to default on loans. However, conditions that lead to 

defaults do occur. Defaults may be triggered by a number of events, 

including unemployment, divorce, or death. These events are not likely to 

trigger defaults if the owner has positive equity in his or her home because 

the sale of the home with realization of a profit is preferable to the loss of 

the home through foreclosure. However, if the property is worth less than 

the mortgage, these events may trigger defaults.

Prepayments of home mortgages can also occur. These may be triggered by 

events such as declining interest rates, which prompt refinancing, and 

rising house prices, which prompt homeowners to take out accumulated 

equity or sell the residence. Because FHA mortgages are assumable, the 

sale of a residence does not automatically trigger prepayment. For 

example, if interest rates have risen substantially since the time that the 

mortgage was originated, a new purchaser may prefer to assume the 

seller’s mortgage.

We hypothesized that foreclosure behavior is influenced by, among other 

things, the (1) level of unemployment, (2) size of the loan, (3) value of the 

home, (4) current interest rates, (5) contract interest rates, (6) home equity, 

and (7) region of the country within which the home is located. We 

hypothesized that prepayment behavior is influenced by, among other 

things, the (1) difference between the interest rate specified in the 

mortgage contract and the mortgage rates generally prevailing in each 

32DRI-WEFA is a leading economic forecasting firm.
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Page 40 GAO-02-773 Mortgage Financing

subsequent year, (2) amount of accumulated equity, (3) size of the loan, and 

(4) region of the country in which the home is located.

Our first regression model estimated conditional mortgage foreclosure 

probabilities as a function of a variety of explanatory variables. In this 

regression, the dependent variable is a 0/1 indicator of whether a given loan 

was foreclosed in a given year. The outstanding mortgage balance, 

expressed in inflation-adjusted dollars, weighted each loan-year 

observation.

Our foreclosure rates were conditional on whether the loan survives an 

additional year. We estimated conditional foreclosures in a logistic 

regression equation. Logistic regression is commonly used when the 

variable to be estimated is the probability that an event, such as a loan’s 

foreclosure, will occur. We regressed the dependent variable (whose value 

is 1 if foreclosure occurs and 0 otherwise) on the explanatory variables 

previously listed.

Our second regression model estimated conditional prepayment 

probabilities. The independent variables included a measure that is based 

on the relationship between the current mortgage interest rate and the 

contract rate, the primary determinant of a mortgage’s refinance activity. 

We further separated this variable between ratios above and below 1 to 

allow for the possibility of different marginal impacts in higher and lower 

ranges.

The variables that we used to predict foreclosures and prepayments fall 

into two general categories: descriptions of states of the economy and 

characteristics of the loan. In choosing explanatory variables, we relied on 

the results of our own and others' previous efforts to model foreclosure 

and prepayment probabilities, and on implications drawn from economic 

principles. We allowed for many of the same variables to affect both 

foreclosure and prepayment. 

Equity The single most important determinant of a loan's foreclosure is the 

borrower's equity in the property, which changes over time because (1) 

payments reduce the amount owed on the mortgage and (2) property 

values can increase or decrease. Equity is a measure of the current value of 

a property compared with the current value of the mortgage on that 

property. Previous research strongly indicates that borrowers with small 
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Page 41 GAO-02-773 Mortgage Financing

amounts of equity, or even negative equity, are more likely than other 

borrowers to default.33

We computed the percentage of equity as 1 minus the ratio of the present 

value of the loan balance evaluated at the current mortgage interest rate, to 

the current estimated house price. For example, if the current estimated 

house price is $100,000, and the value of the mortgage at the current 

interest rate is $80,000, then equity is .2 (20 percent), or 1-(80/100). To 

measure current equity, we calculated the value of the mortgage as the 

present value of the remaining mortgage, evaluated at the current year’s 

fixed-rate mortgage interest rate. We calculated the current value of a 

property by multiplying the value of that property at the time of the loan's 

origination by the change in the state’s median nominal house price, 

adjusted for quality changes, between the year of origination and the 

current year.34 Because the effects on foreclosure of small changes in 

equity may differ depending on whether the level of equity is large or small, 

we used a pair of equity variables, LAGEQHIGH and LAGEQLOW,35 in our 

foreclosure regression. The effect of equity is lagged 1 year, as we are 

predicting the time of foreclosure, which usually occurs many months after 

a loan first defaults.

We anticipated that higher levels of equity would be associated with an 

increased likelihood of prepayment. Borrowers with substantial equity in 

their homes may be more interested in prepaying their existing mortgages, 

and may take out larger ones to obtain cash for other purposes. Borrowers 

with little or no equity may be less likely to prepay because they may have 

to take money from other savings to pay off their loans and cover 

transaction costs. 

For the prepayment regression, we used a variable that measures book 

equity—the estimated property value less the amortized balance of the 

loan—instead of market equity. It is book value, not market value, that the 

33When we discuss the likely effects of one of our explanatory variables, we are describing 

the marginal effects of that variable, while holding the effects of other variables constant. 

34We revised the estimated rate of appreciation in nominal median house prices downward 

by 2 percentage points per year to account for depreciation and the gradual improvement in 

the quality of the existing housing stock over time. 

35Essentially, LAGEQHIGH takes the value of equity minus .2 if equity is greater than 20 

percent, or 0 if equity is less than or equal to 20 percent. LAGEQLOW takes the value of 

equity if equity is 20 percent or less, and .2 if equity is greater than 20 percent. 
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Page 42 GAO-02-773 Mortgage Financing

borrower must pay to retire the debt.36 Additionally, the important effect of 

interest rate changes on prepayment is captured by two other equity 

variables, RELEQHI and RELEQLO, which are sensitive to the difference 

between a loan’s contract rate and the interest rate on 30-year mortgages 

available in the current year. These variables are described below.

Loan-to-Value (LTV) Ratio We included an additional set of variables in our regressions related to 

equity: the initial LTV ratio. We entered LTV as a series of dummy variables, 

depending on its size. Loans fit into eight discrete LTV categories. In some 

years, FHA measured LTV as the loan amount less mortgage insurance 

premium financed in the numerator of the ratio, and appraised value plus 

closing costs in the denominator. To reflect true economic LTV, we adjusted 

FHA's measure by removing closing costs from the denominator and 

including financed premiums in the numerator.

A borrower's initial equity can be expressed as a function of LTV, so we 

anticipated that if LTV was an important predictor in an equation that also 

includes a variable measuring current equity, it would probably be 

positively related to the probability of foreclosure. One reason for 

including LTV is that it measures initial equity accurately. Our measures of 

current equity are less accurate because we do not have data on the actual 

rate of change in the mortgage loan balance or the actual rate of house 

price change for a specific house. 

Loans with higher LTVs are more likely to foreclose. We used the lowest 

LTV category as the omitted category. We expected LTV to have a positive 

sign in the foreclosure equations at higher levels of LTV. LTV in our 

foreclosure equations may capture the effects of income constraints. We 

were unable to include borrowers’ income or payment to income ratio 

directly because data on borrowers’ income were not available.37 However, 

it seems likely that borrowers with little or no down payment (high LTV) 

are more likely to be financially stretched in meeting their payments and, 

36Similarly, for foreclosures within the ARM equations, we defined equity as book equity (the 

estimated property value less the amortized balance of the loan) and not market equity. The 

effects of interest rate changes in the ARM equations were estimated using a separate 

variable.

37We also did not know whether individual borrowers had subsequently acquired second 

mortgages or other obligations that would affect prepayment or foreclosure probabilities. 
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Page 43 GAO-02-773 Mortgage Financing

therefore, more likely to default. The anticipated relationship between LTV 

and the probability of prepayment is uncertain. 

For two equations—long-term refinanced loans and investor-refinanced 

loans—we used down payment information directly, rather than the series 

of LTV variables. We defined down payment to ensure that closing costs 

were included in the loan amount and excluded from the house price.

Unemployment We used the annual unemployment rates for each state for the period from 

fiscal years 1975 through 1995 to measure the relative condition of the 

economy in the state where a loan was made. We anticipated that 

foreclosures would be higher in years and states with higher 

unemployment rates, and that prepayments would be lower because 

property sales slow down during recessions. The actual variable we used in 

our regressions, LAGUNEMP, is defined as the logarithm of the preceding 

year's unemployment rate in that state. 

Interest Rates We included the logarithm of the interest rate on the mortgage as an 

explanatory variable in the foreclosure equation. We expected a higher 

interest rate to be associated with a higher probability of foreclosure 

because higher interest rates cause higher monthly payments. However, in 

explaining the likelihood of prepayment, our model uses information on 

the level of current mortgage rates relative to the contract rate on the 

borrower’s mortgage. A borrower’s incentive to prepay is high when the 

interest rate on a loan is greater than the rate at which money can currently 

be borrowed, and it diminishes as current interest rates increase. In our 

prepayment regression we defined two variables, RELEQHI and RELEQLO. 

RELEQHI is defined as the ratio of the market value of the mortgage to the 

book value of the mortgage, but is never smaller than 1. RELEQLO is also 

defined as the ratio of the market value of the mortgage to the book value, 

but is never larger than 1. When currently available mortgage rates are 

lower than the contract interest rate, market equity exceeds book equity 

because the present value of the remaining payments evaluated at the 

current rate exceeds the present value of the remaining payments 

evaluated at the contract rate. Thus, RELEQHI captures a borrower's 

incentive to refinance, and RELEQLO captures a new buyer's incentive to 

assume the seller's mortgage. 

We created two 0/1 variables, REFIN and REFIN2, that take on a value of 1 

if a borrower had not taken advantage of a refinancing opportunity in the 
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Page 44 GAO-02-773 Mortgage Financing

past, and 0 otherwise. We defined a refinancing opportunity as having 

occurred if the interest rate on fixed-rate mortgages in any previous year in 

which a loan was active was at least 200 basis points38 below the rate on the 

mortgage in any year through 1994, or 150 basis points below the rate on 

the mortgage in any year after 1994.39 REFIN takes a value of 1 if the 

borrower had passed up a refinancing opportunity at least once in the past. 

REFIN2 takes on a value of 1 if the borrower had passed up two or more 

refinancing opportunities in the past. 

Several reasons might explain why borrowers passed up apparently 

profitable refinancing opportunities. For example, if they had been 

unemployed or their property had fallen in value, they might have had 

difficulty obtaining refinancing. This reasoning suggests that REFIN and 

REFIN2 would be positively related to the probability of foreclosure; that 

is, a borrower unable to obtain refinancing previously because of poor 

financial status might be more likely to default. 

Similar reasoning suggests a negative relationship between REFIN and 

REFIN2 and the probability of prepayment; a borrower unable to obtain 

refinancing previously might also be unlikely to obtain refinancing 

currently. A negative relationship might also exist if a borrower's passing 

up one profitable refinancing opportunity reflected a lack of financial 

sophistication that, in turn, would be associated with passing up additional 

opportunities. However, a borrower who anticipated moving soon might 

pass up an apparently profitable refinancing opportunity to avoid the 

transaction costs associated with refinancing. In this case, there might be a 

positive relationship, with the probability of prepayment being higher if the 

borrower fulfilled his or her anticipation and moved, thereby prepaying the 

loan. 

Another explanatory variable is the volatility of interest rates, INTVOL, 

which is defined as the standard deviation of the monthly average of the 

Federal Home Loan Mortgage Corporation's series of 30-year, fixed-rate 

mortgages’ effective interest rates. We calculated the standard deviation 

over the previous 12 months. Financial theory predicts that borrowers are 

38A basis point equals 1/100 of a percentage point. 

39Transaction costs associated with refinancing have fallen in recent years, making it more 

profitable than before to refinance at a smaller decrease in interest rates.
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Page 45 GAO-02-773 Mortgage Financing

likely to refinance more slowly at times of volatile rates because there is a 

larger incentive to wait for a still lower interest rate. 

We also included the slope of the yield curve, YC, in our prepayment 

estimates, which we calculated as the difference between the 1- and 10-

year Treasury rates of interest. We then subtracted 250 basis points from 

this difference and set differences that were less than 0 to 0. This variable 

measured the relative attractiveness of ARMs versus fixed-rate mortgages; 

the steeper the yield curve, the more attractive ARMs would be. When 

ARMs have low rates, borrowers with fixed-rate mortgages may be induced 

into refinancing into ARMs to lower their monthly payments. 

For ARMs, we did not use relative equity variables as we did with fixed-rate 

mortgages. Instead, we defined four variables, CHANGEPOS, 

CHANGENEG, CAPPEDPOS, and CAPPEDNEG to capture the relationship 

between current interest rates and the interest rate paid on each mortgage. 

CHANGEPOS measures how far the interest rate on the mortgage has 

increased since origination, with a minimum of 0, while CHANGENEG 

measures how far the rate has decreased, with a maximum of 0. 

CAPPEDPOS measures how much further the interest rate on the mortgage 

would rise if prevailing interest rates in the market did not change, while 

CAPPEDNEG measures how much further the mortgage's rate would fall if 

prevailing interest rates did not change. For example, if an ARM was 

originated at 7 percent and interest rates increased by 250 basis points 1 

year later, CHANGEPOS would equal 100 because FHA's ARMs can 

increase by no more than 100 basis points in a year. CAPPEDPOS would 

equal 150 basis points, since the mortgage rate would eventually increase 

by another 150 basis points if market interest rates did not change, and 

CHANGENEG and CAPPEDNEG would equal 0. Because interest rates 

have generally trended downward since FHA introduced ARMs, there is 

very little experience with ARMs in an increasing interest rate 

environment.

Geographic Regions We created nine 0/1 variables to reflect the geographic distribution of FHA 

loans, and included them in both regressions. Location differences may 

capture the effects of differences in borrowers' incomes, underwriting 

standards by lenders, economic conditions not captured by the 

unemployment rate, or other factors that may affect foreclosure and 

prepayment rates. We assigned each loan to one of the nine Bureau of the 

Census (Census) divisions on the basis of the state in which the borrower 

resided. The Pacific division was the omitted category; that is, the 
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Page 46 GAO-02-773 Mortgage Financing

regression coefficients show how each of the regions was different from 

the Pacific division. We also created a variable, JUDICIAL, to indicate 

states that allowed judicial foreclosure procedures in place of nonjudicial 

foreclosures. We anticipated that the probability of foreclosure would be 

lower where judicial foreclosure procedures were allowed because of the 

greater time and expense required for the lender to foreclose on a loan.

Loan Size To obtain an insight into the differential effect of relatively larger loans on 

mortgage foreclosures and prepayments, we assigned each loan to 1 of 10 

loan-size categorical variables (LOAN1 to LOAN10). The omitted category 

in our regressions was that of loans between $80,000 and $90,000, and 

results on loan size are relative to those loans between $80,000 and $90,000. 

All dollar amounts are inflation adjusted and represent 1999 dollars. 

Number of Units The number of units covered by a single mortgage was a key determinant in 

deciding which loans were more likely to be investor loans. Loans were 

noted as investor loans if the LTV ratio was between specific values, 

depending on the year of the loan or whether there were two or more units 

covered by the loan. Once a loan was identified as an investor loan, we 

separated the refinanced loans from the purchase-money mortgages and 

performed foreclosure and payoff analyses on each. For each of the 

investor equations, we used two dummy variables defined according to the 

number of units in the dwelling. LIVUNT2 has the value of 1 when a 

property has two dwelling units and a value of 0 otherwise. LIVUNT3 has a 

value of 1 when a property has three or more dwelling units and a value of 0 

otherwise. The missing category in our regressions was investors with one 

unit. Our database covers only loans with no more than four units. 

Policy Year and Refinance 

Indicator

To capture the time pattern of foreclosures and prepayments (given the 

effects of equity and the other explanatory variables), we defined seven 

variables on the basis of the number of years that had passed since the year 

of the loan's origination. We refer to these variables as YEAR1 to YEAR7 

and set them equal to 1 during the corresponding policy year and 0 

otherwise. Finally, for those loan type categories for which we did not 

estimate separate models for refinancing loans and nonrefinancing loans, 

we created a variable called REFINANCE DUMMY to indicate whether a 

loan was a refinancing loan.
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Table 2 summarizes the variables that we used to predict foreclosures and 

prepayments. Table 3 presents mean values for our predictor variables for 

each mortgage type for which we ran a separate regression. 

Table 2: Variable Names and Descriptions

Variable name Variable description

Loan size dummy variables

LOAN1 1 if loan amount is less than $40,000, else 0

LOAN2 1 if loan amount is $40,000 or above but below $50,000, else 0

LOAN3 1 if loan amount is $50,000 or above but below $60,000, else 0

LOAN4 1 if loan amount is $60,000 or above but below $70,000, else 0

LOAN5 1 if loan amount is $70,000 or above but below $80,000, else 0

LOAN6 1 if loan amount is $80,000 or above but below $90,000, else 0

LOAN7 1 if loan amount is $90,000 or above but below $100,000, else 0

LOAN8 1 if loan amount is $100,000 or above but below $110,000, else 0

LOAN9 1 if loan amount is $110,000 or above but below $130,000, else 0

LOAN10 1 if loan amount is at least $130,000, else 0

Economic variables

LOGINT Log of the contract mortgage interest rate

REFINANCE DUMMY 1 if the loan is a refinancing loan, else 0

RELEQLO The ratio of the market value of the mortgage to the book value if the market value is below the book value, 

else 1

RELEQHI The ratio of the market value of the mortgage to the book value if the market value is above the book value, 

else 1

REFIN 1 if, in at least 1 previous year, the mortgage interest rate had been at least 200 basis points below the contract 

rate in any year prior to 1995 or 150 basis points below the contract rate after 1994 and the borrower had not 

refinanced, else 0

REFIN2 1 if, in at least 2 previous years the above situation prevailed, else 0

INTVOL The volatility of mortgage rates, defined as the standard deviation of 30-year fixed-rate mortgage interest rates 

over the previous 12 months

YC The slope of the yield curve, defined as the difference between 1- and 10-year U.S. Treasury interest rates 

minus 250 basis points, but not less than 0

LIVUNT2 1 if the property has two housing units, else 0

LIVUNT3 1 if the property has three or more housing units, else 0

LAGUNEM The log of the previous year's unemployment rate in each state

JUDICIAL 1 if state allowed judicial foreclosure (list of states varies by year), else 0

Policy year dummy variables

YEAR1 1 if in loan's first year, else 0 
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YEAR2 1 if in loan's second year, else 0 

YEAR3 1 if in loan's third year, else 0 

YEAR4 1 if in loan's fourth year, else 0 

YEAR5 1 if in loan's fifth year, else 0 

YEAR6 1 if in loan's sixth year, else 0 

YEAR7 1 if in loan’s seventh year, else 0

Loan-to-value dummy variables

LTV0 1 if LTV equals 0, assumed missing data, else 0 

LTV1 1 if LTV is above 0 and less than 60, else 0 

LTV2 1 if LTV is greater than or equal to 60, but less than 85, else 0

LTV3 1 if LTV is greater than or equal to 85, but less than 92, else 0

LTV4 1 if LTV is greater than or equal to 92, but less than 96, else 0

LTV5 1 if LTV is greater than or equal to 96, but less than 98, else 0

LTV6 1 if LTV is greater than or equal to 98, but less than 100, else 0

LTV7 1 if LTV is greater than or equal to 100, but less than 102, else 0

LTV8 1 if LTV is greater than or equal to 102, but less than 106, else 0

Equity variables

LAGEQLOW The lagged value of market equity (defined as 1 minus the ratio of the present value of the loan balance, 

evaluated at the current mortgage interest rate, to the current estimated house price) if equity is less than or 

equal to 20 percent, else .2

LAGEQHIGH The lagged value of market equity (defined as 1 minus the ratio of the present value of the loan balance, 

evaluated at the current mortgage interest rate, to the current estimated house price minus .2) if equity is 

greater than 20 percent, else 0

BOOKNEG The lagged value of book equity (defined as 1 minus the ratio of the amortized loan balance to the current 

estimated house price) if equity is less than or equal to 20 percent, else .2

BOOKPOS The lagged value of book equity (defined as 1 minus the ratio of the amortized loan balance to the current 

estimated house price minus .2) if equity is greater than 20 percent, else 0

CHANGEPOS The amount by which the interest rate of an ARM has increased since origination, with a minimum of 0

CHANGENEG The amount by which the interest rate of an ARM has decreased since origination, with a maximum of 0

CAPPEDPOS The amount by which the interest rate of an ARM could still rise, if prevailing interest rates in the market did not 

change, with a minimum of 0

CAPPEDNEG The amount by which the interest rate of an ARM could still decline, if prevailing interest rates in the market did 

not change, with a maximum of 0

DOWNPAY The down payment, expressed as a percentage of the purchase price of the house; closing costs were 

excluded from the house price and included in the loan amount

Census division dummy variables

DV_Aa 1 if the loan is in the Mid-Atlantic states (NY, PA, NJ), else 0

DV_E 1 if the loan is in the East South Central states (KY, TN, AL, MS), else 0

DV_G 1 if the loan is in the West North Central states (MN, MO, IA, NB, KS, SD, ND), else 0

DV_M 1 if the loan is in the Mountain states (CO, UT, AZ, NM, NV, ID, WY, MT), else 0

(Continued From Previous Page)

Variable name Variable description
Appendix II

Models Used to Forecast Defaults and 

Prepayments for FHA-Insured Mortgages

Page 49 GAO-02-773 Mortgage Financing

a

DV = Division 

Source: U.S. General Accounting Office. 

Table 3: Means of Predictor Variables

DV_N 1 if the loan is in the New England states (MA, CT, RI, NH, ME, VT), else 0

DV_P 1 if the loan is in the Pacific states (CA, OR, WA), else 0

DV_R 1 if the loan is in the East North Central states (IL, MI, OH, IN, WI), else 0

DV_S 1 if the loan is in the South Atlantic states (FL, GA, NC, SC, VA, MD, DC, DE, WV), else 0

DV_W 1 if the loan is in the West South Central states (TX, OK, LA, AR), else 0

(Continued From Previous Page)

Variable name Variable description

Loan type

Predictor variable 

name Long-term FRM

Long-term FRM

refinance

Short-term

FRM ARM Investor

Investor

refinance

Loan size dummy variables

LOAN1 0.0662 0.0171 0.1535 0.0044 0.0625 0.0292

LOAN2 0.0884 0.0376 0.1245 0.0152 0.0798 0.0533

LOAN3 0.1220 0.0669 0.1355 0.0346 0.1047 0.0846

LOAN4 0.1358 0.1091 0.1383 0.0604 0.1176 0.1198

LOAN5 0.1296 0.1324 0.1211 0.0867 0.1262 0.1371

LOAN6 0.1115 0.1466 0.1041 0.1085 0.1196 0.1345

LOAN7 0.0966 0.1400 0.0782 0.1216 0.1108 0.1140

LOAN8 0.0862 0.1201 0.0585 0.1278 0.0893 0.0852

LOAN9 0.1010 0.1390 0.0589 0.1962 0.1061 0.1200

LOAN10 0.0627 0.0911 0.0276 0.2446 0.0834 0.1224

Economic variables

LOGINT -2.3616 -2.4716 -2.4058 -2.6387 -2.2936 -2.4928

REFINANCE DUMMY - - 0.3406 0.1120 - -

RELEQLO 0.9419 0.9841 0.9736 - 0.9588 0.9820

RELEQHI 1.0622 1.0547 1.0289 - 1.0797 1.0469

REFIN 0.1255 0.0590 0.1143 - 0.1943 0.0572

REFIN2 0.0840 - 0.0778 - 0.1343 -

INTVOL 0.4942 0.4651 0.4743 0.4661 0.4995 0.4525

YC 0.0498 0.0385 0.0451 0.0411 0.0488 0.0463
Appendix II

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Prepayments for FHA-Insured Mortgages

Page 50 GAO-02-773 Mortgage Financing

LIVUNT2 - - - - 0.3039 0.2654

LIVUNT3 - - - - 0.0840 0.1005

LAGUNEMP -2.7891 -2.8617 -2.8267 -2.8688 -2.7719 -2.8600

JUDICIAL 0.3982 0.3023 0.4074 0.4284 0.4750 0.4274

Policy year dummy variables

YEAR1 0.1267 0.1898 0.1449 0.1814 0.1255 0.1827

YEAR2 0.1257 0.1809 0.1424 0.1784 0.1241 0.1776

YEAR3 0.1195 0.1612 0.1343 0.1679 0.1170 0.1623

YEAR4 0.1078 0.1436 0.1230 0.1484 0.1055 0.1453

YEAR5 0.0942 0.1276 0.1110 0.1239 0.0931 0.1295

YEAR6 0.0792 0.1088 0.0966 0.0870 0.0811 0.1083

YEAR7 0.0650 0.0520 0.0666 0.0491 0.0695 0.0576

Loan-to-value dummy variables

LTV0 0.0353 0.7673 0.2457 0.0566 0.0135 0.2717

LTV1 0.0103 - 0.0632 - - -

LTV2 0.0884 0.0536 0.2397 0.0532 0.2257 -

LTV3 0.0958 0.0740 0.0945 0.1259 0.5409 -

LTV4 0.1934 0.0547 0.1151 0.2740 0.0629 -

LTV5 0.1723 0.0276 0.0761 0.3377 0.0457 -

LTV6 0.1781 0.0028 0.0890 0.0583 0.0473 -

LTV7 0.1591 0.0098 0.0611 0.0748 0.0424 -

LTV8 0.0672 0.0041 0.0155 0.0177 0.0161 -

Equity variables

LAGEQLOW 0.1263 0.0740 0.1674 - 0.1535 0.1422

LAGEQHIGH 0.1128 0.0135 0.1533 - 0.1097 0.0492

BOOKNEG 0.1336 0.0945 0.1645 0.1180 0.1660 0.1597

BOOKPOS 0.0970 0.0159 0.1500 0.0146 0.1092 0.0462

CHANGEPOS - - - 1.2829 - -

CHANGENEG - - - -0.5957 - -

CAPPEDPOS - - - 0.2131 - -

CAPPEDNEG - - - -0.1253 - -

(Continued From Previous Page)

Loan type

Predictor variable 

name Long-term FRM

Long-term FRM

refinance

Short-term

FRM ARM Investor

Investor

refinance
Appendix II

Models Used to Forecast Defaults and 

Prepayments for FHA-Insured Mortgages

Page 51 GAO-02-773 Mortgage Financing

Note: ARM = Adjustable rate mortgage; DV = Division; FRM = Fixed-rate mortgage.

Source: U.S. General Accounting Office. 

Estimation Results As previously described, we used logistic regressions to model loan 

foreclosures and prepayments as a function of a variety of predictor 

variables. We estimated separate regressions for fixed-rate purchase 

money mortgages (and refinanced loans) with terms over and under 25 

years, ARMs, and investor loans. We used data on loan activity throughout 

the life of the loans for loans originated from fiscal years 1975 through 

1995. The outstanding loan balance of the observation weighted the 

regressions. 

The logistic regressions estimated the probability of a loan being 

foreclosed or prepaid in each year. The standard errors of the regression 

coefficients are biased downward, because the errors in the regressions are 

not independent. The observations are on loan years, and the error terms 

are correlated because the same underlying loan can appear several times. 

However, we did not view this downward bias as a problem because our 

purpose was to forecast the dependent variables, not to test hypotheses 

concerning the effects of independent variables. 

DOWNPAY - 0.0337 - - - 0.1103

Census division 

dummy variables

DV_A 0.0708 0.0468 0.0746 0.0565 0.1556 0.1160

DV_E 0.0740 0.0398 0.0892 0.0478 0.0504 0.0733

DV_G 0.0885 0.1167 0.1265 0.1178 0.0708 0.0874

DV_M 0.1375 0.2050 0.1311 0.1269 0.1445 0.1808

DV_N 0.0082 0.0118 0.0072 0.0320 0.0240 0.0207

DV_P 0.1425 0.1405 0.0720 0.2025 0.1473 0.1294

DV_R 0.1156 0.0759 0.1334 0.1614 0.1294 0.1249

DV_S 0.2050 0.2127 0.1553 0.2073 0.1724 0.1771

DV_W 0.1579 0.1509 0.2107 0.0479 0.1057 0.0904

(Continued From Previous Page)

Loan type

Predictor variable 

name Long-term FRM

Long-term FRM

refinance

Short-term

FRM ARM Investor

Investor

refinance
Appendix II

Models Used to Forecast Defaults and 

Prepayments for FHA-Insured Mortgages

Page 52 GAO-02-773 Mortgage Financing

In general, our results are consistent with the economic reasoning that 

underlies our models. Most important, the probability of foreclosure 

declines as equity increases, and the probability of prepayment increases 

as the current mortgage interest rate falls below the contract mortgage 

interest rate. As shown in tables 4 and 5, both of these effects occur in 

each regression model and are very strong. These tables present the 

estimated coefficients for all of the predictor variables for the foreclosure 

and prepayment equations.

Table 4 shows our foreclosure regression results. As expected, the 

unemployment rate is positively related to the probability of foreclosure 

and negatively related to the probability of prepayment. Our results also 

indicate that generally the probability of foreclosure is higher when LTV 

and contract interest rate are higher. The overall quality of fit was 

satisfactory: Chi-square statistics were significant on all regressions at the 

0.01-percent level. 

Because the coefficients from a nonlinear regression can be difficult to 

interpret, we transformed some of the coefficients for the long-term, 

nonrefinanced, fixed-rate regressions into statements about changes in the 

probabilities of foreclosure and prepayment. The overall conditional 

foreclosure probability for this mortgage type is estimated to be about 0.6 

percent.40,41 In other words, on average, there is a 6/10 of a 1 percent chance 

for a loan of this type to result in a claim payment in any particular year.42 

By holding other predictor variables at their mean values, we can describe 

the effect on the conditional foreclosure probability of changes in the 

values of predictor variables of interest. For example, if the average value 

of the unemployment rate were to increase by 1 percentage point from its 

mean value (in our sample) of about 6 percent to about 7 percent, the 

conditional foreclosure probability would increase by about 17 percent 

(from 0.6 percent to about 0.7 percent). Similarly, a 1 percentage-point 

increase in the mortgage contract rate from its mean value of about 9.4 

40The conditional foreclosure probability is calculated as F(Z) = EXP(Z)/[1+EXP(Z)], 

where Z = Σi

 (Xi*Bi), where Xi refers to the mean value of the ith explanatory variable and Bi

represents the estimated coefficient for the ith explanatory variable. 

41Conditional foreclosure probabilities for the other mortgage types were estimated as 

follows: long-term, fixed-rate, refinancing mortgages (0.4); short-term, fixed-rate mortgages 

(0.1); ARMs (0.4); investor, nonrefinancing mortgages (0.6); and investor, refinancing 

mortgages (0.3).

42This average is for the dollar worth of a loan, not the number of loans. 
Appendix II

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Prepayments for FHA-Insured Mortgages

Page 53 GAO-02-773 Mortgage Financing

percent to about 10.4 percent would also raise the conditional foreclosure 

probability by 17 percent (from about 0.6 percent to about 0.7 percent). 

Values of homeowners’ equity of 10 percent, 20 percent, 30 percent, and 40 

percent result in conditional foreclosure probabilities of 0.7 percent, 0.5 

percent, 0.3 percent, and 0.2 percent, respectively, illustrating the 

importance of increased equity in reducing the probability of foreclosure. 

Table 5 shows our prepayment regression results. The overall conditional 

prepayment probability for long-term, fixed-rate mortgages is estimated to 

be about 5.0 percent. This means that, in any particular year, about 5 

percent of the loan dollars outstanding will prepay, on average.43

Prepayment probability is quite sensitive to the relationship between the 

contract interest rate and the currently available mortgage rate. We 

modeled this relationship using RELEQHI and RELEQLO. Holding other 

variables at their mean values, if the spread between mortgage rates 

available in each year and the contract interest rate widened by 1 

percentage point, the conditional prepayment probability would increase 

by about 78.5 percent to about 8.9 percent.

Table 4: Coefficients from Foreclosure Equations and Summary Statistics

43Conditional prepayment probabilities for the other mortgage types were estimated as 

follows: long-term, fixed-rate, refinancing mortgages (9.4); short-term, fixed-rate mortgages 

(4.0); ARMs (7.0); investor, nonrefinancing mortgages (5.3); and investor, refinancing 

mortgages (8.0).

Loan type

Predictor variable name

Long-term

FRM

Long-term

FRM

refinance

Short-term

FRM ARM Investor

Investor

refinance

INTERCEPT 3.2482 8.6087 5.2438 4.5287 4.7529 6.3348

Loan size dummy variables

LOAN1 0.4475 0.2204 0.6348 0.4215 0.2254 -0.1478

LOAN2 0.2020 0.3541 0.3597 0.5471 0.1341 0.4173

LOAN3 0.1137 0.2015 0.2922 0.5013 0.0945 0.0454

LOAN4 0.0553 0.0699 0.2003 0.2360 -0.0041 -0.1314

LOAN5 -0.0040 0.1134 -0.0218 0.0372 0.1084 0.0482

LOAN7 0.0581 0.0920 0.0959 0.0193 -0.0508 0.0383

LOAN8 -0.0057 0.2395 0.0364 0.0472 -0.0228 0.4049
Appendix II

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Prepayments for FHA-Insured Mortgages

Page 54 GAO-02-773 Mortgage Financing

LOAN9 0.0620 0.3808 0.3060 0.0420 -0.1013 0.2619

LOAN10 0.1989 0.6909 0.2731 -0.0175 0.1111 0.5410

Economic variables

LOGINT 1.9020 2.7726 3.4284 0.9887 2.6879 2.3728

REFINANCE DUMMY - - -0.1142 0.0614 - -

REFIN 0.2470 0.1066 -0.0759 - 0.2678 0.1213

REFIN2 -0.0432 - -0.2014 - 0.1206 -

LIVUNT2 - - - - 0.2047 -0.6477

LIVUNT3 - - - - 0.3231 -0.4930

LAGUNEMP 1.1972 1.9900 1.2845 1.0957 1.1842 1.4017

INTVOL 0.1592 -0.1074 0.3448 -1.3628 0.1896 -0.3889

Policy year dummy variables

YEAR1 -3.8304 -4.5046 -3.9463 -4.9161 -3.5745 -3.8716

YEAR2 -1.2274 -1.6576 -1.6136 -1.6722 -0.7272 -1.4782

YEAR3 -0.2382 -0.4544 -0.5396 -0.3623 0.1692 -0.6043

YEAR4 0.0755 -0.0616 -0.2060 0.1418 0.3430 -0.1892

YEAR5 0.2002 0.1373 -0.1027 0.3275 0.4363 -0.1745

YEAR6 0.1837 0.0696 -0.0256 0.3163 0.4006 0.1026

YEAR7 0.1127 -0.0529 -0.0537 0.0951 0.2844 0.0122

Loan-to-value dummy variables

LTV0 0.5672 -0.0352 1.3795 -0.9882 0.0154 0.2335

LTV2 -0.0662 - 0.9989 -0.9975 0.2300 -

LTV3 0.1975 - 1.2487 -1.2774 0.3365 -

LTV4 0.3526 - 1.5503 -1.1087 0.2860 -

LTV5 0.4168 - 1.7318 -1.1391 0.2021 -

LTV6 0.4756 - 1.8358 -1.1195 0.2116 -

LTV7 0.4531 - 1.7967 -1.3134 0.2832 -

LTV8 0.4878 - 1.5050 -1.3180 0.3618 -

Equity variables

DOWNPAY - 0.0363 - - - -0.5062

LAGEQLOW -1.5225 -1.7177 -1.3348 - -1.7443 -1.1904

LAGEQHIGH -3.9020 -4.5746 -3.3756 - -3.9786 -8.4145

BOOKNEG - - - -3.5071 - -

BOOKPOS - - - -7.7044 - -

(Continued From Previous Page)

Loan type

Predictor variable name

Long-term

FRM

Long-term

FRM

refinance

Short-term

FRM ARM Investor

Investor

refinance
Appendix II

Models Used to Forecast Defaults and 

Prepayments for FHA-Insured Mortgages

Page 55 GAO-02-773 Mortgage Financing

Note: ARM = Adjustable rate mortgage; DV = Division; FRM = Fixed-rate mortgage.

Source: U.S. General Accounting Office.

Table 5: Coefficients from Prepayment Equations and Summary Statistics

CHANGENEG - - - -0.1689 - -

CHANGEPOS - - - -0.1359 - -

CAPPEDNEG - - - 0.3590 - -

CAPPEDPOS - - - -0.0521 - -

Census division dummy variables

DV_A -0.0099 -0.5780 0.1229 -0.5983 -0.3525 0.2875

DV_E -0.1879 -0.6610 0.0802 -0.9281 0.2039 -0.3442

DV_G 0.1372 -0.3796 0.2421 -1.0558 0.3292 -0.2784

DV_M 0.4131 -0.4091 0.5057 -0.7121 0.6765 -0.1520

DV_N 0.3296 0.2313 1.1054 -0.1953 0.5449 1.2292

DV_R -0.0401 -0.6704 0.0230 -0.5256 -0.1075 -0.5336

DV_S 0.1926 -0.3051 0.2480 -0.3892 0.3419 -0.1205

DV_W 0.4089 -0.2715 0.6314 -0.6648 0.7777 -0.1457

JUDICIAL -0.1268 -0.2249 -0.0553 -0.3880 -0.1609 -0.6069

Summary statistics

Percentage of concordant 

pairs

75.6 81.2 77.9 77.9 78.2 81.6

Percentage of tied pairs 3.6 3.3 8.9 3.3 2.7 3.8

Number of unweighted 

observations

1,415,181 417,351 470,127 324,834 638,465 39,508

(Continued From Previous Page)

Loan type

Predictor variable name

Long-term

FRM

Long-term

FRM

refinance

Short-term

FRM ARM Investor

Investor

refinance

Loan type

Predictor variable name

Long-term

FRM

Long-term

FRM refinance

Short-term

FRM ARM Investor

Investor

refinance

INTERCEPT -16.4525 -19.1547 -24.7524 -6.7953 -16.8554 -16.2149

Loan size dummy variables

LOAN1 -0.4880 -0.6988 -0.5681 -0.3627 -0.6638 -0.5795

LOAN2 -0.3716 -0.5338 -0.3791 -0.3232 -0.4130 -0.2219

LOAN3 -0.2557 -0.3188 -0.2294 -0.2500 -0.2542 -0.1071

LOAN4 -0.1428 -0.2230 -0.1482 -0.1900 -0.1502 0.0056
Appendix II

Models Used to Forecast Defaults and 

Prepayments for FHA-Insured Mortgages

Page 56 GAO-02-773 Mortgage Financing

LOAN5 -0.0932 -0.0919 -0.0567 -0.1222 -0.0585 -0.0020

LOAN7 0.0739 0.0646 0.0766 0.0410 0.0848 0.0853

LOAN8 0.1556 0.1625 0.1341 0.0847 0.1459 0.1424

LOAN9 0.2538 0.2186 0.1976 0.1568 0.2450 0.2379

LOAN10 0.3296 0.2459 0.2188 0.2386 0.3223 0.2632

Economic variables

JUDICIAL - - - 0.0863 - -

RELEQLO 4.4215 1.5685 10.0019 - 6.9996 2.2599

RELEQHI 8.4268 10.2719 10.8365 - 6.4431 9.0518

REFINANCE DUMMY - - 0.1925 0.0341 - -

REFIN -0.3935 -0.6908 -0.4111 - -0.2048 -0.5610

REFIN2 -0.9508 - -0.4845 - -0.8704

LIVUNT2 - - - - -0.2879 -0.4480

LIVUNT3 - - - - -0.3922 -0.4716

LAGUNEMP -0.2701 -1.1570 -0.3359 -1.5208 -0.2674 -0.5189

INTVOL -0.1024 -1.2011 0.0622 -0.0979 0.3306 -0.5775

YC 0.6885 0.8419 1.0384 -0.4600 0.9744 0.7896

Policy year dummy variables

YEAR1 -1.9008 0.0631 -1.3964 -1.4215 -1.6517 -0.2573

YEAR2 -0.4079 1.0418 -0.2208 -0.1486 -0.3997 0.7932

YEAR3 0.0919 1.0217 0.0265 0.4024 -0.0350 0.9102

YEAR4 0.2652 0.7777 0.0403 0.6811 0.1096 0.7018

YEAR5 0.1551 0.4668 0.0165 0.5235 0.0296 0.6927

YEAR6 0.0975 0.3597 -0.0188 0.4072 -0.0503 0.5834

YEAR7 0.2168 0.1701 0.0436 0.3691 0.2097 0.3999

Loan-to-value dummy variables

LTV0 0.0720 0.7797 0.2636 0.0980 -0.3081 0.0588

LTV2 -0.1175 0.4439 0.0807 -0.3452 -0.2383 -

LTV3 -0.0750 0.6455 0.2296 -0.2604 -0.2945 -

LTV4 -0.0287 0.6397 0.2161 -0.1843 -0.0706 -

LTV5 -0.0239 0.5802 0.2606 -0.1585 -0.1903 -

LTV6 -0.1142 0.9316 0.1636 -0.5627 -0.2438 -

LTV7 -0.1513 0.6998 0.1268 -0.6300 -0.2865 -

LTV8 -0.2324 0.6186 0.0935 -0.5439 -0.3329 -

Equity variables

(Continued From Previous Page)

Loan type

Predictor variable name

Long-term

FRM

Long-term

FRM refinance

Short-term

FRM ARM Investor

Investor

refinance
Appendix II

Models Used to Forecast Defaults and 

Prepayments for FHA-Insured Mortgages

Page 57 GAO-02-773 Mortgage Financing

Note: ARM = Adjustable rate mortgage; DV = Division; FRM = Fixed-rate mortgage.

Source: U.S. General accounting Office.

Model Predictions for 

Historical Period

To test the validity of our models, we examined how well they predicted 

actual patterns of FHA's foreclosure and prepayment rates through fiscal 

year 1995. Using a sample of 10 percent of FHA's loans made from fiscal 

years 1975 through 1995, we found that our predicted rates closely 

resembled actual rates. 

To predict the probabilities of foreclosure and prepayment in the historical 

period, we combined the models’ coefficients with information on a loan's 

characteristics and information on economic conditions described by our 

predictor variables in each year from a loan's origination through fiscal 

BOOKNEG 1.3850 1.1641 0.8966 3.1229 1.2066 2.1158

BOOKPOS 0.7538 1.8008 1.2857 0.6881 0.7251 2.6915

CHANGENEG - - - -0.0268 - -

CHANGEPOS - - - -0.0210 - -

CAPPEDNEG - - - -0.1422 - -

CAPPEDPOS - - - -0.3254 - -

DOWNPAY - - - - - -1.5991

Census division dummy variables

DV_A -0.4333 -0.3799 -0.3345 -0.5137 -0.2588 -0.5105

DV_E -0.1131 0.2776 0.0920 -0.0742 -0.1015 0.0622

DV_G 0.1323 0.2054 0.0801 -0.4398 0.1106 0.0850

DV_M 0.1122 0.4687 0.2301 0.0713 0.0085 0.2713

DV_N -0.1944 -0.4707 0.0811 -0.6576 -0.1082 -0.8192

DV_R 0.0730 0.3670 0.2244 -0.1595 0.0960 0.2854

DV_S -0.2765 -0.1145 -0.1266 -0.5416 -0.2497 -0.2040

DV_W -0.3410 -0.0045 -0.2690 -0.3150 -0.3083 -0.0595

Summary statistics

Percentage of concordant 

pairs

78.4 73.1 73.6 71.9 76.3 73.8

Percentage of tied pairs 0.5 0.7 0.7 0.7 0.6 0.8

Number of unweighted 

observations

1,415,181 417,351 470,127 324,834 638,465 39,508

(Continued From Previous Page)

Loan type

Predictor variable name

Long-term

FRM

Long-term

FRM refinance

Short-term

FRM ARM Investor

Investor

refinance
Appendix II

Models Used to Forecast Defaults and 

Prepayments for FHA-Insured Mortgages

Page 58 GAO-02-773 Mortgage Financing

year 1995. If our models predicted foreclosure or prepayment in any year, 

we determined the loan's balance during that year to indicate the dollar 

amount associated with the foreclosure or prepayment. We estimated 

cumulative foreclosure and prepayment rates by summing the predicted 

claim and prepayment dollar amounts for all loans originated in each of the 

fiscal years 1975 through 1995. We compared these predictions with the 

actual cumulative (through fiscal year 1995) foreclosure and prepayment 

rates for the loans in our sample. Figure 12 compares actual and predicted 

cumulative foreclosure rates, and figure 13 compares actual and predicted 

cumulative prepayment rates for long-term, fixed-rate, nonrefinanced 

mortgages.44

Figure 12: Cumulative Foreclosure Rates by Book of Business for 30-Year, Fixed-Rate, Nonrefinanced Mortgages, Actual and 

Predicted, Fiscal Years 1975–1995

Source: GAO analysis of HUD data.

44Although we present figures comparing actual and predicted rates only for long-term, fixed-rate, 

nonrefinanced mortgages, the close resemblance holds true for all loan types.
Appendix II

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Prepayments for FHA-Insured Mortgages

Page 59 GAO-02-773 Mortgage Financing

Figure 13: Cumulative Prepayment Rates by Book of Business for 30-Year, Fixed-Rate, Nonrefinanced Mortgages, Actual and 

Predicted, Fiscal Years 1975–1995

Source: GAO analysis of HUD data.
Page 60 GAO-02-773 Mortgage Financing

Appendix III

Data for Figures Used in This Report ApendxiI

Foreclosure rates in the following tables are expressed as a percentage of 

loan amounts. Specifically, for tables 6 through 15 we compute all rates 

using the original loan amount of the foreclosed loans compared to the 

original loan amount of like loans insured by FHA for the corresponding 

year. For tables 16 we compute foreclosure rates using the unpaid balance 

of foreclosed loans as a percentage of the total value of mortgages 

originated.

Table 6: National 4-Year Cumulative Foreclosure Rates for All FHA Loans Originated 

during Fiscal Years 1990–1998 (Figure 1)

Source: GAO’s analysis of data obtained from HUD.

Table 7: National 4-Year Cumulative Foreclosure Rates for Long-Term, Fixed Rate 

Loans Originated during Fiscal Years 1990–1998 (Figure 2) 

Year of 

origination

Foreclosure

rate

Original amount of

foreclosed loan

Total loans

originated

1990 2.87% $1,468,904,919 $51,171,603,963

1991 2.78 1,334,851,353 47,977,729,478

1992 1.86 923,919,357 49,542,579,739

1993 1.69 1,367,705,598 80,735,908,098

1994 2.24 1,956,485,804 87,234,242,852

1995 3.30 1,517,690,292 46,021,098,615

1996 3.34 2,294,973,060 68,615,725,261

1997 3.16 2,297,495,007 72,668,032,499

1998 2.28 2,232,185,460 97,830,968,343

Year of 

origination

Foreclosure

rate

Original amount

of foreclosed loans

Total loans

originated

1980 3.33% $340,425,000 $10,235,649,629

1981 7.42 578,087,000 7,788,823,419

1982 9.94 569,819,000 5,735,087,556

1983 5.02 1,200,882,000 23,930,937,692

1984 8.11 1,154,103,000 14,231,238,175

1985 7.85 1,782,238,000 22,708,988,850

1986 4.34 2,468,155,000 56,917,684,653

1987 2.74 1,914,245,000 69,782,899,762
Appendix III

Data for Figures Used in This Report

Page 61 GAO-02-773 Mortgage Financing

Note: 1980-1989 loan amounts were estimated from a 10 percent sample.

Source: GAO’s analysis of data obtained from HUD.

Table 8: National 4-Year Cumulative Foreclosure Rates for FHA Fixed- and 

Adjustable Rate Mortgage Loans Originated during Fiscal Years 1990–1998

(Figure 3)

Source: GAO’s analysis of data obtained from HUD.

1988 3.26 1,208,982,000 37,113,171,210

1989 3.07 1,209,371,000 39,405,607,204

1990 2.89 1,308,801,408 45,326,035,945

1991 2.84 1,149,372,455 40,464,875,909

1992 1.93 675,069,579 35,006,571,763

1993 1.61 901,944,638 55,892,535,448

1994 1.98 1,110,636,930 56,140,577,134

1995 2.91 820,737,707 28,195,589,414

1996 3.10 1,332,871,376 43,011,763,810

1997 3.02 1,201,681,220 39,805,525,095

1998 2.18 1,609,113,831 73,826,808,921

Year of origination FRMa

 foreclosure rates

a Long-term fixed-rate loans.

ARM foreclosure rates

1990 2.89% 1.79%

1991 2.84 1.71

1992 1.93 1.72

1993 1.61 2.18

1994 1.98 3.30

1995 2.91 4.29

1996 3.10 4.20

1997 3.02 3.65

1998 2.18 3.59

(Continued From Previous Page)

Year of 

origination

Foreclosure

rate

Original amount

of foreclosed loans

Total loans

originated
Appendix III

Data for Figures Used in This Report

Page 62 GAO-02-773 Mortgage Financing

Table 9: Adjustable Rate Mortgages as Share of All FHA Loans Originated during 

Fiscal Years 1990–1998 (Figure 4)

Source: GAO’s analysis of data obtained from HUD.

Table 10: Share of FHA Long-Term, Fixed-Rate Loans Originated in Selected States 

during Fiscal Years 1990–1998 (Figure 5)

Source: GAO’s analysis of data obtained from HUD.

Year of origination Percentage Amount

1990 1% $376,394,573

1991 4 1,968,220,459

1992 16 7,976,055,601

1993 13 10,509,318,684

1994 18 15,670,591,954

1995 27 12,411,803,262

1996 24 16,806,552,046

1997 34 24,479,889,799

1998 13 12,498,114,087

Selected states Share of all loans Total loans originated

California 13% $55,168,696,004

Texas 8 33,963,938,873

Florida 6 26,002,603,640

New York 4 16,903,498,072

Illinois 3 14,340,445,180

Remaining States 65 271,291,101,670
Appendix III

Data for Figures Used in This Report

Page 63 GAO-02-773 Mortgage Financing

Table 11: National 4-Year Cumulative Foreclosure Rates for FHA Long-Term, Fixed-Rate Loans Originated in Selected States 

during Fiscal Years 1990–1998 (Figure 6)

Source: GAO’s analysis of data obtained from HUD.

Table 12: Share of FHA Adjustable Rate Mortgages Originated in Selected States 

during Fiscal Years 1990–1998 (Figure 7)

Source: GAO’s analysis of data obtained from HUD.

Year of 

origination California Texas Florida Illinois New York

Remaining

states

1990 4.36% 4.41% 4.04% 2.09% 2.78% 2.40%

1991 6.99 3.69 3.40 2.50 2.93 2.10

1992 6.18 2.30 2.40 1.73 2.56 1.31

1993 6.00 1.45 1.78 1.27 1.63 0.92

1994 6.87 1.78 2.23 1.40 1.90 1.19

1995 7.14 2.66 4.44 2.44 2.25 2.07

1996 7.20 2.71 4.80 2.61 2.45 2.05

1997 5.86 2.93 4.81 2.72 2.22 2.16

1998 3.67 2.00 4.16 1.65 1.35 1.63

Selected states Share of all loans Total loans originated

California 20.5% $21,078,783,499

Illinois 9.6 9,806,420,567

Maryland 6.4 6,576,127,681

Colorado 5.5 5,675,242,154

Remaining States 58.0 59,560,366,564
Appendix III

Data for Figures Used in This Report

Page 64 GAO-02-773 Mortgage Financing

Table 13: National 4-Year Cumulative Foreclosure Rates for FHA Adjustable Rate Mortgages Originated in Selected States 

during Fiscal Years 1990–1998 (Figure 8)

Source: GAO’s analysis of data obtained from HUD.

Table 14: Distribution of LTV Categories for FHA Loans Originated during Fiscal 

Years 1990–1998 (Figure 9)

Source: GAO’s analysis of data obtained from HUD.

Year of 

origination California Maryland Colorado Illinois Remaining states

1990 3.16% 0.00% 1.56% 1.71% 1.77%

1991 4.51 1.72 1.04 1.28 1.36

1992 4.97 1.49 0.46 1.40 1.28

1993 5.85 1.36 0.49 1.46 1.26

1994 7.88 2.21 0.74 2.01 1.59

1995 10.20 3.22 1.29 3.02 2.52

1996 10.01 4.02 1.30 2.80 2.57

1997 8.78 3.63 1.01 2.45 2.38

1998 8.58 2.34 0.55 2.48 2.53

Year of origination 0<LTV<90 90<=LTV<95 LTV>= 95

1990 24% 34% 41%

1991 20 28 48

1992 17 25 46

1993 13 18 37

1994 12 16 38

1995 13 23 61

1996 13 23 54

1997 15 25 56

1998 15 23 47
Appendix III

Data for Figures Used in This Report

Page 65 GAO-02-773 Mortgage Financing

Table 15: National 4-Year Cumulative Foreclosure Rates for Selected LTV Classes of 

Long-Term, Fixed-Rate Mortgages Originated during Fiscal Years 1990–1998 

(Figure 10)

Source: GAO’s analysis of data obtained from HUD.

Table 16: Actual and Forecasted Cumulative Foreclosure Rates for FHA Loans 

Insured during Fiscal Years 1996–2001, as of September 30, 2001 (Figure 11)

Note: The number of years of data varies by book of business. For example, there are up to 6 years of 

data on the performance of loans originated in 1996, while there is only 1 year’s data for loans 

originated in 2001. Thus, the foreclosure rates for loans originated in 1996 represent 6-year cumulative 

foreclosure rates, while the foreclosure rates for loans originated in 2001 represent 1-year cumulative 

foreclosure rates.

Source: GAO analysis of data obtained from FHA.

Year of 

origination LTV>= 97 95<=LTV< 97 90<=LTV<95 0<LTV<90

1990 4.97% 3.50% 2.69% 1.79%

1991 3.26 3.20 2.78 2.08

1992 2.87 1.95 1.83 1.60

1993 1.58 1.63 1.51 1.22

1994 2.03 1.99 1.75 1.38

1995 3.23 3.01 2.69 2.27

1996 3.58 3.20 2.91 1.99

1997 3.59 3.24 2.93 1.81

1998 3.16 2.42 2.11 1.34

Year of origination Actual foreclosure rate Forecast foreclosure rate

1996 5.81% 3.44%

1997 4.20 2.62

1998 1.86 1.24

1999 1.05 0.56

2000 0.49 0.24

2001 0.01 0.01
Page 66 GAO-02-773 Mortgage Financing

Appendix IV

Comments from the Department of Housing 

and Urban Development ApendxiIV
Appendix IV

Comments from the Department of Housing 

and Urban Development

(250042) Page 67 GAO-02-773 Mortgage Financing
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