Homeownership: Information on Foreclosed FHA-Insured Loans and HUD-Owned Properties in Six Cities
Released: 1997-10-22T00:00:00

Letter

Background

Lenders usually require mortgage insurance when a home buyer has a down payment of less than 20 percent of the value of the home because foreclosures are more likely on these loans than on those with higher down payments. As the principal provider of federally backed mortgage insurance, FHA insured 32 percent of the insured mortgages originated in 1995. However, FHA fulfills an even larger role in providing insurance for some groups of borrowers, particularly low-income home buyers, minorities, and central city residents.

FHA provides most of its single-family mortgage insurance through the Section 203(b) program, which covers loans for purchasing a new or existing one- to four-family home. The 203(b) program, among other programs, is supported by the Mutual Mortgage Insurance Fund (MMI Fund), which is funded by revenue from insurance premiums and foreclosed property sales. By law, the fund must meet or endeavor to meet statutory capital ratio requirements: that is, it must contain sufficient reserves to cover the estimated future payments of claims on foreclosed mortgages and other costs. Other FHA insurance programs for single-family home loans include the Section 203(k) program, for purchasing or refinancing and rehabilitating a home at least 1 year old, and the Section 234(c) program, for purchasing a unit in a condominium project.

A mortgage loan is commonly considered “in default” when the borrower misses three consecutive monthly payments and a fourth payment is due. At that point, foreclosure proceedings against the borrower become a serious possibility. In the case of FHA-insured loans, once the foreclosure process is completed, the lender files an insurance claim with HUD for its losses (unpaid mortgage balance and interest, along with the costs of foreclosure and other expenses). After the claim is paid, the lender transfers the title to the home to HUD, which is responsible for managing and selling the property. HUD-owned properties generally remain vacant until they are resold.

At the end of fiscal year 1996, HUD had about 24,700 single-family properties in its inventory. The purpose of HUD’s property disposition program is to reduce the inventory of acquired property in a manner that expands homeownership opportunities, strengthens neighborhoods and communities, and ensures a maximum return to the mortgage insurance fund. Although FHA has always received enough in premiums from borrowers and other revenues to cover the costs of foreclosed MMI Fund loans, losses totaled about $12.8 billion in 1994 dollars, or about $24,400 for each foreclosed and subsequently sold single-family home over the 19-year period ending in 1993.

To mitigate losses to FHA and hold lenders accountable for the quality of the loans they make, FHA performs several activities related to the approval, monitoring, and recertification of mortgage lenders participating in FHA’s programs. For example, FHA monitors, by mortgage lender, the percentage of loans in default or on which FHA has paid the lender a claim. FHA also conducts on-site reviews of the loan origination and servicing practices of selected lenders. In addition, in 1996, FHA issued guidelines intended to promote the use of special forbearance plans, mortgage modifications, and other tools to help FHA borrowers in default remain in their homes whenever possible and to mitigate losses to FHA resulting from loan foreclosures.

Early Foreclosure Rates Were Highest in Low-Income Areas

Nationwide, early foreclosures did not occur for 99.68 percent of the FHA-insured single-family loans made during calendar years 1992 through 1994. However, early foreclosure rates were higher for low-income areas than for either medium- or high-income areas. Nationwide, the early foreclosure rate for low-income areas was 0.45 percent (i.e., 4.5 early foreclosures occurring for every 1,000 mortgages insured) compared with 0.30 percent and 0.21 percent for medium- and high-income areas, respectively. Federal regulations require FHA to monitor the performance of FHA-insured loans by mortgage lender but not by income area. Consequently, FHA does not have criteria for determining what would constitute excessively high early foreclosure rates for low-, medium-, or high-income areas nationwide or in a specific geographic region.

Consistent with the nationwide pattern, early foreclosure rates in the six cities were highest for low-income areas, but these rates and the proportion of early foreclosures occurring in each income area varied by city. Within 18 months, foreclosures occurred on 254 of the 50,323 loans made in the six cities, for an early foreclosure rate of 0.50 percent. For the six cities combined, the early foreclosure rates for low-, medium-, and high-income areas were 0.80 percent, 0.45 percent, and 0.30 percent, respectively.

Among the individual cities, the early foreclosure rates for low-income areas ranged from 0.47 percent in Washington, D.C., to 1.45 percent in Dallas. For medium-income areas, they ranged from 0.15 percent in Chicago to 1.02 percent in San Bernadino, and for high-income areas, they ranged from zero percent in Washington, D.C., to 0.86 percent in San Bernadino. San Bernadino had the highest early foreclosure rate (1.05 percent) for all income areas combined. According to HUD and San Bernadino city officials, job losses associated with military base closings and corporate downsizing have been a primary cause of foreclosures on FHA-insured mortgages in San Bernadino. Chicago had the lowest early foreclosure rate (0.26 percent) for all income areas combined. Table 1 shows early foreclosure rates in the six cities by income areas.

For the six cities combined, the percentage of early foreclosures occurring for low-income areas was disproportionately high relative to the percentage of loans made for homes in these areas. As shown in appendix I, for the six cities combined, low-income areas accounted for 44.5 percent (113 of 254) of the early foreclosures, compared with 27.9 percent (14,050 of 50,323) of the loans made.

This pattern also held true for the six cities individually. Among the six cities, the proportion of early foreclosures occurring for low-income areas ranged from 8.9 percent (5 of 56 early foreclosures) in Baltimore to 66.7 percent (6 of 9 early foreclosures) in Washington, D.C., while the corresponding proportions of loans made for properties in these areas were 7.5 percent and 39.4 percent, respectively.

In two of the six cities—Baltimore and Dallas—the percentage of early foreclosures for medium-income areas was disproportionately high relative to the percentage of loans made for homes in these areas. For example, in Baltimore, medium-income areas accounted for 46.4 percent of the early foreclosures, compared with 42.2 percent of the loans made. In high-income areas in each of the six cities, the percentage of early foreclosures was smaller than the percentage of loans made for properties in these areas.

Appendix I provides additional details on early foreclosure rates in the six cities.

Lenders With Early Foreclosures Made a Larger Share of Their Loans in Low- and Medium-Income Areas Than Lenders Without Early Foreclosures

For the six cities combined, lenders with early foreclosures made a larger percentage of their loans for properties in low- and medium-income areas and a smaller percentage of their loans for properties in high-income areas than lenders without early foreclosures. Lenders with early foreclosures made 30.3, 43.1, and 26.6 percent of their loans for properties in low-, medium-, and high-income areas, respectively, while the corresponding figures for lenders without early foreclosures were 24.7, 40.7, and 34.5 percent. This pattern also prevailed in four of the individual cities—Atlanta, Baltimore, Dallas, and Washington, D.C. In San Bernadino, however, lenders with early foreclosures made a smaller proportion of their loans for properties in low-income areas and a larger proportion of their loans for properties in high-income areas than lenders without early foreclosures. Also, in Chicago, lenders with early foreclosures made a smaller share of their loans in medium-income areas than lenders without early foreclosures. The relative proportions of loans made for properties in the different income areas of each city by lenders with and without early foreclosures are shown in table 2.

Additional details about differences in lending patterns among lenders with and without early foreclosures appear in appendix II.

Several Factors Were Associated With Early Foreclosures

For FHA-insured loans made during calendar years 1992 through 1994 in the six cities we reviewed, we found that the following factors were associated with early foreclosure rates: (1) the relative income level of the census tract where the property was located (expressed as the ratio of the per capita income for the census tract to the per capita income for the city), (2) the loan amount, (3) the loan-to-value ratio, (4) the loan interest rate, and (5) the city where the property was located. Other things being equal, loans made for properties in poorer census tracts, smaller loans, loans with higher loan-to-value ratios, and loans with higher interest rates were associated with higher probabilities of early foreclosure. Our analysis also showed that loans made for homes in San Bernadino were associated with higher probabilities of early foreclosure, possibly reflecting the loss of military and defense industry jobs in the San Bernadino area.

Our analysis also showed that loans made in poorer census tracts tended to be smaller and to have higher loan-to-value ratios and higher interest rates—all factors that increased the probability of early foreclosure. The relationship between lower incomes and loans with these characteristics may partially explain why early foreclosure rates were higher in low-income areas than in either medium- or high-income areas. Nonetheless, the association between census tract incomes and early foreclosure rates was statistically significant even after controlling for these other factors.

We tested additional factors but did not find them to have statistically significant associations with early foreclosure rates after accounting for the factors listed above. These factors were the race (white or minority) of the borrower, the age of the borrower, the year of the loan’s origination, and the FHA loan program used (203(b) or other loan program).

Appendix III provides additional information on the results of our statistical analysis.

Length of Time That HUD-Owned Properties Remained Unsold

As of December 31, 1996, HUD held 1,374 single-family properties in its inventory in the six cities combined. Among the six cities, the number of properties in HUD’s inventory that remained unsold ranged from 65 in Atlanta to 471 in Chicago. Our analysis did not disclose a pattern in the median time that these properties remained in HUD’s inventory in different income areas. As shown in table 3, while in Atlanta and Washington the median time in inventory was higher in low-income areas than in high-income areas, in Baltimore, Chicago, and San Bernadino, the median time in inventory was about the same in these areas. In Dallas, the median time in inventory was higher in high-income areas than in low-income areas. According to HUD officials, the length of time properties remain in HUD’s inventory is greatly affected by the economic conditions in each city.

For the six cities combined and for each of the individual cities except Dallas, the proportion of properties that had been in inventory for more than 6 months was greater in low-income areas than in either medium- or high-income areas. (See table 4.)

Additional details about the amount of time HUD-owned properties remained unsold in the six cities appear in appendix IV.

Agency Comments

We provided HUD with a draft of this report for its review and comment. Officials who reviewed the report, including a representative from the Office of the Assistant Secretary for Housing-Federal Housing Commissioner, stated that they generally agreed with the report’s findings. HUD also provided several clarifying comments, which we incorporated into the report as appropriate.

Scope and Methodology

In reporting information relating to early foreclosures on FHA-insured single-family loans endorsed during calendar years 1992 through 1994 in low-, medium-, and high-income areas nationwide, we relied on HUD’s analysis of the number of loans made, the number of early foreclosures, and the early foreclosure rates in the three income areas. To determine early foreclosure rates for the same period in the six cities reviewed, we obtained data from HUD’s database on loans insured by FHA in calendar years 1992 through 1994 and merged this information with 1990 census data.

To further analyze lending and early foreclosure patterns in the six cities, we divided the lenders into two groups—those with no early foreclosures and those with one or more early foreclosures during the periods we reviewed—and compared these groups with respect to the distribution of the loans they made across income areas. To obtain information on factors that contribute to differences in early foreclosure rates among income areas, we performed an analysis to show the extent to which certain variables were associated with differences in the probability of early foreclosure. Appendix III provides information on the model we built to estimate relationships between early foreclosures and factors that contribute to such foreclosures. To compare the length of time HUD-owned properties remained unsold in low-, medium-, and high-income areas in the six cities, we obtained data from HUD’s Single-Family Accounting Management System (SAMS), which tracks properties acquired and sold by HUD. Our analysis focused on single-family properties that remained in HUD’s inventory as of December 31, 1996.

While we did not independently verify the accuracy or test the reliability of FHA’s data, we performed tests to check the internal consistency of the data and worked with agency officials to ensure that we interpreted the data properly. Appendix V provides additional details on our scope and methodology.

We performed our work from December 1996 through September 1997 in accordance with generally accepted government auditing standards.

As agreed with your office, unless you publicly announce its contents earlier, we plan no further distribution of this report until 7 days after the date of this letter. At that time, we will provide copies to the Secretary of HUD and other interested parties. We will also make copies available to others upon request.

Please call me at (202) 512-7631 if you or your staff have any questions. Major contributors to this report are listed in appendix VI.

Early Foreclosure Rates on Loans Made in Calendar Years 1992-94, by Income Areas

Washington, D.C.

Data on Lenders With and Without Early Foreclosures on Loans Made in Calendar Years 1992-94, by Income Areas

GAO’s Econometric Model Used to Identify Factors Associated With Early Foreclosures

This appendix describes the econometric model we developed and the analysis we conducted to estimate the associations between early foreclosures and several explanatory variables. The explanatory variables we tested were the loan-to-value ratio, loan amount, contract interest rate, city where the property was located, and neighborhood income. The equation we estimated used all of the FHA-insured single-family loans endorsed in calendar years 1992 through 1994 in six cities—Atlanta, Georgia; Baltimore, Maryland; Chicago, Illinois; Dallas, Texas; San Bernadino, California; and Washington, D.C. We excluded loans made for properties within the metropolitan statistical area (MSA) but outside the city’s boundaries. We relied on census data to determine the per capita income of the census tracts in the six cities. The data we used, our model, and the results we obtained are discussed in detail in the following sections.

Data Used in This Analysis

For our analysis, we combined FHA’s computerized data from two separatefiles of 2,945,252 mortgages endorsed in calendar years 1992, 1993, and 1994. We then merged the combined FHA data files for the selected cities with census data to obtain income information for the census tracts where the loans were made. From FHA’s records, we obtained information on the initial characteristics of each loan, such as the year of its endorsement, state and city in which it was originated, loan-to-value ratio, loan amount, and loan interest rate. FHA’s files contain information on all of the single-family loans that FHA insured, including loans for condominium units, loans made to refinance existing mortgages, rehabilitation loans, and loans covered under FHA’s special risk insurance program. From the Bureau of the Census, we obtained data on the aggregate household income and total population for each of the six relevant MSAs. We computed the per capita income for each tract by dividing its aggregate household income by its total population. We determined the per capita income for each city by dividing the aggregate household income for all of the census tracts within its borders by its total population.

Within the five states covered by our review (Illinois, Georgia, Texas, California, and Maryland) and the city of Washington, D.C., 859,128 loans were made during the 3-year period. We selected the loans originated in each of the six cities by first using the county codes for the appropriate MSA and then identifying the census tracts that were within the city’s borders according to the listing of tracts supplied to us by an official representative of each city. As indicated in table III.1, 399,011 loans were endorsed in the six MSAs that included the six cities during calendar years 1992 through 1994.

Washington, D.C.

We were able to match FHA loans to census records for 83 percent of the loans (333,045) in the MSA, but not for the remaining 17 percent (65,966 loans). Because we used census tract codes to determine if the loans were within or outside a city, we were not able to determine what percentage of the 65,966 unmatched loans were within a city’s borders. We matched 80 percent of the total number of loans with all six digits of the census tract code and an additional 3 percent with four digits of the census tract code. The four-digit match was necessary because of changes to the definitions of some metropolitan area tracts over time.

In general, each of the MSAs had hundreds of census tracts, but only a fraction of them were located within the city’s borders. We excluded from our analysis 543 loans for properties in Washington, D.C., because invalid census tract codes made it difficult to obtain census tract income and population data. In addition, there were 65,423 loans endorsed in the remaining MSAs that we could not identify as being within one of the cities because their census tract codes were invalid. Another 4,537 loans endorsed in the six states did not have valid county codes, and we were unable to determine if they should have been included within one of the MSAs.

As shown in table III.2, of the 333,045 loans we were able to match with census tracts, 50,323 were made for properties within the six cities’ borders. We were able to find the valid census tract income for virtually all of the 50,323 loans. In other words, when we identified a loan as being for a property in one of the six cities, we were almost always able to determine the total population or aggregate income for that loan’s census tract.

Washington, D.C.

Many FHA-insured loans were refinanced during calendar years 1992 through 1994. Refinanced mortgages accounted for about 32 percent of the loans in the six cities during the 3-year period we examined. Of the loans that were refinanced, about 69 percent had a recorded loan-to-value ratio of zero, and nearly all of these were streamlined refinanced mortgages. Because FHA does not require property appraisals for streamlined refinanced mortgages, the initial loan-to-value ratios for these loans are unknown.

Specification of the Model

A default on a home mortgage loan may be triggered by unemployment, divorce, death, or some other event. Such an event is not likely to trigger a foreclosure if the owner has positive equity in the home because the sale of the home with the realization of a profit is better than the loss of the home through foreclosure. However, if the property is worth less than the mortgage, such an event may trigger a foreclosure.

We hypothesized that the probability of early foreclosure is influenced by, among other things, the loan-to-value ratio, the size of the loan, the loan interest rate, income, and the property’s location. Because the recorded value of the loan-to-value ratio for some loans was zero, we added a variable to our analysis to identify these loans. We used a logistic regression equation to explore how foreclosure rates on loans endorsed in calendar years 1992 through 1994 in the six cities varied for each of these factors. Logistic regression is a standard procedure for analyzing a dichotomous dependent variable, such as whether or not an early foreclosure occurred. We used the results of our logistic regression to estimate how the odds of early foreclosure are expected to change with unit changes in the explanatory factors. In the logistic regression, we used deviation coding for categorical variables, such as the city where the foreclosure occurred. Therefore, the effect for each category is compared to the average effect for all of the categories, rather than to an omitted (or reference) category.

We tested additional factors but did not find them to be significantly associated with early foreclosure rates after accounting for the factors listed above. These additional factors were the race of the borrower (white or other), the age of the borrower, the year of the loan’s endorsement (1992, 1993, 1994), and the loan program used (the MMI Fund’s 203(b) program or other loan program).

We were not able to include all of the factors, such as unemployment rates, that might be related to the probability of early foreclosure in our analysis. This was generally because data were not available. If we had been able to include these other factors, our results with respect to the included factors might have been different. We and other researchers have estimated the probability of ultimate foreclosure and have found other factors that have a significant impact on it. These factors include the borrower’s equity and the prevailing interest rate at the time of default, lagged unemployment, the property’s location (i.e., urban or rural), whether the borrower is a first-time homeowner, and the borrower’s marital status. It is generally agreed that many life-changing events—such as the arrival of children, divorce, and death—may also be related to the probability of foreclosure. However, it should be noted that prior research has associated these other factors only to ultimate loan foreclosure, not to early foreclosure.

Income

To determine if early foreclosure rates were different in lower-income communities, we obtained information on the aggregate income and the total population for each census tract within the borders of the cities we studied. We computed the ratio of the per capita income for each of the tracts to the per capita income for the relevant city to obtain the tract-to-city income ratio. We anticipated that people living in lower-income tracts might have more difficulty meeting their mortgage payments than people in higher-income tracts and that the rate of early foreclosure would, then, be higher in the lower-income tracts than elsewhere. Factors associated with lower-income communities, such as higher unemployment rates and less stability in employment, could limit the ability of borrowers to meet their monthly mortgage payments. Other factors, such as the greater age of the housing stock or the slower appreciation of house prices in lower-income communities, could also affect early foreclosure rates.

Loan-to-Value Ratio

The ratio of the loan amount to the value of the property is an important determinant of whether a loan will end in foreclosure. The loan-to-value ratio on the property changes over time because property values can increase or decrease, and payments reduce the amount owed on a mortgage. Because we were examining foreclosures that occur within 18 months of the loan endorsement date, we anticipated that the change in the loan-to-value ratio within that time period would be so small that the initial loan-to-value ratio would be sufficient to capture the effect of the borrower’s equity percentage on the probability of foreclosure, when the equity percentage is considered to be 1 minus the loan-to-value ratio. Research indicates that borrowers with small amounts of equity (and, hence, higher loan-to-value ratios), especially those with negative equity, are more likely than other borrowers to default.

FHA’s data showed a value of zero for about 22 percent of the loans in the six cities. Although almost all of these loans were refinanced, another 10 percent of the loans were refinanced and had valid loan-to-value ratios. FHA does not require an appraisal for streamlined refinanced loans. When an appraisal is not performed, the loan-to-value ratio is unknown. We have reported that the probability of foreclosure for FHA-insured refinanced loans differs from that for other FHA-insured loans, but we did not include a refinance indicator in the regression. We did, however, add a variable to indicate when a loan was missing a loan-to-value ratio. We did not separately take into account any further differences that may result from other characteristics of refinanced loans that did have valid loan-to-value ratios.

Interest Rate and Loan Amount

We included the interest rate on the mortgage as an explanatory variable in the early foreclosure equation. We expected a higher interest rate to be associated with a higher probability of early foreclosure because a higher interest rate causes a higher monthly payment.

To obtain insight into the differential effect of relatively larger loans on the probability of early foreclosure, we used the loan amount as an explanatory variable. In our previously cited report, we pointed out that, other things being equal, larger loans have lower probabilities of foreclosure than smaller loans. Different rates of appreciation in house prices in low- and higher-income communities may be one factor underlying this phenomenon. We know that larger loans are associated with higher-priced homes. By using the loan amount as a variable in our equation and holding income constant, we were testing the relationship between larger loans and the probability of early foreclosure.

City Where Property Is Located

We used variables to indicate the city where the property was located. We expected that the coefficients for these variables would pick up differences in economic conditions within the city that we could not model explicitly. Some of these differences may include changes in the rates of unemployment, house price appreciation, net migration, and other unknown factors.

Estimation Results

The results of our analysis are summarized in table III.3. In general, our results are consistent with the economic reasoning that underlies our model.

Washington, D.C.

Income: ratio of the per-capita income for the tract to that for the city (ratio x 100)

Interest rate (percent)

Loan amount (dollars in thousands)

Loan-to-value ratio (ratio x 100)

Is the loan-to-value ratio provided in the data equal to zero?

In logistic regression, the coefficients of the variables are not easily interpretable. Therefore, we transformed the original coefficients into a more interpretable form that we termed the “odds change factor.” Specifically, we raised the natural logarithm base, e, to the power equal to the value of the original coefficient to obtain the odds change factor. Odds change factors estimate the effect of each variable on the predicted odds of foreclosure. A value greater than 1 means that the odds of foreclosure are expected to increase, while a value less than 1 predicts a decrease in the odds of foreclosure. For example, the odds change factor for the interest rate variable is 1.16, which means that the odds of early foreclosure increase by 16 percent for each percentage point the interest rate increases. Confidence intervals were also calculated for the original logistic regression coefficients at the 95-percent confidence level and then transformed into the more interpretable form. This means that we would expect the lower and upper bound to include the true odds change factor 95 times out of 100.

We found statistically significant associations between increased rates of early foreclosure and (1) a lower per capita income for a census tract, (2) higher loan-to-value ratios, (3) higher loan interest rates, (4) smaller loan amounts, and (5) loans made for properties located in San Bernadino. We also found that early foreclosure was less likely for loans made for properties in Chicago.

As the per capita income in the census tract in which the property was located increased relative to the per capita income in the entire city, the odds of early foreclosure decreased. For example, the odds of foreclosure for loans on properties located in areas whose per capita income was 91 percent of the citywide per capita income were about 1 percent lower than the odds for properties in areas whose per capita income was 90 percent of the citywide income. Larger mortgages were negatively correlated with the probability of early foreclosure. The odds of early foreclosure were estimated to decrease by about 1 percent for each additional $1,000 borrowed.

The loan-to-value ratio was significantly and positively correlated with the odds of early foreclosure. When the loan-to-value ratio increased by 1 percentage point, the odds of early foreclosure increased by about 6 percent. The odds of early foreclosure for loans with a loan-to-value ratio of zero—mostly streamlined financed loans—were about the same as the odds for loans with a loan-to-value ratio of 90 percent and were about 25 percent lower than the odds for loans with a loan-to-value ratio of 95 percent.

Higher interest rates are associated with an increase in early foreclosures. Holding other things constant, an increase of 1 percentage point in the interest rate was found to increase the odds of early foreclosure by about 16 percent.

We also found that the odds of early foreclosure differed with the city being tested. For example, the odds of early foreclosure were lower than average for Chicago and about twice as high as the six-city average for San Bernadino. We did not obtain statistically significant results for Atlanta, Baltimore, Dallas, or Washington, D.C.

Time in Inventory for Single-Family Properties in Six Cities That Remained Unsold as of December 31, 1996, by Income Areas

Objectives, Scope, and Methodology

Our objectives were to (1) compare early foreclosure rates on FHA-insured single-family loans made in low-, medium-, and high-income areas nationwide and in the six cities; (2) compare across income areas the proportion of loans made in the six cities by FHA-approved mortgage lenders with and without early foreclosures; (3) identify factors that influence early foreclosure rates; and (4) compare the length of time HUD-owned single-family properties remained unsold in low-, medium-, and high-income areas in the six cities.

In reporting information relating to early foreclosures on FHA-insured single-family loans endorsed during calendar years 1992 through 1994 in low-, medium-, and high-income areas nationwide, we relied on HUD’s analysis of the number of loans made, the number of early foreclosures, and the early foreclosure rates in the three income areas. To determine early foreclosure rates for the same period in the six cities reviewed, we obtained data from HUD’s database on loans insured by FHA in calendar years 1992 through 1994 and merged this information with 1990 census data. Detailed information on the data we used are provided in the section of appendix III that discusses the data used in this analysis.

We defined a census tract’s income level as “low” if the per capita income was at or below 80 percent of the city’s per capita income, “medium” if the per capita income was greater than 80 percent but at or below 120 percent of the city’s level, and “high” if the per capita income was greater than 120 percent of the city’s level. Although HUD usually uses the median family income to identify low-, medium-, and high-income census tracts, we were unable to compute the median family income for the six cities from the data we extracted from census records. We therefore used the per capita income as our income measure.

HUD computed early foreclosure rates by income level nationwide for this report using the average household income as the income measure for each MSA. As indicated above, we used the per capita income for each city as the income measure to calculate early foreclosure rates by income level for the six cities. Therefore, our classification of census tracts as low-, medium-, or high-income may differ from HUD’s classification because (1) the average income for the MSA may differ from the per capita income for the city, and (2) the per capita income does not take into account differences in the average household size among the three income groups. While our classification of census tracts differed from HUD’s classification, the relationship between early foreclosure rates and census tract income levels for both computations was similar.

We limited our analysis to early foreclosures, that is, to those occurring within 18 months of the loan endorsement date. To determine whether a foreclosure occurred within that time period, we measured the time elapsed between FHA’s endorsement of the loan and the date the lender foreclosed on the loan. For this report, we included in our calculation of early foreclosure rates loans on which the lender did not actually foreclose but on which FHA paid an insurance claim to the lender within 18 months of the loan endorsement date. We excluded from our calculation of early foreclosure rates nonconveyance foreclosures, such as instances during which a foreclosure occurs but an insurance claim is not paid. In some cases, early foreclosures may not have been reflected in the data from HUD that we used because of the lag between the date of the actual foreclosure and the date it was recorded in HUD’s database. As a result, our analysis may understate the number of early foreclosures by the number of these unrecorded cases.

To further analyze lending and early foreclosure patterns in the six cities, we divided the lenders into two groups—those with no early foreclosures and those with one or more early foreclosures during the periods reviewed—and compared these groups with respect to the distribution of the loans they made across income areas. We determined whether a lender had one or more early foreclosures on a city-by-city basis. Therefore, any lender that made loans in more than one of the six cities could be classified in the group of lenders with early foreclosures in one city and in the group of lenders without early foreclosures in another city.

To obtain information on factors that contribute to differences in early foreclosure rates among income areas, we performed an analysis to show the extent to which certain variables were associated with differences in the probability of early foreclosure. Appendix III provides information on the model we built to estimate relationships between early foreclosures and factors that contribute to such foreclosures. In addition, we reviewed the mortgage finance literature and interviewed officials from HUD’s Office of Insured Single-Family Housing and HUD field office officials in each of the six cities. We also interviewed local government officials and nonprofit housing executives familiar with FHA’s role in the real estate markets in each of the six cities.

To compare the length of time HUD-owned properties remained unsold in low-, medium-, and high-income areas in the six cities, we obtained data from HUD’s Single-Family Accounting Management System (SAMS), which tracks properties acquired and sold by HUD. Our analysis focused on single-family properties that remained in HUD’s inventory as of December 31, 1996. We measured the time in inventory from the date that HUD acquired the property. We excluded properties held off the market as of May 17, 1997 (the date our data extract was created), but we were unable to determine if the remaining properties had been held off the market for any time in the past.

For the six cities reviewed, we matched (both electronically and manually) the property addresses in SAMS to the addresses in the Bureau of the Census’ street address file to identify corresponding census tracts. When an exact match for the zip code and street address did not exist, we manually selected the closest reasonable match. When no reasonable match existed or multiple choices were possible, we excluded the property from our analysis. For the six cities combined, we were able to match about 90 percent (1,232 of 1,374) of the properties in HUD’s inventory with a census tract and data on valid time in inventory.

Major Contributors to This Report

Resources, Community, and Economic Development Division, Washington, D.C.

Chicago Field Office

Glenn G. Davis Dorothy Waniak Steven Westley The first copy of each GAO report and testimony is free. Additional copies are $2 each. Orders should be sent to the following address, accompanied by a check or money order made out to the Superintendent of Documents, when necessary. VISA and MasterCard credit cards are accepted, also. Orders for 100 or more copies to be mailed to a single address are discounted 25 percent.

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