There is a growing body of research at large that attempts to explain differences in the likelihood of default as a function of individual characteristics of the borrower and the loan. A recent study by the Office of Federal Housing Enterprise Oversight (OFHEO)2 analyzes the probability of default (and prepayment) for prime versus nonprime loans where the interest rate for the nonprime loan is well above the average market rate, typically in cases with greater credit risk. On the basis of econometric analysis, the findings confirm that nonprime borrowers are generally more likely to default and that certain other variables affect the default outcome as well, including the age of the loan, credit scores, down payments,
interest rates, house prices, and labor market conditions. One finding of the study is that the probability of default declines as the borrower’s credit score increases.
The Study Team notes that VA LGY has started to collect FICO credit scores in its data systems. This may be useful for further research into default patterns for VA loan
OFHEO Working Paper 02-1, Patterns of Default and Prepayment for Prime and Nonprime Mortgages, March 2001.
borrowers. For example, do FICO scores differ between VA loan and FHA loan borrowers? Recent work examining FHA default rates, found that the inclusion of FICO scores was still in its nascent stage. As FICO scores become available in a larger portion of loan files, the differences in scores and their implications can be examined.
The General Accounting Office (GAO) in GAO-02-773 (July 2002), Mortgage Financing Changes in the Performance of FHA-Insured Loans, determined factors behind increases in FHA’s default and foreclosure rates. GAO described early performance of FHA loans and learned that there were some changes in the mortgage market that contributed to changes in loan performance.
GAO found that newer loans had experienced higher foreclosure rates than rates of older loans. Interestingly, the most recent loans were performing much better than loans made in the 1980s. Loans originated in the late 1990s had higher foreclosure rates than those originated earlier in the decade. Foreclosures were higher in California and for adjustable rate mortgages (ARMs). Loans made on properties in California had a foreclosure rate of 6.4 percent, whereas those made in all other States averaged 2.0 percent. This dramatic difference illustrated a basic economic trend that the increase in the overall foreclosure rate in the 1990s was attributable to the increasing number of loans with higher loan-to-value ratios. This was true to a large extent for loans in California.
Although economic characteristics of borrowers and the dynamics of local and regional housing markets contributed to mortgage foreclosure, other issues contributed to the phenomenon as well. In particular, changes in loan underwriting standards and practices were determined to have played a role in increased rates of foreclosure. These changes were not due to negligence or malfeasance; instead, changes in underwriting standards and practices reflected policy changes designed to increase home ownership. This adjustment of underwriting practices led to significant increases in home ownership, thus meeting the charge of FHA. Still, there was an increase in the rate of foreclosure and, thus, risk associated with FHA’s portfolio.
GAO found that, as of the time of its report, FHA had not been collecting individual-level data on variables such as credit scores and debt-to-income ratios for a sufficiently long period to include these types of variable in risk modeling efforts. As we stated above, as the number of observations of both FHA and VA loan records including FICO scores increases, useful analysis yielding insights into probabilities of default associated with particular scores would be possible. Such analysis can examine the extent to which FICO scores serve as a valuable proxy for risk exposure in the case of FHA and VA loans.
In a study sponsored by HUD to analyze FHA default rates,3 advanced statistical techniques were used to control for certain factors, such as the loan-to-value characteristics of the loan, assets after closing, monthly income, average metropolitan statistical area (MSA)
unemployment rate, MSA house price growth, and geographic concentration of defaulted loans. Variables that are proxy for loan risk, such as the loan-to-value ratio, have the
expected effect on the default outcome. Average MSA unemployment rate and MSA house
Unicon Corporation, Assessing Problems of Default in Local Mortgage Markets, March 2001.
price growth help to control for differences in local economies and housing markets that affect default and foreclosure outcomes. The type of mortgage, such as an ARM, has been shown to contribute to increased rates of foreclosure in FHA loans, when compared with fixed rate mortgage (FRM) loans.4 Other factors, such as different foreclosure laws among the States and the District of Columbia, contribute to variations in foreclosure and default among different jurisdictions.
As was found by GAO, FHA extends home ownership to borrowers not well served in the conventional market. Although this meets FHA’s goal of expanding ownership, there is an increased rate of default. According to the report, by taking actions to reduce the risk of default, FHA will work against extending home ownership.
FHA borrowers in neighborhoods and among lenders with high default rates are more likely to be first-time homebuyers and are often African American. These borrowers also have higher loan-to-value ratios, lower incomes, and smaller values of assets after closing than do borrowers in neighborhoods and among lenders with lower default rates. The analysis showed that atypically high default rates were concentrated among a set of high default neighborhoods and high default rate lenders. Sophisticated analysis was used in efforts to isolate differences between census tracts in default probabilities.
From these reports, certain results or implications are useful to VA. As with all complex programs requiring data to underpin decisionmaking and policy formation—more is better. In particular, as FICO scores become a part of more files, more analysis consistent with that in most other sectors of the financial industry in risk management can be undertaken. Many insights and loss mitigation strategies can be developed from analysis that is more detailed. VA loans, like those of FHA, are designed to increase home ownership to borrowers not well served by conventional lenders. Although not explicit, it can be implied that there is an optimal tradeoff between limiting exposure to default and increasing home ownership.
Results of Multivariate Analysis
The highly aggregated data comparing VA default rates to FHA and conventional loans do not incorporate the effects of age, active duty status, income, qualifying for conventional mortgage, and other variables. Clearly, characteristics of a borrower can influence the rate of default and foreclosure to levels diverging from the simple average rate. The team used multivariate statistical analysis to account and control for the influence of various
characteristics on loan outcomes.
The Study Team examined whether economic and demographic factors identified in prior research on non-VA loans affect the probability of VA loan default. Income, gender, race, active duty status, loan-to-income ratio, age, and credit status affect the probability of default. Similarly, the Study Team examined the effect that these same variables have on the probability of a loan foreclosure.
Abt Associates Inc. Analysis of FHA’s ARM Program and the Performance of ARMs Relative to Other FHA Insured Single Family Loans. Prepared for HUD, December 2000.
Data
The data used for this analysis are an extract of VA Guaranteed and Insured Loan (GIL) administrative data including defaulted but cured loan data. The GIL data extract includes loans originated between January 1999 and June 2003, whereas the cured data include all loans that were defaulted but cured as of April 2003. The GIL data have an indicator for terminated loans that allowed us to identify foreclosed loans. The GIL data and the cured data were merged in order to attach the data fields available in the GIL data only (such as active duty status) to the cured loan data. The data provide extensive administrative information. The 1999 to 2003 data have over one million records.
Although many variables are included, the number of observations included in the analysis is small, relative to the total sample. The Study Team had over 1.3 million observations, but samples of 20,000 observations were used. This was done to adequately match loans in the GIL database with cured loans. A random sample of the loans was taken and merged with the cured data in order to have a similar number of observations in conducting the multivariate analysis.
Results
Descriptive statistics are presented in Table 7-2 and Table 7-3 for loan default and
foreclosure variables, respectively. Multivariate results are presented in Table 7-4 and Table 7-5 for default and foreclosure probability, respectively. The technical information related to the multivariate analysis is included in Appendix I.
Table 7-2. Variables Used for Multivariate Analysis of Loan Defaults
Variable Definition Mean
Number of Observations Cured (Dependent Variable) =1 if defaulted but cured; 0 otherwise .31 20,452
Active Duty =1 if active duty; 0 otherwise .13 20,452
Income Gross monthly income $2,668 20,452
Would Qualify for
Conventional Mortgage =1 if qualifies for conventional5 .13 20,452
Debt Ratio Ratio of Debt: to Monthly Income 27.04% 20,452
Interest Rate Interest Rate 7.37% 20,452
Down Payment Amount of down payment $2,030 20,452
Purchase Price Purchase price of home $106,346 20,452
Guarantee Amount Ratio Ratio of Guarantee amount to Loan amount 33.84% 20,452
Age Age of borrower 39.9 20,452
Source: Study Team analysis and VA loan servicing data on defaulted but cured data
Whether a borrower qualifies for a conventional mortgage is used here as a proxy for lower default/foreclosure risk. Borrowers who qualify for a conventional mortgage have credit and other financial characteristics that create less risk, on average, than those of borrowers who do not qualify for a conventional mortgage.
Table 7-3. Variables Used for Multivariate Analysis of Loan Foreclosure
Variable Definition Mean
Number of Observations Foreclosure (Dependent
Variable) =1 if foreclosed; 0 otherwise .031 13,361
Active Duty =1 if active duty; 0 otherwise .14 13,361
Income Gross monthly income $2,745 13,361
Would qualify for
Conventional Mortgage =1 if qualifies for conventional .12 13,361
Debt Ratio Ratio of Debt to Monthly Income 22.93% 13,361
Interest Rate Interest Rate 6.9% 13,361
Purchase Price Purchase price of home $121,342 13,361
Guarantee Amount Ratio Ratio of Guarantee amount to Loan amount 31.74/% 13,361
Age Age of borrower 37.6 13,361
Source: Study Team analysis and VA GIL data
Table 7-4. Factors Affecting Whether a Loan Defaults Variable
Change in Probability of Being in Default
Active Duty 10.9%
Income -2.7%
Would Qualify for a Conventional Mortgage -21.0%
Debt Ratio 29.0%
Interest Rate .02%
Purchase Price 9.0%
Guarantee Amount Ratio .003%
Age -4.8%
Source: Study Team analysis and VA loan servicing data on defaulted but cured data
Table 7-5. Factors Affecting Whether a Loan Forecloses Variable
Change in Probability of Being in Foreclosure
Active Duty 9%
Income -5%
Would Qualify for a Conventional Mortgage -17%
Debt Ratio 32%
Interest Rate 1.3%
Purchase Price 4%
Guarantee Amount Ratio .012%
Age -2.7%
Source: Study Team analysis and VA GIL data
The results in Table 7-3 show that a borrower on active duty is 10.9 percent more likely to be in default than a borrower who is not on active duty. An increase in income decreases the probability of default. A $100 increase in monthly income decreases the probability of default by 2.7 percent. The effect of the $100 increase is estimated at the mean amount of monthly income, which is $2,668 for default loan borrowers. The effect will vary depending on the amount of the monthly income.
Those who qualify for a conventional mortgage are 21 percent less likely to default. This is intuitive, as those who qualify for a conventional mortgage are, on average, less credit constrained than are borrowers who do not qualify for a conventional mortgage. The debt ratio of a borrower has a very strong effect on the probability of default. A 1 percent
increase above the average of the debt ratio of borrowers leads to a 2.9 percent increase in the probability of default. Age has a negative effect on the probability of default; that is, older borrowers are less likely to default. These findings are consistent with other similar empirical studies.
Purchase price is found to have a positive effect on default. In other words, homes that are more expensive are more likely to be in default. This result may be less intuitive or
counterintuitive and may be attributable to certain regional effects not captured in our model. For instance, California is one of the highest priced areas, and its economy has not been performing as well as much of the rest of the country in recent years. The effect of a poor economy in a local area or State is to increase the occurrence of defaults.
The results also suggest that for the past few years, the mostly stable interest rates have not had a significant impact on defaults. However, when rates are quickly rising, borrowers have difficulty selling or refinancing, whereas dropping rates often help borrowers out of default situations. In the 1980s, when interest rates were in double digits, liquidation rates for VA loans were at their highest level.
In Table 7-5, the variables that contribute to default are significant also in affecting rates of foreclosure. These variables follow those of default with similar orders of magnitude. For example, a VA borrower who is on active duty status is 9 percent more likely to foreclose than a borrower who is not on active duty. This compares similarly to the 10.9 percent higher probability of default for the active duty borrower.
Application of Statistical Analysis to Management
The use of this type of multivariate statistical modeling is common in many financial services organizations as a decision support tool. For example, knowledge of the determinants of foreclosure can be useful in adopting policies that can forestall the loss of a home. Another possible application of this type of analysis is in evaluating the performance of RLCs. The economic and demographic differences that exist across RLCs and over time can have different effects on foreclosure rates and, in turn, on the FATS ratio. Using this type of modeling, one can adjust for the effects of extraneous factors on RLC performance, thereby permitting more accurate assessment of RLC performance. Another potential application is forecasting the workload for supplemental servicing of loans in default.