Mortgage Financing: Changes in the Performance of FHA-Insured Loans
Published: 2002-07-10T00:00:00

Fast Facts:

Highlights:
section_title: What GAO Found
paragraphs: Federal Housing Administration (FHA) loans made in recent years have experienced somewhat higher foreclosure rates than loans made in earlier years. However, recent loans are performing much better than loans made in the 1980s. 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 in the 1990s. Factors not fully captured in the model GAO used may be affecting the performance of recent FHA loans and causing the overall risks of FHA's portfolio to be somewhat greater than previously estimated. Thus, the Mutual Mortgage Insurance Fund may be somewhat less able to withstand worse-than-expected loan performance resulting from adverse economic conditions.

section_title: Letter
paragraphs: 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.  In February 2001 we  reported that a 2 percent capital ratio appeared sufficient to withstand  moderately severe economic downturns that could lead to worse-than- expected loan performance.  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  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-than- expected 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.
subsections: section_title: Background
paragraphs: 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.
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. 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.  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.
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.  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  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. 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.
section_title: Early Performance of FHA Loans Originated during the Late 1990s Has Declined Slightly
paragraphs: 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.
subsections: section_title: 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
paragraphs: We compared the four-year cumulative foreclosure rates across books of  business to measure the performance of FHA’s insured loans.  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. 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.
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 mid- 1980s, 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.
section_title: FHA Foreclosure Rates Have Been Particularly High for Adjustable Rate Mortgages
paragraphs: 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.
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.
section_title: FHA Foreclosure Rates Have Been Particularly High in California
paragraphs: 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, fixed- rate 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  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.
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 one- third 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.
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.
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.
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.
section_title: Difference in LTV Ratios Can Explain Part but Not All of the Difference in Foreclosure Rates
paragraphs: 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.
Generally, as shown in figure 10, higher LTV ratios, which measure  borrowers’ initial equity in their homes, are associated with higher  foreclosure rates. 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 categories by LTV. Thus, differences in LTV alone cannot account for the  observed differences in foreclosure rates.
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  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.
section_title: Program- and Market- Related Changes that Could Explain Higher Foreclosure Rates
paragraphs: Although economic factors such as house-price-appreciation rates are key  determinants of mortgage foreclosure, a number of program- and market- related 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.  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  unable to estimate the effect of changes in FHA’s program and competition  from conventional lenders on FHA loan performance.
subsections: section_title: Changes in FHA’s Underwriting Guidelines Could Have Resulted in Higher Foreclosure Rates
paragraphs: 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. 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. 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.
subsections: section_title: FHA Has Changed How It Defines Long-Term Debt
paragraphs: 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  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.
section_title: FHA Has Changed How It Defines Effective Income
paragraphs: 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.
section_title: FHA Uses Additional Compensating Factors to Qualify Borrowers
paragraphs: 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  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.”
section_title: FHA Has Changed How It Evaluates Borrowers’ Past Credit History
paragraphs: 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 FHA- insured 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. 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. 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.
section_title: Increased Competition and Changes in the Conventional Mortgage Market Could Have Resulted in Higher FHA Foreclosure Rates
paragraphs: 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.
section_title: FHA Has Taken Steps to Improve the Quality of Its Underwriting
paragraphs: 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 mortgage- interest rate must be used when qualifying the borrower (rather than the  lower rate after the buy down). FHA also stipulated that lenders should  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.
section_title: FHA Has Implemented a New Loss Mitigation Program that Could Reduce Foreclosures and Foreclosure Losses
paragraphs: In 1996 Congress passed legislation directing FHA to terminate its Single- Family Mortgage Assignment Program. 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-of- foreclosure and pre-foreclosure sale, options resulting in insurance claims  against the Fund, declined.
section_title: Existing Data Preclude a Full Assessment of the Impact of FHA Program and Conventional Mortgage Market Changes on Mortgage Default Rates
paragraphs: 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  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. 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.
section_title: Performance of Recent Loans Suggests that FHA’s Portfolio May Be Riskier than Previously Estimated
paragraphs: 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. 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  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. 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. 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  the model may be causing loans insured after 1995 to perform worse  thanwould be expected based on the historical experience of older loans.
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  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, 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  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.
section_title: Agency Comments and Our Evaluation
paragraphs: 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.
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.
section_title: Scope and Methodology
paragraphs: 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. 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.
section_title: Models Used to Forecast Defaults and Prepayments for FHA-Insured Mortgages
paragraphs: For an earlier report, 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. These probabilities were estimated using observed patterns  of prepayments and foreclosures in a large set of FHA-insured loans. More  specifically, our models used logistic equations to estimate the logarithm of  the odds ratio, 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.
subsections: section_title: Data and Sample Selection
paragraphs: 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  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. 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.
section_title: Specification of the Model
paragraphs: 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  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.
subsections: section_title: Equity
paragraphs: 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  amounts of equity, or even negative equity, are more likely than other  borrowers to default.
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. 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, 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  borrower must pay to retire the debt. 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.
section_title: Loan-to-Value (LTV) Ratio
paragraphs: 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. 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,  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.
section_title: Unemployment
paragraphs: 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.
section_title: Interest Rates
paragraphs: 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  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 points 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. 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  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.
section_title: Geographic Regions
paragraphs: 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  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.
section_title: Loan Size
paragraphs: 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.
section_title: Number of Units
paragraphs: 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.
section_title: Policy Year and Refinance Indicator
paragraphs: 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.
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.
section_title: Estimation Results
paragraphs: 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.
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. (X*B), where X refers to the mean value of the ith explanatory variable and B  represents the estimated coefficient for the ith explanatory variable. 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.  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.
subsections: section_title: Model Predictions for Historical Period
paragraphs: 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  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.
section_title: Data for Figures Used in This Report
paragraphs: 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.
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