Mortgage Financing: FHA's Fund Has Grown, but Options for Drawing on the Fund Have Uncertain Outcomes
Published: 2001-02-28T00:00:00

Fast Facts:

Highlights:
section_title: What GAO Found
paragraphs: The Mutual Mortgage Insurance Fund has maintained an economic value of at least two percent of the Fund's insurance-in-force, as required by law. GAO's and the Department of Housing and Urban Development's (HUD) analysis show that the Fund had an economic value of $15.8 billion (3.20 percent) and $16.6 billion (3.66 percent), respectively. Given the economic value of the Fund and the state of the economy at the end of fiscal year 1999, a two-percent capital ratio appears sufficient to withstand moderately severe economic downturns that could lead to worse-than-expected loan performance. However, under more severe economic conditions, the economic value of two percent of insurance-in-force would not be adequate. Because of the uncertainty and professional judgment associated with this type of economic analysis, GAO cautions against relying on one estimate or even a group of estimates to determine the adequacy of the Fund's reserves over the longer term. HUD could exercise several options under current legislative authority to reduce the capital ratio for the Fund. It is difficult, however, to reliably measure the impact of policy changes on the Fund's capital ratio and Federal Housing Administration borrowers without using tools designed to estimate the multiple impacts that policy changes often have. Nonetheless, any option that reduces the Fund's reserve, if not accompanied by a similar reduction in other government spending, would result in a budget surplus reduction or a deficit increase.

section_title: Letter
paragraphs: According to the Department of Housing and Urban Development (HUD),  the economic value of the Mutual Mortgage Insurance Fund (Fund) grew  by about $5 billion in 1999, apparently reaching its highest level in at least  the last 20 years. Under this Fund, HUD's Federal Housing Administration  (FHA) provides insurance for private lenders against losses on home  mortgages. Borrowers who obtain FHA-insured mortgages pay insurance  premiums, which are deposited into the Fund. Although the Fund has been  financially self-sufficient for most of its history, it experienced substantial  losses during the 1980s, primarily because foreclosure rates on single- family homes supported by the Fund were high in economically stressed  regions. 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 achieves and  maintains an economic value of at least 2 percent of the Fund's insurance- in-force. The 1990 reforms also required that an independent actuarial  study be conducted annually to measure this capital ratio. In January 2000,  HUD reported that, according to an independent study by Deloitte &  Touche, the estimated capital ratio was 3.66 percent at the end of fiscal  year 1999. With the Fund's capital ratio now substantially above the  minimum required level, proposals have surfaced to reduce the ratio either  by spending some of the Fund's current resources or by reducing the net  cash flows into the Fund.
Concerned about the adequacy of the minimum 2-percent requirement and  about proposals to spend what some were calling excess reserves, you  asked us to determine the conditions under which an estimated capital  ratio of 2 percent would be adequate to maintain the actuarial soundness of  the Fund. Specifically, you asked us to (1) estimate the value of the Fund at  the end of fiscal year 1999, given expected economic conditions, and  compare our estimate to the estimate of the value of the Fund reported by  HUD for that year; (2) determine the extent to which a 2-percent capital  ratio would allow the Fund to withstand worse-than-expected loan  performance due to economic and other factors; and (3) describe some  options for adjusting the size of the Fund if the estimated capital ratio is  different than the amount needed, and describe the impact that these  options might have on the Fund, FHA mortgagors, and the federal budget.
To meet these objectives, we reviewed the laws and regulations governing  FHA's insurance program. In addition, to estimate the value of the Fund  and determine the extent to which a 2-percent reserve would allow the  Fund to withstand worse-than-expected loan performance, we developed  economic models. We also met with HUD officials who administer FHA's  single-family insurance program, the independent contractors that have  analyzed the Fund throughout the 1990s, and other experts to better  understand methodologies used to estimate the value of the Fund. In  addition, we met with officials at the Office of Management and Budget and  the Congressional Budget Office who are building economic models of  FHA's insurance program to provide better information about the Fund's  impact on the federal budget. To explore options for changing the size of  the Fund, we met with HUD officials and other interested parties. To  determine the impact of these changes on the Federal budget, we relied on  our own experts as well as budget experts familiar with FHA's program and  mortgage models at the Office of Management and Budget and the  Congressional Budget Office.
We conducted our work between December 1999 and February 2001 in  accordance with generally accepted government auditing standards. See  appendix I for a further discussion of our scope and methodology.
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, borrowers are required to purchase single-family mortgage  insurance when the value of the mortgage is large relative to the price of  the house. FHA, the Department of Veterans Affairs, and private mortgage  insurers provide virtually all of this insurance. In recent years private  mortgage insurers and conventional mortgage lenders have begun to offer  alternatives to borrowers who want to make little or no down payment.  FHA provides most of its single-family insurance through a program  supported by the Mutual Mortgage Insurance Fund. The Fund is organized  as a mutual insurance fund in that any income received in excess of the  amounts required to cover initial insuring costs, operating expenses, and  losses due to claims may be paid to borrowers in the form of distributive  shares after they pay their mortgages in full or voluntarily terminate their  FHA insurance. The economic value of the Fund depends on the relative  sizes 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 (see fig. 1). To cover these  outflows, FHA deposits cash inflows—up-front and annual insurance  premiums from participating homebuyers and 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  that were designed to place the Fund on an actuarially sound basis required   the Secretary of HUD to take steps to ensure that the Fund attains a  capital ratio of 2 percent of the insurance-in-force by November 2000  and maintains that ratio at a minimum at all times thereafter;   an independent contractor to conduct an annual actuarial review of the   the Secretary of HUD to suspend the payment of distributive shares,  which had been paid continuously from 1943 to 1990, until the Fund is  actuarially sound; and   FHA borrowers to pay more in insurance premiums over the life of their  loans by adding a risk-adjusted annual premium to the one time, up- front premium.
The Federal Credit Reform Act of 1990, enacted as part of the Omnibus  Budget Reconciliation Act of 1990, also reformed budgeting methods for  federal credit programs including FHA's mutual insurance program. The  1990 credit reforms were intended to ensure that the full cost of credit  activities for the current budget year would be reflected in the federal  budget so that the executive branch and the Congress could consider these  costs when making annual budget decisions. As a result, FHA's budget is  required to reflect the subsidy cost to the government—the estimated long- term cost calculated on a net present value basis—of FHA's loan insurance  activities for that year. During the 1990s, the estimated economic value of  the Fund—comprised of capital resources and the net present value of  future cash flows—grew substantially. As figure 2 shows, by the end of  fiscal year 1995, the Fund had 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 has continued to grow  and has always exceeded the amount required for a 2-percent capital ratio.
As a result of the 1990 housing reforms, the Fund must not only meet  capital ratio requirements, but it must also achieve actuarial soundness;  that is, the Fund must contain sufficient reserves and funding to cover  estimated future losses resulting from the payment of claims on foreclosed  mortgages and administrative costs. However, neither the legislation nor  the actuarial profession defines actuarial soundness. Price Waterhouse  (now PricewaterhouseCoopers) in 1989 concluded that for the Fund to be  actuarially sound, it should have capital resources that could withstand  losses from reasonably adverse, but not catastrophic, economic  downturns. The Price Waterhouse report did not clearly distinguish  adverse from catastrophic downturns; however, they said that private  mortgage insurers are required to hold contingency reserves to protect  against catastrophic losses. In turn, rating agencies require that private  mortgage insurers have enough capital on hand to withstand severe losses  that would occur if loans they insure across the entire nation had losses  similar to those experienced in the west south central states in the 1980s.
Because economic downturns put downward pressure on house prices and  incomes, they can stress FHA's ability to meet its obligations. Thus, it is  reasonable that measures of the financial soundness of the Fund would be  based on tests of the Fund's ability to withstand recent recessions or  regional economic downturns. In the last 25 years, we have experienced a  national recession and regional economic declines that did or could have  placed stress on FHA. For example, the nation experienced a recession in  1981 and 1982 that strained mortgage markets. Regionally, states in the  west south central portion of the nation experienced an economic decline  in 1986 through 1989 precipitated by a sharp drop in the price of crude oil.  Similarly, the economic decline experienced by California from 1992  through 1995 placed stress on FHA. Because FHA does substantial  business in these regions of the country, these experiences led to  substantial losses for FHA. In contrast, the economic decline experienced  by the New England states from 1989 through 1991 placed little strain on  FHA because insured mortgages in this region do not make up a large  portion of FHA's total portfolio. However, experiences similar to the New  England downturn, during which the unemployment rate increased by  almost 140 percent and house prices decreased by 5.5 percent, could place  stress on FHA if they occurred in other regions or the nation as a whole.
section_title: The Fund's Estimated Economic Value Exceeds 3 Percent of Insurance-in-Force
paragraphs: On the basis of our economic model of FHA's home loan program and  forecasts of several key economic factors, we estimate that at the end of  fiscal year 1999, the Fund had an economic value of about $15.8 billion.  This value, which is 3.20 percent of the unamortized insurance-in-force,  reflects the robust economy and relatively high premium rates prevailing  through most of the 1990s and the good economic performance forecast for  the future. In comparison, Deloitte & Touche estimated that the Fund's  1999 economic value was over $800 million larger than our estimate—or  about 3.66 percent of its estimate of FHA's unamortized insurance-in-force.  Although we did not evaluate the quality of Deloitte's estimates, we believe  that Deloitte's and our estimates are comparable because of the  uncertainty inherent in forecasting and the professional judgments made in  this type of analysis. However, Deloitte's analysis and ours differ in several  ways, including the time when the analyses were performed and some of  the assumptions made.
subsections: section_title: The Estimated Economic Value of the Fund Reflects the Robust Economy and Increased Premium Rates
paragraphs: Using conservative assumptions, we estimate that at the end of fiscal year  1999, the Fund had an economic value of about $15.8 billion. The economic  value of the Fund consists of the capital resources on hand and the net  present value of future cash flows. Documents used to prepare FHA's 1999  financial statements show that the Fund had capital resources of about  $14.3 billion at the end of that fiscal year. We estimated the relationship  between historical FHA foreclosures and prepayments and certain key  economic factors to forecast foreclosures and prepayments and the  resulting cash flows over the next 30-year period for mortgages insured by  FHA before the end of fiscal 1999. As a result of this analysis, we estimate  that at the end of 1999 the net present value of future cash flows was about  $1.5 billion. Summing the capital resources and future cash flows gives us  an economic value of about $15.8 billion. See appendix II for a detailed  discussion of the forecasting and cash flow models used to estimate the  economic value of the Fund.
We also estimate that the Fund's capital ratio—the Fund's economic value  divided by its insurance-in-force—exceeded 3-percent at the end of fiscal  year 1999. From the individual loan data provided by HUD, we calculated  that the unamortized insurance-in-force at the end of fiscal year 1999 was  about $494 billion and that the amortized value of that insurance, an  estimate of the outstanding balance of the loans and thus FHA's insurance  liability, was about $455.8 billion. Therefore, the economic value of the  Fund represented 3.20 percent of the unamortized insurance-in-force and  about 3.47 percent of the amortized insurance-in-force on September 30,  1999.
The robust economy and the increased premium rates established by the  1990 legislation contributed to the strength of the Fund at the end of fiscal  year 1999. The Fund's economic value principally reflects the large amount  of capital resources that the Fund has accrued. Because current capital  resources are the result of previous cash flows, the robustness of the  economy and the higher premium rates throughout most of the 1990s  accounted for the accumulation of these substantial capital resources.  Good economic times that are accompanied by relatively low interest rates  and relatively high levels of employment are usually associated with high  levels of mortgage activity and relatively low levels of foreclosure;  therefore, cash inflows have been high relative to outflows during this  period.
The estimated value of future cash flows also contributed to the strength of  the Fund at the end of fiscal 1999. As a result of relatively low interest rates  and the robust economy, FHA insured a relatively large number of  mortgages in fiscal years 1998 and 1999. These loans make up a large  portion of FHA's insurance-in-force, because many borrowers refinanced  their FHA-insured mortgages originated in earlier years, probably as a  result of interest rates having fallen to relatively low levels in 1998 and  1999. Because these recent loans have low interest rates and because  forecasts of economic variables for the near future show house prices  rising while unemployment and interest rates remain fairly stable, our  models predict that these new loans will have low levels of foreclosure and  prepayment. As a result, our models predict that future cash flows out of  the Fund will be relatively small. At the same time, we assume that FHA- insured homebuyers will continue to pay the annual premiums that were  reinstituted in 1991. Thus, our models predict that cash flowing into the  Fund from mortgages already in FHA's portfolio at the end of fiscal year  1999 will be more than sufficient to cover the cash outflows associated  with these loans. As a result, the estimated economic value of the Fund is  even higher than the level of its current capital resources.
section_title: Deloitte's Estimates Are Comparable to Ours, but the Analyses Differ in Several Ways
paragraphs: As table 1 shows, Deloitte's independent actuarial analysis of the Fund for  fiscal year 1999 estimated a capital ratio that was somewhat higher than  ours, 3.66 percent rather than 3.20 percent of unamortized insurance-in- force. Although we did not evaluate the quality of Deloitte's estimates, we  did identify some reasons that its estimate of the capital ratio was higher  than ours. The ratio is higher because Deloitte estimates both a higher  economic value of the Fund and a lower amount of insurance-in-force.  Deloitte's higher estimated economic value of the Fund includes a higher  estimated value for capital resources on hand that is somewhat offset by a  lower estimate of the net present value of future cash flows.
Our estimate and that of Deloitte rely on forecasts of foreclosures and  prepayments over the next 30 years, and, in turn, these forecasts  necessarily rest on forecasts of certain economic factors. In addition, the  estimates depend on the choices made concerning a variety of other  assumptions. As a result of the inherent uncertainty and the need for  professional judgment in this type of analysis, we believe that our estimates  and Deloitte's estimates of the Fund's economic value and capital ratio are  comparable.
Although the estimates are comparable, Deloitte's estimates of capital  resources and insurance-in-force differ from ours primarily because the  analyses were conducted at different times. Because Deloitte performed its  analysis before the end of 1999, it had to estimate some data for which we  had year-end values. In particular, Deloitte overestimated the 1999 value of  capital resources by extrapolating from the 1998 value. In contrast, we used  values developed for FHA's 1999 financial statements that were about $1  billion lower than Deloitte's estimate. Using our value for capital resources,  Deloitte's estimated capital ratio would be 3.44 percent rather than 3.66  percent of insurance-in-force. Similarly, Deloitte underestimated the  number of loans that FHA insured in the fourth quarter of fiscal year 1999  and, thus, underestimated the value of loans insured for all of fiscal year  1999 by about $33 billion, though this appears to have had little effect on  the estimated capital ratio.
Our analysis of the net present value of future cash flows and that of  Deloitte also differ in several respects. Both our estimates and Deloitte's  rely on forecasts of future foreclosures and prepayments. In turn, these  forecasts are generated from models that are based on estimated  relationships between the probability of loan foreclosure and prepayment  and key explanatory factors, such as borrowers' home equity and interest  and unemployment rates. Our model differs from Deloitte's in the way that  it specifies these relationships. For example, Deloitte specified changes in  household income as one of the key explanatory factors, while we did not.
The analyses also differ in the assumptions made about some future  economic values and costs associated with FHA's insurance program. For  example, we assumed lower house price appreciation rates and higher  discount rates for calculating net present values than did Deloitte. In  addition, the analyses differ in the way that they use HUD's data. We used a  sample of individual loans while Deloitte grouped loans into categories to  do its analysis. Although these factors could be important in identifying  why the two estimates differ, we could not quantify their impact because  we did not have access to Deloitte's models.
section_title: A 2-percent Capital Ratio Appears Sufficient to Withstand Some Worse-Than- Expected Loan Performance
paragraphs: According to our estimates, worse-than-expected loan performance that  could be brought on by moderately severe economic conditions would not  cause the estimated value of the fund at the end of fiscal year 1999 to  decline by more than 2 percent of insurance-in-force. However, a few more  severe economic scenarios that we examined could result in such poor  loan performance that the estimated value of the fund at the end of fiscal  year 1999 could decline by more than 2 percent of insurance-in-force. Two  of the three scenarios that showed such a large decline extended adverse  conditions more widely than the moderately severe scenarios and,  therefore, are less likely to occur. While these estimates suggest that the  capital ratios are more than sufficient to protect the Fund at this time from  many worse-than-expected loan performance scenarios, factors not fully  captured in our 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. For example, conventional mortgage  lenders and private mortgage insurers have recently lowered the required  down payment on loans. Such actions may have attracted some lower risk  borrowers who would otherwise have insured their loans with FHA. As a  result, the overall riskiness of FHA's portfolio may be greater than we have  estimated, making a given amount of capital less likely to withstand future  economic downturns than we have predicted.
subsections: section_title: At This Time, the Capital Ratio Appears Sufficient to Withstand Moderately Severe Economic Scenarios That Are Based on Recent Historical Experience
paragraphs: Beginning with the robust economy and the value of the Fund in 1999, our  analysis shows that a 2-percent capital ratio appears sufficient to withstand  worse-than-expected loan performance that results from moderately  severe economic scenarios similar to those experienced over the last 25  years. Our model and others that are based on historical experience  suggest that falling house prices and high levels of unemployment are likely  to produce poor mortgage performance. Thus, to test the Fund's ability to  withstand worse-than-expected loan performance, we developed economic  scenarios that are based on certain regional downturns and the 1981-82  national recession.
We tested the adequacy of the capital ratio using economic scenarios that  were based on three recent regional economic downturns—one in the west  south central region of the United States that began in 1986, one in New  England that began in 1989, and one in California that began in 1992—that  produced high mortgage foreclosure rates in those regions. The degree to  which these downturns affected the Fund depended on their severity as  well as on the volume of mortgages insured by FHA in that region. Thus,  while New England suffered a severe downturn in the late 1980s and early  1990s, the Fund did not suffer significantly because the volume of loans  that FHA insures in New England represents a small share of FHA's total  volume of insured loans. Because regional averages diminish the impact of  the adverse economic experience, from each region we selected a state  with particularly poor experience as the basis for our scenarios. We also  adjusted the scenarios to recognize that the forecasts start from the  economic conditions that existed at the end of 1999. See appendix III for  further discussion of the scenarios that we used to test the adequacy of  FHA's capital ratio.
As can be seen in table 2, neither the scenarios that are based on regional  downturns nor the scenario that is based on the 1981-82 national recession  had much of an effect on the value of the Fund. More specifically, in these  worse-than-expected scenarios that are based on specific historical  experiences, the estimated capital ratio never falls below 2.8 percent,  which is only 0.4 percentage points below our estimated capital ratio using  expected economic conditions. However, the national recession had the  greatest impact because it affected FHA's entire portfolio.
section_title: Under More Severe Economic Scenarios, the Capital Ratio Could Fall by More Than 2 Percentage Points
paragraphs: Although the Fund's estimated capital ratio at the end of fiscal year 1999  fell by considerably less than 2 percentage points under economic  scenarios that are based on recent regional experiences and the 1981-82  national recession, our model suggests that extensions of some historical  regional scenarios to broader regions of the country could cause the capital  ratio to fall by more than 2 percentage points. Specifically, to test whether a  2-percent capital ratio could withstand more severe economic conditions,  we extended the regional scenarios to two regions and then to the nation as  a whole. However, we recognize that these extensions are less likely to  occur than the historical scenarios that affected a single region. As table 3  shows, if any of these downturns simultaneously hit two regions where  FHA has significant business—the west south central and Pacific regions— the estimated capital ratio would be less than 2 percentage points lower  than it would be with expected loan performance. In addition, even if the  entire nation experienced a downturn similar to two of the three regional  downturns that we analyzed, the estimated capital ratio would still fall by  less than 2 percentage points. However, a national downturn as severe as  that experienced by Massachusetts from 1989 through 1992 would cause  our estimate of the 1999 capital ratio to fall by more than 2 percentage  points.
Because we were concerned that the historical scenarios we were  considering might not be adequate to test the effect of changes in interest  rates, we developed two additional scenarios: one in which mortgage  interest rates fall and then a recession sets in and one in which mortgage  and other interest rates rise to levels that are higher than those in the  expected economic conditions scenario. The first scenario is more likely to  exhaust a 2-percent capital ratio.
Under a scenario in which mortgage interest rates fall and then a recession  sets in, the drop in interest rates might induce some homeowners to  refinance their mortgages. For those homeowners who refinance outside of  FHA, the fund would no longer be accumulating revenue in the form of  annual premiums; if the homeowners have not had their mortgages for  long, they would receive some premium refunds. Moreover, those  borrowers who use FHA's streamline refinance provision that allows  borrowers to refinance their mortgages without a new appraisal of their  home will likely pay annual premiums for fewer years than if they had not  refinanced. So, cash outflows would have increased and cash inflows  would have decreased before the recession hits. When the recession hits,  cash outflows would increase further because of increased foreclosures  among the remaining borrowers. As table 3 shows, our model predicts that  the capital ratio would fall substantially—by almost 2 percentage points— under this scenario.
A scenario with rising mortgage interest rates will affect various loan types  differently. Because the payments on adjustable rate mortgages increase as  interest rates rise, there is an increased likelihood that borrowers with  these types of mortgages will default. However, since FHA-insured  mortgages are assumable, rising interest rates make fixed-rate mortgages  more valuable to those borrowers holding them. This decreases the  likelihood that borrowers with these types of mortgages will default.  Insurance on loans originated in 1998 and 1999 make up 42 percent of  FHA's portfolio at the end of fiscal year 1999, and the insured loans are  predominately fixed rate mortgages. Consequently, it is not surprising that  a rising interest rate scenario leads to an increase in the value of the Fund.
Because our economic model did not predict regional or national  foreclosure rates as high as those experienced during the 1980s in any of  our scenarios, we estimated cash flows using foreclosure rates that more  closely matched regional experience during the 1980s. Specifically, we  assumed that for mortgages originated from 1989 through 1999, foreclosure  rates in 2000 through 2004 would equal those experienced from 1986  through 1990 by FHA-insured loans that originated between 1975 and 1985  in a given region. As table 3 shows, the capital ratio fell to 0.92 percent  under this scenario. To test an even more severe scenario, one similar to  that used by rating agencies for private mortgage insurers, we also  calculated future cash flows assuming that foreclosure rates in 2000  through 2004 extended the very poor performance of the west south central  mortgages in the 1980s to ever larger portions of FHA's insurance portfolio.  As figure 3 shows, we found that if 36.5 percent of FHA-insured mortgages  experienced these high default rates, the estimated capital ratio for fiscal  year 1999 would fall by 2 percentage points. If about 55 percent of FHA's  portfolio experienced these conditions, a less likely event, the capital ratio  would be 0.
section_title: Other Factors May Affect the Adequacy of the Capital Ratio
paragraphs: Because our models are based on the relationship between foreclosures  and prepayments and certain economic factors from fiscal years 1975  through 1999, they do not account for the potential impact of recent events,  such as changes in FHA's program or in the behavior of the conventional  mortgage market. In addition, our models assume that no additional  changes in FHA's program or the conventional mortgage market that would  affect FHA-insured loans originated through 1999 take place during the  forecast period, which extends from fiscal years 2000 through 2028. To the  extent that any such changes cause foreclosure and prepayment rates on  existing FHA-insured loans to be higher or lower than we have predicted,  the Fund's capital ratio would be different under the various scenarios we  have discussed. Furthermore, our analysis does not attempt to predict how  loans insured by FHA after fiscal year 1999 will behave. Future changes in  FHA's program, such as the premium changes adopted as of January 1,  2001, or in the conventional mortgage market may make future loans  perform better or worse than we might expect from past experience. In  addition, these changes may increase or reduce the amount of cash flowing  into the Fund and thus its ability to withstand worse-than-expected loan  performance in the future.
subsections: section_title: Changes in FHA's Insurance Program
paragraphs: HUD and the Congress can change FHA's insurance program in a variety of  ways, including changes in refund policy and underwriting standards. In  fact, HUD and the Congress have taken the following actions in recent  years that could affect the Fund in ways that are not accounted for in our  models:   HUD has suggested that it will reinstitute distributive shares and  Members of Congress have introduced bills requiring HUD to take that  action. The immediate consequence of this action would be that cash  flows out of the Fund would be higher than our estimates.
During the late 1990s, the Congress required that FHA implement a new  loss mitigation program that encourages lenders to take actions to lower  defaults on FHA-insured mortgages. The program requires that lenders  provide homebuyers with certain options to avoid foreclosure. While it  is hoped that losses from foreclosures will decline as a result of this  program, if foreclosure is simply delayed as a result of forbearance,  losses could ultimately be larger in the long run. In either case, actual  cash flows would likely be different than our estimates.
FHA has also reduced up-front premiums for new homeowners who  receive financial counseling before buying a home. If the program  reduces the likelihood that these homeowners will default, losses would  be lower than we have estimated.
HUD has taken action to improve the oversight of lenders and better  dispose of properties and is continuing to implement new programs in  these areas. Better oversight of lenders could mean that losses on  existing business would be lower than we have predicted, and better  practices for disposing of property could reduce losses associated with  foreclosed properties below the level we have estimated.
Our models do not look at cash flows associated with loans that FHA  would insure after fiscal year 1999. However, recent and future changes in  FHA's insurance program will affect the likelihood that these loans will  perform differently than past experience suggests they will. If, for example,  FHA loosens underwriting standards, there is a greater likelihood that  future loans would perform worse than past experience suggests. In  addition, changes in premiums, such as the recent reductions in up-front  premiums, could reduce cash inflows into the Fund and, therefore, reduce  the Fund's ability to withstand poor loan performance. However, this  premium change could also lower the riskiness of the loans FHA insures.
section_title: Changes in the Conventional Mortgage Market
paragraphs: Recent changes in the conventional mortgage market, especially changes in  FHA's competitors' behavior, may also affect the estimates we have made  concerning the Fund's ability to withstand adverse economic conditions  over the long run. Homebuyers' demand for FHA-insured loans depends, in  part, on the alternatives available to them. In recent years, FHA's  competitors in the conventional mortgage market—private mortgage  insurers and conventional mortgage lenders—are increasingly offering  products that compete with FHA's for those homebuyers who are  borrowing more than 95 percent of the value of their home. These  developments in the conventional mortgage market may have increased the  average risk of FHA-insured loans in the late 1990s. In particular, 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, 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. If this effect, known as adverse  selection, has been substantial, the economic value of the Fund may be  lower than we estimate, and it may be more difficult for the Fund to  withstand worse-than-expected loan performance than our estimates  suggest. In addition, should these competitive pressures persist, newly  insured loans are likely to perform worse than prior experience would  suggest, and then any given capital ratio would be less able to withstand  such performance. FHA is taking some action to more effectively compete.  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: The Impacts of Options for Reducing FHA's Capital Ratio Are Difficult to Predict
paragraphs: Several options are available to the Secretary of HUD under current  legislative authority that could result in reducing FHA's capital ratio. Other  options would require legislative action. Reliably measuring the impacts of  these options on the Fund's capital ratio and FHA borrowers is difficult  without using tools designed to estimate the multiple impacts that policy  changes often have. While HUD has substantially improved its ability to  monitor the financial condition of the Fund, neither the models used by  HUD to assess the financial health of the Fund, nor those used by others,  explicitly recognize the indirect effects of policy changes on the volume  and riskiness of FHA's loans. As a result, the impacts of the various policy  options on the federal budget are difficult to discern. However, any option  that results in a reduction in the Fund’s reserve, if not accompanied by a  similar reduction in other government spending or by an increase in  receipts, would result in either a reduction in the surplus or an increase in  any existing deficit.
subsections: section_title: The Secretary of HUD and the Congress Have Numerous Options Available to Reduce the Capital Ratio
paragraphs: There are several changes to the FHA single-family loan program that could  be adopted if the Secretary of HUD or the Congress believes that the  economic value of the Fund is higher than the amount needed to ensure  actuarial soundness. For example, actions that the Secretary could take  that could reduce the value of the fund include lowering insurance  premiums, adjusting underwriting standards, and reinstituting distributive  shares. However, congressional action in the form of new legislation  would be required to make other program changes that are not now  authorized or clearly contemplated by the statute. These would include  actions such as changing the maximum amount FHA-insured homebuyers  may borrow relative to the price of the house they are purchasing and using  the Fund's reserves for other federal programs. Generally, the Secretary  of HUD, in making any authorized changes to the FHA single-family  program, must meet certain operational goals. These operational goals  include (1) maintaining an adequate capital ratio, (2) meeting the needs of  homebuyers with low down payments and first-time homebuyers by  providing access to mortgage credit, (3) minimizing the risk to the Fund  and to homeowners from homeowner default, and (4) avoiding adverse  selection.
section_title: Potential Effects of Options on the Fund's Capital Ratio and FHA Borrowers Are Difficult to Measure
paragraphs: Reliably estimating the potential effect of various options on the Fund's  capital ratio and FHA borrowers is difficult because the impacts of these  policy changes are complex and tools available for handling these  complexities may not be adequate. Policy changes have not only  immediate, straightforward impacts on the Fund and FHA's borrowers, but  also more indirect impacts that may intensify or offset the original effect.  Implementing these options could affect both the volume and the average  riskiness of loans made, which, in turn, could affect any future estimate of  the Fund's economic value. As a result of this complexity, obtaining a  reliable estimate would likely require that economic models be used to  estimate the indirect effects of policy changes. In 1990, the Congress  enacted legislation designed to provide better information on the Fund's  financial condition. The Omnibus Budget Reconciliation Act requires  annual independent actuarial reviews of the Fund and includes credit  reforms that require HUD to estimate, for loans originated in a given year,  the net present value of the anticipated cash flows over all the years that  the loans will be in existence. The models developed by HUD to comply  with these requirements are based on detailed analyses of the Fund's  historical claim and loss rates and have improved HUD's ability to monitor  the financial condition of the Fund. At this time, however, neither the  models used by HUD to assess the financial health of the Fund, nor those  used by others, explicitly recognize the indirect effects of policy changes  on the volume and riskiness of FHA's loans. As a result, HUD cannot  reliably estimate the impact of policy changes on the Fund. Although it is  difficult to predict the overall impact of a change on the Fund's capital ratio  and thus on FHA borrowers as a whole, different options would likely have  different impacts on current and prospective FHA-insured borrowers.
subsections: section_title: Effect on the Fund's Capital Ratio
paragraphs: Many of the proposals to reduce the capital ratio, such as lowering  premiums or reinstituting distributive shares, will reduce the price of FHA  insurance to the borrower. If no change in the volume of loans FHA insures  is considered, then the effect of lowering premiums, for example, clearly  would be to lower the economic value of the Fund. However, for two  reasons, this price reduction is likely to increase the volume of FHA loans  originated, which would increase both premium income and claims against  the Fund when some of these new loans default. First, by lowering the  price of FHA insurance relative to the price of private mortgage insurance,  this premium reduction would likely induce some borrowers who  otherwise would have obtained private mortgage insurance to obtain FHA  insurance instead, thereby increasing FHA's market share. Second, people  who were deferring home purchases because of the high price of FHA  insurance might buy homes with FHA insurance once the price is lower.  Without a complete analysis of the impact on the volume of loans, reliably  estimating the effect of lowering the premiums on the Fund's economic  value is difficult.
Furthermore, the economic value of the Fund is influenced not only by the  volume of loans FHA insures, but also by the riskiness of those loans.  Therefore, determining the effect a policy change will have on the  economic value of the Fund requires determining how the policy will affect  the riskiness of FHA-insured loans. In the case of lowering up-front  premiums, for example, the new FHA-insured loans could be less risky than  FHA's existing loans. As a result, the new loans would be profitable and  offset the direct impact of lower premiums. Generally, private mortgage  insurers require that borrowers meet higher credit standards than does  FHA. So, to the extent that these new FHA borrowers would have obtained  private mortgage insurance without the lower premiums, they are likely to  have lower risk profiles than the average for all current FHA borrowers. At  the same time, lowering up-front premiums is not likely to attract many  additional higher-risk borrowers who would previously not have qualified  for FHA-insured loans.
Because HUD does not have adequate tools to handle the complexities of  estimating the ultimate impact of policy changes on the volume of FHA- insured loans and the riskiness of those loans, these factors are not always  considered in assessing the impact of policy changes. For example,  assuming that the volume and riskiness of FHA-insured loans will not  change, HUD estimates that the recent reductions in up-front premiums  combined with the introduction of mortgage insurance cancellation  policies will lower the estimated value of the Fund by almost $6 billion over  the next 6 years. Because this estimate does not consider the possible  changes in the volume of loans that will be insured and the riskiness of  those loans, it is an estimate only of the direct impact rather than the full  impact of policy changes. Similarly, a recent study presents estimates that  lowering up-front premiums to 1.5 percent would result in an almost  fivefold increase in the likelihood that cash inflows would be less than  outflows over a random 10-year period. However, this study notes that it  did not look at how these changes would affect the riskiness of new loans.  The complexity of estimating the impact of policy changes on the Fund  implies that economic models would be needed to reliably estimate the  likely outcomes. The most likely sources for such models would be the  studies that compute the economic value of the Fund; however, the models  HUD and others have been using to assess the financial health of the Fund  do not explicitly recognize the impact of policy changes on the economic  value of the Fund. Instead, they assume that FHA's market share remains  static.
section_title: Effect on Borrowers
paragraphs: Although it is difficult to predict the overall impact of a change on the  Fund's capital ratio and thus on FHA borrowers as a whole, different  options would likely have different impacts on various FHA-insured  borrowers. Some proposals would more likely benefit existing and future  FHA-insured borrowers, while others would benefit only future borrowers,  and still others would benefit neither of these groups. One interpretation of  the higher premiums that borrowers paid during the period in which the  economic value of the fund has been rising is that borrowers during the  1990s “overpaid” for their insurance. Some options for reducing the capital  ratio, such as reinstituting distributive shares, would be more likely to  compensate these borrowers. Paying distributive shares would benefit  certain existing borrowers who voluntarily terminate their mortgages. If  these policies continued into the future, they would also benefit future  policyholders. Alternatively, reducing up-front premiums, reducing the  number of years over which annual insurance premiums must be paid, or  relaxing underwriting standards would tend to benefit only future  borrowers. Policy options that propose to use some of FHA's capital  resources for spending on other programs would benefit neither existing  nor future FHA-insured borrowers, but would instead benefit the recipients  of those programs receiving the new expenditures. For example, reducing  the capital ratio by shifting funds from the Fund to subsidize multifamily  housing may primarily benefit renters rather than single-family  homeowners. However, over time such a policy could be sustained only so  long as FHA borrowers continue to pay premiums higher than the cost to  FHA of insuring single-family mortgages.
section_title: Potential Impact of Options on the Federal Budget Is Difficult to Discern
paragraphs: Because of the difficulty in reliably measuring the effect of most actions  that could be taken either by the Secretary of HUD or the Congress on the  Fund's capital ratio, we cannot precisely measure the effect of these  policies on the budget. However, any actions taken by the Secretary or the  Congress that influence the Fund's capital ratio will have a similar effect on  the federal budget. Specifically, any proposal that results in a reduction in  the Fund’s reserve, if not accompanied by a similar reduction in other  government spending or by an increase in receipts, would result in either a  reduction in the surplus or an increase in any existing deficit.
If the Secretary or the Congress adopts policies, such as paying distributive  shares or relaxing underwriting standards, that could reduce the  profitability of the Fund, both the negative subsidy amount reported in  FHA's budget submission and the Fund's reserve would be lower. Some of  these policies—such as paying distributive shares—would affect FHA's  cash flows immediately. Thus, the amount of money available for FHA to  invest in Treasury securities would be lower. The Treasury, in turn, would  have less money available for other purposes, and any overall surplus  would decline or any deficit would rise. If the amounts of cash flowing out  of the Fund exceeded current receipts, FHA would be required to redeem  its investments in Treasury securities to make the required payments. The  Treasury, then, would be required to either increase borrowing from the  public or use general tax revenues to meet its financial obligations to FHA.  In either case, any annual budget surplus would be lower or deficit higher.
section_title: Conclusions
paragraphs: At the end of fiscal year 1999, the Fund had a capital ratio that exceeded 2  percent of FHA's insurance-in-force—the minimum required by law;  however, whether the fund was actuarially sound is not so clear. Neither  the statute nor HUD has established criteria to determine how severe of a  stress the Fund should be able to withstand, that is, what constitutes  actuarial soundness. Our results show that as of the end of fiscal year 1999,  only the most severe circumstances that we analyzed would cause the  current economic value of the Fund to fall below 0.
One method of determining actuarial soundness would be to estimate the  value of the Fund under various economic and other scenarios. In our  analysis, the required minimum capital ratio of 2 percent appears sufficient  to cover most of the adverse economic scenarios we tested, although it  would not be possible to maintain the minimum under all scenarios.  Nonetheless, we urge caution in concluding that the estimated value of the  Fund today implies that the Fund could withstand the specified economic  scenarios regardless of the future activities of FHA or the market. Our  estimates and those of others are valid only under a certain set of  conditions, including that loans FHA recently insured respond to economic  conditions similarly to those it insured in the more distant past, and that  cash inflows associated with future loans at least offset outflows  associated with those loans. However, HUD is changing several policies  that may affect the volume and quality of its future business. Further,  adverse economic events cannot be predicted with certainty; therefore, we  cannot attach a likelihood to any of the scenarios that we tested (even  though we recognize that it is less likely that a severe economic downturn  will affect the whole nation than one or two regions). It is instructive to  remember in considering the uncertainty of the future, that the Fund had  an even higher capital ratio in 1979 when the economic value of the Fund  equaled 5.3 percent of insurance-in-force, but in little more than a decade— after a national recession, the substitution of an up-front premium for  annual insurance premiums, and regional real estate declines—the  economic value of the Fund was negative. Thus, it is important to  periodically reevaluate the actuarial soundness of the Fund.
Today, FHA knows more about the condition of the Fund but could still  improve its evaluation of the impact that unexpected economic downturns  and policy changes may have on the Fund. HUD has already taken some  action that it estimates will lower the value of the Fund, including reducing  up-front insurance premiums on newly insured mortgages. HUD has done  so without the tools necessary to reliably measure the multiple impacts  that these policies are likely to have. While the direct impact of policies  that are likely to reduce the Fund's capital ratio can be estimated with the  models used in the actuarial reviews, those models cannot isolate the  indirect effects on the volume of loans insured by FHA and the riskiness of  those loans.
section_title: Matters for Congressional Consideration
paragraphs: The Congress may want to consider taking action to amend the laws  governing the Fund to specify criteria for determining when the Fund is  actuarially sound. Because we believe that actuarial soundness depends on  a variety of factors that could vary over time, setting a minimum or target  capital ratio will not guarantee that the Fund will be actuarially sound over  time. For example, if the Fund were comprised primarily of seasoned loans  with known characteristics, a capital ratio below the current 2-percent  minimum might be adequate, but under conditions such as those that  prevail today, when the Fund is comprised of many new loans, a 2-percent  ratio might be inadequate if recent and future loans perform considerably  worse than expected. Thus, the Congress may want to consider defining  the types of economic conditions under which the Fund would be expected  to meet its commitments without borrowing from the Treasury.
section_title: Recommendations
paragraphs: If the Congress decides that no further guidance is necessary, to better  evaluate the health of the Fund and determine the appropriate types and  timing of policy changes, we recommend that HUD develop criteria for  measuring the actuarial soundness of the Fund. These criteria should  specify the economic conditions that the Fund would be expected to  withstand and may specify capital ratios currently consistent with those  criteria.
Because many conditions affect the adequacy of a given capital ratio, we  recommend that the independent annual actuarial analysis give more  attention to tests of the Fund's ability to withstand appropriate stresses.  These tests should include more severe scenarios that capture worse-than- expected loan performance that may be due to economic conditions and  other factors, such as changes in policy and the conventional mortgage  market.
To more fully assess the impact of policy changes that are likely to  permanently affect the profitability of certain FHA-insured loans, we  recommend that the Secretary of HUD develop better tools for assessing  the impact these changes may have on the volume and riskiness of loans  that FHA insures. Such analysis is particularly important where the policy  change permanently affects certain loans, as in the case of underwriting  and premium changes. Without a better analytical framework to assess the  full impact of policy changes that permanently affect certain loans, we  recommend that such changes be made in small increments so that their  impact can be monitored and adjustments can be made over time.
section_title: Agency Comments and Our Evaluation
paragraphs: We provided a draft of this report to the Secretary of HUD for his review  and comment. HUD agreed with the report's findings regarding the  estimated value of the fund, and the ability of the fund to withstand  moderately severe economic downturns that could lead to worse-than- expected loan performance. However, HUD expressed concern that the  report did not note the probability of the most stressful scenarios we tested  and FHA's ability to react to adverse developments. HUD also thought our  reference to the substantial decline in the capital ratio that occurred during  the 1980s left a false impression that the Fund is currently in jeopardy. In  addition, HUD expressed concern that the report did not fully recognize the  improvements it has made in analyzing policy changes and monitoring the  performance of the Fund and disagreed with our recommendations. HUD's  letter is reproduced in appendix IV.
In response, we clarified that scenarios in which we extend historical  adverse economic conditions more widely are less likely to occur.  However, we cannot attribute a probability to any scenario we used. We  also acknowledge that the annual actuarial reviews and the annual  reestimates of the Fund required under the housing and credit reforms of  1990 enable HUD to better monitor the performance of the Fund and,  therefore, react to adverse developments. However, we remain concerned  that HUD's analyses of policy changes do not fully recognize the impact  that these policy changes may have on the volume of loans FHA will insure  and the riskiness of those loans. We also disagree that the reference to the  decline in the capital ratio experienced in the 1980s implies that the Fund is  in jeopardy today. In fact, this example serves to illustrate that changes in  the economy and HUD policy can have a dramatic impact on the value of  the Fund.
With regard to our recommendation that HUD develop criteria for  measuring the actuarial soundness of the Fund, HUD seems to infer that we  believe a static capital ratio should be the criterion for measuring actuarial  soundness. We do not recommend a static capital ratio for measuring  actuarial soundness. Rather, we believe that it is important to measure  actuarial soundness under different economic and other scenarios;  therefore, we recommend that HUD specify the conditions that the Fund  would be expected to withstand. We revised this recommendation to make  clear that the definition of actuarial soundness should consider the  economic conditions that the Fund would be expected to withstand.
Regarding our recommendation that the independent annual actuarial  analysis give more attention to tests of the Fund's ability to withstand  appropriate stresses, HUD noted that it believed it was already complying  with this recommendation and asked that our report define more  specifically what tests are needed. In response, we clarified that the annual  actuarial review should include more severe scenarios that capture worse- than-expected loan performance that may be due to economic conditions  and other factors, such as changes in HUD policy and the conventional  mortgage market. HUD's recent actuarial analysis included two  scenarios—an interest rate spike scenario and a lower house price  appreciation scenario—for testing the value of the Fund under a stressed  economic state, and in neither scenario do house prices decline or  unemployment rates rise.
With regard to our recommendation concerning tools for assessing the  impact of policy changes, HUD disagreed that any tools are needed beyond  those that it already has. Specifically, HUD cites the annual analyses done  in compliance with the Federal Credit Reform Act of 1990 and its annual  actuarial reviews that already focus on policy changes. Further, HUD notes  that it has made its program data more accessible for policy analysis  through the creation of the Single Family Data Warehouse. However, we  remain concerned that HUD does not have adequate tools for assessing the  full impact that policy changes may have. Tools such as models for  estimating the change in demand and the risk characteristics of future  loans would enable HUD to better estimate the full impact that policy  changes may have on the value of the Fund. HUD also disagreed with the  idea that any policy actions it takes should be only incremental and  reversible. We revised our recommendation to make clear that incremental  changes are appropriate where a policy change permanently affects certain  loans.
Copies of this report will be distributed to interested congressional  committees; the Honorable Mel Martinez, Secretary of the Department of  Housing and Urban Development; the Honorable Mitchell E. Daniels, Jr.,  the Director of the Office of Management and Budget; and the Honorable  Dan L. Crippen, the Director of the Congressional Budget Office. We will  also make copies available to others on request.
If you or your staff have any questions about this report, please contact me  at (202) 512-8678. Key contributors to this report are listed in appendix V.
section_title: Scope and Methodology
paragraphs: To estimate the economic value of the Federal Housing Administration's  (FHA) Fund as of September 30, 1999, and its resulting capital ratio, we  developed econometric and cash flow models.  These models were based  on models that we developed several years ago for this purpose.  In  developing the earlier models, we examined existing studies of the single- family housing programs of both the Department of Housing and Urban  Development (HUD) and the Department of Veterans Affairs (VA);  academic literature on the modeling of mortgage foreclosures and  prepayments; and previous work that Price Waterhouse (now  PricewaterhouseCoopers), HUD, VA, ourselves, and others had performed  on modeling government mortgage programs.  For our current analysis, we  modified our previous models on the basis of our examination of work  performed recently by PricewaterhouseCoopers, Deloitte & Touche, and  others; discussions we held with analysts familiar with modeling mortgage  foreclosures and prepayments; and program changes made by FHA since  our previous work was performed.  For these models, we used data  supplied by FHA and Standard & Poor’s DRI, a private economic  forecasting company.  We also used information from FHA’s independent  actuarial reviews in our analysis.
Our econometric analysis estimated the historical relationships between  the probability of loan foreclosure and prepayment and key explanatory  factors, such as the borrower's equity and the interest rate.  To estimate  these relationships, we used HUD’s A-43 data on the default and  prepayment experience of FHA-insured home mortgage loans that  originated from fiscal years 1975 through 1999.  To test the validity of our  econometric models, we examined how well the models predicted the  actual rates of FHA's loan foreclosures and prepayments through fiscal  year 1999.  We found that our predicted rates closely resembled the actual  rates.  Next, we used our estimates of these relationships and forecasts of  future economic conditions provided by Standard & Poor’s DRI to develop  a baseline forecast of future loan foreclosures and prepayments for loans  that were active at the end of fiscal year 1999.
To estimate the net present value of future cash flows of the Fund under  expected economic conditions, we used our forecast of future loan  foreclosures and prepayments in conjunction with a cash flow model that  we developed to measure the primary sources and uses of cash for loans  that originated from fiscal years 1975 through 1999.  Our cash flow model  was constructed to estimate cash flows for each policy year through the life  of a mortgage.  An important component of the model was the conversion  of all income and expense streams—regardless of the period in which they  are actually forecasted to occur—into their 1999 present value equivalents.   We then added the forecasted 1999 present values of the future cash flows  to the current cash available to the Fund, which we obtained from  documents used to prepare FHA's 1999 audited financial statements, to  estimate the Fund's economic value and resulting capital ratio.  A detailed  discussion of our models and methodology for estimating the economic  value and capital ratio of the Fund appears in appendix II.
To compare our estimates of the Fund's economic value and capital ratio  with the estimates prepared for FHA by Deloitte & Touche, we reviewed  Deloitte's report and met with its analysts and HUD officials to learn more  about that study's methodology, data, and assumptions.
To determine the extent to which a capital ratio of 2 percent would allow  the Fund to withstand worse-than-expected loan performance, we  developed various scenarios for future economic conditions that we  anticipated would result in substantially worse loan performance than we  forecasted in our scenario using expected economic conditions.  We based  these scenarios on the economic conditions that led to episodes of  relatively high foreclosure rates for FHA single-family loans in certain  regions of the country at different times during the 1975 through 1999  period and on those experienced nationally during the 1981-82 recession.   We developed additional scenarios that extended the adverse regional  economic conditions to larger sections of the country to analyze how well  the Fund could withstand conditions even worse than what we had  experienced in the past 25 years.  We also developed some additional  scenarios with even higher foreclosure rates to further analyze the Fund's  ability to withstand adverse conditions.
Under each of the scenarios that we developed, we used our estimated  relationships between foreclosure and prepayment rates and various  explanatory factors, and the future economic conditions implied by the  scenarios, to forecast future foreclosures and prepayments for loans that  were active at the end of fiscal year 1999.  We then used these forecasts, in  conjunction with our cash flow model, to estimate the economic value and  capital ratio of the Fund under each scenario.  The difference between  these estimates and our estimate under expected economic conditions  shows whether each scenario is likely to result in a reduction of the Fund's  economic value of more than 2 percent and, therefore, whether a 2-percent  capital ratio is likely to be sufficient to allow the Fund to withstand the  worse-than-expected loan performance associated with such a scenario.
Our analysis of the adequacy of FHA’s capital ratio is limited to the  performance of loans in FHA's portfolio as of the end of fiscal year 1999.   That is, our analysis assesses the likelihood that an economic value of 2  percent of the unamortized insurance-in-force would be sufficient to cover  the excess of future payments over future cash inflows (on a net present  value basis) on those loans if they perform worse than expected.  Our  analysis of the ability of the Fund to withstand various adverse economic  conditions requires making the assumption that the adverse conditions  would not also cause loans insured by FHA after fiscal year 1999 to be an  economic drain on the Fund.  Since the 1990 reforms, the cash flows  associated with each year’s loans have been estimated to have a positive  economic value, thereby adding to the economic value of the entire Fund.   However, during adverse economic times, new loans might perform worse  than loans that were insured by FHA during the 1990s.  If the newly insured  loans perform so poorly that they have a negative economic value, then the  loss to the Fund in any of the adverse economic scenarios that we have  considered would be greater than what we have estimated.  Alternatively, if  the newly insured loans have positive economic values, then the Fund  would continue to grow.
To identify other factors, such as recent program and market changes, that  could cause worse-than-expected loan performance, we reviewed the laws  and regulations governing FHA’s insurance program, studied recent  actuarial reviews of the Fund, and interviewed experts.  We considered  these other factors because the relationships estimated in our econometric  models are based on historical relationships since 1975.  As a result, these  models might not capture the effects of recent changes in FHA programs or  the conventional mortgage market on the likelihood that loans insured in  the late 1990s will foreclose or prepay.  In addition, our forecasts of future  cash flows assume that FHA’s program and the private mortgage market  will not change over the 30-year forecast period in any way that would  affect FHA-insured loans originated through 1999.
To identify options for adjusting the size of the Fund and determining the  impact that these options might have, we reviewed the laws and  regulations governing FHA’s insurance program and proposals to use the  Fund’s economic value or otherwise change FHA’s insurance program.   Additionally, we interviewed experts both within and outside the federal  government.  When available, we collected HUD’s estimates of the impact  of various options on the Fund and the estimates of other experts.  To  determine the impact of these changes on the federal budget, we relied on  our own experts as well as those at the Office of Management and Budget  and the Congressional Budget Office.
We conducted our review from December 1999 to February 2001 in  accordance with generally accepted government auditing standards.
section_title: Models Used to Estimate the Economic Value of FHA’s Mutual Mortgage Insurance Fund
paragraphs: We built econometric and cash flow models to estimate the economic value  of HUD's FHA's Mutual Mortgage Insurance Fund (Fund) as of the end of  fiscal year 1999.  The goal of the econometric analysis was to forecast  mortgage foreclosure and prepayment activity, which affect the flow of  cash into and out of the Fund.  We forecasted activity for all loans active at  the end of fiscal year 1999 for each year from fiscal years 2000 to 2028 on  the basis of assumptions stated in this appendix.  We estimated equations  from data covering fiscal years 1975 through 1999 that included all 50 states  and the District of Columbia, but excluded U.S. territories.
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 model 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 can be calculated.  These equations are  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 has been active.  The results of the logistic  regressions were used to estimate the probabilities of a loan being  foreclosed or prepaid in each year.
FHA pays a claim on a foreclosed mortgage and sometimes, depending on  the age of the loan, refunds a portion of the up-front premium when a  mortgage prepays.  These two actions contribute to cash outflows.  Cash  inflows are generated when FHA sells foreclosed properties and when  borrowers pay mortgage insurance premiums.  We forecasted the cash  flows into and out of the Fund on the basis of our foreclosure and  prepayment models and key economic variables.  We then used the  forecasted cash flows, including an estimate of interest that would be  earned, and the Fund's capital resources to estimate the economic value of  the Fund.
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 refinanced and those that were purchase money mortgages (mortgages  associated with home purchase).  Separate estimates were prepared for  each group of long-term loans.  Likewise, investor loans were divided  between mortgages that 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; (3) the model results; (4) the cash flow model, with emphasis on  key economic variables; and (5) a sensitivity analysis that demonstrates the  sensitivity of our forecasts to the values of some key variables.
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 1999 (1,465,852 loans).   For the econometric models related to  long-term, fixed-rate mortgages, we used 25 percent of the long-term loans  in our sample.  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.  We categorized the loans as foreclosed, prepaid, or  active as of the end of fiscal year 1999.
To describe macroeconomic conditions at the national and state levels, we  obtained data from Standard & Poor's DRI, by state, on annual civilian  unemployment rates and data from the 2000 Economic Report of the  President on the implicit price deflator for personal consumption  expenditures.  We used Standard & Poor's DRI data on quarterly interest  rates for 30-year mortgages on existing housing along with its forecast data,  at the state level, on median house prices and civilian unemployment rates,  and at the national level, on interest rates on 1- and 10-year U.S. Treasury  securities.
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/her home because the  sale of the home with realization of a profit is better than 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 prompts refinancing, and  rising house prices, which prompts the take out of accumulated equity or  the sale of 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 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 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 home may be more interested in prepaying their existing mortgage  and taking out a larger one 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 loan 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: 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.  For the long-term  nonrefinanced equation, the ARM equation, and the short-term equation,  we deleted the lower category of LTV loans.  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 some loan type categories, 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 1999 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 high interest rates cause a higher monthly payment.   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 now 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 up 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/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  mortgage 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 farther the interest rate on the  mortgage would rise, if prevailing interest rates in the market did not  change, while CAPPEDNEG measures how much farther 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 downwards 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’ income, 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 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 determinate 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 if 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 4 summarizes the variables that we used to predict foreclosures and  prepayments.  Table 5 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  1999.  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 importantly, 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 6 and 7, 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 6 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 goodness of fit was  satisfactory: Chi-Square statistics were significant on all regressions at the  0.01-percent level.
Because the coefficients from a nonlinear regression can be difficult to  interpret, we transformed some of the coefficients for the long-term,  nonrefinanced, fixed-rate regressions into statements about changes in the  probabilities of foreclosure and prepayment.  Overall conditional  foreclosure probabilities for this mortgage type are estimated to be about  0.5 percent. In other words, on average, there is a ½ of a 1-percent  chance for a loan of this type to result in a claim payment in any particular  year.  By holding other predictor variables at their mean values, we can  describe the effect on the conditional foreclosure probability of changes in  the values of predictor variables of interest.  For example, if the average  value of the unemployment rate were to increase by 1 percentage point  from its mean value (in our sample) of about 6 percent to about 7 percent,  the conditional foreclosure probability would increase by about 20 percent  (from 0.5 percent to about 0.6 percent). Similarly, a 1-percentage-point  increase in the mortgage contract rate from its mean value of about 9.25 to  about 10.25 would also raise the conditional foreclosure probability by 20  percent (from about 0.5 percent to about 0.6 percent).   Values of  homeowners' equity of 10 percent, 20 percent, 30 percent, and 40 percent  result in conditional foreclosure probabilities of 0.8 percent, 0.7 percent,  0.5 percent, and 0.3 percent, respectively, illustrating the importance of  increased equity in reducing the probability of foreclosure. *B), where X refers to the mean value of the ith explanatory variable and the  Bs are the estimated coefficients. 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 rates.  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 one percentage point, the  conditional prepayment probability would increase by about 80 percent to  8.6 percent.
subsections: section_title: Model Predictions for Historical Period
paragraphs: To test the validity of our model, we examined how well the model  predicted actual patterns of FHA’s foreclosure and prepayment rates  through fiscal year 1999.  Using a sample of 10 percent of FHA’s loans made  from fiscal years 1975 to 1999, we found that our predicted rates closely  resembled actual rates.
section_title: Estimation of the Economic Value of the Fund
paragraphs: The economic value of the Fund is defined in the Omnibus Budget  Reconciliation Act of 1990 as the “current cash available to the Fund, plus  the net present value of all future cash inflows and outflows expected to  result from the outstanding mortgages in the Fund.”  We obtained  information on the capital resources of the Fund from documents used to  prepare FHA’s audited financial statements.  These capital resources were  reported to be $14.3 billion.
To estimate the net present value of future cash flows of the Fund, we  constructed a cash flow model to estimate the five primary future outflows  and inflows of cash through 2028 resulting from the books of business  written from fiscal years 1975 through 1999.  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.  Cash flows into the fund from  income from mortgagees' insurance premiums and from the net proceeds  from the sale of foreclosed properties.
To estimate the Fund's cash flow, we first forecasted, for active loans at the  end of 1999, the dollar value of loans predicted to foreclose or prepay in  any year through 2028.  From those estimates, we derived estimates of the  outstanding principal balances for the loans remaining active for each year  in the forecast period.  Our cash flow model used these estimates of  foreclosure and prepayment dollars and outstanding principal balances to  derive estimates of each of the primary cash flows.
We forecasted future loan activity (foreclosures and prepayments) on the  basis of the regression results described above and forecasts of the key  economic and housing market variables made by Standard & Poor's DRI.   Standard & Poor's DRI forecasts the median sales price of existing housing,  by state and year, through fiscal year 2005.  We assumed that after 2005  those prices would rise at 3 percent per year.  In creating the borrower's  equity variable, we used DRI forecasts of existing housing prices by state  and subtracted 2 percentage points per year to adjust for improvements in  the quality of housing over time and the depreciation of individual housing  units.  We also subtracted another 1 percentage point per year from the  company’s forecasts, to be conservative.  We made similar adjustments to  our assumed value of median house price change for the years beyond the  range of these forecasts.  We used DRI forecasts of each state’s  unemployment rate and assumed that rates from fiscal year 2026 on would  equal the rates in 2025.  We also used Standard & Poor's DRI forecasts of  interest rates on 30-year mortgages and 1- and 10-year Treasury securities.
Using the results of the econometric model, the cash flow model estimates  cash flows for each policy year through the life of a mortgage.  An  important component of the model is converting all income and expense  streams—regardless of the period in which they actually occurred—into  1999 present value dollars. We applied discount rates to match as closely as  possible the rate of return FHA likely earned in the past or would earn in  the future from its investment in Treasury securities.   As an  approximation of what FHA earned for each book of business, we used a  rate of return comparable to the yield on 7-year Treasury securities  prevailing when that book was written to discount all cash flows occurring  in the first 7 years of that book's existence.  We assumed that after 7 years,  the Fund's investment was rolled over into new Treasury securities at the  interest rate prevailing at that time and used that rate to discount cash  flows to the rollover date. For rollover dates occurring in fiscal year 1999  and beyond, we used 6 percent as the new discount rate.  As an example,  cash flows associated with the fiscal year 1992 book of business and  occurring from fiscal years 1992 through 1998 (i.e., the first 7 policy years)  were discounted at the 7-year Treasury rate prevailing in fiscal year 1992.   Cash flows associated with the fiscal year 1992 book of business but  occurring in fiscal year 1999 and beyond are discounted at a rate of 6  percent.
Our methodology for estimating each of the five principal cash flows is  described below.
subsections: section_title: Premium Income
paragraphs: Because FHA’s premium policy has changed over time, our calculations of  premium income to the Fund change depending on the date of the  mortgage’s origination.  We describe all premium income, including up- front premiums, even though they play no role in estimating the future cash  flows for the Fund at the end of fiscal year 1999.
For loans originating from fiscal years 1975 through 1983, premiums equal  the annual outstanding principal balance times 0.5 percent.  For loans  originating from fiscal years 1984 through June 30, 1991, premiums equal  the original loan amount times the mortgage insurance premium.  The  mortgage insurance premium during this period was equal to 3.8 percent  for 30-year mortgages and 2.4 percent for 15-year mortgages.  Because  there are no annual premiums for this group of loans, the future cash flows  would include no premium income.  For the purposes of this analysis,  mortgages of other lengths of time are grouped with those they most  closely approximate.  Effective July 1, 1991, FHA added an annual premium  of 0.5 percent of the outstanding principal balance to its up-front  premiums.  The number of years for which a borrower would be liable for  making premium payments depended on the LTV ratio at the time of  origination.  (See tables 8 and 9.)  For loans originating from July 1, 1991,  through the time of our review, premiums equal the original loan amount  times the respective up-front premium plus the product of the annual  outstanding principal balance times the respective annual premium rate for  as many years as annual premiums were required.
Some loans that originated in the 1990s are streamline refinanced  mortgages that are subject to different premium rates.  Since streamline  refinances do not require an appraisal, we decided that mortgages coded in  FHA’s database with an LTV of 0 could reasonably be assumed to represent  streamline refinance business.  For streamline refinance mortgages that  originated before July 1, 1991, we applied the premium rates from table 10.
For all streamline refinance mortgages that originated after July 1, 1991, we  applied the premium rates for non-streamline loans.  That is, for up-front  premium rates, we followed the 15-year or 30-year non-streamline premium  schedule for loans of those maturities.  For annual premium rates and  number of years that annual premiums are paid, we applied the rates for  loans with an LTV of less than 90 percent.
section_title: Claim Payments
paragraphs: Claim payments equal the outstanding principal balance on foreclosed  mortgages times the acquisition cost ratio.  We defined the acquisition cost  ratio as being equal to the total amount paid by FHA to settle a claim and  acquire a property (i.e., FHA’s "acquisition cost" as reported in its database)  divided by the outstanding principal balance on the mortgage at the time of  foreclosure.  For the purposes of our analysis, we calculated an average  acquisition cost ratio for each year’s book of business using actual data for  fiscal years 1975 through 1999.  Acquisition cost ratios generally decreased  over time from a high of 1.51 for loans originating in 1975 to a low of 1.09  for loans originating in 1999.
section_title: Net Proceeds
paragraphs: FHA’s net proceeds from the sale of foreclosed properties depend on both  the lag rate—the proportion of a year that passes between the time of a  foreclosure and the time the proceeds are received—and the loss rate—the  proportion of the cost of the property acquired that is not recovered when  the property is sold.  These are calculated as follows: Net Proceeds = Lag rate x claim payments from previous period x (1 - loss  rate) + (12-lag rate) x claim payments from the current period x (1 - loss  rate).
The lag, which is the number of months between the payment of a claim  and the receipt of proceeds from the disposition of the property, varied as  follows.  Before 1995, the lag was 5.9 months; in 1995, 5.35 months; in 1996,  4.7 months; and in 1997, 5.26 months.  For the years after 1997, we used a  lag of 5.26 months.  To calculate the lag rate for each period, we divided the  lag by 12.
We defined the loss rate as equal to FHA’s reported dollar loss after the  disposition of property divided by the reported acquisition cost over the  historical period.  We determined a loss rate for each year per book of  business for years 1 through 25.  We used an auto-regressive model to  forecast future loss rates.  In addition to past values of loss rates, we used  the origination year and policy year of the loan as independent variables in  this model.  Using the results of this model, we forecast loss rates over the  period from fiscal years 2000 through 2023.  For fiscal years 2024 through  2028, we used the estimated rate for 2023.  Our loss rates averaged 37  percent over the forecast period.
section_title: Refunded Premiums
paragraphs: The amount of premium refunds paid by FHA depends on the policy year in  which the mortgage is prepaid and the type of mortgage.  For mortgages  prepaid between October 1, 1983, and December 31, 1993, refunds were  equal to the original loan amount times the refund rate. However, we  converted these rates to express them as a percentage of the up-front  premium.  In 1993, FHA changed its refund policy to affect mortgages  prepaid on or after January 1, 1994.  For loans prepaying on or after  January 1, 1994, refunds are equal to the up-front mortgage insurance  premium times the refund rate. (See table 11.)
section_title: Administrative Expenses
paragraphs: Administrative expenses equal the outstanding principal balance times the  administrative expense rate.  The estimates of the administrative expense  rates were 0.098 percent for the years before 1995, 0.113 percent for 1995,  0.097 percent for 1996, 0.102 percent for 1997, and 0.103 percent for 1998  and all future years.
section_title: Sensitivity Analysis
paragraphs: We conducted additional analyses to determine the sensitivity of our  forecasts to the values of certain key variables.  Because we found that  projected losses from foreclosures are sensitive to the rates of  unemployment and house price appreciation, we adjusted the forecasts of  unemployment and price appreciation to provide a range of estimates of  the Fund's economic value under alternative economic scenarios.  Our  starting points for forecasts of the key economic variables were forecasts  made by Standard & Poor's DRI, as previously described.
For our low case scenario, we made these forecasts more pessimistic by  subtracting 2 percentage points per year from the forecasts of house price  appreciation rates and adding 1 percentage point per year to the  unemployment rate forecasts.  For our high case scenario, we added 2  percentage points per year to our base case forecast of house price  appreciation rates.  Under these alternatives, we estimated economic  values of about $13.6 billion and about $16.4 billion, respectively, for the  low and high cases, compared with about $15.8 billion for our base case.   These estimates correspond to estimates of the capital ratio of about 2.75  percent and 3.32 percent, respectively, for the low and high cases,  compared with our base case estimate for the capital ratio of 3.20 percent.   These estimates are shown in table 12.
To assess the impact of our assumptions about the loss and discount rates  on the economic value of the Fund, we operated our cash flow model with  alternative values for these variables.  We found that for the economic  scenario of our base case, a 1-percentage-point increase in the forecasted  loss rate resulted in a 0.7-percent decline in our estimate of the economic  value of the Fund.  Conversely, each percentage point decrease in the loss  rate resulted in a 0.7-percent increase in our estimate of economic value.   With respect to the discount rate, we found that for our base case economic  scenario, a 1-percentage-point increase in the interest rate applied to most  periods’ future cash flow resulted in a 0.3-percent increase in our estimate  of economic value.  Conversely, each percentage point decrease in the  discount rate resulted in a 0.4-percent decrease in our estimate of  economic value.
section_title: Development of Scenarios of Adverse Economic Conditions Used to Estimate the Economic Value of the Fund
paragraphs: This appendix describes the scenarios that we used to estimate the ability  of the Fund to withstand adverse future economic conditions.  Each  scenario specifies values of key economic variables, which our models  indicate are associated with mortgage claims and prepayments, during the  forecast period.  We used these values with the forecasting models  presented in appendix II to estimate future mortgage claims and  prepayments.  We then used these forecasted values of claim and  prepayment dollars in our cash flow model to estimate the economic value  of the Fund and the capital ratio under each scenario.
We developed two types of scenarios—historical and judgmental.  We  designed the historical scenarios to test the ability of the Fund to withstand  adverse economic conditions similar to those that adversely affected the  Fund in the 1980s and 1990s.  Because some of these adverse conditions  affected only certain regions, in some scenarios we expanded our analysis  to include estimates of the capital ratio when the historical conditions were  assumed to affect a larger share of FHA's business, including when they  were assumed to affect the entire nation.  In contrast, the judgmental  scenarios that we developed are not based on historical experience.   Instead, they represent conditions that we believe might place stress on the  Fund.
The key economic variables for which we forecast different values in the  different scenarios are the rate of house price appreciation; the  unemployment rate; and, in some instances, certain interest rates,  especially the mortgage interest rate.  In addition, we assumed that FHA's  loss per claim (the loss rate), expressed as a percentage of the claim  amount, was greater than the loss rate that we used in our base case  analysis under expected economic conditions.  We assumed that FHA  would experience higher loss rates when foreclosures were substantially  higher because of the difficulty of managing and disposing of a large  number of properties at the same time.  In addition, the demand for housing  would be likely to fall during an economic downturn, making it more  difficult to dispose of properties than in the base case.
subsections: section_title: Historical Scenarios
paragraphs: Three regional economic downturns and the 1981-82 national recession  form the bases of our historical scenarios.  Each regional downturn was  associated with a regional decline in house prices.  Declining house prices  represent a particularly adverse condition for the Fund because of the  strong negative relationship between borrowers' equity and the probability  of defaults leading to foreclosures.  The three regional economic  downturns, and associated housing price declines, that we used were (1)  the late 1980s' decline in the oil-producing states of the west south central  region; (2) the late 1980s' and early 1990s' decline in New England; and (3)  the early to mid-1990s' decline in the Pacific region, particularly in  California.
For each scenario that is based on a regional downturn, we assumed that  for 4 years the rate of house price change for the part of the nation assumed  to be affected by the downturn equaled the rate of house price change in  the state in that region that we selected to represent the regional  experience.  We selected the experiences of (1) Louisiana, beginning in  1986, to represent the oil price downturn; (2) Massachusetts, beginning in  1988, to represent the New England economic downturn; and (3)  California, beginning in 1991, to represent the California housing market  downturn.  Table 13 shows the median house prices for existing houses in  these states during their economic downturns. In calculating homeowner's  equity, we made the same adjustment to annual changes in median house  prices that we did in our base case, as described in appendix II. Similarly, in  our scenarios that are based on regional downturns, we assumed that  unemployment rates would change in the affected area for 4 years by the  same percentages as those rates changed in Louisiana; Massachusetts; and  California, respectively.
We developed six separate scenarios that are based on each regional  downturn, by varying the scope (i.e., the number of states assumed to be  affected) and timing of the adverse economic conditions in the forecast  period.  Specifically, we used three different scopes.  In the narrowest  scope, we assumed that only the particular region was affected.  That is, for  the scenario based on the downturn in the west south central region in the  late 1980s, we assumed that during 4 years of the forecast period, all of the  states in the west south central region experienced the same changes in  key economic variables as Louisiana experienced from 1987 through 1990.   We then expanded the scope by assuming that two regions in which FHA  has a lot of borrowers, the west south central and Pacific regions, were  affected.  Finally, we then expanded the scope to the entire nation, by  assuming that all states were affected.
Regarding timing, for each scope we developed two scenarios, one in  which the downturn began in 2000 and one in which it began in 2001.   Although we know that an economic downturn did not begin in 2000, we  developed scenarios starting then to test the ability of the Fund to  withstand an economic downturn that occurs when the portfolio contains  many recent loans.  Scenarios in which the downturn does not begin until  2001 would be expected to be less adverse because most of the large  number of borrowers who took out mortgages in 1998 and 1999 would have  seen substantial price appreciation in 2000, thereby reducing the likelihood  of default.
We developed two historical scenarios that are based on the 1981-82  recession and subsequent recovery.  In those scenarios, we assumed that in  each state, the rates of change in house price appreciation and  unemployment for 5 years during the forecast period are the same as they  were from 1981 through 1985.  In one scenario, we assumed that these  adverse conditions replicating 1981 through 1985 began in 2000; in the  other scenario, we assumed that they began in 2001.  Under these  scenarios, some states fared better than in the base case scenario.
Because it will be more difficult to manage and dispose of foreclosed  properties during an economic downturn, we increased the loss rates on  the proportion of mortgages affected by a given scenario during the years  the scenario runs.  We assumed that losses on affected foreclosed  properties would rise to 45 percent of the property’s value.  Without this,  loss rates average about 37 percent.
Our estimates of the economic value of the Fund and the capital ratio for  the historical scenarios are presented in table 14.
Judgmental Scenarios  We developed several judgmental scenarios to test the ability of the Fund to  withstand various types of economic conditions that might adversely affect  the Fund without regard to their relationship to historical experience.  In  one scenario, we assumed that median existing house prices declined by 5  percent per year for 3 consecutive years—an extremely steep rate of  decline—and that unemployment increased compared with the base case,  with both changes beginning in 2001.  Specifically, we increased the  unemployment rates in each state from forecasted levels by 2 percentage  points in 2001; 5 percentage points in 2002, 2003, and 2004; and 2  percentage points in 2005.  In a second scenario, we allowed the mortgage  interest rate to decline in 2000—by 2 percentage points from its forecasted  level—and then to return to forecasted levels.  We did this to precipitate a  wave of refinancing.  We also assumed declining house prices and rising  unemployment beginning in 2001, as in the previous judgmental scenario.  We used this scenario to test what might happen if premium income turns  out to be substantially less than expected and premium refunds  substantially more than expected because of rapid prepayment of loans,  most of which would not default.  In our third scenario, we added 1  percentage point to the base case forecasts of the mortgage interest rate,  and 1- and 10-year Treasury rates for the year 2000, 3 percentage points to  the forecasts of these interest rates between 2001 and 2003, and 1  percentage point in 2004.  We used this scenario to test what might happen  if interest rates were to rise more than anticipated.  In a fourth scenario, we  used the same rising interest rates as in the third scenario and also added  one percentage point to the forecasts of median existing house prices over  that period.
Our estimates of the economic value of the Fund and the capital ratio for  the judgmental scenarios are also presented in table 14.
In another type of judgmental scenario, we did not forecast the economic  variables and then use the forecasted claims and prepayments from our  econometric model, as we did with all of our other scenarios, both  judgmental and historical.  Instead, because none of our other scenarios  produced foreclosure rates nearly as high as FHA experienced in the 1980s,  we developed two scenarios in which we directly assumed higher  foreclosure rates.  First, we assumed that in 2000 through 2004, the  proportion of loans insured in each region experienced for the 1989  through 1999 books of business the same foreclosure rates that the 1975  through 1985 books of business experienced in that region in 1986 through  1990.  This scenario produced a capital ratio of 0.92 percent.  Second, we  assumed that in 2000 through 2004, varying proportions of FHA’s portfolio  experienced for the 1989 through 1999 books of business the same  foreclosure rates that the 1975 through 1985 books of business experienced  in the west south central states in 1986 through 1990.  Because streamline  refinanced mortgages and ARMs did not exist or were minimal parts of  FHA’s portfolio from 1975 through 1985, foreclosure rates were not  adjusted for these types of loans.  For the other products—30-year fixed- rate, 15-year fixed-rate, investor, and graduated payment mortgages— foreclosure rates were adjusted accordingly for each type of product.  For  this scenario, we found that if 36.5 percent of FHA-insured mortgages  experienced these high default rates, the estimated capital ratio for fiscal  year 1999 would fall by 2 percentage points, and if about 55 percent of  FHA's portfolio experienced these conditions, the economic value would  be depleted.
section_title: Comments From the Department of Housing and Urban Development
section_title: GAO Contacts and Staff Acknowledgments
subsections: section_title: GAO Contacts
section_title: Acknowledgments
paragraphs: In addition to those named above, Nancy Barry, Elaine Boudreau, Steve  Brown, Jay Cherlow, Kimberly Granger, DuEwa Kamara, John McDonough,  Salvatore F. Sorebllo Jr., Mark Stover, and Patrick Valentine made key  contributions to this report.
section_title: Related GAO Products
paragraphs: Financial Health of the Federal Housing Administration's Mutual Mortgage  Insurance Fund (GAO/T-RCED-00-287, Sept. 12, 2000).
Level of Annual Premiums That Place a Ceiling on Distributions to FHA  Policyholders (GAO/RCED-00-280R, Sept. 8, 2000).
Single-Family Housing: Stronger Measures Needed to Encourage Better  Performance by Management and Marketing Contractors (GAO/T-RCED- 00-180, May 16, 2000, and GAO/RCED-00-117, May 12, 2000).
Single-Family Housing: Stronger Oversight of FHA Lenders Could Reduce  HUD's Insurance Risk (GAO/RCED-00-112, Apr. 28, 2000).
Homeownership: Results of and Challenges Faced by FHA's Single-Family  Mortgage Insurance Program (GAO/T-RCED-99-133, Mar. 25, 1999).
Homeownership: Achievements of and Challenges Faced by FHA's Single- Family Mortgage Insurance Program  (GAO/T-RCED-98-217, June 2, 1998).
Homeownership: Management Challenges Facing FHA's Single-Family  Housing Operations (GAO/T-RCED-98-121, Apr. 1, 1998).
Homeownership: Mixed Results and High Costs Raise Concerns about  HUD's Mortgage Assignment Program (GAO/RCED-96-2, Oct. 18, 1995).
Homeownership: Information on Single Family Loans Sold by HUD  (GAO/RCED-99-145, June 15, 1999).
Homeownership: Information on Foreclosed FHA-Insured Loans and HUD- Owned Properties in Six Cities (GAO/RCED-98-2, Oct. 8, 1997).
Homeownership: Potential Effects of Reducing FHA's Insurance Coverage  for Home Mortgages (GAO/RCED-97-93, May 1, 1997).
Homeownership:  FHA's Role in Helping People Obtain Home Mortgages  (GAO/RCED-96-123, Aug. 13, 1996).
Mortgage Financing:  FHA Has Achieved Its Home Mortgage Capital  Reserve Target (GAO/RCED-96-50, Apr. 12, 1996).
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