Home Mortgage Guarantees: Issues to Consider in Evaluating Opportunities to Consolidate Two Overlapping Single-Family Programs
Released: 2016-10-31T00:00:00

Letter

Background

Federal Mortgage Guarantee Programs

RHS and FHA operate major federal programs that guarantee mortgage loans. The guaranteed loans generally feature attractive interest rates (comparable to those of prime loans), but serve borrowers who may have difficulty qualifying for conventional mortgage credit (that is, mortgage loans without government guarantees). The programs protect the private lender or other mortgage holder, because the federal government commits to pay part or all of a loan’s outstanding principal and interest if the borrower defaults. In exchange, borrowers are required to pay up- front and annual guarantee fees. Both the RHS and FHA single-family programs guarantee 30-year, fixed-rate mortgages for borrower-occupied homes. The mortgage proceeds can be used to buy, build, or refinance homes. The mortgage loans require little or no down payment from borrowers and allow financing of up-front guarantee fees. These terms generally result in high loan-to-value (LTV) ratios (the amount of the loan divided by the value of the home at origination), including LTV ratios greater than 100 percent.

As shown in figure 1, both the RHS and FHA loan guarantee programs have requirements and benchmarks that lenders use to assess borrower eligibility and qualifications. For example, to be eligible for an RHS loan, the property must be located in an RHS-eligible area and the borrower’s household income must not exceed 115 percent of the area median income, based on household size (which effectively limits the loan amount). Although FHA does not have income or geographic limits, the amount of the loans it guarantees is limited by statute. Both programs also require lenders to assess the borrower’s willingness and ability to repay the loan. To make this assessment, lenders use information collected during the loan origination process—including a borrower’s credit score, a numeric value ranging from 300 to 850 (calculated based on credit reports from the national credit bureaus) that indicates a borrower’s ability to repay future obligations; payment-to-income (PTI) ratio, the percentage of a borrower’s income that goes toward total mortgage debt payments; and the debt-service-to-income (DTI) ratio, the percentage of a borrower’s income that goes toward all recurring debt payments. RHS and FHA established specific benchmarks lenders use to evaluate the borrower’s qualifications. RHS and FHA also allow lenders to consider loans for approval that differ from these benchmarks by considering compensating factors (such as proof of continuous employment or cash reserves) that demonstrate the borrower’s ability to repay the loan.

Both RHS and FHA lenders evaluate the overall creditworthiness of a loan guarantee applicant and determine the associated risk of default using a version of FHA’s automated mortgage score card. Lenders may also review loans manually to assess applicants’ eligibility and qualifications.

Rural Areas

In 1949 Congress authorized separate housing assistance for rural areas and gave USDA responsibility for administering it. Section 520 of the Housing Act of 1949, as amended, defines certain areas as “rural” for purposes of determining RHS program eligibility (in this report, we generally refer to these as RHS-eligible areas). RHS-eligible areas are largely identified based on population, but also consider other factors, such as proximity to metropolitan areas and access to mortgage credit. USDA is to re-evaluate eligibility determinations upon issuance of a decennial U.S. Census of Population and Housing. The eligible areas were most recently updated in 2014 and 2015 to take into account data from the 2010 Census. In contrast, FHA’s single-family loan guarantee program is not restricted to any geographic location.

As noted previously, rurality can be assessed under classification schemes other than statutory definitions. USDA’s Rural-Urban Commuting Area codes classify all census tracts in the United States on a continuum from rural to urban based on daily commuting patterns, urbanization, and population density. The codes can be consolidated into four types of locations.

Urban. Adjoining census tracts in built-up areas, with total population of 50,000 or more. These areas correspond to the U.S. Census Bureau’s urbanized areas.

Suburban. Areas with high commuting flow to urban areas and all areas where 30-49 percent of the population commute to urban areas for work.

Large rural town. Towns with populations between 10,000 and 49,000 and surrounding rural areas where 10 percent or more of the population commutes to the town and 10 percent or more of the population commutes to an urban area for work.

Small town and isolated rural area. Towns with populations of less than 10,000 and their surrounding commuter areas and other isolated rural areas that are more than 1 hour driving distance to the nearest city.

RHS-eligible areas encompass rural areas (that is, large rural towns and small town and isolated rural areas) as well as areas that are more urban and suburban. As shown in figure 2, in 2015, RHS-eligible areas constituted 97 percent of the land area of the United States. Eligible areas also contained 37 percent of the nation’s population. Using the Rural- Urban Commuting Area codes described earlier, rural areas constituted 67 percent of the land area of the United States in 2015. These rural areas contained 12 percent of the population.

Loan Status

Several categories are used to describe the payment status of mortgages:

Current. The borrower is meeting scheduled payments.

Delinquent. The borrower is behind by 30 or more days on scheduled payments.

In the foreclosure process. The borrower generally has been delinquent for more than 90 days (commonly referred to as in default) and the lender has elected to initiate a legal process against the borrower that generally results in the borrower losing the property. The loan is considered active during the foreclosure process.

Terminated with a claim. The borrower is delinquent, unable to pay off the loan balance, and loses title to the property. The government pays a claim (provides reimbursement) to a lender that incurs a loss on a guaranteed loan. The loan is no longer considered active.

Prepaid. The borrower has paid off the entire loan balance before it is due. Prepayment often occurs as a result of the borrower selling the home or refinancing. A prepaid loan is no longer considered active.

As previously noted, in this report we refer to loans that were 90 days or more delinquent, in the foreclosure process, or that terminated with a claim as troubled loans.

As we and others have reported previously, certain loan features are often associated with an increase in mortgage defaults and foreclosures. For example, higher LTV ratios are associated with increased risk of default or foreclosure—especially for borrowers in a negative equity position (when mortgage balances exceed the current value of homes). Borrowers then are limited in their ability to sell or refinance their homes in the event they cannot stay current on their mortgage payments. In addition, lower borrower credit scores at loan origination, higher PTI and DTI ratios, and first-time homebuyers are associated with an increased likelihood of mortgage default and foreclosure.

Overlap and Consolidation

As noted previously, our prior work has assessed the extent to which there is overlap between RHS- and FHA-guaranteed loan programs. In particular, our 2012 report found overlap in the products offered, borrower income levels, and geographic areas served by the two programs. Overlap occurs when programs have similar goals, devise similar strategies and activities to achieve those goals, or target similar users. Our prior work has found that overlap can have positive and negative effects on program implementation, outcomes and impact, and cost- effectiveness. Assessing the presence and extent of any overlap and its positive and negative effects can help congressional decision makers and executive branch leaders identify options to reduce or better manage overlap. Among other things, we have found that addressing overlap may require changes in statute, regulation, or guidance to revise or explicitly define agencies’ roles and responsibilities or program consolidation. In past reports, including in our 2012 report, we have suggested that agencies could increase their efficiency and effectiveness by consolidating their management functions, such as information- technology or administrative-support services. Consolidation is beneficial in some situations and not in others. As a result, a case-by- case analysis is needed to evaluate the goals of the consolidation against realistic expectations of how they can be achieved.

Both Programs Had Similar Loan Characteristics, but RHS Borrowers Had Lower Incomes and Higher Loan-to-Value Ratios Than FHA Borrowers

The mortgages RHS and FHA guaranteed in RHS-eligible areas in 2010– 2014 generally had similar property, borrower, and loan characteristics, underscoring the overlap between the two guarantee programs. But key differences existed in certain characteristics, primarily due to statutory program requirements. Both programs served more than 600,000 borrowers in RHS-eligible areas, although RHS served more borrowers in more rural parts of these areas. In addition, the majority of borrowers in both programs had similar credit profiles, debt burdens, and other demographic characteristics. But consistent with income limits for the RHS program, RHS borrowers generally had lower annual incomes than FHA borrowers. Finally, both RHS and FHA generally guaranteed loans that were less than $150,000 and that had high LTV ratios. In keeping with RHS’s income restrictions, RHS’s loans were smaller than FHA’s, and RHS’s no-down-payment requirement resulted in higher LTV ratios for RHS than for FHA.

Both RHS and FHA Served Large Numbers of Borrowers in RHS-Eligible Areas

In 2010–2014, RHS and FHA both guaranteed large numbers of loans to borrowers in RHS-eligible areas, with FHA serving more borrowers overall and RHS serving a higher number and percentage of borrowers in more rural areas (see fig. 3). In this period, FHA guaranteed about 880,000 loans in RHS-eligible areas, compared with about 614,000 loans for RHS, a 36 percent difference. The difference is partly attributable to the larger size of FHA’s program (loans in RHS-eligible areas represent 25 percent of the loans FHA guaranteed in 2010–2014). But in terms of geographic distribution within RHS-eligible areas, RHS served more borrowers than FHA in more rural areas (areas classified as large rural towns or small town and isolated rural areas using USDA’s Rural-Urban Commuting Area classification). Specifically, in more rural areas, RHS guaranteed approximately 170,000 loans (28 percent of its total), while FHA guaranteed approximately 154,000 loans (18 percent of its total). Conversely, about 72 percent of the loans RHS guaranteed and 83 percent of the loans FHA guaranteed in RHS-eligible areas in 2010–2014 were in areas considered more urban or suburban (using USDA’s Rural- Urban Commuting Area classifications).

Credit and Other Characteristics of RHS and FHA Borrowers Were Similar, but RHS Served More Borrowers with Lower Incomes Than FHA

For loans guaranteed during 2010–2014 in RHS-eligible areas, RHS and FHA borrower characteristics, such as credit score and debt burden, were generally similar. While RHS borrowers had lower incomes, the proportion of borrowers who were racial or ethnic minorities was comparable.

Credit Characteristics

The distribution of borrower credit scores and PTI ratios for loans RHS and FHA guaranteed in RHS-eligible areas was similar overall, although RHS borrowers generally had lower DTI ratios. We and others have previously found that lower credit scores and higher PTI and DTI ratios were associated with poorer loan performance.

Credit score. In RHS-eligible areas, RHS and FHA borrower credit scores at loan origination were generally similar—median scores were 686 for RHS and 683 for FHA. As shown in figure 4, about one-half of RHS and FHA borrowers had credit scores in the 640–699 range. Credit scores also were similar in the higher ranges—41 percent of RHS borrowers and 39 percent of FHA borrowers had scores greater than 700. However, RHS had a smaller proportion of borrowers with scores of less than 640 (9 percent) than FHA did (14 percent). Although FHA’s benchmark for minimum credit score (580 for maximum financing) is lower than RHS’s (640), lenders with whom we spoke noted that many FHA lenders and mortgage investors impose minimum credit score requirements that are higher than the FHA benchmark—typically, 640 or greater—to guard against the increased risk of default associated with lower scores. In addition, FHA policy allows lenders to require borrowers to have a higher credit score (in excess of FHA’s benchmark) when the borrower does not meet the benchmark for another characteristic, such as the PTI or DTI ratio. As a result, several lenders said they expected RHS and FHA borrowers to have similar scores.

Payment-to-income ratio. In RHS-eligible areas, RHS and FHA borrowers generally had similar PTI ratios (see fig. 5). Median ratios for both programs were similar—23 percent for RHS and 24 percent for FHA. RHS’s benchmark for PTI ratio is 29 percent and FHA’s is 31 percent. Roughly three-quarters of the borrowers in both programs had PTI ratios of 29 percent or less (78 percent for RHS and 71 percent for FHA), and about 10 percent of the borrowers in each program had PTI ratios of greater than 31 to 35 percent (a less favorable category). Finally, a larger share of FHA borrowers—about 13 percent—had PTI ratios greater than 35 percent, compared with 5 percent of RHS borrowers.

Debt-service-to-income ratio. In RHS-eligible areas, RHS borrowers generally had lower (more favorable) DTI ratios than FHA borrowers (see fig. 6). The median DTI ratio for RHS borrowers was 37 percent, which was somewhat lower than the median for FHA borrowers (41 percent). RHS’s benchmark for DTI is 41 and FHA’s is 43. Seventy-one percent of RHS borrowers and 51 percent of FHA borrowers had DTI ratios of 41 percent or less. Although both agencies allow lenders to approve borrowers with DTI ratios that exceed their benchmarks when compensating factors are present, several lenders with whom we spoke said that FHA provided greater flexibility than RHS in this regard. Consistent with this view, the share of FHA borrowers with DTI ratios greater than 43 percent was double the corresponding share of RHS borrowers (40 percent and 20 percent, respectively).

In RHS-eligible areas in 2010–2014, most of the borrowers served by each agency had annual incomes of less than $60,000, but RHS borrower incomes were generally lower than those of FHA borrowers. The median income of RHS borrowers was about $44,000, compared with about $57,000 for FHA borrowers—a 28 percent difference. Approximately 42 percent of the loans guaranteed by RHS went to borrowers with annual incomes of less than $40,000 (roughly the median income of all rural households), compared with 26 percent of FHA- guaranteed loans (see fig. 7). RHS also served 13 percent more borrowers in this income category than FHA (about 256,000 RHS borrowers compared with about 226,000 FHA borrowers). In contrast, about 27 percent of FHA borrowers had annual incomes of $80,000 or more, compared with 3 percent of RHS borrowers. These differences are consistent with the RHS program’s statutory income limits (the program is designed to serve low- and moderate-income borrowers).

As shown in table 1, in RHS-eligible areas more than three-quarters of both RHS and FHA borrowers identified as white, although the proportion was larger for RHS (86 percent) than for FHA (77 percent). A smaller proportion of RHS borrowers (8 percent) identified their ethnicity as Hispanic or Latino than did FHA borrowers (11 percent). With respect to racial minority groups, about 5 percent of the borrowers served by each program identified as black or African-American, and roughly 1 percent of each program’s borrowers identified as Asian.

Historically, federal mortgage guarantee programs have played a particularly large role among first-time homebuyers, due partly to their low or no down-payment requirements. In RHS-eligible areas, RHS and FHA borrowers both tended to be first-time homebuyers, with RHS having a higher proportion of first-time homebuyers (85 percent) than FHA (71 percent).

Most RHS- and FHA- Guaranteed Loans in RHS-Eligible Areas Were Less Than $150,000, but RHS’s Had Higher Loan- to-Value Ratios

In RHS-eligible areas in 2010–2014, most RHS- and FHA-guaranteed loans were for less than $150,000, but RHS’s loans generally were smaller than FHA’s (see fig. 8). More specifically, the median loan amount for RHS ($124,000) was 17 percent less than the median for FHA ($146,000). Approximately 69 percent of RHS-guaranteed loans were less than $150,000, compared with about 52 percent of FHA-guaranteed loans. RHS and FHA served almost the same number of borrowers with loans of less than $100,000 (about 200,000 loans each), and these smaller loans accounted for about 33 percent of RHS’s loans and about 23 percent of FHA’s loans. In contrast, about 25 percent of FHA’s loans were above $200,000, compared with 10 percent for RHS. RHS’s smaller loan amounts (relative to FHA’s) are consistent with RHS borrowers’ generally lower incomes.

At origination, both RHS- and FHA-guaranteed loans in RHS-eligible areas had high LTV ratios (above 90 percent)—a median ratio of 101 percent for RHS and 96.5 percent for FHA. As shown in figure 9, approximately 57 percent of RHS-guaranteed loans had LTV ratios greater than 100 percent, indicating that borrowers were in a negative equity position (owed more on their loans than their homes were worth).

Among RHS borrowers with negative equity, 28 percent had LTV ratios from 102 percent to 104 percent (the top of the range for RHS). Although less than 1 percent of FHA-guaranteed loans had LTV ratios of more than 100 percent, FHA loans also had high LTV ratios. Approximately 48 percent of FHA loans had LTV ratios from 96.51 to 100 percent (with 44 percent in the 96.51 to 98.5 percent range), and an additional 35 percent had ratios from 90.01 to 96.5 percent. As previously discussed, higher LTV ratios are generally associated with a higher likelihood of default.

The difference between the LTV ratio distributions for RHS and FHA are consistent with differences in the down-payment requirements of the two programs. RHS does not require borrowers to make a down payment, which allows borrowers to borrow up to 100 percent of the principal amount (prior to financing any allowable fees). In contrast, FHA requires borrowers to make a minimum 3.5 percent down payment, which results in an initial maximum LTV (prior to financing any allowable fees) of 96.5 percent.

Significant Percentages of RHS and FHA Borrowers Could Have Met Key Criteria for the Other Program

We estimated that at least 36 percent of RHS borrowers with loans guaranteed in 2010–2014 could have met key FHA criteria (credit score, debt burden measured by PTI and DTI ratios, and loan amount) and also had the ability to meet FHA’s down-payment requirement. Similarly, at least 22 percent of FHA borrowers in RHS-eligible areas could have met key RHS criteria (credit score, debt burden ratios, and household income). According to most lenders we spoke with, borrowers who meet the criteria for both RHS- and FHA-guaranteed loans primarily consider the up-front and monthly costs of the loans in deciding between the two loan products.

At Least 36 Percent of RHS Borrowers Potentially Could Have Met FHA’s Down-Payment and Other Key Criteria

Based on our analysis of data on RHS-guaranteed loans, we estimated that at least 36 percent of RHS borrowers could have met key criteria to receive FHA-guaranteed loans and also potentially could have made a 3.5 percent down payment (see fig. 10). We analyzed data on loans RHS guaranteed in 2010–2014 and estimated how many RHS borrowers could have met key criteria for the FHA program. That is, the borrowers would have had to meet FHA’s benchmarks for credit scores of 580 or above, PTI ratios of 31 percent or less, and DTI ratios of 43 percent or less, and had loan amounts within the limits for the FHA program. We applied FHA’s benchmarks and did not consider any compensating factors that might have allowed borrowers to qualify without meeting those benchmarks. We also estimated how many RHS borrowers could have also made FHA’s required down payment of at least 3.5 percent (based on borrowers’ liquid assets and LTV ratios). When only credit score, PTI and DTI ratios, and loan amount were considered, at least 70 percent of RHS borrowers could have met these four FHA criteria.

Although at least an estimated 36 percent of the RHS borrowers had reported liquid assets sufficient to meet FHA’s down-payment and other criteria, the extent to which they would have used their liquid assets for a down payment is unknown. For example, according to a lender and an industry association with whom we spoke, RHS borrowers with liquid assets may choose not to make a down payment because they may want to use the funds to improve the home, cover other expenses, or maintain savings for future use. In addition, for RHS borrowers who could not have met the down-payment requirement, the extent to which they could have obtained funds from other sources to meet the requirement is unknown. Some RHS borrowers might have been able to obtain a third- party gift to make the down payment. In 2010–2014, approximately 26 percent of FHA borrowers in RHS-eligible areas received a gift to help make their down payment.

We estimated that a majority of RHS borrowers also could have met individual FHA criteria (see fig. 11). Almost all RHS borrowers could have met FHA’s benchmarks for credit score and loan amount and about 80 percent could have met the benchmarks for PTI and DTI ratios. When only the ability to make FHA’s minimum down payment was considered, we found that at least 51 percent of RHS borrowers could have met this FHA requirement.

These results are not unexpected because many RHS borrowers already would have met RHS’s relatively stricter benchmarks for credit score and PTI and DTI ratios. And as previously discussed, loan amounts for RHS- guaranteed loans were generally smaller than for FHA-guaranteed loans. However, some RHS borrowers would not have met every FHA benchmark. As noted previously, borrowers can qualify for a guaranteed mortgage without meeting every benchmark if certain compensating factors are present.

At Least 22 Percent of FHA Borrowers in RHS- Eligible Areas Could Have Met Key RHS Criteria

Based on our analysis of data on FHA-guaranteed loans, we estimated that at least 22 percent of FHA borrowers in RHS-eligible areas could have met key RHS criteria for credit score, PTI and DTI ratios, and household income (assuming it was equivalent to the borrower’s income) (see fig. 12). We analyzed data on loans FHA guaranteed in 2010–2014 in RHS-eligible areas and estimated how many FHA borrowers could have met key criteria for the RHS program. That is, the borrowers would have had to meet RHS benchmarks for credit scores of 640 or above, PTI ratios of 29 percent or less, and DTI ratios of 41 percent or less. Additionally, the borrower’s household income would have had to be within RHS limits. When only credit score and PTI and DTI ratios were considered, at least an estimated 37 percent of FHA borrowers could have met the RHS criteria.

Although RHS’s benchmarks are relatively stricter than FHA’s and RHS has eligibility restrictions related to household income that FHA does not have, many FHA borrowers also could have met individual criteria for RHS-guaranteed loans (see fig. 13). For example, an estimated 72 percent of FHA borrowers in RHS-eligible areas could have met RHS’s household income requirement (which we measured using borrower income). This may reflect FHA’s relatively large concentration of first-time homebuyers (about 71 percent of the FHA borrowers we analyzed were first-time homebuyers), many of whom may have relatively lower incomes.

In contrast, fewer FHA borrowers (about 51 percent) could have met RHS’s benchmark for DTI ratio. As previously noted, several lenders with whom we spoke said that FHA provides lenders more flexibility than RHS in considering compensating factors related to DTI ratio, which results in some FHA borrowers with DTI ratios in excess of FHA’s benchmark (and consequently also of RHS’s benchmark).

Lender Views and Our Analysis Indicate that the Costs of RHS-Guaranteed Loans Make Them an Attractive Alternative to FHA-Guaranteed Loans

According to most lenders with whom we spoke, borrowers who meet the criteria for both the RHS and FHA programs primarily consider costs in deciding between the two programs. They stated that borrowers typically prefer the RHS-guaranteed loan over the FHA-guaranteed loan because the borrower’s up-front and monthly costs (measured by the amounts borrowers are required to pay) are lower. We asked lenders about other factors borrowers might consider, including how borrowers access the programs and the length of time to obtain the guarantee. They told us that for borrowers who meet the criteria for both programs, differences between the programs—except for cost—were small and had a minimal impact on borrowers. For example, lenders told us that there is little difference in how borrowers access the programs. Specifically, borrowers can access both RHS- and FHA-guaranteed loans through a variety of channels, including nationwide lenders, local banks, and mortgage brokers. Similarly, they explained that RHS’s 2-stage process for issuing guarantees versus FHA’s 1-stage process did not cause borrowers to opt for FHA-guaranteed loans. In addition, a 2016 survey found that up-front and monthly costs were a factor borrowers consider in choosing a home mortgage and that the importance of costs increased for borrowers with lower household incomes.

Both RHS and FHA borrowers pay certain up-front and monthly costs for their guaranteed loans. In addition to closing costs and fees such as payments for home inspections and appraisals, RHS and FHA borrowers pay an up-front guarantee fee, any required down payment, and an annual guarantee fee (charged monthly). RHS and FHA charge the up- front and annual guarantee fees to help offset the cost to taxpayers of the loan guarantee programs. The guarantee fee amounts differ, in part because the programs have different requirements and goals for covering their long-term costs. According to RHS officials, since 2010 RHS has had the goal of making each year’s new cohort of guaranteed loans credit-subsidy-neutral. That is, initially the present value of the lifetime estimated revenue (cash inflow) equals the present value of lifetime estimated expenses (cash outflow).

In contrast, FHA historically has estimated that its loan guarantee program has a negative subsidy rate for each new cohort. That is, initially the present value of lifetime estimated revenue exceeds the present value of lifetime estimated expenses. FHA’s approach is consistent with the statutory requirement that it maintain a 2 percent reserve for its primary mortgage guarantee fund. FHA’s capital reserve represents the amounts in excess of those needed for estimated claims or other costs and is used to cover unanticipated increases in those estimated costs before FHA draws on funds available through its permanent indefinite budget authority. RHS does not have a similar requirement, and therefore draws directly on its permanent and indefinite budget authority to cover all estimated cost increases.

As described in the hypothetical example below, borrower costs (as measured by amounts required at loan closing and paid monthly) would be lower for an RHS-guaranteed loan than for an FHA-guaranteed loan for the same property. On the basis of statements by most of the lenders we spoke with, the example assumes the same interest rate for both loans.

As described previously, FHA requires borrowers to make a 3.5 percent down payment, but RHS has no down-payment requirement. Thus, RHS borrowers would not need to bring any cash for a down payment to closing.

RHS’s up-front guarantee fee is higher than FHA’s. In 2014, RHS’s fee was 2 percent of the loan amount and FHA’s was 1.75 percent. However, according to lenders with whom we spoke, most RHS and FHA borrowers finance their up-front guarantee fee into the loan amount and therefore do not need to bring cash to closing to cover this payment.

Both programs require borrowers to pay an annual guarantee fee (charged monthly) for the life of the loan, which is calculated based on the scheduled outstanding loan balance. In 2014, RHS’s fee was 0.5 percent and FHA’s minimum fee (for loans of $625,000 or less with LTV ratios of 95 percent or more) was 1.2 percent.

As shown in figure 14, a hypothetical RHS borrower purchasing a $125,000 home in 2014 (near the median loan amount for home purchases made with RHS-guaranteed loans from 2010–2014) would have had lower up-front costs (based on the required down payment and assuming financing of up-front guarantee fees) than a borrower with an FHA-guaranteed loan. Assuming a 3.75 percent interest rate, the monthly mortgage payments (including the annual guarantee fee) during the first year would have been $50 less, or 7 percent lower, with an RHS- guaranteed loan than with an FHA-guaranteed loan. Over the life of the loan, assuming the borrower made all monthly scheduled payments for 30 years, the RHS borrower would have paid approximately 58 percent less in annual guarantee fees than the FHA borrower (approximately $11,000 for the RHS borrower versus $27,000 for the FHA borrower).

While RHS-guaranteed loans have lower up-front and monthly costs, they often have higher LTV ratios than FHA-guaranteed loans, because RHS does not require a down payment. In the hypothetical situation presented in figure 14, the RHS borrower’s LTV ratio at origination is 102 percent (a negative equity position), compared with 98 percent for the FHA borrower. Because RHS borrowers often start off with negative home equity, they may have fewer options than FHA borrowers to avoid default if they experience financial troubles. For example, they may find it more difficult to sell or refinance their homes to relieve unsustainable mortgage payments. Additionally, RHS borrowers may take longer than FHA borrowers to build sufficient home equity to refinance into a conventional loan without needing to pay additional guarantee fees.

RHS loans’ higher LTV ratios and lower guarantee fees relative to FHA loans help make loans more affordable. However, these features may also increase financial risks to the federal government from increased loan defaults and less revenue to cover unanticipated costs. Specifically, as discussed further in the next section of this report, higher LTV ratios are associated with a higher probability of troubled loan performance. Furthermore, in setting guarantee fees, RHS does not have to raise sufficient revenue to maintain a capital reserve as FHA does. As a result, RHS’s program is not designed to cover unanticipated cost increases without drawing on its permanent and indefinite budget authority. These trade-offs highlight issues for RHS and FHA to consider in evaluating opportunities to consolidate the programs, as we recommended in 2012 and reaffirm in this report.

RHS-Guaranteed Loans Were More Likely to Be Troubled after 3 Years than FHA-Guaranteed Loans

Our analysis of loans RHS and FHA guaranteed in 2010 and 2011 found some differences in loan performance after 3 years. These differences included RHS’s lower prepayment rates and higher troubled loan rates (the share of loans at least 90 days delinquent, in the foreclosure process, or terminated with a claim) compared with FHA. Our statistical model also estimated that RHS loans would be expected to have higher troubled loan rates than FHA loans, due partly to the higher LTV ratios of RHS borrowers.

After 3 Years of Performance, RHS Had a Higher Troubled Loan Rate Than FHA

After 3 years, the performance of comparable loans that RHS and FHA guaranteed in 2010 and 2011 in RHS-eligible areas differed. As shown in figure 15, RHS had a higher troubled loan rate than FHA (about 7 percent for RHS and 6 percent for FHA). When we restricted the analysis to loans for properties in large rural towns and small town and isolated rural areas (using USDA’s Rural-Urban Commuting Area codes), the troubled loan rates were lower for both agencies, but RHS’s rate (6.1 percent) remained higher than FHA’s (5.4 percent).

Additionally, across all RHS-eligible areas, RHS had a higher percentage of loans that were current (and not prepaid) than FHA did. Specifically, about 78 percent of RHS-guaranteed loans were current after 3 years, compared with 73 percent of FHA-guaranteed loans. In contrast, RHS had a substantially lower proportion of loans that prepaid relative to FHA. Specifically, about 9 percent of RHS-guaranteed loans and 16 percent of FHA-guaranteed loans prepaid after 3 years of performance. See appendix V for additional information about the performance status of RHS- and FHA-guaranteed loans after 2 and 3 years of performance.

According to representatives of some lenders and industry associations with whom we spoke, RHS would be expected to have lower prepayment rates than FHA because of differences in the RHS and FHA programs. As previously noted, prepayments can result from a borrower refinancing into a new loan. The greater initial home equity of FHA borrowers might have allowed them to more quickly reach the level of equity needed to refinance into a conventional loan. A few lenders and an industry association also noted that FHA’s streamline refinance program, available nationwide, allows existing FHA borrowers to refinance into a new FHA- guaranteed loan through a process that involves fewer requirements than FHA’s traditional refinance process. In contrast, RHS had no streamline refinance program available to existing RHS borrowers in 2010 and 2011.

We also analyzed the status of comparable RHS- and FHA-guaranteed loans after 2 years of performance. In contrast to the 3-year troubled loan rates, we found that the agencies had almost identical 2-year troubled loan rates, although RHS’s rate was slightly lower (3.8 percent for RHS and 3.9 percent for FHA). However, similar to the 3-year results, we found that RHS had a higher percentage of current loans and a lower percentage of prepaid loans than FHA after 2 years.

Our Estimates Suggest That RHS Would Be Expected to Have a Higher Troubled Loan Rate Than FHA

We developed a statistical model to examine factors that may explain differences between RHS and FHA troubled loan rates (the share of loans at least 90 days delinquent, in the foreclosure process, or terminated with a claim), including the characteristics of their guaranteed portfolios (represented by loan, borrower, and economic variables). For RHS and FHA separately, we analyzed the statistical relationships between the variables and the probability of the guaranteed loans becoming troubled after 3 years (that is, we estimated changes in the troubled loan rate associated with variations in the value of the variables). In general, we found that the statistical relationships between the variables were similar for both agencies. These similarities suggest that the way the agencies and lenders implement the two guarantee loan programs, including methods for evaluating default risk and ensuring compliance with underwriting standards, would be expected to yield similar troubled loan rates for loans with comparable characteristics. Thus, differences in loan performance are likely attributable to differences in portfolio characteristics.

To estimate the influence of portfolio characteristics on differences in agency troubled loan rates, we ran our model using a combined set of RHS and FHA loans. Specifically, we simulated troubled loan rates for RHS and FHA based on a set of characteristics representing the average loan for each agency and compared these expected rates. Overall, the model estimated that RHS would be expected to have a somewhat higher 3-year troubled loan rate than FHA when considering all portfolio characteristics. Specifically, RHS’s expected troubled loan rate was 6 percent higher than FHA’s (see fig. 16). RHS’s higher expected rate relative to FHA’s is consistent with the pattern observed in the agencies’ actual troubled loan rates.

Additionally, when we isolated the influence of individual portfolio characteristics on expected troubled loan rates, we found that LTV ratio and borrower income were the largest contributors to RHS’s higher expected rate, as follows (see fig. 16):

Our model estimated that RHS’s higher LTV ratios relative to FHA’s were associated with an expected troubled loan rate that was about 11 percent higher than FHA’s.

Similarly, our model estimated that RHS’s lower borrower incomes relative to FHA’s were associated with an expected troubled loan rate that was about 6 percent higher than FHA’s.

RHS would be expected to have relatively riskier characteristics in these areas due to program requirements that are less restrictive than FHA’s for down payment (as reflected in the LTV ratios) and more restrictive for household income.

In contrast, the estimated influences of other characteristics were relatively smaller and associated with lower estimated troubled loan rates for RHS, as follows (see fig. 16):

RHS's lower DTI ratios and loan amounts were each associated with an expected troubled loan rate that was about 5 percent lower for RHS than for FHA.

Similarly, our model estimated borrower credit scores were associated with an expected troubled loan rate that was 3 percent lower for RHS than for FHA.

RHS would be expected to have relatively less risky characteristics along these dimensions due to its stricter benchmarks for credit score and DTI ratio and limits on household income (which indirectly affect the loan amounts).

In contrast to the 3-year results, our statistical model estimated that RHS would have a slightly lower expected 2-year troubled loan rate than FHA given the characteristics of the two agencies’ guaranteed portfolios. RHS’s marginally lower expected rate is consistent with the pattern observed in the agencies’ actual troubled loan rates after 2 years. The estimated influences of individual portfolio characteristics on expected 2- year troubled loan rates were consistent with our estimates for the 3-year rates in terms of direction but differed in magnitude. For example, in the case of LTV ratio, the model estimated that RHS’s higher LTV ratios relative to FHA were associated with an expected 2-year troubled loan rate that was about 6 percent higher than FHA’s expected rate. By comparison, RHS’s corresponding 3-year rate was 11 percent higher than FHA’s.

The change in the estimated influence of LTV ratios may reflect that, as time passes, a borrower’s chances of experiencing events that can make mortgage payments unsustainable (for example, job loss) increase. Borrowers with greater home equity may be better positioned to resolve such situations through refinancing or sale of the home. As previously noted, RHS borrowers often start off with less home equity (as reflected in their higher LTV ratios) than FHA borrowers and may find it more difficult to refinance or sell their homes to avoid default. See appendixes VI and VII for more information on the model and its results.

Agency Comments and Our Evaluation

We provided a draft of this report to USDA and HUD for review and comment. USDA provided comments via e-mail from the audit liaison officer in Rural Development’s Financial Management Division. HUD provided technical comments, which we incorporated into the final report as appropriate.

In its comments, USDA stated that our comparative analysis of the RHS and FHA programs added to the understanding of the programs’ capabilities and expanded upon our 2012 report, which concluded that overlap existed between federal housing programs. USDA also made several more specific points, as follows:

USDA said that the overlap we found between the RHS and FHA programs was minor and that RHS targets a specific underserved segment of the population, which would not attain successful homeownership but for RHS. We maintain that there is significant overlap between the RHS and FHA loan guarantee programs in RHS- eligible areas, as evidenced by the estimated 36 percent of RHS borrowers and 22 percent of FHA borrowers who could have met the criteria for both programs. Furthermore, we found that the majority of RHS borrowers could have met individual FHA eligibility requirements (99 percent for loan amount and 51 percent for down payment) and individual qualifying benchmarks (99 percent for credit score, 85 percent for PTI ratio, and 79 percent for DTI ratio).

Regarding our estimate of RHS borrowers with sufficient liquid assets to make a 3.5 percent down payment, USDA noted that many of them would have had little or no savings left to meet the additional expenses associated with homeownership if they had made such a down payment. USDA also said that the greater affordability of RHS loans compared with FHA loans helps families meet other financial needs. Our report discusses differences in RHS and FHA borrower costs, including the down payment and guarantee fees that make RHS loans more affordable. It also recognizes the view that some borrowers may want to use their liquid assets to improve the home, cover other expenses, or maintain savings for future use rather than make a down payment. However, our report also describes important trade-offs associated with these lower costs. For example, because RHS borrowers are not required to make a down payment and can finance their up-front guarantee fee, they often start off with negative home equity (LTV ratios over 100 percent). As we note in our report, they therefore may have fewer options than FHA borrowers to avoid default if they experience financial trouble. Additionally, RHS’s higher LTV ratios and lower guarantee fees relative to FHA also may increase financial risk to the federal government from higher potential loan defaults and less revenue to cover unanticipated costs of the loan guarantees.

USDA said that for methodological reasons, it did not believe that the differences we identified between expected RHS and FHA troubled loan rates were meaningful. USDA stated that RHS’s higher expected troubled loan rates were partly attributable to RHS’s higher LTV ratios (often greater than 100 percent) and that our performance analysis did not fully account for differences in LTV ratios between the RHS and FHA portfolios. USDA added that there could be intraregional differences in loan characteristics between RHS and FHA that were not reflected in our analysis. We maintain that the differences between expected troubled loan rates for RHS and FHA are meaningful and that our methodology properly accounted for differences between the RHS and FHA portfolios regarding LTV ratios and geographic areas served. Our statistical model included an LTV variable with four categories, including a category for loans with LTV ratios at or above 100 percent (which accounted for most of RHS’s portfolio) and a category for loans with ratios of 96 to 99.9 percent (which accounted for most of FHA’s portfolio). We used that structure based on our analysis of RHS loans, which found that loans with LTV ratios slightly above 100 percent performed worse than those with ratios slightly below 100 percent. We specifically highlight this issue in appendix VI of our report. This appendix contains discussion and analysis showing the importance of differentiating loans at the high end of the LTV range in modeling expected RHS loan performance and comparing the performance of the RHS and FHA portfolios. In addition, our analysis of expected loan performance took multiple steps to account for the potential impact of geographic differences between the RHS and FHA portfolios on loan performance. Specifically, we (1) restricted FHA loans to those for properties in RHS-eligible areas, (2) differentiated, within RHS-eligible areas, between areas that were more rural and more urbanized, (3) included a set of variables in our model to control for house price appreciation at the state level, and (4) included a set of regional indicator variables (representing the nine census divisions) in our model to help control for factors not directly measured by other explanatory variables but that might differ by region.

Finally, USDA said it did not believe that “a single monolithic housing department” would best serve the highly differentiated housing market in rural America and that it was committed to seeking opportunities for intra-agency collaboration to expand mortgage access and improve the quality of housing for rural families. Our report does not state or intend to suggest that a single agency or single program is necessarily the best way to serve rural housing needs. Rather, our report analyzes the extent of overlap between two similar programs and identifies issues that should be considered in assessing opportunities for consolidation. As we note in our report, consolidation may be beneficial in some situations and not in others, but a case-by- case analysis is needed to assess opportunities for improved efficiency and effectiveness and inform congressional decision making. For this reason, we maintain that RHS and FHA should implement our 2012 recommendation to report on and evaluate consolidation opportunities.

As agreed with your office, unless you publicly announce the contents of this report earlier, we plan no further distribution until 30 days from the report date. At that time, we will send copies to interested congressional committees, USDA, and HUD. In addition, this report will be available at no charge on the GAO website at http://www.gao.gov.

If you or your staff have any questions about this report, please contact me at (202) 512-8678 or garciadiazd@gao.gov. Contact points for our Offices of Congressional Relations and Public Affairs may be found on the last page of this report. Key contributors to this report are listed in appendix VIII.

Appendix I: Objectives, Scope, and Methodology

This report compares the characteristics and performance of single-family mortgage loans guaranteed by the Department of Agriculture’s (USDA) Rural Housing Service (RHS) and by the Department of Housing and Urban Development’s (HUD) Federal Housing Administration (FHA) in fiscal years 2010–2014. Specifically, this report (1) compares the property, borrower, and loan characteristics of RHS- and FHA- guaranteed loans in statutorily defined RHS-eligible areas (RHS-eligible areas); (2) estimates the number of RHS and FHA borrowers in RHS- eligible areas who could have met key criteria for the other program and describes factors borrowers consider in choosing between the two programs; and (3) compares the performance of RHS- and FHA- guaranteed loans in RHS-eligible areas.

Data Used in Our Analysis

To address these three objectives, we obtained and analyzed loan-level data from RHS and FHA for single-family home purchase and refinance mortgages the agencies guaranteed under RHS’s Section 502 program and FHA’s Section 203(b) program from 2010 through 2014 (a time frame which allowed for analysis of loan performance over multiple years). To facilitate comparison of the agencies’ guaranteed portfolios, we restricted our analysis to 30-year, fixed-rate mortgages to owner-occupants and excluded loans for units in condominium and cooperative developments. The final data sets included records for 672,736 RHS-guaranteed loans (644,514 purchase loans and 28,222 refinance loans) and 5,200,250 FHA-guaranteed loans (3,528,256 purchase loans and 1,671,994 refinance loans). Each loan record included data on property characteristics (such as the property’s state and zip code), borrower characteristics (such as credit score and measures of debt burden), and loan characteristics (such as loan amount and loan-to-value ratio) at origination. In addition, approximately 80 percent of the RHS loan records had data on borrowers’ liquid assets (cash or other assets, such as stocks and proceeds from the sale of property, which are readily convertible to cash). The data also included information on mortgage payment status, including number of days (RHS) or months (FHA) delinquent and other performance information (such as in the foreclosure process, prepaid, or terminated with a claim). To assess the reliability of these data, we tested the data for missing values, outliers, and obvious errors and reviewed documentation on the processes RHS and FHA used to collect and ensure the reliability and integrity of their data. We also interviewed RHS and FHA officials to discuss interpretations of various data fields. We concluded that the data we used were sufficiently reliable for purposes of comparing the characteristics and performance of RHS- and FHA-guaranteed loans and estimating the extent to which RHS and FHA borrowers could have met key criteria for the other program.

We supplemented the RHS and FHA data with additional information. Specifically, we determined whether each mortgaged property was located in an RHS-eligible area using RHS’s boundary data. The statutory definition of RHS-eligible areas changed in 2014, and some areas that were previously eligible in 2010–2014 were no longer eligible. To identify RHS-guaranteed loans with properties in RHS-eligible areas according to the new definition, we used loan-level data on the property’s location (expressed as latitude and longitude). Of the 672,736 loans, approximately 5 percent or 31,394 loans RHS guaranteed in 2010–2014 were no longer in RHS-eligible areas using the new definition. We excluded these loans from our analysis. As we did not have property addresses for the FHA-guaranteed loans, we used each property’s census tract to determine whether the property was located in an RHS- eligible area.

In addition, we assigned a “rurality” code to each loan using USDA’s Rural-Urban Commuting Area codes. Developed by USDA’s Economic Research Service and the U.S. Health Resources and Services Administration, the codes consist of 10 primary and 21 secondary codes for classifying census tracts in the United States on a continuum from rural to urban based on daily commuting patterns and population density. For ease of presentation, we used the Washington State Department of Health’s four-tiered consolidated taxonomy of the codes. The taxonomy classifies all properties as one of the following:

Urban. Adjoining census tracts in built-up areas with a total population of 50,000 or more.

Suburban. Areas with high commuting flows to urban areas and all areas where 30-49 percent of the population commutes to urban areas for work.

Large rural town. Towns with populations of between 10,000 and 49,000 and surrounding rural areas where 10 percent or more of the population commutes to the town and 10 percent or more of the population commutes to an urban area for work.

Small town and isolated rural area. Towns with populations of less than 10,000 and their surrounding commuter areas and other isolated rural areas that are more than 1 hour driving distance to the nearest city.

This classification system was designed to support descriptive analysis of census tract data by providing information about the general character of an area.

Finally, for each RHS- and FHA-guaranteed loan, we also assigned a census region (based on the state where the property was located) and a 10-year Treasury constant maturity rate at the time of origination (using publicly available data from the Board of Governors of the Federal Reserve System).

Characteristics of RHS- and FHA-Guaranteed Loans

To compare the property, borrower, and loan characteristics of single- family home mortgages RHS and FHA guaranteed in 2010–2014, we reviewed agency documentation on the Section 502 and Section 203(b) programs and analyzed the loan-level data discussed previously. To identify similarities and differences between the two agencies’ guarantee programs, we reviewed relevant statutes and regulations, RHS and FHA policy guidelines, and documentation related to the agencies’ loan-level data. We also reviewed our 2012 report comparing the RHS and FHA single-family loan guarantee programs. We focused our analysis of the loan-level data on purchase loans. However, in appendix III, we present tables showing the characteristics of RHS and FHA refinance loans.

Using data in the agency loan records, we compared the characteristics of RHS and FHA purchase loans for properties located in RHS-eligible areas (based on the statutory definition of eligibility). As noted previously, we did not have the property addresses associated with the FHA loans, but we did have the associated census tracts. We used a property’s census tract to determine whether the property was located in an RHS- eligible area. We considered FHA loans for properties in census tracts where 66.7 percent or more of the tract was in an RHS-eligible area to be RHS-eligible (a total of 880,294 loans). We separately analyzed the characteristics of FHA loans for properties not in RHS-eligible areas and present that analysis in appendix IV.

We compared the property, borrower, and loan characteristics of RHS and FHA purchase loans by calculating the number and percentage of loans that fell into different categories or ranges of values for each characteristic. To analyze property characteristics, we compared the rurality, state, Census region, and construction type (manufactured or not manufactured home) of properties with RHS- and FHA-guaranteed loans. To analyze borrower characteristics, we compared the credit score, payment-to-income (PTI) and debt-service-to-income (DTI) ratios, annual income, first-time homebuyer status, and borrower-identified race and ethnicity of RHS and FHA borrowers. For borrower race and ethnicity, we used the Home Mortgage Disclosure Act classifications, which place borrowers in one of the following categories: American Indian or Alaska Native, Asian, black or African-American, Native Hawaiian or other Pacific Islander, Hispanic or Latino, white, or borrower did not disclose. To analyze loan characteristics, we compared the loan amount, loan-to-value (LTV) ratio (including any financed up-front guarantee fees), and interest rate spread of RHS- and FHA-guaranteed loans.

For refinance loans, RHS provided loan-level data for two refinance programs (refinance of RHS direct loans to guaranteed loans and refinance of existing RHS-guaranteed loans) and FHA provided data on three refinance programs (refinance of conventional loans to FHA- guaranteed loans, refinance of existing FHA-guaranteed loans (not streamlined), and FHA streamline refinance program). Based on our analysis of the program requirements for RHS and FHA refinance loans, we determined that certain aspects of RHS’s and FHA’s program policies made direct comparison of the two programs problematic. For example, FHA allowed for cash-out refinances (where the borrower refinances their mortgage at a higher amount than the loan balance and uses the remaining funds for other purposes), while RHS did not. In addition, less data were available for refinance loans because certain refinance programs do not require collection of information such as LTV ratio and borrower credit score (in the case of streamline refinance loans). As a result, we analyzed the characteristics of each agency’s refinance loans (and present the analysis in app. III) but did not directly compare the results as we did for purchase loans.

Estimates of RHS and FHA Borrowers Who Could Have Met Key Criteria for the Other Loan Guarantee Program

To estimate the number and percentage of RHS and FHA borrowers in RHS-eligible areas who could have met key criteria for the other agency’s guarantee program in 2010–2014, we compared the characteristics of RHS-guaranteed loans to the requirements and benchmarks for FHA’s program and compared the characteristics of FHA-guaranteed loans to the requirements and benchmarks for RHS’s program.

To do this, we first reviewed RHS and FHA program documents and analyzed agency loan-level data. Specifically, we examined statutes and regulations for the RHS and FHA loan guarantee programs and RHS and FHA guidelines and policy documents (such as handbooks, mortgagee letters, and administrative notices) to identify each program’s key eligibility and qualifying criteria, including statutory requirements and underwriting benchmarks (see fig. 17). Statutory requirements included minimum down payments and limits on loan amount and borrower household income. Underwriting benchmarks included target values set by the agencies for borrower credit score and PTI and DTI ratios.

Next, we appended information provided by the agencies to the RHS and FHA loan-level data in conjunction with additional data provided by the agencies. As before, we (1) focused on 30-year, fixed-rate mortgage loans guaranteed by RHS and FHA in 2010–2014 for purchasing single- family homes (excluding loans for units in condominium and cooperative developments) and (2) considered FHA loans to be in RHS-eligible locations if the properties were in census tracts where 66.7 percent or more of the tract was located in an RHS-eligible area. As noted previously, data on RHS borrowers’ liquid assets (cash or other assets, such as stocks and proceeds from the sale of property, which are readily convertible to cash) were not available for approximately 20 percent of RHS borrowers. Our analysis assumed no liquid assets for borrowers for whom data were not available.

The information we appended to the loan-level data included the following, if applicable:

FHA county-level loan limits to each RHS loan record based on the longitude and latitude of the mortgaged property.

RHS annual up-front guarantee fee (as a percentage ranging from 2 percent to 3.5 percent) to each RHS loan record based on the loan’s origination year.

RHS county-level household income limits to each FHA loan record based on the loan’s origination year and associated census tract (for determining the county in which the mortgaged property was located). RHS’s household income limits were different for one-to-four-person households and five-to-eight-person households. As we did not have any information on the size of FHA borrower households, we assumed that the borrower’s income was the only source of household income and that all households consisted of no more than four people (the more restrictive limit).

Using the appended data sets, we calculated the number and percentage of RHS borrowers who could have met FHA’s criteria and the number and percentage of FHA borrowers who could have met RHS’s criteria. To do this, we compared the relevant borrower and loan characteristics of each loan to the applicable requirements and benchmarks for the other agency’s program (as described in more detail below). In applying these benchmarks, we did not consider the presence of compensating factors— that is, borrower strengths that may lead a lender to qualify a borrower who does not meet all benchmarks. For example, RHS and FHA allow lenders to qualify a borrower with a DTI ratio higher than their benchmarks when the borrower has additional cash reserves. As a result, our estimates represent the minimum number of borrowers who might have met criteria for credit score and PTI and DTI ratios for the other program.

RHS Borrowers Who Could Have Met FHA Criteria

To assess the extent to which RHS borrowers could have met FHA’s criteria for borrower credit score and PTI and DTI ratios, we compared pertinent information in the RHS loan records with FHA’s benchmarks for these criteria. Similarly, to assess the extent to which RHS borrowers could have met FHA’s criterion for loan amount, we compared the RHS loan amounts to the applicable FHA loan limit based on the year the loan was originated and the county in which the mortgaged property was located. We first determined the number and percentage of RHS borrowers who could have met individual criteria and then determined the number and percentage who could have met all of the criteria simultaneously.

To estimate the number and percentage of RHS borrowers who could have met FHA’s criterion for down payment (3.5 percent minimum down payment requirement), we used RHS data on borrowers’ liquid assets, LTV ratios, and up-front guarantee fees (as a percentage of the loan amount). For this analysis, we assumed that any LTV ratio below 100 percent reflected money the borrower had paid towards a down payment and that all RHS borrowers had financed the applicable 2010–2014 RHS up-front guarantee fee into their loans. In addition, for each loan, we used the property’s appraised value to calculate the amount the borrower would have been required to pay to make a 3.5 percent down payment. We then determined the number and percentage of RHS borrowers who had (1) sufficient liquid assets to make at least a 3.5 percent down payment, (2) LTV ratios corresponding to at least the minimum down payment amount, or (3) a combination of liquid assets and LTV ratios corresponding to at least the minimum down payment amount. As previously discussed, RHS did not have data on liquid assets for approximately 20 percent of the borrowers, so we assumed that those borrowers had no liquid assets. In addition, it is unknown how many RHS borrowers could have obtained additional assets from a third party to pay the down payment. Thus, our estimate represents the minimum number of RHS borrowers who could have met FHA’s down-payment requirement.

Finally, based on the analysis described above, we estimated the number and percentage of RHS borrowers who could have met FHA criteria for borrower credit score, PTI and DTI ratios, loan amount, and down payment simultaneously. These 36 percent of RHS borrowers represented the group who could have qualified for an FHA-guaranteed home purchase loan.

FHA Borrowers Who Could Have Met RHS Criteria

To assess the extent to which FHA borrowers could have met RHS’s criteria for borrower credit score and PTI and DTI ratios, we compared pertinent information in the FHA loan records with RHS’s benchmarks. We first determined the number and percentage of FHA borrowers who could have met the individual RHS criteria and then determined the number and percentage who could have met all of the criteria simultaneously.

To estimate the number and percentage of FHA borrowers who could have met RHS’s criterion for household income, we compared the amount of each FHA borrower’s income with the applicable RHS income limit. However, FHA’s loan-level data contained information on borrower income but not on household income or size. FHA does not collect data on household income, so we assumed that FHA borrower and household income were equivalent. Under that assumption, we estimated that 72 percent of FHA borrowers fell within RHS’s income limits. By assuming borrower income was the only source of household income, our analysis potentially overestimated the number of FHA borrowers who could have met RHS’s household income limits. To test the sensitivity of our estimate to that assumption, we recalculated our estimate using an alternative assumption. Some FHA households likely had income from someone other than the borrower that could have pushed the household over the RHS income limit. To account for this possibility, we calculated the median difference between RHS borrower incomes and their household incomes and added that amount ($2,960) to the incomes of all FHA borrowers. Under this assumption, the percentage of FHA borrowers who met RHS’s income limits decreased from 72 percent to 70 percent.

Finally, based on the analysis described earlier, we estimated the number and percentage of FHA borrowers who could have met RHS’s criteria for borrower credit score, PTI and DTI ratios, and household income simultaneously. These borrowers represented the group who could have qualified for an RHS-guaranteed home purchase loan. We made the estimates using both income assumptions (the base case of household income equivalent to borrower income and the alternative of household income equivalent to borrower income plus $2,960). The difference between the two estimates was one percentage point (22 percent and 21 percent for the base case and alternative assumptions, respectively).

Results by Rural-Urban Commuting Areas and Loan Cohort

We expanded our analysis of borrowers who could have qualified for the other program by disaggregating the results by location type. Specifically, using USDA’s Rural-Urban Commuting Area classification system, we estimated the number and percentage of RHS and FHA borrowers in urban, suburban, large rural town, and small town and isolated rural area who could have met all of the other program’s key criteria (see table 2).

Finally, we analyzed RHS and FHA loan-level data to estimate the number and percentage of RHS and FHA borrowers by loan cohort (2010–2014) who could have met all of the other program’s key criteria (see table 3).

Factors Considered by RHS and FHA Borrowers in Choosing between the Two Loan Guarantee Programs

To analyze the factors that RHS and FHA borrowers who meet the criteria for both programs consider in choosing between the two, we analyzed data on up-front and monthly costs, interviewed eight lenders and five industry stakeholders, and analyzed a survey published in 2016 of borrower opinions. To describe RHS’s and FHA’s methodologies for calculating up-front and annual costs, we (1) reviewed RHS’s periodic notices and policy guidance on up-front and annual guarantee fees and RHS’s Guarantee Up-front and Annual Fee Calculator and (2) reviewed FHA’s policy handbook and mortgagee letters containing information on up-front and annual costs and FHA’s methodology for calculating guarantee fees.

To compare illustrative up-front, monthly, annual, and lifetime costs of RHS- and FHA-guaranteed loans in 2014, we estimated costs for homes with a $125,000 purchase price and a mortgage interest rate of 3.75 percent. To calculate the up-front and annual costs for each program, we assumed that: the appraised value and purchase price of the property were the same; the full up-front guarantee fee was financed; all closing costs and fees (other than the up-front guarantee fee) were paid by the seller (through seller concessions); the interest rates were identical; and the loan payments were made on time for the life of the loan.

To calculate the up-front costs of RHS-guaranteed loans, we first calculated the amount of the up-front guarantee fee based on the purchase price of the property and the 2 percent up-front fee percentage RHS charged in 2014. Second, we added the up-front fee to the purchase price to calculate the final loan amount.

To calculate the up-front costs for FHA-guaranteed loans, we (1) calculated the amount of the required 3.5 percent down payment based on the appraised value of the property, (2) calculated the base loan amount by subtracting the down payment from the initial purchase price, (3) calculated the amount of the up-front guarantee fee (FHA’s up-front fee was 1.75 percent in 2014) using the base loan amount, and (4) calculated the final loan amount by adding the up-front fee to the base loan amount. For both RHS and FHA loans, we assumed that the amount of cash required at loan closing was equivalent to the amount of the required down payment (3.5 percent for the FHA loan and zero for the RHS loan).

To calculate the illustrative monthly, annual, and lifetime costs of RHS- and FHA-guaranteed loans, we calculated: 1. the monthly principal and interest payment based on the final loan amount (including the financed up-front guarantee fee); 2. the amount of the annual guarantee fee by multiplying the annual average outstanding loan balance by the annual guarantee fee percentage (in 2014, the annual guarantee fee was 0.5 percent for RHS loans and 1.2 percent for FHA loans); 3. the monthly cost of the annual guarantee fee by dividing the amount of the annual guarantee fee by 12 (for 12 months); and 4. the total monthly costs by adding the monthly principal and interest payment to the monthly cost of the annual guarantee fee.

To test the sensitivity of our up-front, monthly, and annual cost calculations, we also calculated those costs assuming alternative home purchase amounts ($80,000, $100,000, $135,000, and $150,000) and interest rates (6 and 15 percent). We found that for all purchase amounts and interest rates, RHS had lower cash required at closing than FHA because RHS had no down-payment requirement. In addition, we found that as the assumed interest rate increased to 6 and 15 percent, RHS’s monthly costs (principal and interest and annual guarantee fees charged monthly) continued to be lower than FHA’s, although the percentage difference between RHS and FHA monthly costs decreased.

To test the sensitivity of our results to the assumption that interest rates for RHS- and FHA-guaranteed loans were identical, we also calculated monthly costs (for a $125,000 home) under scenarios in which the interest rate charged for the RHS loan was higher or lower than the 3.75 percent interest rate charged for the FHA loan. Specifically, we calculated the cost to a hypothetical borrower where the interest rate for the RHS loan was 0.25 percentage points higher (that is, 4.0 percent) and 0.25 percentage points lower (that is, 3.5 percent) than the rate charged for the FHA loan. In both scenarios, the monthly costs of the RHS loan remained lower than the costs of the FHA loan.

To obtain the perspective of program participants, we interviewed a nonprobability sample of eight mortgage lenders selected to capture variation in the geographic areas served, volume of guaranteed loans originated, and mix of RHS and FHA business. To identify these lenders, we analyzed data collected under the Home Mortgage Disclosure Act (including the number of RHS- and FHA-guaranteed loan originations by lender in 2012 and the location of each lender) and RHS and FHA loan- level data for 2010–2014 on the number of guaranteed loans originated by each lender. To supplement our lender interviews and gain perspective on mortgage lending in rural areas, we interviewed mortgage industry groups selected to capture a range of stakeholders in the RHS and FHA loan guarantee programs. These groups included the Credit Union National Association, the Independent Community Bankers of America, National Association of Mortgage Brokers, National Association of Realtors, and Mortgage Bankers Association. Additionally, we reviewed the Consumer Financial Protection Bureau and Federal Housing Finance Agency’s survey of recent mortgage borrowers about their experiences in choosing and taking out a mortgage. Among other things, the survey asked a representative sample of borrowers who obtained a mortgage in 2013 how important a variety of mortgage terms or features (such as a low interest rate, low closing fees, low down payment, and low monthly payment) were in their selection of a mortgage. The survey results were reported in the aggregate and by loan and demographic categories, including loan amount and household income. We reviewed the survey methodology and determined that the survey results were sufficiently reliable for purposes of describing the importance of decision factors borrowers consider when selecting a mortgage loan.

Performance of RHS- and FHA-Guaranteed Loans

To compare the performance of RHS- and FHA-guaranteed home purchase loans in RHS-eligible areas, we analyzed the RHS and FHA loan-level data described previously. We focused on loans guaranteed in 2010–2012 and determined their performance status at 12-month intervals (anniversary months) starting from the month the first payment was due until September 30, 2014. For this comparison, we limited the FHA-guaranteed loans to those obtained by borrowers with incomes within the RHS county-level household income limits (using borrower income as a proxy for household income). We restricted the data in this way to account for RHS’s household income limits and FHA’s lack of such limits, which resulted in FHA serving some borrowers with higher incomes than RHS is allowed to serve. We also limited the set of FHA-guaranteed loans to those for properties in census tracts where more than 95 percent or more of the tract was within an RHS-eligible area. Because the analysis focused on the payment status of the loans, we used the first month a loan payment was due (first payment month) as the starting point for measuring performance. The first payment month may be up to 2 months after the month the loan is guaranteed. We classified the performance status of each loan guaranteed by RHS and FHA into one of the following payment status categories: current, prepaid, 30–89 days delinquent, 90 or more days delinquent or in the foreclosure process, or terminated with a claim. Due to the relatively brief period for our analysis of loan performance (2 and 3 years from the month of first payment), we did not isolate and analyze the foreclosure rate of RHS- and FHA- guaranteed loans. According to the Federal Reserve Bank of Minneapolis, on average loans in foreclosure in 2010 took from 15 months to 2 years to complete foreclosure, meaning that relatively few of the loans we analyzed would have had time to complete this process. We also classified loans in the last two categories as troubled and calculated troubled loan rates for each agency. We compared the actual performance of RHS- and FHA-guaranteed loans after 2 and 3 years from the date of first payment.

To compare the expected performance of RHS- and FHA-guaranteed loans after 2 and 3 years and to examine factors accounting for differences between RHS and FHA troubled loan rates, we developed a statistical model. The model primarily used FHA and RHS loan-level data including borrower income and credit score, loan amount, loan interest rate, and DTI and LTV ratios at loan origination. The model also used data from other sources, including the Federal Housing Finance Agency House Price Index for state nonmetropolitan areas to estimate changes in the value of each mortgaged property and data on the Treasury 10-year constant maturity rate to determine the interest rate spread (that is, the difference between the interest rate on the loan and the Treasury rate for the same period). Prior research has found that these variables influence loan performance, including troubled loan rates. Other variables for which we lacked data, such as borrower employment status, may also influence troubled loan rates.

For RHS and FHA separately, we used logistic regressions to estimate the relationships between relevant loan and borrower characteristics and economic conditions (explanatory variables) and the probability of the loans they guaranteed becoming troubled after 2 and 3 years. That is, we estimated changes in the troubled loan rate associated with variations in the value of the explanatory variables. These relationships are represented by coefficients for each variable. In general, we found that the coefficients were similar for both agencies and consistent with prior research in terms of the direction of the estimated effect.

The similarity in the separate RHS and FHA coefficients allowed us to run our model using a data set that included both agencies’ loans in order to estimate coefficients for the combined loan pool. These coefficients represent the statistical relationships between the explanatory variables and loan performance without regard to which agency provided the guarantee. As a result, they provided a common foundation for estimating and comparing how the portfolio characteristics of each agency influenced their troubled loan rates.

We used these coefficients to simulate 2- and 3-year troubled loan rates using average values of the explanatory variables. Specifically, we first estimated an overall expected troubled loan rate for RHS’s loan portfolio based on RHS-specific averages for all explanatory variables. Second, to isolate the impact of a particular RHS portfolio characteristic on the expected troubled loan rate, we used the RHS-specific average for the corresponding variable while holding all other variables at their average values for the combined RHS-FHA loan pool. We performed the same two steps for FHA’s loan portfolio and compared the results for RHS and FHA. See appendixes VI and VII for additional information on the statistical model and results.

We conducted this performance audit from February 2015 to September 2016 in accordance with generally accepted government auditing standards. Those standards require that we plan and perform the audit to obtain sufficient, appropriate evidence to provide a reasonable basis for our findings and conclusions based on our audit objectives. We believe that the evidence obtained provides a reasonable basis for our findings and conclusions based on our audit objectives.

Appendix II: Characteristics of Home Purchase Loans RHS and FHA Guaranteed in RHS-Eligible Areas in Fiscal Years 2010– 2014

This appendix describes the results of our analysis comparing the characteristics of single-family home purchase loans guaranteed by the Rural Housing Service (RHS) and the Federal Housing Administration (FHA) in fiscal years 2010–2014 in RHS-eligible areas. We calculated the number and percentage of loans that fell into different categories or ranges of values for each borrower, loan, and property characteristic at loan origination by individual loan cohort and for all loan cohorts collectively.

Appendix III: Characteristics of Refinance Loans RHS and FHA Guaranteed in RHS- Eligible Areas in Fiscal Years 2010–2014

This appendix describes the results of our analysis of the characteristics of single-family refinance loans guaranteed by the Rural Housing Service (RHS) and Federal Housing Authority (FHA) in fiscal years 2010–2014 in RHS-eligible areas.

RHS provided loan-level data for two refinance programs: refinancing of RHS direct loans to RHS-guaranteed loans and refinancing of existing RHS-guaranteed loans.

FHA provided data for three refinance programs: refinancing of existing FHA-guaranteed loans (not streamlined), refinancing of existing FHA-guaranteed loans (streamlined), and refinancing of conventional loans to FHA-guaranteed loans.

For selected borrower and loan characteristics, we calculated the number of loans refinanced under each program, the percentage of loans with missing information for the specified characteristic, and the median value of the characteristic (for the 5-year period collectively).

Appendix IV: Characteristics of Home Purchase Loans FHA Guaranteed for Properties Not Located in RHS-Eligible Areas in Fiscal Years 2010–2014

This appendix describes the results of our analysis examining the characteristics of single-family home purchase loans guaranteed by the Federal Housing Administration (FHA) in fiscal years 2010–2014 for properties not in Rural Housing Service (RHS)-eligible areas. Approximately 75 percent (2,646,366) of the home purchase loans FHA guaranteed in 2010–2014 were not located in RHS-eligible areas. We calculated the number and percentage of FHA-guaranteed loans that fell into different categories or ranges of values for each borrower, loan, and property characteristic at loan origination by individual loan cohort and for all loan cohorts collectively.

Appendix V: Performance of Home Purchase Loans RHS and FHA Guaranteed in RHS- Eligible Areas in Fiscal Years 2010–2012

This appendix describes the results of our analysis comparing the performance of single-family home purchase loans guaranteed by the Rural Housing Service (RHS) and the Federal Housing Administration (FHA) in fiscal years 2010–2012 in RHS-eligible areas after 2 and 3 years of performance. We analyzed performance for all loans and by the degree of rurality of the mortgaged property using the Department of Agriculture’s (USDA) Rural-Urban Commuting Areas classification system.

Appendix VI: Description of Our Statistical Model Comparing the Performance of Home Purchase Loans Guaranteed by RHS and FHA

This appendix describes the statistical model we developed to analyze how mortgage loans recently guaranteed under the Section 502 single- family loan guarantee program, administered by the Department of Agriculture’s (USDA) Rural Housing Service (RHS), have performed over time and what factors explain performance empirically. We provide a comparison of RHS loan performance to that of single-family loans guaranteed by the Federal Housing Administration (FHA) through its Section 203(b) program.

Our fundamental approach was to define an individual loan’s performance in terms of its status as observed in a particular anniversary month. Status is a measure of whether a loan is current at a particular time, or, if not, a measure of its delinquency or an indication of its termination. Given knowledge of a loan’s month of first payment, we can define an anniversary month as the month of any particular scheduled payment, such as the 24th or 36th payment month. A loan’s anniversary status is then simply a snapshot of its status in that month.

Given observations of anniversary month status, we developed binary measures of loan performance in that month: troubled (at least 90 days delinquent, in the foreclosure process, or terminated with a claim) on the one hand or nontroubled (current, 30–89 days delinquent, or prepaid) on the other. We then used logistic regressions to estimate the effects of various factors that are understood to influence loan performance on this binary measure. The underlying variation in the values of these explanatory variables across a large set of loans permits us to estimate the effects of these factors on loan performance and to discuss results in terms of changes in expected probabilities of troubled performance.

To undertake our analysis, we obtained loan-level data from RHS and FHA. The agencies provided us with information for loans guaranteed in fiscal years 2010–2014. Specifically, we obtained information on 6,181,365 FHA loans and 687,564 RHS loans, although we analyzed only fixed-rate loans with a 30-year term for home purchases and, given the requirements of the anniversary-month approach, could not include every loan record in the statistical analysis. The agencies provided us with two types of information for each loan: static and performance. In general terms, static information describes loan and borrower characteristics at the time of loan origination, such as the loan amount and the borrower’s credit score. Performance, or history, files contain information that includes a loan’s status in each month (for example, current or 30 days delinquent). The performance information for each loan in our data set covers performance from loan origination through September 2014, the last month of fiscal year 2014. Thus, we have approximately 5 years of observed performance for the earliest loans in our data, but only a few months for the most recent loans in our data. The month of first mortgage payment was not itself a variable in the data we received, but both agencies provided us with guidance to identify the month of first payment using information on other related date fields.

Modeling Considerations

Explanatory Variables

We included a variety of loan, borrower, and other characteristics as explanatory factors in our statistical analyses of mortgage performance. Because of our focus on comparing RHS and FHA loan performance, we developed a modeling approach that would allow us to estimate the effects of a set of explanatory variables separately for RHS and FHA loans as well as pooled together. To accomplish this, we needed the same set of explanatory factors for RHS and FHA loans. Information describing key loan and borrower characteristics was available in both the RHS and FHA data. Key static information at loan origination includes borrower income, credit score, loan-to-value (LTV) ratio, loan amount, debt-service-to-income (DTI) ratio, mortgage interest rate, and an indicator for whether the borrower was a first-time homebuyer. These characteristics are associated with loan performance, as follows.

Borrowers with higher credit scores are less likely to have missed payments than borrowers with lower credit scores.

Equity, which is defined as the value of the property minus the balance of the mortgage, is associated with better loan performance. The LTV ratio measures initial equity; larger down payments at loan origination are associated with better loan performance.

Larger loan amounts require larger payments, which can put more financial stress on households struggling to fulfill various financial commitments.

DTI ratio is a measure of how stretched a household may be to make scheduled mortgage and other debt payments.

Higher interest rates are associated with larger payments, and a mortgage rate with a large spread over a reference rate may represent the lender’s assessment that the borrower presents higher risk of troubled loan performance. Rather than incorporating the mortgage interest rate directly, we calculate the spread of the mortgage rate over the constant maturity 10-year Treasury rate in the month of loan origination.

First-time homebuyer status can be a risk factor, potentially reflecting a new homeowner’s limited experience with the risks and costs of homeownership.

A borrower can build equity over time if housing values appreciate or by paying down their mortgage balance. We used data from a Federal Housing Finance Agency house price index to develop a house price appreciation measure, using state-level indexes of house prices for the nonmetropolitan portions of each state (or a neighboring state in three instances) over the specific interval linking a loan’s origination to a specific payment anniversary. We also included a set of census division dummy variables, a set of first payment quarter dummy variables, and a set of “community type” dummy variables based on USDA’s Rural-Urban Commuting Area code construct. These dummy variables may help control for other factors not directly measured by the other explanatory factors listed, including any that may have varied by region of the country, time of loan origination, or community type.

We also included information for RHS borrowers on the amount of liquid assets available to the borrower at loan origination. This permitted us to explore the effects of a financial cushion at the time of loan origination for RHS loans, but not for FHA loans separately or for the model using RHS and FHA data pooled together.

RHS Program Considerations and Comparable FHA Loans

To present an appropriate comparison group of FHA loans, we considered other issues related to RHS program features so that we could exclude FHA loans that would not qualify for the RHS program. First, RHS has a rural focus, and its loans are available only in specific mapped (RHS-eligible) areas. FHA loan availability is not restricted by geography. To avoid comparing RHS loans to FHA loans outside of RHS- eligible areas, which may perform differently from loans in RHS-eligible areas, we excluded from our analysis FHA (and RHS) loans in census tracts for which less than 95 percent of the tract’s land area is in an RHS- eligible area. As shown in table 43, this excluded most FHA loans but relatively few RHS loans.

Second, to qualify for an RHS loan, a borrower’s household income may not exceed 115 percent of the area median income, a threshold that varies across the country and over time. FHA borrowers are not subject to income restrictions. Thus, there will be FHA borrowers with incomes exceeding the RHS program limits in RHS-eligible areas. To avoid comparing RHS loans to FHA loans that went to higher-income borrowers, we excluded borrowers with incomes that exceeded the RHS program limits. For instance, the borrowers who received the 203,319 FHA loans in table 43 that were made in RHS-eligible areas also had incomes that met the RHS program limits. The number of FHA loans in RHS-eligible areas without regard to borrower income limits was 276,933. We also present results for a subset of loans that are in communities in RHS-eligible areas considered to be the most rural, as defined using the Rural-Urban Commuting Area codes.

Selection of Anniversary Month and Performance Status

We faced a trade-off between the number of loans we could include in the statistical analysis and the degree of loan seasoning we could select. September 2014 is the last month for which we could determine status for any loan, and the earliest loans in our data set were originated in the beginning of fiscal year 2010. As a general approximation, no loan originated after fiscal year 2011 will have a 36-month (3-year) anniversary by the end of fiscal year 2014. In contrast, all loans originated before fiscal year 2014 will have a 12-month (1-year) history available by the end of fiscal year 2014. Typically, relatively few loans exhibit troubled performance early in their history (for instance, at their 1-year anniversary). Thus, more seasoned loans are likely to provide a better indicator of the ultimate performance of a group of loans. Figure 18 shows the relationship between the numbers of loans that would be available for our analysis given the selection of alternative anniversary periods. For illustration, the number of loans refers to the number of loans that would have the requisite history given a choice of anniversary month. Performance of these loans is defined in terms of the share of loans that are troubled. Less than 1.5 percent of loans show troubled performance at 1 year, while more than 6 percent of loans show troubled performance by 3 years.

We chose to measure status as of the 3-year anniversary period to model loan performance, although we do present some results in which we use the 2-year anniversary period.

Functional Form

In terms of functional form, we express each continuous variable as a series of dummy variables that exhaust the continuous variable’s range. For example, the borrower credit score range was split into nine dummies, most containing 20-point portions and others containing 40- point portions of the 850-point credit score range. This permitted flexibility in determining the effects of changes in the value of an explanatory factor on changes in performance, and for comparing the effects as estimated on the set of RHS loans to the effects as estimated on the set of FHA loans. We used this feature to trace the relationship between changes in an explanatory factor across its range and the expected probability of troubled performance.

We provide a table of mean values for this specification later, but table 44 presents information on the distribution of these variables in a more conventional form. These values are for the specification using a 3-year anniversary period, loans in the RHS-eligible areas, and borrowers with incomes meeting the RHS program limits.

Notably, and as discussed elsewhere in this report, the distributions of some key characteristics are quite similar between the two sets of loans—particularly credit score and interest rate spread values. RHS borrowers had lower incomes, smaller loan amounts, and lower DTI ratios. House price appreciation was slightly more favorable for RHS than for FHA. A greater share of RHS borrowers were first-time homebuyers.

Both the RHS and FHA programs feature low or no down payments. However, the programs have different down-payment requirements, which affect the distribution of LTV ratios. FHA loans are concentrated at an LTV value of 96.5 percent, while many RHS loan amounts exceed the value of the property at loan origination—that is, have an LTV ratio exceeding 100 percent. The dummy variable construct helps to model LTV effects given the skewed distribution of values.

We developed two sets of dummy variables to capture LTV effects: one with three categories in which the top category includes all loans with LTV values at or above 96, and the second with four categories in which the top category includes all loans with LTV values at or above 100 and the next-to-top category includes LTV values at or above 96 but less than 100. Most loans for both agencies exceed an LTV value of 96, but the three-category model does not distinguish a value of 102 from a value of 98. The four-category model makes this distinction, but given the nature of the two programs, almost all of the loans that have LTV values exceeding 100 are RHS loans.

The model includes only fixed-rate loans with 30-year terms for home purchases. In addition, we excluded loans for units in condominiums and cooperative developments; observations with missing or extreme values of explanatory variables; observations for which certain date values seem inappropriate—for example, mortgages that appear to terminate before they originate; and observations for which we could not determine an unambiguous measure of the performance status.

Performance Comparison Using Agency-Specific Model Results

We compared the performance of RHS loans to that of FHA loans by first examining agency-specific model results. We present a series of charts that support the view that the two agencies share a similar loan performance model structure. We then used the pooled set of loans to calculate the expected probability of troubled loan performance represented by loans with particular values of the explanatory characteristics. In particular, we compared the predicted performance of a loan with average RHS characteristics to a loan with average FHA characteristics for particular characteristics of interest. Our assumption that loan performance for the two programs could be evaluated using a similar model structure is also supported by the fact that the loan underwriting processes for the two programs share common elements.

We present our primary results for a model specification in which we select an anniversary duration of 3 years; classify our binary measure of loan performance so that troubled loans are those with anniversary month status of 90 days delinquent or worse (including loans in foreclosure or terminated with a claim); approximate RHS program income requirements by imposing borrower household income limits; restrict property locations to those in census tracts with at least 95 percent of land area in RHS-eligible areas; and select a four-category LTV ratio dummy variable structure.

We also present some alternative specifications to highlight the sensitivity of various factors on the results (see tables 45–49 in app. VII for additional information).

In general, our estimates suggest that across all samples and specifications, certain consistent patterns are present for RHS and FHA borrowers. At the same time, there are some differences. For instance, increases in borrower credit scores decrease the expected probability of a loan being troubled by the 3-year anniversary, and there are dramatic declines as credit scores increase from the lower to the higher portions of the credit score range for both RHS and FHA loans (see fig. 19). However, at the lower portions of the range, RHS loans have a higher probability of being troubled, holding other factors constant, than FHA loans. But in both cases these probabilities decrease rapidly throughout the credit score range and converge by the high end of the range. Differences between RHS and FHA estimated coefficients are statistically significant except at the high end of the credit score range, suggesting that loans to RHS borrowers with low credit scores are somewhat more likely to perform worse, other factors held constant, than loans to FHA borrowers with low credit scores.

Increases in borrower income were associated with decreased probability of a loan being troubled by the 3-year anniversary (see fig. 20). There were no significant differences between the estimated RHS and FHA coefficients.

As shown in figure 21, larger loan amounts were associated with higher probabilities of troubled performance by the 3-year anniversary. There were no significant differences between the estimated RHS and FHA coefficients.

As shown in figure 22, increases in the DTI ratio were associated with modest increases in the probability of troubled performance by the 3-year anniversary. The differences in the estimated coefficients in the greater- than-31-to-36 category and the greater-than-46 category were statistically significant (although relatively few RHS borrowers were in the latter category).

There were some differences between the agencies in the estimated effects of mortgage rate spread (see fig. 23). For both agencies, higher spreads were associated with higher estimated probabilities of troubled performance by the 3-year anniversary. However, the estimates for RHS exhibit a relatively flat pattern (that is, the probability of troubled performance was not as sensitive to changes in the mortgage rate spread) compared with the FHA estimates.

As previously noted, both the RHS and FHA programs have low or no down-payment features, and more than one-half of RHS borrowers had LTV ratios at origination that exceeded 100 percent. However, fewer than 1 percent of FHA loans had LTV ratios exceeding 100 percent, which provides little rationale for modeling FHA loan performance on a stand- alone basis using a four-category LTV specification—that is, using a specification with a separate category for loans with LTVs of 100 percent or more. The four-category specification is more appropriate for modeling RHS loan performance. For both agencies, the expected probability of a loan being troubled by the 3-year anniversary increases with LTV values, with the exception of the top category for FHA loans. Our results show that for RHS loans, moving to the LTV category with values at or exceeding 100 percent was associated with an increase in the probability of a loan being troubled by the anniversary date (see fig. 24). The differences between the RHS and FHA coefficient estimates were not statistically significant.

As shown in figure 25, the influence of house price appreciation was modest for RHS and FHA loans, possibly in part because overall rates of house price appreciation were moderate during the period covered by our analysis. The differences between the estimated RHS and FHA coefficients were not statistically significant.

The estimated effects of first-time homebuyer status differed in the agency-specific regressions. Both programs are attractive to first-time homebuyers because of the low (or no) down-payment requirements. In our data set, 85 percent of RHS borrowers and 76 percent of FHA borrowers were first-time homebuyers. However, the share of FHA borrowers who were first-time homebuyers was greater in locations that are not eligible for the RHS program. In the FHA regression, the estimated coefficient for first-time homebuyer status is positive (it is associated with a higher expected probability of troubled loan performance) and statistically significant. In the RHS regression, the estimated coefficient is not statistically significant at the 10 percent level. This suggests that non-first-time homebuyer status is not associated with better loan performance.

Performance Comparison Using Pooled Agency Data

In general, we believe that the performance of RHS and FHA loans is well explained by the model. Additionally, given the similar patterns of association between key explanatory characteristics and the expected probability of troubled loan performance, we believe it is reasonable to use the model and a pooled data set—that is, data from both agencies combined—to investigate how differences between the portfolio characteristics of the two agencies influence loan performance. The presence of a similar underlying model structure is evidenced by the RHS and FHA stand-alone regressions, which showed similar decreasing relationships between borrower credit scores and the expected probability of troubled loan performance (that is, high expected values at low credit scores and steep declines in expected values as scores increase). While the estimates were significantly different across portions of the credit score range, for other important characteristics the similarity of the underlying model structure was confirmed by similarities in the association with troubled loan status and the absence of statistically significant differences between the two agencies. In particular, we observed similar decreasing relationships between borrower income and the expected probability of troubled loan performance and similar increasing relationships between loan amount and expected troubled loan performance.

The advantage in assuming a common model structure and using a pooled data set is that we can use the estimated coefficients to calculate the expected probability of a loan with particular characteristic values being in a troubled status by the 36th month anniversary. One comparison is to calculate these probabilities using the mean values for the entire set of explanatory characteristics observed for each agency (agency-specific mean values). Another method of comparison is to calculate these probabilities using the mean values of the pooled characteristics (pooled mean values) to provide a base value, and then to substitute agency-specific mean values for particular characteristics. For example, if RHS borrowers had riskier characteristics than FHA borrowers, then given the set of coefficient estimates for the pooled data, the expected probability of a loan being troubled by the anniversary period would be higher for RHS than for FHA. To the extent that each agency’s borrower characteristics are similar, the expected probabilities for each agency will be similar. To the extent that the characteristics diverge, the probabilities will diverge. Figure 26 shows the expected troubled loan rate for each agency using the pooled coefficients and the pooled or agency-specific mean values for the various characteristics.

Alternative Model Specifications

We estimated four alternative specifications of the model to determine the extent to which our results varied depending on plausible changes in assumptions and to examine the effect of particular factors relevant to RHS program features.

First, we estimated a specification in which we did not restrict borrower incomes based on RHS household income limits. This had the effect of including many more FHA observations with relatively higher incomes—improving expected FHA performance—and serves to highlight the importance of borrower income.

Second, we also estimated a model specification with three LTV ratio categories instead of four. That is, we combined the top two categories of the range, so that the top category included all LTV ratio values greater than or equal to 96 percent. Given the categorical nature of the way variables are defined, in this specification it becomes impossible to distinguish between LTV ratio values that are slightly less than 100 percent from those that are slightly above. In contrast to the primary specification, this specification predicts better performance for RHS than for FHA for reasons described below.

Third, we also chose a specification in which we included only those locations that were the most rural in character (large rural towns and small town and isolated rural areas) on the basis of the Rural-Urban Commuting Area codes. This permitted us to analyze loan performance in the more rural portions of the RHS-eligible areas. This specification produced similar results to the primary specification in terms of the two agencies’ relative loan performance.

Finally, we chose a specification that used a 2-year anniversary period. This specification produced almost identical expected performance for both agencies.

Under the first alternative model specification, many FHA borrowers within RHS-eligible areas have incomes exceeding RHS income limits. Since higher incomes are associated with a lower risk of a loan being troubled, actual and predicted FHA performance improves when these observations are not excluded from the estimation. Figure 27 shows the results of this first alternative specification. Compared with the results for the primary model specification (see fig. 26 above), the expected probability of a loan becoming troubled by its 3-year anniversary is lower with all characteristics at their pooled mean values. Additionally, the expected probability of troubled loan performance is considerably higher with all characteristics at RHS-specific mean values than it is with all characteristics at FHA-specific mean values. Borrower income is particularly important to this outcome, suggesting that the inclusion of higher-income borrowers in RHS-eligible areas is associated with better FHA performance.

Under the second alternative model specification, we examined the importance of the top end of the LTV ratio range. While few FHA loans had an LTV ratio exceeding 100 percent, many RHS loans did. Within the RHS stand-alone regression, moving from an LTV ratio below 100 percent to one above 100 percent has a positive and significant effect on the probability of troubled loan performance. The second alternative specification uses only three categories of LTV ratio (rather than the four categories used in the primary specification), with the highest category starting at 96 percent.

Figure 28 shows the results of this second alternative specification. In contrast to the results for the primary specification (see fig. 26 above), the expected probability of troubled loan performance is lower with all characteristics at RHS-specific mean values than it is with all characteristics at FHA-specific mean values. In particular, the contribution of LTV ratio appears to favor rather than disadvantage RHS performance because FHA has a larger proportion of loans than RHS in the highest category (which does not distinguish between LTV ratios of 96 to 99.9 percent and those of 100 percent or more). Taken together, our analyses suggest that for RHS borrowers, beginning homeownership with negative equity is associated with a higher probability of troubled performance than beginning with low but positive equity.

Under the third alternative specification, we focused on the possibility that loan performance may be different in the most rural parts of RHS-eligible areas. In this specification, we included only observations corresponding with Rural-Urban Commuting Area codes for locations less integrated into urban areas. Commuting-to-work patterns are an important component of the Rural-Urban Commuting Area classification scheme so that, for example, if a rural area is close enough to an urban area to be attractive to commuters, housing market transactions in those areas may be more standardized than in more rural areas with fewer employment opportunities. There also may be differences in the characteristics of RHS and FHA borrowers in these areas. When we restricted our analysis to observations in large rural towns and small town and isolated rural areas within RHS-eligible areas, we found that the overall incidence of troubled loans was lower in these more rural areas. However, the effects of portfolio characteristics on expected probabilities of troubled loan status were generally similar to those we found in RHS-eligible areas overall (see fig. 29). As a result, figure 29 resembles figure 26 (which does not impose the additional geographic restriction) in many respects.

Finally, we estimated a fourth alternative specification that is the same as our primary specification except that it focuses on a 2-year anniversary period. As with the case of the 3-year specification, we believe that stand- alone estimates for each agency suggest a common underlying structure. Fewer loans are troubled after 2 years than after 3 years; however, we were able to increase the number of observations because more loans had 2 years of history available by the end of fiscal year 2014 than had 3 years of history. In contrast to the 3-year actual troubled loan rates, we found that the agencies had almost identical 2-year actual troubled loan rates (3.8 percent for RHS and 3.9 percent for FHA).

Using the pooled data set, the effects of particular characteristics after 2 years of performance were similar to those observed in the 3-year case (see fig. 30). In contrast to figure 26 above (which describes the results of our primary 3-year specification), the expected probability of troubled performance is slightly lower with all characteristics at RHS-specific mean values than with all characteristics at FHA-specific mean values. This result is consistent with the fact that the actual RHS troubled loan rate, although quite similar to FHA’s, was slightly lower. However, income and LTV ratio characteristics associated with the RHS portfolio disadvantaged RHS performance.

Appendix VII: Results of Statistical Model Comparing the Performance of Home Purchase Loans RHS and FHA Guaranteed in RHS-Eligible Areas in Fiscal Years 2010– 2012

This appendix describes the results of our statistical model comparing the performance of single-family home purchase loans guaranteed by the Rural Housing Service (RHS) and the Federal Housing Administration (FHA) in fiscal years 2010–2012 in RHS-eligible areas after 2 and 3 years of performance. We analyzed performance for RHS and FHA loans separately and for RHS and FHA loans combined using various model specifications. For example, under one specification, we limited the borrowers to those with incomes within the county-level household income limits set by RHS. We restricted the data in this way to account for the absence of FHA limits on borrower household income, which resulted in FHA serving some borrowers with higher incomes than RHS is allowed to serve. Additionally, we analyzed 3-year performance using a specification with four loan-to-value ratio categories and another specification using three loan-to-value ratio categories.

As shown in the following tables, we estimated coefficients for the different categories that comprise each explanatory variable. The coefficient for a particular category is an estimate of the effect of being in that category as distinct from the omitted category. The omitted categories are as follows: borrower credit score (760–799); debt-service- to-income ratio (36 percent to less than 41 percent); loan amount ($100,000–$149,999); borrower income ($50,000–$59,999); first-time homebuyer (non-first-time homebuyer); house price appreciation (decrease of greater than 0 to 3.5 percent for the 2-year performance analysis and decrease of greater than 0 to 3 percent for the 3-year performance analysis); mortgage spread over 10-year Treasury rate (greater than 2 percent to 2.25 percent); loan-to-value ratio (96 percent to 99.9 percent); and months of liquid assets (1 or 2 months).

Appendix VIII: GAO Contact and Staff Acknowledgments

GAO Contact

Daniel Garcia-Diaz, (202) 512-8678 or garciadiazd@gao.gov.

Staff Acknowledgments

In addition to the individual named above, Mathew J. Scirè (Director), Steve Westley (Assistant Director), Patricia MacWilliams (Analyst-in- Charge), Abiud Amaro Diaz, Stephen Brown, William R. Chatlos, Anna Chung, John McGrail, John Mingus, Barbara Roesmann, and Jena Sinkfield made major contributions to this report. Additional support was provided by Melissa Kornblau, Alexandra Martin-Arseneau, and Heneng Yu.