Home Mortgage Guarantees: Issues to Consider in Evaluating Opportunities to Consolidate Two Overlapping Single-Family Programs
Published: 2016-09-29T00:00:00

Fast Facts:

Highlights:
section_title: Why GAO Did This Study
paragraphs: RHS and FHA help borrowers finance homes by guaranteeing single-family mortgage loans made by private lenders, and both operate in rural areas. However, eligibility for RHS guarantees is restricted to RHS-eligible areas and to low- and moderate-income households. A prior GAO report (GAO-12-554) found overlap in the products offered, borrower income levels, and geographic areas served by the two guarantee programs and recommended that RHS and FHA evaluate and report on opportunities for consolidating similar housing programs.
GAO was asked to expand on the analysis in its 2012 report. This report compares the characteristics, performance, and borrower costs of RHS- and FHA-guaranteed loans in RHS-eligible areas.
GAO analyzed RHS and FHA data for home purchase loans guaranteed in fiscal years 2010–2014 (which allowed for analysis of loan performance over multiple years). GAO also interviewed RHS and FHA officials, eight lenders (selected to capture variation in rural areas served, origination volume, and mix of RHS and FHA business), and industry associations.
section_title: What GAO Found
paragraphs: GAO's comparison of single-family home purchase loans guaranteed by the Rural Housing Service (RHS) and the Federal Housing Administration (FHA) in fiscal years 2010–2014 identified significant overlap and some differences in the borrowers served. Within statutorily defined rural areas (RHS-eligible areas):
Both agencies served large numbers of rural borrowers, but FHA served over 35 percent more than RHS, while RHS reached a greater number of borrowers in the more rural parts of RHS-eligible areas.
Most of the borrowers served by each agency had annual incomes below $60,000. But consistent with RHS's statutory income limits, the median borrower income for RHS ($44,000) was well below that for FHA ($57,000).
RHS and FHA borrowers had similar credit scores (around 685 at the median) and ratios of housing expenses to monthly gross income (23–24 percent at the median).
Borrowers in both programs had high loan-to-value (LTV) ratios (loan amount divided by home value). But RHS's no-down-payment requirement and FHA's statutorily required 3.5 percent down payment resulted in higher LTV ratios for RHS than for FHA (medians of 101 and 96.5 percent, respectively).
Significant portions of RHS and FHA borrowers could have met the criteria of the other program. For example, at least 36 percent of RHS borrowers could have met FHA's criteria, including the 3.5 percent minimum down payment.
In RHS-eligible areas, RHS loans guaranteed in fiscal years 2010–2011 performed worse than corresponding FHA loans after 3 years. Specifically, for borrowers whose incomes fell within RHS limits, RHS's 3-year troubled loan rate (the share of loans 90 or more days late, in foreclosure, or terminated with a claim) was 7 percent, compared with 6 percent for FHA. GAO estimated that RHS's loans would be expected to perform worse than FHA's due partly to RHS borrowers' higher LTV ratios.
Borrower costs—at loan closing and paid monthly—were lower for RHS loans than for FHA loans. Due to differences in down-payment requirements, a borrower purchasing a $125,000 home in 2014 would have paid $4,375 more in up-front costs with an FHA loan than with an RHS loan. Also, FHA (which must maintain a capital reserve) charged borrowers a higher annual guarantee fee than RHS, which has no capital requirement. Due largely to the difference in this fee (charged monthly), a borrower's initial monthly payments would have been about 7 percent lower with an RHS loan (assuming a 3.75 percent interest rate).
GAO's analysis provides additional evidence of how the programs overlap in terms of income, location, and borrower qualifications. It also highlights issues for RHS and FHA to consider in evaluating opportunities to consolidate these programs, as GAO recommended in 2012. Specifically, differences in the performance and borrower costs of RHS and FHA loans underscore important tradeoffs. Higher LTV ratios and lower guarantee fees help make mortgages more affordable. However, these features also may elevate financial risks to the federal government from increased loan defaults and less revenue to cover unanticipated costs. Agency consideration of these issues would aid congressional decision-making about potential program consolidation.
section_title: What GAO Recommends
paragraphs: GAO makes no new recommendations in this report but maintains that RHS and FHA should evaluate and report on opportunities to consolidate their similar housing programs.

section_title: Letter
paragraphs: The Department of Agriculture’s (USDA) Rural Housing Service (RHS)  has helped more than 1 million low- and moderate-income families in  rural communities finance homes through its Single Family Mortgage  Guarantee Program. The program protects private lenders against  losses on loans that finance the purchase of properties in areas statutorily  designated as rural (RHS-eligible areas) or that refinance existing RHS  mortgages. At the end of fiscal year 2015, RHS had a portfolio of more  than $112 billion in outstanding guarantees. The Department of Housing  and Urban Development’s (HUD) Federal Housing Administration (FHA)  also protects lenders against losses on single-family mortgage loans (by  providing mortgage insurance) and has a much larger program. At the  end of 2015, FHA had a portfolio of more than $1 trillion in outstanding  guarantees. FHA’s program has no geographic or borrower income  restrictions; therefore, it too guarantees loans in rural areas.
Although the programs have some salient differences, in an August 2012  report, we found overlap in the products offered, borrowers’ income  levels, and geographic areas reached by the RHS and FHA single-family  mortgage guarantee programs. We concluded that there were  opportunities for increased collaboration and consolidation in the  programs. Although Congress ultimately would have to decide on actions  requiring statutory change, we noted that agencies (including RHS and  FHA) could further this effort by exploring the potential benefits and costs  of consolidating overlapping programs. Such analyses represent a key  step on the path to determining the viability of consolidation and helping  inform Congress’s decision-making process. Thus, in 2012, we  recommended that the agencies evaluate and report on the specific  opportunities for consolidating similar housing programs, including those  requiring statutory changes. The agencies (including RHS and FHA)  generally agreed with our recommendation, but they have yet to report on  such opportunities, and our recommendation remains unaddressed.
Additionally, relatively little is known about how the loan performance of  the two programs compares. For example, one of RHS’s performance  goals is to be within a specified range of FHA’s delinquency rates. RHS  has generally met this goal and reported to Congress in April 2015 that  during the prior 5 years (2010–2014) RHS-guaranteed loans generally  performed slightly better than FHA-guaranteed loans. However, our  March 2016 report found RHS’s analysis did not account for the age of  the loans, property location, or other loan and borrower characteristics  that can influence performance. We recommended that RHS improve its  measures comparing RHS and FHA loan performance, potentially by  making comparisons on a cohort basis and limiting comparisons to loans  made in similar geographic areas. RHS has yet to make changes to its  performance measures and our 2016 recommendation remains  unaddressed.
You asked us to expand on the analysis in our 2012 report and compare  the characteristics and performance of rural single-family loans  guaranteed by RHS and FHA. This report (1) compares the property,  borrower, and loan characteristics of RHS- and FHA-guaranteed loans in  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.
To compare the property, borrower, and loan characteristics of RHS- and  FHA-guaranteed loans, we analyzed loan-level data on mortgages for  home purchases guaranteed by the agencies in RHS-eligible areas in  2010–2014. In addition to focusing on home purchase loans in RHS- eligible areas (the areas in which both agencies can operate), we took  other steps to make the RHS and FHA data comparable, such as by  limiting our analysis to 30-year, fixed-rate loans, and by excluding loans  for units in condominium and cooperative developments. We compared  the distributions and number of loans for key property characteristics  (such as the state and census region); borrower characteristics (such as  credit score and debt burden ratios); and loan characteristics (such as  loan amount and down-payment percentage) at the time the mortgages  were originated. To further differentiate the geographic comparisons of  the loans, we used Rural-Urban Commuting Area codes (developed by  USDA’s Economic Research Service and the U.S. Health Resources and  Services Administration) to assess the “rurality” of a property location (as  measured by factors such as commuting patterns and population  density).
To assess the extent to which borrowers could have met key criteria for  both RHS- and FHA-guaranteed home purchase mortgages and the  factors borrowers consider in choosing between the two programs, we  reviewed RHS and FHA documentation on their underwriting  requirements and qualifying benchmarks to understand similarities and  differences in program eligibility and qualification criteria. We analyzed  RHS and FHA data for loans guaranteed in 2010–2014 and applied  general agency requirements and benchmarks to estimate the number  and percentage of RHS and FHA borrowers who could have met key  criteria for each program. Our estimates represent the minimum number  of borrowers who could have met key criteria for the other program. We  also analyzed the costs for RHS- and FHA-guaranteed loans and  calculated illustrative up-front and monthly borrower costs for each  program under various home price and interest rate scenarios. To confirm  key observations from this analysis, 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 gain additional perspective on mortgage  lending in rural areas, we interviewed mortgage industry groups selected  to cover a range of stakeholders in the RHS and FHA guarantee  programs. These included the Independent Community Bankers of  America, National Association of Mortgage Brokers, National Association  of Realtors, and Mortgage Bankers Association.
To compare the performance of RHS- and FHA-guaranteed loans for  home purchases in RHS-eligible areas, we analyzed RHS and FHA loan- level data and determined the performance status (current, delinquent, in  the foreclosure process, prepaid, or terminated with a claim) of loans  guaranteed in 2010–2012 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 county-level  household income limits set by RHS (which we determined using  borrower 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 some FHA borrowers with higher incomes than RHS is  allowed to serve. We classified loans as troubled if they were 90 or more  days delinquent during the anniversary month, were in the foreclosure  process, or had terminated with a claim. We compared the performance  of RHS- and FHA-guaranteed loans after 2 and 3 years of performance  through the end of September 2014. In addition, we developed a  statistical model to examine the extent to which differences between RHS  and FHA troubled loan rates (the number of troubled loans divided by the  number of loans guaranteed) after 2 and 3 years stemmed from the  characteristics of their guaranteed portfolios (for example, loan and  borrower characteristics).
To assess the reliability of the RHS and FHA data, we tested the data for  missing values, outliers, and obvious errors and reviewed documentation  on the process that RHS and FHA used to collect and ensure the  reliability and integrity of their data. We also interviewed knowledgeable  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 for estimating the percentage of borrowers  who could have met key criteria for both programs.
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 I contains  additional information on our objectives, scope, and methodology.
subsections: section_title: Background
subsections: section_title: Federal Mortgage Guarantee Programs
paragraphs: 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.
section_title: Rural Areas
paragraphs: 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.
section_title: Loan Status
paragraphs: 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.
section_title: Overlap and Consolidation
paragraphs: 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.
section_title: Both Programs Had Similar Loan Characteristics, but RHS Borrowers Had Lower Incomes and Higher Loan-to-Value Ratios Than FHA Borrowers
paragraphs: 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.
subsections: section_title: Both RHS and FHA Served Large Numbers of Borrowers in RHS-Eligible Areas
paragraphs: 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).
section_title: Credit and Other Characteristics of RHS and FHA Borrowers Were Similar, but RHS Served More Borrowers with Lower Incomes Than FHA
paragraphs: 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.
subsections: section_title: Credit Characteristics
paragraphs: 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).
section_title: Most RHS- and FHA- Guaranteed Loans in RHS-Eligible Areas Were Less Than $150,000, but RHS’s Had Higher Loan- to-Value Ratios
paragraphs: 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.
section_title: Significant Percentages of RHS and FHA Borrowers Could Have Met Key Criteria for the Other Program
paragraphs: 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.
subsections: section_title: At Least 36 Percent of RHS Borrowers Potentially Could Have Met FHA’s Down-Payment and Other Key Criteria
paragraphs: 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.
section_title: At Least 22 Percent of FHA Borrowers in RHS- Eligible Areas Could Have Met Key RHS Criteria
paragraphs: 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).
section_title: Lender Views and Our Analysis Indicate that the Costs of RHS-Guaranteed Loans Make Them an Attractive Alternative to FHA-Guaranteed Loans
paragraphs: 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.
section_title: RHS-Guaranteed Loans Were More Likely to Be Troubled after 3 Years than FHA-Guaranteed Loans
paragraphs: 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.
subsections: section_title: After 3 Years of Performance, RHS Had a Higher Troubled Loan Rate Than FHA
paragraphs: 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.
section_title: Our Estimates Suggest That RHS Would Be Expected to Have a Higher Troubled Loan Rate Than FHA
paragraphs: 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.
section_title: Agency Comments and Our Evaluation
paragraphs: 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.
section_title: Appendix I: Objectives, Scope, and Methodology
paragraphs: 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.
subsections: section_title: Data Used in Our Analysis
paragraphs: 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).
section_title: Characteristics of RHS- and FHA-Guaranteed Loans
paragraphs: 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.
section_title: Estimates of RHS and FHA Borrowers Who Could Have Met Key Criteria for the Other Loan Guarantee Program
paragraphs: 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.
subsections: section_title: RHS Borrowers Who Could Have Met FHA Criteria
paragraphs: 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.
section_title: FHA Borrowers Who Could Have Met RHS Criteria
paragraphs: 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).
section_title: Results by Rural-Urban Commuting Areas and Loan Cohort
paragraphs: 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).
section_title: Factors Considered by RHS and FHA Borrowers in Choosing between the Two Loan Guarantee Programs
paragraphs: 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.
section_title: Performance of RHS- and FHA-Guaranteed Loans
paragraphs: 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.
section_title: Appendix II: Characteristics of Home Purchase Loans RHS and FHA Guaranteed in RHS-Eligible Areas in Fiscal Years 2010– 2014
paragraphs: 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.
section_title: Appendix III: Characteristics of Refinance Loans RHS and FHA Guaranteed in RHS- Eligible Areas in Fiscal Years 2010–2014
paragraphs: 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).
section_title: Appendix IV: Characteristics of Home Purchase Loans FHA Guaranteed for Properties Not Located in RHS-Eligible Areas in Fiscal Years 2010–2014
paragraphs: 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.
section_title: Appendix V: Performance of Home Purchase Loans RHS and FHA Guaranteed in RHS- Eligible Areas in Fiscal Years 2010–2012
paragraphs: 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.
section_title: Appendix VI: Description of Our Statistical Model Comparing the Performance of Home Purchase Loans Guaranteed by RHS and FHA
paragraphs: 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.
subsections: section_title: Modeling Considerations
subsections: section_title: Explanatory Variables
paragraphs: 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.
section_title: RHS Program Considerations and Comparable FHA Loans
paragraphs: 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.
section_title: Selection of Anniversary Month and Performance Status
paragraphs: 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.
section_title: Functional Form
paragraphs: 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.
section_title: Performance Comparison Using Agency-Specific Model Results
paragraphs: 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.
section_title: Performance Comparison Using Pooled Agency Data
paragraphs: 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.
section_title: Alternative Model Specifications
paragraphs: 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.
section_title: 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
paragraphs: 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).
section_title: Appendix VIII: GAO Contact and Staff Acknowledgments
subsections: section_title: GAO Contact
paragraphs: Daniel Garcia-Diaz, (202) 512-8678 or garciadiazd@gao.gov.
section_title: Staff Acknowledgments
paragraphs: 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.