The Housing Act of 1949 authorized new rural lending programs to farmers, which were administered by RHS’s predecessor, the Farmers Home Administration, within the U.S. Department of Agriculture (USDA). RHS now facilitates homeownership, develops rental housing, and promotes community development through loan and grant programs in rural communities. Over the decades, Congress changed the requirements for rural housing eligibility—for example, by changing population limits— and rural housing programs have evolved to serve low- and moderate- income people of all occupations. The current definition of rural considers factors such as whether an area is contained in an MSA, is “rural in character,” and “has a serious lack of mortgage credit for lower- and moderate-income families.” RHS’s Section 521 Rental Assistance Program is the agency’s largest line-item appropriation, with an annual budget of more than $500 million. The program provides rental subsidies for approximately 250,000 tenants who pay no more than 30 percent of their income for rent (RHS pays the balance to the property owner). The units in which the tenants live are created through RHS’s Section 515 Multifamily Direct Rural Rental Housing Loans and Section 514 Multifamily Housing Farm Labor Loans programs. The Section 515 and 514 programs provide developers loans subsidized with interest rates as low as 1 percent to help build affordable rental housing for rural residents and farm workers. RHS staff determine which areas are eligible for RHS housing programs by interpreting statutory requirements and agency guidance; however, their determinations involve judgment and may be open to question. Additionally, some eligibility requirements often result in areas with similar characteristics receiving different designations. For example, the requirement that an eligible area cannot be part of an MSA often results in ineligibility for what appears to be a rural area. Also, the “lack of credit” in rural areas remains an eligibility requirement, even though USDA has reported that a lack of income and ability to pay the mortgage appear to be the greater problems than a lack of credit for rural Americans. Section 520 of the Housing Act of 1949, as amended, defines rural for most RHS housing programs. Using the statute and instructions promulgated by the national office, state and local (together, field) offices determine the boundaries to delineate eligible areas from ineligible areas—a task field office officials acknowledged is time-consuming, based on judgment, and can be problematic. The statutory definition generally identifies eligible rural areas as those with populations up to 20,000 and defines “rural” and “rural areas” as any open country or any place, town, village, or city that is not part of or associated with an urban area. Specifically, there are several population levels at which communities may be determined eligible, but as a community’s population increases, the statute imposes additional requirements that include being “rural in character” (a concept that is not defined in the statute), having a serious lack of mortgage credit, or not being located within an MSA. Certain communities with populations above 10,000 but not exceeding 25,000 may be “grandfathered in,” based on prior eligibility if they still met the “rural in character” and “lack of credit” criteria. USDA’s instructions give its field offices flexibility in implementing the statute. Field office officials said that drawing the eligibility boundaries required an element of judgment because “rural in character” is open to interpretation—even with the overall national guidance on the statute and review of census populations, MSA standards, maps, aerial photographs, and visits to communities. Even when local supervisors fully understand the local conditions and rural character of an area, finding a way to equitably decide on a boundary is sometimes problematic. For instance, field staff in Maryland told us that in response to December 2002 national guidance, they stopped using natural features such as rivers or mountains as eligibility boundaries for communities. Maryland now uses only roads. Figure 1 shows a new boundary, a road that divides the eligible area on the left from the ineligible area on the right. RHS local office officials told us that the “road only” criteria forced them to find the nearest public road to a populated section of Hagerstown, which happens to go through farmland. The result is that apparently similar rural areas received different designations. Figure 2 shows an area in Brookside, Ohio, where the city line divides the eligible from the ineligible area. The Maryland example illustrates that using the only physical boundary available resulted in one piece of farmland receiving a rural designation and the other not. The Brookside example shows that using a political boundary also did not necessarily result in a readily discernible urban-rural difference. Our analysis of RHS eligible areas nationwide, compared with census data, found approximately 1,300 examples where communities with populations at or below 10,000 were within or contiguous with urban areas that had populations of 50,000 or more. The statute states that eligible communities cannot be a part of or associated with an urban area. Some field staff determinations of eligibility in these cases might be questionable as some of these communities, despite their low populations, might not be considered rural, and thus, eligible. For example, field staff told us that Belpre, Ohio, is eligible for RHS programs because it meets both the population and “rural in character” requirements. However, Belpre is contiguous with Parkersburg, West Virginia, which has a population of more than 33,000 (see fig. 3). In addition, the 2000 census considers Belpre, along with Parkersburg and Vienna, West Virginia, as part of an urbanized area because its total population exceeds 50,000. Although it is across the Ohio River from Parkersburg, bridges have connected Belpre and Parkersburg for decades and, according to a Belpre city employee, many people from Belpre work in Parkersburg. Furthermore, most of Belpre has a population density of 1,000 people or more per square mile, which the Census Bureau considers “densely settled” and a measure of urbanization. For these reasons, it is unclear whether Belpre meets the eligibility requirements. Changes to the way eligibility is defined might allow RHS to better designate “rural” areas and treat communities with similar characteristics more consistently. For instance, eliminating the MSA requirement and “grandfathering” might help RHS better serve its clients. To illustrate, we found rural communities with populations exceeding 10,000 that were directly impacted by the MSA and “grandfather” restrictions. Because MSAs are county-based and may contain both urban and rural areas, the MSA restriction and the grandfathering of certain communities resulted in some communities being eligible while others with similar demographic profiles were ineligible. We looked at two communities within the Bakersfield, California, MSA, which is basically rural outside the environs of Bakersfield (see fig. 4). Lamont was grandfathered because it lost eligibility when its population went above 10,000 at the 1980 census. Taft’s population was already over 10,000 prior to the 1980 census, so Taft was not eligible for grandfathering. The right side of the figure shows what would happen if MSAs and grandfathered eligibility were removed from the equation and a density- based system such as the Census Bureau’s urbanized areas/urban clusters were used to indicate changes in population. Taft would be in its own urban cluster outside of the Bakersfield urbanized area, which happens to include Lamont. Based on our visit, we believe this scenario, where the more rural community would be the one eligible, is more in line with the overall purpose of the legislation than the current situation. In another example, by eliminating the MSA criterion, RHS could review the eligibility of Washington Court House and Circleville, Ohio, based on population and rural character criteria. Additionally, using density-based mapping could help RHS draw boundaries around these communities, which although Census-designated as “urban clusters,” still meet rural housing program population requirements (see fig. 5). The statute imposes a requirement to demonstrate a serious lack of mortgage credit for lower- and moderate-income families in communities with populations of 10,001 to 25,000. RHS has a policy stating that a serious lack of mortgage credit at rates and terms comparable with those offered by the agency exists in all rural areas. However, a study by USDA’s Economic Research Service concluded that credit problems in rural areas are primarily limited to sparsely populated or remote rural areas; such communities generally do not fall into the population range specified above. Many of the RHS officials and industry experts with whom we spoke also saw the primary “credit” problem as lack of income rather than lack of credit. Additionally, eligibility requirements for RHS programs are based on income levels. The agency uses funding set asides, funding allocations, application reviews, and state-level strategic plans to determine areas and populations of greatest need. As a result, RHS program activity already is focused on income issues, and given RHS’s blanket policy, the “lack of credit” requirement is not central to determining participant eligibility. We reported that weaknesses in RHS’s budget estimation and oversight of rental assistance funds had resulted in largely overestimated budget levels and increased the risk that the agency was not efficiently or appropriately budgeting and allocating resources. Additionally, RHS lacked sufficient internal control to adequately monitor the disbursement of rental assistance funds. In March 2004, we reported that since 1990, RHS had consistently overestimated its budget needs for the rental assistance program. Concern had arisen about this issue because in early 2003 RHS reported hundreds of millions of dollars in unexpended balances tied to its rental assistance contracts. Specifically, in estimating needs for its rental assistance contracts, RHS used higher inflation factors than recommended, did not apply the inflation rates correctly to each year of the contract, and based estimates of future spending on recent high usage rather than average rates. First, the agency used inflation factors that were higher than those recommended by OMB for use in the budget process. Second, RHS did not apply its inflation rate separately to each year of a 5-year contract, but instead compounded the rate to reflect the price level in the fifth year and applied that rate to each contract year. The result was an inflation rate that was more than five times the rate for the first year. For example, using these two methods, RHS overestimated its 2003 budget needs by $51 million or 6.5 percent. Third, RHS based its estimates of future expenditure rates on recent maximum expenditures, rather than on the average rates at which rental assistance funds were being expended. Additionally, our analysis of rental assistance payment data showed that the agency had overestimated its budget needs almost every year since 1990, the earliest year for which we gathered data. Where we were able to obtain sufficient data from RHS, our analysis showed that if RHS had used and correctly applied OMB inflation rates to its base per-unit rates, its estimates would have been closer to actual expenditures (see fig. 6). We also reported that RHS was not adhering to internal control standards regarding segregation of duties, rental assistance transfers, and tenant income verification reviews. A single employee within the agency was largely responsible for both the budget estimation and allocation processes for the rental assistance program. According to GAO internal control standards, key duties and responsibilities need to be divided or segregated among different people to reduce the risk of error or fraud. Moreover, RHS did not have a comprehensive policy for transferring rental assistance. As a result, insufficient guidance on the transfer process limited RHS’s ability to move unused rental assistance to properties that had tenants with the greatest need. Finally, because RHS conducts reviews infrequently and covers a small percentage of tenant files, the agency cannot reasonably ensure that tenants’ income and assets, and ultimately rental assistance payments, are adequately verified. RHS’s national, state, and local offices share responsibility for monitoring the rental assistance program, with the local offices performing the primary supervisory review every 3 years. These triennial supervisiory reviews are RHS’s primary tool for detecting misreporting of tenant income, which may result in unauthorized rental assistance payments. But the shortcomings in the review process increase the risk that RHS will provide rental assistance to tenants that may not be eligible. Alternate methods of verifying tenant information, such as internal database checks and wage matching, also have limited effectiveness but could help improve internal control if properly designed or implemented. Today we are releasing a report addressed to the RHS Administrator on internal control issues in the Information Resource Management (IRM) databases. We issued the report as a follow-up to our work addressing the definition of rural used for rural housing programs. During the earlier review, we identified several issues that raised concerns about the accuracy of the information in the IRM databases. For example, while we originally intended to geocode (match) 5 years of the national RHS housing loan and grant portfolio to specific communities, the time needed to ensure the reliability of the data required us to limit much of our analysis to five states. In reviewing 29,000 records for five states we found incorrect, incomplete, and inconsistent entries. For example, over 8 percent of the community names or zip codes were incorrect. Additionally, inconsistent spellings of community names distorted the number of unique communities in the database. More than 400 entries lacked sufficient information (street addresses, community names, and zip codes) needed to identify the community to which the loan or grant had been made. As a result, some communities served by RHS were double counted, others could not be counted, and the ability to analyze the characteristics of communities served was compromised. Since these data form the basis of information used to inform Congress (and the public) about the effectiveness of RHS programs, data accuracy is central to RHS program management and the ability of Congress and other oversight bodies to evaluate the agency and its programs. While the agency has worked to improve its management information systems (for example, since 2002, the agency has spent $10.3 million to improve its management information systems including developing single and multifamily program data warehouses which were designed to improve its reporting capabilities), the system still relies upon information collected and entered from field offices. However, RHS does not have procedures for second-party review of the data in IRM systems. Moreover, while the IRM databases have edit functions in place that are intended to prevent the entry of nonconforming data (such as the entry of a community name in a street address field), the functions are not preventing incorrect or incomplete entries. Until RHS can demonstrate that its edit functions or other data entry design features can ensure the accuracy and completeness of the data in the IRM databases, second-party review would be necessary. Our 2002 report to this subcommittee on RHS’s Section 515 multifamily program concluded that with little new construction and limited prepayment at that time, maintaining the long-term quality of the aging housing stock in the program portfolio had become the overriding issue for the program. We found that RHS did not have a process to determine and quantify the portfolio’s long-term rehabilitation needs. As a result, RHS could not ensure that it was spending its limited funds as cost- effectively as possible, providing Congress with a reliable or well- supported estimate of what was needed to ensure the physical and fiscal “health” of the multifamily portfolio, and prioritizing those needs relative to the individual housing markets. We recommended that USDA undertake a comprehensive assessment of long-term capital and rehabilitation needs for the Section 515 portfolio. We also recommended that USDA use the results of the assessment to set priorities for immediate rehabilitation needs and develop an estimate for Congress on the amounts and types of funding needed to deal with long-term needs. In response to our recommendation, RHS commissioned a consulting firm to assess the condition and rehabilitation needs of its multifamily portfolio. RHS released the study in November 2004. The principal findings—that the housing stock represented in the portfolio is aging rapidly and that property reserves and cash flows are not sufficient for basic maintenance or long-term rehabilitation needs—are in line with our findings in 2002. The study concludes that continuing the status quo would put undue stress on the rental assistance budget and proposes leveraged solutions that combine market-based solutions with private-sector funding as a more cost-effective alternative to using only federal dollars. In addition, the study concludes that while its proposed solutions will cost more than current budget levels, delaying actions to address the portfolio’s physical, fiscal, and market issues will result in even greater budget needs in the future. RHS has made progress in improving program management over the past few years. For example, when we began our work on the multifamily loan program in June 2001, agency officials could not provide us with the number of properties in the portfolio or a list of where properties were located. Today, with the exception of some database errors we pointed out that RHS officials have committed to correct, RHS knows where its multifamily properties are located and has developed a revitalization strategy to deal with the physical, fiscal, and market issues identified. However, the agency still faces challenges in areas that include the basic question of how best to determine what areas are rural, how to best manage rental assistance (the largest budget item in RHS), and how to ensure that data entered into management information systems are accurate. Despite these challenges, opportunities exist to provide more flexibility and improve existing processes that could better help RHS serve its clients while responding to the challenges of current fiscal and budget realities. For example, while determining what areas are eligible for rural housing programs will always require an element of judgment, several changes to the current eligibility requirements could help RHS make more consistent eligibility determinations. If MSAs were removed from the eligibility criteria, RHS officials could make determinations for more communities based on population data and “rural character.” And, using an alternative measure such as the Census Bureau’s urbanized areas and urban cluster classifications as a guide could help RHS better draw boundaries around rural areas, because the density-based measures provide finer-scale information. Additionally, eligible communities within MSAs would not need to be “grandfathered” based on previous eligibility, a provision which essentially gives these communities an advantage over similar though ineligible towns located in MSAs. Finally, the “lack of credit” requirement could be removed with no detriment to RHS housing programs. We noted further opportunities for improvement in RHS’s largest program—the rental assistance program, which has an annual budget of over $500 million and provides rental subsidies to about 250,000 rural tenants. Problems with its budget estimating processes caused the agency to consistently overstate its spending needs, resulting in hundreds of millions of dollars in unexpended balances. Consistently overstating funding needs for one program also undermines the congressional budget process by making funds unavailable for other programs. In addition, RHS’s internal controls had not provided reasonable assurance that rental assistance resources were being used effectively. We questioned whether internal control weaknesses were preventing rental assistance funds from going to properties with the neediest tenants. RHS has recently moved on a number of fronts to correct the many rental assistance program shortcomings identified in our reports. For example, RHS has told us that it will follow OMB budget estimation guidance, that it is correcting the program’s segregation of duty issues, has issued standardized guidelines on rental assistance transfers, and is revamping its supervisory review process. While it is too early for us to fully review the impact of these changes, we believe that changes in how rental assistance budgets are estimated and the application or strengthening of internal controls, consistent with our recommendations, would result in greater efficiency and resource savings in this pivotal program. Finally, in reviewing RHS property data for selected states, we identified various errors that raise questions about the accuracy of agency’s data. Although the agency is making efforts to improve its data systems, our findings suggest additional measures could ensure more accurate data entry and reporting, particularly at the field level. In addition to improving the accuracy of the information, such an effort could ensure that RHS’s investment in system upgrades would provide more meaningful and useful information to the agency itself, Congress, and the public. To improve eligibility determinations in rural housing programs, we suggested that Congress may wish to consider eliminating the MSA criterion, recommending that RHS use density measures as a basis for its eligibility decisions, phasing out the practice of “grandfathering” communities, and eliminating the “lack of credit” requirement. To help the agency verify tenant information, we also suggested that the Congress consider giving RHS access to the Department of Health and Human Services’ National Directory of New Hires (New Hires), which includes centralized sources of state wage, unemployment insurance, and new hires data for all 50 states, and it would provide nationwide data for wage matching. Congress already granted HUD the authority to request and obtain data from New Hires in January 2004, and as part of its initiative to reduce improper rent subsidies for its rental assistance program, HUD is making New Hires information available to public housing authorities who are responsible for, among other things, verifying tenant income and calculating rent subsidies correctly. HUD plans to make the data from the new hires database available to property owners by fiscal year 2006. To more accurately estimate rental assistance budget needs, we recommended that the Secretary of Agriculture require program officials to use and correctly apply the inflation rates provided by OMB in its annual budget estimation processes. To ensure that rental assistance funds are effectively distributed to properties that have tenants with the greatest need, we recommended that the Secretary of Agriculture require program officials to establish centralized guidance on transferring unused rental assistance, improve sampling methods to ensure a sufficient number of tenant households are selected for supervisory reviews, and improve tenant verification of information, including more effective use of alternate methods of income verification. To improve data entry and accuracy, we recommend that RHS formally advise field staff to establish a second-party review of data in the IRM databases are accurate and complete, require correction of errors in existing information, and ensure that system edit functions are properly functioning. USDA generally agreed with our matters for congressional consideration, stating that our report on eligibility articulates how the use of MSAs has resulted in disparate treatment of some communities. USDA added that applying a density-based measure might have merit but that further study would be needed to properly define such a measure for nationwide application. We concur with this position. In addition, USDA stated that the “lack of credit” requirement could be removed with no detriment to RHS housing programs. USDA initially disagreed with our finding that its rental assistance budget estimates were too high, questioning whether we demonstrated that using inflation rate projections from the President’s Budget would provide a more accurate budget estimate. However, USDA has now reported that it will adopt OMB estimates, and it appears that RHS now agrees with our report findings. USDA also generally agreed with most of our recommendations on monitoring and internal controls. RHS has recently issued regulations and an asset management handbook on transferring unused rental assistance and expanded guidance on income verification. Also, it appears that RHS is acting on our recommendation to improve sampling methods to ensure a sufficient number of tenant households are selected for supervisory reviews; that is, the agency has informed us that it is revamping that process. Finally, the RHS Administrator has generally agreed to implement our recommendations on the IRM databases. Mr. Chairman, this concludes my statement. I would be pleased to respond to any questions you or members of the Subcommittee may have. For more information regarding this testimony, please contact William B. Shear at (202) 512-4325 or shearw@gao.gov or Andy Finkel at (202) 512- 6765 or finkela@gao.gov. Individuals making key contributions to this testimony also included Martha Chow, Katherine Trimble, and Barbara Roesmann. 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Summary: The rural America of 2005 is far different from the rural America of the 1930s, when the federal government first began to provide housing assistance to rural residents. Advances in transportation, computer technology, and telecommunications, along with the spread of suburbia, have linked many rural areas to urban areas. These changes, along with new fiscal and budget realities, raise questions about how Rural Housing Service (RHS) programs could most effectively and efficiently serve rural America. This testimony is based on a report on how RHS determines which areas are eligible for rural housing programs, three reports on RHS's rental assistance budgeting and distribution processes, and a report we are releasing today on internal control issues with RHS's loans and grants databases. GAO found that while RHS has significantly improved the housing stock in rural America and has made progress in addressing problems, several issues prevent the agency from making the best use of resources. Specifically: (1) Statutory requirements for program eligibility, including those related to metropolitan statistical areas (MSA), "grandfathering" communities, and demonstrating a "serious lack of mortgage credit," are of marginal utility. For example, using density measures rather than MSAs might allow RHS to better differentiate urban and rural areas, and phasing out the "grandfathering" of communities could better ensure that RHS makes more consistent eligibility determinations; (2) RHS has consistently overestimated its rental assistance budget needs by using higher inflation rates than recommended by the Office of Management and Budget and incorrectly applying those rates. Also RHS lacked sufficient internal controls to adequately monitor the use of rental assistance funds, particularly for fund transfers and income verifications. RHS has been taking actions that should correct many of the rental assistance shortcomings GAO identified; and (3) GAO found incorrect, incomplete, and inconsistent entries in RHS's loans and grants databases. Until RHS can demonstrate that its system edit functions or other design features can ensure the accuracy of data in its databases, second-party review is necessary to meet internal control standards.