HUD’s CDBG, HOME, and HOPE VI programs and Treasury’s CDFI, Low- Income Housing Tax Credit, and New Markets Tax Credit programs are among a number of federal programs that fund housing and community and economic development. In varying degrees, these programs leverage other funds to help finance their initiatives and projects. As we reported in a May 2007 report, some of these programs define leveraging as using one source of funds to attract additional sources of funds, while others define leveraging more broadly as the layering or combining of different sources of funds. Further, and as described below, some of these programs leverage at the institutional and project levels, while some leverage only at the project level. At the institutional level, an organization (such as a group of investors or a community or other development authority) pools funds from multiple sources, which are then used to finance a portfolio of projects. At the project level, an organization (such as a state or local agency) leverages funds as necessary to finance discrete projects. We highlight below the purpose, structure, and activities of the three HUD programs and three Treasury programs that we reviewed. Appendix II describes in more detail how leveraging occurs in the selected Treasury programs. The CDBG program is the federal government’s principal community development program. It provides annual grants on a formula basis to entitlement communities—principal cities of metropolitan statistical areas, other metropolitan cities with populations of at least 50,000, and qualified urban counties—and states to develop viable urban communities by providing decent housing and a suitable living environment, and by expanding economic opportunities, principally for low- and moderate- income persons. Under the CDBG program, communities and states develop their own programs and funding priorities. However, all funded activities must meet one of three national objectives: primarily benefit low- and moderate-income persons, aid in the prevention or elimination of slums and blight, or meet community development needs of particular urgency (because existing conditions pose a serious and immediate threat to the health or welfare of the community and other financial resources are not available to meet such needs). Although the CDBG program has no statutory or regulatory leveraging requirement, some projects funded under the program use CDBG funds to leverage additional funds to finance development costs. In fiscal year 2007, Congress appropriated approximately $3.7 billion to the CDBG program for formula distribution, and HUD allocated these funds to 1,133 entitlement communities, 49 states, and Puerto Rico. HOME provides formula grants to states and localities—certain cities, counties, or consortiums of cities and counties—to fund a wide range of activities to benefit low-income people. Under the HOME program, states and localities may use program funds to finance a broad range of activities, such as providing eligible homeowners and new homebuyers with home purchase or rehabilitation financing assistance and building or rehabilitating housing for rent or ownership. States and localities also may use HOME funds to provide tenant-based rental assistance. The program requires states and localities to match 25 percent of expended program funds with monetary or certain in-kind contributions, such as donated materials or voluntary labor. This match requirement was designed to elicit local resources in support of affordable housing. Like the CDBG program, the HOME program has no statutory or regulatory leveraging requirement; however, some projects funded under the program use HOME funds to leverage additional funds to finance development costs. In fiscal year 2007, Congress appropriated approximately $1.8 billion to the HOME program, and HUD allocated these funds to 589 localities, the 50 states, and Puerto Rico. HOPE VI is part of HUD’s effort to transform public housing. By providing funds for a combination of capital improvements and community and supportive services, the HOPE VI revitalization grant program seeks to (1) improve the living environment for residents of severely distressed public housing through the demolition, rehabilitation, reconfiguration, or replacement of obsolete units; (2) revitalize sites on which such severely distressed public housing is located, and contribute to the improvement of the surrounding neighborhood; (3) provide housing that will avoid or decrease the concentration of very-low income families; and (4) build sustainable communities. Any public housing agency (PHA) that has severely distressed public housing units in its inventory is eligible to apply for a HOPE VI revitalization grant. Recipients of revitalization grants must match 5 percent of the grant with other funds, and HUD awards PHAs that demonstrate an ability to leverage additional points in the HOPE VI application process. In fiscal year 2006, HUD made four HOPE VI revitalization grants totaling approximately $72 million. Through the CDFI program, Treasury’s CDFI Fund provides CDFIs with financial assistance in the form of grants, loans, equity investments, and deposits to enhance their ability to make loans and investments and provide services for the benefit of designated investment areas, targeted populations, or both. CDFIs must match (leverage) their financial assistance awards dollar-for-dollar with funds of the same type (equity investment, loan, deposit, or grant) from nonfederal sources. CDFI funds can be used for economic development (job creation, business development, and commercial real estate development), affordable housing (housing development and homeownership), and community development financial services (provision of basic banking services to underserved communities and financial literacy training). In 2007, Treasury made approximately $26 million in financial assistance awards to 49 CDFIs. Under the Low-Income Housing Tax Credit program, states are authorized to allocate federal tax credits to private investors as an incentive to develop rental housing for low-income households. The equity generated by the sale of the credits is used to lower the financing costs of housing developments by reducing the debt or equity the developer otherwise would incur or contribute. Investors who purchase the tax credits may claim the credits annually for 10 years. To receive Low-Income Housing Tax Credit financing, properties must meet certain rent and tenant income requirements: (1) at least 20 percent of the units in the property must be reserved for individuals or families with incomes of 50 percent or less of the area median income, or at least 40 percent of the units must be reserved for individuals or families with incomes of 60 percent or less of the area median income; and (2) rents for affordable units are restricted to 30 percent of the applicable income limit (that is, 50 percent or 60 percent of the area median income). Each state receives an allocation of the greater of $1.75 per capita or $2 million annually, adjusted by a cost of living factor ($1.95 or $2.275 million in 2007). The program costs the federal government an estimated $5 billion annually in forgone tax revenue and is the government’s largest housing production program. The New Markets Tax Credit program permits taxpayers to receive a credit against federal income taxes for making qualified equity investments in designated Community Development Entities (CDE), which must in turn make investments in low-income communities. Qualified low-income community investments include (1) any capital or equity investment in, or loan to, any qualified, active, low-income community business; (2) the purchase from another CDE of any loan made by such entity that is a qualified low-income community investment; (3) financial counseling and other services to businesses located in, and residents of, low-income communities; and (4) certain equity investments in, or loans to, a CDE. The credit provided to the investor totals 39 percent of the cost of the investment and is claimed over a 7-year period. In addition, Treasury scores those applications in which CDEs demonstrate an ability to leverage additional funds more favorably. In fiscal year 2007, Treasury awarded $3.9 billion in New Markets Tax Credits (totaling approximately $1.5 billion in forgone federal tax revenue) to 61 CDEs. The leverage measures (such as ratios) HUD and Treasury reported for the selected programs in performance, budget, and other documents lacked transparency because the agencies generally did not disclose the limitations of the data or the methods used to calculate them. Based on our review of available leveraging data and interviews with HUD and Treasury officials, we found that the leverage measures the agencies reported for the selected programs were based on incomplete data and thus did not capture the actual extent of leveraging in the programs. We also found that while the agencies generally reported measures that described the ratio of all other funds (federal, state, local, and private funds) to program funds, alternative measures that described the total federal investment or total private investment in a program provided considerably different results—also potentially of value to decision makers—about the extent of leveraging in a program. Further, no agency- specific or governmentwide guidance directs what agencies should disclose about the leverage measures they report for the selected programs; however, we regularly have reported that clearly communicating data limitations and their potential impact may foster appropriate use of data. Consequently, absent specific information on how these measures were calculated and their limitations, decision makers would not have sufficient information to understand their meaning and determine how they could and should be used in performance assessment, budgeting, and other contexts. Based on our review of available leveraging data and interviews with HUD and Treasury officials, we found that the leverage measures the agencies reported for the selected programs were based on incomplete data and did not capture the actual extent of leveraging that may have occurred in each of the programs. We also found that HUD and Treasury did not always disclose these limitations when they published the measures. Table 1 describes the limitations associated with the underlying data used for determining leverage measures for each of the selected programs. In our assessment of HUD’s and Treasury’s use of leverage measures in strategic planning, annual performance and budget documents, on their Web sites, and in other published reports, we found that the agencies did not routinely disclose the limitations to the leveraging data (outlined in table 1) they used to compute leverage measures for the selected programs. For example, the only place in which Treasury included discussions of known limitations to the data used to calculate a leverage measure for the CDFI program was in periodic agency reports on the extent of leveraging in the program. Treasury’s Web site and key performance and budgeting documents provide little to no information on data limitations associated with the CDFI program leverage measure. Similarly, while HUD cited leverage measures on its Web site and in budget documents for the HOME and HOPE VI programs, respectively, the agency did not disclose the limitations of the data used to compute the reported leverage measures for the programs. Further, no agency-specific or governmentwide guidance directs what agencies should disclose about the leverage measures they report for the selected programs; however, we regularly have reported on the need for agencies to collect and report on credible and reliable data for performance budgeting and other purposes. For example, cautioning decision makers and others about significant data limitations allows them to judge the credibility of the data and use them in appropriate ways. We also noted that all data have limitations that may hinder their use for certain purposes, and decision makers and others may not have enough familiarity with the data to recognize the significance of the shortcomings. Therefore, we concluded that appropriate use of data may be fostered by clearly communicating how and to what extent data limitations affect assessments of performance. OMB also has stressed the importance of making clear to policymakers and others what individual performance indicators measure. According to OMB, doing so helps decision makers understand what should be expected of an overall program. To the extent that HUD and Treasury were not clear about the limitations of the measures they calculated for the selected programs, they potentially misrepresented (either positively or negatively) the extent of leveraging that occurred in these programs. If decision makers are unaware of the limitations of the agencies’ reported leverage measures and take them at face value, they could misuse them in making funding decisions or performance evaluations on the programs (which also may have implications on the budget process). In our assessment of HUD’s and Treasury’s use of leverage measures in strategic planning, annual performance and budget documents, on their Web sites, and in other published reports, we also found that the agencies did not routinely disclose information on the methods they used to calculate leverage measures for the selected programs. For instance, in its fiscal year 2008 budget justification, HUD reported that the HOPE VI program leveraged $634 million over a 6-month period in 2007, without further explanation of how the measure was derived. Similarly, Treasury’s Web site noted that on average CDFIs leveraged program funds 20 to 1, but did not explain what types of funds (public or private) were leveraged. Based on our discussions with agency officials, we found that the leverage measures HUD and Treasury calculated for each of the selected programs generally described the ratio of all other funds contributed to a program (including other federal, state, local, and private funds) to program funds. However, these measures can be calculated in multiple ways that describe leveraging from different perspectives, such as the extent that federal funds are used with nonfederal funds or public funds are used with private funds, which underscore the importance of disclosing the calculation methods used. As illustrated in figure 1, leverage measures for a single program could vary considerably depending on how funding categories were combined (that is, program funds, other federal funds, state and local funds, and private funds). Scenario A in figure 1 generally represents how the agencies presented leverage measures for the selected programs. The alternate leverage measures presented in scenarios B and C provide additional information that could be more useful to policymakers and investors than measures that describe the ratio of all other funds to program funds. For example, to help inform decisions made as part of the annual appropriations process, policymakers may be interested in determining the extent of total federal contributions made to projects funded under a particular program (scenario B). Alternatively, to assess the potential risk of investing in a federally sponsored development project in a low-income community, a private investor might be interested in knowing the proportion of private investment to public investment in the program (scenario C). Some private investors might perceive a relatively low ratio as an indication that the program carried a high level of investment risk and thus a higher potential for losses. Further, more detailed information on all the different sources of funding could be useful in describing the extent to which one federal program is leveraging funds from another federal program (that is, the extent to which federal programs cross-subsidize one another) and could be particularly relevant to policymakers during annual budget deliberations. In addition, for the CDFI and New Markets Tax Credit programs (which leverage at both the institutional and the project levels), disclosing information on institutional and project-level leveraging could be more useful to policymakers and investors than a total program leverage measure. For example, providing such information would assist policymakers and investors in understanding the extent to which institutional leveraging could be used to manage project-level investment risks—a program with a high institutional leverage ratio but a low project leverage ratio might be one which invests in riskier projects than a program with a low institutional leverage ratio but a high project leverage ratio. As we discussed in our previous report on leveraging federal funds, investments at the institutional level generally are isolated from the investment risks associated with discrete projects. In appendix II we present multiple calculation scenarios for each of the selected programs. Consistent with our hypothetical demonstrations in figure 1, our calculations show considerably different results between the leverage measures the agencies reported (that is, the ratio of all other funds to program funds) and measures that describe either (1) the ratios of nonfederal funds to federal funds and private funds to public funds or (2) institutional and project leverage ratios. As a result of not having more specific information about how these measures were calculated, decision makers would not have sufficient information to understand their meaning and how they can and should be used in performance assessment, budgeting, and other contexts. Further, as previously discussed, there is no agency-specific or governmentwide guidance on what agencies should report about how (or the extent to which) leveraging occurs in their programs. However, in other contexts, our prior work and that of OMB has stressed the value in agencies’ disclosing this type of information to ensure decision makers not only are aware of what is being reported about a program, but how that information can and should be used to inform their budget, performance, and other decisions. Leverage measures can provide basic financial information about the programs included in our review; however, their relevance in assessing the performance of these programs varies considerably. For all of the programs we reviewed, leverage measures can describe inputs, or the resources used to support program activities, and may be useful for conveying basic financial information. To the extent that leveraging is a goal or core (expected) activity of a program (as in the three Treasury programs), leverage measures generally can describe program outputs, or the products or services delivered (such as total leveraged funds), and may be used along with other performance indicators to assess the efficiency and effectiveness of a program in meeting its goals. In cases where leveraging is not clearly and appropriately linked to program goals and activities (as in the three HUD programs), use of such measures to describe program outputs could be misleading and result in adverse consequences, such as giving funding priority to projects that leverage more over those that leverage less, but which may fill a greater or more immediate need within a community. Although leveraging had limited relevance to the goals and activities of the selected HUD programs, we found that OMB and the agency often cited leverage measures for the programs in performance- and budget-related reviews and documents. Their continued use of leverage measures in these contexts could unnecessarily encourage HUD to place more importance on leveraging than meeting the stated goals of the CDBG, HOME, and HOPE VI programs. Leverage measures generally can be used to describe the sources and amounts of funds contributed to a program, and if linked to a program’s goals and core activities, they also can provide more detailed information about the program’s performance. On a basic level and for all of the programs we reviewed, leverage measures convey information on inputs— that is, the specific sources of funds used to implement program activities. For example, leverage measures can provide information on the relative contributions made by different types of investors (private and public) to a program or project and the overall resources committed—this information could be used to inform agency budgeting exercises or financial analyses. To the extent that leveraging is a goal or core (expected) activity of a program, leverage measures generally can describe program outputs (in addition to program inputs) and be used with other performance indicators to measure the efficiency or effectiveness of a program in reaching its goals (see fig. 2). Previously we have reported that for performance measures to be useful in assessing program performance, they should be linked or aligned with program goals and cover the activities that an entity is expected to perform to support the intent of the program. Generally, leveraging would not be an outcome measure for any of the selected programs—outcomes describe program benefits or consequences (such as the impact of leveraging on community development), whereas outputs generally measure quantities produced (total dollars leveraged). Leverage measures can be used to assess the performance of programs that were designed to leverage (that is, in which leveraging is directly related to the goals and core activities of the program), but are less meaningful in assessing the performance programs that do not have explicit leverage requirements. Each of the three Treasury programs was designed to leverage other funds in a number of ways and, as a result, leveraging directly relates to each program’s goals and core activities and leverage measures can be used to describe program outputs. Under the CDFI program, CDFIs must match federal program funds at least dollar- for-dollar with nonfederal funds as a condition of receiving program funds. The match requirement is intended to increase the sustainability of CDFIs (by increasing private-sector investment in them) as well as their ability to make investments serving low-income individuals and communities. Although not required to do so, CDFIs use program and match funds to leverage debt and further increase their lending resources. Funding recipients (for example, small businesses) also may use their grants or loans from CDFIs to leverage additional funds to help finance their projects. In this way, leveraging at the project level also relates closely to the CDFI program’s goal of increasing investment in low-income individuals and communities. Similarly, the tax credit programs were designed to automatically generate private-sector equity investments in the production of affordable housing (in the case of the Low-Income Housing Tax Credit program) and community and economic development (in the case of the New Markets Tax Credit program). Further, the application processes for both programs were designed to encourage additional leveraging. Under the Low-Income Housing Tax Credit program, in order to limit the federal share of housing development project costs, states are to provide no more tax credits to projects than necessary for their financial viability. Under the New Markets Tax Credit program, Treasury considers CDEs’ potential to leverage other sources of funds (in addition to the qualified low-income community investment they plan to make using the tax credit equity) for the projects they sponsor as a factor in scoring the tax credit allocation applications. In cases where leverage measures are not clearly and appropriately linked to program goals and core activities, use of such measures to describe program outputs could result in adverse consequences; for example, by encouraging agencies to place more importance on leveraging than on meeting their stated goals. This trade-off is directly apparent in the use of leverage measures as outputs for the CDBG and HOME programs. While leveraging may be a strategy some funding recipients employ (either by choice or out of necessity) to meet these programs’ goals, none of these programs originally was designed to leverage (meaning, leveraging generally is not a goal or core activity in these programs). Thus, using leveraging to assess the success or impact of these programs in meeting their goals may result in agencies and funding recipients serving fewer lower-income communities or households (as originally intended by these programs) and more moderate-income communities and households (those that are better able to attract additional funds because they pose relatively less risk to investors). HUD set a leveraging goal for the HOPE VI program in the agency’s most recent strategic plan and its fiscal year 2007 annual performance plan and fiscal year 2008 budget justification. According to HUD officials, while leveraging has long been a rating factor in the program’s application process, its relative importance in financing HOPE VI developments has increased over time as program appropriations have declined. While leveraging may help HUD meet the HOPE VI program goal to create mixed-income communities, its use may involve trade-offs, as it may conflict with another program goal—providing housing for extremely-low, very-low, and low-income households. For example, increased reliance on leveraged funds from other programs or sources that may have different requirements (such as higher income limits) potentially could affect the demographic composition of HOPE VI developments. Previously, we have reported several limitations to the usefulness of leverage measures in providing detailed information about federal programs and the projects they fund (regardless of whether or not those programs were designed to leverage). Although leveraging can be a useful tool and public- and private-sector officials regard it favorably, according to many of the officials we contacted, if considered independently of other information, leverage measures can provide misleading information about the success or impact of a program or project. For example, many said that factors such as the local economy or availability of investors within a certain geographic area could have a positive or negative impact on a project’s ability to leverage additional funds, and thus its leverage ratio. That is, projects in vibrant communities likely may have higher leverage ratios than those in distressed communities. As a result, leverage measures are not sufficient to make judgments about the relative success of projects or programs without other descriptive information. Leverage measures also do not account for the level of substitution of federal funds for otherwise available private funds that might occur in programs or projects. Although difficult to measure, information on substitution might be useful in assessing how effectively federal funds were utilized in a program or project. Officials we contacted noted that having information on the risk position of different contributions to a project might be useful in assessing the extent of substitution that occurred. For instance, the level of substitution in a project in which the federal government assumed more risk (by taking a subordinate position) than nonfederal investors could be lower than the level of substitution in a project in which the federal government assumed less risk (by taking a senior position). When OMB and the agencies cited leverage measures in performance- and budget-related reviews and documents, they did not always link leveraging to program goals and core activities—in some cases, OMB and the agencies used leveraging to assess the performance of the selected programs despite its limited relevance to program goals and core activities. According to OMB officials, the agency considers leveraging to be an output measure for each of the selected HUD programs. Consistent with this view, OMB used leveraging as an output measure in its PART reviews of these programs, although leveraging generally was not linked to the goals and core activities of the programs. For example, in its 2003 PART review of the CDBG program, OMB recommended that HUD implement a new performance measurement system that included information on the amount of money leveraged from other sources. The agency developed steps to address this recommendation in the program improvement plan it developed with OMB in 2006 (in response to the PART assessment’s finding that the program lacked specific annual performance measures that demonstrated progress on achieving long-term goals). We have noted that federal programs, in particular federal block grant programs, have faced difficulties but could benefit from defining program goals and performance measures that go beyond describing program activities to describe outcomes or results. However, because leveraging is not a required activity or explicit goal of the CDBG program (as discussed previously), its value in evaluating the performance of the program is limited. Further, in its PART review of the HOME program, OMB used leverage measures to compare the performance of the HOME program with that of the CDBG program. Such a comparison does not facilitate evaluations of these programs in the context of their intended goals (neither of which is to leverage). While using leveraging as an output measure for the CDFI and New Markets Tax Credit programs is consistent with the programs’ goals and core activities as discussed above, OMB identified leveraging as an outcome measure for the CDFI program in its 2004 PART review despite the fact that, as discussed previously, leveraging cannot be used to measure the impact of the program. Further, the agency equated leveraging with program effectiveness in its 2004 PART review of the New Markets Tax Credit program. (As described in fig. 2, outcome measures are used to assess the effectiveness of programs in achieving desired results. As we have discussed throughout this report, outcome measures should be designed to assess the benefits or consequences of a program— leverage measures by themselves cannot provide information on the impact these programs have had on their targeted populations and communities.) As we observed in our 2004 review of OMB’s PART process, the goals and measures OMB defines in its PART reviews are designed to meet the needs of executive decision makers during the budget formulation process, and thus may be inconsistent with the goals and measures federal agencies have developed in response to GPRA, which may be developed at a higher, strategic level and less relevant to OMB’s budget decision-making process. As a result of OMB’s focus on the budget process, we found that its judgment about appropriate goals and measures for a program may be substituted for agency judgments. These findings generally are consistent with our observations on OMB’s use of leverage measures in the PART reviews of the selected programs we reviewed for this report. We observed that the agencies identified leveraging as a performance measure in their performance- and budget-related reports for some of the selected programs despite its sometimes limited relevance to program goals and core activities. Table 2 describes HUD’s use of leverage measures for the HOME and HOPE VI programs in its strategic planning and other performance- and budget-related documents or contexts. In the case of the HOME program, although leveraging was not linked to the program’s goals and core activities, HUD equated more leveraging with better performance by ranking states and localities on their ability to leverage other sources of funds. For the HOPE VI program, HUD primarily used leveraging as a measure for its goal of providing decent, affordable housing through the improvement of the physical quality of public housing. However, HUD generally did not discuss how leveraging would help the agency in achieving this goal. HUD also linked leveraging to the HOPE VI goal of creating mixed-income housing. Although increased leveraging in a program designed to provide affordable housing could result in trade-offs, HUD’s performance- and budget-related documents did not discuss the impact of (or the potential unintended consequences of) leveraging on the ability of the program to meet this goal. Finally, as described in table 3, Treasury generally linked leveraging with the goals and core activities of the CDFI and New Markets Tax Credit programs. For example, Treasury noted that leveraging in the CDFI program helps build CDFIs’ capacity to make loans and other investments in low-income communities. Because Treasury to date has not reported publicly the extent of leveraging in the New Markets Tax Credit program, the agency’s performance- and budget-related documents only discuss the extent of institutional leverage in the program. As with the CDFI program, Treasury linked institutional leveraging to the program’s goal of attracting private-sector capital to low-income communities. With the increased focus of federal agencies on performance management, budgeting, and financial reporting, leveraging has come to be seen as an effective and efficient means of delivering more impact per dollar of federal investment, particularly in a period of increasingly tight budgets and competing funding priorities. While agencies have collected and presented leveraging information in strategic planning, performance, and budget reports, and on their Web sites, agencies disclose little or no information on methods of data collection or how leverage measures were calculated, in part because there is no agency-specific or governmentwide guidance on how to calculate, describe, and use leverage measures in a manner that is consistent with the programs’ design. Information on methodology is important in the leveraging context because of the limitations of leveraging measures and data collection issues. For example, in the case of the CDBG and HOME programs, leveraging may be underestimated because HUD’s database does not distinguish between zero responses (for example, where no leveraging occurred) and blank responses (for example, where leveraging data may be incomplete). Moreover, measures such as ratios may not disclose the details necessary to understand which component funding sources were being compared, and as demonstrated, the ratios can vary considerably depending on what information an agency is trying to convey about a program (for example, the extent of public or private investment in a program). Further, data collection and completeness are issues because not all the programs are required to report leveraging, and in many cases agencies are unable to capture data on all leveraging that may be occurring in a program (for example, project leveraging). Absent specific information on how leverage measures were calculated and their potential limitations, decision makers do not have sufficient information to understand their meaning and how they can and should be used in performance assessment, budgeting, and other contexts. Moreover, the relevance of leveraging to performance measurement is dependent on the context of the program being analyzed. Because leveraging is not an intended activity carried out to achieve program goals or a goal unto itself for some of the selected housing and community and economic development programs in our review, measures such as ratios are not indicative of program or project performance (outcomes and impact). Rather, such measures are indicative only of resource utilization. Nevertheless, even in cases where they were not reflective of program performance, agencies presented leverage measures in strategic plans, annual performance plans, performance and accountability reports, and budget justifications. The use of leverage measures in such contexts could lead decision makers to presuppose that the information was indicative of program impacts in cases where leveraging actually might say very little about the success of a program, such as the ability of a program to improve the living conditions of the urban poor. Despite the issues surrounding the utility of leverage measures, we note the valid and useful purposes for which the measures may be used, particularly in instances where leveraging is an intended activity or goal. For instance, decision makers and practitioners in the area of affordable housing and community and economic development may utilize leverage measures to report basic information on how federal funds were combined with other funds for a program or project. Such information could be instructive in ascertaining trends in the involvement of private- sector investors or local governments in federally sponsored initiatives, or identifying demographic trends that could adversely or positively affect the ability of program funds to attract other funds. Additionally, the measures may aid management and Congress in their oversight of programs and strategic planning for future budgets. Further, when directly linked to program goals and activities and considered with other performance measures, leverage measures also could provide insight into the success of a program, including its impact on targeted populations and communities. The valid and useful purposes to which leverage measures may be put underline the importance of transparency for federal agencies in communicating the limitations of such measures and how they are calculated. The agencies administering the housing and community and economic development programs we reviewed could improve the transparency of the leverage measures they use by including information about the completeness and accuracy of the data and methods used to compute the measures. Further, the agencies could discuss the relevance of leveraging to a program’s stated goals and activities. The publication of such information in conjunction with the measures themselves would increase the accuracy of the information being conveyed and provide perspectives that would allow various users to assess the potential of the measures to serve as relevant and accurate indicators of program or project outputs and, in some cases, outcomes or impact. However, the opportunities to better describe, assess, and report the role of leveraging in housing and community and economic development programs do not rest solely with the agencies administering those programs. OMB, because it plays a key role in assessing the performance of federal agencies and developing and tracking compliance with performance goals, has an opportunity to refine its understanding and use of leverage measures in future PART and other performance reviews by carefully considering the role of leveraging in carrying out program goals and activities. Specifically, in its performance assessments of the selected programs, OMB could provide information on how leveraging may support or conflict with a program’s intended purpose. This is particularly important because the accuracy of measures and the relationship of leveraging to program goals and thus performance can vary considerably across the housing and community and economic development programs we reviewed. To ensure that leverage measures provide accurate, useful, and relevant information to Congress and others, we recommend that the Secretaries of HUD and the Treasury consider disclosing the following when they publish such measures for the programs included in our review: Presentation of leverage measures should be accompanied by information about the completeness and accuracy of the data and the method(s) used to calculate the measures (for example, with leverage ratios, information on what sources of funds were compared, such as private funds to public funds or nonfederal funds to federal funds). Presentation of leverage measures should be accompanied by a discussion of the relevance of the measure in assessing the program’s performance. For example, the agencies should discuss the extent to which leverage measures are linked to program goals and core activities. We further recommend that the Director of OMB provide guidance to help agencies determine how to calculate, describe, and use leverage measures in a manner consistent with the programs’ design; and re-evaluate the use of leverage measures and disclose their relevance to program goals and activities in future PART or other performance reviews of the selected programs. We received written comments on a draft of this report from HUD and Treasury, which are included in appendixes V and VI, respectively. HUD’s Office of Public and Indian Housing also provided technical comments related to the HOPE VI program, which we incorporated as appropriate. We also provided a draft of this report to OMB for review, but no comments were provided. In a letter from the Acting Deputy Assistant Secretary for Grant Programs, HUD noted that it was pleased with the results presented in our draft report, but provided several detailed comments on and suggested changes to our findings related to the CDBG and HOME programs (see app. V). Specifically, HUD expressed concern that the draft report (1) did not sufficiently emphasize that the CDBG and HOME programs do not have statutory or regulatory leveraging requirements; (2) did not sufficiently emphasize that the agency currently does not publish a leverage measure for the CDBG program; (3) incorrectly stated that the agency did not disclose limitations to the data or methods used to calculate leverage measures for the HOME program, which are reported on HUD’s Web site; and (4) contends that leveraging affects the funding decisions HUD makes for CDBG and HOME (HUD noted that all funding decisions are made at the state or local level and are not approved by the agency). With respect to HUD’s first two concerns, we incorporated additional language into the report to further emphasize that the CDBG and HOME programs do not have leveraging requirements and that the agency does not publish a leverage measure for the CDBG program. In its letter, HUD agreed to work to improve the quality of leveraging data CDBG grantees report to the agency, which would address, in part, our recommendation that the agencies disclose information about the completeness and accuracy of the data and the method(s) used to calculate leverage measures. Concerning HUD’s comment that the draft report incorrectly stated that the agency did not disclose limitations to the data or methods used to calculate leverage measures for the HOME program, which are reported on HUD’s Web site, we acknowledge that HUD’s Web site included information on the method used to calculate leverage measures for the HOME program (that is, the ratio of other funds to program funds). However, HUD has not provided information on the limitations to the data used to calculate those measures. Specifically, the database HUD used to collect leveraging data for the program did not distinguish between nonresponses, which default to zero, and actual entries of zero; assuming that some grantees failed to enter funding information, the total amount of leveraging that occurred in the program (or in a specific state or locality) potentially would be underestimated. Accordingly, we did not change the report. Finally, with respect to HUD’s concern that the draft report contends that leveraging affects the funding decisions HUD makes for CDBG and HOME, our report did not state that HUD or grantees make funding decisions based on leveraging; rather, the report noted the potential consequences of using leveraging as a performance indicator for programs that were not designed to leverage. Specifically, we found that leveraging may be a strategy some funding recipients employ, either by choice or out of necessity, to meet the goals of the CDBG and HOME programs. Thus, using a leverage measure to assess the impact or success in meeting goals may create adverse or conflicting incentives for the agency and its grantees as well as Congress and other decision makers; for example, by giving funding priority to projects that leverage more over those that leverage less, but which may fill a greater or more immediate need within a community. In response to this comment, we added language to the report to emphasize that HUD has not identified leveraging as a performance measure for either program. In a letter from the Director of the Community Development Financial Institutions Fund, Treasury expressed appreciation for our finding that each of the agency’s programs included in our review was designed to leverage. Although Treasury did not specifically comment on our recommendations, it provided several detailed comments primarily related to the agency’s calculation of leverage measures for the CDFI and New Markets Tax Credit programs (see app. VI). Specifically, Treasury commented on our findings that (1) the leverage measures Treasury reported for its programs lacked transparency because the agency did not disclose the limitations of the data or the methods used to calculate them; (2) the leverage measures did not reflect the actual extent of leveraging in the CDFI program due to incomplete data; (3) missing project-level data for the New Markets Tax Credit program potentially led to misestimations of leveraging in the program; and (4) the leverage measure Treasury calculated for the New Markets Tax program was a multiplier ratio, not a leverage ratio. With respect to its first comment on our findings, Treasury stated that on multiple occasions the agency has publicly disclosed its calculation method for the CDFI program. In the report, we listed two publications in which Treasury disclosed its calculation methodologies and limitations to the data it used to compute a leverage ratio for the CDFI program. To this list, we added the additional report Treasury cited in its letter. However, we continue to believe that disclosure of the methodologies and limitations of the data used to calculate the leverage measures is important, particularly in key budget and performance documents, which policymakers often rely on to make funding and management decisions. As discussed in the report, Treasury did not disclose such information about its leverage calculation for the CDFI program in these key documents. For example, in the fiscal year 2006 Performance and Accountability Report and Justification for Appropriations and Performance Plans, Treasury reported leverage ratios for the CDFI program and emphasized its importance in achieving program goals, but did not include any discussions of the measures’ data limitations or calculation methods. Accordingly, we did not change our finding that Treasury’s reporting of such information was inconsistent and that it should further disclose its data limitations and calculation methods in key budget and performance documents. Concerning Treasury’s comment on our finding that the leverage measure the agency calculated for the CDFI program did not reflect the actual extent of leveraging due to incomplete data, Treasury stated that although it was aware that the match leverage—that is, the ratio of nonfederal match funds to program funds—may actually exceed the statutory requirement of a 1 to 1 ratio, it is not appropriate or necessary to include excess matching funds that exceed the requirement. We continue to believe that excluding excess matching funds from the leverage calculation (which typically includes all other sources of funds) understates the actual extent of leveraging that occurs in the program. Accordingly, we did not change the report in this regard. If Treasury chooses to continue to exclude such amounts from future, published leverage calculations for the program, we believe that it should disclose this and its potential impact on the leverage measure, consistent with the recommendations included in this report. Concerning Treasury’s comment on our finding that missing project-level data for the New Markets Tax Credit program potentially led to misestimations of leveraging in the program, Treasury stated in its letter that the leverage measure for the program would not substantially be different if complete data were available and that the calculated measures provided a reasonable approximation of the leveraging that occurs in the program. We reported that (1) leverage data were not available for 26 percent of New Markets Tax Credit projects and (2) Treasury assumed that CDEs contribute 100 percent of tax credit equity to qualified low- income community investments, even though CDEs are permitted to retain up to 15 percent of such equity for administrative and other purposes. We noted that the former case could lead to an underestimation of the extent of leveraging and the latter an overestimation of the extent of leveraging that occurred in the program. As discussed above with respect to the CDFI program, these data limitations potentially could have an impact on the leverage measure Treasury calculated for the program. In its letter, Treasury agreed with our description of these limitations, but did not provide any specific evidence of the impact of missing project-level data on these measures. Treasury also acknowledged the importance of disclosing such information, stating it would make every effort to include a discussion of these and other data limitations, as well as its calculation methodologies, when and if it publishes leverage measure for the program. In response to these comments, we did not change the report. Finally, with respect to our finding that the leverage measure Treasury calculated for the New Markets Tax Credit program for purposes of this report was a multiplier ratio, Treasury stated that the measure was a leverage ratio, calculated consistent with GAO guidance outlined in the report. However, we reported the measure Treasury reported for the New Markets Tax Credit program was not a leverage ratio, but rather a money multiplier or multiplier ratio. A multiplier ratio measures the total amount of investment $1 in tax credits potentially can generate in low-income communities, whereas a leverage ratio measures the additional amount of investment relative to a source of funds (such as program funds). According to Treasury officials with whom we spoke, the agency included the cost of the credit ($0.25) on “both sides of the ratio,” consistent with the calculation of a multiplier ratio, but overstating the extent of leveraging that occurred in the program. Our purpose in making a distinction between leverage ratios and multiplier ratios was to highlight the need for adequate disclosure of calculation methods and data limitations so that decision makers understand how to interpret these measures and how these measures compare with those reported by other programs. Without such information, it would not be possible for decision makers to assess the reliability of the measures or the comparability of the measures reported by other programs. If Treasury publishes the measure it calculated for the New Markets Tax Credit program, we believe it is incumbent upon the agency to provide a discussion as to how the measure was calculated in an attempt to provide complete information to decision makers (see app. III). Further, Treasury acknowledged in its letter it would do so, stating it would “make every effort to include a discussion of data methodologies and limitations” when it publishes leverage measure for the program. Accordingly, we did not change the report in response to this comment. HUD’s and Treasury’s letters also included several comments that were technical in nature, which we incorporated as appropriate. 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 of this report to the Ranking Member, Subcommittee on Housing and Community Opportunity, Committee on Financial Services, the Secretaries of Housing and Urban Development and the Department of the Treasury, the Director of the Office of Management and Budget, and other interested congressional committees. We also will make copies available to others upon request. In addition, the report will be available at no charge on the GAO Web site at http://www.gao.gov. If you or your staff have any questions concerning this report, please contact me at (202) 512-8678 or at shearw@gao.gov. Contact points for our Office 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 VII. The objectives of this report were to examine (1) the leverage measures the Department of Housing and Urban Development (HUD) and the Department of the Treasury (Treasury) reported for the selected housing and community and economic development programs and the transparency of the data and methods used to calculate them and (2) the relevance of leverage measures in assessing the performance of the selected programs. Our review focused on HUD’s Community Development Block Grant (CDBG), HOME Investment Partnership (HOME), and HOPE VI programs and the Treasury’s Community Development Financial Institutions (CDFI), Low-Income Housing Tax Credit, and New Markets Tax Credit programs. To examine the leverage measures HUD and Treasury reported for each of the selected programs and the transparency of the data and methods used to calculate them, we reviewed relevant program regulations and guidance, our prior reports, and reports of others, and interviewed agency officials and other stakeholders. Based on this information, we requested from HUD and Treasury data they use to measure the extent of leveraging (for example, data on the sources and amounts of funds, or other financial data, commonly referred to as “leveraging data”) in the CDBG, HOME, and HOPE VI programs and the CDFI and New Markets Tax Credit programs, respectively. We did not request Low-Income Housing Tax Credit data from HUD or Treasury because neither maintains a database with detailed information on leveraging. For both the CDBG and HOME programs, we requested leveraging data on completed program activities that were aggregated at the local level from HUD’s Integrated Disbursement and Information System (IDIS), which contains information on activities funded by a number of grant programs (including the CDBG and HOME programs). The CDBG data were from December 1, 2005, and May 1, 2007, and the HOME data were from October 1, 2005, and September 30, 2006. To assess the reliability of the data for both programs we (1) performed basic electronic testing of data elements associated with the financing used by state and local agencies that administer the programs—for example, we checked for missing data; (2) reviewed existing information about the data and IDIS; (3) replicated the leverage measure that HUD reported for each program; and (4) interviewed agency officials knowledgeable about the data. As a result of these tests, we found several limitations with these data, specifically that they were largely self-reported by program administrators and were not validated. In addition, IDIS does not distinguish between nonresponses, which default to zero, and actual zero (that is, $0) responses; as such, the data may underreport the total amount of leveraging that occurred in the programs. Further, the data may be incomplete because HUD does not require state and local agencies to report leveraging data because leveraging is not a required activity in either the CDBG or the HOME program, and HUD only started collecting leveraging data for the CDBG program in December 2005 (only about half of all program administrators have reported relevant data to the agency). Due to these limitations, we were unable to determine the reliability of the precise dollar amounts that were used in combination with the CDBG and HOME funds. We use the leverage measures that HUD derived from the data to illustrate how leverage measures can be calculated in different ways, but the values should not be used to represent actual dollars leveraged. To assess the reliability of HUD’s HOPE VI program leveraging data on the 55 HOPE VI projects completed (that is, projects in which all phases of construction were fully completed and actual funding amounts were reported) as of March 2006, we (1) performed basic electronic testing of data elements associated with the financing used by the public housing agencies that administer the program; (2) reviewed existing information about the data and HUD’s HOPE VI Internet-based Grant Management Reporting System Prototype (HOPE VI database); and (3) interviewed HUD officials knowledgeable about the data. In addition, we interviewed officials from five randomly selected public housing agencies (PHA) that received a HOPE VI grant to determine the accuracy and completeness of the data in the HOPE VI database as it pertained to the PHAs’ specific HOPE VI project. We determined that the data were sufficiently reliable for the purpose of this report. For the CDFI program, we discussed with agency officials the calculation method used to compute the program’s leverage measure, including any assumptions made, the completeness and accuracy of the data used in the calculation, and any other known limitations to the measure or the data used to calculate it. Unlike the CDBG, HOME, and HOPE VI programs, we did not request project-level data Treasury uses to calculate a leverage measure for the program. Rather, Treasury provided us with a spreadsheet containing the calculation method and nationally aggregated data used to calculate leverage measures for each of the last 6 reporting years. We determined that Treasury’s calculation method was appropriate and supporting data were sufficiently reliable for the purpose of calculating an approximation of the funds being leveraged in the program. However, based on our conversations with agency officials, we also noted several limitations in Treasury’s calculation method and the supporting data. Specifically (1) the data were largely self-reported by CDFIs and were not validated and (2) Treasury assumed that matching contributions do not exceed $1 for every $1 in program funds, which likely understates the extent of institutional-level leveraging in the program (because many of the CDFIs exceed the match requirement, according to Treasury officials). To assess the reliability of the data Treasury provided on project-level leveraging in the New Markets Tax Credit program, we (1) performed basic electronic tests of the data elements associated with the financing used by Community Development Entities (CDE), (2) reviewed existing information about the data, (3) replicated the project-level leverage measure Treasury calculated for the program, and (4) interviewed agency officials knowledgeable about the data. We determined that the project- level data were sufficiently reliable for purposes of calculating a project- level leverage measure for the program. However, we also noted some limitations in the project-level data, specifically that (1) they were largely self-reported by CDEs and were not validated, and (2) about 26 percent (139 out of 538) of the CDEs that were awarded New Markets Tax Credits did not report data. To determine what leveraging data were available for the Low-Income Housing Tax Credit program at the state level and whether such data were maintained electronically, we conducted a telephone survey of the entire population of 57 allocating agencies, which included 50 state agencies, the District of Columbia, Puerto Rico, and the Virgin Islands; one suballocating agency in the District of Columbia; two suballocating agencies in the State of New York; and a suballocating agency in Chicago. Our pretested survey achieved a 79 percent response rate. On the basis of 45 responses to the following questions—(1) Does your agency have data in its database on the specific types of financing sources that are used in each Low-Income Housing Tax Credit project? and (2) Does your agency have the dollar amounts contributed by each financing source used in the project in the database?—we found that 25 allocating agencies collect the dollar amounts contributed by specific financing sources and keep that data electronically. Because not all allocating agencies collected leveraging data and those that did used different software applications to maintain their data, we determined that it would be difficult to collect aggregate data to report a national leverage measure for the program. To examine the relevance of leverage measures in assessing the performance of the selected programs, we reviewed our reports and those of the Office of Management and Budget (OMB) on performance measurement; agency strategic plans and annual performance plans, budget justifications and performance and accountability reports; and industry, and other literature such as agency press releases and Web sites. We also interviewed representatives from Treasury, HUD, and OMB. Additionally, we interviewed representatives of the following industry groups and other organizations involved in housing and community and economic development initiatives: City of Chicago Department of Housing; Coalition of Community Development Financial Institutions; Coastal Enterprises, Inc.; Council of State Community and Economic Development Agencies; International Economic Development Council; Harvard University’s Joint Center for Housing Studies; John D. and Catherine T. MacArthur Foundation; Living Cities: The National Community Development Initiative; Local Initiatives Support Corporation; National Association of Affordable Housing Lenders; National Association of Development Organizations; National Association of Housing and Redevelopment Officials; National Community Development Association; National Community Investment Fund; National Council of State Housing Agencies; New Hampshire Community Loan Fund; New Markets Tax Credit Coalition; Western Massachusetts Enterprise Fund, Inc. Further, as part of this work, we conducted site visits and collected information on how federal funds have been leveraged in housing and community and economic development projects, 20 of which we toured. Specifically, we conducted site visits in Chicago, Illinois; Laredo and San Antonio, Texas; Chester and Philadelphia, Pennsylvania; Portland and Salem, Oregon; and Seattle and Tokeland, Washington. We selected these areas to obtain perspectives from a variety of regions with attributes such as difficult-to-develop areas, rural and urban classifications, and lower- and higher-cost areas that affect the extent of leveraging. We used a nongeneralizable, illustrative sampling approach to select a range of housing and community and economic development projects or initiatives to tour. More specifically, our criteria were (1) projects were substantially completed in the last 5 years, (2) funds of each of the programs in our review were utilized in at least one of the projects selected, and (3) projects had two or more funding sources. In the cases where program officials maintained comprehensive lists of projects, we used such lists to randomly select projects; otherwise, we used available project information (for example, from program administrator Web sites) in conjunction with information from program administrators to select projects that generally met our criteria. In selecting the projects we illustrate in this report, we further considered several factors including the availability and completeness of leveraging data, the creativity in the projects’ financing and design, the type of development or initiative, whether the project was located in a rural area, and the general geographic location of the project. Appendix II provides examples of how federal funds have been leveraged in the selected programs. We conducted this performance audit in Chicago, Illinois; San Antonio and Laredo, Texas; Philadelphia and Chester, Pennsylvania; Portland and Salem, Oregon; Seattle and Tokeland, Washington; and Washington, D.C., from November 2006 to January 2008 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. Figures 3 through 6 describe how the CDFI, Low-Income Housing Tax Credit, and New Markets Tax Credit programs leverage funds for housing and community and economic development. As illustrated in figure 3, CDFIs must match CDFI program funds dollar- for-dollar with funds from nonfederal sources such as local governments or private foundations (match leverage). CDFIs use these program and match funds to attract private debt from lenders (debt leverage). Together, match and debt leverage represent institutional leverage in the CDFI program. CDFIs use the pooled equity and debt to make loans to a number of development projects. Additional leverage also may occur at the project level—individual projects may use their CDFI funds to leverage funding (equity or debt) from other investors, such as foundations, nonprofits, banks, and local governments. Projects (borrowers) repay principal and interest to their investors, including the CDFI. CDFIs use these payments to make subsequent loans to additional projects and repay lenders. As illustrated in figure 4, states, through their state housing finance agencies, are authorized to allocate Low-Income Housing Tax Credits to housing projects. Project developers can sell their tax credits directly to an investor(s) or a syndicator (which assembles a group of investors and acts as the group’s representative). The money investors pay for the tax credits is paid into the projects as equity financing. Generally, investors (including individuals, foundations, and state and local governments) contribute this equity, which is combined with non-tax credit financing sources (such as mortgages) in individual projects to fund development costs. As illustrated in figure 5, under the New Markets Tax Credit program, Treasury competitively awards tax credits to CDEs (such as a financial institution or nonprofit organization), which offer the credits to investors (including individuals, groups of investors, or corporations). The equity or debt generated from such an offering is used to finance eligible investments or projects, as described above. In turn, these investments and projects may use New Markets Tax Credit equity to leverage additional equity and debt to finance development or related costs. As illustrated in figure 6, rather than offering tax credits directly to investors, under a leveraged transaction structure, a CDE may offer credits to an investment fund. The investment fund pools equity generated from the credit offering with other equity and debt, and loans the funds to the CDE, which in turn makes qualified low-income community investments. Table 4 outlines the leverage measures HUD and Treasury calculated for the CDBG, HOME, HOPE VI, CDFI, Low-Income Housing Tax Credit, and New Markets Tax Credit programs. Following table 4, we present several leverage measure calculation scenarios for the selected programs. Figures 7, 8, and 9 illustrate the leverage ratios HUD reported for the CDBG, HOME, and HOPE VI programs, respectively (scenario A), and our recalculations of the those measures to convey the ratio of nonfederal funds to federal funds (scenario B) and public funds to private funds (scenario C). Based on cumulative data from December 1, 2005, to May 1, 2007, HUD estimated the leverage ratio for the CDBG program to be 4.98 to 1 (other funds to CDBG program funds). However, we could not determine in which category some CDBG funding data belonged; thus, we excluded these data and revised the 4.98 to 1 ratio to 4.04 to 1 (scenario A in fig. 7). Using the revised dataset, we then recalculated the measure to convey the ratio of nonfederal funds to federal funds (1.84 to 1, scenario B in fig. 7) and the ratio of private funds to public funds (0.55 to 1, scenario C in fig. 7). Based on data on HOME activities completed in fiscal year 2006, HUD estimated the leverage ratio for the HOME program to be 4.00 to 1 (all other funds to HOME program funds). Using these same data, we recalculated the measure to convey the ratio of nonfederal funds to federal funds (1.15 to 1, scenario B in fig. 8), and the ratio of private funds to public funds (0.62 to 1, scenario C in fig. 8). Using data on the 55 HOPE VI projects completed (that is, projects in which all phases of construction were fully completed) as of March 31, 2006, we determined the leverage ratio of other funds to program funds to be 1.13 to 1 (scenario A in fig. 9). Using the same data, we recalculated the measure to convey the ratio of nonfederal funds to federal funds (0.67 to 1, scenario B in fig. 9). Unlike the HUD programs, we generally were not able to recalculate the leverage measures Treasury reported for the CDFI and New Markets Tax credit programs to convey the extent of public and private investment in them because available leveraging data did not distinguish between or correctly categorize public and private contributions in either program. However, we were able to deconstruct the measures to approximate the extent of institutional and project leverage in them (see figs. 10 and 11). In the CDFI program, match leverage (that is, the ratio of nonfederal match funds to program funds) represented approximately one-fifth of total institutional leverage, while debt leverage (that is, additional private debt CDFIs were able to attract with program funds and matched funds combined) represented four-fifths of total institutional leverage, according to Treasury data. A majority of the total leverage that occurred in the CDFI program in fiscal year 2005 occurred at the project level—according to Treasury data, for every $1 of federal funds the agency contributed to CDFIs, they were able to leverage an additional $21.31 at the project level (see fig. 10). In the New Markets Tax Credit program, institutional leverage (that is, net equity generated through the offering of the credits) represented approximately 58 percent of total leveraging that occurred in the program, while project leverage represented approximately 42 percent of total leveraging (see fig.11). To determine how federal funds have been leveraged in HUD’s CDBG, HOME, and HOPE VI programs and Treasury’s CDFI, Low-Income Housing Tax Credit, and New Markets Tax Credit programs, we toured and obtained information on a number of projects that used funds from the selected programs in combination with other federal, state, local, and private funds for housing and community and economic development. Appendix I describes how we selected the communities to visit and the projects to include in this report. HUD’s CDBG, HOME, and HOPE VI programs and Treasury’s CDFI, Low- Income Housing Tax Credit, and New Markets Tax Credit programs are among a number of federal programs that fund housing and community and economic development. Specific information about the features of these programs follows: CDBG provides annual grants on a formula basis to entitlement communities and states to develop viable urban communities by providing decent housing and a suitable living environment, and by expanding opportunities, principally for low- and moderate-income persons. Under CDBG, communities and states develop their own programs and funding priorities. HOME provides formula grants to states and localities to fund a range of activities that buy, build, and rehabilitate affordable housing for rent or homeownership or provide direct rental assistance to low-income households. HOPE VI is part of HUD’s effort to transform public housing by providing grants that fund the demolition of severely distressed public housing; the capital costs of major rehabilitation, new construction, and other physical improvements; and other resident-related services. Through the CDFI program, Treasury provides CDFIs with financial assistance in the form of grants, loans, equity investments, and deposits to enhance their ability to make loans and investments and provide services for the benefit of low-income communities and persons. CDFI funds can be used for economic development, affordable housing, and community development financial services. Under the Low-Income Housing Tax Credit program, states are authorized to allocate federal tax credits as an incentive to private investors to develop rental housing for low-income households. The equity generated by the sale of tax credits is used to lower the financing costs of housing developments by reducing the debt or equity the developer otherwise would incur or contribute. The New Markets Tax Credit program permits taxpayers to receive a credit against federal income taxes for making qualified equity investments in CDEs. CDEs use the equity generated by the sale of the credits to make investments in qualified low-income businesses. Figures 12 through 14 illustrate several projects that leveraged federal funds for the development of affordable housing. Figures 15 through 20 illustrate several projects that leveraged federal funds for community and economic development activities. In addition to the contact named above Marianne Anderson, William Bates, Elizabeth Curda, Daniel Garcia-Diaz, Terence Lam, Alison Martin, John McGrail, Jackie Nowicki, Josephine Perez, Barbara Roesmann, Cory Roman, and Charles Wilson Jr. made key contributions to this report. Leveraging Federal Funds for Housing, Community, and Economic Development. GAO-07-768R. Washington, D.C.: May 25, 2007. Performance Budgeting: PART Focuses Attention on Program Performance, but More Can Be Done to Engage Congress. GAO-06-28. Washington, D.C.: October 28, 2005. Performance Budgeting: Observations of the Use of OMB’s Program Assessment Rating Tool for the Fiscal Year 2004 Budget. GAO-04-174. Washington, D.C.: January 30, 2004. Tax Administration: IRS Needs to Further Refine Its Tax Filing Season Performance Measures. GAO-03-143. Washington, D.C.: November 22, 2002. Public Housing: HOPE VI Leveraging Has Increased, but HUD Has Not Met Annual Reporting Requirement. GAO-03-91. Washington, D.C.: November 15, 2002. Performance Reporting: Few Agencies Reported on the Completeness and Reliability of Performance Data. GAO-02-372. Washington, D.C.: April 26, 2002. Managing for Results: Challenges Agencies Face in Producing Credible Performance Information. GAO/GGD-00-52. Washington, D.C.: February 4, 2000. Performance Plans: Selected Approaches for Verification and Validation of Agency Performance Information. GAO/GGD-99-139. Washington, D.C.: July 30, 1999. Agency Performance Plans: Examples of Practices that Can Improve Usefulness to Decisionmakers. GAO/GGD/AIMD-99-69. Washington, D.C.: February 26, 1999. Grant Programs: Design Features Shape Flexibility, Accountability, and Performance Information. GAO/GGD-98-137. Washington, D.C.: June 22, 1998. Managing for Results: Analytic Challenges in Measuring Performance. GAO/HEHS/GGD-97-138. Washington, D.C.: May 30, 1997.

Summary: This is the second of two reports on the leveraging of federal funds in housing and community and economic development programs. Leveraging involves using a source of funds to attract other funds or combining multiple sources of funds. This report examines (1) the leverage measures and the transparency of the data and methods used to calculate them, and (2) the relevance of such measures in assessing performance that the Department of Housing and Urban Development (HUD) and the Department of the Treasury (Treasury) reported for six selected programs. To complete this work, GAO reviewed agency policies and reports, interviewed officials, and analyzed agency data. The leverage measures (such as ratios) HUD and Treasury reported for the selected programs in performance, budget, and other documents lacked transparency because the agencies generally did not disclose the limitations of the data or the methods used to calculate them. Based on its review of available leveraging data and interviews with HUD and Treasury officials, GAO found that the leverage measures the agencies reported for the selected programs were based on incomplete data and thus did not capture the actual extent of leveraging in the programs. GAO also found that while the agencies generally reported measures that described the ratio of all other funds (federal, state, local, and private funds) to program funds, alternative measures that described the total federal investment or total private investment in a program provided considerably different results--also potentially of value to decision makers--about the extent of leveraging in a program. GAO regularly has reported that clearly communicating data limitations and their potential impact may foster appropriate use of data; however, no agency-specific or governmentwide guidance directs what agencies should disclose about the leverage measures they report for the selected programs. Consequently, absent specific information on how these measures were calculated and their limitations, decision makers would not have sufficient information to understand their meaning and determine how they could and should be used in performance assessment, budgeting, and other contexts. Leverage measures can provide basic information about the programs GAO reviewed; however, their relevance in assessing the performance of these programs varies considerably. For all of the programs GAO reviewed, leverage measures can describe inputs, or the resources used to support program activities, and may be useful for conveying basic financial information. To the extent that leveraging is a goal or expected activity of a program (as in the three Treasury programs), leverage measures generally can describe program outputs, or the products or services delivered (such as total leveraged funds), and may be used along with other performance indicators to assess the efficiency and effectiveness of a program in meeting its goals. In cases where leveraging is not clearly and appropriately linked to program goals and activities (as in the three HUD programs), use of such measures to describe program outputs could be misleading and result in adverse consequences. Although leveraging had limited relevance to the goals and activities of the selected HUD programs, GAO found that the Office of Management and Budget (OMB) and the agency often cited leverage measures for the programs in performance- and budget-related reviews and documents. Their continued use of leverage measures in these contexts could unnecessarily encourage HUD to place more importance on leveraging than meeting the stated goals of the selected programs.