Low-Income Housing Tax Credit: Improved Data and Oversight Would Strengthen Cost Assessment and Fraud Risk Management
Released: 2018-09-18T00:00:00

Letter

Background

Credit Allocation and Cost Oversight

Each state receives an annual LIHTC allocation. Allocating agencies then evaluate developers’ proposals to use tax credits to help develop new or rehabilitate existing housing against their QAPs. The QAPs identify agencies’ priority housing needs and contain selection criteria for awarding credits. In addition to meeting criteria outlined in a QAP, projects awarded tax credits must remain affordable to qualifying households for at least 30 years.

The amount of LIHTCs allocating agencies award to a project is primarily based on the project’s eligible basis. The agencies should allocate no more credits than they deem necessary to ensure the project’s financial feasibility through the 10-year credit period. To determine financial feasibility, Section 42 requires allocating agencies to consider the reasonableness of developmental and operating costs, any proceeds or receipts expected to be generated through the tax benefit, and the percentage of credit amounts used for project costs other than the cost of intermediaries such as syndicators (discussed later in this section). Section 42 also requires allocating agencies to evaluate available private financing and other federal, state, and local funding a developer plans to use and adjust the award accordingly.

Allocating agencies must review costs to determine the credit amount at three points in time: application (when the proposal is submitted), allocation (when the agency commits to providing credits to a specific project), and placed in service (when the project is ready for occupancy under state and local laws). When a project is placed in service, the developer must submit a final cost certification to the allocating agency. This certification details a project’s total costs and eligible basis. In general, the cost certification must be accompanied by an unqualified audit report from a certified public accountant, conducted in accordance with generally accepted auditing standards. An agency’s QAP (or related documents) may outline policies and procedures for reviewing costs.

Investors and Project Financing

Once a project is awarded tax credits, developers often attempt to obtain funding for the project by attracting investors willing to contribute equity financing. Developers typically sell an ownership interest in their LIHTC projects in exchange for equity from investors (a process commonly referred to as selling tax credits). The equity contributions (or investments) reduce debt burden on LIHTC projects, making it possible for project owners to offer lower, more affordable rents. Generally, investors buy an ownership interest in a LIHTC partnership (commonly referred to as buying tax credits) to lower their tax liability.

Investors in LIHTC projects may invest directly or through intermediaries known as syndicators. Direct investors are typically larger institutional investors, such as banks that have the internal capacity to fund and manage the acquisition, underwriting, and management of the underlying development project. Under the direct investment model, an investor owns a “limited” partner interest in the partnership owning the underlying property, with the developer typically assuming the “general” partner interest (see fig. 1).

Alternatively, investors may invest in a fund organized and managed by a syndicator. The syndicator-managed funds are limited partnerships in which investors own the limited partner interest in the fund (upper-tier partnership), with the fund in turn owning the limited partner interest in various property partnerships (lower-tier partnership). The money investors pay for a partnership interest in the fund is paid to associated LIHTC projects as equity financing. Syndicators manage two types of funds: proprietary (or single-investor) funds and multi-investor funds (see fig. 2). In both cases, the syndicator originates potential investments, performs underwriting, and presents the potential investments to investors.

Syndicators receive a fee from investors—typically a percentage of the gross equity raised—for their services in establishing, originating, underwriting, and closing on projects for investment funds. This fee is often referred to as an “acquisition fee” or an “upper-tier syndication fee.” The syndicator also may charge a fee to each project partnership in a fund for project-specific legal and accounting costs. This fee is often referred to as a “lower-tier syndication fee.”

LIHTC projects typically do not produce income through rents for investors. Rather, investors use the credits to offset their income tax liabilities over the 10-year credit period. As a result, for a LIHTC investment to be financially beneficial to an investor, the present value of 10 years of LIHTCs and any related benefits, such as taxable losses and depreciation, generally must exceed the amount the investor contributes in equity. This consideration, in part, drives the price investors are willing to pay for tax credits. Under normal economic conditions, equity pricing per tax credit has ranged from the $0.80s to mid-$0.90s per $1.00 of tax credit.

Projects often require financing in addition to investors’ equity contributions to cover development costs. This gap may be filled by federal, state, local, and private sources—for example, certain HUD grants and loans, state tax credits modeled after the federal program, and mortgage loans without government guarantees. A developer also may defer its developer fee to cover all or a portion of a funding gap.

Program Oversight

IRS and allocating agencies jointly administer the LIHTC program, with other entities providing additional types of oversight, as follows.

IRS administration of the LIHTC program includes developing and publishing regulations and guidance, enforcing taxpayer compliance, and overseeing allocating agencies’ monitoring of taxpayer compliance. The IRS Office of Chief Counsel, with assistance from Treasury’s Office of Tax Policy, develops and publishes regulations and guidance based on requirements in Section 42. In general, IRS collects and reviews information necessary for tax administration, including data on LIHTCs awarded and other information necessary to check the amount claimed on tax returns. According to IRS officials, IRS also regularly communicates with allocating agencies and stakeholders about LIHTC compliance issues and best practices at industry meetings and conferences.

IRS relies on allocating agencies to administer and oversee the LIHTC program in states. In addition to awarding credits to qualified projects, allocating agencies are responsible for monitoring LIHTC properties for compliance with program requirements (for example, rent ceilings, tenant income, and habitability). Noncompliance with LIHTC requirements may result in IRS denying claims for the credit in the current year or recapturing (taking back) credits claimed in prior years.

Investors and syndicators also monitor projects by performing due diligence in relation to their viability and eligibility for tax credits, in part to ensure they receive the expected tax credits.

Although not an administering agency, HUD plays a role in collecting data on the program. Specifically, the agency has to collect information on LIHTC tenant characteristics, as mandated in the Housing and Economic Recovery Act of 2008. Since 1996, HUD voluntarily has collected LIHTC project-level data because of the importance of the credits as a source of funding for low-income housing. HUD also has a role in designating difficult development areas and qualified census tracts.

In addition, NCSHA has identified recommended practices to allocating agencies for administering the LIHTC program, including oversight of QAPs and cost verification.

LIHTC Project Costs Varied Widely, and Scale, Location, and Tenant Characteristics Explained Some Differences

Median Cost of LIHTC Projects Was About $200,000 Per Unit, and the Range and Composition of Costs Varied by Construction Type

The median per-unit cost of the LIHTC projects completed in our 12 selected allocating agency jurisdictions in 2011–2015 was $204,000. The median per-unit cost of new construction projects was about $50,000 higher than for rehabilitation projects ($218,000 compared to about $169,000). For new construction projects, the median per-unit cost was about $38,000 higher in urban areas than in nonurban areas (about $230,000 compared to $192,000). For rehabilitation projects, the median per-unit cost was about $72,000 higher in urban areas than in nonurban areas (about $196,000 compared to $124,000). The development costs we report may be somewhat understated, because the documentation we obtained from allocating agencies did not consistently include the value of all costs—for example, donated land— which we discuss later in this report.

As shown in figure 3, the median per-unit LIHTC equity investment was about $147,000 for new construction projects (about 67 percent of the total development cost) and $103,000 for rehabilitation projects (about 61 percent of the total development cost). Other funding sources, such as private loans or state and local programs, made up for differences between project costs and equity investments. We estimated equity investments for the selected projects based on their LIHTC allocations and the reported prices investors paid for the credits. The median credit price increased from about $0.80 in 2011 to about $0.93 in 2015.

Although rehabilitation projects generally had lower per-unit costs than new construction, both types of projects had similar proportions of hard and soft costs (see fig. 4). Hard costs (which include land, existing structures, and construction) were roughly 70 percent of new construction and rehabilitation project costs. Costs for acquisition of existing structures were proportionally higher and construction costs proportionally lower for rehabilitation projects than for new construction. Land costs were close in proportion. Soft costs (which include contractor fees, architect and engineer fees, developer fees, and other soft costs such as construction loan financing) were proportionally similar for new construction and rehabilitation projects—roughly 30 percent.

Project Cost Trends Differed by Construction Type and Are Difficult to Compare to Market-Rate Projects

In nominal terms, the median per-unit cost of new construction projects increased by about 13 percent during 2011–2015, and the median per- unit cost of rehabilitation projects decreased by about 21 percent. After accounting for inflation, the median per-unit cost for new construction projects increased by about 7 percent (from about $208,000 to $222,000 in 2015 dollars), while the median per-unit cost for rehabilitation projects decreased by about 26 percent (from about $207,000 to $153,000 in 2015 dollars). However, this analysis does not account for changes in the composition of projects that were built (such as size or location). In addition, the overall trends were substantially affected by certain allocating agencies.

For example, California accounted for about 24 percent of the new construction projects in our sample. During 2011–2015, the median per-unit cost of California’s new construction projects increased by about 11 percent (about 18 percent in nominal terms), while the median per-unit cost of all other new construction projects in our sample decreased by about 4 percent (in nominal terms, increased by about 2 percent).

Additionally, New York City accounted for about 19 percent of the rehabilitation projects in our sample, and the median per-unit cost of its projects declined by about 33 percent (about 32 percent in nominal terms) in 2011–2012. During this same period, the median per-unit cost of all other rehabilitation projects increased by about 13 percent (about 15 percent in nominal terms) but did not show a clear trend in 2011–2015.

To provide some context for the project costs and trends discussed above, we compared the annual rates of change for median new construction costs—generally site work, construction materials and labor, and contractor fees—to the annual rates of change in a Bureau of Labor Statistics index for construction costs that tracks price changes for various types of new construction.

The median per-unit construction cost of the LIHTC projects (unadjusted for inflation) and the index both increased over the analysis period—by 11 percent and 10 percent, respectively. However, while the index consistently increased annually by an average of about 2 percent, the magnitude and direction of changes for the LIHTC projects varied, increasing by as much as about 8 percent in 2013–2014 and decreasing by about 5 percent in 2014–2015.

Figure 6 shows the annual median per-unit construction costs for new construction LIHTC projects and a projected trend if they had increased at the rate of the Bureau of Labor Statistics index beginning in 2011. These results suggest that factors besides the price of construction inputs (such as material, labor, and contractor fees) drove changes in the median cost of LIHTC projects completed during 2011–2015. Project locations and characteristics varied each year, and a number of these factors were associated with per-unit costs, as discussed later.

To provide context for our cost analysis, we also examined the feasibility of comparing LIHTC development costs to development costs for market- rate projects. However, we were unable to obtain data on market-rate developments from industry groups we contacted that represented developers and lenders, or from researchers who had conducted similar studies. Additionally, allocating agencies did not consistently maintain key project data—such as gross square footage, number of stories, or construction wages—needed to benchmark LIHTC project costs using a construction cost estimation tool. We discuss these and other data challenges in greater detail later in this report.

Nonetheless, several factors provide possible explanations for why construction costs, developer fees, and other soft costs may differ between LIHTC and market-rate projects:

Durability. LIHTC project developers may have incentive to use more durable (and potentially more expensive) construction components than they might for market-rate developments. They may seek to limit replacement costs before the end of the 15-year compliance period— after which they may seek additional LIHTCs for rehabilitation or convert units to market-rate. As revenue from tenant rents is generally lower for LIHTC projects than for market-rate projects, and because investors prefer not to refinance during the 15-year compliance period and lower their returns, LIHTC project owners are more limited in their ability to recapitalize aging projects. On the other hand, market forces may encourage market-rate developers to provide higher-grade finishes and amenities than LIHTC developers in some markets.

Agency and local requirements. Allocating agencies can use QAP minimum standards and scoring incentives to influence the types of projects developers propose and build. Although these preferences can help achieve a variety of policy priorities, some can increase costs. For example, QAPs may provide developers with incentives to pursue historic preservation projects or require them to add on-site commercial space or amenities such as community rooms. Green building and energy-efficiency standards are also common QAP incentives that can increase development costs, although they may offset some future operating costs through lower utility expenses. Some QAPs also may incentivize urban infill projects on sites that require extensive demolition or environmental remediation, which add to costs.

Profit motive. LIHTC projects may be less attractive financially for developers than market-rate projects because they yield lower profits from rental income. Accordingly, allocating agencies allow a developer fee, for which tax credit equity generally pays. For the projects in our sample, developer fees represented about 11 percent of development costs at the median. In comparison, market-rate developers are generally compensated through rental income or from the sale of their developments.

Other soft costs. LIHTC projects may have higher soft costs (other than developer fees) compared to market-rate and other types of affordable developments for a number of reasons, including the following:

Financing projects through LIHTC equity is a complex process that can result in higher legal, accounting, and syndication fees and can also require developers to hire outside consultants and develop sophisticated internal capacity.

LIHTC developers also generally rely on multiple public and private funding sources in addition to tax credit equity to fully finance projects. For example, projects in California used about six funding sources in addition to LIHTC equity, on average. These additional sources can increase legal, accounting, and other fees due to the costs associated with seeking additional sources, writing applications, and complying with further appraisal, audit, and regulatory requirements. Securing additional funding sources also can delay the development process, which may increase land holding and interest expenses.

LIHTC Project Costs Varied across Selected Allocating Agencies

As shown in figure 7, the median per-unit cost of new construction projects across the 12 selected allocating agencies ranged from a low of about $126,000 in Texas to a high of $326,000 in California. The median per-unit cost was less than $200,000 for 4 of the 12 allocating agencies (Arizona, Georgia, Ohio, and Texas); from $200,000 to $300,000 for 6 of the 12 allocating agencies (Florida, Illinois, New York, New York City, Pennsylvania, and Washington); and greater than $300,000 for 2 of the 12 agencies (Chicago and California).

Median per-unit costs for rehabilitation projects were lower and varied less than those for new construction projects, ranging from a low of about $107,000 in Illinois to a high of about $258,000 in both Chicago and New York. In all selected allocating agencies, the median per-unit cost for rehabilitation projects was lower than for new construction projects. For example, the median in California was about $184,000, compared to about $326,000 for new construction. For additional details on the cost of rehabilitation projects, see appendix III.

As also shown in figure 7, within individual allocating agencies, the cost difference between the least and most expensive project was as little as $104,000 per unit (Georgia) and as much as $606,000 per unit (California). Project costs tended to be clustered around the median for each allocating agency, but were still widely distributed between the 25th and 75th percentiles for some allocating agencies. For example, the difference between the 25th and 75th percentiles was more than $75,000 in half of the locations we reviewed (California, Chicago, Illinois, New York, New York City, and Pennsylvania).

Although projects costs were among the highest for the Chicago and New York City allocating agencies, they were within the range of costs for five other cities that had comparable population and density and were in the jurisdictions of other allocating agencies within our sample (see fig. 8).

Hard costs as a proportion of total development costs varied among the selected allocating agencies. Agencies’ hard costs ranged from about 66– 76 percent for new construction projects completed in 2011–2015, with soft costs accounting for the remainder (see fig. 9). The proportions of hard and soft costs were generally similar across higher- and lower-cost locations. For example, California had the highest median per-unit cost among selected allocating agencies, but had hard and soft costs (about 67 and 33 percent) proportionally similar to those in Texas (about 68 and 32 percent) and Georgia (about 69 and 31 percent), where median per- unit costs were among the lowest.

In relation to hard costs, median per-unit construction costs were highest in Chicago, where construction costs constituted about 72 percent of total development costs (but were about 63 percent elsewhere, on average). In comparison, construction costs in California were just 56 percent of total development costs due to higher land costs (about 12 percent of total development costs, but about 5 percent elsewhere, on average).

For soft costs, developer fees and other soft costs (such as construction loan interest and permit fees) varied more widely across the allocating agencies than architect and engineer fees and contractor fees. Developer fees ranged from about 6 percent of development costs in Chicago to about 13 percent of development costs in Florida. Other soft costs similarly ranged from about 7 percent of development costs in Pennsylvania to about 14 percent of development costs in California. In comparison, architect and engineer fees ranged from about 3 percent to 5 percent of development costs, and contractor fees ranged from about 5 percent to 9 percent of development costs.

Scale, Location, and Other Characteristics of LIHTC Projects Explained Some Cost Differences

By design, the LIHTC program gives allocating agencies flexibility to address local housing needs and agency priorities through their award processes. As a result, the characteristics of each agency’s LIHTC projects generally can be expected to reflect the real estate conditions, built environment, and populations of the areas they serve. For example, in locations with less density and inexpensive land, low-rise multibuilding developments may be more cost-effective, while in locations with higher density and expensive land, taller single-building developments may be more cost-effective. Therefore, it is important to consider the cost reasonableness of LIHTC developments within the context of local conditions.

As previously noted, we developed a regression model to examine the relationship between the cost of developing LIHTC projects and various building, location, and other variables. Our model results indicate that a number of key characteristics were associated with significant increases or decreases in the per-unit costs of LIHTC projects that received tax credit awards from our selected allocating agencies. Differences in the prevalence of these characteristics among the allocating agencies help explain the cost variation among and within them. While our results indicate that these characteristics may have directly or indirectly affected per-unit cost, their specific effects varied by allocating agency, suggesting that our estimates are sensitive to the particular conditions of the locations we sampled.

First, construction type (new construction or rehabilitation) and scale (number of units and unit size, measured by number of bedrooms)—were associated with cost, controlling for other characteristics.

Construction type. We previously noted that the median per-unit cost for new construction was about $50,000 higher than the per-unit cost for rehabilitation projects, but after controlling for other characteristics, we estimated this difference to be $39,000. New construction projects were more costly than rehabilitation projects because they had higher construction costs (primarily site work, materials, and labor). For perspective, $39,000 represents about 19 percent of the median per-unit cost ($204,000) of projects in our sample.

Number of units. In general, we found that per-unit costs decreased as the number of units in a project increased, consistent with economies of scale in construction. Specifically, we estimated that the per-unit cost of projects with more than 100 units was about $85,000 less than projects with fewer than 37 units (see fig. 10). In addition, we estimated that the per-unit cost of projects with 37–50 or 51–100 units was about $31,000 or $56,000 lower, respectively, than projects with fewer than 37 units.

However, due to data limitations, our analysis does not account for building type—for example high-rise or low-rise structures—that may have affected per-unit cost. To account for some variation in building type, we compared projects with one or more larger buildings (60 or more units) to projects with more typical building designs. We found that the per-unit cost of projects with larger buildings—which were also taller on average—was about $15,000 more (about 7 percent of the median per- unit cost). This difference may be attributable to specific design requirements of larger and taller structures, such as construction materials and sprinkler systems.

Unit size (number of bedrooms). As would be expected when comparing costs on a per-unit basis, we estimated that projects with larger units had higher per-unit costs. We estimated that the per-unit cost decreased by about $2,000 (or about 1 percent of the median per-unit cost) as the number of units with fewer than two bedrooms increased by10 percent. Conversely, the per-unit cost increased by about $3,000 as the number of units with more than two bedrooms increased by 10 percent.

Second, we also found that the types of organizations that developed LIHTC projects and the tenants they targeted were associated with per- unit cost, after controlling for other characteristics.

Tenant type. We estimated that the per-unit cost of projects targeted to seniors was about $7,000 lower than nonsenior projects (or about 3 percent of the median per-unit cost). Compared to nonsenior projects, units in senior projects generally had less residential square footage (for which we did not control), which may help explain their lower per-unit costs.

Target income level. We also estimated that the per-unit costs of projects targeted to predominantly low-income tenants was about $11,000 more than for mixed-income projects (or about 5 percent of the median per-unit cost). Mixed-income projects might be expected to have higher costs as they generate more rent revenue to support higher development costs. But, because LIHTC allocations are calculated based on the ratio of low-income units to total units, predominantly low-income projects receive proportionally more LIHTC equity, which may allow them to support higher development costs. For example, we estimated that projects targeted towards predominantly low-income tenants generated LIHTC equity equal to about 67 percent of development cost, whereas mixed-income project generated LIHTC equity equal to about 50 percent of development cost.

Nonprofit participation. Section 42 requires a portion of each state’s tax credit allocation to be set aside for projects involving a qualified nonprofit organization. We estimated that the per-unit cost of these projects was about $15,000 more than projects not in the set-aside (or about 7 percent of the median per-unit cost). Other studies of the LIHTC program have suggested potential explanations for this result. For example, nonprofit organizations may focus more on populations that are more costly to serve, such as special-needs tenants who may require additional or enhanced facilities. Additionally, nonprofit developers may have higher costs because they are often smaller, produce fewer projects, and may need to spend more time and resources on activities such as fundraising and market research, compared to their for-profit counterparts.

Third, controlling for other characteristics, we found that a number of geographic and economic variables were associated with cost differences.

Location. We estimated that urban locations were associated with a per- unit cost about $13,000 higher than for suburban locations (or about 6 percent of the median per-unit cost), and that per-unit costs in rural areas were not statistically different from suburban areas. Consistent with this estimate, the data in our sample show that per-unit land and construction costs were greater in urban areas than in nonurban areas.

In addition, urban projects were more likely to include parking structures, which we found were associated with a per-unit cost increase of about $56,000 in California and Arizona (or about 27 percent of the median per- unit cost), where parking structure data were available. Among these projects, about 98 percent of projects with parking structures were in urban areas.

Urban projects were also located in closer proximity to transit, which we found increased per-unit construction costs. In an alternative specification of our model limited to projects near fixed-guideway transit stations, we estimated that the per-unit construction costs of projects that were 0.5 miles or less from a transit station—known as transit-oriented developments—were about $17,000 more than projects that were between 0.5 miles and 1.0 miles from a transit station.

Local housing market and economy. As discussed previously, difficult development areas are those with high construction, land, and utility costs relative to area median gross income; qualified census tracts are areas with higher rates of low-income households or poverty rates. We did not find that projects in these areas were associated with cost differences compared to projects outside these areas.

However, we found cost differences among projects in difficult development areas and qualified census tracts when we estimated alternative specifications of our model that excluded some geographic, economic, and local housing market variables that may be associated with the areas and tracts. For example, using a model specification that excluded local property values, we estimated that difficult development areas were associated with about a $9,000 increase in per-unit costs. In a separate estimation that excluded poverty rates and some other economic and geographic variables, we estimated that projects in qualified census tracts were associated with a per-unit cost increase of about $18,000 (or about 9 percent of the median per-unit cost). In both cases, the project characteristics of interest (difficult development area or qualified census tract) are likely associated with the excluded variables mentioned, as difficult development areas are characterized by high land costs and qualified census tracts are characterized by high poverty rates, among other factors. In the absence of the excluded geographic or local housing market variables, the estimated influence of these project characteristics is more pronounced.

Finally, we found that the presence of federal funding sources in addition to LIHTC were associated with cost differences, after controlling for other characteristics.

American Recovery and Reinvestment Act funding. We estimated that projects that received funding through either of two LIHTC programs (Tax Credit Assistance Program or Section 1602 Program) under the American Recovery and Reinvestment Act of 2009 (ARRA) were associated with a decrease of about $13,000 in per-unit costs (or about 6 percent of the median per-unit cost). Projects received ARRA funds during a period of economic recovery, and the relative scarcity of private funds may have motivated developers to pursue less costly projects. Because about 91 percent of projects that received ARRA funds were completed in 2011– 2012, we restricted our ARRA estimate to projects completed in that period.

We estimated that soft costs were about $4,000 per unit lower for ARRA projects than for non-ARRA projects. Soft costs, which we previously mentioned were about one-third of total development costs, may have been lower for ARRA projects because proportionately fewer of these projects used tax credit equity to fund development costs. For example, about 30 percent of these projects received ARRA funds entirely in lieu of tax credits. As a result, ARRA projects may have had lower or no tax credit partnership and syndication costs. However, we did not estimate a significant difference in construction costs between ARRA and non-ARRA projects.

Rural Development funding. Projects that received at least one Rural Development loan or grant, from the Department of Agriculture, were associated with about a $32,000 decrease in per-unit cost (or about 16 percent of the median per-unit cost). However, projects that received these loans or grants may have had unique characteristics that affected cost. According to an allocating agency official from California—where about 19 percent of the projects we reviewed used at least one Rural Development loan or grant—projects that received these funds may have had lower total development costs because high-cost projects were not financially feasible in some rural areas due to lower rents and less local public funding. In addition, projects to house seasonal farm workers that receive funding from Rural Development’s Section 514/516 Farm Labor Housing programs may lack some amenities—such as in-unit kitchens and bathrooms—that increase costs and are more common in other LIHTC projects. Furthermore, private loans guaranteed through Rural Development’s Section 538 Guaranteed Rural Rental Housing Program are subject to per-unit limits, which may have hindered the feasibility of higher-cost projects.

Other federal funding. We also estimated that projects that received HOPE VI funds were associated with about an $18,000 increase in per- unit costs (or about 9 percent of the median per-unit cost).

However, the cost increase that we estimated may not have fully captured all additional costs associated with these projects. Several of the 23 HOPE VI projects included in our sample were phases of larger HOPE VI Revitalization Grant projects and may have included only the project costs associated with a smaller portion of a multibuilding development. In addition, some predevelopment expenses associated with the overall grant project, such as the demolition of existing structures and tenant relocation, may not have been included in the cost certifications we reviewed.

In contrast to the HOPE VI projects we reviewed, we did not find that projects that received Community Development Block Grant (CDBG) or HOME Investment Partnerships Program (HOME) funds had statistically different per-unit total development costs. However, like HOPE VI projects, CDBG and HOME projects were associated with increases in per-unit construction costs (about $15,000 or $6,000, respectively). The presence of HOME funds also was associated with an increase in per-unit soft costs (about $2,000), while CDBG or HOPE VI funds were not strongly associated with differences in per-unit soft costs.

While these sources were associated with cost differences, controlling for other characteristics, the association may not be entirely causal. The use of CDBG, HOME, and HOPE VI funds may have directly increased construction costs, as fund usage can trigger federal prevailing wage requirements. On the other hand, CDBG and HOME funding (for example) may have been used in addition to LIHTC equity to fill funding gaps for projects with particularly high costs.

Finally, to examine the relationship our model characteristics had on the per-unit cost of low- and high-cost projects, we compared the characteristics of new construction projects below the 25th percentile for per-unit cost against those above the 75th percentile.

As shown in table 1, projects below the 25th percentile generally had a higher proportion of characteristics that were associated with decreases in per-unit cost. These projects were larger, had smaller units, were more often targeted toward seniors, and were located in rural areas. In comparison, projects above the 75th percentile generally had a higher proportion of characteristics associated with increases in per-unit cost (or less of a decrease). These projects were smaller, had larger units, were more often located in urban areas, and were built in more expensive real estate markets, as the following examples illustrate.

About 70 percent of the projects below the 25th percentile had either 51–100 units or more than 100 units—which we found were associated with lower per-unit cost—compared to just 46 percent of the projects above the 75th percentile.

About 40 percent of the projects below the 25th percentile were senior projects—which we also found were associated with lower per-unit costs—compared to 18 percent for projects above the 75th percentile.

About 88 percent of the projects above the 75th percentile were in urban areas—which we found were associated with higher per-unit costs—compared to 71 percent of the projects below the 25th percentile.

Allocating Agencies Took Steps to Manage and Verify Development Costs, but LIHTC Policies Do Not Require Detailed Cost Information

Allocating agencies used approaches that include cost and fee limits and cost-based scoring criteria to manage project-development costs. A few agencies adopted additional measures such as detailed contractor certifications at project completion to help guard against a risk of fraud involving misrepresentation of contractor costs, but LIHTC policies do not require these enhancements.

The 57 Allocating Agencies Managed Development Costs through Approaches That Included Cost and Credit Limits, Fee Limits, and Scoring Criteria

As shown in table 2, the eligibility requirements and scoring systems that the 57 allocating agencies used to evaluate credit applications generally included approaches that seek to limit development costs or incentivize lower costs. For information on the approaches each of the agencies used, and in what combination, see appendix VI.

The types and number of cost-management approaches employed by each agency varied, as illustrated in table 3. More than one-third of the agencies used all four types of cost-management approaches we identified (one or more cost limits, credit allocation limits, fee limits, and cost-based scoring criteria). In contrast, a few agencies used just one type of approach. The number of approaches used by an agency is not necessarily indicative of the effectiveness of its cost management.

Additionally, the way that agencies implemented each type of approach varied.

The cost-management approaches agencies identified in their QAPs and related documents were as follows.

Cost limits. More than two-thirds of the allocating agencies (39 of 57) set limits on the total development cost for each project or set limits on the total eligible basis (or both). Total development cost is the overall cost to develop a project, whereas eligible basis typically includes costs associated with acquisition, construction and rehabilitation, and most soft costs, but excludes costs associated with land, permanent financing, and tax credit syndication. For information on cost limits for each of the 57 agencies, see appendix VI, table 32.

Thirty-three agencies set limits on the total development cost for each project. For example, Illinois limited total costs by bedroom type, number of units, and location, based on the agency’s analysis of historical cost data.

Ten agencies set cost limits on a project’s eligible basis, and their approaches to these limits varied. For example, two agencies adopted universal eligible basis limits of $250,000 per unit (Pennsylvania) and $300,000 per unit (New York City), whereas most others had multiple limits based on project characteristics such as type (new construction or rehabilitation), number of bedrooms, and location.

Six agencies, including Georgia, applied cost limits from a HUD program that insures mortgages for rental housing for moderate- income families. According to Georgia officials, adopting the HUD limits was more cost-effective than developing cost limits based on a market analysis.

Credit allocation limits. About two-thirds (34) of the allocating agencies had limits on the amount of LIHTCs available, generally per project or per developer, and the limits varied by type and amount. For information on credit allocation limits for each of the 57 agencies, see appendix VI, table 33.

Twenty-nine agencies had allocation limits per project, which included dollar limits (from $500,000 to $2.5 million) and percentage limits (from 10 percent to 60 percent of an agency’s total available credits per project), and two of these agencies also had a per-unit limit. For example, Illinois limited credits per project to the lesser of $1.5 million or 28,500 credits per unit. California limited credits per project to $2.5 million, and Washington limited credits to 10 percent of the agency’s total available credits.

Fourteen agencies had credit limits per developer or for the number of projects a developer can sponsor in a given year. One of these agencies also had a per-unit limit. The developer credit limits included dollar limits (from about $1.2 million to $3 million per developer) and percentage limits (from 10 percent to 25 percent of the agency’s total available credits). For example, Pennsylvania limited credits to $1.2 million per developer, and Washington limited developers to 15 percent of the agency’s total LIHTCs and two projects per application round. Another agency limited the number of projects (two) a developer can sponsor in a given year.

Fee limits. Fifty-one agencies limited developer fees and 47 also limited contractor fees. The agencies’ approaches to developer and contractor fee limits varied. As for other limits, 14 agencies limited fees for other project team members such as architects. For information on fee limits for each of the 57 agencies, see appendix VI, table 34.

Twenty-seven agencies had a flat limit on developer fees based on a percentage of the total development cost (typically 15 percent, although percentages ranged from 8 percent to 20 percent), while two others had dollar caps ($13,000 and $18,000 per unit).

Twenty-one agencies set tiered limits for developer fees based on the number of units in or cost of the project. For example, Arizona and Texas based their two- and three-tiered limits on the number of units in a project. Chicago and Illinois had tiered percentage limits based on a project’s development costs.

Twenty-five agencies had separate developer fee limits for acquisition costs, ranging from 4 percent to 15 percent, or tiered limits based on development costs.

Fourteen agencies set dollar caps on the total fees developers could receive per project, ranging from $1 million to $3.75 million.

Twenty-seven agencies also limited fees earned by related-party developers and contractors. For example, Pennsylvania set a related-party developer fee limit (12 percent) lower than its developer fee limit (15 percent). Illinois required related-party developers to reduce their fees by their related general contractor’s profit.

Cost-based scoring criteria. A large majority (51) of the allocating agencies used a competitive scoring process that incorporated one or more cost-based criteria to award LIHTCs. For information on cost-based scoring criteria for each of the 57 agencies, see appendix VI, table 35.

Twenty-four agencies awarded points to projects with costs under an agency’s limits. For example, Washington awarded points to projects for which the developer fee was below the agency’s limit of 15 percent.

Eighteen agencies awarded points to projects with comparatively lower costs. For example, New York City awarded points to projects with costs below the median total development cost of all submitted applications.

Eleven agencies awarded points to applications for credit efficiency, which many of the agencies measured by the dollar amount of credits requested relative to the number of units proposed. For example, Ohio awarded a sliding scale of points to projects based on the ratio of the credits requested to the proposed number of units, with lower ratios (representing greater credit efficiency) earning more points.

Three agencies’ competitive scoring criteria included penalties for developers with poor past cost performance. For example, they awarded negative points to developers that exceeded cost limits or provided incomplete cost information for previous projects.

In addition, 35 agencies included a cost-based criterion in their application scoring tiebreakers. For example, Arizona included a credit efficiency criterion as a tiebreaker.

Other cost-related approaches (12 selected agencies). Through our interviews and review of documentation, we also identified several other steps that our 12 selected allocating agencies took to manage LIHTC project costs at application and during construction.

Officials from two agencies (Georgia and Ohio) told us that their cost- reasonableness reviews included identifying high-cost outliers. For example, Ohio replaced its total development cost limit with a process for identifying and removing from consideration projects with the highest total development costs compared with other competing applications.

Chicago and Florida officials said they required or encouraged a bid process for selecting contractors or subcontractors. Florida officials told us that competitive selection of subcontractors, rather than using related-party subcontractors, provided cost transparency and could lead to lower costs.

Similarly, New York City officials told us that nearly all the agency’s LIHTC projects received funds from a city subsidy loan program that can require competitive selection of contractors, and the agency reviewed each contractor bid for cost reasonableness.

Illinois required third-party cost reviews of some projects as part of its cost-reasonableness review. Projects with related parties and all rehabilitation projects had to provide a construction cost breakdown completed by an independent third party. Additionally, Georgia’s QAP provided discretion to the agency to require a third-party cost review as needed.

According to officials from 11 of the 12 agencies, policies they used to discourage cost increases during construction included restrictions on change orders, such as by requiring agency approval and documenting a project’s cost increases (8 agencies); requiring developers or general contractors to pay for cost increases using contingency funds, profits, or other sources of funding (10 agencies); and penalizing developers for cost increases in future application rounds (5 agencies).

Nine of the 12 selected agencies conducted site inspections directly or by a third party to monitor construction progress, ranging from one visit to biweekly site visits. For example, New York officials said they conducted regular and unannounced site visits. Officials from the other 3 agencies said they did not conduct site visits and relied on other public funding partners, private lenders, developers, and syndicators to monitor projects during construction and in some cases, provide monitoring reports for the agency’s review.

Although officials from many of the selected allocating agencies acknowledged the importance of managing LIHTC development costs, for the most part agencies have not determined the specific cost effects of their approaches. A June 2016 report by Enterprise Community Partners recognized the complexity of assessing the cost implications of individual agency actions, while also noting that the wide range of agency approaches represented an opportunity for experimentation, innovation, and sharing of leading practices. The report recommended that as agencies establish goals and make changes to QAPs, they should regularly evaluate cost trends and outcomes. But as discussed later in the report, limitations in the cost-related data allocating agencies collect and the format in which they maintain them have hampered such evaluation.

Some Allocating Agencies Have Enhanced Cost- Verification Requirements to Manage a Fraud Risk, but LIHTC Policies Do Not Require It

While a few allocating agencies have implemented additional cost- certification controls—such as contractor-level certifications—to help address the risk of fraud involving misrepresentation of contractor costs, there are no LIHTC requirements to do so. Rather, allocating agencies oversee costs at project completion by reviewing final developer cost certifications. LIHTC regulations require developers of projects with more than 10 units to submit a cost certification, which includes total project costs and eligible basis, to the allocating agency and for the certification to be audited by a certified public accountant. As illustrated in figure 11, developer cost certifications do not break out specific contractor costs; rather, they aggregate contractor costs into several broad categories.

While the extent of fraud in the LIHTC program is not known, federal legal actions involving LIHTC projects in Florida highlight the risk of unscrupulous developers, contractors, and subcontractors inflating costs and obtaining excess program resources for personal financial gain. For example, according to the Department of Justice’s U.S. Attorney’s Office for the Southern District of Florida:

Several developers and contractors conspired in a contract inflation scheme affecting numerous LIHTC projects. The scheme involved submitting fraudulently inflated cost information to the allocating agency, resulting in $36 million in excess LIHTCs and federal grants. Seven individuals pled guilty and received sentences that included forfeiture of fraudulently obtained funds and for three individuals, prison time.

In another scheme affecting four LIHTC projects, developers working with a related-party contractor and subcontractor submitted fraudulently inflated cost information to the allocating agency. Under a prosecution agreement, the subcontractor has paid $5.2 million in forfeiture and fines.

But only a limited number of allocating agencies—5 of the 12 we selected and at least 4 of the remaining 45 agencies—have additional cost- certification controls to help address the risk of fraud involving misrepresentation of contractor costs. These controls are outlined in the agencies’ QAPs. Agencies outside of the 12 we selected for more detailed review could have requirements beyond what appears in their QAPs. However, two national accounting firms with LIHTC practices confirmed that, as of early 2018, a limited number of allocating agencies had implemented controls to address the risk of fraud involving misrepresentation of contractor costs.

Of the 12 selected agencies, 4 required general contractor cost certifications, which provide information that can be used to corroborate costs listed in developer cost certifications (see fig. 12). More specifically, Florida and Ohio required general contractor cost certifications for all projects, and Arizona and Georgia required cost certifications only from related-party general contractors.

In addition, California required auditors performing developer cost certifications for projects with related parties to audit to the level of the subcontractor. According to one national accounting firm, this may involve examining source documents from subcontractors (such as invoices, fee agreements, contracts, or deeds) to verify consistency with construction line items in the developer cost certification.

Among the 45 remaining agencies, Delaware, Kentucky, Michigan, and Missouri had QAPs that required general contractor cost certifications for all projects. None of the 45 agencies’ QAPs cited a requirement for cost certifications for related-party general contractors.

Officials from a few of the 12 selected agencies and a LIHTC accounting firm told us that unrelated parties also may present a fraud risk. The LIHTC development community is small in some markets, and unrelated developers and contractors may work together repeatedly. These relationships may pose risks similar to related-party relationships by increasing opportunities to collude in misrepresenting costs.

Requiring information beyond the developer cost certification provides greater cost transparency, which may help to deter or detect misrepresentation of costs. Federal LIHTC regulations do not require developers to provide contractor- or subcontractor-level cost information to LIHTC allocating agencies, or for auditors to verify the consistency of these costs with the developer cost certification. As a result, the regulations do not fully address the risk of fraud involving misrepresentation of contractor costs.

Federal internal control standards state that management should consider the potential for fraud when identifying, analyzing, and responding to risks. IRS and Treasury officials told us they have not considered implementing changes to the cost-certification requirement and that neither allocating agencies nor industry groups had suggested to them that the existing regulation needed clarification. They suggested that allocating agencies could enhance the requirement at their discretion.

In contrast, NCSHA revised its recommended practices for allocating agencies in 2017, advising that agencies should require additional cost certification due diligence for all housing credit developments. According to NCSHA, this additional due diligence may include audits of general contractors—alone or with an additional review of a sampling of subcontractor invoices—to verify consistency with the developer cost certification. However, NCSHA’s recommended practices are voluntary and it remains to be seen how many agencies implement these enhanced measures and in what form.

Moreover, NCSHA, a national accounting firm, some developers, and several of the selected allocating agencies told us that additional cost- certification requirements can provide more detailed cost information and help deter fraud by providing more cost transparency to allocating agencies and auditors. Two of these allocating agencies estimated that requiring general contractor cost certifications could increase project costs by about $5,000–$15,000. NCSHA and two other selected agencies noted that additional cost certification requirements would not significantly increase project costs.

Under the existing federal cost certification requirement—which stops at the developer level—the vulnerability of the LIHTC program to a known fraud risk is heightened, particularly in states in which allocating agencies have not implemented additional cost certification measures.

Weaknesses in Data Quality and Federal Oversight Constrain Assessment of LIHTC Costs

Data Limitations Hinder Detailed Evaluation of LIHTC Development Costs

Data limitations, including inconsistencies among allocating agencies in the collection, definition, and format of key variables, constrain analysis and oversight of LIHTC development costs. While we were able to provide a cost analysis earlier in this report, our analysis was limited to those variables we were able to consistently collect and that were similarly defined across the selected allocating agencies.

LIHTC regulations require developers to submit cost certifications to allocating agencies and the agencies to evaluate all sources and uses of funds for each project. However, IRS does not specifically require allocating agencies to collect and report cost-related data that would facilitate programwide assessment of development costs. IRS officials said that doing so would be inconsistent with their authority and role, which is focused on taxpayer compliance rather than program evaluation. As a result, allocating agencies have flexibility in what cost-related data to collect, how to maintain these data, and how to define variables for purposes of program evaluation.

Our tax expenditure evaluation guide suggests federal agencies assess (determine and define) what data are needed to evaluate tax expenditures. Without standardized, accessible data on LIHTC development costs, federal agencies and credit allocating agencies cannot rigorously assess the factors that drive costs, the reasonableness of costs, and the efficiency of LIHTCs in producing affordable housing. Currently, no standards exist for collecting and maintaining data related to LIHTC project costs.

Agencies Inconsistently Collected or Defined Key Variables

In conducting our evaluation of LIHTC development costs, we aimed to collect data that would allow us to assess costs associated with federal preferences for LIHTC developments outlined in Section 42; assess costs associated with certain allocating agency preferences, which we identified through a literature review and interviews with selected industry groups; and compare LIHTC development costs to market-rate development costs, a potentially useful step in assessing the reasonableness of project costs as required under Section 42.

Comprehensive information about project costs and characteristics is needed to conduct such an evaluation. However, inconsistencies in allocating agencies’ collection or definition of certain variables complicated our efforts to estimate statistical associations with costs, as follows.

Developer characteristics. Allocating agencies did not maintain information on developers in a manner that readily permitted classification by for-profit or nonprofit status. We estimated the association between nonprofit status and development costs based on projects that received credits under nonprofit set-asides. A limitation of this approach is that it does not account for projects with nonprofit developers that received credits apart from the set-asides. For example, almost 80 percent of Washington’s projects in our sample had a nonprofit developer, but only 32 percent received credits under the nonprofit set-aside.

Additionally, allocating agencies maintained tax identification numbers that would allow them to assess the influence of developer experience or incumbency—that is, how frequently a developer is awarded credits—on costs. But this information was not part of our data set, and we found that alternative variables (such as developer name) were unreliable for purposes of conducting a similar analysis.

Tenant type. Allocating agencies identified and defined tenant types differently, partly as a result of their specific QAP priorities. For example, New York defined 39 distinct tenant types and Texas defined 2 (family and elderly). Consequently, we could not standardize tenant types across agencies and estimate associations with development costs, other than for projects targeted to seniors, a population for which there is a specific federal definition.

Energy efficiency. Among our 12 selected allocating agencies, only California, Florida, and Texas collected information needed to assess the influence of energy-efficiency features on project-development costs. This information generally took the form of whether a project received a Leadership in Energy and Environmental Design (LEED) certification, a component of which is energy efficiency.

Payment of prevailing wages. Some states also may require the payment of prevailing wages (generally, the hourly wage and benefits paid to the majority of workers in a particular area). In addition, certain federal funding sources commonly used as gap financing in LIHTC projects require the payment of prevailing wages. However, the agencies in our sample did not consistently capture information on whether projects paid these wages.

Proximity to transit or other amenities. Most of the selected allocating agencies required or awarded points to projects located near certain amenities such as grocery stores, hospitals, or public transit. However, none maintained readily accessible data indicating which completed projects had this characteristic. Therefore, to estimate statistical associations between a development’s proximity to transit and development costs, we merged project address information with federal and local transit data. We were not able to estimate associations between other amenities and development costs.

Square footage. Four of the 12 selected allocating agencies independently determined, or provided us with information we could use to calculate, the gross square footage of projects. Construction cost per gross square foot is a commonly used measure in the construction industry and useful for comparing LIHTC project costs to construction industry benchmarks. Additionally, because it encompasses the entire size of the structure, this measure relates project cost to project scale more precisely than other common measures, such as cost per unit and cost per residential square foot.

Building type. The selected allocating agencies varied in how they defined and classified building types—such as single-family, multifamily, high-rise, mid-rise, or low-rise. As previously discussed, we classified projects generally based on the number of units and number of buildings they contained because data inconsistencies precluded more precise classifications.

Number of residential and nonresidential buildings. All of the selected allocating agencies collected data on the number of residential buildings in each project, but only five collected data on the number of nonresidential buildings. As with gross square footage, this information would allow cost assessments based on a project’s entire physical footprint. Additionally, this information would allow agencies to refine per- unit cost measures by subtracting the cost of nonresidential spaces (for example, community or other common areas) from per-unit cost totals.

Primary construction materials. The project documents we reviewed from the selected allocating agencies generally did not include data on the primary construction materials (for example, steel, concrete, brick, or wood). Including this information in data maintained on completed projects would help better explain cost variances between otherwise similar projects (for example, a 3-story building constructed with brick versus a 3-story building constructed with wood). This information is similarly useful for comparing LIHTC project costs to construction industry benchmarks.

Number of stories per building. A few agencies, including Arizona, California, and Texas, collected data on the number of stories per building in each of their projects. As previously discussed, development costs may increase for taller structures due to design requirements. As a result, data on the number of stories would facilitate cost comparisons across similar structures and assessment of costs against construction industry benchmarks.

Total syndication expenses. As discussed later in this report, none of the selected allocating agencies collected information on total tax credit syndication expenses. This information is necessary for understanding the cost of developing affordable-housing projects with LIHTCs.

Agencies Maintained Data in Different Formats

We also found that the 12 allocating agencies maintained cost-related LIHTC data in a variety of formats, ranging from paper records or electronic files for individual projects to electronic spreadsheets with information on multiple projects, as shown in the following examples.

Illinois provided us with scanned copies of paper applications and cost certifications for each project.

California provided us with a mix of scanned copies of paper and electronic applications and cost certifications for individual projects.

Ohio provided us with a consolidated (or single) electronic spreadsheet containing line-item costs for all projects.

This variation made it difficult to efficiently collect the data and put them in a format suitable for analyzing cost trends and drivers. To create a data set suitable for analysis, we manually entered data for 1,356 projects with paper files and consolidated data from spreadsheets using statistical software for 493 projects.

Agencies did not collect data using standardized cost categories for analysis. As a result, we met with individual allocating agency officials to define each variable and ensure that we consistently categorized data across the agencies. Some examples of differences in how the data were defined include the following:

New York City did not separate construction-related fees from construction costs. As a result, we were not able to compare construction costs for projects in New York City to construction costs for projects from the other 11 allocating agencies.

Some allocating agencies—for example New York—did not include a line item for syndication expenses on their cost certifications. On cost certifications without a syndication line item, developers generally are expected to report those costs on the legal or partnership line item. As a result, we were unable to report information on syndication expenses incurred at the project level.

Similarly, some allocating agencies’ cost certifications combined line- item costs that others did not. For example, 11 of the selected allocating agencies required developers to separately report general contractor overhead, profit, and general requirements, while 1 (New York City) generally required developers to combine the three costs under one line item. As a result, we had to create broad cost categories and were not able to assess costs at the line-item level.

Ways in Which Standardized Data Can Facilitate Agencies’ Cost Assessments

Few of the selected allocating agencies comprehensively or systematically evaluated data to determine the effect of their policies, including their cost-management approaches, on project development costs. Our analysis in the previous sections of this report highlighted ways in which allocating agencies can use and benefit from standardized data, including for project cost assessments.

Individual allocating agencies could use data to more effectively identify cost drivers and trends over time. We have discussed how certain project characteristics were associated with higher and lower per-unit development costs. Our analysis illustrates how agency priorities and practices may influence costs, as shown in the following examples.

Texas had the lowest median per-unit development costs among the selected agencies and tended to award credits to large garden-style apartments (low, clustered buildings).

Georgia also had comparatively lower development costs. The agency funded the highest percentage of senior projects among the selected states (48 percent) and also funded the lowest percentage of urban projects (55 percent).

Washington had among the lowest soft costs as a percentage of total development costs. Agency officials told us they used a consolidated application for awarding public funds—including LIHTCs, state tax credits, and HOME funds—that streamlines the application process for developers and reviewers and helps reduce soft costs.

California had the highest land costs and soft costs among the selected agencies. The agency prioritized funding projects in job centers (urban areas) and completed projects used six funding sources in addition to tax credit equity, on average.

Chicago had the highest construction costs as a percentage of development costs among the 12 selected agencies, and did not have a cap on development costs or eligible basis.

Florida had the highest developer fees among the selected agencies.

Our analysis showed the median developer fee in Florida was about $2.1 million for projects completed in 2011–2015; the next highest median fee was about $1.5 million (in New York and Texas). The agency’s 2017 QAP set developer fees generally at 16 percent of development costs, one of the highest rates among the selected agencies.

In turn, agencies that have identified their cost drivers and trends could look to the experience of other agencies for examples of relevant ways to contain costs. For example, agencies with comparatively high costs— either overall or in particular cost categories—might benefit from considering the cost-management approaches of agencies with lower costs.

Complete Data on Total Tax Credit Syndication Expenses Are Lacking

Syndication expenses represent a significant cost of producing affordable housing with LIHTCs, but complete data on syndication partnerships generally were lacking. As shown in figure 13, syndication expenses include expenses at the upper-tier and lower-tier partnerships of a LIHTC deal. Investors pay for upper-tier expenses in the form of a syndication fee, similar to a load fee paid to a mutual fund manager. The fee covers expenses related to establishing, originating, underwriting, and closing on projects for the investment fund and is paid out of the equity investors contribute to the partnership. As a result, the fee facilitates equity investment in a fund’s LIHTC projects, while also reducing the amount of the equity investment available to each project. At the lower-tier partnership level, a project developer may pay a fee to the syndicator for project-specific legal and accounting expenses. The lower-tier syndication fee is typically less than the upper-tier fee.

In a February 2017 report on the role of LIHTC syndicators, we cited an industry stakeholder’s estimate that upper-tier syndication fees for LIHTC funds were 2–5 percent of equity. According to a 2018 report by a national accounting firm, upper-tier syndication fees ranged from 5–8 percent of equity for multi-investor funds closed in recent years. For perspective, 2–8 percent of a $7.6 million investment (the estimated median amount for our 12-agency project sample) is $152,000–$608,000. The accounting firm report also noted that the market for acquiring projects and attracting investor capital is highly competitive. As a result, syndicators may reduce or defer their fees to attract projects and investor capital.

IRS regulations require project developers to report syndication expenses on their final cost certifications. IRS officials told us that the regulations require the reporting of all syndication expenses, including upper-tier and lower-tier fees, on the cost certification. They said the regulation helps to ensure that allocating agencies have complete information to assess the financial feasibility of projects, as required under Section 42. Additionally, written guidance for IRS examiners states that syndication costs need to be accounted for, although they are not includable in eligible basis (allowable costs for calculating tax credit awards), to ensure they have not been accumulated with other costs for a line item on the certification.

However, our 12 selected allocating agencies did not require developers to report upper-tier syndication expenses on final cost certifications and generally did not have data on these expenses. Allocating agency officials told us that developers generally report costs directly attributable to the project (including lower-tier syndication expenses) on the cost certifications.

In explaining their practices, allocating agency officials said they did not consider upper-tier syndication expenses to be project costs because they are not directly incurred by the developer. Some of the officials noted that developers select investors based on the net equity (gross equity minus upper-tier expenses) or net price offered in exchange for the tax credits, and therefore may not be aware of the fees investors pay syndicators. Additionally, accounting firm officials said that if upper-tier expenses were included on the cost certification, they would not be able to access or verify documentation from the upper-tier partnership when auditing cost certifications because the upper- and lower-tier partnerships are separate legal entities.

Outside of the cost-certification process, some of the selected allocating agencies said they receive investor letters or other documentation from syndicators that disclose upper-tier syndication expenses. These letters typically state the gross and net equity amounts attributable to each project, or a gross and net credit price offered in exchange for a developer’s credits. Some of the letters we reviewed also detailed the syndicator’s services and related expenses in addition to gross and net equity amounts or credit prices (for example, amounts for investor fees, organizational and offering expenses, acquisition expenses, and reserves and working capital). These examples suggest that information on upper- tier syndication expenses is available and allocable to specific projects.

The gap between IRS’s expectations and allocating agencies’ practices developed, in part, because IRS has not clearly communicated expectations to allocating agencies about reporting of upper-tier syndication expenses. None of the documents IRS pointed to—the regulations, Technical Advice Memorandum, or Revenue Ruling previously cited—draw a clear distinction between upper- and lower-tier expenses, leaving the requirement open to interpretation. The documents also do not address issues that developers, allocating agencies, and auditing firms may have in obtaining and reviewing upper-tier fees.

Federal internal control standards state that management should externally communicate—to contractors and regulators, among others— the necessary quality information to achieve the entity’s objectives. Without clear communication to allocating agencies on how to report syndication costs, IRS lacks assurance that the cost-certification requirement provides the level of financial transparency and accountability it expects.

More complete collection of data on syndication expenses also would help answer key questions in our 2013 tax expenditures evaluation guide, which provides a framework for evaluating the effectiveness of tax expenditures. Examples of questions relevant to syndication expenses include the following:

What are the costs of the resources used to generate the tax expenditure’s benefits? The costs of using syndicators cannot be known without disclosure of the upper-tier expenses for which LIHTC investors pay from their equity contributions.

Who actually benefits from the tax expenditure? Disclosure of the fees syndicators receive would aid assessment of the benefits received by syndicators in relation to benefits received by other LIHTC program participants.

The ability to answer these questions more fully would help Congress assess the costs, benefits, and efficiency of the LIHTC program relative to affordable housing programs that use delivery mechanisms other than tax expenditures.

No Federal Agency Monitors and Assesses LIHTC Development Costs

No federal agency monitors or assesses LIHTC development costs, which are key to evaluating the efficiency and effectiveness of the tax credit program. In a July 2015 report on federal oversight of LIHTC, we found that although IRS is the only federal agency responsible for overseeing the LIHTC program, it does not assess the performance of the program. IRS officials said the agency’s role is focused on ensuring taxpayer compliance and that the agency generally does not have the authority or funding to assess the performance of tax expenditures, including LIHTC.

Unlike for the LIHTC program, Treasury collects and reports data on the New Markets Tax Credit program, for which Treasury has a more direct administrative role. The Community Development Financial Institutions Fund within Treasury uses its Awards Management Information System and its Community Investment Impact System to collect and report detailed information on New Markets Tax Credit projects, including certain cost and project characteristics data. Treasury produces annual research reports and periodic research briefs using these data.

Consistent with a recommendation in our July 2015 report, IRS and Treasury officials said HUD may be better equipped to determine what data should be collected to assess LIHTC performance. Although HUD is the government’s lead housing agency, it currently plays a limited role in collecting and reporting data for the LIHTC program. Specifically, HUD collects and periodically reports information on LIHTC tenant characteristics as mandated by the Housing and Economic Recovery Act of 2008. In addition, since 1996, HUD voluntarily has collected LIHTC project-level data in its LIHTC database. While HUD may have the technological capacity to collect and maintain additional LIHTC data, absent additional authority, the agency does not have access to IRS taxpayer (developers and allocating agencies) data, including cost data. If HUD or another agency were given authority to collect and report on these data, it likely would need additional budgetary resources to carry out this function.

Our tax expenditure evaluation guide outlines information Congress could consider when determining which federal agencies should manage the evaluation of tax expenditures. The guide cites statutory requirements that set the expectation that agencies should consider tax expenditures in measuring and communicating progress in achieving their missions and goals. It also states that for tax expenditures without logical connections to program agencies, Treasury may be the most appropriate agency to conduct an evaluation. Historically, IRS and Treasury (the agencies with the authority to oversee the LIHTC program) have devoted few resources to that task. And although HUD has a logical connection to LIHTC as the lead federal housing agency, it does not have oversight authority, access to key data, or existing resources to carry out additional data collection for and assessments of the LIHTC program. Without federal monitoring and assessment of LIHTC development costs, federal agencies and Congress do not have information to assess the tax credit’s efficiency and effectiveness.

Conclusions

The LIHTC program plays an important role in addressing the housing needs of low-income renters, but some LIHTC projects have been scrutinized for high or fraudulent development costs. Our analysis provides a broad perspective on development costs across a range of allocating agencies and illustrates the types of insights than can be gained from standardized data on project costs and characteristics. These include identification of cost drivers and trends that may help target cost-management efforts.

However, our work also identified shortcomings in program data and administration that hamper oversight and are inconsistent with federal evaluation criteria and internal control standards.

Although the LIHTC program represents the largest source of federal assistance for developing affordable housing, Congress has not specifically designated an agency to evaluate the program’s performance. Without a designated entity for collecting, maintaining, and assessing data on LIHTC project costs, federal agencies and Congress lack information needed to oversee billions of dollars in tax expenditures.

The current IRS cost-certification requirement for LIHTC projects is limited to aggregated developer costs and does not directly address a known fraud risk. General contractor cost certifications required by some allocating agencies may help deter fraud by providing information that can be used to corroborate developer cost certifications. But because IRS does not require general contractor cost certifications for LIHTC projects, the LIHTC program may be vulnerable to fraud involving misrepresentation of costs.

The lack of standards for collecting and maintaining data related to LIHTC project costs has resulted in inconsistent data quality and formats among allocating agencies. In the absence of a federal agency designated to collect data and assess program performance, greater standardization of cost data by allocating agencies would lay a foundation for deeper analysis of cost drivers and cost-management practices by allocating agencies and industry stakeholders. This analysis could be used to help increase the efficiency of the LIHTC program.

IRS has not clearly communicated how allocating agencies should collect and review syndication expenses—particularly, upper-tier fees—to meet a regulatory requirement. As a result, information on a significant program cost is not transparent or available to conduct the types of financial assessments IRS expects allocating agencies to perform.

Matter for Congressional Consideration

Congress should consider designating an agency to regularly collect and maintain specified cost-related data from credit allocating agencies and periodically assess and report on LIHTC project development costs. (Matter for Congressional Consideration 1)

Recommendations for Executive Action

We are making a total of three recommendations to IRS: IRS’s Associate Chief Counsel, in consultation with Treasury’s Assistant Secretary for Tax Policy, should require general contractor cost certifications for LIHTC projects to verify consistency with the developer cost certification. (Recommendation 1)

To help allocating agencies analyze development cost trends and drivers and make comparisons to other agencies, IRS's Commissioner of the Small Business/Self-Employed Division should encourage allocating agencies and other LIHTC stakeholders to collaborate on the development of more standardized cost data, considering information in this report about variation in data elements, definitions, and formats. (Recommendation 2)

IRS’s Associate Chief Counsel, in consultation with Treasury’s Assistant Secretary for Tax Policy, should communicate to credit allocating agencies how to collect information on and review LIHTC syndication expenses, including upper-tier partnership expenses. (Recommendation 3)

Agency and Third- Party Comments and Our Evaluation

We provided a draft of this report to IRS, Treasury, and HUD for their review and comment. IRS provided written comments that are reprinted in appendix VII. Treasury and HUD did not provide comments. We also provided a draft to NCHSA for its review and comment. NCSHA provided written comments that are reprinted in appendix VIII.

IRS disagreed with our recommendation to require general contractor cost certifications for LIHTC projects. IRS said it was not clear whether the recommendation would uncover and deter misrepresentation of contractor costs. We maintain that requiring general contractor cost certifications would help address this fraud risk by providing greater cost transparency to allocating agencies and auditors. Our report notes that a number of allocating agencies already have similar controls and that the Florida agency began requiring general contractor cost certifications in response to fraudulent contract-inflation schemes that were the subject of federal legal actions. Furthermore, NCSHA’s recommended practices advise allocating agencies to implement additional cost certification due diligence for all LIHTC projects. We believe that general contractor cost certifications should be required to help ensure the efficient and effective use of federal resources programwide.

IRS disagreed with the recommendation in our draft report to collaborate with LIHTC stakeholders to develop a framework for the collection of cost- related data. The purpose of this recommendation was to promote creation of more standardized data to help allocating agencies analyze cost trends and drivers and make comparisons to other agencies. IRS said that in the absence of specific authorization, it collects data only to the extent necessary for tax administration, and that collecting LIHTC cost data is not necessary for that purpose. IRS added that without statutory authorization or a tax administration need, any data collection would be a misuse of IRS resources. In response, we modified the recommendation in our final report to give IRS greater flexibility in promoting standardization of LIHTC cost data in ways consistent with its authority. For example, IRS could encourage development of more standardized data in its communications with LIHTC allocating agencies and stakeholders at industry meetings and conferences. Our report recognizes that IRS has not had a role in assessing the performance of tax expenditures. For this reason, our report also states Congress should consider designating an agency to regularly collect and maintain specified cost-related data from allocating agencies and assess and report on LIHTC project-development costs.

Finally, IRS disagreed with our recommendation to communicate to allocating agencies how to collect and review information on LIHTC syndication expenses, including upper-tier partnership expenses. IRS said that existing regulations require agencies to collect and evaluate all sources and uses of project funds and that this covers syndication expenses, including upper-tier partnership expenses. IRS said to the extent that we were recommending that it revise regulations, the agency did not necessarily have the authority to mandate how allocating agencies collect syndication expense data. IRS’s response suggests the reporting requirements are clear. However, as stated in our report, the 12 allocating agencies we reviewed and other LIHTC stakeholders did not share IRS’s understanding of the requirement. Consequently, the allocating agencies did not require developers to report upper-tier syndication expenses and generally did not have data on the expenses. In its comments on our report, NCSHA also expressed surprise at IRS’s explanation (see discussion below and app. VII). Finally, our report does not state that IRS should revise its regulations. Rather, it recommends that IRS communicate its requirement to allocating agencies. The wording of our recommendation provides IRS the flexibility to communicate the requirement in whatever way it deems appropriate. As a result, we made no changes to the recommendation.

In its comments, NCSHA expressed concerns about our recommendation and matter for congressional consideration about collecting and analyzing LIHTC cost data. NCSHA questioned the cost-effectiveness of requiring consistent data across states and did not believe that cross-state comparisons were critical for evaluating LIHTC. For example, NCSHA said the utility of comparing Hawaii costs to Arkansas costs was not clear. NCSHA also noted LIHTC was designed to give allocating agencies flexibility, including in program design and data collection. We maintain consistent data are important for program management and oversight. While cost drivers in states differ, our report notes that at least one allocating agency has funded a study to compare development costs with neighboring states. While we understand the LIHTC program gives states flexibilities, a more standardized approach to data collection would not restrict allocating agency funding decisions or prevent agencies from collecting data they consider important. Furthermore, consistent data collection would facilitate state and federal evaluations of the cost- effectiveness of a multibillion dollar tax expenditure. NCSHA also expressed concern that Congress might require the data collection but not appropriate funds to implement the mandate. Our report acknowledges that if Congress were to grant an agency the authority to collect and report on LIHTC cost data, that agency likely would need additional budgetary resources to carry out this function.

Regarding our recommendation on general contractor cost certifications, NCSHA noted that more allocating agencies were likely to adopt NCSHA’s recommended practices and require or encourage such certifications. However, allocating agencies voluntarily adopt recommended practices, and some agencies may view a general contractor cost certification as unnecessary. NCSHA added that instances of fraud were rare in the 30-year history of LIHTC, and affected agencies had responded in each known instance. We noted in our report that under the existing federal cost certification requirement—which stops at the developer level—the vulnerability of the LIHTC program to misrepresentation of general contractor costs is heightened. And while known instances of fraud schemes (such as the Florida examples cited in our report) may be limited, the true extent of fraud in the program is unknown. Federal internal control standards state that management should consider the potential for fraud when identifying, analyzing, and responding to risks. Requiring general contractor cost certifications for all LIHTC projects could help address this known fraud risk and further strengthen the integrity of the program.

Regarding our recommendation on syndication expenses, NCSHA was surprised IRS officials told us LIHTC regulations require reporting of all syndication expenses (including upper-tier expenses) on the project cost certification. NCSHA said it long understood that the cost certification must include only costs paid by the project partnership for the individual property (the developer) and that IRS never communicated otherwise. NCSHA also identified some potential difficulties with collecting and reporting information on upper-tier syndication fees. While our report discusses some similar concerns, it also provides examples of at least two allocating agencies that collect such information. NCSHA’s response further supports our finding of a gap between IRS expectations and allocating agency practices for reporting syndication expenses and underscores the need for IRS to more clearly communicate its expectations on how to collect and review this information.

Finally, NCSHA said findings from its recently commissioned study of LIHTC development costs, which had not been released as of August 2018, were generally consistent with cost analyses in our report. NCSHA said its study and other information suggest LIHTC development costs generally were consistent with overall apartment development costs and grew at a similar or slower rate. We believe broad comparisons between LIHTC and non-LIHTC development costs should be viewed with caution. As our report notes, numerous limitations in available LIHTC cost data (among other factors) make it difficult to produce methodologically sound comparisons. If implemented, our recommendations to improve collection and analysis of LIHTC data could help overcome some of these difficulties.

We are sending copies of this report to the appropriate congressional committees, the Secretary of the Treasury, the Secretary of Housing and Urban Development, and other interested parties. In addition, the report will be available at no charge on the GAO website at http://www.gao.gov.

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

Appendix I: Objectives, Scope, and Methodology

The objectives of this report were to analyze (1) development costs for Low-Income Housing Tax Credit (LIHTC) projects completed in 2011– 2015 in selected locations and factors affecting these costs, (2) steps allocating agencies have taken to oversee LIHTC development costs, and (3) factors limiting assessment of LIHTC development costs.

We selected 12 credit allocating agencies (representing 10 states and 2 cities) as the focus for key parts of our analysis discussed in more detail later in this appendix:

Arizona Department of Housing

California Tax Credit Allocation Committee

Chicago Department of Planning and Development

Florida Housing Finance Corporation

Georgia Department of Community Affairs

New York City Department of Housing Preservation and Development

New York State Division of Housing and Community Renewal

Ohio Housing Finance Agency

Pennsylvania Housing Finance Agency

Texas Department of Housing and Community Affairs

Washington State Housing Finance Commission To select these agencies, we ranked all states in order of their credit ceiling amount for 2015 and selected the two highest-ranking states in each of five geographic regions (West, Southwest, Midwest, Southeast, and Northeast). We then selected for review the 12 allocating agencies within those 10 states that administered 9 percent LIHTCs. These allocating agencies accounted for 50 percent of the total 9 percent credit ceiling amount in 2015.

To obtain general information for all of our objectives, we interviewed officials from the 12 selected allocating agencies, the Department of Housing and Urban Development (HUD), Department of the Treasury (Treasury), and Internal Revenue Service (IRS). We also interviewed representatives from 10 groups representing allocating agencies, developers, investors, syndicators, and other LIHTC interests, including Affordable Housing Investors Council; Affordable Housing Tax Credit Coalition; Recap Real Estate Advisors; Housing Partnership Network; Enterprise Community Partners; Mortgage Bankers Association; National Association of Home Builders; National Association of State and Local Equity Funds; National Council of State Housing Agencies (NCSHA); and Stewards of Affordable Housing for the Future. Additionally, we interviewed representatives of two national accounting firms— CohnReznick LLP and Novogradac & Company LLP—that have LIHTC practices and have conducted research on the LIHTC program.

Data Used in Our Analysis of Costs and Characteristics

To analyze the development costs of LIHTC projects completed in 2011– 2015 in selected locations and characteristics associated with project costs, we created and analyzed a database of costs and characteristics for the 1,849 LIHTC projects that submitted final cost certifications to the 12 selected allocating agencies in that period and for which the cost certification was available.

Collecting LIHTC Project Data

We first requested relevant documentation and data from the selected allocating agencies. Specifically, we requested the final cost certification for all projects that received 9 percent LIHTCs and were submitted in 2011–2015. We also included projects for which the selected allocating agencies initially reserved a tax credit allocation but exchanged the allocation for American Recovery and Reinvestment Act of 2009 funds.

In addition to cost certifications, we also requested documentation and data that described project characteristics associated with project costs. We determined relevant characteristics to collect through a review of existing housing-agency-sponsored literature on LIHTC project costs. We identified existing literature through a literature search, and we confirmed the completeness of the literature with selected industry groups. The project characteristics we collected from the selected allocating agencies included the following:

Address (street, city, state, and zip code)

Construction type (new construction or rehabilitation)

Income limits for low-income units

Number of buildings (residential and non-residential)

Number of units (low-income, market-rate, and employee-occupied)

Square footage (gross and residential)

Structural features (the presence of an elevator, green building certifications, and parking structures)

Net tax credit price

Tenant type (senior or nonsenior)

Unit sizes (number of bedrooms)

Year of completion (year final cost certification signed)

We used manual data entry and a statistical program to input the project costs and characteristics into individual databases we created for each selected allocating agency. We verified the accuracy of the manual data entries by having a second analyst review the entries of the first analyst. Additionally, a second analyst reviewed the statistical programs we created and a sample of the databases they created to verify their accuracy. After compiling the 12 databases, we compared our list of projects against HUD’s LIHTC database to verify the completeness of our sample. For projects that we determined had been omitted, we requested their documentation and data from the relevant allocating agency, which we then manually entered into our databases and verified in the manner previously described.

Consolidating LIHTC Project Data

To perform analyses across all sampled projects, we consolidated the 12 allocating agency databases into one sample-level database. We first interviewed each of the selected allocating agencies to define data elements—including how to treat missing data—and determine the comparability of the data they provided. We also requested additional documentation and data, such as missing project addresses and data elements we identified after our initial data request. Additionally, we interviewed a national accounting firm that specializes in LIHTC cost certifications to further define cost data and learn more about their comparability across allocating agencies.

We then categorized project costs into aggregated categories. Line items in cost certifications were not comparable across all selected allocating agencies due to differences in how data were reported. For example, market study costs were listed separately on some cost certifications but aggregated with appraisal costs on others. To improve the comparability of cost data across allocating agencies, we developed and implemented a plan to categorize and consolidate cost data using a statistical program. We developed the plan by reviewing the overlap between the line-item costs we collected. We also reviewed a study of multiple allocating agencies that was conducted by an accounting firm specializing in LIHTC cost certifications and which used a similar methodology to consolidate costs. Based on our plan, we categorized costs into three hard-cost and four soft-cost categories:

Construction: Costs related to the direct physical development of the project site and structures. These include change orders; construction trade material and labor (such as electrical, masonry, or roofing); contingencies; demolition; environmental remediation; furniture, fixtures, and equipment; landscaping and fencing; off- site and on-site improvements; other property assets (such as maintenance, office, or playground equipment); prevailing wages; site security (if listed separately from contractor fees); tenant relocation; and utilities during construction.

Existing structures: The purchased or appraised value of acquired structures.

Land: The purchased or appraised value of acquired or leased land.

Architect and engineer fees: Fees for architectural design and supervision and engineer services.

Contractor fees: Contractor general requirements, overhead, and profit.

Developer fees: Developer overhead and profit.

Other soft costs: Costs related to financing, tax credit partnership and syndication, predevelopment, professional services, and other indirect construction activities, as shown in the following examples. These include accounting; agency fees (such as application, reservation, allocation, extension, compliance monitoring, and waivers fees); appraisals; broker fees and closing costs; capital needs assessments; certifications; construction-management fees; project supervision or monitoring; consultant fees; credit reports; environmental reports (such as asbestos and lead-paint tests); green building and energy efficiency design services; impact and utility connection fees; inspections; insurance (such as builders risk, general liability, hazard, and title insurance); surveys; legal fees; loan fees and interest (such as for predevelopment loans, construction loans, bridge loans, and permanent loans); market studies; payment or performance bonds; permits and other local fees; real estate taxes (during construction); soil borings and tests; and title searches and recording.

We also collected each project’s total development cost and eligible basis from the cost certification. To isolate development costs, we subtracted from each project’s total development cost all costs associated with prefunded reserves and postconstruction activities, such as marketing and rent-up period operating expenses.

We also developed and implemented a plan to consolidate project characteristics data into the sample-level database using a statistical program. We interviewed officials and reviewed documentation from selected allocating agencies about data definitions to determine the comparability of the characteristics data we collected. We then recoded comparable data elements using a standard coding system across all 12 allocating agencies. We conducted verification checks on the programs we created and the final database.

To assess the reliability of the project data, we tested each data field for missing values, obvious errors, and outliers—for example, whether per- unit costs were more than two standard deviations from an allocating agency’s average. We communicated some outliers and inconsistencies to relevant allocating agency officials and made corrections to the database as necessary. We concluded that the data were sufficiently reliable for purposes of comparing LIHTC development costs within and across allocating agencies and for examining development cost drivers and trends. As an additional test, we compared summary statistics from applicable data elements in our database to comparable data elements in HUD’s LIHTC database. We found that our data elements did not differ in significant ways from HUD’s.

Incorporating Location Data from Secondary Sources

We then merged several additional location characteristics into our database from federal and public statistical sources. We first validated project addresses and then used them to determine the census tract for each project. We then used census tracts to incorporate data from the American Community Survey, including census tract size and population (which we used to calculate population density), median home value, poverty rate, and unemployment rate.

Using the census tract, we also identified the Rural-Urban Commuting Area codes classification for each project, which we recoded to categorize each project as rural, suburban, or urban. We also identified whether each project was located in a qualified census tract or difficult development area using the 2017 HUD lists. Lastly, we used geographic information system software and the Department of Transportation’s Fixed-Guideway Transit Network database to identify the distance from each project to the nearest transit station (train and bus rapid transit stations).

Before conducting our analyses, we prepared data analysis plans and interviewed selected representatives from industry groups and researchers to inform our efforts. We also clarified data interpretations and limitations with officials from the selected allocating agencies on an as-needed basis.

Costs and Characteristics of LIHTC Projects

To describe the costs and characteristics of LIHTC projects, we calculated and compared summary statistics for relevant database elements. To account for inflation, we converted all costs to 2015 dollars using the calendar-year, chain-weighted Gross Domestic Product price index. We also normalized costs by dividing the total development cost by the number of units. We then calculated and compared summary statistics for key categories, such as the number and median per-unit cost of new construction projects, and subcategories, such as the number and median per-unit cost of new construction projects in urban areas. We also repeated these analyses for each selected allocating agency.

To compare the cost of Chicago’s and New York City’s projects to other urban locations, we calculated and compared their median per-unit costs to costs in five other cities within our 12-agency sample that had comparable populations and densities. Using 2010 Census data, we selected the five densest cities (people per square mile) with populations of 300,000 or more, population densities of 5,000 or more people per square mile, and 10 or more new construction projects completed in 2010–2015. They were Los Angeles, Miami, Philadelphia, San Francisco, and Seattle. To identify all projects within the five selected cities, we matched the three-digit zip code prefixes associated with their U.S Postal Service area (known as a sectional center facility) to the zip codes for sampled projects.

To determine the composition of project costs in terms of hard and soft costs, we compared the sum of all hard costs and the sum of all soft costs to the sum of all total development costs by construction type. Hard costs included existing structures, land, and construction costs; soft costs included architect and engineer fees, contractor fees, developer fees, and other costs. We also compared the cost categories (such as construction costs) using the same approach as for hard and soft costs. We then repeated these steps for each selected allocating agency.

We also reviewed how LIHTC equity investments differed by construction type. We first calculated the equity investment for each project by multiplying the LIHTC allocation by the net credit price (both adjusted to 2015 dollars). We then calculated and compared the median per-unit equity investment and the percentage of the median per-unit total development cost that it comprised for new construction and rehabilitation projects.

To determine how total development costs changed over time, we calculated and compared the median per-unit cost for each year by construction type. We then repeated these steps for each allocating agency to determine how their costs changed over time. We also repeated the sample-level analysis over time excluding California’s projects from the new construction pool and New York City’s projects from the rehabilitation pool because, in both cases, their costs were among the highest, changed sharply in some years, and represented roughly one-fifth of all new construction and rehabilitation projects, respectively.

To determine how LIHTC construction costs changed over time relative to a federal index of construction costs, we calculated and compared the annual rates of change in the median per-unit cost of construction and contractor fees for sampled new construction projects to the rates of change in the annual averages for the Bureau of Labor Statistics’ Producer Price Index by Commodity for Final Demand: Construction. This index tracks monthly price changes for construction materials, labor, equipment, and contractor fees. To account for the delay between when construction costs were incurred and projects completed, we compared the annual rates of change for the LIHTC projects to the annual rates of change in the average index value from the prior year. We also used the prior-year rate of change to generate a projection of LIHTC construction costs to determine how the sample trend differed from the index trend. For example, we calculated the projected cost in 2012 by inflating the actual cost in 2011 by the change in the average index value in 2010– 2011.

To determine the association between the project characteristics we collected and per-unit development cost, we developed a statistical model and used ordinary least squares regression to estimate the controlled effect of specified characteristics on per-unit cost. For more detail on our statistical model and results, see appendix II. To further describe how project characteristics may have influenced costs, we calculated and compared summary statistics for the model characteristics among new construction projects below the 25th percentile or above the 75th percentile for per-unit cost within each allocating agency.

Steps Taken to Assess Allocating Agencies’ Oversight of LIHTC Development Costs

To analyze steps allocating agencies have taken to oversee LIHTC development costs, we reviewed the Qualified Allocation Plans (QAP) and related documents (for example, policy manuals) for all 57 allocating agencies as of 2017. These agencies included all 50 states, the District of Columbia, the 4 U.S. territories that received a LIHTC allocation in 2017 (Guam, Northern Mariana Islands, Puerto Rico, and U.S. Virgin Islands), and the Cities of Chicago and New York. We conducted a structured analysis of the QAPs and related documents to gather information about agencies’ policies and practices for managing and verifying project-development costs. We defined “cost management” as practices allocating agencies used to contain or limit development costs and fees, such as cost limits, credit allocation limits, fee limits, and cost- based scoring criteria. We defined “cost verification” as practices the agencies used to confirm the accuracy of project costs following construction—that is, whether the amount paid equaled the amount billed.

To obtain supplementary information on allocating agency approaches to cost management, we interviewed officials and reviewed additional documentation from the 12 selected allocating agencies, identified previously. Through this work, we identified a number of other steps those agencies took to limit LIHTC development costs. While the results of our supplementary work cannot be generalized to all allocating agencies, they provide additional insight into the cost-management approaches and cost-verification requirements of a diverse group of allocating agencies. For further context on cost-management approaches, we reviewed GAO and industry reports that analyzed allocating agency QAPs from prior years.

We also interviewed federal officials to obtain information about relevant LIHTC requirements and cost-management practices used in other federal programs that support development of affordable multifamily housing. Specifically, we spoke with IRS and Treasury officials about LIHTC cost-verification requirements and the approaches of allocating agencies to cost management. In addition, we interviewed HUD officials to identify cost-verification practices used in the HOME Investment Partnerships Program and the Federal Housing Administration’s Multifamily Mortgage Insurance programs. To obtain additional information about allocating agency practices and the cost-certification process, we interviewed representatives of NCSHA, CohnReznick LLP, and Novogradac & Company LLP.

Steps Taken to Evaluate Factors Limiting Assessment of LIHTC Development Costs

To analyze factors limiting assessment of LIHTC development costs, we assessed the data we collected from the 12 allocating agencies. We identified and documented the consistency in cost-related variables agencies collected in several key documents and data sources, and how they defined the variables. We documented the formats in which agencies provided and maintained the data we requested and steps we took to standardize and combine data. We compared the variables the agencies collected against federal tax credit allocation priorities outlined in Section 42 of the Internal Revenue Code (Section 42), as well as certain allocating agency priorities. In addition, we reviewed an off-the- shelf software package for cost-estimation to determine what project characteristics were required to calculate estimates with the software, and evaluated the extent to which the selected agencies collected these characteristics.

We also reviewed Section 42 and related regulations to ascertain requirements for reporting syndication expenses to allocating agencies and IRS, and interviewed IRS and Treasury officials about these requirements. We interviewed the selected allocating agencies about their practices for collecting and reviewing syndication expense information. We also interviewed CohnReznick LLP and Novogradac & Company LLP about the different fees syndicators charge to investors and developers, and the extent to which these fees are reported to allocating agencies. Finally, we reviewed our prior work on federal oversight of the LIHTC and other tax credit programs.

We conducted this performance audit from May 2015 to September 2018 in accordance with generally accepted government auditing standards. Those standards require that we plan and perform the audit to obtain sufficient, appropriate evidence to provide a reasonable basis for our findings and conclusions based on our audit objectives. We believe that the evidence obtained provides a reasonable basis for our findings and conclusions based on our audit objectives.

Appendix II: Description of Our Statistical Model to Examine Factors Associated with Development Costs for Low-Income Housing Tax Credit Projects

This appendix provides an overview of our statistical analysis of factors associated with the cost of producing affordable rental housing supported by the Low-Income Housing Tax Credit (LIHTC). We developed a regression model that explains the costs based on a number of project characteristics and other factors. As described in appendix I, we developed a data set based primarily on information from 12 selected allocating agencies. The data set contains detailed information on 1,849 LIHTC projects with final cost certifications signed in 2011–2015 and provides broad geographic coverage, including urban, suburban, and rural locations. whether a project was located in a qualified census tract or a difficult development area.

We augmented these data with information from the American Community Survey and from USDA to enable us to control for certain neighborhood characteristics that may be associated with the cost of developing and constructing LIHTC projects.

Key Characteristics of the Projects

Table 4 below provides an overview of project costs and some key attributes of projects in our sample and highlights the variation across the allocating agencies. The average total cost per unit in our data set is about $220,000 (in 2015 dollars). The average total cost per unit was greater than $300,000 in California and Chicago and less than $150,000 in Georgia and Texas. Construction costs were greater than or approaching $200,000 in Chicago and New York City and less than $100,000 in Georgia and Texas. Project scale varied across the agencies, reflecting differences in built environments, property costs, and other factors and averaged 66 units and 7.5 buildings.

The cost of land and existing structures can be a large component of project development costs. Land costs can scale with project size (an apartment complex of 12 buildings could require twice as much land as a complex of 6 buildings) as well as with underlying market land values. The median land value across all projects was about $400,000, and was more than $1,000,000 in California and Florida. But the median land cost in New York City was about $1, suggesting that land and structures were donated. Given the market values of New York City real estate, total development costs for some New York City projects are likely to be understated when compared to projects in other jurisdictions.

Variable Definitions

Variables Describing Project Characteristics

The data set includes detailed information on program characteristics (discussed previously) that we used to define explanatory variables. We included the size of projects as defined by total units and placed them in four size categories (fewer than 37 units, 37–50 units, 51–100 units, and more than 100 units).

To develop a project-type categorization, we incorporated information on the number of residential buildings. Projects can come in many combinations of building count and building size (number of units). For instance, a 60-unit project could be a single 60-unit building, 10 6-unit buildings, or 30 2-unit buildings. We distinguished projects in which the average building size had at least 60 units (“larger buildings” category) and projects with at least 20 buildings (“many buildings” category). We placed all remaining projects in a large residual category. This category is somewhat independent of size and primarily is meant to distinguish among types of projects that might require specialized construction or project-management skills.

We also created variables to provide information on the distribution of units by number of bedrooms within each project. Bigger units, those with more bedrooms, are more costly to build. We created three unit size categories: 0-1 bedroom, 2 bedrooms, and 3 or more bedrooms. We defined the values as shares of total units in the category. For example, if a given project had 80 units, 20 of which had 1 bedroom, 40 of which had 2 bedrooms, and 20 of which had 3 bedrooms, the values for these variables would be 0.25, 0.5, and 0.25 respectively. The values sum to 1 across the categories.

We used binary variables to indicate if projects were new construction or rehabilitation. New construction is generally thought to be more expensive than rehabilitation on average, given site work and possible demolition requirements. We also developed variables to indicate if a project was targeted to seniors and if it served low-income tenants exclusively or a mix of low-income and other tenants.

We used two variables (yes or no binaries) to indicate if a project was in a qualified census tract or difficult development area. Within the LIHTC program, the size of the credit awarded for a given project may be increased if the project is located in such areas.

We also used information on other project characteristics that would affect costs, which we obtained for some, but not all, allocating agencies. For instance, for two agencies we could indicate that the project included parking structures (as opposed to a surface parking lot or stand-alone garage or carports), and for three agencies, that projects were built according to Leadership in Energy and Environmental Design (LEED) standards.

Variables Describing Project Financial Support and Developer Type

Variables from Other Sources to Control for Neighborhood and Geography

A broad set of factors related to local conditions, as well as conditions such as whether project locations are rural or urban, likely influence the costs of developing and building projects. Thus, we also used codes developed by USDA (the Rural-Urban Commuting Area codes) to place each project into rural, suburban, or urban categories. because a given dollar amount of rent represents access to different housing quality in different places. That is, neighborhoods in which rents are high or low may share common characteristics across the country.

We also used a series of allocating agency dummy variables and a series of project year dummy variables to control for otherwise unmeasured factors that may be common across projects or conditions in each agency jurisdiction or year, respectively.

Information on Omitted Categories for Categorical Variables

Many of the explanatory variables in the model are categorical variables, and thus the coefficient estimates presented in the tables in this appendix need to be interpreted in terms of differences from an omitted category. The omitted categories are for project scale, projects with fewer than 37 units; for project type, all projects in which there are fewer than 60 units per building and fewer than 20 residential buildings; for unit size, the 2-bedroom group; for age of housing stock, median year built between 1945 and 1994; for contract rent, neighborhoods in which the median contract rent is between the 25th percentile and median values of the state-wide contract rent; and for geographic area, suburban.

Some allocating agencies did not have complete information about whether other program funding, such as funding from Rural Development or ARRA programs, were used for projects. Conceptually, these variables are yes or no binaries. One approach is to add an “unknown” category in addition to the usual yes or no binary. That is, the categorization becomes “known yes,” “known no,” and “unknown.” An alternative approach is to treat missing information as the absence of the characteristic of interest. Using the three-category approach generally yielded virtually identical results to the alternative in which “missing” information was treated as the absence of the characteristic.

In general, we used a traditional binary structure. In one case, we kept the three-category structure. Specifically, we created a measure across agencies as to whether projects were targeted solely to low-income tenants or to a mix of low-income and other tenants. In many cases and across many agencies, we were not able to reliably make this determination using information in the data set. For estimation purposes, we included the unknown and known low-income category binary variables and omitted the known mixed-income category. The interpretation of the known low-income category is still the difference from the known mixed-income category. Other variables are binary, indicating the presence of the characteristic (such as if the project used a Rural Development loan or not, or was in a qualified census tract or not).

Regression Strategy

Following Cummings and DiPasquale, we estimated a regression model to explain total development costs per unit—and alternatively, measures of construction costs and soft costs separately—as depending on these project and neighborhood characteristics. We developed a base case model including the variables discussed previously and estimated this model using all 1,849 observations. The pooled sample, because it provides a broad range of conditions and policy responses, can permit a similarly broad view of the influences on LIHTC project costs.

At the same time, we wanted to have some idea about how sensitive broad, overall results were to the influence of conditions and policy responses of particular jurisdictions. (We would expect housing market conditions and housing policy responses to differ across agencies.) Thus, we also present the same model estimated on three different subsamples in which the projects of particular allocating agencies were excluded. The pooled sample and subsample results are shown in table 5 later in this appendix.

Specifically, we present results on samples excluding projects in California, New York City, and Texas in turn.

California had the highest average total cost, highest (observed) land costs, and biggest program in terms of allocation of tax credits and units placed in service.

New York City is a completely urban jurisdiction. About 75 percent of its projects were rehabilitation projects (compared to about one-third for the entire sample). More than half of its projects were in neighborhoods in which the median year housing stock was built was 1945 or before (compared to about 15 percent for the entire sample).

Texas had the lowest total cost and lowest construction costs and soft costs per unit, with many large, multibuilding projects that may be impractical in some other contexts. It was second to California in allocation of tax credits and units built.

Housing conditions in the three jurisdictions and policy options favored by these jurisdictions may not represent conditions and policy options easily available or desirable in other jurisdictions.

We also present estimates explaining construction costs per unit and soft costs per unit as alternatives to total costs. The construction cost measure includes costs for site and structure work and fees paid to the building contractor. We defined a broad soft cost measure to include predevelopment costs, financing costs, legal fees, architect and engineer fees, developer fees, and project-level partnership and syndication fees. Some factors may be more associated with the construction-cost component and less associated with the soft cost project-development component, or vice versa. These results are shown in table 6.

Sensitivity Analysis

We also present results using the pooled sample set for three variations of the base specification. The first variation omitted the property value variable. Property values vary within states and metropolitan areas, as well as across the states. We examined the extent the presence of this control affected the influence of other factors. The second variation omitted variables related to neighborhood characteristics. The third variation omitted the variables related to other types of housing support (for example, HOME funds). These results are shown in table 7. received final cost certifications in 2011 and 2012. In table 9 we present results concerning possible cost-related features (parking structures, LEED certification, and developer type) for specific agencies and a subset of projects.

We addressed whether our estimates were sensitive to the possibility that observed values for total cost might be artificially low when land or structures were acquired at very low or zero cost. We restricted projects to those in which land and structure costs accounted for at least 1 percent of total development costs and estimated our model on this subsample using both total costs and construction costs as dependent variables. We present our results in table 10.

We examined whether the results were sensitive to the form in which some credits were granted in New York City. That is, credits awarded in New York City to many single-building projects appeared to be part of larger neighborhood clusters under common development. In an alternative version, we aggregate project-level information to the level of multibuilding project clusters. We present the results in table 11.

Finally, we looked at whether proximity to transit affected project costs. Some allocating agencies may offer incentives for transit-oriented developments—or projects within certain proximity to public transit. These areas may have higher land and construction costs due to higher density and demand within urban environments. Using projects within 2 miles of a transit station and various distance ranges, we estimated the association with per-unit total and construction costs. We present the results in table 12.

Regression Specification

We used ordinary least squares estimation with heteroscedasticity consistent standard errors. This model allowed us to make statements concerning the association of explanatory factors on project costs, given that other explanatory factors were held constant. As is the case in such models, we generally only can discuss associations between explanatory factors and the cost measure to be explained, and not causality. For example, the use of other sources of government funding may have directly increased construction costs, as fund usage can trigger federal prevailing wage requirements. On the other hand, these other funding sources may have been used in addition to LIHTC equity to fill funding gaps for projects with particularly high costs. Additionally, econometric estimates can be sensitive to model specification, variable definitions, and the omission of variables (for example, due to unavailable data) relevant to the outcome of interest.

Because the data used to estimate the model include only LIHTC projects that were placed in service, we cannot make statements about how the costs of developing these projects may compare to other potential LIHTC projects or to projects developed and financed by the private sector. It is probably true that allocating agencies could have selected lower-cost (or higher-cost) projects compared to those actually selected, but whether or not this counterfactual housing would have better served the low-income population is a different question.

Estimation Results

Our results are presented in tables 5 through 12. Our estimates include allocating agency and project year dummy variables, which are not presented in the tables. The allocating agency dummy variables are agency-specific intercept shifts, given the estimation of common slopes, and largely pick up unexplained deviations from the pooled average costs. The project year dummy variables were estimated to be small and only rarely statistically significant. We also estimated a version in which each agency and project year combination had its own intercept shift, but these results were quite similar. The dependent variable in most cases is total development cost per unit, adjusted for inflation.

Base Case Results and Sensitivity to Included Allocating Agencies

level. Without California in the sample, per-units costs in the “many buildings” projects indicator were estimated to be more than $10,000 higher than more typical projects, controlling for other characteristics. This amount was estimated to be much smaller and statistically insignificant with California observations. The share of 3-bedroom units was associated with higher cost per unit and was not particularly sensitive to the sample, although the degree to which a higher share of smaller units led to reduced cost per unit was less clear. Costs to develop senior projects were modestly lower, but estimates and statistical significance were sensitive to the agencies included.

Projects targeted exclusively to low-income households (most projects) were estimated to be more costly to develop than mixed-income projects. These results were quite sensitive to the presence of projects approved by the New York City allocating agency. More than 40 percent of the mixed-income projects in the entire sample were in New York City. Many of New York City’s mixed-income projects had donated land and might not be comparable from a cost perspective to mixed-income projects in other locations. When we excluded New York City projects, our estimates showed no statistically significant difference in per unit costs for low- and mixed-income projects.

Notably, Rural Development loans were associated with sizeable effects on costs (costs were lower). This may be partly due to the types of projects supported by Rural Development loans, such as farm labor housing (which may lack some amenities that can increase costs) and program limits on costs per unit. Projects supported by HOME and CDBG funds were estimated to be more costly to develop, although these differences were not generally statistically significant. The effect of HOPE VI financial support was estimated to be large and statistically significant, but only about 1 percent of projects in the sample were supported with this program. The projects that received financial support from this source might be idiosyncratic, or could include other unobserved characteristics that influence costs. For example, tenant relocation requirements for HOPE VI projects may have contributed to the higher per-unit costs. $15,000. Projects in neighborhoods with low rents (relative to the state distribution) were estimated to be less costly, typically in the range of $20,000–$30,000 per unit. Costs in neighborhoods with higher rents were estimated to be modestly higher, but rarely significant. Older neighborhoods were associated with higher costs per unit, while newer neighborhoods were associated with lower costs per unit, as compared to projects in neighborhoods in which the median year built was between 1945 and 1994 (and controlling for other characteristics). In the pooled sample, estimated magnitudes were about $18,000 higher in older neighborhoods and about $17,000 lower in newer neighborhoods.

Examining Construction and Soft Cost Components

Table 6 shows that many of the same factors affected total costs, construction costs, and soft costs similarly. For instance, all costs scaled with project size and new construction, and many of the neighborhood effects remained significant. A higher share of 3-bedroom units was associated with higher costs in all cost categories. “Larger buildings” projects had higher total costs and construction costs, but modestly negative and insignificant soft costs. The latter result is consistent with the idea that soft costs scale with the number of units, but not with the size or number of buildings in a project.

Projects with Rural Development loans were associated with lower construction and soft costs. For construction costs, the result is consistent with the loans being able to be used for projects characterized by lower- than-average costs of construction. Soft costs may be affected more directly to the extent that Rural Development loans provide a key source of funding that may reduce the difficulty of other project financing efforts. The HOME indicator was associated with modestly significant higher construction and soft costs. Slightly more than one-third of projects across all allocating agencies received HOME funds.

Finally, the lower costs associated with senior projects were more statistically significant for soft costs than total costs or construction costs.

Sensitivity to Specification

In table 7, we present model variations that exclude, in turn, particular portions of the base case explanation. Other remaining factors, including those associated with the LIHTC program, may be sensitive to the omitted factors. For instance, the estimated effect of a Rural Development loan may be sensitive to the presence of a rural control variable, or the estimated effect of a location in a qualified census tract may be sensitive to other indicators of neighborhood characteristics.

Because the value of land influences the total cost of housing development, we first excluded the home value variable (a measure of variation in property values within and across allocating agency jurisdictions). Estimates of the effect of other neighborhood measures, such as housing stock age and rent quartiles, changed in the absence of the property value measure. The age of housing stock variables were highly significant with and without the inclusion of the property value measure. In the model with the property value measure included, the difference between the estimated cost in an older neighborhood and the estimated cost in a newer neighborhood is about $35,000. That is, the estimated cost in an older neighborhood was about $18,000 more and the estimated cost in a newer neighborhood was about $17,000 less than the estimated cost in in a neighborhood in which the median year built was between 1945 and 1994. In the model with the property value measure excluded, this difference increased to about $50,000, which may reflect the underlying correlation of age of neighborhood and property value that we observe in our data set. For projects in locations in the upper half of the state contract rent distribution, the estimate became much larger and statistically significant at the 1 percent level. poverty rate measure became much smaller, decreasing from about 390 to about 125, and insignificant. In the sample, the 25th percentile poverty rate was about 14 percent, and the 75th percentile value about 37 percent. In the base case, an increase of 23 percentage points represented an increase in total costs per unit of about $9,000, but in the specification without the measure of property value the estimate was about $2,900 (controlling for other characteristics in both specifications). The overall fit, expressed as adjusted R-squared, was reduced from 0.648 to 0.618 in the absence of the property value measure.

Compared to the base case, most results were not particularly sensitive to the absence of the neighborhood variables (housing stock age, rent quartiles, and poverty rate). However, the qualified census tract variable became larger (from about $7,000 to about $18,000) and statistically significant in the absence of the neighborhood variables. The property value effect also became somewhat larger, suggesting that costs increased by about $41,000 per unit, compared to $33,000 in the base case, given a change in property value from the first to the third quartile and controlling for other characteristics. The overall fit worsened from 0.648 to 0.627.

The omission of the other housing program support variables had very little effect, which is not that surprising given the lack of large effects other than the presence of Rural Development loans. The overall fit, expressed as adjusted R-squared, was reduced from 0.648 to 0.641.

Examining Effects of the American Recovery and Reinvestment Act of 2009

Activities funded through nonrefundable tax credits require the entities claiming the credit to have (or expect to have) sufficient federal income tax liability to make the credit desirable. During the 2007–2009 recession, some investors in tax credit-related activities saw reductions in their tax liability. ARRA created the possibility that low-income housing projects could be supported by federal grants that allocating agencies would allocate in much the same manner as they allocated tax credits.

Of all LIHTC projects receiving some ARRA support, more than 90 percent had final costs certified in 2011 and 2012. Thus, we examined the effects of ARRA, expressed as a binary indicator of participation, using the same model but with projects restricted to those that were certified in 2011 and 2012. That is, we believe this was the time period for which ARRA was likely to be most relevant and thus any effects likely to be most pronounced. About one-half of the projects in our data for project years 2011 and 2012 received some ARRA support.

We present results for total costs, construction costs, and soft costs separately, the motivation being that grant funding may reduce the costs of project finance and syndication relative to the traditional credit-based context (see table 8). Construction costs might be expected to be less directly affected by a change in the project finance regime.

In general, the overall results are similar to those presented in table 6. The ARRA indicator is negative and significant in the total and soft cost versions, and negative but insignificant in the construction cost context. The ARRA coefficient was estimated to reduce soft costs by a little more than $4,000 per unit, holding other factors constant. For context, the average soft cost per unit during this time period was about $53,000.

Examining Effects of Variables Not Available for All Allocating Agencies

nonprofit developers do not expect to earn a return on investment, so they may be able to develop projects at lower cost. Nonprofit and for- profit developers also may select different kinds of projects, so it is possible that nonprofit developers more often pick projects that are more costly in observable and unobservable characteristics.

Table 9 provides the results of total cost models estimated using the relevant allocating agency subsamples. In both the parking structure and LEED models, we included categories for missing information. The omitted category is the known absence of parking or LEED construction, respectively. Both of these subsamples were heavily weighted by California projects.

The estimated effect of parking structures was quite large and statistically significant at the 1 percent level. Regardless of the true magnitude of the effect, projects in which parking structures were included clearly were likely to cost more. It is unlikely that all projects envision tenants with cars. For those that do, a surface parking option often may be feasible, but when it is not, project costs will be larger.

LEED certification was associated with costs of about $19,000 more per unit than other projects, holding other factors constant. LEED projects represent about 18 percent of projects in which LEED status was clearly known. Most LEED projects were new construction, and only about 5 percent of the rehabilitation projects with known LEED status were built to LEED standards.

Nonprofit set-aside provisions were associated with an increase in total cost per unit of about $15,000, controlling for other characteristics. Nonprofit set-aside projects had different characteristics from those of projects developed without nonprofit set-asides. For instance, nonprofit set-aside projects typically were smaller, more likely to be in older neighborhoods, less likely to be in low-rent neighborhoods, and less likely to receive Rural Development loans—characteristics we estimated to be associated with increases in total cost per unit. When we estimated the model shown in table 9, but without the set-aside indicator, and multiplied the coefficients by mean values of the explanatory variables calculated separately for each group, we calculated that per-unit costs for projects developed without the set-aside are about $220,000 and the estimated cost for projects developed with the set-aside are about $250,000. As shown in table 9, the fact that we estimated an increase in total cost per unit even while controlling for other factors suggests that unobserved factors may be important. For instance, as mentioned in the body of this report, nonprofit organizations may focus more on populations that are more costly to serve, such as special-needs tenants who may require additional or enhanced facilities.

Examining Effects of Donated Land or Property

estimations, the fits improved, providing some evidence that the excluded observations introduced some noise to the estimation.

In table 11, we examined the effect of aggregating certain projects in New York City. In principle, observations in a regression should be independent from one another. When individual building-level observations appear to be parts of larger projects under common development, this condition is violated. In New York City, it appears that separate tax credit allocations were made to single-building projects in close proximity to other tax credit projects awarded to the same developers at the same time or in consecutive years. For example, three buildings being renovated by the same developer in the same relatively small area could be considered as three separate one-building projects or one three-building project. Clustering the single-building projects as one project for the model made very little difference in the estimates, but led to modest improvements in the overall fit of the model and reduced the number of observations because of the aggregation of projects.

We also examined the association between LIHTC costs and the proximity of projects to public transit. Some allocating agencies offered incentives for the production of transit-oriented LIHTC developments— projects within 0.5 mile of a transit station. Research generally describes transit-oriented developments as compact, mixed-use, walkable neighborhoods located near transit facilities. These types of developments are intended to advance other policy goals, such as furthering opportunities for employment.

We used the Department of Transportation’s Fixed-Guideway Transit Network database to identify the distance from each project to the nearest transit station (train and bus rapid transit). For this model specification, we restricted our estimates to projects within 2 miles of a transit station because not all transit agencies reported station locations to the Department of Transportation database—making our transit distance variable quite large for some projects. As shown in table 12, while we did not find that projects within 0.5 mile of a transit station had significantly different costs than those between 0.5 and 1 mile (the omitted category), we did find that per-unit construction costs were about $17,000 greater for transit-oriented developments, controlling for other characteristics.

Finally, table 13 presents the mean values for our full project sample and base case model.

Appendix III: Development Costs for LIHTC Projects Completed in 2011–2015, for 12 Allocating Agencies

This appendix provides data on the development costs of Low-Income Housing Tax Credit (LIHTC) projects completed in 2011–2015 that received tax credits from 12 selected allocating agencies. Figure 14 shows how median per-unit costs for new construction and rehabilitation projects changed over that period for each allocating agency. Table 14 (new construction projects) and table 15 (rehabilitation projects) break down the median per-unit costs into hard and soft costs and their component parts. Tables 16 and 17 provide data on alternative cost measures—cost per-bedroom and per-square foot—although this information was not available for all 12 allocating agencies. All the cost data in this appendix are presented in 2015 dollars. For additional information on the cost categories we describe, see appendix I.

Projects Completed in 2011–2015, for 12 2011 (dollars)

2012 (dollars)

2013 (dollars)

2014 (dollars)

2015 (dollars)

Two Studies Identifying Associations between Project Characteristics and Per-Unit Cost California

Two of the five studies we reviewed used statistical models to identify the association between project characteristics and per-unit cost.

The authors of a 2014 study sponsored by several California agencies found that the median per-unit cost (excluding land costs) of 400 new construction projects approved for 4 percent or 9 percent LIHTCs in 2001–2011 was $276,000. Using a regression analysis to control for multiple characteristics, they found a variety of characteristics were associated with differences in per-unit costs.

Similar to our results, the authors found that per-unit costs decreased as the number of units increased or as the unit size decreased.

Projects with buildings that had four or more stories were also about 10 percent more expensive per-unit. The authors found higher land costs tended to indirectly increase construction costs, because developers responded by building taller and more often included structured parking—another cost driver.

Also similar to our results, they estimated that senior projects were less costly than projects targeted to families (by about 18 percent), and projects from nonprofit developers were more expensive than projects from for-profit developers (by about 9 percent).

The authors of the California study also reviewed characteristics that we did not. For example, they found that projects with a higher degree of construction quality, durability, and energy efficiency had higher costs. Local factors, such as design review and approval requirements, also added to per-unit total cost.

While data limitations prevented the authors from comparing the cost of LIHTC projects to market-rate developments in a conclusive way, they found that the per-unit construction costs of LIHTC projects in their sample were within the 50th and 75th percentile of estimated costs for market-rate projects with similar height, area, location, and wages.

Washington

The authors of a 2009 study sponsored by the Washington State Department of Commerce reviewed 65 affordable multifamily housing projects, including 41 LIHTC projects that received funding from the state’s Housing Trust Fund in 2003–2009. The average per-unit cost of new construction projects was about $177,000. Similar to our results, about 62 percent of the cost was attributed to construction.

Using a regression analysis to control for multiple characteristics, the authors found that projects financed with LIHTCs tended to be larger and more expensive than affordable non-LIHTC projects.

Architect fees were most strongly associated with per-unit costs, because architect fees may have approximated the complexity of the projects’ designs.

Similar to our results, they found higher costs among urban projects relative to rural ones.

In contrast to our results, the authors did not find that per-unit costs decreased as the number of units increased. Rather, for new construction LIHTC projects in urban areas, per-unit construction costs increased as the number of units increased. According to the authors, the cost increases may have been due to amenities associated with larger urban projects, such as structured parking.

The authors also noted several characteristics that were not associated with per-unit costs, including the presence of a special needs population or the developer type.

Three Studies Comparing Cost Differences

The remaining three studies we reviewed compared cost differences among groups, typically by comparing averages between exclusive categories (for example, senior and nonsenior projects). But they did not statistically control for characteristics that may have differed among projects.

Colorado

The authors of a 2016 study sponsored by the Colorado Housing and Finance Authority analyzed 247 LIHTC projects that applied for 4 percent or 9 percent LIHTCs in Colorado in 2011–2016. They found the average per-unit cost of new construction projects increased by about 32 percent during this period to about $258,000 in 2016. The authors noted that the increase may have stemmed from the decreasing size of projects in Colorado and the increasing cost of construction.

The authors studied the characteristics of the highest- and lowest-cost projects and stated that only two characteristics (project size and year of application) were consistently different between the groups. For projects that received 9 percent credits, characteristics such as location, developer type, and tenant types did not consistently differ between the highest- and lowest-cost projects.

The authors also conducted 25 interviews with architects, consultants, developers, and general contractors, who stated that the most significant contributor to cost increases was higher labor costs due in part to shortages among skilled laborers and federal prevailing wage requirements. In addition, developers stated that while affordable housing developers were more focused on the long-term durability of their projects than market-rate developers, hard costs were generally similar between affordable and market-rate projects. However, soft costs tended to be higher as a result of legal fees associated with LIHTC syndication.

New Mexico (and Other States)

The authors of a 2014 study sponsored by the New Mexico Housing Mortgage Finance Agency reviewed cost drivers across 259 new construction projects that received 9 percent LIHTCs in 2006–2013 from multiple allocating agencies—Arizona, Colorado, Nevada, New Mexico, Texas, and Utah. The authors found the average per-unit cost (including reserves) ranged from about $124,000 in Texas to about $199,000 in Colorado. In New Mexico, average per-unit costs generally decreased in 2007–2010 and then increased thereafter through 2013. Similar to our results, the authors found that hard and soft costs comprised about 65 and 35 percent of project costs, respectively, among the states.

Although the authors of the New Mexico study did not use a statistical analysis that would have controlled for multiple differences among project characteristics, the authors reported differences in construction costs among several groups.

Similar to our results, the authors found slightly lower per-unit construction costs among senior projects compared to nonsenior projects, and that the largest projects (60 units or more) were generally less costly than the smallest projects (30 units or fewer).

In contrast to our results, they noted higher per-unit construction costs among rural projects compared to urban projects.

Also in contrast to our findings, the authors did not find a difference in the per-unit construction costs of nonprofit and for-profit developers.

Minnesota

In a 2013 study, a research intern working for the Minnesota Housing Finance Agency reviewed the costs of 412 affordable housing projects that applied for agency financing in 2003–2012, including 216 LIHTC projects, to determine the extent to which costs changed in response to cost containment strategies. The author found that the average per-unit cost of new construction LIHTC projects in the Minneapolis-St. Paul metropolitan area was about $237,000.

Similar to our results and those of the other studies we reviewed, the author estimated that construction costs comprised about 61 percent of LIHTC project costs.

Also similar to our findings, the author found that the per-unit cost of all affordable new construction projects generally increased during the sample period while the per-unit cost of rehabilitation projects generally decreased.

For LIHTC projects specifically, the per-unit cost decreased by about 8 percent compared to about an 18 percent decrease among non- LIHTC affordable projects in 2003–2012. The author noted that these decreases are important as they coincided with an increased focus by the housing agency on characteristics expected to have increased costs, such as green building standards.

The author also noted that the housing agency previously found—in a separate study using its predictive cost model—that construction costs for the agency’s affordable housing projects were about 12 percent higher than estimates for similar market-rate projects in the same geographical area.

Appendix VI: Cost-Management Approaches for Each Allocating Agency, as of 2017

This appendix provides information on cost-management approaches of allocating agencies, based on our review of qualified allocation plans (QAP) and related documents for 57 agencies as of 2017. The agencies were located in all 50 states, the District of Columbia, the 4 U.S. territories that received a Low-Income Housing Tax Credit (LIHTC) allocation in 2017 (Guam, the Northern Mariana Islands, Puerto Rico, and the U.S. Virgin Islands), and two suballocating agencies (Chicago and New York City). See table 29 for the name and location of each agency.

We identified four main approaches that agencies used to manage project-development costs: cost limits, credit allocation limits, fee limits, and cost-based scoring criteria. Agencies implemented these approaches in various ways, as shown in table 30.

In addition, the types and number of cost-management approaches employed by each agency varied, as shown in table 31. The quantity of approaches used by an agency is not necessarily indicative of the quality or effectiveness of an agency’s cost management, which we were unable to measure.

Cost limits

● - - - - ● ● ● - - ● ● - - - - - ● ● ● ● ● - ● - - - ● - ● - ● - - ● ● - ● ● - ● ● ● ● ● ● ● - ● ● ● ● ● ● - ● ● ● - ● ● ● ● ● - ● ● ● - ● ● ● ● ● ● ● ● ● ● ● ● ● - ● ● ● ● ● The extent of each agency’s practices for each type of cost-management approach also varied, as shown in tables 32–35.

Total development cost limits

Fee

Fee

Appendix VII: Comments from the Internal Revenue Service

Appendix VIII: Comments from the National Council of State Housing Agencies

Appendix IX: GAO Contact and Staff Acknowledgments

GAO Contact

Staff Acknowledgments

In addition to the contact named above, Steve Westley (Assistant Director), Cory Marzullo (Analyst in Charge), Stephen Brown, Heather Chartier, Farrah Graham, Brandon Kruse, John McGrail, John Mingus, Marc Molino, Ed Nannenhorn, Daniel Newman, and Barbara Roesmann made key contributions to this report.