Low-Income Housing Tax Credit: Improved Data and Oversight Would Strengthen Cost Assessment and Fraud Risk Management

LIHTCs encourage private investment in low-income rental housing and have financed about 50,000 housing units annually since 2010.The LIHTC program is administered by IRS and credit allocating agencies (state or local housing finance agencies). The program has come under increased scrutiny following reports of high or fraudulent development costs for certain LIHTC projects. GAO was asked to review the cost-efficiency and effectiveness of the LIHTC program.

This report examines (1) development costs for selected LIHTC projects and factors affecting costs, (2) allocating agencies' oversight of costs, and (3) factors limiting assessment of costs. GAO compiled and analyzed a database of costs and characteristics for 1,849 projects completed in 2011–2015 (the most recent data available when compiled) from 12 allocating agencies. The agencies span five regions and accounted for about half of the LIHTCs available for award in 2015. GAO also reviewed the most recent allocating plans and related documents for 57 allocating agencies and reviewed federal requirements.

GAO identified wide variation in development costs and several cost drivers for Low-Income Housing Tax Credit (LIHTC) projects completed in 2011–2015. Across 12 selected allocating agencies, median per-unit costs for new construction projects ranged from about $126,000 (Texas) to about $326,000 (California). Within individual allocating agencies, the variation in per-unit cost between the least and most expensive project ranged from as little as $104,000 per unit (Georgia) to as much as $606,000 per unit (California). After controlling for other characteristics, GAO estimates that

larger projects (more than 100 units) cost about $85,000 less per unit than smaller projects (fewer than 37 units), consistent with economies of scale.

Allocating agencies use measures such as cost and fee limits to oversee LIHTC development costs, but few agencies have requirements to help guard against misrepresentation of contractor costs (a known fraud risk). LIHTC program policies, while requiring high-level cost certifications from developers, do not directly address this risk because the certifications aggregate costs from multiple contractors. Some allocating agencies require detailed cost certifications from contractors, but many do not. Because the Internal Revenue Service (IRS) does not require such certifications for LIHTC projects, the vulnerability of the LIHTC program to this fraud risk is heightened.

Weaknesses in data quality and federal oversight constrain assessment of LIHTC development costs and the efficiency and effectiveness of the program. GAO found

inconsistencies in the types, definitions, and formats of cost-related variables 12 selected agencies collected.

allocating agencies did not capture the full extent of a key indirect cost—a fee paid to syndicators acting as intermediaries between project developers and investors that IRS requires be collected.

IRS does not require allocating agencies to collect and report cost-related data that would facilitate programwide assessment of development costs. Further, Congress has not designated any federal entity to maintain and analyze LIHTC cost data.

Even without a designated federal entity, opportunities exist to advance oversight of development costs. In particular, greater standardization of cost data would lay a foundation for allocating agencies to enhance evaluation of cost drivers and cost-management practices.

Congress should consider designating a federal agency to maintain and analyze LIHTC cost data. GAO also makes three recommendations to IRS to enhance collection and verification of cost data. IRS disagreed with the recommendations and said it lacked certain data collection authorities. GAO maintains the recommendations would strengthen program oversight and integrity and modified one of them to allow IRS greater flexibility in promoting data standards.

Low-Income Housing Tax Credits (LIHTC) are the largest source of  federal assistance for developing affordable rental housing and  represented an estimated $8.4 billion in foregone revenue in 2017. The  program encourages private investment in low-income housing through  tax credits, but the cost of this housing has come under increased  scrutiny following reports of high or fraudulent development costs in  certain LIHTC projects. In addition, analysis of trends and variation in  LIHTC development costs and of federal and state efforts to oversee  these costs has been limited to date.

The LIHTC program, established under the Tax Reform Act of 1986, has  financed approximately 50,000 housing units annually since 2010. The  program is jointly administered by the Internal Revenue Service (IRS)  within the Department of the Treasury (Treasury) and by credit allocating  agencies, typically state housing finance agencies established to meet  affordable housing needs of their residents.

You requested we review the cost-efficiency and effectiveness of the  LIHTC program. This report analyzes (1) development costs for 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.

To analyze development costs for LIHTC projects, we created and  analyzed a database of costs and characteristics for 1,849 projects that  submitted final cost certifications (which detail a project’s total costs,  including the costs used in calculating credit awards) to 12 selected  allocating agencies in 2011–2015. The 12 allocating agencies accounted  for 50 percent of the total 2015 credit ceiling amount and spanned the five  major geographic regions. Although the database we created includes  nearly all projects completed by the 12 allocating agencies in 2011–2015,  it is not generalizable to all allocating agencies. To describe costs and  characteristics of LIHTC projects, we calculated summary statistics  (distributions and medians) for key elements in our database, and  compared results across the 12 agencies. We also developed a  regression model to estimate relationships between development costs  and relevant project and location characteristics. We interviewed officials  from the 12 agencies, selected industry groups, and selected researchers  to discuss our data collection and analysis. To assess the reliability of  the project data, we tested the data for missing values, outliers, and obvious errors and interviewed allocating agency officials about  interpretations of various data fields, among other things. We concluded  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.

To analyze steps allocating agencies took to oversee LIHTC development  costs, we reviewed the 2017 (or most recent as of August 2017) Qualified  Allocation Plans (QAP) and related documents for 57 allocating agencies  to identify cost-management and cost-verification approaches (policies  and practices to limit development costs and fees and confirm the  accuracy of project costs). We interviewed IRS and Treasury officials for  information and perspectives on LIHTC cost-verification requirements and  Department of Housing and Urban Development (HUD) officials to identify  development cost-verification practices of other federal housing  programs. We also interviewed officials from the 12 selected allocating  agencies, representatives from two national accounting firms with  expertise in LIHTC, and the National Council of State Housing Agencies  (NCSHA) about cost management and the cost-certification process.

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 and how they defined 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  reviewed Section 42 of the Internal Revenue Code (Section 42) and  related regulations to ascertain requirements for reporting development  costs and other information to allocating agencies and IRS. We also  interviewed IRS and Treasury officials about these requirements. For  more information on our scope and methodology, see appendix I.

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.

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.

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.

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.

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.

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)

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:

California Tax Credit Allocation Committee

Chicago Department of Planning and Development

New York City Department of Housing Preservation and Development

New York State Division of Housing and Community Renewal

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.

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)

Number of buildings (residential and non-residential)

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

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

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.

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.

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.

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).

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.

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.

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.

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.

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)

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.

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.

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.

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.

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.

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

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

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.

Developers can apply for federal Low-Income Housing Tax Credits to help them build affordable housing projects. The amount of credit depends largely on project costs.

Project costs we looked at varied widely (shown below), and federal oversight of costs is limited. Federal and state agencies could use data on variables that affect cost—such as square footage and building type—to better monitor the tax credit. State agencies that administer the credit aren't required to report the kind of detailed cost data that could reduce fraud risks.

We recommended ways to make better data available to the government and improve oversight of the credit.