A variety of tools exist for promoting affordable housing development. Public debate often centers on which tools to use rather than how to implement the chosen tools. In spite of this focus, administrative decisions for policy implementation can have a large impact on housing policy outcomes. This study examines how administrative choices about third-party service providers influences the policy outcomes of subsidized affordable housing development.

Since its creation in 1986, the Low-Income Housing Tax Credit (LIHTC) program has become the greatest catalyst for publicly-subsidized housing production in the United States. The program is co-managed by three different groups: 1) the U.S. Internal Revenue Service annually determines the pool of tax credits, 2) the U.S. Department of Housing and Urban Development (HUD) sets broad program requirements, and 3) state housing finance agencies allocate tax credits to public and private housing developers based on each state’s housing priorities. Housing developers compete for tax credits, and each state housing finance agency enjoys discretion in determining the criteria for awarding credits to developers. Although credits are awarded based on particular project proposals, this research indicates that developer characteristics are inherently tied to housing project outcomes.

This study evaluates the relationship between housing developer characteristics and the type of housing that is produced. Developer characteristics include local vs. national housing developers, frequency of program participation, and nonprofit/for-profit/public status. Housing outcomes considered include the number of housing units produced per project, special populations targeted, and income restrictions for residents. The findings indicate that certain types of housing developers specialize in certain types of housing. This research is the first to incorporate organizational-level characteristics into the national LIHTC dataset. The main implication of this study is that developer characteristics should be considered more explicitly in the credit allocation process.

This research uses national data from HUD’s administrative records on the over 36,000 housing projects constructed using LIHTC between 1987 and 2011. This project-based dataset has been enhanced with some organizational variables, enabling analysis at an organizational (i.e., developer) level in addition to the project level. These data are combined with census tract level data from 1990, 2000, and 2010. The analysis uses cluster effects models to account for both state and year effects.