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Development and Analysis of the National Low-Income Housing Tax Credit Database, 1996


Authors: Abt Associates    

Report Acceptance Date: September 1996 (60 pages)

Posted Date: September 01, 1996

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The Low-Income Housing Tax Credit (LIHTC) is the most important resource for creating affordable housing in the United States today. The LIHTC provides State housing agencies with the equivalent of more than $3 billion in annual budget authority that they can use to leverage a vast amount of capital to respond to locally identified rental housing needs.

This report uses the LIHTC database to provide previously unavailable information about LIHTC projects. For example, an average of 1,300 projects and 56,000 units are placed in service annually and average project size has increased from 37 units in 1992 to 45 units in 1994. More significantly, policy analysts and researchers can use these data to construct reliable samples for more-detailed studies of the LIHTC. For example, the database contains addresses and other information needed to locate projects. It also contains variables that are useful for stratifying samples, such as project size and whether the project was newly constructed or rehabilitated. Encouraging more analysis of the LIHTC was the major motivation behind the creation of this database. Since its inception, the LIHTC has made possible the creation of several hundred thousand affordable housing units in communities across the country. In order to understand the variety of needs this housing serves, we must look across the diversity of State experience with the program. But, given the decentralized nature of the LIHTC program, in which 54 State and local housing finance agencies independently allocate tax credits, no comprehensive source of information is available to those wishing to answer questions such as who resides in tax-credit projects, where they come from, and how their rents compare to their incomes. A good sampling frame provides analysts with the necessary starting point to design studies that can produce reliable conclusions.


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