Points for Place: Can State Governments Shape Siting Patterns of Low-Income Housing Tax Credit Developments?

Abstract

There is considerable controversy about the allocation of Low-Income Housing Tax Credits (LIHTC). Some charge that credits are disproportionately allocated to developments in poor, minority neighborhoods without additional investments and thereby reinforcing patterns of poverty concentration and racial segregation. We examine whether Qualified Allocation Plans, which outline the selection criteria states use when awarding credits, can serve as an effective tool for directing credits to higher opportunity neighborhoods (or neighborhoods that offer a rich set of resources, such as high-performing schools and access to jobs) for states wishing to do so. To answer this question, we study changes in the location criteria outlined in allocation plans for 20 different states across the country between 2002 and 2010, and observe the degree to which those modifications are associated with changes in the poverty rates and racial composition of the neighborhoods where developments awarded tax credits are located. We find evidence that changes to allocation plans that prioritize higher opportunity neighborhoods are associated with increases in the share of credits allocated to housing units in lower poverty neighborhoods and reductions in the share allocated to those in predominantly minority neighborhoods. This analysis provides the first source of empirical evidence that state allocation plans can shape LIHTC siting patterns.

Keywords:

• Low-income housing
• tax credit
• location
• state

Acknowledgments

We thank Yiwen Kuai, Roman Pazuniak, and Michael David Williams for excellent research assistance on this project. We would also like to thank the Department of Housing and Urban Development as well as the Open Society Foundations for generously supporting this research. The substance and findings of this work are dedicated to the public. The authors are solely responsible for the accuracy of the statements and interpretations contained in this publication. Such interpretations do not necessarily reflect the views of the government or the Open Society Foundations.

Notes

1. Baum-Snow and Marion (2009) show that these incentives are reinforced by the greater generosity of the tax credit in qualified census tracts, or tracts officially designated as economically distressed.

2. For more details on the LIHTC program see https://www.novoco.com/resource-centers/affordable-housing-tax-credits/lihtc-basics/about-lihtc

3. Statutorily Mandated Designation of difficult development areas and qualified census tracts for 2015, 79 Fed. Reg. 59,855 (October 4, 2014).

4. For a full review of opportunities indicators in 2015 QAP see National Housing Trust (2015). To search for the most up-to-date opportunity indicators in a state QAP readers can use the following site and search for opportunity housing: http://www.prezcat.org/catalog-search

5. Some developers may propose to use tax credits to preserve affordable developments located in low-poverty areas too.

6. In a few cases, where QAP were missing from the Novogradac website, we obtained them from individual state housing finance agencies. Occasionally, we needed to refer to a 2009 QAP if a state did not issue a 2010 QAP. Specifically, we looked at the following websites: http://www.novoco.com/low_income_housing/lihtc/qap_2002.php and http://www.novoco.com/low_income_housing/lihtc/qap_2010.php. Maryland and Washington did not include many of the siting policies in the QAP itself, but rather in a supplementary document. Washington’s policies are found in “Exhibit D” Policies Tax Credit Application, which can be accessed at http://www.novoco.com/low_income_housing/resource_files/qap/washington_policies_10.pdf. We received Maryland’s policy documents directly from the Maryland Housing Finance Agency.

7. As the HUD data were not always complete, missing some allocation years, we relied on the state lists to fill in gaps. Specifically, we supplement HUD data on allocations between 2003 and 2005 in Georgia, Indiana, Mississippi, New Jersey, Massachusetts, and Wisconsin. For more recent years, we had to rely more heavily on state HFA websites.

8. For a complete enumeration of the changes included in each category, please reference the appendix to this article.

9. To be clear, a basis boost will not increase the chance that a proposed development receives an allocation of credits from the state. Such a boost may, however, encourage more developers to propose projects in high-opportunity neighborhoods because the basis boost makes the potential tax credits more valuable.

10. 26 U.S.C. § 42(m)(1)(A)(ii).

11. 26 U.S.C § 42(m)(B)(ii)(III).

12. As a part of HERA, Congress amended the LIHTC statute to give states the flexibility to provide a 30% basis boost for projects where additional subsidy was required to make a project financially feasible. Prior to HERA, the basis boost had only been available to projects located in qualified census tracts or difficult development areas.

13. We also reran our analysis without changes in basis boosts and results remain unchanged.

14. As states often changed both the scores given to each of these categories as well as the overall points allocated, we calculated the share of points allocated to a priority in each time period and looked at the percentage point change.

15. We also conducted sensitivity analyses, where we coded changes as only 0/1, and then also only coded large changes with a 1. Results are not significant when we limit our analysis to 0/1 for all changes, but when we include 1 only for large changes (2 or 3) then results are consistent. Thus, it seems that the states with dramatic changes to one of these categories are the states driving these results.

16. We ran this same analysis using data from the 2007–2011 5-year ACS estimates and find results that are even stronger, in terms of both magnitude and statistical significance. Given our preference to rely on tract estimates that are not impacted by these new allocations we prefer to rely on the 2006–2010 5-year ACS estimates as our primary data source. Full results are available from the authors upon request.

17. When relying on our alternative measures of neighborhood minority concentration, those with non-Hispanic white shares of less than 20% and 30%, we find similar results, although they are slightly attenuated. For ease of presentation these results are not included, but are available from the authors upon request.

18. The coefficients on the remainder of the individual indices move in the hypothesized direction, although they also rarely reach conventional levels of significance.

19. For ease of exposition we have left these results out of the article, but they are available from the authors upon request.