Gateway to Opportunity? Disparities in Neighborhood Conditions Among Low-Income Housing Tax Credit Residents

Abstract

A key goal of housing assistance programs is to help lower income households reach neighborhoods of opportunity. Studies have described the degree to which Low-Income Housing Tax Credit (LIHTC) developments are located in high-opportunity neighborhoods, but our focus is on how neighborhood outcomes vary across different subsets of LIHTC residents. We also examine whether LIHTC households are better able to reach certain types of neighborhood opportunities. Specifically, we use new data on LIHTC tenants in 12 states along with eight measures of neighborhood opportunity. We find that compared with other rental units, LIHTC units are located in neighborhoods with higher poverty rates, weaker labor markets, more polluted environments, and lower performing schools, but better transit access. We also find that compared with other LIHTC tenants, poor and minority tenants live in neighborhoods that are significantly more disadvantaged.

Keywords:

• Low-income housing
• opportunity
• minorities
• tax credit
• neighborhood

Acknowledgment

We would like to thank Kirk McClure, Katherine O'Regan, Alex Schwartz, participants at the Urban Affairs Association conference, and two anonymous reviewers for insightful comments. All errors are our own.

Notes

1. Raw data available at https://www.huduser.gov/portal/datasets/lihtc.html.

2. In December 2016, the IRS issued Revenue Ruling 2016–29, which stated that the IRS Code does not require or encourage state agencies to reject LIHTC proposals that do not have formal approval from the locality where a project is proposed to be developed. The IRS also issued Notice 2016–77, which stated that LIHTC Qualified Allocation Plans may only give preference to projects in Qualified Census Tracts (QCT) if they are part of a “concerted community revitalization plan” that goes beyond just the LIHTC project.

3. McClure (2008) shows that LIHTC residents are more likely to live in low-poverty neighborhoods than renter households with incomes below 30% of the area median income.

4. Given that the share of households relying on public transportation is very highly correlated with income, for this portion of our analysis we add a control for median household income in our regressions.

5. For more information on this data set, see http://www.locationaffordability.info/About_Data.aspx.

6. Data available at http://lehd.did.census.gov/led/onthemap/. For a detailed description of how these costs are calculated, see http://locationaffordability.info/LAPMethodsV2.pdf.

7. For this analysis, we exclude 12 metropolitan areas in which no census tract contains over 5% of the total jobs.

8. These are generally MSA fixed effects, but in the few MSA that include multiple states, we include a separate fixed effect for counties in separate states. For example, we include one fixed effect for the parts of the New York City MSA that are in New York and another for the parts that are in New Jersey. Results are unchanged when we simply control for MSA fixed effects.

9. The median Hispanic/white dissimilarity index is 37.2, and the median black/white dissimilarity index is 46.9. Data are available at https://s4.ad.brown.edu/projects/diversity/Data/data.htm.