Poster #43 - Opportunity for Who? Analyzing the Moving Patterns of Ohio LIHTC Residents

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Abstract

The Low-Income Housing Tax Credit (LIHTC) program is the largest place-based housing policy in the United States, subsidizing a large portion of affordable housing development. An important component of the LIHTC process is establishing selection criteria for LIHTC properties to ensure developers build projects that reflect the needs of the state, reflected in the Qualified Allocation Plan (QAP). In recent years, more QAPs have introduced opportunity-based scoring to increase the chances of selecting projects developed in neighborhoods with high levels of opportunity for low-income households. This scoring practice is justified on the basis of helping low-income households move to better communities and improve their economic situation. However, little is known about the actual moving patterns of LIHTC residents. This primarily stems from the lack of data on the LIHTC tenant’s previous address, which this study overcomes by combining two unique datasets.

The study leverages individual-level administrative data from Ohio Housing Finance Agency on LIHTC residents from 2015-2019.This data provides demographic characteristics of the LIHTC resident as well as the ZIP code of the LIHTC project they reside in. We link LIHTC residents to the Ohio Consumer Credit Panel (CCP), which contains individual-level credit characteristics for most Ohio residents. Notably, the CCP contains the ZIP code of the individual’s household. This allows one to determine the ZIP code the LIHTC tenant resided in prior to moving into a LIHTC property.

In this paper, I address the following questions: Are LIHTC tenants more likely to move within or across neighborhoods when moving into a LIHTC project? Do tenants move across neighborhoods at a higher rate when the LIHTC project is placed in a higher “opportunity” neighborhood?I estimate a series of regression models to predict the likelihood of a LIHTC tenant moving ZIP codes to reside in a LIHTC property. The first regression model predicts the probability of moving ZIP codes based on demographic and financial characteristics of LIHTC tenants. The next several regression models introduce the opportunity level of the LIHTC property as an explanatory variable, proxied by various components of the opportunity score that the Ohio Housing Finance Agency uses to score applications. Such proxies include area median income (AMI), unemployment rate, and educational attainment obtained through U.S. Census Data. While this analysis is not causal, it contains important implications for how practitioners structure the selection criteria for LIHTC projects and how scholars theorize the neighborhood effects of LIHTC developments.

Author

• Brandon Frye, The Ohio State University
  Presenting Author