The Effect of Regional Capacity on Low-Income Housing Voucher Lease-Up Rates

Leechen at laptop

Students

2021
College Dental Medicine

Faculty

Assistant Professor

Project Summary

As an architecture major, working on low-income housing policy might seem more indirectly than directly related to my academic year work. However, design relies upon many other fields of study, and an understanding of neighborhood and economic dynamics is important. Over the course of the ten weeks, I worked with Prof. Vincent Reina to analyze the nationwide problems and successes of the federal government’s Section 8 Housing Choice Voucher Program (HCV) for low-income renters. The current aim of the project is to determine the extent to which regional administrative, advocacy, and financial capabilities impact different local success rates. 

To begin, I created an index for those capabilities, together defined as capacity, as it relates to the program. I pieced this together through several dozen literature reviews, finding the best practices and common barriers of regional successes. Since the program is mainly administered by one entity per region, known as the Public Housing Authority, researchers have suggested many capacity indicators for that individual authority. However, regional capacity depends on additional factors: relationships with related regional nonprofits, partnering with counseling or workshopping services for staff or low-income residents, the presence of fair housing or low-income housing advocacy groups, and local political conditions. Counseling and workshopping was of particular interest to me, as many previous studies discuss the barriers in not only applying for and finding housing through the program, but adjusting to placement communities and eventually achieving economic self-sufficiency.

The last half of my research was heavy on data analysis. Having determined the possible factors of regional success, I looked for suitable datasets that quantized those factors. Using a publically available national dataset of nonprofit characteristics, a dataset of Housing Authority characteristics, and some sets of background neighborhood control characteristics; I isolated possible indicators such as nonprofit organization type and age, housing authority size, average income of families in the program, and housing authority expenditures. My analysis demonstrated a need for all indicators to be considered, as they did not always correlate with each other. For example, higher expenditures per renter did not necessarily correlate with a lower or higher total housing authority performance as judged by the U.S. Department of Housing and Urban Development. To continue the research, other indicators of program success will be added to the analysis, such as the number and income of participants.

Besides policy design, this experience taught me a lot about the impact of intraregional relations, administrative structure, and local economics on problems facing housing authorities and low-income renters. I also learned about the difficulties in informing policies with statistics. I cannot count how many times I had to consult with my mentor and a graduate assistant on problems with incomplete datasets, unreliable findings, and incomparable metrics. I was able to work through these problems firsthand with other researchers to make sense of a whole host of factors to consider, participating in the discussion around the evaluation and design of policies. I am thankful for the opportunity to do so.