Project Abstract

Substandard homes severely impact resident wellbeing: deficient housing quality is associated with asthma and respiratory illness, lead poisoning, accidental injury, anxiety and depression, and poor academic outcomes. Data on housing quality is limited to MSA-level estimates of housing adequacy and subjective assessments by the local appraisal district, so it’s difficult for housing advocates to understand where housing quality issues are most acute and how to direct resources for repair. The project purpose is twofold: (1) identify neighborhoods in Dallas where there is poor housing quality and (2) develop a sampling and surveying approach to collect granular data within high-repair neighborhoods.

Project Description & Overview

Housing quality matters for the mental, emotional, and physical health of residents, but the 2019 American Housing Survey reports that 27,600 housing units in the Dallas-Fort Worth Arlington MSA are severely inadequate. Research indicates that housing quality issues are more severe for people of color, people living in poverty, single parents, and renters. Although there is great need within Dallas’ housing stock, we lack actionable data to elevate the issue of housing quality, better direct limited resources, and advocate for more resources to ensure Dallas residents have a healthy home. Through this Capstone project, we hope to leverage existing datasets to design a methodology for calculating housing quality at a smaller geographic unit in order to identify neighborhoods in Dallas where there is disproportionately poor housing quality that needs to be remedied. This could take the form of a housing quality index that contemplates various data sources and takes into account both renter- and owner-occupied units. In addition, the CUSP team will estimate the cost of housing repair needs in Dallas (see 2019 report from the Philadelphia Fed entitled, Measuring and Understanding Home Repair Costs, as an example using 2017 AHS data). Finally, the CUSP team will develop a sampling and surveying approach to collect more granular data within neighborhoods indicating a high need for repairs. This framework can then be deployed on-the-ground in Dallas in target neighborhoods to better understand specific needs and direct resources, like home repair programs, to units where they’re most needed.

Datasets

Datasets available for this project include Dallas Central Appraisal District property-level data, the Census Bureau’s bi-annual American Housing Survey, the American Community Survey (for relevant household data), CoStar multifamily data (e.g., property class, unit features — like A/C, and property age), and City of Dallas code violations. Other potential datasets include multifamily and single family rental inspections by the City of Dallas, units with failed inspections from the Dallas Housing Authority, and MLS property listing data. Potential datasets require additional steps for CPAL.

Competencies

Specific skills that would be useful for students to have include spatial analysis and regression, econometric modeling, hedonic and/or multilinear regression, and sampling design. Nice-to-have is some understanding of housing quality/adequacy and its impact on residents.

Learning Outcomes & Deliverables

Expected deliverables include:

1. A descriptive analysis of housing quality in the City of Dallas, e.g., through the creation of a housing quality index applied to the smallest geographic unit possible;
2. An estimate of what existing home repairs in the City of Dallas cost (see 2019 report from the Philadelphia Fed entitled, Measuring and Understanding Home Repair Costs, as an example using 2017 American Housing Survey data);
3. A sampling and surveying framework that can be used on-the-ground in neighborhoods to collect unit-level data on housing quality and repair needs. If travel to Dallas is permissible for fieldwork, students could visit to test the sampling and surveying framework in a neighborhood that indicates high need based on the housing quality index. If travel is not permissible, CPAL staff or volunteers will use the framework for data collection efforts.

Students

Danielle Bayer, Riccardo Negri, Vaidehi Raipat, Yilin Sang