JDS Journal of Data Science 1683-86021680-743X1680-743X School of Statistics, Renmin University of China JDS1116 10.6339/23-JDS1116 Data Science in Action Race-Specific Risk Factors for Homeownership Disparity in the Continental United States RichardsonRachel E.rachel.richardson@pnnl.gov1∗ LeachDamon T.1 WinansNatalie M.1 DegnanDavid J.1 PrymolennaAnastasiya V.2 BramerLisa M.1 Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99354, USA Environmental Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99354, USA Corresponding author. Email: rachel.richardson@pnnl.gov. 202413122023224591604 Supplementary Material

Open-source code, additional visualizations and tables, as well as original datasets are available in a public GitHub repository:

https://github.com/PNNL-CompBio/HomeownershipDisparity_2015_2019

Tables

Table 1: Descriptions of variables

Table 2: Division county percentages per Race dataset

Table 3: Number of counties in each dataset

 Plots

Figure 1: Dataset timeline

Figure 2: Important variables for White models with and without outliers

Figure 3: Correlation between predictor variables

Figure 4: Model performance on 10% holdout data

1012202229102023 2024 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.2024 Open access article under the CC BY license.

The United States has a racial homeownership gap due to a legacy of historic inequality and discriminatory policies, but factors that contribute to the racial disparity in homeownership rates between White Americans and people of color have not been fully characterized. In order to alleviate this issue, policymakers need a better understanding of how risk factors affect the homeownership rates of racial and ethnic groups differently. In this study, data from several publicly available surveys, including the American Community Survey and United States Census, were leveraged in combination with statistical learning models to investigate potential factors related to homeownership rates across racial and ethnic categories, with a focus on how risk factors vary by race or ethnicity. Our models indicated that job availability for specific demographics, and specific regions of the United States were factors that affect homeownership rates in Black, Hispanic, and Asian populations in different ways. Based on the results of this study, it is recommended policymakers promote strategies to increase access to jobs for people of color (POC), such as vocational training and programs to reduce implicit bias in hiring practices. These interventions could ultimately increase homeownership rates for POC and be a step toward reducing the racial wealth gap.

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