Race-Specific Risk Factors for Homeownership Disparity in the Continental United States
Volume 22, Issue 4 (2024), pp. 591–604
Pub. online: 13 December 2023
Type: Data Science In Action
Open Access
Open Access
Received
10 December 2022
10 December 2022
Accepted
29 October 2023
29 October 2023
Published
13 December 2023
13 December 2023
Abstract
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.
Supplementary material
Supplementary MaterialOpen-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
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Table 1: Descriptions of variables
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Table 2: Division county percentages per Race dataset
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Table 3: Number of counties in each dataset
Plots
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Figure 1: Dataset timeline
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Figure 2: Important variables for White models with and without outliers
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Figure 3: Correlation between predictor variables
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Figure 4: Model performance on 10% holdout data
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