Journal of Data Science

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.

Racial and ethnic representation in home ownership rates is an important public policy topic for addressing inequality within society. Although more than half of the households in the US are owned, rather than rented, the representation of home ownership is unequal among different racial and ethnic groups. Here we analyze the US Census Bureau’s American Community Survey data to conduct an exploratory and statistical analysis of home ownership in the US, and find sociodemographic factors that are associated with differences in home ownership rates. We use binomial and beta-binomial generalized linear models (GLMs) with 2020 county-level data to model the home ownership rate, and fit the beta-binomial models with Bayesian estimation. We determine that race/ethnic group, geographic region, and income all have significant associations with the home ownership rate. To make the data and results accessible to the public, we develop an Shiny web application in R with exploratory plots and model predictions.

The 2020 Census County Assessment Tool was developed to assist decennial census data users in identifying deviations between expected census counts and the released counts across population and housing indicators. The tool also offers contextual data for each county on factors which could have contributed to census collection issues, such as self-response rates and COVID-19 infection rates. The tool compiles this information into a downloadable report and includes additional local data sources relevant to the data collection process and experts to seek more assistance.