JDS Journal of Data Science 1683-86021680-743X1680-743X School of Statistics, Renmin University of China JDS1113 10.6339/23-JDS1113 Data Science in Action Exploring Racial and Ethnic Differences in US Home Ownership with Bayesian Beta-Binomial Regression MedriJhonatanjm192@usf.edu1∗ ChannagiriTejasvitchannagiri@gmail.com1∗ LuLu1 Department of Mathematics & Statistics, University of South Florida, USA Corresponding and joint first authors. Email: jm192@usf.edu or tchannagiri@gmail.com. 202431102023224605620 Supplementary Material

We have included a separate Supplementary section with additional discussion of the data, modeling analyses and results, and a description of a Shiny app developed in R for data exploration. The app features a user-friendly web interface created with the R packages Shiny (Chang et al., 2022) and shinyWidgets (Perrier et al., 2023), enabling users to perform customized and interactive explorations of the data and models presented in this work. The current version of the interface was initially showcased at the American Statistical Association (ASA) Data Challenge Expo 2022 (in the Joint Statistical Meeting (JSM) 2022), and was subsequently refined and expanded in this article. The code for the Shiny app and all our analyses may also be found at https://github.com/jmedri/JSM2022_HomeOwnership.

16120233172023 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.

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.

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