Exploring Racial and Ethnic Differences in US Home Ownership with Bayesian Beta-Binomial Regression
Volume 22, Issue 4 (2024), pp. 605–620
Pub. online: 31 October 2023
Type: Data Science In Action
Open Access
Open Access
Received
16 January 2023
16 January 2023
Accepted
31 July 2023
31 July 2023
Published
31 October 2023
31 October 2023
Abstract
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.
Supplementary material
Supplementary MaterialWe 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.
References
Ascari R, Migliorati S (2021). A new regression model for overdispersed binomial data accounting for outliers and an excess of zeros. Statistics in Medicine, 40(17): 3895–3914. https://doi.org/10.1002/sim.9005
Delgadillo L (2009). A model of factors correlated to homeownership: The case of Utah. Family and Consumer Sciences Research Journal, 30: 3–36. https://doi.org/10.1177/1077727X01301001
Deslatte A, Tavares A, Feiock RC (2018). Policy of delay: Evidence from a Bayesian analysis of metropolitan land-use choices. Policy Studies Journal, 46(3): 674–699. https://doi.org/10.1111/psj.12188
Ferrari S, Cribari-Neto F (2004). Beta regression for modelling rates and proportions. Journal of Applied Statistics, 31(7): 799–815. https://doi.org/10.1080/0266476042000214501
Flippen CA (2010). The spatial dynamics of stratification: Metropolitan context, population redistribution, and black and Hispanic homeownership. Demography, 47(4): 845–868. https://doi.org/10.1007/BF03214588
Gabry J, Simpson D, Vehtari A, Betancourt M, Gelman A (2019). Visualization in Bayesian workflow. Journal of the Royal Statistical Society. Series A. Statistics in Society, 182(2): 389–402. https://doi.org/10.1111/rssa.12378
Goodman LS, Mayer C (2018). Homeownership and the American dream. The Journal of Economic Perspectives, 32(1): 31–58. https://doi.org/10.1257/jep.32.1.31
Haseman JK, Kupper LL (1979). Analysis of dichotomous response data from certain toxicological experiments. Biometrics, 35(1): 281–293. https://doi.org/10.2307/2529950
Bengtsson H (2017). matrixStats: Functions that Apply to Rows and Columns of Matrices (and to Vectors). R package version 0.52.2. Available at https://github.com/HenrikBengtsson/matrixStats.
Hui SK, Cheung A, Pang J, et al. (2010). A hierarchical Bayesian approach for residential property valuation: Application to Hong Kong housing market. International Real Estate Review, 13(1): 1–29. https://doi.org/10.53383/100117
Jones LD (1989). Current wealth and tenure choice. Real Estate Economics, 17(1): 17–40. https://doi.org/10.1111/1540-6229.00471
Krivo LJ, Kaufman RL (2004). Housing and wealth inequality: Racial-ethnic differences in home equity in the United States. Demography, 41(3): 585–605. https://doi.org/10.1353/dem.2004.0023
Kuebler M (2013). Closing the wealth gap: A review of racial and ethnic inequalities in homeownership. Sociology Compass, 7(8): 670–685. https://doi.org/10.1111/soc4.12056
Najera-Zuloaga J, Lee DJ, Arostegui I (2018). Comparison of beta-binomial regression model approaches to analyze health-related quality of life data. Statistical Methods in Medical Research, 27(10): 2989–3009. https://doi.org/10.1177/0962280217690413
Ospina R, Ferrari SLP (2012). A general class of zero-or-one inflated beta regression models. Computational Statistics & Data Analysis, 56(6): 1609–1623. https://doi.org/10.1016/j.csda.2011.10.005
Paleologos EK, Elhakeem M, Amrousi ME (2018). Bayesian analysis of air emission violations from waste incineration and coincineration plants. Risk Analysis, 38(11): 2368–2378. https://doi.org/10.1111/risa.13130
Pebesma E (2018). Simple features for R: Standardized support for spatial vector data. The R Journal, 10(1): 439–446. https://doi.org/10.32614/RJ-2018-009
Perrier V, Meyer F, Granjon D (2023). shinyWidgets: Custom Inputs Widgets for Shiny. https://github.com/dreamRs/shinyWidgets, https://dreamrs.github.io/shinyWidgets/.
Prentice RL (1986). Binary regression using an extended beta-binomial distribution, with discussion of correlation induced by covariate measurement errors. Journal of the American Statistical Association, 81(394): 321. https://doi.org/10.1080/01621459.1986.10478275
Robb BH (2021). Council Post: Homeownership and the American Dream. https://www.forbes.com/sites/forbesrealestatecouncil/2021/09/28/homeownership-and-the-american-dream/.
Sanchez-Moyano R (2021). Geography and Hispanic homeownership: A review of the literature. Journal of Housing and the Built Environment, 36(1): 215–240. https://doi.org/10.1007/s10901-020-09745-5
Tennekes M (2018). tmap: Thematic maps in R. Journal of Statistical Software, 84(6): 1–39. https://doi.org/10.18637/jss.v084.i06
US Census Bureau (2021a). Data Suppression. Technical report, US Census Bureau. Available at https://www.census.gov/programs-surveys/acs/technical-documentation/data-suppression.html.
US Census Bureau (2021b). Understanding and Using the American Community Survey Public Use Microdata Sample Files: What Data Users Need To Know. Technical report, US Census Bureau. Available at https://www.census.gov/content/dam/Census/library/publications/2021/acs/acs_pums_handbook_2021.pdf.
Vehtari A, Gelman A, Gabry J (2017). Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Statistics and Computing, 27: 1413–1432. https://doi.org/10.1007/s11222-016-9696-4
Vehtari A, Gelman A, Simpson D, Carpenter B, Bürkner PC (2021). Rank-normalization, folding, and localization: An improved Rˆ for assessing convergence of MCMC (with discussion). Bayesian Analysis, 16(2): 667–718. Publisher: International Society for Bayesian Analysis. https://doi.org/10.1214/20-BA1221
Wickham H, Averick M, Bryan J, Chang W, McGowan LD, François R, et al. (2019). Welcome to the tidyverse. The Journal of Open Source Software, 4(43): 1686. https://doi.org/10.21105/joss.01686
