Journal of Data Science

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.

Researchers and practitioners of many areas of knowledge frequently struggle with missing data. Missing data is a problem because almost all standard statistical methods assume that the information is complete. Consequently, missing value imputation offers a solution to this problem. The main contribution of this paper lies on the development of a random forest-based imputation method (TI-FS) that can handle any type of data, including high-dimensional data with nonlinear complex interactions. The premise behind the proposed scheme is that a variable can be imputed considering only those variables that are related to it using feature selection. This work compares the performance of the proposed scheme with other two imputation methods commonly used in literature: KNN and missForest. The results suggest that the proposed method can be useful in complex scenarios with categorical variables and a high volume of missing values, while reducing the amount of variables used and their corresponding preliminary imputations.