A Model of Factors Correlated to Homeownership: The Case of Utah

Abstract

This article examines the relationship between homeownership and socioeconomic, demographic, and market factors in Utah. Units of analyses were census‐designated places. The goal was to provide a model that can be replicated by housing specialists and consumer scientists to gain a better understanding of how homeownership (dependent variable) differs from place to place and how this variation relates to socioeconomic index, population density, affordability ratio, and the median value of owner occupied housing units (independent variables). The 1990 data set was analyzed using bivariate and multivariate analyses. Homeownership percentages were regressed on the linear combination of the socioeconomic scale, log of population density, and affordability ratios. Log of population density was the factor that explained most of the variance. The interaction equation slightly improved the explanatory power, accounting for more than 50% of the variance.

Exploring Racial and Ethnic Differences in US Home Ownership with Bayesian Beta-Binomial Regression

“…The remaining columns show the predicted effect on the home ownership rate for different changes in the income. (Delgadillo, 2009;Moore, 1991). We discuss a possible remedy to this shortcoming in Section 5.…”

“…The model tends to overestimate the home ownership rate in such counties, because they have a relatively high income but a relatively low home ownership rate. Thus, further predictors, such as the population density (Delgadillo, 2009), may be useful to better control for these differences between counties.…”

“…Moreover, some authors have developed Bayesian models, such as hierarchical (Hui et al, 2010) or averaging methods (Erdoğdu et al, 2021), to explain residential property valuation or housing prices. In the study of home ownership, methods using ordinary least squares and logistic regression (binomial family) have been used (Moore, 1991;Goodman and Mayer, 2018;Jones, 1989;Delgadillo, 2009). In this article, we used Bayesian beta-binomial regression to help explain racial/ethnic differences in home ownership rates.…”