A spatio-temporal analysis of single-family home values in the greater Orlando region

Home equity represents the largest component of wealth for households in the US. The accurate estimation of home values is important for different sectors of the economy. The current study develops a new method – grouped generalized ordered probit (GGOP) model – for examining the impact of independent variables on single-family home values. The proposed methodology is developed using home property value data for the greater Orlando region from 2013 to 2022. The host of independent variables considered include structural characteristics, demographic characteristics, land use characteristics, transportation infrastructure characteristics, and temporal characteristics. The model estimation process includes the estimation of a GGOP model, spatial lag GGOP and spatial error GGOP models. The spatial lag model offers significantly improved fit compared to other models. The independent variables such as parcel area, living area, number of bathrooms, population, median income, number of jobs, school quality, proximity to downtown, distance to SunRail stations and year of analysis significantly affect single-family home values in the Greater Orlando region. The model results were validated using data not considered for estimation. An elasticity analysis was also conducted to offer insights on independent variable impact magnitudes. The model findings can be employed to quantify property tax revenues for new developments and homeowner/ community losses in the event of natural disasters.