Estimation of Housing Price Variations Using Spatio-Temporal Data

This paper proposes a hedonic regression model to estimate housing prices and the spatial variability of prices overmultiple years. Using themodel, maps are obtained that represent areas of the city where there have been positive or negative changes in housing prices. The regression-cokriging (RCK)method is used to predict housing prices. The results are compared to the cokrigingwith external drift (CKED) model, also known as universal cokriging (UCK). To apply the model, heterotopic data of homes for sale at different moments in time are used. The procedure is applied to predict the spatial variability of housing prices in multi-years and to obtain isovalue maps of these variations for the city of Granada, Spain. The research is useful for the fields of urban studies, economics, real estate, real estate valuations, urban planning, and for scholars.This work was conducted within the framework of a research project granted by CEMIX-6/16 and financed by Banco Santander