Local Spatiotemporal Modeling of House Prices: A Mixed Model Approach
The real estate market has long provided an active application area for spatial-temporal modelling and analysis and it is well known that house prices tend to be not only spatially but also temporally correlated. In the spatial dimension, nearby properties tend to have similar values because they tend to share similar characteristics but house prices tend to vary over space due to differences in these characteristics. In the temporal dimension, current house prices tend to be based on property
... based on property values from previous years and in the spatial-temporal dimension, the properties on which current prices are based tend to be in close spatial proximity. To date, however, most research work on house prices has adopted either a spatial perspective or a temporal one. Relative few efforts have been devoted to the situation where both spatial and temporal effects coexist. Even fewer analyses have allowed for both spatial and temporal variations in the determinants of house prices. Using 10-years of house price data in Fife, Scotland (2003-2012, this research applies a mixed model approach, semi-parametric geographically weighted regression (GWR), to explore, model and analyse the spatiotemporal variations in the relationships between house prices and associated determinants. The study demonstrates the mixed modelling technique provides better results than standard approaches to predicting house prices by accounting for spatiotemporal relationships at both global and local scales.