Time, space and hedonic prediction accuracy: evidence from Corsican apartment markets

In this study, we propose a hedonic housing model to address spatial and temporal latent structures simultaneously. With the development of spatial econometrics and spatial statistics, economists can now better assess the impact of spatial correlation on house prices. However, the simultaneous handling of spatial and temporal correlation is still under development. Since the commonly used spatial econometric models cannot appropriately account for the two kinds of correlation simultaneously, we propose using a hierarchical spatiotemporal model from spatial statistics. Based on a Bayesian framework and a Stochastic Partial Differential Equation approach, estimation is carried out via Integrated Nested Laplace Approximations. We then perform an empirical study on apartment transaction prices in Corsica (France) using the proposed model. The empirical results demonstrate that the prediction performance of the hierarchical spatiotemporal model is the best among all candidate models. Moreover, the hedonic estimates are affected by spatial effects and temporal effects. Ignoring these effects could result in serious forecasting issues.