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Time, space and hedonic prediction accuracy evidence from the Corsican apartment market

Yuheng Ling


In this study, we propose a hedonic housing model to address spa- tial 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. How- ever, the simultaneous handling of spatial and temporal correlation is still under development.
Since spatial econometric models are limited to account for two kinds of cor- relation simultaneously, we propose using a hierarchical spatiotemporal model from spatial statistics. Based on a Bayesian framework and a stochastic par- tial differential equation (SPDE) approach, the estimation is carried out via INLA.
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 housing estimates are affected by spatial effects and temporal effects. Ignoring these effects could result in serious forecasting issues.

Keywords : Hierarchical spatiotemporal model · Hedonic price model · INLA-SPDE · Apartment market

Working Paper TerRa n°13

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