A Spatial and Temporal Autoregressive Local Estimation for the Paris Housing Market
Résumé
This original study examines the potential of a spatiotemporal autoregressive Local (LSTAR) approach in modelling transaction prices for the housing market in inner Paris. We use a data set from the Paris Region notary office (“Chambre des notaires d'Île-de-France”) which consists of approximately 250,000 transactions units between the first quarter of 1990 and the end of 2005. We use the exact X -- Y coordinates and transaction date to spatially and temporally sort each transaction. We first choose to use the spatiotemporal autoregressive (STAR) approach proposed by Pace, Barry, Clapp and Rodriguez (1998). This method incorporates a spatiotemporal filtering process into the conventional hedonic function and attempts to correct for spatial and temporal correlative effects. We find significant estimates of spatial dependence effects. Moreover, using an original methodology, we find evidence of a strong presence of both spatial and temporal heterogeneity in the model. It suggests that spatial and temporal drifts in households socio-economic profiles and local housing market structure effects are certainly major determinants of the price level for the Paris Housing Market.
Cette étude originale évalue l'apport d'une modélisation spatio-temporelle autorégressive (STAR) pour expliquer l'évolution des prix des transactions de logements sur Paris et sa première couronne. Nous utilisons la méthode STAR introduite par Pace, Barry, Clapp and Rodriguez (1998), qui incorpore un filtre spatio-temporel à la fonction hédonique standard. La recherche indique une forte présence d'interdépendances spatiales et temporelles dans ces sous-marchés qui semblent déterminantes dans l'analyse des prix immobiliers de logements à Paris.
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