A SPATIAL AND TEMPORAL AUTOREGRESSIVE LOCAL ESTIMATION FOR THE PARIS HOUSING MARKET
研究了时空自回归局部方法在巴黎内城住房交易价格建模中的潜力，发现空间依赖效应显著，且存在强烈的时空异质性。
ABSTRACT This original study examines the potential of a spatiotemporal autoregressive Local (LSTAR) approach in modeling 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 STAR approach proposed by Pace et al., 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.