A spatio-temporal autoregressive model for multi-unit residential market analysis

Abstract

By splitting spatial effects into building and neighborhood effects, this paper introduces a two order spatio-temporal autoregressive model with the consideration of heteroscedasticity problem arising from the nature of the data. The empirical results based on 55282 condominium transactions in Singapore from 1990 to 1999 show that in multi-unit residential market, a two order spatio-temporal autoregressive model incorporates more spatial information into the model, thus outperforms the models originally developed in the market for single family. It is also found that Bayesian estimation method can efficiently detect and correct heteroscedasticity, indicating that the Bayesian estimation method is more suitable for estimating real estate hedonic function than the conventional OLS estimation. By examining pair wise correlations of spatio-temporal lagged residuals, it is also found that there may be a trade off between heteroscedastic robustness and the incorporation of spatial information in model implementation. The model can be used to construct specific price indices for any condominium or building within a condominium.