A spatio-temporal model of house prices in Korea: a Bayesian approach

다국어 초록 (Multilingual Abstract)

The purpose of this article is to show how spatiotemporal correlations can be incorporated when estimating regression coefficients and when predicting house prices. The empirical analysis is conducted using standard OLS model and spatial temporal autoregressive models. The data set used in the estimation of the hedonic models consists of transaction data of apartments for Gangnam-gu, Seoul from January 2006 to December 2015. Data processing is facilitated by the use of a vector-based Geographic Information Systems (GIS). By considering spatiotemporal effects, this paper extends Pace et al. (1998)’s spatiotemporal autoregressive model (STAR) with the consideration of the heteroscedasticity problem arising from the nature of the data. The family of the spatio-temporal autoregressive models produces better goodness of fit and out-of-sample performance than the family of the traditional hedonic models based on OLS with and without trend models. Our Bayesian heteroscedastic STAR model has significantly increased the R^2 to 0.8725, increased by 230.2% relative to OLS without trend model and increased by 103.7% relative to OLS with trend model. It also has significantly reduced root mean square errors (RMSE) and the median absolute errors. Based on the results of this study, if the house price is estimated by using the BSTAR model, the government policy makers can accurately understand the movements of the housing market and can establish and implement policies and investors will be able to invest objectively.

참고문헌 (Reference)

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