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The Estimation of Seoul Apartment Prices Using Spatiotemporal Autore g ressive Models
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The estimation of the housing price has mainly been studied in a traditional way of the regression analysis. When spatial and temporal effects are disregarded in the model, however, those effects lead to distort and mislead parameter estimates and statistical inference. Using 1,265 observations on apartment prices in the southern Seoul from 1995 to 2000, this article demonstrates the substantial benefits obtained by modeling the spatial as well as the temporal dependence of the data. Specifically, the spatiotemporal autoregression with twenty variables reduced root mean squares error (RMSE) by 39.9% relative to an indicator-based model with twenty-one variables.
Abstract
The estimation of the housing price has mainly been studied in a traditional way of the regression analysis. When spatial and temporal effects are disregarded in the model, however, those effects lead to distort and mislead parameter estimates and statistical inference. Using 1,265 observations on apartment prices in the southern Seoul from 1995 to 2000, this article demonstrates the substantial benefits obtained by modeling the spatial as well as the temporal dependence of the data. Specifically, the spatiotemporal autoregression with twenty variables reduced root mean squares error (RMSE) by 39.9% relative to an indicator-based model with twenty-one variables.