The analysis of hidden spatial features is crucial for the improvement of hedonic regression models for analyzing the structure of land and housing prices. If critical variables representing the influence of spatial f...The analysis of hidden spatial features is crucial for the improvement of hedonic regression models for analyzing the structure of land and housing prices. If critical variables representing the influence of spatial features are omitted in the models, the residuals and the coefficients estimated usually exhibit some kind of spatial pattern. Hence, exploration of the relationship between the spatial patterns and the spatial features essentially leads to the discovery of omitted variables. The analyses in this paper were based on two exploratory approaches: one on the residual of a global regression model and the other on the geographically weighted regression (GWR) technique. In the GWR model, the regression coefficients are al- lowed to differ by location so more spatial patterns can be revealed. Comparison of the two approaches shows that they play supplementary roles for the detection of lot-associated variables and area-associated variables.展开更多
基金Supported by the Special Coordination Funds for Promoting Sci-ence and Technology, and the Research Grant-In-Aid provided by the Ministry of Education, Culture, Sports, Science, and Technol-ogy, Japan
文摘The analysis of hidden spatial features is crucial for the improvement of hedonic regression models for analyzing the structure of land and housing prices. If critical variables representing the influence of spatial features are omitted in the models, the residuals and the coefficients estimated usually exhibit some kind of spatial pattern. Hence, exploration of the relationship between the spatial patterns and the spatial features essentially leads to the discovery of omitted variables. The analyses in this paper were based on two exploratory approaches: one on the residual of a global regression model and the other on the geographically weighted regression (GWR) technique. In the GWR model, the regression coefficients are al- lowed to differ by location so more spatial patterns can be revealed. Comparison of the two approaches shows that they play supplementary roles for the detection of lot-associated variables and area-associated variables.
