Predicting Housing Price Using Spatial Panel Data Models in Korea
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초록
The purpose of this study is to examine which of the following spatial econometric models can most precisely predict a housing price by using spatial panel data: the Spatial Autoregressive Model (SAR), the Spatial Errors Model (SEM), the Spatial Lagged Term with Spatially Correlated Error Structure Model(SAC) and the Spatial Durbin Model (SDM). The study includes 15 Korean metropolitan cities, and its period ranged from August 2011 to March 2017. The results of the analyses suggests that the SDM is the most accurate method for predicting housing price, since it shows the highest scores, R2 = 0.309, log likelihood = 2468.605. The root mean square errors (RMSE) of 0.0065, is also lower compared to the SAR, SEM and SAC. on results of the SDM analysis, the rate of fluctuation of apartment sales price appeared rising 0.402% and 0.421% correspondingly to the common increase of 1% of the indices of Chonsei and consumer prices respectively. Whereas it appeared rising 0.017% and 0.003% respectively by the common increase of 1% of the housing transaction volume and consumer sentiment index, it fell 0.002% correspondingly to the common increase of 1% of the mortgage loan and unsold housing volume.
Abstract
The purpose of this study is to examine which of the following spatial econometric models can most precisely predict a housing price by using spatial panel data: the Spatial Autoregressive Model (SAR), the Spatial Errors Model (SEM), the Spatial Lagged Term with Spatially Correlated Error Structure Model(SAC) and the Spatial Durbin Model (SDM). The study includes 15 Korean metropolitan cities, and its period ranged from August 2011 to March 2017. The results of the analyses suggests that the SDM is the most accurate method for predicting housing price, since it shows the highest scores, R2 = 0.309, log likelihood = 2468.605. The root mean square errors (RMSE) of 0.0065, is also lower compared to the SAR, SEM and SAC. on results of the SDM analysis, the rate of fluctuation of apartment sales price appeared rising 0.402% and 0.421% correspondingly to the common increase of 1% of the indices of Chonsei and consumer prices respectively. Whereas it appeared rising 0.017% and 0.003% respectively by the common increase of 1% of the housing transaction volume and consumer sentiment index, it fell 0.002% correspondingly to the common increase of 1% of the mortgage loan and unsold housing volume.
참고문헌 (22)
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- "An apartment sales and Chonsei price data" (2016)[URL]
- "The Bank of Korea"[URL]
- "The Ministry of Land, Infrastructure and Transport"[URL]
- "The Statistics Korea"[URL]
- "The Korea Research Institute for Human Settlements"[URL]