Geographically Weighted Regression model (GWR) based spatial analysis of house price in Shenzhen

Through applying spatial statistical analysis, Geographical Weighted Regression (GWR) model and GIS technology, this study aims at finding the relationship between the effects of various factors and spatial distribution of residential house price. The traditional regression models are reviewed firstly, the model without the consideration of spatial characteristics cannot reach very nice precision to simulate the spatial distribution of the house price. In this study, the spatial statistical model, coupled with GIS as well as GWR model, is developed. The proposed model is validated using the house price data in Shenzhen, China, when considering these factors such as the land price, transportation, the distance to the commercial center, the distance to hospital, school, the house type, the brand of the house etc. It is demonstrated that our approach provides an effective model to present the distribution of the residential house price and serve as a tool for house price appraisal during the property tax levy process.