- 期刊
Extracting Housing Prices' Influence Factors based on a GWR Model--A Case Study of Fuzhou, China
摘要
In order to promote the economic development and implement the concept of sustainable development, a healthy housing market is of considerable importance. Thus, monitoring housing prices can provide essential implications for equitable housing policies. The development of the big data and the popularization of various on-line platforms allow us to obtain the research data from internet. This study uses mainly open internet data of housing prices, supplemented by other data sources such as house transaction data, open street map data, POI data, remote-sensing data and urban planning data, to study the spatial patterns of housing prices and the influence factors in Fuzhou. Taking the residential community of Fuzhou in 2020 as the basic research unit, we use spatial autocorrelation analysis and ordinary Kriging interpolation method to estimate the spatial distribution of housing prices, and employ Geographically Weighted Regression (GWR) model to explore influence factors on housing prices. On the whole, the housing prices and its influencing factors exhibit obvious spatial heterogeneity. The central of the city is the area where high housing prices clustered, while the other areas possess comparatively lower price. In addition, the neighborhood and location characteristics affect the housing prices more than structure characteristics do. In particular, the distance to the nearest bus and metro station, together with the distance to the Minjiang river and city center, are the major influence factors compared with the others.