Housing is among the most pressing issues in urban China and has received considerable scholarly attention. Researchers have primarily concentrated on identifying the factors that influence residential property prices and how such mechanisms function. However, few studies have examined the potential factors that influence housing prices from a big data perspective. In this article, we use a big data perspective to determine the willingness of buyers to pay for various factors. The opinions and geographical preferences of individuals for places can be represented by visit frequencies given different motivations. Check-in data from the social media platform Sina Visitor System is used in this article. Here, we use kernel density estimation (KDE) to analyse the spatial patterns of check-in spots (or places of interest, POIs) and employ the Getis-Ord Gi* method to identify the hot spots for different types of POIs in Shenzhen, China. New indexes are then proposed based on the hot-spot results as measured by check-in data to analyse the effects of these locations on housing prices. This modelling is performed using the hedonic price method (HPM) and the geographically weighted regression (GWR) method. The results show that the degree of clustering of POIs has a significant influence on housing values. Meanwhile, the GWR method has a better interpretive capacity than does the HPM because of the former method’s ability to capture spatial heterogeneity. This article integrates big social media data to expand the scope (new study content) and depth (study scale) of housing price research to an unprecedented degree.
School of Resources and Environmental Science, Wuhan University, Wuhan, China;Department of Geography, Kent State University, Kent, Ohio, United States of America;School of Resources and Environmental Science, Wuhan University, Wuhan, China;Key Laboratory of GIS, Ministry of Education, Wuhan University, Wuhan, China;Key Laboratory of Digital Mapping and Land Information Application Engineering, National Administration of Surveying, Mapping and Geoinformation, Wuhan University, Wuhan, China;School of Resources and Environmental Science, Wuhan University, Wuhan, China;Key Laboratory of GIS, Ministry of Education, Wuhan University, Wuhan, China;Key Laboratory of Digital Mapping and Land Information Application Engineering, National Administration of Surveying, Mapping and Geoinformation, Wuhan University, Wuhan, China;School of Resources and Environmental Science, Wuhan University, Wuhan, China;School of Resources and Environmental Science, Wuhan University, Wuhan, China;Key Laboratory of GIS, Ministry of Education, Wuhan University, Wuhan, China;Key Laboratory of Digital Mapping and Land Information Application Engineering, National Administration of Surveying, Mapping and Geoinformation, Wuhan University, Wuhan, China;Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan, China
Recommended Citation:
Chao Wu,Xinyue Ye,Fu Ren,et al. Spatial and Social Media Data Analytics of Housing Prices in Shenzhen, China[J]. PLOS ONE,2016-01-01,11(10)