Distribution Pattern of Urban Housing Price Based on Multiple Big Data:A Case Study of Beijing,Shanghai,Guangzhou,and Wuhan

The housing price has been paid close attention by people in all walks of life,and the development of big data provides a new data environment for the study of urban housing price.Housing price data of four national central cities (Beijing,Shanghai,Guangzhou and Wuhan) are taken as research samples.With the help of software GIS,exploratory spatial data analysis method is used to depict the spatial distribution pattern of urban housing price,and commonness and difference of spatial distribution of housing price are explored.The conclusions are as below:①regional imbalance of housing price in national central cities is significant.②Spatial distribution of urban housing price in Beijing,Shanghai,Guangzhou and Wuhan presents a polycentric pattern,and there is obvious spatial agglomeration.③The internal change of housing price in different cities has significant spatial difference.Beijing,Shanghai,Guangzhou and Wuhan are taken as typical city samples for research,with reference and practical significance,which could help to effectively predict spatial development trend of housing prices in other first and second tier cities.The research aims to provide certain reference for the government implementing real estate control policies according to local conditions,project location and reasonable pricing of real estate developers.

Modeling urban housing price： The perspective of household activity demand

Existing studies about the modeling of urban housing price have figured out sets of factors and the main focus is on the relative spatial location. Generally, this line of research is descriptive rather than modeling in nature. The underlying reasons for the distribution of housing price are largely unexplored and more research is needed. The paper therefore attempted to systematically explore the spatial heterogeneities of urban housing price based on the urban activity interaction rule. Using Beijing as a case study, this study first developed a new measurement of accessibility which directly depicts the cost and possibilities to access opportunities of different activities such as employments, educational, shopping and medical services. From the perspective of demands of different households, the paper then modelled the relationships between urban housing price and these accessibilities and found that the distribution pattern of housing price can be relatively well represented by this model that the R^2 could achieve 0.7. We investigated the relationship between housing price and the demands of different kinds of households categorized by households of one-generation, two-generation, three-generation and four-and-plus-generation and found that the demands of household of four-and-plus-generations is the most highly correlated with housing prices. The reason might be that this kind of household has more household members and the demands are more diverse and complex, which is more similar to the distributions of all kinds of activity opportunities in the real world. In the end of the paper, some implications for policy-making are proposed based on the results of the analyses.