TROPICAL GEOGRAPHY ›› 2018, Vol. 38 ›› Issue (3): 384-393.doi: 10.13284/j.cnki.rddl.003027

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Spatial Pattern and Causes of Rental Housing in Guangzhou

LU Junwen1,YUAN Qifeng2,HUANG Zhe1,LI Zhigang3,4   

  1. (1.School of Geography and Planning,Sun Yat-Sen University,Guangzhou 510275,China;2.School of Architecture,South China University of Technology,Guangzhou 510641,China;3.School of Urban Planning,Wuhan University,Wuhan 430072,China;4.Hubei Residential Environment Research Center of Engineering and Technology,Wuhan 430000,China)
  • Online:2018-05-05 Published:2018-05-05

Abstract: With rapid growing and expanding in urban China, it is of great significance to study how the housing space is being used, especially from the perspective of housing leasing. Existing studies paid plenty of attention to rental housing space and have found that historical inertia during the progress of market-oriented transformation, development of urban rail transit, the adjustment of urban structure and the construction of new towns are the main factors affecting the spatial distribution of rental housing in big cities in China, but these conclusions are mostly drawn from studies conducting on town and subdistrict scale, there is still a lack of empirical research about those from community scale. By using open data from government websites and business websites, this paper draws a picture of the rental housing space in Guangzhou, and attempts to make a demonstration analysis. It is found that rental housing space and commodity housing space are separate obviously in central Guangzhou, while there is a more obvious tendency of sitting along the traffic lines for rental housing space and it even gather in somewhere outside the central city. It employs spatial econometric approaches as K-means spatial clustering analysis to help explore the spatial distribution patterns of rental housing space, and the results show that the rental housing space of Guangzhou is divided into six different types including central leasing-dominated regions, central owner-occupied regions, secondary leasing-dominated regions, periphery leasing-dominated regions, owner-occupied regions of low resident population and the remainders. The results above verified the theoretical assumptions presented earlier in some extent, which represents the general characteristics of the distribution pattern of rental housing space in big cities in China, but these also reflect some particularity of the distribution pattern of rental housing space in Guangzhou. After extensive study, the causes of the spatial distribution patterns of rental housing in Guangzhou are summarized as follows: 1) The rise of investment-oriented housing-purchase boom around 2005 gave birth to the dividing pattern of central leasing-dominated regions and central owner-occupied regions in central urban area of Guangzhou. 2)The rapid development of urban rail transit truly plays an important role in shaping the secondary leasing-dominated regions, but in Guangzhou, the emerge of some of the secondary leasing-dominated regions are inextricably linked with urban development strategies like "extending to east and opening up to south" as well. 3) Industrial suburbanization and residential suburbanization are the forces resulting in the distribution pattern of periphery leasing-dominated regions like industry new towns and large residential communities, but in cross-boundary area between Guangzhou and Foshan (Guang-fo city), the force of Guang-fo integration is nonnegligible, which results in a unique type of periphery leasing-dominated region in Guangzhou. As the new policy of “Renting and buying a house enjoy equal rights” launched, this research may conduct much more significance in assisting housing policy, community administration and spatial planning. Around this theme and considering the situation in Guangzhou, it is instructive to improve the supply of public service and emphasize on distribution of public interest in central city, and quicken the pace of designing pertinent policies for rental housing space along the traffic lines and outside central city.

Key words: rental housing, open data, spatial pattern, Guangzhou