Study on the Spatial Identification of Housing Vacancy

Housing Vacancy Rate (HVR) is an important index in assessing the healthiness of residential real estate market. Due to lack of clear and effectively evaluation criterion, the understanding of housing vacancy in China is then rather limited. This paper quantitatively analyzed spatial identification and difference pattern of house vacancy at different scale in China by using nighttime light data and micro-blog check-in data, in order to make up the deficiency of traditional methods in the aspects of data missing and differential approach. The nighttime light intensity for non-vacancy area is estimated after removing the nighttime light intensity from non-residential sources of NPP-VIIRS light data and difference of nighttime light caused by the different urban area ratio. Then, the HVR is calculated for the spatial pattern analysis. This paper analyzed the spatial strength of residents activities by using micro-blog check-in data, based on density-based spatial clustering of applications with noise and heat map. The 30 sample cities were selected to identify house vacancy from 100 cities which ghost city index were high. The following conclusions were drawn through the study: The HVR of eastern coastal cities and regions with rapid development of economy were lower, while the phenomenon of house vacancy in central and western regions were more obvious. The HVR increased from eastern coastal regions to inland areas. What’s more, the phenomenon of house vacancy in middle and small cities were more distinct from the aspect of urban scale. The house vacancy of China were divided into five types: industry or resources driven, government planned, epitaxy expansionary, environmental constraint and speculative activate by taking the factors of natural environment, social economic development level, and population density into consideration. This may shed light on policy implications for Chinese urban development.