Spatial pattern and spatial heterogeneity of influencing factors of housing quality in urban china

Accurately identifying the spatial distribution characteristics and the spatial heterogeneity of influencing factors of housing quality in 337 prefecture-level and above cities in China can provide an important scientific basis for profoundly understanding regional development gap and promoting the high-quality development of urban housing in China. Based on the data of China's population census by county in 2020 and the data of socio-economic characteristics of each city, the spatial statistical method is used to identify the spatial agglomeration characteristics of housing quality in urban China, and a multiscale geographically weighted regression model (MGWR) is established to explore the spatial heterogeneity of the influencing factors of housing quality. The conclusions can be drawn as follows: Firstly, the spatial distribution of housing quality in urban China shows a spatial characteristic of “high in the southeast and low in the northwest” with the Hu Line as the boundary, and a spatial pattern of a slight rebound in Xinjiang and Inner Mongolia within the borders; the spatial distribution of housing area in urban China shows a spatial pattern of “high in the south and low in the north”; the spatial distribution of housing facilities in urban China shows a spatial pattern of “high in the southeast and northwest, and low in the northeast and southwest”. Secondly, the goodness of fit of MGWR results is significantly higher than that of the ordinary least squares regression model and geographically weighted regression model, and the different influencing factors have different working scales. The key influencing factors are per capita GDP, per capita general budget expenditure of local finance, population aging and the proportion of illiterate population. Thirdly, the results of the MGWR model show that both of the socio-economic and demographic factors have a significant spatial heterogeneity impact on housing quality in urban China. The positive impact of per capita GDP and per capita investment in real estate development presents a “north-south” spatial disparity; the impact of the proportion of non-agricultural industry output value, population aging and the proportion of illiterate population shows a “east-west” spatial disparity; the high value areas affected by per capita general budget expenditure of local finance are the central Guizhou urban agglomeration areas; the high-value areas affected by the urbanization rate are Northeast China and Inner Mongolia; the high-value areas affected by the average schooling years are the urban agglomeration areas along the Yellow River in Ningxia; the high-value areas affected by the households size are the central and southern Liaoning urban agglomerations.