Administrative hierarchy, housing market inequality, and multilevel determinants: a cross-level analysis of housing prices in China

Numerous studies have examined the determinants of housing prices in Chinese cities. However, most of them ignored the hierarchical structural characteristics of housing prices because of the use of ordinary least squares and hedonic pricing to model the housing prices for a single city. Therefore, this study explores the multi-level determinants of housing prices and their interactions at different levels. To this end, it proposes a three-level hierarchical linear model (HLM) using observations from 146,099 communities nested in 1120 counties of 31 provinces in China as a case study. The results of the hierarchical linear regression indicate significant variances in average housing prices. This finding suggests HLM to be appropriate when dealing with housing prices inherently nested at multiple geographic levels. Overall, housing values in China are not only determined by accessibility factors but are also driven by multi-level socioeconomic aspects. Among the selected variables, high-speed railway shows a significant positive effect, while ordinary railway shows a significant negative effect on housing values at the community level. At the county level, rural–urban migration and per capita living space have significant positive impacts on housing value. At the province level, the relationship between rural–urban migration and housing prices depends on economic development and urban employment. Similarly, the average wages of urban employment influence the relationship between per capita living space and housing prices. These results suggest that contextual effects exist between the determinants of housing prices at county and province levels.