Using 2016 data from the National Migrant Population Dynamic Monitoring, this article establishes a relationship network to describe migrant workers' interprovincial hukou transfer intention in China. Spatial analysis methods and the eigenvector spatial filtering gravity model are employed to examine the spatial pattern and determinants of the hukou transfer intention network. The results show that (a) most interprovincial migrant workers in China are less educated, middle aged, with middle‐ or low‐income levels, and job oriented, and their average interprovincial hukou transfer intention is 0.361; (b) there is significant network autocorrelation in the intention network, which presents a clustered and unbalanced spatial pattern where higher ranking intention flows from relatively less developed regions to more developed megacities; (c) provinces' hukou attractiveness to migrant workers demonstrates a random spatial pattern, but the hukou exclusion patterns are spatially concentrated: The north‐east provinces are hotspots, whereas several central and north‐east provinces are cold spots; (d) among geographical factors, distance exerts a negative influence on migrant workers' hukou transfer intention, whereas population size does not matter at origin or destination. Socio‐economic factors, especially disposable income, play the most significant role in impacting migrant workers' hukou transfer intention; and (e) as for individual factors, migrant workers' interprovincial migration is an economic decision based on family development: Those with more children, higher ratios of income to cost, or higher education levels are reluctant to transfer their hukous. Besides, the job and housing conditions of migrant workers are also closely related to their hukou transfer intentions.
The housing sales market in China has flourished and gained considerable interest, while the housing rental market has lagged behind and been ignored over the past two decades. With the acceleration of urbanization, the housing rental demand is rising rapidly. Exploring and comparing the influencing factors on housing sale prices and rental prices has significance for sustainable urban planning and management. Using house purchase transaction and rent transaction data in 2017, as well as the average housing price and rent data in 2016 in Beijing, China, this paper compares the spatial distribution and it employs the hedonic price model and quantile regression model to quantify the average and distributional effects of micro-level influencing factors on housing prices and housing rents. Results show that housing prices and housing rents both have a decentralized distribution with multiple centers, but rents of residential communities with high housing prices may not necessarily be high. Both homeowners and renters prefer properties with good structural, locational, and neighborhood characteristics, as well as a good school attendance zone, whereas they still differ in terms of preferences. Homeowners prefer a higher-quality living environment. Renters are more concerned with proximity to an employment center and public transit convenience. Moreover, the price premium of school quality for homeowners exceeds the premium for renters. Higher-priced homeowners or renters differ in the preferences from lower-priced homeowners or renters. Higher-priced homeowners and higher-priced renters are more willing to live in property with a larger number of bedrooms, proximity to a major employment center, park, or school, as well as a location in a school attendance zone with higher school quality.
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