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Investigating the Determinants of Housing Rents in Hangzhou, China: A Spatial Multilevel Model Approach

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Abstract

Accelerating the cultivation and development of the residential rental property (hereafter, rental property) market is an indispensable part of improving China’s housing market system, and the rationality of rental prices has become the focus of social attention in this development process. Drawing on geospatial big data, such as rental data collected from 5i5j and Points of Interest (POI) facilities in Hangzhou, this paper examines spatial distribution characteristics of rental property and associated rents in Hangzhou using GIS spatial analysis and employs a spatial multilevel model to investigate the determinants of such rents. The results indicate that the spatial distribution and kernel density distribution of rental property and associated rents in Hangzhou are alike, being characterized by a similar center-edge structure. Furthermore, considering the positive spatial autocorrelation of rents in Hangzhou, three spatial proxy variables are filtered out through eigenvector spatial filtering analysis to reduce the spatial autocorrelation problem. In addition, the spatial multilevel model witnesses the best goodness-of-fit when compared with the traditional ordinary last squares (OLS) and multilevel models. The results of the spatial multilevel model show that residential rents in Hangzhou are affected by both individual-level and street-level factors. At the individual level, building characteristics such as house area, number of bedrooms, decoration grade, story, and age are the major determinants. At the street level, distance to cultural and sports facilities is negatively associated with rents, while distance to bus stations, 3A hospitals (three first-class hospitals), and commercial complexes is positively associated with them. Comparing the impact intensity of various distance variables, distance to city center and public transit has the largest impact on rents in Hangzhou, followed by distance to educational, medical, and living facilities.

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Data Availability

Rental properties data were collected from rental property listings in Hangzhou on the 5i5j online rental platform (https://hz.5i5j.com/) in December 2020.

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Funding

This work was supported by the National Natural Science Foundation of China (42001120), Fundamental Research Funds for the Provincial Universities of Zhejiang (GB202103004).

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Authors and Affiliations

  1. School of Management, China Academy of Housing & Real Estate, Zhejiang University of Technology, 310023, Hangzhou, China

    Dongsheng Zhan, Chunxin Xie & Juanfeng Zhang

  2. College of Applied Arts and Science, Beijing Union University, Beijing, 100191, China

    Bin Meng

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  1. Dongsheng Zhan
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  2. Chunxin Xie
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Correspondence to Bin Meng.

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Zhan, D., Xie, C., Zhang, J. et al. Investigating the Determinants of Housing Rents in Hangzhou, China: A Spatial Multilevel Model Approach. Appl. Spatial Analysis 16, 1707–1727 (2023). https://doi.org/10.1007/s12061-023-09530-1

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