Dongsheng Zhan, Chunxin Xie, Juanfeng Zhang, Bin Meng
{"title":"杭州住房租金影响因素的空间多层次模型研究","authors":"Dongsheng Zhan, Chunxin Xie, Juanfeng Zhang, Bin Meng","doi":"10.1007/s12061-023-09530-1","DOIUrl":null,"url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":46392,"journal":{"name":"Applied Spatial Analysis and Policy","volume":"16 4","pages":"1707 - 1727"},"PeriodicalIF":2.0000,"publicationDate":"2023-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Investigating the Determinants of Housing Rents in Hangzhou, China: A Spatial Multilevel Model Approach\",\"authors\":\"Dongsheng Zhan, Chunxin Xie, Juanfeng Zhang, Bin Meng\",\"doi\":\"10.1007/s12061-023-09530-1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>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.</p></div>\",\"PeriodicalId\":46392,\"journal\":{\"name\":\"Applied Spatial Analysis and Policy\",\"volume\":\"16 4\",\"pages\":\"1707 - 1727\"},\"PeriodicalIF\":2.0000,\"publicationDate\":\"2023-07-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Applied Spatial Analysis and Policy\",\"FirstCategoryId\":\"90\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s12061-023-09530-1\",\"RegionNum\":4,\"RegionCategory\":\"社会学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENVIRONMENTAL STUDIES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Spatial Analysis and Policy","FirstCategoryId":"90","ListUrlMain":"https://link.springer.com/article/10.1007/s12061-023-09530-1","RegionNum":4,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENVIRONMENTAL STUDIES","Score":null,"Total":0}
Investigating the Determinants of Housing Rents in Hangzhou, China: A Spatial Multilevel Model Approach
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.
期刊介绍：
Description
The journal has an applied focus: it actively promotes the importance of geographical research in real world settings
It is policy-relevant: it seeks both a readership and contributions from practitioners as well as academics
The substantive foundation is spatial analysis: the use of quantitative techniques to identify patterns and processes within geographic environments
The combination of these points, which are fully reflected in the naming of the journal, establishes a unique position in the marketplace.
RationaleA geographical perspective has always been crucial to the understanding of the social and physical organisation of the world around us. The techniques of spatial analysis provide a powerful means for the assembly and interpretation of evidence, and thus to address critical questions about issues such as crime and deprivation, immigration and demographic restructuring, retailing activity and employment change, resource management and environmental improvement. Many of these issues are equally important to academic research as they are to policy makers and Applied Spatial Analysis and Policy aims to close the gap between these two perspectives by providing a forum for discussion of applied research in a range of different contexts
Topical and interdisciplinaryIncreasingly government organisations, administrative agencies and private businesses are requiring research to support their ‘evidence-based’ strategies or policies. Geographical location is critical in much of this work which extends across a wide range of disciplines including demography, actuarial sciences, statistics, public sector planning, business planning, economics, epidemiology, sociology, social policy, health research, environmental management.
FocusApplied Spatial Analysis and Policy will draw on applied research from diverse problem domains, such as transport, policing, education, health, environment and leisure, in different international contexts. The journal will therefore provide insights into the variations in phenomena that exist across space, it will provide evidence for comparative policy analysis between domains and between locations, and stimulate ideas about the translation of spatial analysis methods and techniques across varied policy contexts. It is essential to know how to measure, monitor and understand spatial distributions, many of which have implications for those with responsibility to plan and enhance the society and the environment in which we all exist.
Readership and Editorial BoardAs a journal focused on applications of methods of spatial analysis, Applied Spatial Analysis and Policy will be of interest to scholars and students in a wide range of academic fields, to practitioners in government and administrative agencies and to consultants in private sector organisations. The Editorial Board reflects the international and multidisciplinary nature of the journal.
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