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Identifying the determinants of housing prices in China using spatial regression and the geographical detector technique
Wang, Yang1,2; Wang, Shaojian3; Li, Guangdong4; Zhang, Hongou1,2; Jin, Lixia1,2; Su, Yongxian1,2; Wu, Kangmin1,2
2017-02-01
Source PublicationAPPLIED GEOGRAPHY
ISSN0143-6228
Volume79Pages:26-36
Corresponding AuthorWang, Shaojian(1987wangshaojian@163.com) ; Li, Guangdong(ligd@igsnrr.ac.cn)
AbstractThis study analyzed the direction and strength of the association between housing prices and their potential determinants in China, from a tripartite perspective that takes into account housing demand, housing supply, and the housing market. A data set made up of county-level housing prices and selected factors was constructed for the year 2014, and spatial regression and geographical detector technique were estimated. The results of the study indicate that the housing prices of Chinese counties are heavily influenced by the administrative level of the county in question. On the basis of results obtained using Moran's I, the study revealed the presence of significant spatial autocorrelation (or spatial agglomeration) in the data. Using spatial regression techniques, the study identifies the positive effect exerted by the proportion of renters, floating population, wage level, the cost of land, the housing market and city service level on housing prices, and the negative influence exerted by living space. The geographical detector technique revealed marked differences in the relative influence, as well as the strength of association, of the seven factors in relation to housing prices. The cost of land had a greater influence on housing prices than other factors. We argue that a better understanding of the determinants of housing prices in China at the county level will help Chinese policymakers to formulate more detailed and geographically specific housing policies. 2016 Elsevier Ltd. All rights reserved.
KeywordHousing prices Spatial regression Geographical detector China
DOI10.1016/j.apgeog.2016.12.003
WOS KeywordURBAN-GROWTH ; MARKETS ; CITIES ; RENTS ; LAND ; FUNDAMENTALS ; DYNAMICS ; MIGRANTS ; REGION ; WAGES
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[41601151] ; National Natural Science Foundation of China[1401164] ; National Natural Science Foundation of China[41501175] ; Natural Science Foundation of Guangdong Province[2016A030310149]
Funding OrganizationNational Natural Science Foundation of China ; Natural Science Foundation of Guangdong Province
WOS Research AreaGeography
WOS SubjectGeography
WOS IDWOS:000395353300003
PublisherELSEVIER SCI LTD
Citation statistics
Cited Times:15[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/64810
Collection中国科学院地理科学与资源研究所
Corresponding AuthorWang, Shaojian; Li, Guangdong
Affiliation1.Guangzhou Inst Geog, Guangdong Open Lab Geospatial Informat Technol &, Guangzhou 510070, Guangdong, Peoples R China
2.Guangdong Acad Innovat Dev, Guangzhou 510070, Guangdong, Peoples R China
3.Sun Yat Sen Univ, Guangdong Prov Key Lab Urbanizat & Geosimulat, Sch Geog & Planning, Guangzhou 510275, Guangdong, Peoples R China
4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
Recommended Citation
GB/T 7714
Wang, Yang,Wang, Shaojian,Li, Guangdong,et al. Identifying the determinants of housing prices in China using spatial regression and the geographical detector technique[J]. APPLIED GEOGRAPHY,2017,79:26-36.
APA Wang, Yang.,Wang, Shaojian.,Li, Guangdong.,Zhang, Hongou.,Jin, Lixia.,...&Wu, Kangmin.(2017).Identifying the determinants of housing prices in China using spatial regression and the geographical detector technique.APPLIED GEOGRAPHY,79,26-36.
MLA Wang, Yang,et al."Identifying the determinants of housing prices in China using spatial regression and the geographical detector technique".APPLIED GEOGRAPHY 79(2017):26-36.
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