IDEAS home Printed from https://ideas.repec.org/a/gam/jlands/v11y2022i12p2299-d1003775.html
   My bibliography  Save this article

Spatial Pattern of the Determinants for the Private Housing Rental Prices in Highly Dense Populated Chinese Cities—Case of Chongqing

Author

Listed:
  • Guiwen Liu

    (School of Management Science and Real Estate, Chongqing University, Chongqing 400045, China)

  • Jiayue Zhao

    (School of Management Science and Real Estate, Chongqing University, Chongqing 400045, China)

  • Hongjuan Wu

    (School of Management Science and Real Estate, Chongqing University, Chongqing 400045, China)

  • Taozhi Zhuang

    (School of Management Science and Real Estate, Chongqing University, Chongqing 400045, China)

Abstract

The private housing rental market has rapidly developed and demonstrated its outstanding contribution to improving affordability for the floating population in China. However, the forming pattern of private housing rental prices (PHRP) remains poorly understood in China’s highly dense populated cities. This study aims to comprehensively investigate the determinants of PHRP and depict their spatial pattern, considering the diverse functions of different areas within the city. A theoretical framework of the factors that influence PHRP has been developed based on an extensive literate study. Taking Chongqing city as a case, a Multiscale Geographically Weighted Regression (MGWR) analysis based on data from Lianjia.com and 58.com was conducted to investigate the spatial pattern of those influencing factors. The PHRP in Chongqing were mainly shaped by the factors of traffic condition and the neighborhood environment. The main findings highlighted that the influence of traffic condition on rental prices is more dominating in the industrial and financial zones, and the neighborhood factors represent spatial heterogeneity in the educational and commercial zones. This study provides a comprehensive examination of the spatial pattern of PHRP’s determinants in highly dense populated Chinese cities, extending the understanding of factors influencing housing rental prices. Practically, it provides scientific and reliable recommendations for the local governments and housing agencies in developing housing properties that consider the needs of the floating population. Moreover, tenants in highly dense populated cities benefit from suggestions about looking for proper accommodation with high value and accessibility in different functional zones of the city.

Suggested Citation

  • Guiwen Liu & Jiayue Zhao & Hongjuan Wu & Taozhi Zhuang, 2022. "Spatial Pattern of the Determinants for the Private Housing Rental Prices in Highly Dense Populated Chinese Cities—Case of Chongqing," Land, MDPI, vol. 11(12), pages 1-22, December.
    • Handle: RePEc:gam:jlands:v:11:y:2022:i:12:p:2299-:d:1003775
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-445X/11/12/2299/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2073-445X/11/12/2299/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hee-Jung Jun, 2022. "Spillover effects in neighborhood housing value change: a spatial analysis," Housing Studies, Taylor & Francis Journals, vol. 37(8), pages 1303-1330, September.
    2. Siqi Zheng & Matthew E. Kahn, 2013. "Does Government Investment in Local Public Goods Spur Gentrification? Evidence from Beijing," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 41(1), pages 1-28, March.
    3. Lin Jiang & Yani Lai & Ke Chen & Xiao Tang, 2022. "What Drives Urban Village Redevelopment in China? A Survey of Literature Based on Web of Science Core Collection Database," Land, MDPI, vol. 11(4), pages 1-16, April.
    4. Qingyun Du & Chao Wu & Xinyue Ye & Fu Ren & Yongjun Lin, 2018. "Evaluating the Effects of Landscape on Housing Prices in Urban China," Tijdschrift voor Economische en Sociale Geografie, Royal Dutch Geographical Society KNAG, vol. 109(4), pages 525-541, September.
    5. Hao Xi & Lin Tang & Changchun Feng, 2022. "Research on the Measurement Method of Benchmark Price of Rental Housing," Land, MDPI, vol. 11(5), pages 1-15, May.
    6. Polloni, Stefano, 2019. "Traffic calming and neighborhood livability: Evidence from housing prices in Portland," Regional Science and Urban Economics, Elsevier, vol. 74(C), pages 18-37.
    7. Geniaux, Ghislain & Martinetti, Davide, 2018. "A new method for dealing simultaneously with spatial autocorrelation and spatial heterogeneity in regression models," Regional Science and Urban Economics, Elsevier, vol. 72(C), pages 74-85.
    8. Rosen, Sherwin, 1974. "Hedonic Prices and Implicit Markets: Product Differentiation in Pure Competition," Journal of Political Economy, University of Chicago Press, vol. 82(1), pages 34-55, Jan.-Feb..
    9. Kelvin J. Lancaster, 1966. "A New Approach to Consumer Theory," Journal of Political Economy, University of Chicago Press, vol. 74(2), pages 132-132.
    10. Yang, Linchuan & Chau, K.W. & Wang, Xu, 2019. "Are low-end housing purchasers more willing to pay for access to basic public services? Evidence from China," Research in Transportation Economics, Elsevier, vol. 76(C).
    11. Li, Han & Wei, Yehua Dennis & Wu, Yangyi, 2019. "Analyzing the private rental housing market in Shanghai with open data," Land Use Policy, Elsevier, vol. 85(C), pages 271-284.
    12. Sihong Xiong & Ya Wu & Shihai Wu & Fang Chen & Jianzhong Yan, 2020. "Determinants of migration decision-making for rural households: a case study in Chongqing, China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 104(2), pages 1623-1639, November.
    13. Hu, Lirong & He, Shenjing & Han, Zixuan & Xiao, He & Su, Shiliang & Weng, Min & Cai, Zhongliang, 2019. "Monitoring housing rental prices based on social media:An integrated approach of machine-learning algorithms and hedonic modeling to inform equitable housing policies," Land Use Policy, Elsevier, vol. 82(C), pages 657-673.
    14. James Todd & Anwar Musah & James Cheshire, 2022. "Assessing the impacts of Airbnb listings on London house prices," Environment and Planning B, , vol. 49(1), pages 206-222, January.
    15. Michael Beenstock & Daniel Felsenstein & Dai Xieer, 2021. "Long-term housing affordability in spatial general equilibrium," Housing Studies, Taylor & Francis Journals, vol. 36(6), pages 935-968, July.
    16. Xiaokuai Shao & Yujin Cao & Yangchuan Teng & Jidong Chen & Liutang Gong, 2022. "The Consumption‐Stimulating Effect of Public Rental Housing in China," China & World Economy, Institute of World Economics and Politics, Chinese Academy of Social Sciences, vol. 30(1), pages 106-135, January.
    17. Xing Gao & Zijia Wang & Mengqiu Cao & Yuqi Liu & Yuerong Zhang & Meiling Wu & Yue Qiu, 2022. "Neighbourhood satisfaction in rural resettlement residential communities: the case of Suqian, China," Housing Studies, Taylor & Francis Journals, vol. 37(8), pages 1497-1518, September.
    18. Geoffrey K. Turnbull & Velma Zahirovic-Herbert & Minrong Zheng, 2018. "Uncertain School Quality and House Prices: Theory and Empirical Evidence," The Journal of Real Estate Finance and Economics, Springer, vol. 57(2), pages 167-191, August.
    19. Hanna Kettunen & Hannu Ruonavaara, 2021. "Rent regulation in 21st century Europe. Comparative perspectives," Housing Studies, Taylor & Francis Journals, vol. 36(9), pages 1446-1468, October.
    20. Yang, Linchuan & Chu, Xiaoling & Gou, Zhonghua & Yang, Hongtai & Lu, Yi & Huang, Wencheng, 2020. "Accessibility and proximity effects of bus rapid transit on housing prices: Heterogeneity across price quantiles and space," Journal of Transport Geography, Elsevier, vol. 88(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Peng, Ying & Tian, Chuanhao & Wen, Haizhen, 2021. "How does school district adjustment affect housing prices: An empirical investigation from Hangzhou, China," China Economic Review, Elsevier, vol. 69(C).
    2. Jose Torres-Pruñonosa & Pablo García-Estévez & Josep Maria Raya & Camilo Prado-Román, 2022. "How on Earth Did Spanish Banking Sell the Housing Stock?," SAGE Open, , vol. 12(1), pages 21582440221, March.
    3. Alice Barreca, 2022. "Architectural Quality and the Housing Market: Values of the Late Twentieth Century Built Heritage," Sustainability, MDPI, vol. 14(5), pages 1-24, February.
    4. Sofia Vale & Felipa de Mello-Sampayo, 2021. "Effect of Hierarchical Parish System on Portuguese Housing Rents," Sustainability, MDPI, vol. 13(2), pages 1-17, January.
    5. Cankun Wei & Meichen Fu & Li Wang & Hanbing Yang & Feng Tang & Yuqing Xiong, 2022. "The Research Development of Hedonic Price Model-Based Real Estate Appraisal in the Era of Big Data," Land, MDPI, vol. 11(3), pages 1-30, February.
    6. Kang, Yuhao & Zhang, Fan & Peng, Wenzhe & Gao, Song & Rao, Jinmeng & Duarte, Fabio & Ratti, Carlo, 2021. "Understanding house price appreciation using multi-source big geo-data and machine learning," Land Use Policy, Elsevier, vol. 111(C).
    7. Jin, Tanhua & Cheng, Long & Liu, Zhicheng & Cao, Jun & Huang, Haosheng & Witlox, Frank, 2022. "Nonlinear public transit accessibility effects on housing prices: Heterogeneity across price segments," Transport Policy, Elsevier, vol. 117(C), pages 48-59.
    8. Qingchun Liu & Peixiong Zhao & Yan Xiao & Xin Zhou & Jun Yang, 2022. "Walking Accessibility to the Bus Stop: Does It Affect Residential Rents? The Case of Jinan, China," Land, MDPI, vol. 11(6), pages 1-17, June.
    9. Yue Ying & Mila Koeva & Monika Kuffer & Kwabena Obeng Asiama & Xia Li & Jaap Zevenbergen, 2020. "Making the Third Dimension (3D) Explicit in Hedonic Price Modelling: A Case Study of Xi’an, China," Land, MDPI, vol. 10(1), pages 1-26, December.
    10. Sheng Li & Yi Jiang & Shuisong Ke & Ke Nie & Chao Wu, 2021. "Understanding the Effects of Influential Factors on Housing Prices by Combining Extreme Gradient Boosting and a Hedonic Price Model (XGBoost-HPM)," Land, MDPI, vol. 10(5), pages 1-15, May.
    11. Trojanek, Radoslaw & Huderek-Glapska, Sonia, 2018. "Measuring the noise cost of aviation – The association between the Limited Use Area around Warsaw Chopin Airport and property values," Journal of Air Transport Management, Elsevier, vol. 67(C), pages 103-114.
    12. Shimizu, Chihiro, 2014. "How Are Property Investment Returns Determined? : Estimating the Micro-Structure of Asset Prices, Property Income, and Discount Rates," HIT-REFINED Working Paper Series 12, Institute of Economic Research, Hitotsubashi University.
    13. Dan Horsky & Sanjog Misra & Paul Nelson, 2006. "Observed and Unobserved Preference Heterogeneity in Brand-Choice Models," Marketing Science, INFORMS, vol. 25(4), pages 322-335, 07-08.
    14. Ilir Nase & Jim Berry & Alastair Adair, 2016. "Impact of quality-led design on real estate value: a spatiotemporal analysis of city centre apartments," Journal of Property Research, Taylor & Francis Journals, vol. 33(4), pages 309-331, October.
    15. Laura Blow & Martin Browning & Ian Crawford, 2004. "Nonparametric methods for the characteristic model," CeMMAP working papers 18/04, Institute for Fiscal Studies.
    16. Marco Helbich & Wolfgang Brunauer & Eric Vaz & Peter Nijkamp, 2014. "Spatial Heterogeneity in Hedonic House Price Models: The Case of Austria," Urban Studies, Urban Studies Journal Limited, vol. 51(2), pages 390-411, February.
    17. Frondel, Manuel & Kussel, Gerhard & Sommer, Stephan & Vance, Colin, 2019. "Local cost for global benefit: The case of wind turbines," Ruhr Economic Papers 791, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen, revised 2019.
    18. Brockmeier, M., 1991. "Entwicklung und Aufhebung von Reinheitsgeboten im Nahrungsmittelbereich – Analyse und Bewertung," Proceedings “Schriften der Gesellschaft für Wirtschafts- und Sozialwissenschaften des Landbaues e.V.”, German Association of Agricultural Economists (GEWISOLA), vol. 27.
    19. Erica L. Groshen & Brian C. Moyer & Ana M. Aizcorbe & Ralph Bradley & David M. Friedman, 2017. "How Government Statistics Adjust for Potential Biases from Quality Change and New Goods in an Age of Digital Technologies: A View from the Trenches," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 187-210, Spring.
    20. Celia Bilbao-Terol, 2009. "Impacts of an Iron and Steel Plant on Residential Property Values," European Planning Studies, Taylor & Francis Journals, vol. 17(9), pages 1421-1436, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jlands:v:11:y:2022:i:12:p:2299-:d:1003775. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.