IDEAS home Printed from https://ideas.repec.org/p/hhs/kthrec/2022_003.html
   My bibliography  Save this paper

The relationship between owner-occupied housing prices and rental housing rents: evidence from Beijing, China

Author

Listed:
  • Song, Zisheng

    (Department of Real Estate and Construction Management, Royal Institute of Technology)

  • Wilhelmsson, Mats

    (Department of Real Estate and Construction Management, Royal Institute of Technology)

  • Zalejska-Jonsson, Agnieszka

    (Department of Real Estate and Construction Management, Royal Institute of Technology)

Abstract

The relationship between housing prices or rents with other economic factors has been widely analysed. However, few studies use cross-sectional data to analyse the relationship between owner-occupied and rental housing sectors. This paper aims to develop a cross-sectional rent-price model and estimate the interconnected relationship between different market segments. Based on the transactional data of owner-occupied and rental housing in 2015–2018 in Beijing, China, we empirically conduct analyses of cross-sectional rent-price interconnectivity in total housing markets and segments such as housing size and school district. As expected, we find a bi-directional relationship between prices and rents in Beijing that goes in both directions, indicating that housing of different tenures substitute each other, and substitutional effects are significantly different across submarkets. Condominium prices have a more significant impact on rents than vice versa.

Suggested Citation

  • Song, Zisheng & Wilhelmsson, Mats & Zalejska-Jonsson, Agnieszka, 2022. "The relationship between owner-occupied housing prices and rental housing rents: evidence from Beijing, China," Working Paper Series 22/3, Royal Institute of Technology, Department of Real Estate and Construction Management & Banking and Finance.
  • Handle: RePEc:hhs:kthrec:2022_003
    as

    Download full text from publisher

    File URL: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-312116
    File Function: Full text
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Dimitrios Staikos & Wenjun Xue, 2017. "What drives housing prices, rent and new construction in China," International Journal of Housing Markets and Analysis, Emerald Group Publishing Limited, vol. 10(5), pages 662-686, October.
    2. Hill, Robert J. & Syed, Iqbal A., 2016. "Hedonic price–rent ratios, user cost, and departures from equilibrium in the housing market," Regional Science and Urban Economics, Elsevier, vol. 56(C), pages 60-72.
    3. Denise DiPasquale & William C. Wheaton, 1992. "The Markets for Real Estate Assets and Space: A Conceptual Framework," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 20(2), pages 181-198, June.
    4. Patrick Bayer & Kyle Mangum & James W. Roberts, 2021. "Speculative Fever: Investor Contagion in the Housing Bubble," American Economic Review, American Economic Association, vol. 111(2), pages 609-651, February.
    5. Lu, Xun & Su, Liangjun & White, Halbert, 2017. "Granger Causality And Structural Causality In Cross-Section And Panel Data," Econometric Theory, Cambridge University Press, vol. 33(02), pages 263-291, April.
    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. Norbert Hiller & Oliver Lerbs, 2022. "Wie stark reagiert der deutsche Wohnungsbau auf steigende Kapitalmarktzinsen? [How Strongly Does German Residential Construction React to Rising Capital Market Interest Rates?]," Wirtschaftsdienst, Springer;ZBW - Leibniz Information Centre for Economics, vol. 102(9), pages 716-723, September.
    2. Adrian Fernández-Pérez & Marta Gómez-Puig & Simón Sosvilla-Rivero, 2025. "El Clasico of Housing: Bubbles in Madrid and Barcelona’s Real Estate Markets," Documentos de Trabajo del ICAE 2025-03, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    3. Xiao-Li Gong & Jin-Yan Lu & Xiong Xiong & Wei Zhang, 2025. "Liquidity constraints, real estate regulation, and local government debt risks," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 11(1), pages 1-28, December.
    4. Kajuth, Florian & Knetsch, Thomas A. & Pinkwart, Nicolas, 2013. "Assessing house prices in Germany: Evidence from an estimated stock-flow model using regional data," Discussion Papers 46/2013, Deutsche Bundesbank.
    5. William M. Doerner & William G. Doerner, 2011. "Collective Bargaining and Job Benefits in Florida Municipal Police Agencies, 2000-2009," Working Papers wp2011_01_02, Department of Economics, Florida State University, revised Oct 2012.
    6. Jérôme Coffinet & Thomas Ferrière & Dorian Henricot, 2018. "Commercial real estate: is there a risk of a financial bubble? [Immobilier commercial : un risque de bulle financière ?]," Bulletin de la Banque de France, Banque de France, issue 219.
    7. Philippe Bracke, 2013. "House Prices and Rents: Micro Evidence from a Matched Dataset in Central London_x0003_," ERSA conference papers ersa13p112, European Regional Science Association.
    8. Daniel Melser & Robert J. Hill, 2019. "Residential Real Estate, Risk, Return and Diversification: Some Empirical Evidence," The Journal of Real Estate Finance and Economics, Springer, vol. 59(1), pages 111-146, July.
    9. Dong, Feng & Jia, Yandong & Wang, Siqing, 2022. "Speculative Bubbles and Talent Misallocation," Journal of Economic Dynamics and Control, Elsevier, vol. 141(C).
    10. Ryan Fox & Peter Tulip, 2014. "Is Housing Overvalued?," RBA Research Discussion Papers rdp2014-06, Reserve Bank of Australia.
    11. Fabozzi, Frank J. & Xiao, Keli, 2017. "Explosive rents: The real estate market dynamics in exuberance," The Quarterly Review of Economics and Finance, Elsevier, vol. 66(C), pages 100-107.
    12. Edward C. H. Tang, 2021. "Speculate a lot," Pacific Economic Review, Wiley Blackwell, vol. 26(1), pages 91-109, February.
    13. Baltagi, Badi H. & Li, Jing, 2015. "Cointegration of matched home purchases and rental price indexes — Evidence from Singapore," Regional Science and Urban Economics, Elsevier, vol. 55(C), pages 80-88.
    14. Juan Carmona & Markus Lampe & Joan Rosés, 2017. "Housing affordability during the urban transition in Spain," Economic History Review, Economic History Society, vol. 70(2), pages 632-658, May.
    15. Carlos Garriga & Athena Tsouderou & Pedro Gete, 2019. "Housing Dynamics without Homeowners. The Role of I," 2019 Meeting Papers 1407, Society for Economic Dynamics.
    16. Mandell, Svante, 2009. "Policies towards a more efficient car fleet," Energy Policy, Elsevier, vol. 37(12), pages 5184-5191, December.
    17. Li, Yaoyao & Qi, Yuan & Liu, Licheng & Hou, Yuchen & Fu, Shuya & Yao, Jingtao & Zhu, Daolin, 2022. "Effect of increasing the rental housing supply on house prices: Evidence from China’s large and medium-sized cities," Land Use Policy, Elsevier, vol. 123(C).
    18. Yanjiang Zhang & Hongyi Fan & Qingling Liu & Xiaofen Yu & Shangming Yang, 2023. "How a Short-Lived Rumor of Residential Redevelopment Disturbs a Local Housing Market: Evidence from Hangzhou, China," Land, MDPI, vol. 12(2), pages 1-15, February.
    19. Basse, Tobias & Desmyter, Steven & Saft, Danilo & Wegener, Christoph, 2023. "Leading indicators for the US housing market: New empirical evidence and thoughts about implications for risk managers and ESG investors," International Review of Financial Analysis, Elsevier, vol. 89(C).
    20. Łaszek, Jacek & Olszewski, Krzysztof, 2014. "The behaviour of housing developers and aggregate housing supply," MPRA Paper 60478, University Library of Munich, Germany.

    More about this item

    Keywords

    rents; owner-occupied housing prices; housing subsectors’ relationship;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • R32 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Other Spatial Production and Pricing Analysis

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    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:hhs:kthrec:2022_003. 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: Cecilia Hermansson (email available below). General contact details of provider: https://edirc.repec.org/data/ifkthse.html .

    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.