IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0311174.html
   My bibliography  Save this article

An analysis of production efficiency in China’s real estate industry based on a two-stage DEA model

Author

Listed:
  • Jiening Meng
  • Wei Bu

Abstract

To examine the resource utilization in different phases such as development and sales within China’s real estate industry, this paper employs a two-stage Data Envelopment Analysis (DEA) model to measure the production efficiency of the real estate industry across 31 provinces, municipalities, and autonomous regions of China from 2014 to 2022. By examining both overall and phase-specific trends, the study utilizes a panel Tobit model to explore the factors affecting efficiency. Empirical results indicate that the leverage ratio of companies, per capita GDP of regions, and real estate regulatory policies significantly impact production efficiency. Further analysis of regional heterogeneity and its effect on production efficiency revealed that the per capita residential building area, which reflects the housing stock configuration in different regions, exhibits a significant single threshold effect. This not only objectively assesses the utilization of real estate resources in different areas but also delves deeper into the principal factors and their mechanisms affecting the production efficiency of the real estate industry, thus providing theoretical support and policy recommendations for effectively enhancing production efficiency.

Suggested Citation

  • Jiening Meng & Wei Bu, 2024. "An analysis of production efficiency in China’s real estate industry based on a two-stage DEA model," PLOS ONE, Public Library of Science, vol. 19(10), pages 1-16, October.
    • Handle: RePEc:plo:pone00:0311174
    DOI: 10.1371/journal.pone.0311174
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0311174
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0311174&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0311174?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Randy Anderson & Danielle Lewis & Leonard Zumpano, 2000. "X-Inefficiencies in the Residential Real Estate Market: A Stochastic Frontier Approach," Journal of Real Estate Research, Taylor & Francis Journals, vol. 20(1-2), pages 93-104, January.
    2. Li, Yaoyao & Zhu, Daolin & Zhao, Jiangmeng & Zheng, Xiaodong & Zhang, Lanyue, 2020. "Effect of the housing purchase restriction policy on the Real Estate Market: Evidence from a typical suburb of Beijing, China," Land Use Policy, Elsevier, vol. 94(C).
    3. Zhongbao Zhou & Wenbin Liu, 2015. "DEA Models with Undesirable Inputs, Intermediates, and Outputs," International Series in Operations Research & Management Science, in: Joe Zhu (ed.), Data Envelopment Analysis, edition 127, chapter 15, pages 415-446, Springer.
    4. Robert T. Kleiman & James E. Payne & Anandi P. Sahu, 2002. "Random Walks and Market Efficiency: Evidence from International Real Estate Markets," Journal of Real Estate Research, American Real Estate Society, vol. 24(3), pages 279-298.
    5. Xiao-Xiao Liu & Yao-Yao Song & Hui-Hui Liu & Guo-Liang Yang, 2022. "Regional efficiency of the real estate industry in 35 large and medium-sized cities in China: a meta-frontier SBM approach," Post-Communist Economies, Taylor & Francis Journals, vol. 34(3), pages 376-408, April.
    6. Liu, Wenbin & Zhou, Zhongbao & Ma, Chaoqun & Liu, Debin & Shen, Wanfang, 2015. "Two-stage DEA models with undesirable input-intermediate-outputs," Omega, Elsevier, vol. 56(C), pages 74-87.
    7. Randy I. Anderson & Danielle Lewis & Leonard V. Zumpano, 2000. "X-Inefficiencies in the Residential Real Estate Market: A Stochastic Frontier Approach," Journal of Real Estate Research, American Real Estate Society, vol. 20(1), pages 93-103.
    8. Pijnenburg, Katharina, 2017. "The spatial dimension of US house prices," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 54(2), pages 466-481.
    9. Amemiya, Takeshi, 1984. "Tobit models: A survey," Journal of Econometrics, Elsevier, vol. 24(1-2), pages 3-61.
    10. Rajiv D. Banker & Ram Natarajan, 2008. "Evaluating Contextual Variables Affecting Productivity Using Data Envelopment Analysis," Operations Research, INFORMS, vol. 56(1), pages 48-58, February.
    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. Victoria Wojcik & Harald Dyckhoff & Sebastian Gutgesell, 2017. "The desirable input of undesirable factors in data envelopment analysis," Annals of Operations Research, Springer, vol. 259(1), pages 461-484, December.
    2. Pooja Bansal & Aparna Mehra, 2018. "Multi-period additive efficiency measurement in data envelopment analysis with non-positive and undesirable data," OPSEARCH, Springer;Operational Research Society of India, vol. 55(3), pages 642-661, November.
    3. Malte L. Peters & Stephan Zelewski, 2021. "Upper and lower satisficing levels in efficiency analysis: a corporate social responsibility perspective," Sustainability Nexus Forum, Springer, vol. 29(3), pages 187-195, December.
    4. Calcagno, R. & Renneboog, L.D.R., 2004. "Capital Structure and Managerial Compensation : The Effects of Renumeration Seniority," Discussion Paper 2004-120, Tilburg University, Center for Economic Research.
    5. Barros, Carlos Pestana & Williams, Jonathan, 2013. "The random parameters stochastic frontier cost function and the effectiveness of public policy: Evidence from bank restructuring in Mexico," International Review of Financial Analysis, Elsevier, vol. 30(C), pages 98-108.
    6. Simplice A. Asongu & Mushfiqur Rahman & Mohammad Alghababsheh, 2022. "Information Technology, Business Sustainability and Female Economic Participation in Sub-Saharan Africa," Working Papers 22/057, European Xtramile Centre of African Studies (EXCAS).
    7. Mohammad Nourani & Qian Long Kweh & Evelyn Shyamala Devadason & V.G.R. Chandran, 2020. "A decomposition analysis of managerial efficiency for the insurance companies: A data envelopment analysis approach," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 41(6), pages 885-901, September.
    8. Andrés Felipe Martínez, 2006. "Determinantes de la supervivencia de empresas industriales en el área metropolitana de Cali 1994-2003," Ensayos Sobre Economía Regional (ESER) 2320, Banco de la República - Economía Regional.
    9. Subal C. Kumbhakar & Christopher F. Parmeter & Valentin Zelenyuk, 2022. "Stochastic Frontier Analysis: Foundations and Advances I," Springer Books, in: Subhash C. Ray & Robert G. Chambers & Subal C. Kumbhakar (ed.), Handbook of Production Economics, chapter 8, pages 331-370, Springer.
    10. Renneboog, L.D.R. & Szilagyi, P.G., 2009. "Shareholder Activism through the Proxy Process," Other publications TiSEM cc25d736-2965-4511-b100-1, Tilburg University, School of Economics and Management.
    11. Alexander Cotte Poveda, 2012. "Estimating Effectiveness of the Control of Violence and Socioeconomic Development in Colombia: An Application of Dynamic Data Envelopment Analysis and Data Panel Approach," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 105(3), pages 343-366, February.
    12. Brannlund, Runar & Nordstrom, Jonas, 2004. "Carbon tax simulations using a household demand model," European Economic Review, Elsevier, vol. 48(1), pages 211-233, February.
    13. Fadzlan Sufian & Fakarudin Kamarudin, 2014. "The impact of ownership structure on bank productivity and efficiency: Evidence from semi-parametric Malmquist Productivity Index," Cogent Economics & Finance, Taylor & Francis Journals, vol. 2(1), pages 1-27, December.
    14. Eunsil Seok & Akhgar Ghassabian & Yuyan Wang & Mengling Liu, 2024. "Statistical Methods for Modeling Exposure Variables Subject to Limit of Detection," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 16(2), pages 435-458, July.
    15. Verbeek, M.J.C.M. & Nijman, T.E., 1992. "Incomplete panels and selection bias : A survey," Discussion Paper 1992-7, Tilburg University, Center for Economic Research.
    16. Lynne, Gary D. & Franklin Casey, C. & Hodges, Alan & Rahmani, Mohammed, 1995. "Conservation technology adoption decisions and the theory of planned behavior," Journal of Economic Psychology, Elsevier, vol. 16(4), pages 581-598, December.
    17. Giovanni Marin & Francesca Lotti, 2017. "Productivity effects of eco-innovations using data on eco-patents," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 26(1), pages 125-148.
    18. Yang, Guo-liang & Fukuyama, Hirofumi & Chen, Kun, 2019. "Investigating the regional sustainable performance of the Chinese real estate industry: A slack-based DEA approach," Omega, Elsevier, vol. 84(C), pages 141-159.
    19. Coates, Dennis & Humphreys, Brad, 2011. "Game Attendance and Competitive Balance in the National Hockey League," Working Papers 2011-8, University of Alberta, Department of Economics.
    20. Getachew A. Dagne, 2016. "A growth mixture Tobit model: application to AIDS studies," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(7), pages 1174-1185, July.

    More about this item

    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:plo:pone00:0311174. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.