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Investigating the Impact of Public Services on Rental Prices in Chinese Super Cities Based on Interpretable Machine Learning

Author

Listed:
  • Ruibing Kou

    (School of Design and Art, Changsha University of Science and Technology, Changsha 410114, China)

  • Yifei Long

    (Yibin Institute of Urban and Rural Planning Research, Yibin 644000, China)

  • Yixin Zhou

    (Queen’s Business School, Queen’s University Belfast, Belfast BT9 5EE, UK)

  • Weilong Liu

    (Lingnan College, Sun Yat-sen University, Guangzhou 510275, China)

  • Xiang He

    (School of Natural and Built Environment, Queen‘s University Belfast, Belfast BT7 1NN, UK)

  • Qiao Peng

    (Queen’s Business School, Queen’s University Belfast, Belfast BT9 5EE, UK)

Abstract

In China, approximately 20% of the permanent population are renters, with 91% of leased land concentrated in first-tier and new first-tier cities. Education and healthcare are primary concerns for residents, significantly influencing rental decisions due to the household registration (hukou) system, competitive educational environment, and uneven distribution of medical resources. This study explores the distinct factors affecting rental decisions in China’s super cities, differing from other countries where renters prioritize proximity to work or urban amenities. Using advanced interpretable machine learning techniques, the study analyses rental markets in Beijing, Shanghai, and Shenzhen. The random forest model demonstrates superior performance in rent prediction across all three cities. The results indicate that the impact of public service resources on rent is notably higher in Beijing and Shanghai, while in Shenzhen, balanced urban planning results in property characteristics being more prominent in tenant preferences. These findings enhance the understanding of global rental market dynamics and provide recommendations for promoting sustainable rental housing development. The scientific novelty of this study lies in its application of advanced machine learning models to identify and quantify the unique influences of public service resources on rental markets in different urban contexts.

Suggested Citation

  • Ruibing Kou & Yifei Long & Yixin Zhou & Weilong Liu & Xiang He & Qiao Peng, 2024. "Investigating the Impact of Public Services on Rental Prices in Chinese Super Cities Based on Interpretable Machine Learning," Sustainability, MDPI, vol. 16(17), pages 1-24, September.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:17:p:7861-:d:1474455
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    References listed on IDEAS

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    1. Paul Waddell & Arezoo Besharati-Zadeh, 2020. "A Comparison of Statistical and Machine Learning Algorithms for Predicting Rents in the San Francisco Bay Area," Papers 2011.14924, arXiv.org.
    2. Julie T. Miao & Nicholas A. Phelps, 2022. "Urban Sprawl as Policy Sprawl: Distinguishing Chinese Capitalism’s Suburban Spatial Fix," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 112(4), pages 1179-1194, April.
    3. Zhang, Chuanchuan & Jia, Shen & Yang, Rudai, 2016. "Housing affordability and housing vacancy in China: The role of income inequality," Journal of Housing Economics, Elsevier, vol. 33(C), pages 4-14.
    4. Paul Cheshire & Stephen Sheppard, 2004. "Capitalising the Value of Free Schools: The Impact of Supply Characteristics and Uncertainty," Economic Journal, Royal Economic Society, vol. 114(499), pages 397-424, November.
    5. Gibbons, Stephen & Machin, Stephen & Silva, Olmo, 2013. "Valuing school quality using boundary discontinuities," Journal of Urban Economics, Elsevier, vol. 75(C), pages 15-28.
    6. Chihiro Shimizu & Tsutomu Watanabe, 2010. "Housing Bubbles in Japan and the United States," Public Policy Review, Policy Research Institute, Ministry of Finance Japan, vol. 6(3), pages 431-472, March.
    7. Dean Hanink & Robert Cromley & Avraham Ebenstein, 2012. "Spatial Variation in the Determinants of House Prices and Apartment Rents in China," The Journal of Real Estate Finance and Economics, Springer, vol. 45(2), pages 347-363, August.
    8. Wallace, Jeremy, 2014. "Cities and Stability: Urbanization, Redistribution, and Regime Survival in China," OUP Catalogue, Oxford University Press, number 9780199378999.
    9. Mo, Jiawei, 2018. "Land financing and economic growth: Evidence from Chinese counties," China Economic Review, Elsevier, vol. 50(C), pages 218-239.
    10. Weldensie T Embaye & Yacob Abrehe Zereyesus & Bowen Chen, 2021. "Predicting the rental value of houses in household surveys in Tanzania, Uganda and Malawi: Evaluations of hedonic pricing and machine learning approaches," PLOS ONE, Public Library of Science, vol. 16(2), pages 1-20, February.
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