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

Ruibing Kou, Yifei Long, Yixin Zhou, Weilong Liu, Xiang He and Qiao Peng ()
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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

Sustainability, 2024, vol. 16, issue 17, 1-24

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.

Keywords: rental market; educational institution; medical facility; Chinese super cities; interpretable machine learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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