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A Longitudinal Rental Analysis of Sub-divided Units in Hong Kong by Machine Learning Algorithms

Author

Listed:
  • Ka Man Leung
  • Yu Cheung Wong
  • Kin Kwok Lai
  • Dah Ming Chiu

Abstract

This paper presents a pioneer longitudinal rental analysis of sub-divided units (SDUs) in Hong Kong, employing first-hand data collected from surveys conducted across five time points between 2017 and 2023. Five widely used machine learning algorithms, multiple linear regression, random forest, decision tree, support vector regression and gradient boosting algorithm, are employed. This study aims to identify the key factors influencing the SDU rental values, focusing on variables including physical facilities, locational characteristics, and temporal trends. The longitudinal nature of the data allows for an examination of how rental values have changed over time. As SDU data with detailed internal characteristics are not publicly available, this study provides timely information and insights for the informal housing market and social welfare policy development, contributing to informed decision-making in addressing housing challenges.

Suggested Citation

  • Ka Man Leung & Yu Cheung Wong & Kin Kwok Lai & Dah Ming Chiu, 2025. "A Longitudinal Rental Analysis of Sub-divided Units in Hong Kong by Machine Learning Algorithms," ERES eres2025_137, European Real Estate Society (ERES).
    • Handle: RePEc:arz:wpaper:eres2025_137
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    JEL classification:

    • R3 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location

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