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Predicting industrial property prices with explainable artificial intelligence

Author

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  • Tris Kee
  • Winky K.O. Ho

Abstract

The industrial property market in Hong Kong is a dynamic and complex sector, marked by unique characteristics and atypical market behaviour. This study leverages the predictive power of Gradient Boosting Machines (GBM) to uncover the intricate relationships that drive property prices. Key features such as location, square footage, floor level and proximity to mass transit railway stations are analysed, with Shapley values providing a transparent and interpretable measure of each feature’s impact. Our findings reveal striking non-linear interactions among these features, vividly depicted through beeswarm plots showcasing wide SHAP value distributions that oscillate across the baseline. These insights illuminate the nuanced interplay between property attributes and their market valuation, offering a fresh perspective on the industrial property sector. By equipping stakeholders with actionable intelligence, this research empowers data-driven decision-making, fostering a deeper understanding of the forces shaping property prices in one of the world’s most dynamic real estate markets.

Suggested Citation

  • Tris Kee & Winky K.O. Ho, 2026. "Predicting industrial property prices with explainable artificial intelligence," Journal of Property Research, Taylor & Francis Journals, vol. 43(2), pages 167-187, April.
    • Handle: RePEc:taf:jpropr:v:43:y:2026:i:2:p:167-187
    DOI: 10.1080/09599916.2025.2550976
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