preprints.org > computer science and mathematics > artificial intelligence and machine learning > doi: 10.20944/preprints202409.0875.v1 (registering DOI)

Preprint Article Version 1 This version is not peer-reviewed

Version 1 : Received: 10 September 2024 / Approved: 11 September 2024 / Online: 11 September 2024 (08:48:15 CEST)

This study explores the industrial property market in Hong Kong, a sector characterized by its unique features and atypical market behaviour. Particularly, this research employs machine learning techniques to predict property prices based on a set of features, including location, square footage, floor level, accessibility to mass transit railway station and the like. To ensure transparency and understanding, the Shapley value is employed to quantify the relative importance of each feature in predicting property prices. Our analysis reveals the existence of non–linear relationships among these features, as demonstrated by the wide distribution of SHAP values for most features, which are illustrated in a beeswarm plot that span both sides of the baseline. This finding indicates a complex interaction among such features as square footage, age, floor level, carpark, proximity to mass transit railway stations, location, and property prices. The results contribute valuable insights into the relationships between industrial property characteristics and their corresponding values, thereby equipping stakeholders with enhanced understanding of the market to support informed decision–making.

Industrial property prices; Machine learning; Explainable Artificial Intelligence

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

* All users must log in before leaving a comment