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

Ka Man Leung, Yu Cheung Wong, Kin Kwok Lai and Dah Ming Chiu

ERES from European Real Estate Society (ERES)

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.

Keywords: Informal Housing; rental analyses; sub-divided units; time series analysis (search for similar items in EconPapers)
JEL-codes: R3 (search for similar items in EconPapers)
Date: 2025-01-01
New Economics Papers: this item is included in nep-big and nep-mac
References: Add references at CitEc
Citations:

Downloads: (external link)
https://eres.architexturez.net/doc/oai-eres-id-eres2025-137 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arz:wpaper:eres2025_137

Access Statistics for this paper

More papers in ERES from European Real Estate Society (ERES) Contact information at EDIRC.
Bibliographic data for series maintained by Architexturez Imprints ().