Longitudinal modelling of housing prices with machine learning and temporal regression
International Journal of Housing Markets and Analysis
ISSN: 1753-8270
Article publication date: 25 April 2022
Issue publication date: 14 August 2023
Abstract
Purpose
The purpose of this paper is to model housing price temporal variations and to predict price trends within the context of land use–transportation interactions using machine learning methods based on longitudinal observation of housing transaction prices.
Design/methodology/approach
This paper examines three machine learning algorithms (linear regression machine learning (ML), random forest and decision trees) applied to housing price trends from 2001 to 2016 in the Greater Toronto and Hamilton Area, with particular interests in the role of accessibility in modelling housing price. It compares the performance of the ML algorithms with traditional temporal lagged regression models.
Findings
The empirical results show that the ML algorithms achieve good accuracy (R2 of 0.873 after cross-validation), and the temporal regression produces competitive results (R2 of 0.876). Temporal lag effects are found to play a key role in housing price modelling, along with physical conditions and socio-economic factors. Differences in accessibility effects on housing prices differ by mode and activity type.
Originality/value
Housing prices have been extensively modelled through hedonic-based spatio-temporal regression and ML approaches. However, the mutually dependent relationship between transportation and land use makes price determination a complex process, and the comparison of different longitudinal analysis methods is rarely considered. The finding presents the longitudinal dynamics of housing market variation to housing planners.
Keywords
Citation
Zhang, Y., Rahman, A. and Miller, E. (2023), "Longitudinal modelling of housing prices with machine learning and temporal regression", International Journal of Housing Markets and Analysis, Vol. 16 No. 4, pp. 693-715. https://doi.org/10.1108/IJHMA-02-2022-0033
Publisher
:Emerald Publishing Limited
Copyright © 2022, Emerald Publishing Limited