Housing price prediction incorporating spatio-temporal dependency into machine learning algorithms. (December 2022)

• Record Type: Journal Article
• Authors: Soltani, Ali; Heydari, Mohammad; Aghaei, Fatemeh; Pettit, Christopher James
• Is Part Of: Cities. Volume 131(2022)
• Publication Date: 2022-12
• Subjects: Housing price -- Value estimation -- Spatio-temporal modelling -- Machine learning -- Australia; City planning -- Periodicals; Urban policy -- Periodicals; 711.4
• DOI: 10.1016/j.cities.2022.103941
• Languages: English
• ISSNs: 0264-2751

Abstract

Abstract: Conventional housing price prediction methods rarely consider the spatiotemporal non-stationary problem in a large data volumes. In this study, four machine learning (ML) models are used to explore the impacts of various features – i.e., property attributes and neighborhood quality - on housing price variations at different geographical scales. Using a 32-year (1984–2016) housing price dataset of Metropolitan Adelaide, Australia, this research relies on 428, 000 sale transaction records and 38 explanatory variables. It is shown that non-linear tree-based models, such as Decision Tree, have perform better than linear models. In addition, ensemble machine learning techniques, such as Gradient-Boosting and Random Forest, are better at predicting future housing prices. A spatiotemporal lag (ST-lag) variable was added to improve the prediction accuracy of the models. The study demonstrates that ST-lag (or similar spatio-temporal indicator) can be a useful moderator of spatio-temporal effects in ML applications. This paper will serve as a catalyst for future research into the dynamics of the Australian property market, utilizing the benefits of cutting-edge technologies to develop models for business and property valuation at various geographical levels. Highlights: Spatio-temporal non-stationary is significant in explanation of the variations in housing price; A proposed spatiotemporal lag is incorporated to increase the accuracy of the models; Non-linear tree-based models have better performance than the linear model. Ensemble ML techniques are powerful methods to produce better predictive performance.