Housing price prediction incorporating spatio-temporal dependency into machine learning algorithms

《Housing price prediction incorporating spatio-temporal dependency into machine learning algorithms》

打印

关键字: Housing price;Value estimation;Spatio-temporal modelling;Machine learning;Australia

作者单位: UniSA Business, University of South Australia, Adelaide, 5001, Australia;Department of Urban Planning, Tarbiat Modares University, Tehran, Iran;Department of Urban Planning, Shiraz University, Shiraz, Iran;City Futures Research Centre, University of New South Wales, Sydney, 2052, Australia;UniSA Business, University of South Australia, Adelaide, 5001, Australia;Department of Urban Planning, Tarbiat Modares University, Tehran, Iran;Department of Urban Planning, Shiraz University, Shiraz, Iran;City Futures Research Centre, University of New South Wales, Sydney, 2052, Australia

摘要: 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.