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- W4293235959 endingPage "103941" @default.
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- W4293235959 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." @default.
- W4293235959 created "2022-08-27" @default.
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- W4293235959 date "2022-12-01" @default.
- W4293235959 modified "2023-10-02" @default.
- W4293235959 title "Housing price prediction incorporating spatio-temporal dependency into machine learning algorithms" @default.
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- W4293235959 doi "https://doi.org/10.1016/j.cities.2022.103941" @default.
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