Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/155087
Title: Comparing econometric analyses with machine learning approaches: a study on Singapore private property market
Authors: Bian, Tingbin
Chen, Jin
Feng, Qu
Li, Jingyi
Keywords: Social sciences::Economic development
Issue Date: 2020
Source: Bian, T., Chen, J., Feng, Q. & Li, J. (2020). Comparing econometric analyses with machine learning approaches: a study on Singapore private property market. Singapore Economic Review, 1-24. https://dx.doi.org/10.1142/S0217590820500538
Project: M4012113
Journal: Singapore Economic Review
Abstract: We aim to compare econometric analyses with machine learning approaches in the context of Singapore private property market using transaction data covering the period of 1995-2018. A hedonic model is employed to quantify the premiums of important attributes and amenities, with a focus on the premium of distance to nearest Mass Rapid Transit (MRT) stations. In the meantime, an investigation using machine learning algorithms under three categories-LASSO, random forest and artificial neural networks is conducted in the same context with deeper insights on importance of determinants of property prices. The results suggest that the MRT distance premium is significant and moving 100m closer from the mean distance point to the nearest MRT station would increase the overall transacted price by about 15,000 Singapore dollars (SGD). Machine learning approaches generally achieve higher prediction accuracy and heterogeneous property age premium is suggested by LASSO. Using random forest algorithm, we find that property prices are mostly affected by key macroeconomic factors, such as the time of sale, as well as the size and floor level of property. Finally, an appraisal on different approaches is provided for researchers to utilize additional data sources and data-driven approaches to exploit potential causal effects in economic studies.
URI: https://hdl.handle.net/10356/155087
ISSN: 0217-5908
DOI: 10.1142/S0217590820500538
Rights: © 2020 World Scientific Publishing Company. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SSS Journal Articles

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