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.

中文翻译：

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比较经济分析与机器学习方法：新加坡私人房地产市场研究

我们的目标是使用涵盖 1995 年至 2018 年期间的交易数据，在新加坡私人房地产市场的背景下，将计量经济学分析与机器学习方法进行比较。采用特征模型来量化重要属性和便利设施的溢价，重点关注到最近的大众捷运 (MRT) 站的距离溢价。与此同时，在同一背景下使用机器学习算法在三类——LASSO、随机森林和人工神经网络下进行了一项调查，对房地产价格决定因素的重要性有更深入的了解。结果表明，捷运距离溢价显着，移动 100从平均距离点到最近的地铁站更近米将使整体交易价格增加约 15,000 新加坡元 (SGD)。LASSO 建议机器学习方法通​​常可以实现更高的预测精度和异构财产年龄溢价。使用随机森林算法，我们发现房地产价格主要受关键宏观经济因素的影响，例如销售时间，以及房产的规模和楼层水平。最后，为研究人员提供了对不同方法的评估，以利用额外的数据源和数据驱动的方法来利用经济研究中的潜在因果效应。