The widely applied hedonic regression approach for the relationship between property prices and housing attributes is subject to assumptions and specifications of models as well as the availability and content of second-hand official data. In a cross-disciplinary spirit, this study employs machine learning techniques to examine hedonic apartment prices in the Tokyo Metropolitan Area of Japan based on online sales data extracted by web-parsing technology. With 14,579 apartment observations, two machine learning regressions—decision tree (DT) and random forest (RF)—are compared to conventional ordinary least squares regression (OLS) for hedonic modelling. Empirical results demonstrated that RF regressions led to the highest accuracy in model prediction performance, followed by DT and OLS. The comparison with results across models revealed that the housing features that have consistent influences on apartment prices tend to be those associated with living quality (including management funds, repair fund fees, floor size, located floor, total floor of the building, and location in Tokyo). Other commonly appreciated features, such as southward orientation or corner-lot location, did not demonstrate importance, possibly due to changes in residents’ preferences. In this big-data era, the adaptation of real-time data and machine learning approaches should add value to the variable selection process and model performance.

中文翻译：

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机器学习方法在东京都市区实时公寓价格中的应用

房地产价格与住房属性之间关系的广泛应用的特征回归方法取决于模型的假设和规范以及二手官方数据的可用性和内容。本研究本着跨学科的精神，根据网络解析技术提取的在线销售数据，采用机器学习技术来检查日本东京都市区的享乐公寓价格。通过对 14,579 个公寓的观察，将两种机器学习回归——决策树 (DT) 和随机森林 (RF)——与传统的普通最小二乘回归 (OLS) 进行特征建模进行比较。经验结果表明，RF 回归导致模型预测性能的准确性最高，其次是 DT 和 OLS。模型结果对比显示，对公寓价格具有一致影响的住房特征往往是与生活质量相关的特征（包括管理资金、维修资金费用、楼层面积、所在楼层、建筑物总楼层和所在地区的位置）。东京）。其他普遍欣赏的特征，例如南向或角落位置，没有表现出重要性，可能是由于居民偏好的变化。在这个大数据时代，实时数据和机器学习方法的适应应该为变量选择过程和模型性能增加价值。和在东京的位置）。其他普遍欣赏的特征，例如南向或角落位置，没有表现出重要性，可能是由于居民偏好的变化。在这个大数据时代，实时数据和机器学习方法的适应应该为变量选择过程和模型性能增加价值。和在东京的位置）。其他普遍欣赏的特征，例如南向或角落位置，没有表现出重要性，可能是由于居民偏好的变化。在这个大数据时代，实时数据和机器学习方法的适应应该为变量选择过程和模型性能增加价值。