The Application of Machine Learning Approaches on Real-Time Apartment Prices in the Tokyo Metropolitan Area

Abstract

Abstract 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.