Predicting demand for rental office space using RNN and the DiPasquale-Wheaton's office rent model

A case of the rental office market in Tokyo

Abstract

There are growing concern about the impact of COVID-19 pandemic on commercial real estate market and related financial systems. This research perform a comparative analysis of office rent and demand forecast in the Tokyo office market. Firstly, we estimate office rent trend after 2020 using the DiPasquale and Wheaton’s office rent model (DiPW, hereinafter), which is one of traditional regression-based forecasting strategies from real estate economics. Secondly, we construct office space demand model using recurrent neural network (RNN) and embed it into the DiPW model. We also apply dimension reduction via dynamic factor analysis (DFA) to summarize macro-economic trend and compare these forecast models in terms of predictive accuracy. By combining RNN and DFA, we examine the predictive relationship between office space demand and macro-economic trend to find the following. First, the prediction accuracy is improved by introducing dynamic factors to machine learning model. Secondly, we find that not only GDP related indices but also economic indices related to labor, firm and public finance are important factors in forecasting office space demand in Tokyo.