New Behavioural Big Data Methods for Predicting Housing Price

Abstract

Housing market price prediction is a big challenge. The 2008 global recession strongly showed that even the most sophisticated traditional economic models failed to foresee the crisis. New developments of behavioural economic theory indicate that the information from micro-level’s decision making will bring new solution to the age-old problem of economic forecasting. Additionally, the information revolution and big data methods have provided a new lens to study economic problems apart from traditional methodologies. This research provides the theoretical link between irrationality and big data methods. Empirically, big data methods will be used in forecasting the housing market cycle in Australia. Specifically, Google trends is included as a new variable in a time series auto-regression model to forecast housing market cycles.