Abstract
This study analyzes the relationship between the housing and stock markets, focusing on housing market bubbles. Stock market dynamics generally have a more significant impact on housing price movements than housing market dynamics have on stock dynamics. However, if housing market information is provided as a signal, housing price movements can predict stock market volatility. Accordingly, we build a machine learning-based early warning system (EWS) for the housing market using a long short-term memory (LSTM) neural network. Applying the generalized supremum augmented Dickey-Fuller test to extract the bubble signal in the housing market, we find that the signal simultaneously detects future changes in the housing market prices and future stock market volatility, and our EWS effectively detects the bubble signal. We confirm that the LSTM approach performs better than other benchmark models, the random forest and support vector machine models.
Highlights
Housing market bubbles are widely recognized but present an intractable risk [1], [2]
We develop a dynamic early warning system (EWS) by integrating a crisis classifier and the long short-term memory (LSTM) neural network
We evaluate the housing bubble predictor based on the LSTM neural network in comparison with those based on our two baseline models: the random forest (RF) and support vector machine (SVM) models
Summary
Housing market bubbles are widely recognized but present an intractable risk [1], [2]. A housing market bubble indicates that the market is unstable owing to abnormally high housing prices. It is difficult to directly apply the classical time-series models to the housing market because housing prices are unstable and can be affected by various external variables, including real estate policies [10]. One way to overcome these limitations and effectively predict housing market bubbles is an early warning system (EWS) [11]. We define the signal for a housing market bubble following Phillips et al [12] and predict the signal using the LSTM neural network.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
