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.

中文翻译：

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基于机器学习的住房和股票市场预警系统

本研究分析了房市与股市的关系，重点关注房市泡沫。股票市场动态对房价变动的影响通常比房地产市场动态对股票动态的影响更大。然而，如果提供住房市场信息作为信号，房价变动可以预测股市波动。因此，我们使用长短期记忆 (LSTM) 神经网络为住房市场构建了一个基于机器学习的预警系统 (EWS)。应用广义最高增强 Dickey-Fuller 检验提取房市泡沫信号，我们发现该信号同时检测到房市价格和未来股市波动的未来变化，我们的 EWS 有效地检测到了泡沫信号。