Machine Learning-Based Early-Warning System for Coupled Risks Within the Land–Real Estate–Finance Nexus

Abstract

Real estate market risk has long been a core policy concern for governments worldwide, as risks across the land, real estate, and financial markets are inherently deeply coupled. Accurate measurement and early warning of systemic risks in the land–real estate–financial market system constitute a critical prerequisite for forestalling major economic fluctuations. In the era of big data, the proliferation of high-frequency, large-scale, and multi-dimensional market data poses formidable challenges to conventional risk assessment frameworks. Grounded in the perspective of interlinkages among the land, real estate, and financial markets, this paper employs the BEKK-GARCH model to construct a time-varying composite risk index and further incorporates the CNN-LSTM machine learning model to provide early warning of coupled risks in the land–real estate–financial market system. The empirical results demonstrate that the constructed composite risk index exhibits strong validity and high sensitivity, and the findings remain robust under stochastic scenarios. Compared with other benchmark early-warning models, the CNN-LSTM model delivers superior overall early-warning performance. The findings of this study carry significant practical implications for dynamically monitoring and providing early warning of real estate market risks in the context of big data, curbing cross-market risk contagion, and safeguarding the sustainable and sound development of the land–real estate–finance nexus.