Macro Financial Prediction of Cross Border Real Estate Returns Using XGBoost LSTM Models
Keywords:
Real estate forecasting, International investment, Gradient boosting, LSTM, SHAP analysis, Macro-financial indicators, Cross-border modelingAbstract
This study develops a combined prediction model for international real estate returns using gradient boosting and long short-term memory networks. The model integrates macro-financial and property-specific indicators with a lagged input structure to capture both contemporaneous and delayed effects. Quarterly data from twelve countries between 2010 and 2023 are used to train and evaluate the model. Empirical results show that the proposed method consistently outperforms conventional approaches, including linear regression, random forest and standalone recurrent networks. The average root mean squared error (RMSE) is reduced by over 14%, with particularly notable gains in high-volatility markets. Variable attribution using SHAP values confirms the substantial influence of interest rate spread, inflation and capital account openness on return variation. Interaction effects between key indicators and sensitivity to variable exclusion further enhance model transparency. The study suggest that the integration of nonlinear feature modeling and sequence learning offers measurable improvements in short-term return forecasting. The approach provides a viable tool for international real estate investment analysis under changing macroeconomic conditions.
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