Real Estate

International migration is often considered an important factor in housing market dynamics, yet its relationship with residential property prices remains uncertain, particularly in developing economies. This study examined whether changes in international migration are associated with changes in residential property prices in South Africa, both in the short term and over a longer period. Annual data covering 1976–2022 were analysed using an unrestricted error-correction model and the Pesaran, Shin and Smith bounds-testing approach. The results show that changes in the number of international migrants were not significantly associated with changes in residential property prices in the short term. The analysis also found no evidence of a lasting relationship between international migration and residential property prices over the longer term. In other words, the data do not provide sufficient statistical evidence to conclude that changes in international migration were linked to changes in South African residential property prices during the study period. In contrast, the results show that changes in residential property prices tended to persist over time, with price changes in one year being positively associated with changes in the following year. These findings suggest that migration should not be assumed to be an important explanation for national housing-price movements without supporting empirical evidence. They also highlight the importance of examining short-term and long-term relationships separately when studying migration and housing-market dynamics.

The period 2018–2026 offers the first opportunity to observe a complete, closed mortgage interest rate cycle in the Czech Republic: rates fell to a historic trough of 1.98% (March 2021), rose to 5.37% (September 2023) and stabilized around 4.5% from 2025. This paper examines whether the relationship between mortgage rates and housing prices is reversible over such a cycle. The analysis draws on the EVAL software (version 2.0), which has continuously collected and evaluated real estate advertising across all Czech municipalities since 2007, combined with Czech National Bank and Czech Statistical Office data; methods include phase-specific logarithmic regressions, Chow tests, asymmetric difference regressions, cross-correlation analysis and a composite affordability indicator, the Mortgage Payment Burden (MPB). The point estimates are asymmetric: rate increases are followed by lower listing volumes within four months and lower prices within seven, whereas rate cuts show no detectable direct effect; the difference between the two responses is, however, not statistically significant at the available sample size, so the asymmetry is suggestive rather than conclusive. The price–rate trajectory forms an open hysteresis loop—at an identical rate of 4.5%, the median price was 35.5% higher after the cycle than during tightening. Adjustment ran through quantities rather than prices; the correction was deepest in cheap peripheral regions, and by April 2026 the national MPB reached 41.2% of the average wage, its historical maximum—a result that survives plausible alternative wage-growth assumptions. The evidence is consistent with monetary policy acting as an effective brake but a weak accelerator of the housing market, although the reduced-form design cannot separate the effect of rates from that of the concurrent macroeconomic shocks.

Real estate valuation increasingly requires methodologies that combine predictive accuracy, economic interpretability, and model transparency. This study proposes a Generalized Explainable Hybrid Econometric–Machine Learning Framework integrating econometric modelling, machine learning, and Explainable Artificial Intelligence (XAI) within a unified analytical architecture. The framework is organized around a generalized conceptual valuation equation and implemented through a workflow comprising data quality assessment, econometric benchmarking, hyperparameter optimization, nonlinear machine-learning modelling, five-fold cross-validation, and SHAP-based explainability. The methodology is demonstrated using an internally constructed Azerbaijan commercial real estate dataset and independently assessed using the publicly available UCI residential benchmark dataset through an identical Python-based computational pipeline. Under the complete income-capitalization benchmark, the Artificial Neural Network achieved the highest predictive performance for the Azerbaijan dataset, whereas Random Forest performed best for the external benchmark. A leakage-reduced robustness analysis further showed that the framework retained meaningful predictive capability after excluding Net Operating Income and the Capitalization Rate, with XGBoost providing the strongest performance under the reduced specification. SHAP analysis identified economically meaningful valuation drivers, highlighting income generation in the commercial market and transport accessibility in the residential benchmark. The proposed framework provides a transparent, explainable, and transferable decision-support methodology for modern real estate valuation.