Real Estate

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.

This article provides a critical and thematically structured literature review of Envi-ronmental, Social, and Governance (ESG) urbanism as it intersects with the right to the city, green gentrification, affordable housing, public-private partnerships, and partici-patory governance. Drawing from 52 scholarly and research-led sources centered on 2020–2025, while re-taining selected foundational earlier works, the study examines how ESG frameworks are adopted, contested, and operationalized across diverse urban contexts. While ESG has emerged as a dominant paradigm in urban planning and real estate, the review reveals its frequent co-optation by market-driven agendas, which risk reproducing socio-spatial inequalities under the guise of sustainability. At the same time, the literature highlights promising alternatives rooted in environmental justice, multispecies ethics, legal reform, and community-led planning. The review advances the argument that ESG must be reframed not as a universal compliance model, but as a situated, justice-oriented framework capable of responding to the complex ecological and social realities of contemporary urbanization. By foregrounding relational governance, inclusive design, and equitable urban futures, the article contributes to an emerging research agenda that challenges technocratic sus-tainability and reclaims ESG as a transformative tool for spatial and environmental justice.