Search Results (675)

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. Full article

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. Full article

The value of money is shaped by economic, political, and social dynamics, prompting individuals to rely on different saving and investment instruments to preserve and grow their wealth over time. Focusing on Romania as a representative emerging-economy context, this study documents a survey-based dataset capturing individuals’ self-assessed financial literacy, saving habits, investment preferences, and perceptions regarding the safety and benefits of different investment instruments. Primary data were collected between November 2024 and February 2025 from 875 respondents across 41 Romanian counties and Bucharest, using a computer-assisted web interviewing (CAWI) questionnaire. They were analyzed using chi-square goodness-of-fit tests to examine whether responses regarding self-assessed financial literacy, risk perception, and investment behavior departed from expected distributions. The findings show that, although respondents most frequently rated their financial literacy as medium to high, they allocate a limited share of savings to investment, favor cryptocurrency and real estate, and prioritize safety over returns. By openly documenting the survey design, sample structure, and analytical procedure, this paper aims to support the reuse, replication, and cross-country comparison of behavioral financial datasets, while offering practical insights for policymakers and financial institutions designing evidence-based interventions in emerging markets. Full article

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. Full article

This study addresses organizational adaptation under conditions of prolonged market instability within capital-intensive real estate sectors. It investigates the multidimensional alignment between crisis-era decisions and reported post-crisis organizational actions among 144 active housing developers in Istanbul. A two-stage analytical method was employed: nonlinear canonical correlation analysis (OVERALS) evaluated the global association between the two action sets, while co-occurrence network analysis and bridge centrality identified the structural positioning of individual practices. The findings indicate a non-random pattern of association that is broadly consistent with evolutionary resilience perspectives, without implying direct causal relationships or testing temporal performance shifts. Knowledge-oriented and relational practices—specifically corporate social responsibility, R&D and new market activity, and consultant reports—exhibited the highest cross-set connectivity with reported post-crisis adjustments. Defensive actions displayed more heterogeneous cross-set positions: project suspension was non-bridging under the baseline threshold, whereas budget control retained a comparatively prominent position in the bridge-strength ranking. This pattern suggests that defensive actions were not uniformly peripheral within the observed network. Overall, this study offers an exploratory framework for understanding how crisis-era action configurations correspond to reported organizational practices in highly volatile urban property markets. Full article

The cost–performance relationship in mutual funds is a longstanding open question in financial economics, particularly when costs are assumed to exert a single, linear effect on returns. This study proposes an integrated causal machine learning framework to revisit this question using a panel of Hungarian open-ended public investment funds across all major asset classes—equity, bond, absolute yield, misc, money market, real estate, and commodity—covering 2017–2024. Six machine learning algorithms are benchmarked for return prediction, and Double Machine Learning, with fund-level cluster-robust inference and year fixed effects, is applied to estimate the effect of the Total Expense Ratio (TER) on next-year returns, under the identifying assumptions stated in the paper, while flexibly controlling for a set of observed fund-level confounders (size, NAV dynamics, volatility, past and cumulative performance, and fund age) without imposing a linear functional form. To move beyond average effects, a Causal Forest model—tuned using an out-of-fold, effect size-neutral selection criterion—estimates heterogeneous treatment effects across funds, and SHAP-based interpretation uncovers the mechanisms underlying this heterogeneity. The results show that, once the outcome is measured in the year following the one in which TER is observed and panel dependence is properly accounted for, the average TER effect is not robustly different from zero at the full-sample level; where a statistically robust effect emerges, it is negative rather than positive, concentrated in equity and absolute-yield funds, and largely confined to the period after 2022, which coincided with the war in Ukraine, rising interest rates, and heightened market volatility, although the research design does not identify which, if any, of these developments drove the change. Average-effect models are shown to conceal this heterogeneity, and the results are further shown to be sensitive to two methodological choices that might otherwise appear secondary—the timing convention linking cost and return, and the criterion used to select among competing heterogeneous-effects specifications—underscoring the importance of making such choices explicit. These findings demonstrate the added value of combining predictive and causal machine learning, together with identification-robust and panel-robust inference, for uncovering heterogeneity that conventional econometric approaches overlook and offer a transferable methodological template for causal machine learning applications in finance and other high-dimensional decision-making domains. Full article

In this study, which is the result of a thesis defended in 2020, we evaluate how the market and the state have allied themselves in promoting popular and social interest housing in the city of São Paulo through a set of municipal regulatory frameworks and financing programs from Caixa Econômica Federal, “Caixa”, respectively. The market and the state have adapted and integrated; this association is both politically and economically valuable for the state—despite it having given up on greater urban regulation—and opportune and profitable for the market. This thesis yielded an important result: in 2018, most of the developments launched in the municipality were financed through “Caixa’s” Minha Casa, Minha Vida Program. In other words, the market and the state are allied to address the housing deficit in the municipality, with quantitatively expressed results. We highlight that this formal establishment of private housing, focused on the popular market, was facilitated by not only current municipal laws but also the participation of the state in the federal sphere through funding mechanisms like “Caixa” that have a monopoly on financing this segment. Studying this real estate production, as well as municipal regulatory changes and economics in national terms, enables the creation of an urban space generated through intervention by the state—which acts simultaneously as a regulating and financing agent—and the development of the market as an encouraging and productive agent. Full article

In recent years, the city of Naples has seen increasing attention focused on the mural dedicated to Diego Armando Maradona, located in the Spanish Quarter. Originally a spontaneous expression of street art and initially perceived by the local community as a simple artwork, the mural has gradually transformed into a powerful symbol of identity, becoming a true cultural icon of the city. This process has generated effects that transcend the artistic value of the artwork, contributing to the enhancement of the entire surrounding urban context and altering the social and economic dynamics of the quarter. By considering the Maradona Mural as the epicentre of a range of territorial and socio-economic effects, this paper aims to explore the issue of measuring the influence exerted by its presence on the market values of residential properties located in the Spanish Quarter of Naples. From this perspective, street art can be interpreted as a positive externality capable not only of fostering processes of urban regeneration and the redevelopment of formerly marginalised or degraded areas but also of influencing the dynamics of the local real estate market. Property values reflect not only the intrinsic characteristics of the properties but also the environmental and territorial attributes that contribute to the formation of market prices. From this perspective, properties can be considered as beneficiaries of externalities, whose economic value can be estimated through variations in house prices attributable to changes in environmental and territorial conditions. Full article

Real estate investment decisions are shaped by a complex environment of macroeconomic, demographic, and structural factors, where traditional linear assessment methods often fail to capture nonlinear relationships influencing aggregate housing market performance. The aim of this article is to develop a data-driven artificial intelligence framework for assessing how macroeconomic and structural conditions influence aggregate housing market performance and for providing a conceptual basis for evaluating real estate investment strategies under different economic contexts. The study uses machine learning algorithms that allow for modeling complex relationships between investment return indicators and key macroeconomic factors, such as economic growth rates, price dynamics, population concentration, and long-term structural changes. Unlike traditional econometric methods, the proposed approach identifies nonlinear and regime-dependent relationships between macroeconomic conditions and housing market performance, providing insights that can support the interpretation of different investment strategies. The results show that the factors determining investment returns are not universal, and their significance depends on the broader economic regime and market structure. This allows us to examine how changing macroeconomic conditions influence aggregate housing market performance and to discuss the potential implications for different investment strategies. The study contributes by proposing an artificial intelligence-based methodological framework that combines predictive modelling with explainable AI to support the analysis of macroeconomic influences on housing markets and to inform strategic real estate investment decision-making within complex socioeconomic systems. Full article

Urban regeneration has become a key strategy to address urban decline, infrastructure aging and socio-economic challenges, aiming to enhance quality of life and stimulate local development. Beyond physical renewal, it involves social, cultural and economic dimensions that contribute to the revitalization of communities. Recent literature frames regeneration as an adaptive and multifaceted process, drawing on concepts such as change management, dynamic capabilities and sensemaking to interpret urban transformation as a form of institutional learning. This study adopts a qualitative case study approach focused on the Libertà district in Bari (Italy), using document analysis, field observation, and stakeholder mapping to investigate both tangible and intangible outcomes of regeneration policies. Particular attention is devoted to understanding how socio-institutional and spatial transformations are translated into economic outcomes, using residential property values as an exploratory indicator of economic change. In this regard, variations in residential property values are considered as an exploratory indicator of economic dynamics associated with regeneration, while acknowledging that property price increases do not necessarily correspond to benefits for all community members and may also reflect issues related to affordability and social displacement. The findings suggest that regeneration should be understood as a coordinated and collaborative process involving multiple stakeholders and institutional actors. The analysis highlights the relevance of cultural initiatives, stakeholder interactions and changes in the physical environment as key elements of the transformation process. Furthermore, the study discusses the extent to which changes in the local real estate market may be associated with broader regeneration dynamics, recognizing the limitations of establishing direct causal relationships. Overall, the research proposes an original perspective on urban regeneration as a learning-based and organizational process in which social, institutional, and spatial transformations interact with economic change in complex and context-dependent ways. Full article

Climate change creates physical and transition risks that may affect property value. Yet, these risks can remain weakly reflected in real estate markets when professional valuations rely on historical evidence and incomplete information. This study examines whether valuers’ awareness of climate change acts as a mechanism through which climate risk is transmitted to property valuation practice and market prices. An online survey of 117 professional valuers in Greece provides exploratory evidence. The empirical analysis shows that valuers are generally aware of climate change, but awareness is only partially translated into practice. Flooding and extreme weather are considered more frequently than sea-level rise or temperature increase. Lack of data, methodology and regulation are the main reported barriers. Cluster analysis, acting as a heuristic classification, identifies three valuer profiles: experienced climate-aware anticipators, practice-oriented climate integrators, and low-engagement traditionalists. Awareness is positively associated with climate-change consideration in practice, but perceived barriers do not significantly moderate this relationship. The findings suggest that closing the valuation gap requires professional guidance, localised climate-risk data, training and clearer standards. A theoretical microeconomic model, not tested by the empirical analysis, shows the pathways through which valuers’ awareness can affect real estate demand, supply and sustainable equilibrium. Full article

The present research investigates the relationship between urban regeneration initiatives and residential real estate market dynamics by assessing market price appreciation associated with the factors most commonly considered in housing transactions. The study focuses on the Esquilino district in the city of Rome (Italy) with particular attention to the redevelopment of Piazza dei Cinquecento, the major public space located in front of Roma Termini railway station. The intervention aims to improve urban accessibility, reduce traffic congestion, and enhance public space quality through a new spatial configuration and the creation of a tree-lined area. The objective of the study is to verify whether, and to what extent, the ongoing regeneration project has influenced residential property values. To achieve this goal, an econometric analysis is implemented to quantify the contribution of different housing and locational attributes to residential asking prices and to identify the variables that significantly affect value formation within the local market. Given that the initiative is still in progress and approaching completion, the analysis adopts a diachronic perspective by comparing two distinct temporal stages: the ante project phase (second half of 2021) and the in itinere phase (first half of 2025). Building on the findings of a previous pre-intervention study, the research systematically examines changes in market behaviors over time, with the dual purpose of identifying variations in price determinants and analyzing the associations between the current urban transformations and the residential real estate market. The results indicate a substantial stability in the main determinants of residential property prices across the two periods, suggesting that the regeneration initiative has not yet been fully capitalized into market behaviors. However, variations in the contribution and functional relationships of some spatial variables highlight preliminary signs of market adjustment during the ongoing transformation process. The study highlights the importance of monitoring for assessing how urban regeneration processes are progressively incorporated into real estate market dynamics. The proposed framework provides a transferable tool for evaluating regeneration processes in different urban contexts, supporting evidence-based decision-making and the comparative assessment of urban transformation strategies. Full article

This study examines the effect of road traffic noise on apartment prices in Kraków, treated on an equal footing with the other price-forming attributes. The analysis draws on 6244 transactions from the primary and secondary markets concluded between January 2021 and June 2022. Multiple regression in linear and semi-logarithmic form was applied, and outliers were removed iteratively on the basis of standardised residuals; the sensitivity of the estimates to the adopted elimination threshold was examined and the results were compared with robust regression. The effect of road noise differs between market segments. In the primary market, in the sample after outlier elimination, the noise attribute is statistically significant and the NSDI equals 0.17% of the price per 1 dB (95% CI: 0.13–0.21%). In the secondary market, the estimate is close to zero, and the difference between the segments is significant (z = 6.0; p < 0.001). In the full samples, where prices are strongly differentiated by dwelling standard not recorded in the register, the noise effect does not reach significance, which points to the conditional character of the estimates. The influence of noise also depends on the position relative to the city centre: it is strongest in the intermediate zone (0.23%/dB), weaker on the periphery, and disappears in the central zone, where the elevated sound level is offset by the advantages of the location. The diagnostics covered VIF coefficients, sensitivity analysis, and Moran’s I for the residuals. The results, set against international research on noise capitalisation, support the routine inclusion of the acoustic climate in the valuation of new dwellings and provide a monetary reference point for noise abatement policy. Full article

This article investigates the factors that determine the dynamics of mortgage lending in Bulgaria and the Euro area by using ordinary least squares (OLS) regression models based on stationary time series over the period 2010–2025. The results show that in Bulgaria, the dynamics of mortgage lending are determined primarily by wage growth, inflation, and the high liquidity of the banking system, which increases banks’ capacity to extend new loans. In contrast, in the Euro area, the main factor driving mortgage lending trends is interest rates on mortgage loans, with the development of the real estate market, as measured by house price index, also exerting a significant influence. The findings further indicate that, despite the high degree of economic integration between Bulgaria and the European Union, the factors determining mortgage lending differ, which justifies the need for separate modeling of mortgage loans in the two economies. Moreover, mortgage lending transmission mechanisms differ substantially across the two economies despite their close monetary integration, highlighting the importance of country-specific institutional characteristics. The faster growth of mortgage lending by Bulgarian banks compared to those in the Euro area does not yet pose risks to the stability of Bulgaria’s banking system. Full article

This study examines commercial real estate market dynamics and valuation performance using an applied panel-data econometric framework within the context of an emerging market economy. While traditional valuation approaches primarily emphasize static income–capitalization relationships, they often overlook the combined influence of liquidity conditions, market risk, and macroeconomic environment on commercial real estate performance. The empirical analysis is based on a balanced panel dataset of commercial properties in Azerbaijan covering the period 2021–2025. The dataset includes net operating income, capitalization rates, liquidity conditions, market risk indicators, and macroeconomic variables. A two-equation modelling framework is employed to separate valuation determinants from capitalization rate dynamics, thereby reducing potential endogeneity between property values and return expectations. The empirical findings indicate that net operating income is consistently and positively associated with commercial property values across the estimated panel-data models. In addition, the results suggest that market liquidity and market risk provide complementary information for understanding commercial real estate performance within the analyzed emerging market setting. Higher values of the risk index are positively associated with capitalization rates, while the estimated relationship between market risk and property value remains sensitive to model specification. Macroeconomic variables exhibit limited statistical significance and are interpreted primarily as indicators of common market-wide conditions. The study contributes to the literature by applying an integrated panel-data framework to an illustrative emerging market case (Azerbaijan) while providing a transparent empirical framework that may be adapted to comparable commercial real estate markets. Full article