Search Results (514)

Over the last two decades, touristification has become one of the most influential concepts for understanding the relationship between tourism and urban change. However, existing research remains strongly shaped by the experiences of large metropolitan areas and globally recognized tourist destinations, leaving intermediate urban contexts comparatively underexplored. This paper addresses this gap by examining the Atlantic Urban System of the north-western Iberian Peninsula as a framework for understanding touristification beyond dominant metropolitan paradigms. Drawing on debates on touristification, ordinary cities, intermediate urbanism, and regional development, the article argues that the polycentric structure, historical peripherality, and growing integration of this urban system create distinctive conditions shaping tourism-related urban change. Building on previous work on the Atlantic Urban Axis, the paper conceptualizes the Atlantic Urban System as a territorial configuration composed largely of interconnected intermediate cities and develops a framework for analysing touristification through five interrelated dimensions: territorial position, housing market structure, tourism development trajectories, urban governance, and urban form. Rather than proposing a new urban type, the article highlights the analytical value of examining tourism-related transformations within a specific territorial configuration and contributes to ongoing efforts to diversify the geographical foundations of urban theory. Full article

Increasingly stringent regulations regarding climate policy and the sustainable development paradigm determine the transformation of contemporary multi-family housing typology, manifested by a growing share of single-aspect micro-apartments (units with exterior exposure on only one facade). This article identifies the phenomenon of the energy-efficiency paradox, wherein highly insulated buildings successfully trap winter heat but inadvertently escalate summer cooling demands. Consequently, the primary operational challenge becomes limiting excessive solar heat gains in summer, which directly translates into high cooling energy demand, rather than solely mitigating heat losses in winter. Sustainable construction requires moving beyond the narrowly defined reduction of envelope thermal transmittance towards holistic adaptation to climate change and ensuring adequate indoor environmental quality. The methodology is based on a coupled energy-economic analysis, evaluating thermal balances and their direct financial implications for end-users. The variant analysis of solar heat gains conducted for a reference 30 m² dwelling in Warsaw proves that architectural optimization should not be determined solely by short-term investment profit maximization. Effective engineering optimization in construction requires the implementation of a full building life cycle perspective. Unfavorable glazing orientation and the lack of cross-ventilation necessitate the use of energy-intensive air-conditioning systems, which directly increases the building’s carbon footprint and generates hidden operating costs (differences reaching over 145 PLN annually for heating and approximately 70 PLN for cooling). The findings highlight the necessity for a critical reevaluation of design priorities for compact apartments, integrating social justice (by reducing information asymmetry in the real estate market, where buyers are often unaware of these future cooling burdens) with long-term economic rationality and the resilience of the built environment to extreme weather events. Full article

This article employs exploratory data-driven modeling to examine the relationships between selected macroeconomic indicators, residential property prices, and housing affordability pressures in Poland between 2020 and 2024. This turbulent period was selected for analysis because of the unprecedented volatility triggered by the COVID-19 pandemic and the geopolitical shocks associated with the war in Ukraine, both of which severely disrupted macroeconomic stability and construction supply chains. The study examines how key economic variables—including inflation, gross domestic product (GDP), unemployment, and average and minimum wage dynamics—are associated with residential property price dynamics within the framework of construction economics. Using statistical modeling techniques, including linear regression and Pearson correlation analysis, the study quantifies the strength, direction, and dynamics of these relationships across primary and secondary housing sectors. Our findings reveal a distinct comparative pattern of associations: average wage growth and inflation emerge as the macroeconomic indicators most strongly associated with property valuations, while macroeconomic growth and unemployment dynamics exhibit asymmetric associations across market segments. Notably, the findings suggest that the primary sector may be more sensitive to credit-related demand shocks and policy interventions, whereas the secondary sector appears to respond more directly to broader consumer trends and household purchasing capacity. By integrating macroeconomic data into a sectoral analysis, this study provides an exploratory empirical basis for discussing sustainable housing strategies. The results underscore the necessity of aligning investment and production cycles in the construction sector with macroeconomic stability to maintain long-term residential purchasing capacity and support resilient urban development. Full article

Low-rise terraced housing dominates residential construction in non-metropolitan Taiwan, yet empirical evidence on priority divergence between developers and homebuyers in such markets remains scarce. Using a case study of Changhua County, we applied the Analytic Hierarchy Process (AHP) to quantify priority structures of 35 construction managers and 58 homebuyers. Both groups evaluated an identical five-dimensional hierarchy encompassing fourteen directly surveyed factors across location selection, housing price, financing, construction risk, and building planning. Consistency ratios were 0.028 (developers) and 0.019 (homebuyers). The results indicate substantial differences in relative priorities. Developers prioritized Construction Risk (0.368), with Government Regulations (A42) ranking first globally (0.152). Homebuyers prioritized Location Selection (0.412), with Transportation Convenience (A11) ranking first (0.223). The Location Selection dimension gap (0.221 points) and Construction Risk gap (0.251 points) represent the largest divergences. Factor-level rank inversions are pronounced: Transportation Convenience ranks first for homebuyers but fifth for developers; Government Regulations ranks first for developers but fourteenth for homebuyers. The findings suggest that supply-side and demand-side stakeholders may place different emphasis on project feasibility and residential use, with implications for site selection, floor plan design, and buyer communication in regional markets. Full article

Traditional approaches to urban real estate green value assessment rely heavily on single structured data sources. Such methods often provide limited interpretability and fail to capture multidimensional green attributes accurately. To address these limitations, this study constructs a multimodal assessment framework that integrates image, text, and spatial information. A housing price prediction model is developed based on a Multi-Layer Perceptron architecture. Results show that the proposed method is superior to traditional models (such as the Hedonic pricing model, Ridge regression, and eXtreme Gradient Boosting, as well as single-modality control models). The core evaluation metric, mean squared error, reaches 0.0505 ± 0.0021. SHapley Additive exPlanations analysis shows that the text modality provides the largest contribution to model prediction, accounting for 51.45% of the global contribution. However, this dominance reflects the model’s dependence on textual green signals rather than the establishment of causal relationships. The result may also be influenced by marketing language bias and symbolic sustainability signals. The image modality contributes 38.48%, while the spatial modality contributes 10.07%, indicating a complementary relationship among the three modalities. Green premium analysis confirms that the model achieves higher prediction accuracy for high-priced residences and effectively captures differences in green premium across housing price tiers. This study provides a new technical pathway for real estate green value assessment. Full article

The rapid expansion of online real estate (RE) platforms has intensified information overload, making property search and decision-making increasingly complex. Real estate recommendation systems (RERSs) have emerged as essential decision-support tools; however, their development has not kept pace with advances in explainable artificial intelligence (XAI), transfer learning (TL), and fairness-aware machine learning. This PRISMA-compliant systematic review synthesizes 59 peer-reviewed studies published between 2005 and 2025 to critically examine algorithmic approaches, data modalities, evaluation practices, and ethical considerations in RERS research. Our analysis reveals a substantial lag in the adoption of state-of-the-art AI techniques: While deep learning is employed in 15% of studies, no reviewed work implements state-of-the-art post hoc XAI or TL frameworks, despite their relevance for addressing interpretability and data scarcity challenges. Furthermore, we identify systemic research biases, including reliance on proprietary datasets (80%), geographic concentration in Asia (56%), the dominance of residential property studies (91%), and limited fairness auditing despite documented discrimination risks in housing markets. To address these gaps, we propose a trust-based evaluation (T-EVAL) framework that integrates predictive accuracy, user trust, fairness, and market efficiency, and introduces a comprehensive nine-layer conceptual architecture for transparent, ethical, and data-efficient next-generation RERS. This review establishes an empirical benchmark for technology adoption gaps and outlines a research agenda for advancing responsible AI in RE decision-support systems. Full article

Major infrastructure investments alter accessibility and urban development patterns, yet their impact on housing prices varies significantly across regions. The prevailing interpretation attributes this heterogeneity to supply differences or regulatory constraints, treating land use regulations as exogenous variables. Nevertheless, even two regions with a nominally similar regulatory framework may produce substantially different outcomes in the housing market, depending on the effectiveness of rule implementation. This paper argues that this approach overlooks a critical variable: the ability of regional authorities to coordinate, regulate, permit, and implement spatial development in a predictable and timely manner. In line with this, a conceptual framework is developed, grounded in the literature on spatial and multi-level governance, in which regional institutional capacity is proposed as a potential mediator of capitalization around project milestones (announcement, funding, construction, operation), rather than as a backdrop. This capacity shapes outcomes through three interrelated dimensions: the responsiveness of supply, which depends on administrative capacity and regulatory consistency; the coherence of governance across jurisdictions within functional urban areas; and the management of land value through land value capture instruments. From this framework, testable propositions are derived regarding the intensity, timing, and spatial distribution of price effects. The study does not empirically estimate changes in housing prices, nor does it test the propositions put forward. Instead, it develops the conceptual framework and organizes the spatial and institutional units of observation required for a subsequent empirical test. The framework is specified spatially through Section A, Line 4 of the Athens Metro to organize the project’s spatial units, administrative jurisdictions, land uses, and milestones for future analysis. The contribution is threefold: conceptual, as it elevates regional institutional capacity from a contextual to an explanatory variable; theoretical, in that it bridges urban economics with the governance literature; and policy-relevant, since it repositions the reform of regional governance as a constituent element of housing policy and as a factor that may shape sustainable spatial development outcomes. Full article

Affordable housing delivery is a global concern, especially in developing countries; in the same way, Pakistan is also facing this challenge due to haphazard urbanization. Governance failures, and socio-economic and cultural constraints make policy interventions difficult. This research synthesizes market, regulatory, governance and cultural factors to investigate the critical barriers of affordable housing delivery. To analyze this phenomenon, Partial Least Squares Structural Equation Modeling (PLS-SEM) was performed. Formative and reflective constructs were evaluated using primary data from 200 professionals in Punjab, Pakistan. The findings show that while market and financial restrictions have no evident impact, policy and governance failure, institutional and regulatory impediments, and socio-cultural constraints have a considerable impact on housing affordability delivery. The model highlights the predominance of institutional and cultural variables over simply financial reasons, accounting for 77.8% of the variance in housing affordability delivery. The findings give vital insights to policymakers pursuing housing affordability solutions in developing countries. Full article

This paper examines the drivers of house price cycles across EU countries between 2005 and 2024 and measures their synchronicity. We used panel data methods—fixed effects, dynamic panel models (Arellano–Bond GMM), and a pooled VAR framework—to capture static and dynamic relationships between house price growth and key macroeconomic variables. The results show that the dynamics of house prices are highly persistent. GDP growth has a clear positive effect, while higher unemployment and interest rates push prices down. Migration flows, however, are not statistically significant at the EU aggregate level. Property taxation shows a positive coefficient, which probably reflects structural and institutional differences rather than a direct dampening effect on prices. Dynamic analysis suggests that macroeconomic shocks have persistent and economically meaningful impacts on house price growth. Hierarchical cluster analysis revealed three distinct groups of countries, meaning that house price cycles are only partially synchronized across the EU. Unlike previous studies that typically examine individual determinants or synchronization separately, this study integrates panel econometric methods, dynamic VAR analysis, and hierarchical clustering within a unified framework to jointly assess macroeconomic drivers, dynamic interactions, and structural heterogeneity of house price cycles across EU countries. In general, common macroeconomic drivers and structural heterogeneity coexist—this is important for the stability of the housing market and sustainable development. Full article

Urban sprawl in Iran has previously been examined through spatial measurement, driver classification, and multi-criteria weighting approaches. However, less attention has been given to the hierarchical structure through which governance, market, infrastructure, demographic, and regulatory conditions reinforce one another over time. This study develops a structural interpretation of urban sprawl in Iran’s major metropolitan regions by integrating expert refinement of key drivers with Interpretive Structural Modeling and MICMAC analysis. Rather than ranking drivers by relative importance, the analysis identifies their causal positioning within the wider sprawl system. The findings show that institutional fragmentation, weak enforcement capacity, and limited metropolitan coordination occupy the deepest structural levels, shaping downstream outcomes such as speculative land development, infrastructure-led peripheral expansion, housing pressure, and the growth of outlying settlements. The study contributes to urban-sprawl scholarship by reframing Iranian metropolitan expansion as a governance-embedded spatial process and by identifying leverage points for coordinated intervention. Policy responses should therefore prioritize institutional alignment, enforceable growth-management mechanisms, and infrastructure investment that supports compact rather than dispersed metropolitan development. Full article

In post-socialist countries, the transition from state-controlled housing provision to market-driven residential development has significantly reshaped urban housing production. This transformation has introduced new development dynamics associated with increasing pressures toward economic efficiency and affordability, which may influence the implementation of spatial standards. In this context, this paper examines the extent to which newly built apartments comply with national minimum housing standards in Serbia. The study is based on an empirical dataset comprising 31 multifamily housing projects and 1155 apartment units designed in Niš in the period from January 2024 to April 2025. Each apartment is classified according to its typology and analysed in relation to the minimum floor area prescribed by national regulations. The results reveal a selective pattern of compliance, with the lowest levels observed in two-room apartments, which represent the dominant segment of housing production. This suggests that spatial optimization may be most pronounced in the lower segment of medium-sized dwellings, where dominant housing typologies and regulatory thresholds converge. The findings are interpreted within the framework of post-socialist housing transition, highlighting the uneven relationship between regulatory standards and contemporary housing production patterns. Full article

Post-disaster reconstruction programmes create an irreversible window for embedding or foreclosing residential energy efficiency at scale. This study examines the structural determinants of per capita residential electricity consumption (K_MES) across all 81 provinces of Türkiye over 2013–2022 using a balanced province-year panel. We develop two complementary panel models, both estimated by two-way fixed effects (province + year) with cluster-robust standard errors, and supported by GLS-AR(1) and random-effects GLS robustness checks. Note that K_MES measures the electricity component of residential energy use only; we, therefore, also estimate the building-stock model with a constructed total-energy dependent variable that combines residential electricity (H_MES) and natural-gas consumption (X_DG) in kWh-equivalent units. Model 1 isolates the macroeconomic transmission channel through which exchange-rate volatility shapes residential electricity demand. Because the USD/TRY rate has no cross-sectional variation, its identifying power in two-way fixed effects comes from its interaction with province-level natural-gas-heating exposure (sh_gas × EV_DA). The interaction is robustly negative across all full-sample specifications (β ≈ −0.022, p < 0.01), indicating that provinces with greater gas-heating penetration are buffered against currency-depreciation pass-through into electricity demand. Provincial GDP carries the dominant direct macro coefficient (β ≈ 0.27–0.29, p < 0.01), establishing income elasticity rather than the exchange rate as the headline aggregate driver. Model 2 decomposes the building stock by structural system, filler material, heating system, and heating fuel. The dominant predictors are the share of electric heating (β ≈ 1.16–1.27, p < 0.01) and the share of AC-only heating (β ≈ −1.0 to −1.13, p < 0.05), with a total-energy specification reaching R² = 0.92. In the comparative subsample of the eleven Kahramanmaraş-affected provinces, masonry construction emerges as the dominant pre-disaster predictor of per capita electricity consumption (β = 14.04, p < 0.05), revealing structurally distinct stock characteristics that pre-date the February 2023 earthquake. Two re-framings are required. First, since the panel covers 2013–2022, the disaster-province estimates capture pre-disaster structural heterogeneity rather than post-disaster market rupture. Second, the macroeconomic mechanism that prior work attributed to the exchange-rate level is more accurately understood as a fuel-mix-mediated exposure channel. The combined evidence implies that mandatory building-code enforcement and natural-gas grid extension are complementary policy levers in the 488,000-unit Turkish Housing Development Administration reconstruction programme: gas grid expansion reduces the macroeconomic vulnerability of residential energy demand, while masonry-replacement construction standards address the largest pre-disaster structural determinant of energy intensity in the affected region. Full article

Housing inequalities remain a major challenge for contemporary urban governance, as they combine economic, social, spatial, and demographic dynamics that are difficult to capture through single indicators. This paper develops a data-driven assessment of housing inequalities in Portugal between 2015 and 2025, drawing on official national and European statistics and applying a Business Intelligence (BI) and urban analytics framework oriented towards policy monitoring. Official data from Statistics Portugal and Eurostat are integrated through an analytical pipeline including automated extraction via public APIs, data enrichment, and visual analytics. The workflow follows a CRISP-DM-inspired structure, creating a set of normalized indicators to capture different dimensions of housing conditions. The results point to a structurally polarized housing market. Housing valuations increased across all regions, but at uneven rates, reinforcing territorial disparities rather than convergence. Metropolitan and tourism-oriented regions experienced faster appreciation and indirect effects, while year-over-year growth in completed dwellings slowed after 2021–2022, indicating an uneven supply response. Beyond its empirical findings, the primary contribution of this study lies in demonstrating how BI and data science methodologies can be operationalized to monitor housing inequalities using official statistics. The proposed framework is replicable and can be adapted to other territorial and policy contexts. Full article

As a policy-driven land use transition initiative bridging poverty eradication and sustainable development, China’s Poverty Alleviation Relocation (PAR) program exemplifies how state-led resettlement can reconfigure land use patterns while balancing immediate livelihood security with long-term community capacity development. The integration of large-scale PAR communities into new urbanization is a critical postrelocation task that is essential for consolidating poverty eradication achievements and enhancing endogenous development capacity. This study examined how the configuration of policy instruments shapes the endogenous development capacity of PAR communities during their transition to new urbanization. Employing a “tool–goal” analytical framework, we conducted a content analysis of 38 provincial-level policy documents (2021–present) using NVivo 20 software. The findings reveal that while local governments have established a preliminary policy system, structural imbalances persist: (1) uneven deployment of policy tools, (2) underutilization of demand-based policy tools, (3) tool–goal misalignment, and (4) insufficient market/societal participation in government-led measures. The discussion further reveals that the land use transition in the PAR program emphasizes the “living mode” (housing and public services) over the “livelihood mode” (productive resources and nonagricultural employment), creating structural dependency and leaving industrial land underutilized—as evidenced by weak policy support for industrial development (14.83%) and labour outmigration from resettlement areas. Drawing on the sustainable livelihoods framework, we further demonstrate how this exogenous-dominated policy mix disproportionately enhances physical and financial capital while constraining the accumulation of human and social capital—the very foundations of endogenous development capacity. To address these issues, we propose three key recommendations: (1) optimizing the policy mix to strengthen the endogenous development capacity of PAR communities; (2) realigning policy tools with objectives to achieve diversified yet coordinated goals; and (3) addressing implementation gaps to better leverage market mechanisms and social forces in promoting the sustainable urban integration of resettlement areas. Full article

The real estate market is a complex and dynamic sector that plays a key role in economic stability and wealth generation. In many regions, real estate assets represent around 80% of household wealth, while rising housing prices have turned access to housing into a major social and economic challenge. In this context, the availability of accurate and accessible information is essential for decision-making by buyers, investors, and public administrations. This study proposes the development of an advanced technological tool based on Artificial Intelligence and Machine Learning techniques to predict and analyze real estate market dynamics within a specific geographic area. Using the city of Madrid as a case study, the research presents a digital application capable of estimating the market value of a property by analyzing comparable recently sold properties and incorporating key housing characteristics. By entering an address and a set of property features, the system generates a precise and data-driven valuation. The results demonstrate that AI-based approaches can significantly improve the accuracy and accessibility of real estate valuation processes. The proposed methodology enables real-time price estimation, graphical comparisons, and dynamic market analysis. Furthermore, the framework is scalable and can be extended to other geographic areas where relevant data are available, providing valuable insights for both academic research and practical decision-making in the real estate sector. Full article