Campus managers need to balance the competing claims of different stakeholders of higher education institutions (HEIs). Previous studies in general management have indicated that the decision-making processes are highly related to stakeholders’ attributes (power, legitimacy and urgency). However, such studies have not yet been conducted in the field of campus management. Therefore, this paper aims to identify the importance of key stakeholders in campus development and relevant key performance indicators (KPIs) that are essential for decision- makers in determining and explaining how their efforts are progressing towards achieving various stakeholders’ goals. To explore the underlying importance of campus stakeholders, a theory on stakeholder salience and a questionnaire survey among Polish public HEIs were employed. In turn, to identify various KPIs, the content analysis of institutions’ strategies was conducted. Empirical research suggests that in Polish public HEIs, authorities and academic staff continue to be regarded as the most important stakeholders (at different stages of the building’s life cycle, respectively). This contrasts with the growing market-oriented approach seen in higher education globally. Moreover, the KPIs presented in HEIs’ strategies are predominantly result-focused and rarely user-centric (which have begun to be perceived as the most significant with respect to building performance).
This paper discusses the rapidly developing property sector in Edinburgh, Scotland, which in recent years has been shaped by growth in tourism and the expansion of the short-term rental market. The study aims to investigate how Airbnb listings can be categorised into the tiers offering low, medium and high potential earnings for investors. Research methodology includes the K-Means clustering, based on features, such as location, availability, property type, number of bedrooms, bathrooms and price. The paper further examines what variables influence the investors’ revenue streams most. The application of the Random Forest Model to the K-Means clustering results showed that pricing, availability and guest capacity were the principal variables affecting earnings. Incorporating predictive modelling into the market categorisation enabled this study to offer a practical, data-based framework for potential investors who are considering various options for investment in properties with an intent to offer them for short-term rent via Airbnb.
The interrelationship between housing and land prices has long been a puzzle in China. Using a panel dataset of 286 prefecture-level cities from 2011 to 2020, we construct a spatial simultaneous-equations model and estimate it with the feasible generalized spatial three- stage least squares (FGS3SLS) approach. Results show significant mutual causality and spatial spillovers between housing and land prices. A 1% rise in local housing prices increases local land prices by about 0.71%, with a comparable effect from neighboring housing markets. Regional heterogeneity is evident: the housing-to-land price effect is strongest in western cities (0.93%) and weaker in eastern cities (0.53%), reflecting differences in local government incentives and market structures. By capturing both bidirectional causality and spatial-temporal variation, this study offers new evidence on the mechanisms linking housing and land markets and provides insights for regionally differentiated housing and land policies in China.
The transformation of the real estate sector towards net-zero emissions is a fundamental transition that requires a need for alignment between the performance of the physical asset and the use of environmental, social, and governance-linked capital. Green investment strategies can be an important solution to ensure the liquidity of assets; however, the existing gap between the practical implementation and the real estate sector decarbonization agenda has been widely observed. The existing studies in the field have often focused on the financial appraisal, technical, and regulatory governance separately, which limits a comprehensive understanding of the whole picture. This study, therefore, aims to narrow down the research-practice gap by methodically investigating the link between green investment and decarbonization by adopting a structured interpretive synthesis approach through a PRISMA-informed review of 74 related articles. The review identified five clusters of drivers (headed by green premium) and eight clusters of structural barriers, starting from the split-incentive problem to green investment. The results confirmed that the sustainable business model is achieved through five major transition pathways: technological adoption, deep retrofit, stakeholder engagement, life-cycle carbon management, and circular economy. In addition, 11 policy initiatives ranging from mandatory performance standards to sustainable public procurement were identified and synthesized to address the long-standing market failures. The findings of this research, when structured in a unified decision-making framework, would assist real estate professionals and policymakers in effectively aligning capital use and physical resilience for long-term value capture in the decarbonized economy.
Sustainable hospitals require careful selection of green building materials (GBMs) to reduce eco-impact and improve resilience. This is crucial as hospitals consume significant resources and their material choices influence durability, availability, and indoor air quality. Existing studies did not model uncertainty effectively, calculate experts’ weights methodically, and determine personalized/combined ranks of GBMs. This paper aims to address these gaps by developing an integrated decision framework that evaluates factors/criteria, assigns importance values, and ranks GBMs systematically. The methodology combines hyperbolic fuzzy data with attitudinal variance, LOPCOW, and choice-based WISP methods to determine experts’ weights, factor importance, and material grades. The proposed rank algorithm produces both personalized and cumulative grades. Results show durability, material availability, and indoor air quality as the top three factors, with hempcrete, cross-laminated timber, and rammed earth as the leading GBMs. This framework contributes by offering stakeholders a rational, uncertainty-resilient tool for sustainable hospital design.
This study investigates the relationships between Environmental, Social, and Governance (ESG) attributes and financial performance indicators in the listed real estate sector across Brazil, Chile, and Mexico. Utilising a comprehensive dataset from Bloomberg and annual financial reports, and employing panel regression and Granger causality tests, the analysis reveals significant bidirectional and unidirectional causal linkages between ESG practices and financial performance. Key findings indicate that energy efficiency and governance disclosures are instrumental in augmenting both financial performance and firm valuation. The robust positive correlations between ESG variables and financial metrics substantiate the strategic importance of sustainability, particularly in terms of governance transparency and environmental stewardship. Policy recommendations advocate for incentivising energy-efficient practices, standardising ESG reporting frameworks, and fostering gender diversity to promote sustainable development within the listed real estate sector.
This bibliometric analysis provides valuable insights into the evolving field of building maintenance. It not only charts the existing literature but also critically identifies key processes, essential research areas, central themes, gaps, and connections within the building maintenance (BM) sector while assessing the current state of research. This review utilised treemap analysis, thematic evolution, thematic mapping, and factor analyses, to analyse 483 Scopus-related studies between 1948 (first published article) and 2024. Microsoft Excel, Open Refine, VOSviewer, Biblioshiny, and biblioMagika were employed to preprocess and standardise the BM-related data. Subsequently, assessments of frequency, impact, and bibliometric networks were conducted. The analysis revealed significant growth in sustainability, innovative technologies, and maintenance management areas. Notably, Malaysia and the United Kingdom emerged as leading contributors to BM-related research. Facilities is recognised as a prominent publication in the sector. Major themes identified included maintenance, architectural design, building management, information management, construction, and facilities management. Additionally, several underexplored areas were highlighted, such as maintenance budgets, public housing, commercial buildings, maintenance costs, cost analysis, and maintenance procedures. Overall, the findings provide substantial insights for scholars and industry professionals, delineating trends, challenges, areas for further exploration, future policy considerations, and advocating for sustainable practices in building maintenance.
Urban renewal is widely recognized as a driver of urban transformation, yet its impacts on housing prices remain contested. This study systematically reviews 153 peer-reviewed publications (2013–2023) using the PRISMA framework, integrating bibliometric mapping, text mining, and article-level statistical testing. The analysis shows that while descriptive evidence suggests variation across renewal types, scales, and contexts, chi-square tests confirm that only context is significantly associated with housing price outcomes. Urban-core projects are more often linked to appreciation, whereas peri-urban initiatives display mixed or negative effects. By contrast, project type and scale do not yield statistically significant associations, underscoring the primacy of location and local conditions. Beyond empirical results, the study highlights critical social implications, including affordability pressures, displacement risks, and uneven community stability. These findings provide a more differentiated and context-sensitive understanding of the renewal–price nexus, offering valuable guidance for policymakers and researchers concerned with equitable and sustainable urban development.
The aim of this study is to compare the risk-return performances of real estate investment funds, a rising real estate capital market instrument in Türkiye, and housing prices, which skyrocketed in recent years in Türkiye under high inflationary periods. The research was conducted for the data obtained between March 2020 and May 2024 in 4 different periods (2020–2024), (2021–2024), (2022–2024) and (2023–2024). The risk-return performance analysis used monthly and quarterly returns of the housing prices and real estate investment funds. Sharpe ratio was calculated for each investment tool. Due to the negative Sharpe ratios obtained for some real estate investment funds, the performances were retested by the Modified Sharpe ratio. The research also used the consumer price index (CPI) as the primary benchmark tool. The research results revealed that except for the last period analyzed, where the increase in housing prices slowed, housing prices overperformed the real estate investment funds. The quarterly data analysis gave better performance results for the REIFs compared to the monthly data. This study is the most comprehensive research regarding the number of real estate investment funds and the period it covered. Establishing Türkiye Real Estate Investment Fund Index was also suggested for the first time.
Shocks from climate change and transitioning to a low-carbon economy can have a green swan effect on economies. To assess the extent to which green swan events may disrupt housing markets in the future, this study examines how the prices of carbon trading pilot sites in four Chinese cities (Beijing, Shanghai, Tianjin, and Chongqing) affect housing prices in these cities and whether the green swan effect increases with a rise in policy risks. This paper first performs simulations to determine the extreme risk of housing returns (value at risk) and whether this risk is affected by the exogenous shock of carbon returns. Then, this study examines the correlation between carbon prices and housing prices from the perspectives of returns and volatility. In terms of risk transmission effects, all four real estate markets are affected by carbon price risk spillovers. It verifies that China’s real estate market may be negatively impacted by “green swans” when carbon prices experience significant fluctuations. The findings provide investors with a means to evaluate whether the housing market is susceptible to a green swan effect, and also underscore the need for authorities to evaluate the impact of carbon reduction policies on the housing market.
As a new engine of economic growth, digital trade is playing an increasingly pivotal role in shaping urban dynamics, including housing markets. This study investigated how the development of digital trade influenced city-level house prices in China. A digital trade index and an urban AI index were constructed using the entropy-TOPSIS method and text mining techniques, respectively. Empirical results showed that digital trade exerted a significant and robust positive linear effect on urban house prices, with no evidence of a nonlinear relationship. AI significantly strengthened this effect, acting as a positive moderator, while trade openness weakened it. Further heterogeneity analysis revealed that the impact of digital trade was more pronounced in coastal and high-income cities, yet AI integration substantially boosted this effect in non-coastal and low-income cities, suggesting strong potential for digital catch-up in underdeveloped regions. These findings indicated that digital trade, AI adoption, and regional characteristics jointly shape urban housing outcomes. Therefore, beyond advocating for stronger governmental support for digital infrastructure and emerging technologies, this study also highlighted the importance of enhancing AI capability and optimising trade openness strategies to ensure balanced urban development and sustainable real estate growth.
This paper employed wavelet coherence analysis, wavelet quantile correlation and wavelet local multiple correlation to systematically investigate the influence of multiple types uncertainty indicators on the US REITs market. The findings revealed significant time-frequency characteristics and quantile dependencies between different types of uncertainty indicators and the REITs market: geopolitical risk exhibited a safe-haven function at medium-high quantiles in high-frequency bands; market volatility showed a significant negative correlation with REITs across all quantiles in the lowest frequency band; economic policy uncertainty was positively correlated with REITs at medium-high quantiles in medium-frequency bands; financial stress was negatively correlated with REITs at most quantiles in medium-frequency bands; and commodity market uncertainty demonstrated a pronounced frequency-quantile dependency. The multivariate WLMC analysis reveals that uncertainty indicators exhibit frequency-dependent dominance patterns, with FSI dominating long-term impacts, VIX and FSI alternating in medium-term frequencies. Particularly during the COVID-19 crisis, the associations between these uncertainty indicators and the REITs market were generally enhanced and exhibited persistence in the medium-to-long-term frequency domain. The findings of this study not only enriched the theoretical understanding of the relationship between REITs markets and macroeconomic uncertainty but also provided important empirical evidence for investors’ risk management and policymakers’ market regulation.
This study explores off-post housing preferences and satisfaction among U.S. military personnel and civilian employees stationed at Camp Humphreys in South Korea—the largest overseas U.S. military installation. Using the Importance–Performance Analysis framework, the research identifies key factors shaping residential experiences and strategic housing needs in host communities. While attributes such as English communication and clarity of lease agreement support resident satisfaction, unmet expectations regarding parking and shared facility management highlight critical service gaps. However, beyond individual preferences, the findings carry broader implications for real estate market responsiveness and urban policy. The structured nature of military housing allowances minimizes price sensitivity, suggesting that quality, service reliability, and cultural adaptability hold greater value for this tenant segment. These insights point to opportunities for local governments and developers to differentiate through certified multilingual real estate services and tailored infrastructure planning. By aligning investment strategies with the unique needs of foreign military populations, stakeholders can enhance property values, reduce tenant turnover, and foster community integration. The study contributes to the discourse on military housing policy and provides a model for optimizing real estate services in globalized urban contexts.
Machine learning (ML) in the real estate industry has transformed property assessment, administration, and promotion, tackling significant issues including market instability and pricing precision. Notwithstanding considerable progress in predictive, descriptive, prescriptive analytics, and automation, current research mostly emphasises technological and operational efficiencies, overlooking the integration of environmental, social, economic, and governance (ESEG) sustainability dimensions. This monitoring constrains the advancement of comprehensive and accountable real estate solutions corresponding to sustainable development objectives. This study aims to address these gaps by systematically analyzing publication trends, key contributors, and thematic clusters, incorporating sustainability principles via a combination of bibliometric and content analysis approaches. The study uncovers publication trends, key research themes, and their alignment with ESEG criteria. The results highlight significant research clusters in predictive and descriptive analytics while revealing a notable deficiency in sustainability-focused studies. Implications of this study underscore the necessity for incorporating ESEG dimensions into ML-driven real estate practices, promoting resilient, equitable, and environmentally responsible industry advancements. This study provides actionable insights for stakeholders to enhance sustainable ML adoption, fostering long-term viability and societal well-being in the real estate sector.
This paper investigates the market effects of a reference price policy (the RP policy, hereafter) for resale housing of selected residential projects in Shenzhen, China. The RP policy sets ONE reference price per square meter for each regulated residential project, and requires housing listing prices as well as banks’ valuation in mortgage lending to be below the reference price, but it does not limit transaction prices. Using housing listing, transaction, and rental data, we have two findings. First, the policy lowers the probability of transaction in the regulated projects, which prolongs home sellers’ time on the market; the deeper the reference price is below the would-be listing price of a residential project, the more substantial the probability of transaction is reduced. But we do not find evidence that it significantly reduces housing transaction prices. Second, the RP policy induces more homes in regulated projects to be leased out and reduces housing rents. We explain the two findings as the outcome of home buyers’ and sellers’ reference-dependent and regret-avoidant behaviors. Additionally, we discuss the spillover of the policy effects from regulated to unregulated projects, as well as the possible confounding effect of the RP policy’ role as finance constraint for buyers.
This study examines the impact of anti-social behaviour (ASB) on property prices. By analysing over 14,500 market transactions in Northern Ireland, we find that the prevalence of ASB within a neighbourhood exerts a direct and negative influence on house prices, albeit with diminishing effect at the margin. Furthermore, the dampening effects of ASB are more pronounced in districts characterised by higher population density, proximity to the capital city (Belfast), and lower property values. A thorough analysis of district-level data across a wide range of statistical indicators further indicates that the adverse impact of ASB on property prices is most acute in areas marked by social and economic deprivation, including factors such as income, employment, education, and access to services. Lastly, our submarket analysis suggests that the apartment sector and public housing are disproportionately affected by ASB in terms of price depreciation compared to other property types.
Statistical data indicates a rising trend in the frequency and unpredictability of floods globally. Regions traditionally less affected by flooding are reportedly experiencing an increased impact, underscoring the widespread nature of this phenomenon. Over the past decade, the overall incidence of floods has significantly increased, affecting billions of people worldwide. If appropriate measures are not taken, floods driven by climate change, urbanization, and the consequences of human activities will continue to increase in frequency and intensity, leading to even greater economic, social, and environmental losses. Proper selection of management strategies and risk reduction measures is becoming particularly important for reducing flood risks. Multi-Criteria Decision Making (MCDM) methods are well-suited for addressing such complex, multi-criteria problems. Therefore, this study aims to explore the research fields of MCDM application in flood management and identify the most widely used methods. This is done to clarify their benefits and enhance their applicability. The Systematic Literature Review (SLR) revealed that the research field is broad and dynamic, evolving over the decades. However, the application of MCDM remains popular and, according to current trends, continues to gain popularity. New research fields are also emerging, such as “Identification and/or Mapping of Flood-Prone Areas” and “Sustainable Infrastructure Assessment”, highlighting scientist growing concern about the importance of evaluating vulnerable areas and applying sustainable solutions to address flood management challenges. The findings are particularly relevant to real estate and property management, as they support the development of evidence-based frameworks for assessing property-level flood resilience and for guiding investment decisions to protect built assets.
We develop a Housing Rental Availability Index (HRAI) to measure rental housing availability across Poland by incorporating household income, rental prices, and supply-side factors. The HRAI is constructed using the structured parametric approach, offering a comprehensive and adaptable framework for analysing rental housing dynamics in Poland. Based on the Polish rental market data from 2021 to 2024, the HRAI reveals that supply constraints and rent fluctuations have a greater impact on rental availability than household income. This result challenges traditional affordability metrics. Sensitivity analysis confirms that rental availability emerges from the interaction of supply and demand rather than from either factor alone. This integrated approach positions HRAI as an “anti-separatist” indicator and presents an original approach to examining rental housing availability. The index can help local and national policymakers design targeted rent subsidies, address supply-demand imbalances, and promote spatial equity. Our findings highlight the value of combining economic measures into a single availability index and provide a framework for applying the HRAI in both academic research and housing policy decisions.
Digitalization has reshaped the real estate industry, significantly transforming the role of the agent. In an era of disruptive technologies, real estate agents face both new challenges and opportunities, where adaptability and the integration of digital tools are essential for success. This article examines how digitalization has influenced the agent’s role and assesses its importance in the brokerage process from the customer’s perspective. Self-administered online surveys have been used for data gathering, obtaining 412 valid responses from people who have participated in the process of buying or selling a property in Spain. Results reveal the need for the agent who still plays a decisive role in the buying and selling process and is considered well prepared, providing confidence for many clients. Digitalization has redefined the agent’s profile, requiring a combination of technical and human skills and the importance of empathy and personal contact has become crucial. Those professionals who incorporate technology while focusing on delivering an exceptional customer experience will be the ones who thrive in this new era of digitalized real estate. The manuscript enriches academic literature on the importance of the real estate agent in a country such as Spain where it has been hardly studied.
This study examines the dynamics of rental time on market (TOM) for residential properties in Szczecin, Poland, from 2016 to 2023. Using data from the regional MLS system administered by the West Pomeranian Association of Realtors (ZSPON), the analysis spans four distinct periods: pre-pandemic (2016–2019), pandemic onset (2020), transitional recovery (2021), and post-pandemic normalization (2022–2023). The study investigates two primary hypotheses: (H1) TOM differs significantly across pandemic phases; and (H2) TOM is correlated with rental price levels. Employing Kaplan-Meier survival analysis and General Linear Modeling (GLM), the results confirm both hypotheses. TOM increased sharply during early pandemic phases and decreased in subsequent years, though not returning to pre-pandemic levels. Additionally, listings with mid-range prices consistently showed the shortest TOM, while overpriced and underpriced listings were associated with longer market exposure. The findings contribute to the understanding of how external shocks, such as the COVID-19 pandemic, induce both temporary and persistent changes in rental market behavior. The study also offers practical implications for real estate professionals, suggesting that TOM-based modeling can enhance pricing and listing strategies, particularly during periods of uncertainty or recovery.
