International Journal of Strategic Property Management
The International Journal of Strategic Property Management publishes original interdisciplinary research on strategic management of property. More information ...
Copyright © 2021 The Author(s). Published by Vilnius Gediminas Technical University. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Property price and anti-social behaviour: An empirical study of Northern Ireland
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.
Copyright (c) 2025 The Author(s). Published by Vilnius Gediminas Technical University. http://creativecommons.org/licenses/by/4.0
Urban flood disaster risk management using multi-criteria decision-making methods: A scoping review
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.
Housing rental availability index: A tool for addressing supply-demand challenges in rental markets
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.
Housing Rental Availability Index (HRAI)
Relevance of the real estate agent in the Spanish residential market in the age of digitalisation
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.
Analyzing rental time and pricing trends before, during, and after the COVID-19 pandemic
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.
Establishment of housing transfer inspection items using SEM: Empirical study in Taiwan
Disputes during the housing transfer process often lead to dissatisfaction for homeowners. This study aims to address these issues by (1) proposing a comprehensive house inspection guideline with key items and criteria, and (2) evaluating how inspection services influence homebuyers’ purchase intentions and perceived value. Based on comprehensive literature review and expert input, eight major inspection categories and 43 criteria were identified. These formed the basis for a Structural Equation Modeling (SEM) framework and three hypotheses. A pilot survey with 50 participants confirmed strong reliability (Cronbach’s Alpha: 0.880–0.945). Of 500 distributed questionnaires, 206 valid responses were collected. SEM analysis showed that inspection services significantly enhance both purchase intentions and perceived value, with R² values exceeding 79% in most areas, except for environmental inspections. Building structure, water supply/drainage and water leakage are the most influential findings in shaping perceived value. These results highlight the importance of targeted inspection services in addressing homebuyer perceived value and improving the overall housing experience in Taiwan.
Sustainability, risk, and social responsibility: The new triad in real estate management
The increasing importance of sustainability, risk, and social responsibility in real estate management reflects evolving societal demands, regulatory pressures, and market dynamics. Motivated by the need to align real estate practices with environmental goals and social equity, this study explores how these three pillars can be systematically integrated into property management. The aim is to develop a holistic framework that transforms risks into opportunities and promotes long-term value creation. Using a mixed-methods approach, including literature analysis and semi-structured expert interviews conducted within the Romanian real estate sector, this study investigates how ESG criteria and digital technologies are currently applied in practice. Key findings reveal that tools such as BIM, digital twins, and ESG reporting enhance transparency, operational efficiency, and stakeholder engagement. The research concludes that integrating the sustainability–risk–responsibility triad provides strategic advantages, enhances resilience, and strengthens the role of real estate management in advancing sustainable development.
Explainable AI-based mass appraisal: Insights from machine learning applications in Korea’s residential property market
Mass appraisal plays a pivotal role in real estate management, facilitating property tax assessments, mortgage evaluations, and urban planning across large geographical areas. In regions like Korea, where real estate markets are rapidly evolving, valuation models based on multiple linear regression are valued for their simplicity and interpretability but often fall short in capturing complex market dynamics. In contrast, machine learning (ML) models, while addressing non-linear relationships between property characteristics and market values and offering superior predictive performance, are often criticized for their “black-box” nature, which raises concerns over interpretability in transparency-critical domains like property tax assessments and policy planning. To address these concerns, this study investigates the application of Explainable AI (XAI) techniques in the mass appraisal of residential properties in Korea, integrating XAI methods with both multiple linear regression and random forest models. Using SHAP (SHapley Additive exPlanations) and PFI (Permutation Feature Importance) values, the study analyzes feature importance and predictive contributions, offering insights into the factors driving property valuations. Additionally, a temporal analysis was conducted by segmenting the data into time intervals to examine how feature importance and predictive contributions evolve over time. By combining high predictive performance with transparent and interpretable insights, the findings demonstrate that XAI can enhance the usability of both traditional and advanced automated valuation models (AVMs) for real-world decision-making in the Korean real estate sector.
Tokenization and the future of property investment: A new paradigm for real estate
Tokenization has improved liquidity, access to markets and ownership models for real estate investments. This research aims to analyze applications, enablers and challenges of tokenization by conducting a systematic literature review of 75 peer-reviewed journal articles that discuss real estate tokenization using the Technology-Organization-Environment (TOE) framework. The findings show that tokenization allows more efficient markets with automation, transparency, and liquidity; new investment methods in real estate, such as crowdfunding and fractional ownership; and reductions of any limits of traditional real estate through the reduction of costs, faster transactions, and higher investor engagement. Nevertheless, there are still barriers to real estate tokenization including technological risks, regulatory fragmentation, and the reluctance of organizations towards decentralized asset structures. These are supported by the literature review findings. These barriers highlight the necessity of investor education, technology standardization, and regulatory harmonization with the aim of promoting wider adoption. This paper lays the groundwork for future research into risk management, scalability, and institutional integration of real estate tokenization by highlighting important gaps in existing literature and consolidating previous studies. Ultimately, even though tokenization has a lot of potential to change the real estate market, its long-term success depends on the development of accurate legal frameworks and the seamless incorporation of tokenized assets into conventionnel financial systems.
Dynamic effect of sports parks on housing prices: Evidence from Hangzhou Asian Games Park
The construction of sports parks often involves significant investment and extended development timelines, making it crucial to evaluate their economic impacts for informed policymaking and strategic urban planning. While research on the externalities of urban parks has grown, the specific economic effects of sports parks remain underexplored. This study examines the impact of the Hangzhou Asian Games Park on housing prices using 31,329 transaction records from 2018 to 2023. Employing a hedonic pricing model and a difference-in-differences approach, we find that housing prices increase by 1.6% for every 1 km reduction in distance to the park. Spatial heterogeneity is observable, as the effect diminishes with increasing distance. Temporally, the park’s planning announcement triggered a 0.7% increase in price-distance elasticity, compared to a 0.2% increase after its opening. These findings suggest that the anticipatory market response was stronger than the realized benefits after the park’s completion. This study quantifies the spatial and temporal dynamics of the economic value of sports parks, providing valuable insights for urban planning, public policy, and investment strategies.
Double trouble: Time-varying connectedness between stock and housing markets
Joint new records in the stock and housing markets are now gradually becoming a focal point of controversy in Taiwan. Based on the local heterogeneity of real estate assets, this study proposes setting up a two-market transmission mechanism between the stock and city-level housing markets to fully reply to this question. The estimation results using the Diebold-Yilmaz spillover method offer some critical information: the fact that the overheated housing market is precisely caused by the Taiwan stock market, which serves as evidence of the wealth effect. As far as the housing network is concerned, it is interesting to note that housing prices in Taipei as the source city spill out from near to far: New Taipei, Taichung and ultimately Kaohsiung. All these things make it clear that the authorities pay special attention to the status of the stock market as well as to inter-city differences in terms of housing spillovers.
Tail risk diversification strategy with flight-from-loss approach: Evidence from U.S. REITs
This study introduces a novel portfolio allocation strategy, the flight-from-loss approach, designed to diversify tail risk in the REIT market. The strategy reallocates capital toward assets that have historically outperformed during periods of extreme REIT losses, aiming to reduce downside risk and improve portfolio efficiency. Using U.S. REIT data from 1993 to 2023, we demonstrate that our portfolio approach reduces tail risk significantly, while also enhancing Sharpe ratios compared to a REIT-only benchmark portfolio. These diversification benefits are particularly significant during market crises, such as the subprime mortgage crisis, when risk reduction exceeds 30%. Our analysis further reveals that the minimum-variance and tangency portfolio approaches consistently outperform the equal-weight method in both risk control and performance efficiency. To test the strategy’s generalizability, we applied it to the Fama-French 30 industry portfolios, where the results of some industries indicate even stronger risk reduction and Sharpe ratio gains than in the REIT market. These findings suggest that the flight-from-loss strategy offers a practical, cross-sector solution for managing concentrated portfolio risks.
The housing price dynamics and macroeconomic factors: Evidence of Taiwan housing market
The housing price in Taiwan has seen a sharp increase since 2020 after a long period of stability from 2011 to 2019. To provide a more granular analysis on this substantial rise of the housing prices since 2020 in Taiwan, we applied the hedonic pricing model to analyze the completed and presale residential housing across four types of houses: luxury, first-class, second-class and suite. By incorporating relevant macroeconomic variables from 2014 to 2022, we try to capture the demand of homebuyers across the three biggest cities in Taiwan. In the completed housing market, we find that the suite prices in Taipei and Taichung cities are more significantly affected by Taiwan’s continuing economic growth, whereas the suite prices in Kaohsiung city are hindered by its historically stagnant employment market. Notably, suite prices in Kaohsiung display greater sensitivity to construction cost changes and broader economic cycles compared to the suite prices in Taipei and Taichung. These findings highlight the unique dynamics of real estate markets in northern, central, and southern Taiwan, providing valuable insights for housing investors, prospective homebuyers in different regions and policymakers in their decision making.
How does the withdrawal of rural homesteads impact rural resilience in China? A MADM model
Rural Resilience represents the ability of maintaining their core functions when facing internal changes and recovering to original conditions through transformation. Withdrawal from rural homesteads (WRH) is considered as one of critical strategy for rural revitalization of China but its systemic impacts on rural resilience remain underexplored. This study develops a multidimensional resilience evaluation framework encompassing economic, social, cultural, environmental, and governance dimensions through a Delphi-structured expert consultation process with 16 specialists. Considering the complexity of rural socio-ecological systems and the interplay among various dimensions of rural resilience, this paper uses the Fuzzy Decision-Making Trial and Evaluation Laboratory methodology to analyze the causal relationships between 22 resilience indicators. Results reveal economic resilience and social resilience as dominant causal dimensions, with economic diversification promotion and collective land marketization emerging as key drivers. Cultural and environmental dimensions exhibit effect characteristics, demonstrating dependence on economic, social, governance interventions. Notably, villagers’ income improvement and cooperative mechanisms demonstrate high centrality, while indicators related to culture and environment rank as vulnerable nodes. These findings provide policymakers with a prioritized intervention framework, emphasizing the need for economic restructuring coupled with institutional safeguards to balance developmental and conservation objectives in rural spatial reorganization processes.
fuzzy decision-making trial and evaluation laboratory (Fuzzy-DEMATEL)
Enhancing real estate mass appraisal in Type II metropolitan cities: A GIS-MGWR approach
At present, China’s real estate appraisal sector confronts a number of challenges, including low appraisal efficiency, significant human influence, lack of objectivity, and absence of unified standards. Particularly, conducting a scientific, precise, and efficient mass appraisal of existing housing is vital for fostering the industry‘s healthy development. This study adopts Zhangjiakou City in Hebei Province as a case study, integrating Geographic Information Systems (GIS) and utilizing the Ordinary Least Squares (OLS), Geographically Weighted Regression (GWR), Semi-parametric Geographically Weighted Regression (SGWR), and Multiscale Geographically Weighted Regression (MGWR) models to assess the prices of existing housing. The research delves into the specific challenges in mass appraisal of real estate within Type II metropolitan cities. The study reveals spatial heterogeneity in the prices of existing housing in Zhangjiakou City and shows that the MGWR model excels in mass appraisal of housing prices in Type II metropolitan cities. This research offers strategic guidance for real estate market investment and transactions in Zhangjiakou City and provides valuable references for other Type II metropolitan cities in real estate appraisal practices, market analysis, and policy formulation.
Multiscale Geographically Weighted Regression (MGWR)
Forecasting pandemic-induced changes in real estate market values through machine learning approaches
In this study, a new temporal segmentation method is used to forecasting the real estate market based on the structural and spatial attributes of 676 houses in Niğde, Türkiye, from the years 2019 to 2022. Artificial Neural Networks (ANN), Random Forest (RF), Decision Tree (DT), and K-Nearest Neighbours (KNN) were employed for model development and comparative performance analysis. According to the results, the ANN model that used temporal variables showed the most successful performance by achieving the highest R2 for 2019 (1. period: 0.979, 2. period: 0.990, 3. period: 0.914, 4. period: 0.831) and 2022 (1. period: 0.971, 2. period: 0.975, 3. period: 0.586, 4. period: 0.896) scores. Additionally, the COD values (5%–10%) and PRD values (0.98 to 1.03) remained within the acceptable range, further validating the model’s reliability. RF model showed more effective performance than other models by achieving the highest R2: 0.510 for 2019 and R2: 0.509 for 2022 when temporal variables were excluded. These findings highlight the importance of integrating time-sensitive parameters into valuation models to improve forecast accuracy and robustness. The study offers a replicable, flexible methodology for crisis-responsive valuation, providing valuable insights for policymakers, investors, and urban planners aiming to mitigate risks and enhance resilience in real estate market decision-making.
“Quo Vadis urban areas?”: (Re)thinking the future of urban areas using interpretive structural modeling
The world’s population continues to grow at an unprecedented rate, with urban areas experiencing a more rapid rise in population density than rural regions. This demographic shift compels decision-makers to address pressing urban challenges and rethink future structures of cities. However, a vision for global sustainable urban growth remains elusive, as planners often lack comprehensive, credible and dynamic models to guide decision-making. The main purpose of this study is to propose a process-oriented methodology that integrates cognitive mapping, interpretive structural modeling (ISM) and a matrice d’impacts croisés multiplication appliquée à un classement (MICMAC) analysis to evaluate and prioritize key determinants of urban development. Group work sessions involving decision-makers from diverse fields were conducted to identify critical variables influencing urban development. Unlike traditional models, the proposed approach emphasizes participatory decision-making. By combining cognitive mapping and ISM-MICMAC, this study enables the identification of causal relationships among variables and allows decision-makers to anticipate trends and prioritize challenges effectively. The findings were further validated by an external expert to ensure neutrality and reliability. Overall, this study provides a theoretical contribution to decision-making methodologies while offering a practical framework for urban planners to influence cities toward a sustainable future.
Matrice d’Impacts Croisés Multiplication Appliquée à un Classement (MICMAC)
Impact of financial market development on housing prices: Evidence from China
Housing prices in cities of China had soared by 70% from 2012 to 2021, attracting increasing public concern. Financial market development may suppress housing prices by allowing speculators to channel capital into financial markets rather than merely into the property sector, whereby it plays a critical role in shaping housing prices theoretically. This study aims to investigate the impact of financial market development on housing prices with a sample of 261 cities in China from 2011 to 2021, concluding that financial market development is negatively related to housing prices based on the GMM model. Correspondingly, a potential policy to decrease housing prices is to promote financial market development.
A dual hesitation fuzzy VIKOR method with incomplete attribute weights for property service quality evaluation
The evaluation of property service quality is a typical multi-index evaluation problem, which has the characteristics of comprehensiveness and incomplete information in the modern service industry environment. To solve the above problem, we propose a dual hesitation fuzzy VIKOR comprehensive decision method based on incomplete attribute weights for evaluating property service quality. The method integrates innovative distance measurement, comprehensive weighting, and VIKOR decision technology to address challenges such as incomplete information, unknown weights, and evaluator hesitation. By incorporating the information value of dual hesitation fuzzy set hesitancy, we have designed a new distance measurement formula and developed a comprehensive weighting method that combines subjective and objective data. The dual hesitation fuzzy VIKOR model utilizes these tools to calculate utility values and regret values in order to make scientifically grounded decisions. This approach provides robust support for enhancing property service quality and demonstrates broad potential applications in fuzzy information processing and other domains.
Estimating spatiotemporal heterogeneous effects of haze pollution on housing rents in China
Severe haze pollution significantly affects urban life quality and employment location choices, which in turn impact rental housing demand and housing rents. While numerous studies have delved into the effects of haze pollution on the real estate market, with a particular focus on housing prices, there is a notable scarcity of research on its impact within China’s rental housing market. This study employs spatial panel data encompassing 289 Chinese cities from 2015 to 2021 and applies a Geographically and temporally weighted regression (GTWR) model to investigate the spatiotemporal heterogeneous effects of haze pollution on housing rents in urban China. Our results indicate that the GTWR model’s goodness-of-fit surpasses that of the OLS, GWR, and TWR models. The results of the GTWR model reveal that haze pollution has a negative effect on housing rents in China, with the eastern region experiencing a notably stronger negative impact than the western region. Moreover, this negative impact becomes increasingly stronger over time. In addition, population, economic, and social factors significantly impact housing rents in Chinese cities. These findings offer valuable insights into the relationship between haze pollution and housing rents, assisting policymakers in assessing the economic value of air pollution control in urban China.
A study on house price index performance: Mix adjustment and hierarchical linear growth repeat-sales models
In this study, we examined the differences between three house price indexes constructed using hedonic price, mix adjustment, and hierarchical linear growth repeat-sales modeling. The data consisted of housing sales across 13 administrative districts in Kaohsiung City from the third quarter of 2013 to 2022. The predictions were compared using the mean standard error, mean absolute percentage error, mean absolute error, and root-mean-square error. The results revealed that the hedonic price index performed the best; its prediction scores, as reflected by the four aforementioned metrics were 0.072, 1.176, 0.181, and 0.181, respectively. The index with the second best performance was the mix adjustment model, with scores of 0.154, 1.905, 0.293, and 0.293. The worst-performing index was the repeat-sales model, with scores of 0.309, 2.804, 0.439, and 0.439. After comparing the annual prediction errors of the three models, it became apparent that the hedonic price index had the best performance, followed by the mix adjustment index, and then the hierarchical linear growth repeat-sales index.
hierarchical linear growth repeat-sales model
Spatiotemporal patterns and prediction of multi-region house prices via functional mixed effects model
House prices have always been a popular indicator for real estate market monitoring. This study explores the spatiotemporal patterns of house prices at the community level in San Francisco from January 2009 to April 2024. A functional spatiotemporal semiparametric mixed effects (FST-SM) model was proposed to analyze the Zillow Home Value Index (ZHVI), considering spatiotemporal variations. This response is associated with known influences and unknown latent random effects. The random-effects component was expanded using functional principal components. The conditional autoregressive (CAR) structure of the principal component scores was adopted to analyze nonparametric time trends and spatiotemporal correlations. The proposed model was compared with other time-series models in terms of spatiotemporal prediction. The results show that the prediction accuracy of the proposed model is higher than that of other regular models. In summary, a functional mixed effects model was proposed to describe spatiotemporal patterns and forecast house prices. This study can provide valuable references for decision-making by local governments, real estate suppliers, and house buyers.
Evaluating embedding models for text classification in apartment management
The recent proliferation of embedding models has enhanced the accessibility of textual data classification. However, the crucial challenge is evaluating and selecting the most effective embedding model for a specific domain from a vast number of options. In this study, we address this challenge by assessing the performance of embedding models based on their effectiveness in downstream tasks. We analyze consultation records maintained by an apartment management body in South Korea, and convert this textual data into numerical representations using various embedding models. The vectorized text is then categorized using a k-means clustering algorithm. The downstream task, specifically, the classification of consultation records, is evaluated using a quantitative metric (Silhouette score) and qualitative approaches (domain-specific knowledge and visual inspection). The qualitative approaches yield more reliable results than the quantitative approach. These findings are expected to be valuable for the various stakeholders in property management.
Development drivers for urban regeneration and livability in worn-out neighborhoods
Development driver projects are recognized as tools for revitalizing and regenerating worn-out urban fabrics. Recent studies emphasize the importance of development driver projects for regeneration across three scales: macro, medium, and micro. However, research has lacked an examination of the impact of indicators at all three scales. To fill this research gap, the present study investigates the effect of development driver indicators on the regeneration of the worn-out fabric of Semnan, one of Iran’s historic and significant cities. This research initially identifies 16 indicators across the three scales. Data were collected from 385 residents and analyzed using the Phi coefficient and structural equation modeling. The results of the Phi test indicated that development projects could act as “nuclei for urban transformation of the worn-out fabric of Semnan”. Furthermore, the structural equation modeling analysis revealed that development driver indicators at the macro scale, such as parks (0.71) and landscape design (0.66), have a significant impact on regeneration. This study emphasizes the importance of a comprehensive approach to regeneration and suggests that active stakeholder participation in various stages of regeneration is essential. The findings of this study can serve as guidance for policymakers and urban planners in Iran and other countries.
Evaluating smart city technology efficiency and citizen satisfaction using data envelopment analysis
This study employs Data Envelopment Analysis (DEA) to evaluate the efficiency of the top 20 smart cities in converting Research and Development (R&D) investments into desired outcomes. Using national R&D expenditure (2015–2022) as input and ten criteria from the IMD 2024 Smart City Index report as outputs, the analysis reveals varying levels of efficiency among leading smart cities. Seven cities achieved perfect efficiency scores, while others, including some high-ranking cities, showed unexpected inefficiencies. This study provides valuable insights into resource utilization and identifies specific areas for improvement across structural and technological dimensions. The limitations include the focus on top-performing cities and the use of national R&D data as a proxy for city-specific investments. The findings of this study offer a foundation for policymakers and urban planners to optimize resource allocation and improve smart city initiatives, contributing to the ongoing development of sustainable urban environments in the face of technological advancements and urban challenges.
Apartment prices, the business cycle and time on market: Evidence from Bucharest
The issue of time on market (TOM) correlation with the sale price remains under-explored considering the importance and complexity of the housing market. This paper argues that TOM is influenced by variables other than transaction prices and tests the hypothesis that the business cycle is important in explaining the dynamics of TOM and driving transaction prices in the housing market. In testing this hypothesis, the paper investigates the role of transaction prices and TOM in the housing market in Bucharest, Romania using granular observations of 32,000 price listings over the period 2013–2017, a time-scale that captures the economic recovery phase following the global financial crisis. The analysis shows that spatial correlation is strong for TOM rather than weak and that reinforcing spatial effects evidenced among TOM in transactions of closed units would reflect the strong clustering in prices but are balanced in a type of (contrary sign) distribution effect that diminish the whole spatial impact in TOM in similar size, describing a corrective mechanism leading to a more balanced impact on TOM. Results show that GDP affects transaction prices pro-cyclically (0.062%) and with persistence (0.054%), while only GDP growth (the cycle) influences TOM (0.352%).
Residential satisfaction indicator: Latin American evidence
Latin American construction companies, particularly in Ecuador, operate in a highly competitive environment. In this context, achieving customer satisfaction is a primary objective for the success of real estate projects. This research aims to develop a comprehensive residential satisfaction index that includes sustainability dimensions, based on global satisfaction indices and structural equation modeling. This study will enable residential builders to assess their performance from the customer’s perspective and make strategic decisions aimed at improving quality, which, in turn, will lead to greater customer satisfaction in future projects. The findings of this research will provide valuable input for decision-making in the real estate sector, with a focus on the Latin American context.
Land purchasing behavior of real estate enterprises: An organizational status perspective
Based on resource dependency and signaling theories, this study examines how organizational status affects land purchasing behavior in listed real estate enterprises on China’s Shanghai and Shenzhen A-shares from 2006 to 2023. The findings indicate that the organizational status of real estate enterprises positively impacts their land purchase area, price, and quantity. Further analysis reveals that managerial overconfidence mediates this relationship. Heterogeneity analysis shows that the organizational status of state-owned real estate enterprises positively influences all three types of land purchasing behaviors, while for non-state-owned enterprises, it only significantly affects the area and price of land purchases, not the quantity. This study enriches the theory on land purchasing behavior of real estate enterprises, expands the application scope of organizational status, reveals the mechanisms through which organizational status affects land purchasing behavior and provides valuable insights for guiding rational competition among real estate enterprises, optimizing land resource utilization, and promoting the healthy and sustainable development of the real estate industry.
Supply decision of existing apartment: Case study of apartment transactions in Gangdong district, Seoul, Korea
Despite its importance, homeowners’ supply type decision has been under-researched due to data limitations. We construct a dataset of 4,037 apartment units in Gangdong district, Seoul, Korea over a 46-quarter period (2011Q1–2022Q2). We simultaneously analyse both the asset and space markets. The results show that the estimated probability and estimated volume of sale from supply type choice models are statistically significant in both the sales and rental price models. The key findings of our study are two. We find that the housing sales and rental markets interact and co-move together. We also demonstrate that the institution of the two-year lease contract period influences the transaction probabilities in both the sales and rental markets. For the supply type decision model, the estimated sale-transaction (rental-transaction) price significantly impacts the sale-transaction (rental-transaction) volume. However, no significant relationship was found between the estimated sale price and rental-transaction volume, nor between the estimated rental price and sale-transaction volume. The results of the forecast-performance comparison show that the integrated supply model_T/probability (calibrated) is the best for sales volume, whereas the two-year lagged value is the best for rental-volume estimation. This demonstrates institutional effects in the rental-housing market in Korea.
Does China’s housing supply-demand relationship impact urban innovation capability
Unlike existing literature that explores the impact of house prices on urban innovation, this paper skillfully examines the relationship between the housing market and urban innovation from the perspective of the housing supply-demand (S-D) relationship. Utilizing panel data from 284 prefecture-level cities in China spanning from 2005 to 2020, this study investigates the respective impacts of housing supply, housing demand, and their interplay on urban innovation capacity (UIC). Our findings indicate that housing supply positively influences UIC, with a coefficient of 0.060; specifically, for every 1% increase in housing supply, UIC increases by 0.06%. Similarly, housing demand also significantly affects UIC, with a coefficient of 0.060, suggesting that a 1% increase in housing demand corresponds to a 0.060% rise in UIC. However, we observe a significant negative effect of the housing S-D relationship on UIC, with a coefficient of –0.049, indicating that an increase in the housing S-D ratio detrimentally impacts urban innovation. Furthermore, our analysis reveals that as the housing supply-demand ratio rises, house prices also tend to increase. Additionally, we identify heterogeneity in our results, indicating variations in the housing supply-demand ratio’s impact on the innovation capacity of cities across different regions.