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.

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.

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.

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.

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.

This paper introduces insights into the Turkish real estate market, which can be generalized globally. It primarily aims to find the best forecasting algorithms for the housing price index and compare their prediction performance over three, six, nine, and twelve months ahead by using recurrent neural networks (RNN) with a comparison of out-of-sample predicting power of econometrical models. For these purposes, we employ three RNN architectures in twenty-four settings, revealing that certain RNN architectures are the best predictors in forecasting the Turkish real housing price index. The RNN architectures outperform traditional econometric models; however, the more months forecasted, the lower the prediction power. The lagged values of the price-to-rent ratio, real rents, and the lagged USDTRY values contribute more than the other predictors in forecasting the real housing price index. The outcomes suggest that stocks, real estate investment trusts, and gold are neither complementary nor competing financial instruments since housing is an illiquid asset.

The agent-led construction system is a construction mode that emphasizes the introduction of professional management teams instead of government departments to develop public projects in China, including social security housing. In reality, the problem in practice is that the government owners’ management system of the agent market is not sound enough, and it cannot effectively motivate the agents. Existing research has not paid enough attention to this agent construction market. To reveal the effective incentives of agent developers, using the stimulus–organism–response theory, this study constructed a structural equation model and proposed a research hypothesis about the effect of project governance elements on project performance. The study found that governance elements including internal contractual governance, external contractual governance, and relational governance, had a positive incentivizing effect on the project performance. The psychology and behavior of the construction agent played a partial mediating role. The results provide a policy implication for city government to improve the incentive system for agent construction of security housing projects.

Shopping center plays an important role in distribution system and marketing. These canters provide an appropriate atmosphere for customers; so that, the customers achieve the best service within a short time. However, there is an intense competition among shopping centers to attract more customers for increasing the profit. Therefore, a powerful model assists authorities in identifying the critical competitive aspects and directing their efforts toward performance improvement. However, a number of strategies have been developed to identify the most relevant components. The Delphi technique under intuitionistic fuzzy environment, called intuitionistic fuzzy Delphi method (IFDM) study, is a group-based technique that can simply formulate the uncertainty imposed by decision making circle. On the other hand, multi criteria decision making (MCDM) method such as analytical network process (ANP) is a mathematical tool for taking into account mutual relationships in order to rank a number of criteria. Nonetheless, the ANP is unable to account for the uncertainty involved in the decision-making process. Similarly, the intuitionistic fuzzy set (IFS) can express ambiguity and vagueness by utilizing the given scale. Because the IFS is robust in dealing with complexity and ambiguity, the IFS-GANP (an integrated model of the IFS and ANP methods under group decision) can result in a more specific description of the situation. As a result, the IFS-GANP approach outperforms both conventional ANP and fuzzy ANP. To demonstrate the model’s feasibility, a case study rating the essential aspects impacting the attractiveness of retail centers is shown. The result demonstrates factor C31 (Location) with value of 0.202 plays the greatest role in attracting customers.

Urban worn-out textures, due to their ancient and historically valuable physical structures, exhibit low adaptability to urban developments and advancements. Tehran, as a city with a rich historical background, also boasts considerable worn-out textures. Due to economic, geographical, and political conditions, a large population resides in these textures. In this regard, the present research aims to investigate the livability status of residents, identify key indicators, and design scenarios in the worn-out textures of Tehran city. This paper proposes a new approach to MicMac that promotes driving forces to scenarios. The results indicate that the livability of residents in worn-out textures of Tehran city, particularly in central regions, is in undesirable conditions. Furthermore, the analysis of the MicMac matrix showed that housing prices, residents’ income, investment, and an increase in economic activities, as well as the role of urban management, are driving forces that have a vital impact on the livability of residents in worn-out textures. These driving forces have depicted three scenarios for the livability of worn-out textures in Tehran city until the year 2032.