Search Results (648)

With the rapid development of urbanization and tourism in China, increasing attention has been paid to the protection and utilization of historical and cultural heritage, while tourists’ demands for travel experiences have gradually shifted towards in-depth cultural perception. This paper selects Beijing Fayuan Temple Historic and Cultural District as the research case, and adopts methods such as the LDA (Latent Dirichlet Allocation) topic model, collection and analysis of online text data, and field research to explore the current situation of pedestrian space in Fayuan Temple District and its optimization strategies from the perspective of tourists’ perception. The study found that the dimensions of tourists’ perception of the pedestrian space in Fayuan Temple District mainly include six aspects: historical buildings and relics, tour modes and transportation, natural landscapes and environment, historical figures and culture, residents’ life and activities, and tourists’ experiences and visits. By integrating online text data, questionnaire surveys, and on-site behavioral observations, the study constructed a “physical environment-cultural experience-behavioral network” three-dimensional IPA (Importance–Possession Analysis) evaluation model, and analyzed and evaluated the high-frequency perception elements in tourists’ spontaneous evaluations. Based on the current situation evaluation of the pedestrian space in Fayuan Temple District, this paper puts forward optimization strategies for the perception of pedestrian space from the aspects of block space, transportation usage, landscape ecology, digital technology, and cultural symbol translation. It aims to promote the high-quality development of historical blocks by improving and optimizing the pedestrian space, and achieve the dual goals of cultural inheritance and utilization of tourism resources. Full article

Using panel data on 99 Italian provinces in the period between 2005 and 2020, the research investigates the effects of fundamental economic factors on the home sales at the provincial level, in order to build a forecasting model using a non-linear artificial intelligence approach (MLP-Multiple Linear Perceptron neural network). There are multiple objectives to this: (a) to test the hypothesis that national, regional and local fundamentals such as interest rates, income, inflation rate, unemployment and demography affect the activity’s degree of the housing market; (b) to verify the effectiveness of a neural network in describing the dynamics of the real estate market; (c) to build a simulation model capable of predicting the effect of changes in fundamentals, also due to economic policy measures, on the market. Empirical results show that neural networks offer better capabilities than linear models in representing the complex relationships between the economic situation and the real estate market. The study provides useful information for regulators to improve the effectiveness of monetary policy to stabilize real estate markets as well as for stakeholders to draw up scenarios of market development. Full article

Real estate plays a pivotal role in fostering national economic growth and ensuring social stability. In China, housing constitutes the largest fixed asset for the majority of households. Given the extensive network of upstream and downstream industries associated with real estate, the government places significant emphasis on its regulation and development, employing a variety of policy instruments to maintain market stability. This study adopts a quantitative approach to conduct a text analysis of China’s real estate policies through the lens of knowledge mapping and LDA topic modeling, while also comparing policy content across 21 different cities. The findings indicate that real estate policy in China transcends mere market regulation. It also encompasses governance within the construction industry as well as provisions for housing security. Furthermore, due to the diverse roles that real estate plays in economic development and urban construction, there is notable regional heterogeneity in policy priorities. By text analysis of real estate policies, this study provides a systematic overview of policy content, thereby laying a foundation for more nuanced and regionally differentiated research within the realm of real estate policy. Full article

The complexities of megaprojects, particularly major transportation infrastructure projects (MTIs), require technological innovation that advances economic, social, and ecological objectives. Traditional engineering innovation emphasizes economic gains while neglecting sustainability. Therefore, implementing green innovation (GI) in MTIs is essential. This research examines key factors and correlations influencing MTI-GI to strengthen theoretical understanding and guide effective implementation. First, literature and interviews are used to identify MTI-GI influencing factors through the technology–organization–environment (TOE) framework. Second, an intuitive fuzzy number approach reduces subjectivity in expert scoring and, combined with the DEMATEL method, constructs a fuzzy DEMATEL model to quantify factor importance and identify critical drivers. Critical factors are then analyzed to formulate GI promotion strategies. Results reveal that MTI-GI influencing factors span technology, organization, and environment dimensions. Prioritizing green technological innovation and feedback mechanisms, optimizing organizational structures, and aligning with regional environmental characteristics are crucial for successful MTI-GI implementation. These findings support GI expansion in MTIs and offer targeted strategies for managing complex systems. Full article

Surrealism proposed expanding reality with dreamlike expressions. Chilean architect Roberto Matta embraced this movement in the 1930s when he was working with Le Corbusier and created imaginative apartment illustrations. Based on listings of new real estate projects in Chile, this research developed virtual reality (VR) models of apartments that integrate features from Matta’s drawings, and they were examined concerning housing demands. This study’s methodology involved the interpretation of Roberto Matta’s illustrations in three-dimensional environments, the characterization of the real estate supply, and a summary of current apartment designs and their spatial distribution. Subsequently, two real estate-inspired VR apartment models were created that integrated features of Matta’s drawings. Later, a qualitative pilot study was carried out, applying VR-assisted interviews with five participants. They were asked about the association of the models with domestic spaces, functionality, and connection to social interest. Results show the positive appreciation of spaciousness and the novelty of architectural elements, but also a resistance to complex shapes. Participants associated the VR models with wealthy young artists and recreational spaces. The models developed have novel features and layouts that can suggest residential possibilities. Full article

This study aims to investigate residents’ marginal willingness to pay (WTP) for accessible urban green spaces (UGSs) in Shanghai from 2012 to 2021, using a comprehensive UGS accessibility (UGSA) indicator based on an improved nSFCA method. The UGSA indicator is incorporated into a hedonic pricing model, and multiple global regressions with multilevel data structures are employed to identify a suitable and accurate estimation strategy to determine the impact of UGSA on housing prices. The results show that WTP for UGSA varies significantly across categories and years, as well as between homebuyers and renters, with homebuyers having a much higher WTP compared to renters. Furthermore, neighborhood UGSA is generally more preferred than utmost UGSA. By differentiating UGSA into “neighborhood” and “utmost” levels and conducting a decade-long longitudinal analysis of both homebuyers and renters, this study contributes to two key academic debates: the spatial scaling of amenity valuation and the role of property rights in the capitalization of public goods. Employing a robust spatial econometric framework, our research provides novel insights into these complex dynamics within a hyper-dense urban context. The research contributes to the understanding of the economic value of UGSA by providing valuable insights for urban planning, policy-making, and real estate development, highlighting the importance of considering the spatial, temporal, and heterogeneous aspects of UGSA when estimating its economic value. Full article

As vision and mission statements embody the directions set forth by an organization, their connection to the Sustainable Development Goals (SDGs) must be made explicit to guide overall decision-making in taking strides toward the sustainability agenda. The semantic alignment of these strategic statements with the SDGs is investigated in a previous study, although several limitations need further exploration. Thus, this study aims to advance two contributions: (1) utilizing the capabilities of LLMs (Large Language Models) in text semantic analysis and (2) integrating fuzziness into the problem domain by using a novel intuitionistic fuzzy set extension of the PROBID (Preference Ranking On the Basis of Ideal-average Distance) method. First, a systematic approach evaluates the semantic alignment of organizational strategic statements with the SDGs by leveraging the use of LLMs in semantic similarity and relatedness tasks. Second, viewing it as a multi-criteria decision-making (MCDM) problem and recognizing the limitations of LLMs, the evaluations are represented as intuitionistic fuzzy sets (IFSs), which prompted the development of an IF extension of the PROBID method. The proposed IF-PROBID method was then deployed to evaluate the 47 top Philippine corporations. Utilizing ChatGPT 3.5, 7990 prompts with repetitions generated the membership, non-membership, and hesitance scores for each evaluation. Also, we developed a cohort-dependent SDG–vision–mission matrix that categorizes corporations into four distinct classifications. Findings suggest that “highly-aligned” corporations belong to the private and technology sectors, with some in the industrial and real estate sectors. Meanwhile, “weakly-aligned” corporations come from the manufacturing and private sectors. In addition, case-specific insights are presented in this work. The comparative analysis yields a high agreement between the results and those generated by other IF-MCDM extensions. This paper is the first to demonstrate two methodological advances: (1) the integration of LLMs in MCDM problems and (2) the development of the IF-PROBID method that handles the resulting inherently imprecise evaluations. Full article

To address the issue of spatial mismatch of real estate resources at its source, we incorporate the multidimensional urbanization speeds into the real estate market stock-flow model, simulate the regional real estate resource allocation process from a dynamic perspective, and explore the impact of urbanization on housing prices, as well as the characteristics that a coordinated multidimensional urbanization should possess. Utilizing data on population flow, economic development, and the relative increment of newly built housing units that meet delivery standards from 2008 to 2022 in 35 large- and medium-sized cities in China. We employ the dynamic panel system GMM approach to estimate the direct effect of single-dimensional urbanization on housing prices, and utilize the threshold effect model to examine the comprehensive effect of multidimensional urbanization on housing prices. The findings reveal that population, economic, and spatial urbanization influence housing prices by altering the flow of real estate supply and demand, with their effects being significantly shaped by the scarcity of stock real estate resources. The dynamic coordination of multidimensional urbanization ρ has a significant threshold effect on housing price changes. When Vsu and Vpu reach the optimal match, the real estate market achieves dynamic equilibrium, and housing prices remain relatively stable. This not only underscores the significance of multidimensional urbanization as a driver of urban housing price variations but also provides valuable insights for cities on how to adjust the quantity of new residential construction (or land supply) during the dynamic urbanization process, thereby enhancing the spatial allocation rationality of real estate resources from the source. Full article

This study examines the integration of Environmental, Social, and Governance (ESG) principles within Romania’s residential real estate sector, concentrating on Constanța, a rapidly evolving urban centre in a transitional economy. Drawing on qualitative data from semi-structured interviews with local real estate professionals and secondary analysis of policy and market documents, the research uncovers inconsistencies in ESG implementation. Environmental compliance is advancing, largely driven by EU regulations such as the European Grean Deal, the Corporate Sustainability Reporting Directive and the Energy Performance of Buildings Directive. Voluntary certification schemes like BREEAM and LEED are emerging as benchmarks for environmental performance; however, their uptake remains limited and insufficiently tailored to local conditions. Meanwhile, the social and governance dimensions lag behind, characterised by inconsistent application and weak institutional backing. Key barriers to effective ESG integration in Romania’s residential real estate sector include weak regulatory enforcement, fragmented policies, limited green finance, low awareness, and a lack of standardised social value metrics. The study concludes that without moving beyond mere regulatory compliance to a framework embedding social inclusivity and adaptive governance, ESG efforts risk perpetuating existing inequalities. It calls for a reconceptualisation of ESG frameworks, developed for mature markets, to better suit transitional urban contexts and support long-term resilience in residential real estate. Full article

The real estate market has a major impact on the economy and everyday life. Accurate real estate valuation is essential for buyers, sellers, investors, and government institutions. Traditionally, valuation has been conducted using various estimation models. However, recent advancements in information technology, particularly in artificial intelligence and machine learning, have enabled more precise predictions of real estate prices. Machine learning allows computers to recognize patterns in data and create models that can predict prices based on the characteristics of the property, such as location, square footage, number of rooms, age of the building, and similar features. The aim of this paper is to investigate how the application of machine learning can be used to predict real estate prices. A machine learning model was developed using four algorithms: Linear Regression, Random Forest Regression, XGBoost, and K-Nearest Neighbors. The dataset used in this study was collected from major online real estate listing portals in Bosnia and Herzegovina. The performance of each model was evaluated using the R² score, Root Mean Squared Error (RMSE), scatter plots, and error distributions. Based on this evaluation, the most accurate model was selected. Additionally, a simple web interface was created to allow for non-experts to easily obtain property price estimates. Full article

Air pollution has a significant impact on the housing market, both in terms of property prices and buyer preferences, as well as urban development. Below, we present the main aspects of this impact. These may include a decline in property values in polluted areas, a change in buyer preferences (more buyers are taking environmental factors into account when choosing a home, including air quality—both outdoor and indoor—which translates into increased demand in ‘green’ neighborhoods), the development of energy-efficient and environmentally friendly buildings, the impact on spatial planning and urban policy, health effects, and the rental market. The study showed that air pollution has a significant negative impact on housing prices in Warsaw, particularly in relation to two pollutants: nitrogen dioxide (NO₂) and particulate matter (PM_2.5). As their concentrations decreased, housing prices increased, with the highest price sensitivity observed for smaller flats on the secondary market. The analysis used GRM and OLS statistical models, which confirmed the significance of the relationship between the concentrations of these pollutants and housing prices (per m²). NO₂ had a significant impact on prices in the primary market and on the largest flats in the secondary market, while PM_2.5 affected prices of smaller flats in the secondary market. No significant impact of other pollutants, meteorological factors, or their interaction on housing prices was detected. The study also showed that the primary and secondary markets differ significantly, requiring separate analyses. Attempts to combine them do not allow for the precise identification of key price-determining factors. Full article

This study investigates the contributions of five key sectors—insurance, materials, utilities, real estate, and transport—to the financial markets of six Gulf Cooperation Council (GCC) countries from 2004 to 2023. Grounded in the Sectoral Linkage Theory and Endogenous Growth Theory, the study employs a Panel Autoregressive Distributed Lag (Panel ARDL) model to examine both short-term and long-term sectoral impacts on financial market resilience. The findings reveal that the insurance and transport sectors offer short-term market stimulation, but lack persistent effects. Conversely, the materials, utilities, and real estate sectors exhibit strong, long-run contributions to financial stability and economic diversification. These results highlight the asymmetric impact of sectoral dynamics on market performance in resource-rich contexts. This research contributes to the literature by providing empirical evidence on sectoral interdependence in oil-dependent economies and highlights the importance of structural diversification for sustainable financial resilience. The study provides actionable insights for policymakers and investors seeking to enhance market resilience and reduce reliance on hydrocarbon revenues through targeted sectoral development. Full article

Climate change poses significant challenges to the economic and social sustainability of urban dwellers, particularly in the real estate market, where rising temperatures are affecting property values. While most research focuses on how climate change impacts buyers and sellers, this study shifts attention to renters, who may be more vulnerable to climate-induced price increases. By analyzing rental price and climate data, this study uses ordinary least squares (OLS) and fixed-effects regressions to assess the impact of temperature fluctuations on rental rates across 50 major U.S. metropolitan areas. The findings reveal a positive and significant relationship between rising temperatures and rental rates, particularly in the Northeastern and Southern U.S. These results suggest that targeted policy interventions may help ease financial pressures on vulnerable renters and support more sustainable urban development over time. The analysis also highlights the potential role of energy efficiency measures in rental housing to lower energy costs and alleviate rent burdens. Additionally, the findings indicate that local policymakers may consider rent stabilization strategies and investments in urban green infrastructure to protect low-income renters, reduce localized heat exposure, and promote long-term urban resilience. Full article

This study analyzes the links between urban sustainability goals and the location of office buildings. We propose a concept of a sustainable location of office buildings, one that meets the needs of real estate investors and users and is consistent with the goals of sustainable urban development. The main goal of this study is to develop a method for mapping location potential, which can be used as a tool in the decision-making process of selecting the location of new office buildings. A location with high potential is consistent with the sustainability goals that meet the needs of investors and users with minimal environmental burden. The literature studies on sustainable urban development as well as the analysis of the results of the survey of office space user preferences allow for the determination of the essential characteristics of sustainable office locations: public transportation accessibility, mixed land use, walkability and clean transportation accessibility, parking space, and land reuse. Using these metrics in GIS, a spatial analysis was conducted to map different location potentials in Krakow and to answer the question of whether and to what extent existing office buildings meet these criteria. Full article

Despite the growing relevance of subnational development strategies in emerging economies, the literature lacks empirical applications of classical trade models to territorial productive specialization. This study addresses this gap by adapting the Heckscher–Ohlin–Samuelson (HOS) model to identify optimal specialization patterns in intermediate municipalities with asymmetric factor endowments. Using data from 2017 to 2021 for Tunja and Chiquinquirá (Colombia), we estimate capital-to-labor ratios and sectoral factor intensities to detect specialization aligned with local comparative advantages. The results show that Tunja exhibits capital-abundant conditions favoring specialization in sectors such as real estate, construction, and financial services, while Chiquinquirá demonstrates labor-intensive dynamics suitable for tourism and service industries. Methodologically, the study extends the HOS model to subnational scales, offering a robust analytical tool for regional policy formulation. This article contributes to the academic debate by bridging international trade theory and regional development, and it provides empirical evidence to support place-based industrial policies. Our findings emphasize the importance of aligning productive strategies with structural endowments to foster inclusive and sustainable development in emerging territories. Full article