Search Results (2,394)

The city of Jackson, Mississippi, has experienced frequent, devastating floods, with flooding along tributaries of the Pearl River that cross the city being widespread and disproportionately affecting low-income neighborhoods. We reviewed the history of flood management and public access along the Pearl River, conducted community meetings with flood-affected residents, visited tributaries affected by frequent flooding, assessed options to reduce flood risks for Town Creek (a tributary traversing the city center), and reviewed the 2024 Draft Environmental Impact Statement (DEIS) by the US Army Corps, which proposed a flood risk management project. Our community meetings, site visits, and interviews with residents highlighted the frequent flooding of low-income neighborhoods along Pearl River tributaries. Thus, a flood risk reduction program should effectively address tributary flooding. In our review of the DEIS, we applied criteria from the National Environmental Protection Act indicating that environmental impact statements should provide a complete identification and description of environmental impact, and found that the DEIS failed to do this. We found that the alternative selected as the likely ‘National Economic Development’ plan would expand real-estate development opportunities while exacerbating flooding in low-income neighborhoods and increasing inequalities in Jackson. Full article

This study delineates transaction price submarkets of dwellings in Vienna by performing spatiotemporal clustering and analysing the change in purchasing prices in these clusters between 2018 and 2022. The submarkets are created using a novel spatiotemporal clustering method referred to as Multidimensional Spatiotemporal Change–DBSCAN (MDSTC-DBSCAN), which incorporates the temporal change in transaction prices along with spatial proximity to identify spatial areas with similar transaction prices. It represents an advancement over MDST-DBSCAN for this use case, as it considers the change over time as valuable information rather than a constraint that further splits the clustering groups. The results of the case study in Vienna indicate variations in price growth rates among the submarkets (i.e., contiguous regions with similar prices and price growth rates) that confirm the importance of considering the temporal changes in transaction prices. With respect to the Viennese case study, a lower Moran’s I value was observed for 2022 compared to previous years (2018 to 2021), indicating a higher level of homogeneity in transaction prices. This finding was also supported by the cluster analysis, as less expensive clusters demonstrated higher rates of price increase compared to more expensive clusters. Future research can enhance the algorithm’s usability and broaden its potential use cases to other multidimensional spatiotemporal event data. Full article

With the new phase of urbanization in China, enhancing urban spatial quality has become a key task in urban development. As an important indicator of residents’ willingness to live, housing prices provide valuable feedback from their perspective for improving spatial quality. Taking Wuhan as a case study, this paper constructs an indicator system with 12 explanatory variables, including a subjective evaluation of buildings generated using deep learning techniques. Using OLS and GWR models, the study analyzes the factors influencing housing prices and their spatiotemporal dynamics in Wuhan’s core urban areas from 2016 to 2024, encompassing the full cycle of housing price fluctuations from an upward to a downward trend. The findings reveal that, as housing prices return to more rational levels, the impact of location factors diminishes, while the influence of community quality factors—such as property fees, green space ratio, and building quality—significantly increases. Factors such as proximity to hospitals also exhibit a certain degree of spatiotemporal complexity. This trend highlights residents’ growing attention to housing quality and living environments, marking a fundamental shift in the behavior of homebuyers. The results of this study provide crucial insights into the evolution of residential preferences and the spatiotemporal dynamics of the housing market. They offer significant theoretical and practical references for understanding residents’ housing needs from their perspective, thereby promoting the healthy development of the real estate market and improving urban spatial quality. Full article

Accurately estimating house values is a critical challenge for real-estate stakeholders, including homeowners, buyers, sellers, agents, and policymakers. This study introduces a novel approach to this problem using Kolmogorov–Arnold networks (KANs), a type of neural network based on the Kolmogorov–Arnold theorem. The proposed KAN model was tested on two datasets and demonstrated superior performance compared to existing state-of-the-art methods for predicting house prices. By delivering more precise price forecasts, the model supports improved decision-making for real-estate stakeholders. Additionally, the results highlight the broader potential of KANs for addressing complex prediction tasks in data science. This study aims to provide an innovative and effective solution for accurate house price estimation, offering significant benefits for the real-estate industry and beyond. Full article

The construction industry is one of the most environmentally sensitive sectors, significantly impacting the adoption of sustainable development practices. Environmental, social, and governance (ESG) pillars are essential for assessing corporate sustainability performance, revealing risks, and guiding improvement. Despite the widespread use of indicators, a notable gap exists in ESG frameworks oriented to assess company performance within the sector, with limited research on achieving standard tools. This study proposes a practical standardized framework of indicators for the European construction industry and provides a set of KPIs for the Italian context, serving as a tool to measure and report ESG performance. The methodology consists of the selection of indicators from established protocols for assessing and reporting ESG criteria, such as the Global Reporting Initiative (GRI) and Global Real Estate Sustainability Benchmark (GRESB). The selection process resulted in the identification of 118 indicators, categorized into 44 environmental, 54 social, and 20 governance indicators, enabling construction companies to comprehensively measure and report their ESG performance in accordance with disclosure regulations. The result of this work serves policymakers seeking to develop standardized frameworks specific to the construction industry, for defining expert panels to evaluate mandatory disclosures from companies, and as guidance for companies who need guidelines to assess their sustainability performance and ensure compliance and alignment with existing frameworks. Full article

Passenger transportation in Japan’s main metropolitan areas is operated by the JR companies, which were privatized and divested from Japan National Railways (JNR) in 1987 and by 16 major private railway companies with large-scale operations. Although their core business is transportation, the major private railway companies have adopted a strategy of diversification, and they engage, e.g., in real estate and distribution businesses. This study examines the relationship between the degree of business diversification and the production efficiency of Japan’s major private railway companies from the perspective of a future business model. To this aim, this study applies data envelopment analysis combined with the Malmquist index to the data of the railway companies from 1987 to 2019. We focus on four phases of activities: cost, operational resource, operational output, and total revenue. This study is the first to analyze the diversified management of Japanese railroad companies by evaluating their production efficiency and its changes over time. The results of the analysis reveal that, while all companies’ earnings have generally increased, the less diversified they are, the more they struggle to optimize personnel and other overhead expenses and resources, lowering production efficiency. Full article

In the realm of real estate, each property owns a unique set of characteristics that distinguish it from others. While each property has its own distinctive features, the appraisal process prioritises only those qualities that meaningfully affect the value in the given market context. However, in the dynamically evolving market situation, expectations of real estate buyers can also transform. This study aims to explore how the surrounding environment and micro-location aspects affect the property value, which can deliver valuable outcomes for real estate market participants and researchers. For that purpose, the authors selected nine factors, called non-market attributes, that may affect the estimated value: air quality, noise emissions, green areas, rivers and water reservoirs, kindergartens and primary schools, universities, medical facilities, shopping centres and religious buildings. Moreover, apart from non-market attributes, the authors selected six market attributes usually used for the determination of residential real estate values according to the Polish regulations in this field. The detailed analysis of factors influencing the property value has been conducted based on the residential apartments in the district Zwięczyca in Rzeszów. Specifically, with the use of Pearson’s total correlation coefficients, authors explored market and non-market attributes and examined their relationships with unit transaction prices, attempting to answer the research question on whether non-market attributes can differentiate market values of residential apartments, when local real estate markets are considered. The results demonstrate that all selected market factors have a visible effect on analysed real estate prices and might be adopted for appraisal. Among nine non-market factors, only three of them have a pronounced effect on prices and might be used for the valuation of residential properties on the local market. The combined database of market and non-market factors reveals eight attributes (five market and three non-market) affecting prices of residential apartments. Full article

Urban street comfort is a crucial measure of street environmental quality. However, traditional evaluations primarily focus on physical elements, often neglecting pedestrian perceptions. In this study, considering five core evaluation dimensions—safety, mobility, aesthetics, perceptibility, and convenience—an innovative quantitative evaluation model is proposed to assess pedestrian-perceived comfort on urban streets by integrating physical environmental factors and subjective experiences. This analysis comprises two steps: evaluation indicator extraction and weight application. Indicators are extracted from multi-source data (street-view images, real-time traffic data, points of interest, and pedestrian surveys) using a deep learning method. A comprehensive weighting method combining entropy weight and the analytic hierarchy process is used to determine the relative importance of each factor. This study focuses on Nanjing as a case study, and the results reveal significant variations across the five dimensions and their 11 secondary indicators. Street environment safety (0.143) is critical for street safety, while the degree of street traffic congestion (0.121) dominates street mobility. Street aesthetics is primarily influenced by building enclosure (0.105), and street convenience is strongly affected by the number of surrounding bus stops (0.260). Spatial analysis indicates higher comfort levels in urban centers due to well-developed infrastructure, whereas peripheral areas face challenges from inadequate facilities. Notably, areas around parks demonstrate elevated pedestrian-perceived comfort levels, highlighting the importance of green spaces. Overall, the proposed evaluation system provides new insights from the perspective of pedestrian experience and offers valuable guidance for urban planning and policy. Full article

Crude oil prices have a key meaning for the economies of most countries. Their levels shape the general production costs in many sectors. Oil prices are also a base for financial derivatives like CFD contracts, which are popular nowadays. Due to these reasons, the possibility of an effective prediction of the direction of future changes in the price of crude oil is especially significant. Most existing works focus on the analysis of daily closing prices. This kind of approach results, on the one hand, in losing important information about the dynamics of changes during the day. On the other hand, it does not allow for the modelling of short-term price changes that are especially important in cases of financial derivatives having crude oil as their base instrument. The goal of the following article is the analysis of possible applications of a binary–temporal representation in the modelling and construction of effective decision support systems on the crude oil market. The analysis encompasses all researched state models, e.g., those applying mean and trend analysis. Also, the selection of parameters was optimized for Brent crude oil rates. The presented research confirms the high effectiveness of our state modelling system in predicting oil prices on a level that allows for the construction of financially effective investment decision support systems. The obtained results were verified based on proper backtests from different quotation periods. The presented results can be used both in scientific analyses and in the construction of investment support tools for the crude oil market. Full article

The determinants of hospital project or healthcare project (HP) success are divergent and difficult to generalize because of the heterogeneous perceptions of various stakeholders. There is also a paucity of HP life cycle success evaluations from planning to post-construction phases. Meanwhile, the successful delivery and continual functionality of HPs are pivotal for sustainable development, as evident in the United Nations’ Sustainable Development Goal 3 about ensuring healthy lives and promoting wellbeing for all people. To contribute to sustainable development, a novel evaluation framework is essential to define robust metrics of selected key performance indicators (KPIs) for monitoring and controlling HPs at the life cycle phases thereof. Fuzzy set theory, namely the bisector error method (BEM), was applied to questionnaire survey outputs of an expert panel to establish performance metrics of HPs within five grades, namely, poor, average, good, very good and excellent. The novel evaluation framework comprising indexes, indicators and grades are demonstrated on hypothetical HPs to provide objective, reliable and practical outcomes for performance comparison, benchmarking and improvement purposes. The findings show that a high standard is required for excellent planning, execution, and performance in HPs. The life cycle success evaluation framework is foundational in policymaking. Thus, policymakers can track the success of HPs by linking the performance metrics to goals and policy priorities in benchmarking and strategic planning for sustainable development in HPs. Full article

Understanding the determinants of housing price movements is an ongoing subject of debate. Estimating these determinants becomes a valuable tool for predicting price trends and mitigating the risks of market volatility. This article presents a systematic review analyzing studies that compare various machine learning (ML) tools with hedonic regression, aiming to assess whether real estate price predictions based on mathematical techniques and artificial intelligence enhance the accuracy of hedonic price models used for valuing residential properties. ML models (neural networks, decision trees, random forests, among others) provide high predictive capacity and greater explanatory power due to the better fit of their statistical measures. However, hedonic regression models, while less precise, are more robust, as they can identify the housing attributes that most influence price levels. These attributes include the property’s location, its internal features, and the distance from the property to city centers. Full article

How to effectively improve the filtration characteristics of polyester fiber filtration materials in public buildings is particularly important for ensuring the health of indoor environments. This study uses the impregnation method to prepare composite materials by using the characteristics of graphene and its derivatives and, on this basis, enhances the filtration characteristics of the composite materials by applying an external voltage. The structure and particle filtration performance of the composite materials are tested and analyzed. The results indicate that the filtration efficiency of the prepared composite filter material is significantly improved compared to polyester fiber materials. When the applied voltage is 4 V, the new composite filter material has the highest weight filtration efficiency for particulate matter, with filtration efficiencies of 71.3%, 45.3%, and 35.7% for PM₁₀, PM_2.5, and PM_1.0, respectively. The filtration efficiency is highest when the power on time is 80 s. At this time, the filtration efficiency of the filter material for PM₁₀, PM_2.5, and PM_1.0 is 70.6%, 43.8%, and 35.3%, respectively. The new composite filter material has a significant lifting effect on particles with a diameter of 0–2.5 μm. It provides reference value for research and the application of new filtering materials. Full article

Based on complex system theory and multi-dimensional coupling analysis paradigm, this study constructs a dynamic model covering land use, real estate development, and carbon emissions, and deeply explores the internal mechanism and evolution law of land development system in the process of moving toward a low-carbon path. Firstly, through nonlinear dynamics and bifurcation analysis, this study identifies three typical transformation paths that the system may experience: gradual, transitional, and hybrid, emphasizing the nonlinear, phased, and highly context-dependent characteristics of the transformation process. On this basis, early warning indicators and robustness analysis methods are introduced, which provide operational tools for identifying critical turning points in the system and improving the effectiveness and resilience of regulatory strategies. Furthermore, this paper proposes a multi-level regulation mechanism design framework, which combines the immediate feedback with the historical cumulative effect to achieve the refined guidance of land development patterns and carbon emission paths. The results provide a scientific basis and practical enlightenment for land use optimization, green infrastructure construction, and industrial structure adjustment under the background of realizing the “3060” dual carbon goal and the reform of territorial spatial planning in China. In the future, it is necessary to strengthen the empirical calibration of parameters, data-driven optimization, and collaborative research of multiple policy tools to further improve the applicability and decision-making reference value of the model. Full article

The real estate market plays a pivotal role in most nations’ economy, showcasing continuous growth. Particularly noteworthy is the rapid expansion of the digital real estate sector, marked by innovations like 3D visualization and streamlined online contractual processes, a momentum further accelerated by the aftermath of the Coronavirus Disease 2019 (COVID-19) pandemic. Amidst this transformative landscape, artificial intelligence emerges as a vital force, addressing consumer needs by harnessing data analytics for predicting and monitoring rental prices. While studies have demonstrated the efficacy of machine learning (ML) algorithms such as decision trees and neural networks in predicting house prices, there is a lack of research specifically focused on rental property prices, a significant sector in Brazil due to the prohibitive costs associated with property acquisition. This study fills this crucial gap by delving into the intricacies of rental pricing, using data from the city of São Carlos-SP, Brazil. The research aims to analyze, model, and predict rental prices, employing an approach that incorporates diverse ML models. Through this analysis, our work showcases the potential of ML algorithms in accurately predicting rental house prices. Moreover, it envisions the practical application of this research with the development of a user-friendly website. This platform could revolutionize the renting experience, empowering both tenants and real estate agencies with the ability to estimate rental values based on specific property attributes and have access to its statistics. Full article

With the current downturn in the real estate and construction industries, the construction job market is saturated, and architects are facing an urgent employment crisis. Architectural education should understand the skill requirements of individuals in the labor market and make adjustments accordingly. This study examines the evaluation of and demand for skills from the perspective of employers. A research questionnaire was constructed based on the Kano theoretical model and distributed to 810 practitioners, and the results were analyzed. The results of the data analysis of demand attributes, importance, and group differences showed that the construction industry pays the most attention to strengthening professional soft abilities, while improving personal comprehensive abilities and maintaining professional hard abilities can support architects in maintaining strong competitiveness in the job market. Furthermore, different groups and regions have different needs for architectural skills. The cultivation of skills in colleges and universities must be targeted. This study provides an adjusted direction for architectural education and training and also provides guidance for architectural practitioners in improving their skills and expanding their career development with the trend of industry saturation. Full article