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Search Results (219)

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Keywords = real estate decision

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14 pages, 3044 KiB  
Article
Shared Office Tenants, Developers, and Urban Sustainability Goals—A Method for Assessing the Sustainable Location of Office Buildings Using GIS
by Agnieszka Telega and Ivan Telega
Sustainability 2025, 17(16), 7307; https://doi.org/10.3390/su17167307 - 13 Aug 2025
Viewed by 264
Abstract
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 [...] Read more.
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
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19 pages, 1155 KiB  
Article
Role of Egoistic and Altruistic Values on Green Real Estate Purchase Intention Among Young Consumers: A Pro-Environmental, Self-Identity-Mediated Model
by Princy Roslin, Benny Godwin J. Davidson, Jossy P. George and Peter V. Muttungal
Real Estate 2025, 2(3), 13; https://doi.org/10.3390/realestate2030013 - 5 Aug 2025
Viewed by 287
Abstract
This study explores the role of egoistic and altruistic values on green real estate purchase intention among young consumers in Canada aged between 20 and 40 years. In addition, this study examines the mediating effects of pro-environmental self-identity between social consumption motivation and [...] Read more.
This study explores the role of egoistic and altruistic values on green real estate purchase intention among young consumers in Canada aged between 20 and 40 years. In addition, this study examines the mediating effects of pro-environmental self-identity between social consumption motivation and green real estate purchase intention. A quantitative cross-sectional research design with an explanatory nature is employed. A total of 432 participating consumers in Canada, comprising 44% men and 48% women, with a graduate educational background accounting for 46.7%, and the ages between 24 and 35 contributing 75.2%, were part of the study, and the data collection used a survey method with a purposive sampling, followed by a respondent-driven method. Descriptive and inferential statistics were performed on the scales used for the study variables. A structural equational model and path analysis were conducted to derive the results, and the relationships were positive and significant. The study results infer the factors contributing to green real estate purchase intention, including altruistic value, egoistic value, social consumption motivation, and pro-environmental self-identity, with pro-environmental self-identity mediating the relationship. This study emphasizes the relevance of consumer values in real estate purchasing decisions, urging developers and marketers to prioritize ethical ideas, sustainable practices, and building a feeling of belonging and social connectedness. Offering eco-friendly amenities and green construction methods might attract clients, but creating a secure area for social interaction is critical. To the best of the authors’ knowledge, this research is the first to explore the role of egoistic and altruistic values on purchase intention, mainly in the housing and real estate sector, with the target consumers being young consumers in Canada. Full article
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27 pages, 471 KiB  
Article
Multi-Granulation Covering Rough Intuitionistic Fuzzy Sets Based on Maximal Description
by Xiao-Meng Si and Zhan-Ao Xue
Symmetry 2025, 17(8), 1217; https://doi.org/10.3390/sym17081217 - 1 Aug 2025
Viewed by 151
Abstract
Rough sets and fuzzy sets are two complementary approaches for modeling uncertainty and imprecision. Their integration enables a more comprehensive representation of complex, uncertain systems. However, existing rough fuzzy sets models lack the expressive power to fully capture the interactions among structural uncertainty, [...] Read more.
Rough sets and fuzzy sets are two complementary approaches for modeling uncertainty and imprecision. Their integration enables a more comprehensive representation of complex, uncertain systems. However, existing rough fuzzy sets models lack the expressive power to fully capture the interactions among structural uncertainty, cognitive hesitation, and multi-level granular information. To address these limitations, we achieve the following: (1) We propose intuitionistic fuzzy covering rough membership and non-membership degrees based on maximal description and construct a new single-granulation model that more effectively captures both the structural relationships among elements and the semantics of fuzzy information. (2) We further extend the model to a multi-granulation framework by defining optimistic and pessimistic approximation operators and analyzing their properties. Additionally, we propose a neutral multi-granulation covering rough intuitionistic fuzzy sets based on aggregated membership and non-membership degrees. Compared with single-granulation models, the multi-granulation models integrate multiple levels of information, allowing for more fine-grained and robust representations of uncertainty. Finally, a case study on real estate investment was conducted to validate the effectiveness of the proposed models. The results show that our models can more precisely represent uncertainty and granularity in complex data, providing a flexible tool for knowledge representation in decision-making scenarios. Full article
(This article belongs to the Section Mathematics)
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26 pages, 2624 KiB  
Article
A Transparent House Price Prediction Framework Using Ensemble Learning, Genetic Algorithm-Based Tuning, and ANOVA-Based Feature Analysis
by Mohammed Ibrahim Hussain, Arslan Munir, Mohammad Mamun, Safiul Haque Chowdhury, Nazim Uddin and Muhammad Minoar Hossain
FinTech 2025, 4(3), 33; https://doi.org/10.3390/fintech4030033 - 18 Jul 2025
Viewed by 488
Abstract
House price prediction is crucial in real estate for informed decision-making. This paper presents an automated prediction system that combines genetic algorithms (GA) for feature optimization and Analysis of Variance (ANOVA) for statistical analysis. We apply and compare five ensemble machine learning (ML) [...] Read more.
House price prediction is crucial in real estate for informed decision-making. This paper presents an automated prediction system that combines genetic algorithms (GA) for feature optimization and Analysis of Variance (ANOVA) for statistical analysis. We apply and compare five ensemble machine learning (ML) models, namely Extreme Gradient Boosting Regression (XGBR), random forest regression (RFR), Categorical Boosting Regression (CBR), Adaptive Boosting Regression (ADBR), and Gradient Boosted Decision Trees Regression (GBDTR), on a comprehensive dataset. We used a dataset with 1000 samples with eight features and a secondary dataset for external validation with 3865 samples. Our integrated approach identifies Categorical Boosting with GA (CBRGA) as the best performer, achieving an R2 of 0.9973 and outperforming existing state-of-the-art methods. ANOVA-based analysis further enhances model interpretability and performance by isolating key factors such as square footage and lot size. To ensure robustness and transparency, we conduct 10-fold cross-validation and employ explainable AI techniques such as Shapley Additive Explanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME), providing insights into model decision-making and feature importance. Full article
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33 pages, 10985 KiB  
Article
Integrating AHP-Entropy and IPA Models for Strategic Rural Revitalization: A Case Study of Traditional Villages in Northeast China
by Chenghao Wang, Guangping Zhang and Yunying Zhai
Buildings 2025, 15(14), 2475; https://doi.org/10.3390/buildings15142475 - 15 Jul 2025
Viewed by 385
Abstract
Traditional villages are critical to preserving cultural heritage and promoting sustainable rural development. This study evaluates the development potential of 47 traditional villages in Jilin Province from the perspectives of spatial planning, architectural conservation, and rural real estate revitalization. A Development Potential Assessment [...] Read more.
Traditional villages are critical to preserving cultural heritage and promoting sustainable rural development. This study evaluates the development potential of 47 traditional villages in Jilin Province from the perspectives of spatial planning, architectural conservation, and rural real estate revitalization. A Development Potential Assessment (DPA) framework is constructed based on five dimensions: geographical position, cultural resources, socio-economic factors, natural ecology, and living environment. The AHP-entropy weighting method is applied to ensure objectivity in scoring, while kernel density analysis and coefficient of variation techniques identify spatial patterns and internal disparities. To further inform strategic planning and targeted investment, an Importance–Performance Analysis (IPA) model is introduced, aligning resource advantages with development performance. Key findings include the following: (1) significant spatial heterogeneity, with higher potential concentrated in the southeast and lower levels in the northwest; (2) cultural and socio-economic dimensions are the most influential factors in differentiating development types; and (3) a subset of villages shows a disconnect between resource endowment and realized potential, indicating the need for tailored design interventions and investment strategies. This research offers a visual and data-driven basis for differentiated revitalization strategies, integrating urban science methods, architectural thinking, and real estate development logic. It supports refined policy implementation, spatial design decisions, and the activation of underutilized rural assets through context-sensitive planning. Full article
(This article belongs to the Section Architectural Design, Urban Science, and Real Estate)
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18 pages, 4817 KiB  
Article
Residential Mobility: The Impact of the Real Estate Market on Housing Location Decisions
by Fabrizio Battisti, Orazio Campo, Fabiana Forte, Daniela Menna and Melania Perdonò
Real Estate 2025, 2(3), 9; https://doi.org/10.3390/realestate2030009 - 3 Jul 2025
Viewed by 1416
Abstract
In the context of increasing digitization, integrating ICT technologies, artificial intelligence, and remote working is altering residential mobility patterns and housing preferences. This study examines the housing market’s impact, focusing on how residential affordability affects residential choices, using a case study of the [...] Read more.
In the context of increasing digitization, integrating ICT technologies, artificial intelligence, and remote working is altering residential mobility patterns and housing preferences. This study examines the housing market’s impact, focusing on how residential affordability affects residential choices, using a case study of the Metropolitan City of Florence. The analysis employs a methodology centered on the Debt-to-Income Ratio (DTI), which cross-references real estate market values (source: Agenzia delle Entrate and leading real estate portals) with household income brackets to identify affordable areas. The results reveal a clear divide: households with incomes below EUR 26,000 per year (representing about 69% of the population) are excluded from the central urban property market. This evidence confirms regional and national trends, emphasizing a growing mismatch between housing costs and disposable incomes. The study concludes that affordability is a technical–financial parameter and a valuable tool for supporting inclusive urban planning. Its application facilitates the orientation of effective public policies and the identification of socially sustainable housing solutions. Full article
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21 pages, 1632 KiB  
Article
Real Estate Market Forecasting for Enterprises in First-Tier Cities: Based on Explainable Machine Learning Models
by Dechun Song, Guohui Hu, Hanxi Li, Hong Zhao, Zongshui Wang and Yang Liu
Systems 2025, 13(7), 513; https://doi.org/10.3390/systems13070513 - 25 Jun 2025
Viewed by 524
Abstract
The real estate market significantly influences individual lives, corporate decisions, and national economic sustainability. Therefore, constructing a data-driven, interpretable real estate market prediction model is essential. It can clarify each factor’s role in housing prices and transactions, offering a scientific basis for market [...] Read more.
The real estate market significantly influences individual lives, corporate decisions, and national economic sustainability. Therefore, constructing a data-driven, interpretable real estate market prediction model is essential. It can clarify each factor’s role in housing prices and transactions, offering a scientific basis for market regulation and enterprise investment decisions. This study comprehensively measures the evolution trends of the real estate markets in Beijing, Shanghai, Guangzhou, and Shenzhen, China, from 2003 to 2022 through three dimensions. Then, various machine learning methods and interpretability methods like SHAP values are used to explore the impact of supply, demand, policies, and expectations on the real estate market of China’s first-tier cities. The results reveal the following: (1) In terms of commercial housing sales area, adequate housing supply, robust medical services, and high population density boost the sales area, while demand for small units reflects buyers’ balance between affordability and education. (2) In terms of commercial housing average sales price, growth is driven by education investment, population density, and income, with loan interest rates serving as a stabilizing tool. (3) In terms of commercial housing sales amount, educational expenditure, general public budget expenditure, and real estate development investment amount drive revenue, while the five-year loan benchmark interest rate is the primary inhibitory factor. These findings highlight the divergent impacts of supply, demand, policy, and expectation factors across different market dimensions, offering critical insights for enterprise investment strategies. Full article
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16 pages, 976 KiB  
Review
Life-Cycle Cost Assessment in Real Estate Decision-Making Processes: Scope, Limits and Shortages of Current Practices—An Integrative Review
by Salvador Domínguez Gil, Gema Ramírez Pacheco and Silvia Alonso de los Ríos
Sustainability 2025, 17(12), 5577; https://doi.org/10.3390/su17125577 - 17 Jun 2025
Viewed by 663
Abstract
Life-cycle cost assessment has gained increasing relevance across sectors related to urban and building development. In real estate and public procurement decision-making, it offers a comprehensive view of property costs beyond the initial investment, which aligns with European Sustainable Development policies and new [...] Read more.
Life-cycle cost assessment has gained increasing relevance across sectors related to urban and building development. In real estate and public procurement decision-making, it offers a comprehensive view of property costs beyond the initial investment, which aligns with European Sustainable Development policies and new taxonomies in sustainable investment. Life-cycle cost assessment supports sustainable design decisions by integrating multiple perspectives and methodologies, including Whole Life Costing and Net Present Value calculations. This approach enables a comprehensive evaluation of long-term costs and benefits, assessing their impact on economic viability and profitability throughout the investment life cycle. However, several challenges persist in standardizing methodologies, developing comprehensive data inventories, and ensuring consistency in result interpretation. The absence of universally accepted frameworks and guidelines introduces additional limitations for practitioners, including estimation inaccuracies, biased assessments, unreliable probability judgments, and the neglect of indirect consequences in decision-making. This review particularly emphasizes the need for interdisciplinary research to advance the integration of costs and benefits of externalities and intangibles associated with social and environmental criteria. Full article
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35 pages, 5841 KiB  
Article
A Network Analysis of the Real Estate Fluctuation Propagation Effect in the United States
by Wenwen Xiao, Xuemei Pei, Wenhao Song and Lili Wang
Buildings 2025, 15(12), 2013; https://doi.org/10.3390/buildings15122013 - 11 Jun 2025
Viewed by 331
Abstract
Under the background of intensified global economic fluctuations, to prevent the systemic risk of real estate (e.g., the U.S. subprime crisis), this study constructs a linkage network of the real estate industry in the U.S. based on the complex network method, reveals the [...] Read more.
Under the background of intensified global economic fluctuations, to prevent the systemic risk of real estate (e.g., the U.S. subprime crisis), this study constructs a linkage network of the real estate industry in the U.S. based on the complex network method, reveals the fluctuation diffusion mechanism, identifies the key pivotal industries through the network characteristic indicators, and analyses the characteristics of the fluctuation conduction paths by applying the industrial fundamental association trees. The study found that (1) the U.S. real estate industry is a ‘supply hub’ industry, with first-order and second-order weighted degrees of mean 6.78, 3.98, and significant asymmetry in the supply structure of the industrial network; (2) industries like architectural, engineering, and related services (541300), nonresidential maintenance and repair (230301), and electric power generation, transmission, and distribution (221100) show high degree centrality and betweenness centrality. Their strong propagation and control capabilities form real estate fluctuations’ core transmission mechanisms; (3) foundational association trees reveal long, broad propagation paths where financial investment and energy-supply sectors act as “traffic hubs,” decisively influencing risk diffusion depth and breadth. Targeted policy recommendations address four dimensions: optimizing industrial chain structures, strengthening financial risk isolation, improving housing supply systems, and enhancing policy coordination. This aims to help China avoid U.S.-style real-estate-bubble risks and achieve coordinated real estate macroeconomy development. Full article
(This article belongs to the Section Architectural Design, Urban Science, and Real Estate)
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17 pages, 1253 KiB  
Article
The Intangible Value of Brisbane’s Urban Megaprojects: A Property Market Analysis
by Maximilian Neuger and Connie Susilawati
Buildings 2025, 15(12), 2011; https://doi.org/10.3390/buildings15122011 - 11 Jun 2025
Viewed by 490
Abstract
This study investigated the intangible value transferred from urban megaprojects to surrounding residential property markets, focusing on Brisbane’s transformative urban regeneration projects currently in the development pipeline. The research objectives were twofold: first, to empirically investigate the dynamics of property markets influenced by [...] Read more.
This study investigated the intangible value transferred from urban megaprojects to surrounding residential property markets, focusing on Brisbane’s transformative urban regeneration projects currently in the development pipeline. The research objectives were twofold: first, to empirically investigate the dynamics of property markets influenced by urban megaprojects and second, to assess the impact of a specific case study on these markets through a longitudinal analysis of residential sales data. Drawing from environmental economics, the concept of willingness to pay (WTP) is used to quantify externalities associated with urban megaprojects. The research constructs a comprehensive dataset integrating geospatial and property-specific data. Through revealed preference methods, the intangible value transferred from mixed-use developments is identified and quantified via residential transaction prices. Utilising hedonic price modelling, this study systematically analysed residential transaction data to estimate implicit prices associated with spatial proximity to megaprojects. A comprehensive dataset integrating property-specific attributes, geospatial proximity measures, and temporal dynamics of project development phases underpins this analysis. This research and its findings advance the existing literature in several important dimensions. That is, this research represents the first microeconomic assessment of the property market’s impacts resulting from mixed-use megaprojects in Brisbane, offering novel empirical insights for both academic and practical applications, how urban megaprojects shape residential property values, and informing stakeholders involved in urban planning, policymaking, and real estate investment decisions. Practitioners and policymakers can leverage these insights to inform policy frameworks and strategic decisions. At the governmental level, the results offer applicable insights for urban revitalisation strategies, particularly relevant to central business districts undergoing similar developments. Private sector stakeholders can utilise these outcomes to anticipate market adjustments, managing supply and demand fluctuations more effectively. Full article
(This article belongs to the Section Architectural Design, Urban Science, and Real Estate)
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34 pages, 2660 KiB  
Article
Monetizing Digital Innovation in the AEC Industry: Real Estate Value Creation Through BIM and BMS Integration
by Edison Atencio, Costanza Mariani, Riccardo Accettulli and Mauro Mancini
Buildings 2025, 15(11), 1920; https://doi.org/10.3390/buildings15111920 - 2 Jun 2025
Viewed by 587
Abstract
The real estate sector is increasingly recognizing facility management (FM) as a key driver of asset value. Among emerging technologies, Building Information Modeling (BIM) and Building Management Systems (BMSs) stand out for their potential to enhance FM efficiency by integrating design data with [...] Read more.
The real estate sector is increasingly recognizing facility management (FM) as a key driver of asset value. Among emerging technologies, Building Information Modeling (BIM) and Building Management Systems (BMSs) stand out for their potential to enhance FM efficiency by integrating design data with building operations across the entire lifecycle, from construction to maintenance, performance monitoring, and renovation. While their technical applications have been widely studied, the financial impact of these tools on FM remains underexplored. This paper addresses that gap by estimating the economic value generated by implementing BIM and BMS in real estate facility management. Based on thirteen semi-structured interviews with professionals from the Italian real estate sector, we identified and quantified cost-saving factors and challenges related to digital adoption. These cost efficiencies, when recurring and quantifiable, can improve net operating income (NOI), thereby supporting higher asset valuations under income-based real estate appraisal methods. The results show that integrating BIM and BMS in facility management may generate average annual cost savings of 5.81% relative to asset value, with coordination improvements alone accounting for up to 3.28% per year. Based on a 30-year simulation, these savings correspond to a positive Net Present Value (NPV), supporting the financial viability of digital FM adoption in real estate. This study offers empirical evidence to support investment decisions in digital FM technologies and contributes to bridging the gap between innovation and financial evaluation in the real estate sector. Full article
(This article belongs to the Special Issue Architectural Design Supported by Information Technology: 2nd Edition)
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23 pages, 2220 KiB  
Article
The Impact of ESG Certifications on Class A Office Buildings in Madrid: A Multi-Criteria Decision Analysis
by Alfonso Valero
Standards 2025, 5(2), 14; https://doi.org/10.3390/standards5020014 - 21 May 2025
Viewed by 711
Abstract
This study investigates the impact of Environmental, Social, and Governance (ESG) certifications on the performance of Class A office buildings within Madrid’s Central Business District (CBD). Employing a Multi-Criteria Decision Making (MCDM) methodology, the research evaluates 21 office properties, analyzing the influence of [...] Read more.
This study investigates the impact of Environmental, Social, and Governance (ESG) certifications on the performance of Class A office buildings within Madrid’s Central Business District (CBD). Employing a Multi-Criteria Decision Making (MCDM) methodology, the research evaluates 21 office properties, analyzing the influence of ESG certifications on key performance indicators, including green building certifications, valuation, market perception, and financial outcomes. The findings reveal that ESG-certified buildings demonstrate superior performance, commanding higher valuations, mitigating brown discounts, and achieving increased rental rates, thereby enhancing their investment attractiveness. These results underscore the importance of ESG certifications in the Spanish office market and provide valuable insights for investors, developers, and policymakers navigating the integration of sustainability and commercial real estate. Full article
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21 pages, 5455 KiB  
Article
Research on Spatial Differentiation of Housing Prices Along the Rail Transit Lines in Qingdao City Based on Multi-Scale Geographically Weighted Regression (MGWR) Analysis
by Yanjun Wang, Zixuan Liu, Yawen Wang and Peng Dai
Sustainability 2025, 17(9), 4203; https://doi.org/10.3390/su17094203 - 6 May 2025
Cited by 1 | Viewed by 1091
Abstract
Urban sprawl and excessive reliance on motorization have led to many urban problems. The balance of supply and demand in the real estate market, as well as price fluctuations, also face many challenges. Urban rail transit not only alleviates traffic congestion and air [...] Read more.
Urban sprawl and excessive reliance on motorization have led to many urban problems. The balance of supply and demand in the real estate market, as well as price fluctuations, also face many challenges. Urban rail transit not only alleviates traffic congestion and air pollution, but also significantly reduces residents’ commuting time, broadens urban accessibility, and reshapes the decision-making basis for residents when choosing residential locations. This study takes the 1st, 2nd, 3rd, 4th, 8th, 11th, and 13th metro lines that have been opened in Qingdao City as examples. It selects 12,924 residential samples within a 2 km radius along the rail transit lines. By using GIS spatial analysis tools and the multi-scale geographically weighted regression (MGWR) model, it analyzes the spatial differentiation characteristics of housing prices along the rail transit lines and the reasons and mechanisms behind them. The empirical results show that housing prices decrease to varying degrees with the increase in the distance from the rail transit. For every additional 1 km from the rail transit station, the housing price increases by 0.246%. Through model comparison, it was found that MGWR has a better fitting degree than the traditional ordinary least squares method (OLS) and the previous geographically weighted regression model (GWR), and reveals the spatial heterogeneity of the influence of urban rail transit on housing prices. Different indicator elements have different effects on housing prices along these lines. The urban rail transit factor in the location characteristics has a positive impact on housing prices, and has a significant negative correlation in some areas. The significant influence range of the distance to the nearest metro station on housing prices is concentrated within a radius of 373 m, and the effect decays beyond this range. The total floors, building area, green coverage rate, property management fee, and the distance to hospitals and parks in the neighborhood and structural characteristics have spatial heterogeneity. Analyzing the areas affected by the urban rail transit factor, it was found that the double location superposition effect, the networked transportation system, and the agglomeration of urban functional axes are important reasons for the significant phenomena in some local areas. This research provides a scientific basis for optimizing the sustainable development of rail transit in Qingdao and formulating differentiated housing policies. Meanwhile, it expands the application of the MGWR model in sustainable urban spatial governance and has practical significance for other cities to achieve sustainable urban development. Full article
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12 pages, 4263 KiB  
Article
Leveraging Humanized Performance Labeling to Drive Sustainable Building Choices
by Azadeh Omidfar Sawyer and Sanaz Saadatifar
Architecture 2025, 5(2), 30; https://doi.org/10.3390/architecture5020030 - 27 Apr 2025
Viewed by 640
Abstract
Climate change is a pressing global challenge, significantly influenced by human actions. Considering that buildings account for approximately 40% of total US energy use in the United States, this study examines how humanized energy labeling can influence home buyers’ preferences, shaping total energy [...] Read more.
Climate change is a pressing global challenge, significantly influenced by human actions. Considering that buildings account for approximately 40% of total US energy use in the United States, this study examines how humanized energy labeling can influence home buyers’ preferences, shaping total energy demand and usage. “Humanized energy and carbon data” refers to the simplification of complex energy metrics into accessible formats for non-expert audiences. By presenting energy data in a user-friendly manner, this approach aims to empower consumers to prioritize energy-efficient buildings, consequently driving demand for sustainable practices in the building sector. To test this approach, a survey of 163 participants was conducted. Participants were presented with six building façade designs in two rounds: one without energy, carbon, or utility cost data, and the second with comprehensive performance information. Results revealed that 77.3% of participants shifted their preferences after reviewing energy-related data. Furthermore, the study found consistent impacts across demographic groups, highlighting the broad applicability of humanized labeling. These findings confirm the potential of humanized energy labeling to influence housing decisions, driving demand for sustainable practices in real estate. By empowering consumers with accessible information, this approach contributes to mitigating climate change while fostering informed decision-making in the housing market. Full article
(This article belongs to the Special Issue Sustainable Built Environments and Human Wellbeing)
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35 pages, 3058 KiB  
Systematic Review
Advancement of Artificial Intelligence in Cost Estimation for Project Management Success: A Systematic Review of Machine Learning, Deep Learning, Regression, and Hybrid Models
by Md. Mahfuzul Islam Shamim, Abu Bakar bin Abdul Hamid, Tadiwa Elisha Nyamasvisva and Najmus Saqib Bin Rafi
Modelling 2025, 6(2), 35; https://doi.org/10.3390/modelling6020035 - 24 Apr 2025
Cited by 2 | Viewed by 7189
Abstract
This systematic review investigates the integration of artificial intelligence (AI) in cost estimation within project management, focusing on its impact on accuracy and efficiency compared to traditional methods. This study synthesizes findings from 39 high-quality articles published between 2016 and 2024, evaluating various [...] Read more.
This systematic review investigates the integration of artificial intelligence (AI) in cost estimation within project management, focusing on its impact on accuracy and efficiency compared to traditional methods. This study synthesizes findings from 39 high-quality articles published between 2016 and 2024, evaluating various machine learning (ML), deep learning (DL), regression, and hybrid models in sectors such as construction, healthcare, manufacturing, and real estate. The results show that AI-powered approaches, particularly artificial neural networks (ANNs)—which constitute 26.33% of the studies—, enhance predictive accuracy and adaptability to complex, dynamic project environments. Key AI techniques, including support vector machines (SVMs) (7.90% of studies), decision trees, and gradient-boosting models, offer substantial improvements in cost prediction and resource optimization. ML models, including ANNs and deep learning models, represent approximately 70% of the reviewed studies, demonstrating a clear trend toward the adoption of advanced AI techniques. On average, deep learning models perform with 85–90% accuracy in cost estimation, making them highly effective for handling complex, nonlinear relationships and large datasets. Machine learning models achieve an average accuracy of 75–80%, providing strong performance, particularly in industries like road construction and healthcare. Regression models typically deliver 70–80% accuracy, being more suitable for simpler cost estimations where the relationships between variables are linear. Hybrid models combine the strengths of different algorithms, achieving 80–90% accuracy on average, and are particularly effective in complex, multi-faceted projects. Overall, deep learning and hybrid models offer the highest accuracy in cost estimation, while machine learning and regression models still provide reliable results for specific applications. Full article
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