Urban expansion effects on real estate ecosystems: Identification and neutrosophic analysis of causal dynamics

• Autor(a) principal: Correia, I.
• Data de Publicação: 2024
• Outros Autores: Ferreira, F., Zopounidis, C., Ferreira, N.
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10071/31338
• topic: Cognitive mapping; Decision-making support; DEcision MAking trial and evaluation laboratory (DEMATEL); Neutrosophic logic; Real estate; Urban expansion

Real estate ecosystems are the result of symbiotic relationships between different types of real estate, including residential, commercial, or industrial properties. Maintaining a balance among these categories is essential for these ecosystems' ability to function properly. Real estate currently is largely a product of urban expansion caused by population increases and distances from primary-sector industries. The unplanned growth of urban areas can have profound consequences for local residents’ health and the environment. However, when well structured, urbanization can have a positive effect on societies’ well-being. The relationship between this expansion and its impact on real estate ecosystems is an extremely complex, subjective topic, so decision makers cannot apply just one solution when making plans. This study develops a multicriteria analysis model to support decision making processes, which provides a structured understanding of urban expansion effects on real estate ecosystems and deals with the inherent subjectivity and complexity of this decision problem. Cognitive mapping and decision-making trial and evaluation laboratory (DEMATEL) technique were combined in a neutrosophic environment to incorporate uncertainty and produce a more realistic analysis system, thereby overcoming various limitations of previous research on urban expansion (e.g., unclear way by which relevant factors/criteria are identified in most cases, and lack of analyses dealing with the dynamics of their causal relationships). Decision makers with professional experience in relevant areas collaborated to provide essential input used to create the proposed model. External experts validated the results, confirming the potential of our analysis system for real-life applications.