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Ribeiro, M., Ferreira, F., Jalali, M. & Meidute-Kavaliauskiene, I. (2017). A fuzzy knowledge-based framework for risk assessment of residential real estate investments. Technological and Economic Development of Economy. 23 (1), 140-156
M. I. Ribeiro et al., "A fuzzy knowledge-based framework for risk assessment of residential real estate investments", in Technological and Economic Development of Economy, vol. 23, no. 1, pp. 140-156, 2017
@article{ribeiro2017_1732201778765, author = "Ribeiro, M. and Ferreira, F. and Jalali, M. and Meidute-Kavaliauskiene, I.", title = "A fuzzy knowledge-based framework for risk assessment of residential real estate investments", journal = "Technological and Economic Development of Economy", year = "2017", volume = "23", number = "1", doi = "10.3846/20294913.2016.1212742", pages = "140-156", url = "https://journals.vgtu.lt/index.php/TEDE/article/view/631" }
TY - JOUR TI - A fuzzy knowledge-based framework for risk assessment of residential real estate investments T2 - Technological and Economic Development of Economy VL - 23 IS - 1 AU - Ribeiro, M. AU - Ferreira, F. AU - Jalali, M. AU - Meidute-Kavaliauskiene, I. PY - 2017 SP - 140-156 SN - 2029-4913 DO - 10.3846/20294913.2016.1212742 UR - https://journals.vgtu.lt/index.php/TEDE/article/view/631 AB - Risk analysis of residential real estate investments requires careful analysis of certain variables (or determinants). Because real estate is a key sector for economic and social development, this risk analysis is seen as critical in supporting decision processes relating to buying or selling residential properties, partly due to the pressures caused by the current economic environment. This study aims to develop a conceptual reference model for risk assessment of residential real estate using fuzzy cognitive mapping. This fuzzy model allows cause-and-effect relationships between determinants to be identified and better understood, which in turn allows for better informed investment decisions. The results show that the use of cognitive maps reduces the number of omitted criteria and favors learning with regard to how the criteria relate to each other, holding great potential and versatility in structuring complex decision problems. Practical implications, strengths and weaknesses of our proposal are discussed. ER -