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Export Reference (APA) 
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
Export Reference (IEEE) 
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
Export BibTeX 
@article{ribeiro2017_1765615656771,
	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"
}
Export RIS 
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  -