Fuzzy Consensus Qualitative Risk Analysis as a framework for the evaluation of risk events in real estate development projects

This paper presents a Fuzzy Consensus Qualitative Risk Analysis framework to identify and prioritize risks encountered in real estate projects, which is applied to developing countries. The framework incorporates the consensus and quality of experts in the process of evaluating risk events and is composed of (1) a Fuzzy Expert System (FES)to determine qualification of experts;(2) Fuzzy Similarity Aggregation Algorithms to aggregate experts’ opinions; and (3) a threedimensional prioritization approach to rank the risks, qualitatively. Risks are identified through a literature review and interviews with experts who rank the risks in terms of their probability of occurrence, impact and level of detection; each is described using five linguistic terms that are defined by membership functions (MFs) on a 5-point rating scale. The FES determines an importance weight factor for each expert, based on a set of predetermined qualification attributes. Experts’ opinions are aggregated in a linguistic framework, based on the proximity of their opinions on the scale to ensure that their aggregated decision is a result of common agreement. The importance weight factor is combined with the consensus weight factor of each expert in the aggregation process using a scalar modifier and the Euclidean Distance Measure Function is used to determine the linguistic criticality of every risk event. A threedimensional prioritization approach applies a set of ranking rules to every risk that enables experts to rank and visualize the priority of the risks in a three-dimensional space. The framework contributes to the Real estate industry by solving a major problem for project teams in developing countries to Risk Analysis IX 179 www.witpress.com, ISSN 1743-3517 (on-line) WIT Transactions on Information and Communication Technologies, Vol 47, © 2014 WIT Press doi:10.2495/RISK140161 qualitatively evaluate risks in a fully supported linguistic framework, using fuzzy logic, which addresses the vagueness and imprecision that exist in the decision-making process.