Population growth and increasing trend towards urbanization have caused housing demand to exceed its supply, particularly in urban areas in developing countries. Furthermore, housing industry motivates many subsidiary industries and plays a leading socio-economic role in such countries. Therefore, successful completion of housing projects is of great significance quantitatively and qualitatively.This study aims to propose a framework to evaluate the critical success factors (CSFs) in housing projects considering the interrelationship among factors and criteria. The factors were initially identified through literature review and then refined and categorized using a two-round Delphi method and finally prioritized using fuzzy analytic network process (FANP). To demonstrate the implementation of the proposed model, a case study was carried out on an urban residential building project in Tehran. The framework proposed in this study can be applied as a decision support system for decision makers, project managers and practitioners involved in the housing sector.

Due to the urban population’s growth and increasing demand for the renewal of old houses, the successful completion of Residential Building Projects (RBPs) has great socioeconomic importance. This study aims to propose a framework to predict the success of RBPs in the construction phase. Therefore, a 3-step method was applied: (1) Identifying and ranking Critical Success Factors (CSFs) involving in RBPs using the Delphi method, (2) Identifying and selecting success criteria and defining the Project Success Index (PSI), and (3) Developing an ANN model to predict the success of RBPs according to the status of CSFs during the construction phase. The model was trained and tested using the data extracted from 121 RBPs in Tehran. The main findings of this study were a prioritized list of most influential success criteria and an efficient ANN model as a Decision Support System (DSS) in RBPs to monitor the projects in advance and take necessary corrective actions. Compared with previous studies on the success assessment of projects, this study is more focused on providing an applicable method for predicting the success of RBPs. Doi: 10.28991/cej-2020-03091612 Full Text: PDF