Review of the literature has indicated that the application of historical data combined with predictive analytics can improve cost estimation in construction projects. However, a major obstacle regards being able to estimate projects at the conceptual phase. Conceptual cost estimating occurs at the early stage of a project after order-of-magnitude estimates are completed. Detailed project information, however, is quite limited during this early phase, resulting in considerable uncertainty In this paper, the application of decision support system technology is investigated. Decision support systems (DSS) are software systems that utilize sophisticated algorithmic approaches to solve problems. A suitable underlying predictive model, within the DSS, was selected to support the unique needs of university-related construction. This resulting system provides an automatic and user-friendly environment to support project budgeting, and make decisions among proposed project alternatives. The DSS contains two main components for data collection and data analysis. The data analysis component encompasses: (1) an estimating module predicting the capital cost of the project, (2) a quality assessment module determining the accuracy of the results from the first module, and (3) a support module that depicts visualization information for the decision-makers via a Graphical User Interface.