This thesis presents a framework to conduct a quantity take-off (QTO) and cost estimate within the Building Information Modeling (BIM) Environment. The product of this framework is a model-based cost estimating tool. The framework addresses the cost uncertainty associated with the detailed information defining BIM model element properties. This cost uncertainty is due to the lack of available tools that address detailed QTO and cost estimation using solely a BIM platform. In addition, cost estimators have little experience in leveraging and managing information within semantic-rich BIM models. Unmanaged BIM element parameters are considered a source of uncertainty in a model-based cost estimate, therefore they should be managed and quantified as work items. A model-based system, which assists the estimators to conduct a QTO and cost estimate within the BIM environment, is developed. This system harnesses BIM element parameters to drive work items associated with the parameter’s host element. The system also captures the cost of scope not modeled in the design team’s BIM models. The system consists of four modules 1) establishing estimate requirements, 2) planning and structuring the estimate, 3) quantification and costing, and 4) model-based historical cost data collection. The complete system can produce a project cost estimate based on the 3D BIM Model. This framework is supported by a computation engine built within an existing virtual design and construction (VDC) model review software. The computation engine supports BIM authoring and reviewing BIM data. The Framework’s quantification and costing module was compared to existing methods in a case study. The outcomes of the model-based system demonstrated improved cost estimate accuracy in comparison to the BIM QTO method and improved speed compared to the traditional methods. The framework provides a systematic workflow for conducting a detailed cost estimate leveraging the parameters stored in the BIM models.