Bidding is a commonly used method by contractors to secure business for their companies. A typical bid price consists of three main cost components; direct costs, indirect costs and mark-up. While there are methods for estimating direct and indirect costs with or without contingency, there are fewer tools for estimating mark-ups. This study introduces Multiple Regression (MR), Artificial Neural Network (ANN) and Adaptive Nero-Fuzzy Inference System (ANFIS) techniques for estimating markups, utilizing data collected from contractors in Canada and the USA. To facilitate the modeling process, thirty factors, recognized to impact the mark-ups of estimates and 23 of which are clustered in into five independent categories: need for work, job uncertainty, job complexity, market condition, and owner capability. The ANN, ANFIS and MR models are developed using “MATLAB 2017a” and the results obtained by these methods are compared to their respective performance against the actual estimated markups by the contractors. The ANFIS model yielded more accurate estimates than the other two (2) models.