At early stages of a construction project, the design information and scope definition are very limited, hence; during conceptual cost estimation, achieving high accuracy is very difficult. The level of uncertainty included in the cost estimations should be emphasized for making correct decisions. By using range estimating, the level of uncertainties can be identified in cost estimations. This study represents integrations of parametric and probabilistic cost estimation techniques in a comparative base. Combinations of regression analysis, neural networks, case?based reasoning and bootstrap method are proposed for the early range cost estimations of mass housing projects. The methods are applied using the data of mass housing projects financed by Housing Development Administration of Turkey (TOKI). Results of the three different approaches are compared for predictive accuracy and predictive variability, and suggestions for early range cost estimation of construction projects are made. This study should be especially useful to professionals of cost estimation and bid preparation fields who need practical methods to be applied in conceptual cost estimation processes.