Reducing cost overruns through data-driven methods used in uncertainty analyses

Abstract

This article focuses on how data from completed projects can be applied to uncertainty analysis. Cost estimates and risk assessments in public construction projects rely on expert opinions and, by little extent, historical figures. By analysing the relationship between project features and budget deviation through statistical methods, we find that total area and estimated square meter price is significantly negatively correlated to cost overruns. Smaller projects tend to have higher cost overruns than larger ones. We argue that cost risk analyses can be improved by such insight.