A machine-learning model for estimating construction renovation costs

Purpose – A serious concern for construction costs has been the presence of uncertainties in construction operations and how they affect project performance. Several models exist for predicting construction project costs. However, the models overlook the effects of uncertainties on construction costs. This study, therefore, aims to develop a predictive model that considers uncertainty when estimating building renovation project costs. Design/methodology/approach – The study employed project scope factors and 45 uncertainty factors in the model development. SHapley Additive exPlanations (SHAP) was used to reveal the uncertainty factors that had a significant impact on the construction costs and to improve the performance of the model. The study then used the outcome of the sensitivity analysis along with the project scope factors to train and test a prediction model using XGBoost. Findings – The study found crude oil price, project complexity, delays in payment, regulatory requirements and Inappropriate design to have the most significant impact on construction renovation project costs. The XGBoost model for predicting construction renovation project costs has produced promising outcomes with an accuracy of 91.20%. Practical implications – Findings from this study will enable project managers and stakeholders to make informed decisions, optimise resource allocation and mitigate project risks. Originality/value – To improve the cost performance of construction renovation projects, it is essential to take uncertainty into account, its impact on predictions and the accuracy and value of model predictions. In this study, a novel machine learning approach was developed to predict the construction cost of renovation projects by leveraging the uncertainty factors.