Predicting ENR construction cost index using machine-learning algorithms

Construction Cost Index (CCI) is calculated monthly and published by Engineering News-Record (ENR). CCI is utilized for capital project budgeting and construction cost estimation, especially in cases where mid- and long-term forecasts are needed. Accurate prediction of CCI helps avoid underestimating and overestimating project costs but the current prevailing time series prediction models do not show promising results, especially in mid- and long-term forecasting. The capability of two machine-learning algorithms, k nearest neighbor (k-NN) and perfect random tree ensembles (PERT), are utilized to enhance CCI forecasting, especially in the mid- and long-term. The proposed machine-learning algorithms are able to significantly enhance the predictability of forecasting CCI in all the scenarios, short-, mid-, and long-term. Data from January 1985 to December 2014 is collected from ENR and bureau of labor statistics to conduct empirical studies and quantitatively measure the performance of the proposed methods. As the outcomes show, the prediction accuracies of both proposed methods are better than those of current prevailing time series models under all the tested scenarios. It is anticipated that cost estimators can benefit from CCI forecasting by incorporating predicted price variations in their estimates and preparing more-precise bids for contractors and developing more-accurate budgets for owners.;Construction Cost Index (CCI) is calculated monthly and published by Engineering News-Record (ENR). CCI is utilized for capital project budgeting and construction cost estimation, especially in cases where mid- and long-term forecasts are needed. Accurate prediction of CCI helps avoid underestimating and overestimating project costs but the current prevailing time series prediction models do not show promising results, especially in mid- and long-term forecasting. The capability of two machine-learning algorithms, k nearest neighbor (k-NN) and perfect random tree ensembles (PERT), are utilized to enhance CCI forecasting, especially in the mid- and long-term. The proposed machine-learning algorithms are able to significantly enhance the predictability of forecasting CCI in all the scenarios, short-, mid-, and long-term. Data from January 1985 to December 2014 is collected from ENR and bureau of labor statistics to conduct empirical studies and quantitatively measure the performance of the proposed methods. As the outcomes show, the prediction accuracies of both proposed methods are better than those of current prevailing time series models under all the tested scenarios. It is anticipated that cost estimators can benefit from CCI forecasting by incorporating predicted price variations in their estimates and preparing more-precise bids for contractors and developing more-accurate budgets for owners.;