Construction Cost Predication Model Using Macro Economic Indicators

Craig Capano, Ph.D., Jeanette Hariharan, Ph.D., and Hashem Izadi Moud, Ph.D.
Florida Gulf Coast University
Fort Myers, Florida

Ashish Asutosh, Ph.D. Candidate
University of Florida
Gainesville, Florida

Estimating future costs of construction is an important component to the success of any contracting company. Traditionally a cost modifier has been utilized to offset cost escalations or volatility predictions. Construction estimators and contractors have also attempted to utilize a variety of prediction models. This paper establishes a basis for reliable forecasting and explores the possibility of developing prediction models using time series Neural Networks (NN) by utilizing historic data of three accepted macro-economic composite indicators (MEI) and two accepted construction industry cost indices. The use of these macro-economic indicators for NN-based models may be used to predict cost escalations for construction. Nonlinear autoregressive NN models are constructed through using the macro-economic data and the construction cost data to determine if a reliable time-series predictive model could be established. The results of these models indicated that there is a high correlation between the macro-economic escalations, independent factors, and the construction cost escalations, dependent factors, over time. Use and knowledge of these correlations could aid in the prediction of cost escalations during construction.

Key Words:  Construction forecast, estimating, economic indicators, neural networks

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