Download PDFOpen PDF in browserConstruction Cost Predication Model Using Macro Economic Indicators8 pages•Published: September 25, 2020AbstractEstimating 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.Keyphrases: construction forecast, economic indicators, estimating, neural networks In: Tom Leathem (editor). Associated Schools of Construction Proceedings of the 56th Annual International Conference, vol 1, pages 382-389.
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