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Construction Cost Predication Model Using Macro Economic Indicators

8 pagesPublished: September 26, 2020

Abstract

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.

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

Links:
BibTeX entry
@inproceedings{ASC2020:Construction_Cost_Predication_Model,
  author    = {Craig Capano and Jeanette Hariharan and Hashem Moud and Ashish Asutosh},
  title     = {Construction Cost Predication Model Using Macro Economic Indicators},
  booktitle = {Associated Schools of Construction Proceedings of the 56th Annual International Conference},
  editor    = {Tom Leathem},
  series    = {EPiC Series in Built Environment},
  volume    = {1},
  pages     = {382--389},
  year      = {2020},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2632-881X},
  url       = {https://easychair.org/publications/paper/Zfb3},
  doi       = {10.29007/tjbv}}
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