As large-scale building projects increase in frequency, their construction costs become a matter of great concern, especially because of their lengthy construction periods. In particular, recent volatile fluctuations of construction material prices have fueled problems like cost forecasting. Many researchers try to accurately estimate cost escalations, but price forecasting for numerous construction materials requires a simplified and automated process. The research in this paper develops an automated time-series material cost forecasting (ATMF) system including both autoselected procedures for determining a best-fitting model and an autoextracting module for forecasting values using the Box-Jenkins approach. If the modeling process is simplified and iterative arbitrary decisions for the modeler eliminated, each future prices of a large number of materials can be forecast differently. Thus, the ATMF system can be utilized for predicting future trends in construction material costs. Further, an out-of-sample forecast applying several material price data confirms that this system can be effectively applied to material cost estimation at a more detailed level in object-based cost planning. The proposed system can thus help decision makers in the construction industry deal with changes in economic conditions and design by estimating cost escalations caused by volatile factors such as inflation.
|Number of pages
|Journal of Construction Engineering and Management
|Published - Nov 2012
- Autoregressive moving-average models
- Construction costs
- Time-series analysis