Dynamic Regression Models for Prediction of Construction Costs

Abstract

Accurate prediction of construction costs in the market is essential to effectively estimate costs for construction projects. In the construction industry, cost indexes that are reported in series are often used to explain the change of construction costs. By tracking the trend of such quantitative contemporaneous cost index and making frequent and regular forecasts of the future values of the index, one can develop a deeper understanding of prices of resources used for construction. Incorporating such an understanding and prediction into estimating will help practitioners manage construction costs. This paper proposes two dynamic regression models for the prediction of construction cost index. Comparison of the proposed models with the existing methods proves that the new models provide several advantages and improvements.