Cost Forecasting for Building Materials under Conditions of Uncertainty: Methodology and Practice

Most large construction projects face the problem of cost overruns and failures to meet deadlines mainly due to changes in the cost of building materials. A lot of studies proved the high importance of the cost of building materials for the project budget and highlighted a number of factors that determine the cost of materials. However, modern unstable economic dynamics lead to the need not only to observe sufficient accuracy of quantity and cost calculations regarding primary building materials but also to carefully predict the cost, taking into account uncertainty factors (changes in the geopolitical situation, the impact of the pandemic, changes in the technological structure, etc.). This article proposes the use of a calculation and expert methodology for forecasting the cost of building materials on the example of building bars for two regions of the Russian Federation. This study includes a review of literature, which showed the dependence of the dynamics of the cost of construction on the prices of building materials, confirmed the impact of economic and noneconomic factors of the dynamics of prices of building materials and the impact of risk and uncertainty. Based on the literature review, it is also concluded that it is necessary to expertly adjust the results of the economic and mathematical modeling of the building materials’ price trend line under the influence of noneconomic factors of uncertainty. The statistics of the prices of building materials in Russia were analyzed, and the main causes of price dynamics (economic and noneconomic) were identified. The ARIMA model was selected to build a series of dynamics of prices of reinforcement steel, an expert adjustment of the forecast was made taking into account uncertainty factors. The method of calculation and expert forecasting of prices of building materials was proposed, and the forecast of prices of steel reinforcement in the regions of Russia was calculated on its basis. In conclusion, this study demonstrates the algorithm and practical results of forecasting the cost of building materials under the conditions of uncertainty, as well as recommendations for the implementation of predictive analytics tools in construction practice.

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