Modeling the impact of inflation and price volatility of construction materials on the budget and timelines of infrastructure projects

Abstract

The study is devoted to the quantitative assessment of the impact of inflation and price instability on the budget and implementation timelines of large infrastructure projects under conditions of macroeconomic turbulence. The aim of the research is to develop a stochastic simulation model that enables the transition from point cost estimates to probabilistic ranges of total expenditures and construction duration. The empirical basis consists of retrospective series of consumer and producer price indices, as well as quotations of key construction materials for the period from 2008 to 2023, grouped into more than one hundred and eighty nomenclature items. For each resource group, a statistical analysis of growth rates, volatility, and autocorrelation was conducted, distribution laws most accurately describing price dynamics were selected, and correlations between materials were evaluated. Based on this, a general-purpose software environment was used to implement a Monte Carlo model reproducing thousands of possible trajectories of price changes over a five-year horizon for a typical cost structure of an infrastructure facility and accounting for the impact of cost deviations on the project schedule. The simulation results demonstrate that even with moderate inflation and volatility parameters, the expected budget overruns and average delays exceed classical contingency reserves, while under high volatility scenarios, the standard deviation of budget deviations becomes comparable to its mean level. Scenario analysis, including baseline, inflationary, volatile, and combined modes, reveals a nonlinear nature of the influence of price factors, where the simultaneous growth of inflation and volatility leads to a multiple increase in the composite risk index and the formation of distributions with pronounced heavy tails. Based on empirical distributions of budget and schedule deviations, Value at Risk indicators, are calculated for various confidence levels, allowing for the substantiation of financial reserves and the determination of acceptable risk thresholds for investors and public clients. It is shown that inflation primarily shifts the outcome distribution toward cost escalation, whereas volatility significantly widens the range of possible values and increases the probability of extreme overruns and prolonged delays. The practical significance of the research lies in the fact that the proposed model can serve as a tool for stress testing and substantiation of reserve sizes, as well as a basis for designing adaptive contracts with flexible price indexation mechanisms aligned with the risk profile of a specific project.