Budget overruns driven by unpredictable building material prices are a persistent challenge in emerging economies, yet reliable price forecasting tools for structural materials such as steel reinforcement bar (RFT) remain largely unavailable to building project teams. This study develops an Autoregressive Distributed-Lag (ARDL) model to forecast RFT prices in Egypt using macroeconomic leading indicators. A structured filtering pipeline —stationarity testing, Variance Inflation Factor (VIF) screening, and Granger causality testing—reduced 22 candidate variables to nine predictors: producer price index (PPI), loan rate (LR), discount rate (DR), Egyptian Stock Exchange Index (EGX30), money supply (M0), exchange rate (ER), iron ore prices (ORE), American stock index (S&P500), and Hang Seng Index (HSI). ARDL bounds testing confirmed cointegration across look-back periods (LBP) of 3, 6, and 9 months. Comprehensive diagnostics confirmed model stability, homoscedasticity, and the absence of serial correlation. The ARDL was benchmarked against an Ordinary Least Squares (OLS) baseline and a first-difference vector autoregression (VAR) model. Evaluated as genuine multi-step (dynamic) forecasts over the held-out crisis-period window, the 3-month model achieved an out-of-sample mean absolute error of approximately 5.9% in price terms, retaining a clear advantage over the OLS baseline at that horizon. The proposed framework provides quantity surveyors, cost consultants, and project managers with a practical tool for setting RFT budgets at the tendering stage and optimising procurement timing. It is transferable to other building materials (cement, structural steel sections, glazing) across emerging-market construction economies.
Gomaa et al. (Mon,) studied this question.