Argaw Gurmu
Purpose This study aims to address the research gap in forecasting output-based Building Cost Index (BCI) for building projects by integrating macroeconomic indicators into a robust econometric model. Design/methodology/approach Quarterly Australian data from 1997 to 2024 on BCI, gross domestic product (GDP), construction price index, petroleum prices and Producer Price Indexes (PPI) were analysed using Granger causality and Johansen cointegration tests to examine temporal and long-run equilibrium relationships. Based on these results, a Vector Error Correction Model (VECM) was developed and validated. Findings GDP, petroleum prices and PPIs for electrical, steel and other materials emerged as leading indicators of BCI. Residual diagnostics confirmed no serial correlation and homoscedasticity, validating the VECM assumptions. Out-of-sample forecasts achieved RMSE = 11.48, MAE = 8.54, MAPE = 6.79% and Theil’s U = 0.05, indicating strong predictive accuracy. Research limitations/implications This research used Australian data; therefore, its applicability to other regions may be limited. Future studies should replicate the model across diverse geographic and economic contexts to validate its robustness. Practical implications The model enables construction managers and policymakers to anticipate cost escalation and plan budgets proactively, particularly during economic volatility. For example, rising petroleum prices or material costs can signal future BCI increases, supporting timely procurement and risk mitigation strategies. Originality/value This research introduces a validated VECM tailored to output-based BCI, offering a practical tool for early cost planning and strategic decision-making in building projects.