The Construction Cost Index (CCI) is a key measure of price fluctuations in major construction resources, playing a crucial role in costestimation and price trend analysis. Accurate CCI forecasting is essential to prevent cost underestimation or overestimation, ensuring theeconomic feasibility of construction projects. This study forecasts the CCI using a multivariate time series model, Vector Autoregression(VAR), to address the limitations of univariate models, especially during economic uncertainty. Through statistical validation, three leadingindicators were identified: construction order amount, business survey index (BSI), and producer price index (PPI) for structural steel. Theproposed model was optimized using the Akaike Information Criterion (AIC), while benchmark models ARIMA, VAR (CPI, PPI), and SVRwere optimized through grid search. Model validation was conducted using data from January 2000 to April 2023, segmented into threeeconomic phases: stability, heightened uncertainty, and a combined period. Walk-forward cross-validation assessed predictive performance overshort-term forecasts of 3 months, mid-term forecasts of 6 months, and long-term forecasts of 12 months, with evaluation based on averagedperformance metrics over multiple iterations. Results showed that the proposed model achieved the lowest error and highest accuracy in shortandmid-term forecasts. For long-term forecasts, SVR recorded the lowest error; however, qualitative analysis indicated that the proposedmodel more effectively captured overall trends in a balanced manner. By integrating key market indicators, this approach provides a robustmethod for CCI forecasting, enhancing cost predictability in the construction industry.