A Granular Framework for Construction Material Price Forecasting: Econometric and Machine-Learning Approaches

This study develops a forecasting framework t hat leverages the Construction Specifications Institute (CSI) MasterFormat as the target data structure, enabling predictions at the six - digit section level and supporting detailed cost projections across a wide spectrum of building materials. To enhance p redictive accuracy, the framework integrates explanatory variables such as raw material prices, commodity indexes, and macroeconomic indicators. Four time - series models, Long Short - Term Memory (LSTM), Autoregressive Integrated Moving Average (ARIMA), Vecto r Error Correction Model (VECM), and Chronos - Bolt, were evaluated under both baseline configurations (using CSI data only) and extended versions with explanatory variables. Results demonstrate that incorporating explanatory variables significantly improves predictive performance across all models. Among the tested approaches, the LSTM model consistently ach ieved the highest accuracy, with RMSE values as low as 1.390 and MAPE values of 0.957, representing improvements of up to 59 % over traditional statistical time - series model, ARIMA. Validation across multiple CSI divisions confirmed the framework's scalability, while Division 06 (Wood, Plastics, and Composites) is presented in detail as a demonstration case. This research offers a robust methodology that enables owners and contractors to improve budgeting practices and achieve more reliable cost estimation at the Definitive level. INTRODUCTION 1.1 Motivation The construction industry continues to demonstrate steady long - term growth, with global activity projected to reach US$9.8 trillion by 2026 [1] . Major upcoming programs in the United States, such as the Los Angeles 2028 Olympics and TSMC's fabrication facility in Arizona [2] [3], highlight the scale of high - value projects in the near future. However, volatility in construction material prices has emerged as a critical challenge, creating significant uncertainty for contractors in project planning, budgeting, and cost management. Price fluctuations, driven by raw material costs, macroeconomic conditions such as inflation and interest rates, and supply - demand imbalances, have amplified risks of cost overruns and delays [4] [5] [6] [7] [8] . Traditional econometric methods (i.e.,multiple regression analysis) and modern econometric methods (i.e., univariate, and multivariate time series methods) have faced limitations in effectively capturing the high - frequency volatility observed in constructi on material prices [9] . These models often struggle to handle the complexity of input data and exhibit limited predictive accuracy in real - world applications.