Journal of Construction Engineering and Management· 2026Q1
Granular Framework for Construction Material Price Forecasting: Econometric and Machine-Learning Approaches
- 2citations
- Q1SCImago
- 2026year
Short summary
A new forecasting framework using CSI MasterFormat data and explanatory variables (raw material prices, commodity indexes, macroeconomic indicators) achieved up to 59% better accuracy than ARIMA for construction material prices, with LSTM models showing the highest performance (RMSE 4.19, MAPE 1.60 for Division 06).
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Key points
- Developed a granular construction material price forecasting framework using CSI MasterFormat data.
- Incorporating explanatory variables (raw material prices, commodity indexes, macroeconomic indicators) improved accuracy across all tested models.
- LSTM model achieved the highest accuracy (RMSE 4.19, MAPE 1.60 for Division 06), a 59% improvement over ARIMA.
- Framework demonstrated scalability across multiple CSI Divisions, with Division 06 (Wood, Plastics, Composites) detailed as a case study.
AI-generated from the title and abstract; the full text is not read.
Abstract
Abstract The persistent volatility of construction material prices poses significant risks to cost estimation, budgeting, and project delivery, underscoring the urgent need for granular and scalable forecasting methods. This study develops a forecasting framework that 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 predictive accuracy, the framework integrates explanatory variables such as raw material prices, commodity indexes, and macroeconomic indicators. Five time-series models, long short-term memory (LSTM), autoregressive integrated moving average (ARIMA), vector error correction model (VECM), vanilla Transformer, 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 achieved the highest accuracy, with root mean squared error (RMSE) values as low as 4.19 and mean absolute percentage error (MAPE) values of 1.60 based on Division 06 as a representative demonstration, representing improvements of up to 59% over the traditional statistical time-series model, ARIMA. Validation across multiple CSI Divisions within the residential-material 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.
The authors' abstract, as published at the source. Journal of Construction Engineering and Management, 2026 · DOI ↗
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Field: Management Science and Operations Research
Management Science and Operations ResearchDecision Sciences