Building material price data for predictive cost estimation in construction

This study introduces the Building Materials Dataset as a comprehensive resource for analyzing historical and real-time fluctuations in construction material prices in Nigeria. The dataset was compiled through structured surveys with material suppliers and secondary data from reputable online marketplaces and industry reports. Designed to support cost estimation, procurement planning, and financial forecasting, it enables researchers, policymakers, and industry professionals to assess market trends, optimize budgeting strategies, and mitigate financial risks associated with price volatility. Structured in a tabular, time-series format, the dataset captures key information on material types, pricing variations over multiple years, and economic factors influencing costs. Preprocessing steps included data cleaning, Min-Max normalization, and feature engineering to enhance predictive modeling. The dataset was integrated into the BuildCES system, where a Long Short-Term Memory (LSTM) model was applied for price prediction and cost estimation, facilitating data-driven procurement optimization and contract management. Future enhancements will incorporate Blockchain technology to ensure transaction transparency, real-time pricing updates via API integration, and geospatial data mapping to analyze regional price disparities. By systematically documenting construction material prices in Nigeria, this dataset addresses the gap in structured data availability, providing a scalable foundation for research, industry benchmarking, and policy development. By fostering a data-driven approach to construction management, the dataset contributes to economic stability, transparency, and sustainability in the industry.