Price Prediction Models for Construction Sector – Efficiency and Interpretability

The construction sector, essential for the global economy, is characterized by high price volatility, influenced by economic factors such as material costs, labor market fluctuations, and oil price variations. At the same time, advances in technology and artificial intelligence (AI) have opened new opportunities for improving price predictability through the application of advanced machine learning models. This research aims to compare the performance of several price prediction models, such as Ridge Regression, Random Forest, XGBoost, and Neural Networks, to identify the most efficient model applied in the construction sector The data used in this study were extracted from the Federal Reserve Economic Data (FRED) database and include key variables such as the number of construction employees, job vacancies, the unemployment rate, and oil prices. Based on this data, the four predictive models mentioned were applied and compared, using evaluation metrics such as Mean Squared Error (MSE) and R-squared to determine which model provides the best price prediction. The results provide a detailed assessment of each model and highlight the importance of using AI technologies in managing economic risks in the construction industry, contributing to the optimization of decision-making strategies for companies in this sector. The study provides a valuable set of tools and predictive models that can support managers in the construction industry in making informed and strategic decisions.