Accurate building construction cost prediction is critical, especially for sustainable projects (i.e., green buildings). Green building construction contracts are relatively new to the construction industry, where stakeholders have limited experience in contract cost estimation. Unlike conventional building construction, green buildings are designed to utilize new technologies to reduce their operations’ environmental and societal impacts. Consequently, green buildings’ construction bidding and awarding processes have become more complicated due to difficulties forecasting the initial construction costs and setting integrated selection criteria for the winning bidders. Thus, robust green building cost prediction modeling is essential to provide stakeholders with an initial construction cost benchmark to enhance decision-making. The current study presents machine learning-based algorithms, including extreme gradient boosting (XGBOOST), deep neural network (DNN), and random forest (RF), to predict green building costs. The proposed models are designed to consider the influence of soft and hard cost-related attributes. Evaluation metrics (i.e., MAE, MSE, MAPE, and $R^2$) are applied to evaluate and compare the developed algorithms’ accuracy. XGBOOST provided the highest accuracy of 0.96 compared to 0.91 for the DNN, followed by RF with an accuracy of 0.87. The proposed machine learning models can be utilized as a decision support tool for construction project managers and practitioners to advance automation as a coherent field of research within the green construction industry.

As a fundamental feature of green building cost forecasting, external support is crucial. However, minimal research efforts have been directed to developing practical models for determining the impact of external public and private support on green construction projects’ costs. To fill the gap, the current research aims to develop a mathematical model to explore the balance of supply and demand under deflationary conditions for external green construction support and the accompanying spending adjustment processes. The most current datasets from 3578 green projects across Northern America were collected, pre-processed, analyzed, post-processed, and evaluated via cutting-edge machine learning (ML) techniques to retrieve the deep parameters affecting the green construction cost prediction process. According to the findings, public and private investments in green construction are projected to decrease the cost of green buildings. Furthermore, the impact of public and private investment on green construction cost reduction during deflationary periods is more significant than its influence during inflation. As a result, decision-makers may utilize the suggested model to monitor and evaluate the yearly optimal external investment in green building construction.