Integrating AI models for cost prediction in green housing in diverse climates − an innovative framework for stakeholder understanding in Pakistan

Diverse climatic conditions, ranging from hot deserts to highland climates, pose significant challenges in predicting the cost impact of green housing which is an essential for significant reduction in carbon emissions to mitigate the effects of climate change. This study employs an innovative approach using hybrid AI model to predict green housing costs, emphasizing stakeholder understanding within Pakistan’s unique socio-economic and climatic contexts. Data was collected from multiple climatic zones, focusing on eighteen key factors influencing green housing costs. The dataset underwent rigorous cleaning, preprocessing, and analysis, including density distribution, cumulative probability, and sensitivity assessments, with results visualized for better interpretation. A hybrid AI model was developed to enhance prediction accuracy by integrating algorithms like Support Vector Machine (SVM), Decision Tree and K-Nearest Neighbor (KNN). Machine learning models were trained, tested, and compared using metrics for model evaluation i.e., R-squared (R2), Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Square Error (MSE). The hybrid model demonstrated superior performance with results having R2: 0.99, explaining 99.99 % of the dataset variance. Additionally, Pearson’s correlation matrix revealed that level of awareness (−0.97), climate-responsive design (−0.86), and material inventory (−0.83) exhibited the strongest negative correlations with cost impact, while site area temperature (0.72) had the most significant positive correlations. External validation using an independent dataset of 659 samples (R-squared: 0.81) and Taylor diagram analysis (standard deviation < 2.9 %, correlation > 0.82) further validated proposed model’s superiority over existing models. These findings provide a comprehensive cost prediction framework aiding stakeholders in making informed decisions on sustainable and cost-effective green housing.