Green AI driven quantile boosting ensembles for sustainable real estate valuation

In today’s volatile economic environment, the accurate estimation of apartment prices is crucial for maintaining financial predictability. Nevertheless, the development of high-performing artificial intelligence (AI) models often entails substantial computational costs, which, in turn, raise critical concerns regarding environmental sustainability, economic viability, and equitable accessibility. This study addresses these challenges by proposing a comprehensive, energy-efficient framework for real estate price estimation grounded in the principles of Green AI. Utilizing a dataset of over 13 million geolocated listings in Turkey (January 2018–September 2024), we implement an end-to-end machine learning pipeline, including data preparation, outlier detection, quantile-based ensemble modeling, hyperparameter tuning using Optuna, and model evaluation. The performance of models—LightGBM, Gradient Boosting Regressor, XGBoost, and Random Forest—is assessed through a dual lens of estimation accuracy and environmental efficiency, incorporating metrics such as training time, energy consumption, and prediction latency. Results show that LightGBM offers the best balance between accuracy and resource efficiency, making it the recommended baseline for large-scale or real-time valuation systems, while GBR and XGBoost may be used in R&D settings under energy constraints. The paper also highlights the importance of integrating dynamic market indicators and climate risk variables to enhance model robustness and sustainability. Overall, this research advances a holistic framework for responsible and scalable real estate valuation, contributing to the growing field of sustainable AI deployment in high-impact sectors. • National-scale dataset enables robust training for quantile-based interval modeling. • Hierarchical outlier detection improves data quality and reduces computational load as a green preprocessing step. • Quantile-based valuation provides probabilistic price intervals for risk-aware real estate appraisal. • Green AI framework integrates ML ensembles for multi-objective optimization of accuracy and environmental cost.