Machine Learning Based Buyer Segmentation and Investment Profiling for Real Estate Market Intelligence

This publication proposes a comprehensive machine learning framework for buyer segmentation and investment profiling in the real estate sector. The methodology combines unsupervised clustering for identifying buyer segments with supervised learning for predicting investment profiles. The framework incorporates feature engineering, explainability, fairness, and governance considerations, making it suitable for practical deployment in real estate agencies, developers, and proptech platforms. Supporting resources, including the implementation code, cleaned dataset, exploratory analysis notebook, and Streamlit dashboard, are available through the associated GitHub repository to encourage reproducibility and future research.