Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{207130,
        author = {Ishita Das},
        title = {AI-Powered Buyer Segmentation for Real Estate Using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {2},
        pages = {4314-4328},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=207130},
        abstract = {The rapid digital transformation of the real estate industry has resulted in the generation of large volumes of customer and transaction data, creating opportunities for data-driven decision-making while simultaneously introducing challenges in identifying meaningful buyer patterns. Traditional customer segmentation techniques often rely on manual classification or demographic information, which fail to capture complex behavioural and investment characteristics of buyers. Consequently, organizations face difficulties in developing targeted marketing strategies, optimizing customer engagement, and improving investment planning.
This paper presents an AI-powered buyer segmentation framework that leverages machine learning techniques to identify distinct groups of real estate buyers based on demographic, financial, and behavioural attributes. The proposed framework utilizes customer and property transaction datasets containing buyer demographics, investment behaviour, property ownership details, financing preferences, and satisfaction metrics. After performing comprehensive data preprocessing, feature engineering, and data transformation, the K-Means clustering algorithm was employed to discover hidden customer segments without requiring predefined class labels. The optimal number of clusters was determined using the Elbow Method and Silhouette Score to ensure meaningful segmentation.
To enhance the practical applicability of the proposed framework, an interactive business intelligence dashboard was developed using Streamlit and Plotly. The dashboard enables stakeholders to explore buyer behaviour through dynamic filters, key performance indicators, and interactive visualizations, supporting real-time business analysis and decision-making. The identified buyer segments provide valuable insights into customer investment capacity, purchasing behaviour, financing patterns, and regional distribution, enabling organizations to implement personalized marketing strategies and improve customer relationship management.
The experimental results demonstrate that the proposed framework effectively transforms raw real estate data into actionable business intelligence. By integrating machine learning with interactive visualization, the developed system provides a scalable and practical solution for customer segmentation, strategic planning, and data-driven decision-making within the real estate industry.},
        keywords = {Buyer Segmentation, Machine Learning, K-Means Clustering, Real Estate Analytics, Business Intelligence, Streamlit, Customer Segmentation.},
        month = {July},
        }