The search for suitable real estate has become one of the most critical challenges in urban areas, characterized by information asymmetry and inefficient filtering. The vast, unorganized data from property listings not only overwhelms potential buyers and renters but also poses severe difficulties in finding properties that match specific, nuanced requirements. To address this issue, the present project proposes an innovative solution: "Prop Connect," an intelligent web portal that converts scattered real estate data into a useful, personalized resource. The system employs a machine learningbased recommendation engine that captures user preferences. The captured user data and property listings are then passed through advanced filters and a collaborative filtering model to separate irrelevant properties from highmatch suggestions. Once processed, the user is presented with a high-quality, ranked list of properties. This portal can be used for buying, selling, and renting, thereby promoting efficiency and data-driven decisions. The project demonstrates a cost-effective and user-friendly method to reduce search time while simultaneously creating a valuable, streamlined service. This approach not only contributes to a more efficient market but also highlights the importance of data management in tackling modern urban challenges
Kumbhar et al. (2026) studied this question.