RentScout: Intelligent Rentals With Predictive Pricing and Sentiment-Aware Search

The Indian rental market continues to face ongoing challenges in property discovery, pricing transparency, and data-driven decision-making. To address these issues, this study introduces RentScout, an AI-powered platform that combines rental price prediction, sentiment analysis of tenant reviews, and adaptive property recommendations in a unified and scalable framework. Unlike prior solutions, RentScout is specifically designed for the Indian context, leveraging both structured data (e.g., property attributes) and unstructured data (e.g., user reviews and interaction patterns) to deliver relevant insights. The platform was built using React, Node.js, Express, and PostgreSQL. Preliminary testing indicated a 27 % improvement in engagement when compared with nonpersonalized recommendation baselines. Additionally, the system achieved 92 % accuracy in rental price estimation and an F1-score of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{8 8 \%}$</tex> in sentiment classification. By enhancing user trust and promoting transparent decision-making, RentScout represents a practical application of AI for modernizing India's rental housing ecosystem.