A Novel Approach to Rental Market Analysis for Property Management Firms Using Large Language Models and Machine Learning

This research paper presents a novel approach to Rental Market Analysis for Property Management Firms using Large Language Models (LLMs) and Machine Learning techniques. The proposed system leverages LLM-based web scraping to extract data from dynamic websites, enabling the automated collection of relevant market information. By employing LLMs, the system generates insightful comparisons between property management firms and their listed properties, providing a comprehensive understanding of the competitive landscape. Additionally, an ensembled machine learning approach, utilizing multiple models, is developed to accurately predict rental prices. The integration of these cutting-edge technologies empowers property management firms with a dashboard that offers insightful analytics, predictive capabilities, and generated insights for data-driven decision-making. The system’s architecture combines Python, ReactJS, AWS, PowerBI, PostgreSQL and OpenAI APIs to create a user-friendly interface that facilitates seamless data interaction and enhances insight generation. By automating data collection, analysis, and insight generation, this novel approach revolutionizes traditional rental market analysis processes, enabling property management firms to stay competitive and optimize their business strategies in dynamic rental markets.