REALESTATE HOUSEPRICE PREDICTION

Abstract

The rapid growth of AI-driven real estate analytics and increasing complexity of property valuation have created significant demand for intelligent house price prediction systems. Traditional valuation methods rely heavily on manual analysis and market expertise, which are time-consuming and inconsistent. This paper presents the design and implementation of an AI-Based Realestate Houseprice Prediction System that leverages Machine Learning, Data Analysis, Regression Models, and predictive analytics to estimate property prices accurately. The system analyses features such as location, area, number of bedrooms, amenities, market trends, and property images to generate accurate predictions. A web-based dashboard allows users to input property details and instantly receive estimated house prices. Experimental results demonstrate improved prediction accuracy and efficient performance for real-world real estate applications.