Property Price Prediction Using Machine Learning

Abstract

To make smart investment choices in the rapidly evolving property sector market of today, it's critical to have reliable tools for estimating property prices This study aims to come up with a comprehensive machine learning-based system for forecasting property prices. The proposed solution incorporates historical property data and takes into consideration the future development plans to enhance prediction accuracy.

Using crucial factors that effects the property prices such as location, square footage, count of bedrooms, and bathrooms, this study presents a machine learning model that assists in estimating residential sector pricing. The model also accounts for actual developments that may impact property value, such as new highways, train stations, airports, retail centres, and other public infrastructure. The system also integrates financial tools, including an EMI calculator and a rent estimator based on user-defined profit margins. In the advanced phase, a decade’s worth of historical data, combined with categorized property classes and reasons for annual price shifts, was used to further enhance accuracy.  A “Future Factors Database” was incorporated to account for upcoming infrastructure projects, enabling future growth predictions. The system is developed using Python and Flask, with plans to transition from CSV to a MySQL database for dynamic data handling.