Property Price Predictor

The real estate sector increasingly relies on digital tools to facilitate informed investment decisions. However, the complexity and variability of property prices—driven by multiple parameters such as location, size, and amenities—make accurate estimation a challenging task. To address this, our project presents a web-based Property Price Predictor application, developed entirely using HTML, CSS, and JavaScript. The system is designed to operate on the client side, eliminating the need for a backend server or machine learning model integration, thereby reducing latency and improving accessibility. The application allows users to estimate property prices dynamically by selecting specific attributes including total square footage, number of bedrooms and bathrooms, and location. These user inputs are processed in real time against a curated JSON dataset that contains diverse property listings. Upon matching the input with available data, the application displays the predicted price along with relevant property details such as area type, availability, and society information. The intuitive interface ensures that even non-technical users can navigate the platform with ease. This project highlights the potential of lightweight, browser-based solutions for solving data-driven problems in domains traditionally dominated by server-heavy architectures. In addition to its utility for property buyers and sellers, the application serves as a demonstration of how front-end web technologies can be leveraged to build scalable, portable, and interactive tools for real-time data exploration. This work not only contributes to the development of smarter digital platforms in real estate but also provides a foundation for future integration with predictive analytics and cloud-based data pipelines.