Estate Eagle

https://github.com/owenz0523/Estate-Eagle

Inspiration 📍

Our inspiration for the Real Estate Price Tracker project stems from a desire to bring transparency and predictive insights to the real estate market. By integrating cutting-edge technologies such as blockchain for secure and immutable data storage, and machine learning for accurate future price predictions, we aim to create a comprehensive platform that benefits both buyers and sellers. Our web application, built with NextJS and Tailwind, offers a user-friendly interface for property searches, price tracking, and market analysis. With real-time updates facilitated by Avalanche subnets and a robust backend powered by Node.js and Supabase, our solution not only enhances user experience but also sets a new standard in the real estate industry.

What it does 👭👬

The Real Estate Price Tracker project is designed to provide users with a comprehensive tool for tracking and predicting real estate prices. The platform features a web application built with NextJS and Tailwind, offering a user-friendly interface for property searches, price tracking, and market analysis. Key functionalities include:

1. User Authentication: Ensures secure access and personalized experience for users through Clerk.
2. Property Search and Query: Allows users to search for properties and view detailed information.
3. Dashboard for Tracking Prices: Provides a central hub for users to monitor the prices of properties they are interested in.
4. Price History Graph and Market Predictions: Displays historical price trends and future market predictions using data analysis and machine learning models.
5. Alerts and Notifications: Keeps users informed with real-time updates on property price changes and market trends.

The backend, powered by Node.js and Express, provides robust API endpoints and utilizes Supabase for storing user and property information. The project also incorporates blockchain technology through the NEAR protocol for secure data storage and Avalanche subnets for processing real-time updates. Additionally, AI and data analysis tools, potentially leveraging Google AI Platform or Python ML libraries, are used to predict future property prices and trends based on historical data. If time permits, the backend infrastructure will be implemented on AWS for enhanced scalability and performance.

How we built it ⌨️

We built the Real Estate Price Tracker project by combining a modern web application framework with powerful backend and blockchain technologies. Our frontend was developed using NextJS and Tailwind to create an intuitive and responsive user interface, complete with user authentication via Clerk. For the backend, we used Node.js and Express to set up RESTful API endpoints, while Supabase was chosen to store user and property information securely. We integrated blockchain technology using the NEAR protocol for immutable data storage and leveraged Avalanche subnets for real-time updates. Machine learning models, potentially running on Google AI Platform or using Python ML libraries, were employed to predict future property prices based on historical data. To ensure scalability and robustness, we planned to deploy our backend infrastructure on AWS. Each team member contributed to different aspects of the project, following a structured task order to ensure seamless integration and development.

Challenges we ran into ⏰

During the development of the Real Estate Price Tracker project, we encountered several challenges that tested our technical skills and problem-solving abilities. Firstly, integrating the blockchain with our backend was complex due to the intricacies of the NEAR protocol and Avalanche subnets, requiring us to spend additional time understanding and implementing secure and efficient data transactions. Secondly, ensuring real-time updates and synchronization between the backend and the frontend proved challenging, particularly in handling high-frequency data changes and maintaining system performance. Thirdly, training and deploying our machine learning models for accurate future price predictions demanded significant computational resources and fine-tuning, leading us to optimize our algorithms and explore different machine learning frameworks. Additionally, we faced difficulties in implementing user authentication seamlessly, ensuring both security and user-friendliness. Despite these challenges, our collaborative effort and determination allowed us to overcome these obstacles and deliver a functional and innovative real estate tracking platform.

Accomplishments that we're proud of 🎉

We are proud of several key accomplishments in the development of the Real Estate Price Tracker project. Firstly, we successfully integrated blockchain technology using the NEAR protocol, ensuring secure and immutable storage of real estate data, which significantly enhances the transparency and reliability of our platform. Secondly, we developed a highly responsive and user-friendly web application using NextJS and Tailwind, providing users with an intuitive interface for property searches and market analysis. Thirdly, our implementation of real-time updates with Avalanche subnets and efficient backend operations with Node.js and Supabase ensured seamless data synchronization and high performance. Additionally, our machine learning models for predicting future property prices demonstrated accurate and insightful market trends, providing users with valuable foresight. Lastly, we achieved a robust user authentication system with Clerk, balancing security and ease of use, which is critical for user trust and engagement. These accomplishments reflect our team's technical proficiency, innovative approach, and dedication to delivering a high-quality product.

What we learned 🗞️

Throughout the development of the Real Estate Price Tracker project, we learned several valuable lessons that have enhanced our technical and collaborative skills. Firstly, we gained a deep understanding of blockchain integration, particularly the complexities involved in using the NEAR protocol and Avalanche subnets for secure and efficient data transactions. Secondly, we improved our proficiency in building scalable web applications using NextJS and Tailwind, ensuring an optimal user experience. Thirdly, we learned the importance of real-time data synchronization and the challenges associated with maintaining high performance in a dynamic environment. Our experience with machine learning models provided us with insights into the nuances of training, fine-tuning, and deploying algorithms for accurate market predictions. Additionally, we discovered the critical balance between security and usability in implementing user authentication systems. Lastly, this project underscored the importance of effective communication and teamwork, as our coordinated efforts were crucial in overcoming technical obstacles and delivering a successful product.

What's next for Estate Eagle 🔍

Next for Estate Eagle, we plan to expand and enhance our platform to provide even more value to our users. Our immediate goals include:

1. Enhanced Machine Learning Models: Further refining our machine learning algorithms to improve the accuracy of our price predictions and market trend analysis. We aim to incorporate more diverse datasets and advanced predictive techniques to offer deeper insights.

2. Mobile Application Development: Developing a mobile version of the Estate Eagle platform to provide users with seamless access to our features on the go, ensuring a consistent and responsive experience across all devices.

3. Advanced Data Visualization: Introducing more sophisticated data visualization tools to help users better understand market trends, price histories, and future predictions, making it easier to make informed decisions.

4. Integration with More Real Estate Platforms: Expanding our data sources by integrating with additional real estate listing platforms and APIs to provide users with a more comprehensive view of the market.

By focusing on these areas, we aim to make Estate Eagle the go-to platform for real estate market analysis and predictions, providing unparalleled value and insights to our users.