🏠 AI Real Estate Insights – Project Story

✨ Inspiration

We were inspired by the increasing complexity of real estate data and how time-consuming it can be for investors, buyers, and analysts to manually research market trends, property risks, and investment potential. With AI rapidly transforming industries, we wanted to explore how Large Language Models (LLMs) could simplify property research by generating insights from natural language queries.

💡 What We Built

We created a prototype web app that uses Gemini API to answer real estate-related questions.
Users can input queries like:

• “What are the risks of buying property in downtown LA?”
  
• “Summarize recent trends in rental prices in Austin.”

The app fetches AI-generated insights and displays them in a clean, simple interface. We intentionally kept it frontend-only (without authentication or backend) to focus on showcasing the AI interaction and fast prototyping.

🛠️ How We Built It

We used:

• Next.js (App Router) for frontend development
• Tailwind CSS for styling
• Gemini API to access a Model for answering queries

🚧 Challenges We Faced

• Learned how to handle CORS and API key placement safely for frontend-only calls—since exposing keys on the frontend isn’t secure for production, we focused on short-term testing rather than security.

📚 What We Learned

• How to work with Gemini APIs in a frontend environment
• Rapid prototyping skills: focusing on an MVP that demonstrates core functionality under time constraints

🚀 Conclusion

This project gave us hands-on experience with AI integration in real estate tech. We hope it sparks ideas for more AI-powered tools that democratize access to real estate insights for everyone—from casual home buyers to seasoned investors.

Built With

• javascript
• node.js
• tailwind