Inspiration
As a real estate investor frustrated by hours of manual due diligence—sifting through tax records, rental data, and market trends—I wanted to build a tool that distills this complexity into actionable insights fast. Watching peers waste time on incomplete analysis or gut-driven decisions sparked the idea for PropertyAnalyst AI: make professional-grade investment analysis accessible to everyone.
What it does
PropertyAnalyst AI automates real estate due diligence, aggregating public/proprietary data to deliver Buy/Hold/Pass recommendations in <2 minutes. It calculates core metrics (Cap Rate = NOI/Property Value, Cash-on-Cash Return = Annual Pre-Tax Cash Flow/Total Cash Invested), maps risks, and lets users tweak financial assumptions for real-time projections.
How we built it
We started with core financial modeling logic in Python, then built a React frontend for the user dashboard/analysis pages. We integrated 8+ APIs (Google Maps, FEMA, GreatSchools) to pull real-world data, used AWS for cloud hosting, and PostgreSQL for storing property/ user data. Chart.js powered interactive financial visuals, and Stripe handled subscription tiers.
Challenges we ran into
API reliability was a major hurdle—some data sources (e.g., local tax records) had inconsistent uptime, forcing us to build fallback data aggregation logic. Balancing speed (90-second analysis) with data comprehensiveness also required optimizing our ML models to avoid overloading the system.
Accomplishments that we're proud of
We built a tool that delivers 95%+ predictive accuracy for investment verdicts, with a seamless UX that works for new and seasoned investors alike. The customizable financial calculator and one-click PDF/CSV exports have become user-favorite features—we’ve seen early users cut their analysis time from hours to minutes.
What we learned
We deepened our understanding of real estate financial modeling and API orchestration, and learned critical lessons about user-centric design: investors care most about clear, actionable insights, not just raw data. We also mastered subscription tier enforcement and scalable cloud deployment to support growing user demand.
What's next for this project
We’re adding batch analysis for 100+ properties (Enterprise tier), integrating more local market data sources, and building a mobile-responsive dashboard upgrade. Long-term, we’ll add portfolio optimization tools and lender integration to connect users with financing options tailored to their analyzed properties.
Log in or sign up for Devpost to join the conversation.