Find Your Perfect

Inspiration

As a homeowner who struggled to find an agent that understood my investment-focused goals (instead of generic "top-rated" picks), I saw a gap: real estate matching needed to be personal, not one-size-fits-all. I wanted to build something that prioritized compatibility over arbitrary rankings—for buyers, sellers, investors, and anyone relocating.

What it does

RealEstateAgent Match lets users complete a 5-step preference quiz (scenario, budget, location, etc.) to get matched with 3-5 local agents aligned with their needs. Each agent profile features verified performance metrics, transparent commission info, and a booking system for instant consultations.

How we built it

We started by mapping user journeys (welcome → quiz → results → profile → booking) and defining AI matching logic. Frontend was built with React for smooth step-by-step navigation; backend used Python/TensorFlow for the matching algorithm, with PostgreSQL storing agent data and transaction history. AWS hosted the platform for scalability.

Challenges we ran into

The biggest hurdle was real-time conflict resolution for simultaneous booking requests—ensuring one time slot didn’t get double-booked. We also struggled with normalizing agent performance data (e.g., list-to-sale ratio) across different neighborhoods to make fair comparisons.

Accomplishments that we're proud of

We built an AI algorithm that delivers relevant agent matches in <5 seconds, with a seamless booking flow that auto-locks time slots and sends reminders. The platform also prioritizes transparency—only verified transaction reviews are displayed, no fake ratings.

What we learned

We deepened our understanding of user-centric AI (balancing data accuracy with usability) and the complexity of real estate industry metrics. We also learned how to design for edge cases (e.g., no agent matches, fully booked agents) to keep the user experience smooth.

What's next for this project

Short-term: Add agent comparison tools to let users side-by-side commission/performance data. Long-term: Integrate user accounts (to save quiz progress) and a mobile app. We also plan to partner with local real estate boards to expand agent databases across more regions.

Built With

• javascript
• python
• react
• tensorflow