Stop Renting

Inspiration

After watching friends struggle for years with rental fatigue and insufficient down payments to buy homes, I knew there had to be a better way. Many of us in our 20s-40s have stable careers but can’t qualify for mortgages alone—co-buying felt like the solution, yet there was no streamlined tool to make it safe and simple. That’s where CoBuyer Finder was born: to turn the stress of co-buying into a structured, secure process.

What it does

CoBuyer Finder is a web platform that connects people seeking to co-purchase homes based on shared goals (location, budget, timeline). It uses AI to assess compatibility across finances, lifestyle, and home expectations, provides ready-to-use legal co-ownership agreements, lets users build custom co-ownership rules, and plans clear exit strategies—all for a one-time $99 fee.

How we built it

We started with defining core user pain points: unclear matching, legal uncertainty, and no exit planning. We designed a minimalist UI with a trusted color palette (navy, beige, coral) for approachability, built out frontend pages (React/Tailwind) for intuitive navigation, integrated PDF tools for template downloads, and structured backend logic (Node.js/MongoDB) to power matching and assessment workflows.

Challenges we ran into

The biggest hurdle was balancing simplicity with comprehensiveness—users needed legal rigor without overwhelming jargon. We also struggled to calibrate the AI compatibility scoring to feel fair and accurate, testing dozens of criteria combinations to ensure the score reflected real-world compatibility, not just arbitrary metrics.

Accomplishments that we're proud of

We created a platform that’s fully accessible (no login required) and addresses every critical step of co-buying—from matching to exit. Users can preview legal templates, generate personalized co-ownership rules, and see clear compatibility insights in minutes, making an intimidating process feel manageable.

What we learned

We learned that user trust is everything—transparency in pricing (one flat fee, no hidden costs) and clarity in legal language were non-negotiable. We also realized how important it is to center the platform on real user behaviors: our target demographic values simplicity and professionalism over flashy features.

What's next for this project

Next, we plan to add real-time messaging between matched co-buyers, integrate mortgage guidance tools tailored to regional (North American/European) lending rules, and expand legal templates to cover more regional jurisdictions. We also want to refine the AI assessment with user feedback to make compatibility scoring even more precise.

Built With

• amazon-web-services
• figma
• mongodb
• restapi
• typescript