NestFinder: A Full-Stack Real Estate Platform with AI Features

NestFinder is a comprehensive real estate discovery platform that I built to explore the full capabilities of the Next.js ecosystem. The platform features interactive property maps, secure authentication, payment processing, and AI-assisted features, all backed by a robust relational database managed through Prisma ORM.

I chose Next.js for this project because real estate platforms have unique rendering requirements. Listing pages need to be statically generated for fast initial loads and SEO, while search results need server-side rendering to handle dynamic query parameters. Individual property pages can be statically generated at build time, but the search functionality requires real-time database queries. Next.js hybrid rendering model allows me to use both approaches within the same application, with the framework automatically choosing the right rendering strategy for each page.

The interactive maps are powered by Leaflet, which I chose over Google Maps because of its lower cost and greater flexibility. Each property listing includes a map view showing the property location along with nearby amenities like schools, hospitals, and public transit stations. I built a custom clustering algorithm that groups nearby properties when the map is zoomed out, then splits them into individual markers as the user zooms in. This was necessary because displaying thousands of markers simultaneously would overwhelm the browser.

Payment processing goes through Stripe, which handles premium listing placements and featured property promotions. Property owners can purchase various listing packages that give their properties enhanced visibility. The Stripe integration includes webhook handling for asynchronous payment events, which required careful consideration of idempotency to prevent duplicate processing.

Claude AI integration was one of the most exciting features. When a property owner creates a listing, they can optionally use AI to generate a compelling property description. The AI takes raw data about the property, such as the number of bedrooms, square footage, and key features, and generates a natural language description that highlights the property's best attributes. I also implemented an AI-powered search recommendation system that suggests properties based on the user's browsing history and saved searches.

Prisma ORM manages the database schema and provides type-safe database access. The schema includes models for users, properties, listings, bookings, payments, and reviews, all with appropriate relations and constraints. Prisma migrations handle schema changes smoothly, and the generated TypeScript types eliminate an entire class of runtime errors.

One of the biggest challenges was optimizing map performance. Loading hundreds of property markers simultaneously caused significant frame drops. I solved this by implementing viewport-based loading, where only properties visible in the current map view are fetched and rendered. As the user pans or zooms, new data is fetched dynamically with debouncing to prevent excessive API calls.