Austin Airalytics: Turning Austin’s Airbnb Chaos into Clarity

I’ve always been fascinated by how raw data can tell stories- if you know how to listen. Austin Airalytics started as a personal quest to tame the wild world of Airbnb listings in my hometown. Picture this: thousands of CSV files dumped from rental platforms, each one a tangled mess of prices, locations, and hidden gems. My goal? Transform that chaos into an interactive dashboard where anyone could slice through the noise and find their perfect spot in the Texas sun.

At its heart, Austin Airalytics is a full-stack playground built on Next.js, where I poured in raw Austin Airbnb data and watched it bloom into something beautiful and usable. Users can browse listings like flipping through a digital magazine, filter down to the essentials, dive into property details, and even spot market trends through snappy visualizations. It’s all about making complex data feel approachable: performance-first, scalable, and just plain fun to use.

The Features That Make It Tick

I built this with real users in mind, focusing on the things that matter when you’re hunting for a rental:

• The Listings Dashboard: A paginated table that lets you sort by price, neighborhood, or room type with a click. It’s like having a personal real estate agent who never sleeps.

The interactive listings dashboard in action.

• Smart Filtering: Sidebar controls for price ranges, amenities, and property types—dial in exactly what you need from thousands of options without the headache.
• Property Deep Dives: Click into any listing for high-res images, host bios, reviews, and pricing history. It’s the full story, not just the highlights.
• Map Magic: Leaflet-powered maps with clustering to handle dense Austin data. Custom markers make navigation feel like a treasure hunt.
• Mobile-Ready Design: Tailwind CSS ensures it looks sharp on any screen, because who wants a clunky rental search on their phone?
• Featured Gems: Curated highlights on the homepage to spotlight the standouts.

Under the Hood: The Tech That Powers It

Diving into the build, I leaned hard on Next.js 15’s App Router for that lightning-fast SSR and static generation—load times that feel instant. React and TypeScript on the frontend, paired with Tailwind for styling and Shadcn/UI for those polished components. It’s clean, consistent, and responsive.

On the backend, tRPC was a game-changer: type-safe APIs without the REST boilerplate. Data lives in a SQLite database via Drizzle ORM, with migrations keeping everything tidy. Processing those massive CSVs—over 10,000 listings—was a beast, so I wrote custom TypeScript scripts for cleaning and transforming, ensuring high performance from the ground up.

Performance was non-negotiable. Lazy loading for images, virtualization for big lists, and React Query for efficient fetching. The map? Leaflet with clustering plugins to keep things smooth even with thousands of pins. Analytics crunch numbers via Python (Pandas and Matplotlib) and Rust for speed, cached in JSON for quick hits.

The Roadblocks and the Wins

Building this wasn’t all smooth sailing. Handling 10,000+ listings with images and geo-data pushed my optimization skills to the limit. I remember staring at laggy renders, thinking, “This has to be faster.” Enter memoization with useMemo and useCallback, plus Leaflet’s clustering—suddenly, it was buttery smooth.

Then there was the data mess: CSVs from different sources, inconsistent formats, missing values. I built a Zod-validated pipeline with automated cleaning scripts to handle the edge cases. No more surprises.

Type safety across the stack? Tricky, but tRPC and TypeScript caught bugs before they bit. It’s the kind of setup that makes you sleep better at night.

Why This Project Matters to Me

Austin Airalytics isn’t just code. It’s a demonstration I can wrangle big data, architect full stacks, and optimize for real-world performance. It showcases Next.js mastery, TypeScript rigor, Tailwind elegance, and integrations that bring maps and charts to life. Plus, the data processing chops with Rust and Python, Drizzle for databases, and tRPC for APIs.

As a portfolio piece, it shines because it solves real problems: turning rental market chaos into clarity. Employers and clients see how I scale data, polish UIs, and deliver experiences that feel intuitive. It’s the blend of frontend finesse, backend muscle, and analytics smarts that makes complex apps click.

In the end, it’s about more than tech… it’s about making data dance. And in a world drowning in information, that’s a skill worth having.