The Story
This was a freelance project I picked up on Fiverr. Seven months. One client. A problem that seemed impossible at first.
The client ran a fleet of rental cars on Turo—the Airbnb of car rentals. His challenge? He had no idea what the competition was charging. No way to see which cars were in demand. No data to make smart pricing decisions. Just guesswork.
He wanted a Chrome extension that could give him real-time market analytics. Pricing trends. Booking rates. Vehicle performance across the entire United States.
The only problem: Turo doesn't have a public API.
The Challenge
Turo is the largest peer-to-peer car rental marketplace in the world. But like many platforms, they keep their data locked down. No API. No exports. No official way to access vehicle or market information.
For a rental host trying to run a profitable business, this is crippling. You can't see what competitors charge. You don't know if your market is saturated. You have no visibility into which vehicles are in high demand.
The core challenge was clear: How do you collect data from hundreds of thousands of vehicles across an entire country—without any official access?
Turo's map-based search interface. This is where I discovered the geographic coordinate parameters that made the entire solution possible.
The Research
I spent about a month just researching. Poking around. Looking for cracks.
Early notes and logic flow sketches during the research phase.
Then I found one.
Turo's map-based search accepts geographic coordinates as parameters. When you search for cars in a specific area, you're essentially querying a bounding box defined by latitude and longitude.
This gave me an idea: What if I divided the entire country into a grid?
A custom Python tool that generates a coordinate-based grid covering the entire United States. The blue outline shows country boundaries, divided into rectangular cells for systematic querying.
The Solution
Divide the entire United States into a grid of rectangular cells. Then query each cell to get all vehicles within its boundaries.
Simple in theory. But there was a catch.
Turo's search only returns a maximum of 200 vehicles per query. In high-density areas like Los Angeles or New York, there could be thousands of vehicles in a single cell.
So I built a recursive subdivision algorithm:
- Query a grid cell
- If it returns 200+ vehicles, split it into 4 smaller cells
- Query each smaller cell
- Repeat until every vehicle is captured
The algorithm in action over California. High-density areas like Los Angeles automatically subdivide into smaller quadrants to capture all vehicles beyond the 200-vehicle limit.
The system keeps subdividing until it reaches street-level precision if needed.
A close-up showing how the algorithm subdivides down to street-level accuracy in densely populated areas. No vehicle gets missed.
Live Scraper Dashboard
To visualize the entire operation, I built a real-time dashboard that shows scraping progress across the United States.
The Next.js-powered plotter showing scraping progress across the country. Yellow cells indicate low vehicle density, colored cells show higher concentrations, and dark cells are zero-vehicle areas.
The Infrastructure
The entire system is containerized with Docker, making it easy to deploy, scale, and maintain across environments.
The stack includes:
- Python for the core scraping engine and grid calibration
- Node.js + Express for the API layer
- Next.js + React for the analytics dashboard
- Firebase for real-time data sync
- PostgreSQL for structured data storage
- Puppeteer for browser automation
- TypeScript throughout for type safety
The Analytics Dashboard
The final product—a complete market intelligence platform for Turo hosts.
The complete analytics dashboard showing all key metrics: vehicle count, All-Star host percentage, vehicle type distribution, price distribution, booking rates by category, and a searchable vehicle table with demand indicators.
The Result
What started as "this might be impossible" became a fully functional product:
- Real-time pricing analytics across any region
- Competitor analysis by vehicle type, rating, and pricing tier
- Demand indicators showing which vehicles are booking fast
- Booking rate breakdowns by category and protection plan
- Interactive maps showing market density and opportunity zones
Collecting data from hundreds of thousands of vehicles across the United States. Updated in real-time. All from a platform with no public API.