ChatGPT made an AirBnB scraper to #1 build your own DB of detailed listing data, and #2 get pricing & occupancy stats from the source itself (replacing external-products like AirDNA, Rabbu, etc.)

Unlocking Airbnb Data: Building a Custom Scraper for Enhanced Listing Insights

Are you an Airbnb host or researcher looking to gain deeper insights into the market? Recent developments have made it possible to create personalized tools that tap directly into Airbnb’s own platform, bypassing traditional external sources. I’ve developed a solution that not only helps you build your own comprehensive database of listing details but also provides real-time pricing and occupancy statistics—effectively replacing third-party tools like AirDNA or Rabbu.

Creating a Tailored Airbnb Data Platform

Leveraging advanced automation and modern web development, I built a Chrome extension with Cursor (using OAuth 3) and a dedicated website integrated with Supabase. This setup includes custom database tables, row-level security, and schema configurations—all linked seamlessly to enable authentication and data access for the extension. This means you can collect and analyze Airbnb data directly from the source, ensuring accuracy and relevance.

Why This Matters

As an owner managing multiple properties in the West LA and San Fernando Valley regions, I’ve noticed significant limitations with existing external analytics services. Tools like AirDNA have been invaluable, but often rely on estimations that can be inflated or outdated. For instance, I’ve observed discrepancies in pricing estimates and occupancy figures—sometimes due to the way they calculate discounts or filter property features.

Airbnb itself doesn’t provide advanced filtering options or sorting capabilities directly on their listings page, making it challenging to extract meaningful insights without external tools. By directly accessing the platform’s data, you can overcome these limitations and obtain granular, accurate information—such as availability filters, amenities like pools or jacuzzis, and precise occupancy rates.

Benefits of a Direct Data Approach

• Accuracy: Fetch real-time, source-based data that reflects current market conditions.
• Customization: Tailor filters and queries to match your specific property features and market segment.
• Cost-Effectiveness: Reduce dependency on paid third-party services, saving money and ensuring data integrity.
• Insightful Trends: Understand neighborhood-level dynamics, pricing strategies, and occupancy patterns more effectively.

Practical Application: A Personal Case Study

I recently used this approach for my own Woodland Hills property—a four-bedroom, two-bath, non-pool home. The tool’s insights aligned closely with actual occupancy rates and nightly pricing, confirming its reliability. Our occupancy was notably lower than industry projections, and the suggested rate of $480-$515 per night was spot on with market realities. This validation underscores the potential of