How to scrape domain and web asset listings from Flippa
- Minexa.ai
- Jun 14
- 3 min read
Flippa lists thousands of websites, SaaS products, and online businesses for sale at any given moment. If you are tracking acquisition opportunities, monitoring valuation multiples across categories, or building a dataset of digital asset transactions, manually copying that data is not a realistic option. This guide shows how to extract it cleanly using the Minexa.ai Chrome extension.
What Flippa exposes on its search page
The Flippa search page at flippa.com/search surfaces a paginated list of active listings. Each card contains a meaningful set of structured data points: the listing identifier, asking price, monthly net profit, profit and revenue multipliers, business age, geographic location, category, monetization model, thumbnail image, and a watch link. These fields are consistent across listing cards, which makes the page well-suited for automated extraction.
Understanding what each field represents helps when building a dataset for analysis. The listing_id field (for example, listing-12806224) is the stable anchor for each record. It can be used to deduplicate runs and to construct direct listing URLs for downstream enrichment. The total_price field captures the asking price as displayed, such as USD $572,072, while monthly_price records the net profit figure per month. Together with profit_multiplier and valuation_multiplier, these four fields give you the core pricing logic of every listing in one row.
The business_age field records how long the asset has been operating, the watch_class field exposes the per-listing watch identifier used internally by Flippa, and the watch_item_link field provides the relative endpoint for the watch interaction. The thumbnail_image_src field gives you the CDN-hosted image URL for each listing card. The nested image_urls_and_details and website_category objects bundle category label, verification status, location, business description, type, industry, and monetization model into a single structured field per listing.
Watch the full extraction walkthrough
The video below covers the complete process from opening the Minexa.ai extension on the Flippa search page through to exporting the structured dataset.
If you have not installed the extension yet, you can get it from the Chrome Web Store: Install Minexa.ai.
How the extraction works, step by step
Open your browser and navigate to flippa.com/search. Apply any filters you need before starting the extraction. Once the page has loaded with the results you want, open the Minexa.ai extension.
After the extension opens, click the 'I'm on the right page' button to confirm you are on the target page. Minexa.ai will begin analysing the page structure automatically.
Minexa.ai detects the pagination method used by Flippa and presents the options it found. Review the detected pagination and click Continue to proceed to the next configuration step.
You will then be shown options for pagination logic and whether to scrape list-level data or follow each listing into its detail page. For this guide, list-level extraction covers all the core fields. Select the appropriate option and continue.
On the next screen, choose between simple and advanced configuration. Simple mode is sufficient for extracting all the fields described in this guide. Minexa.ai will highlight the full data container on the page once you start the job.
Once detection completes, all extracted data points are visible in the extension panel. You can navigate between them to verify the fields before running the full job.
After the job completes, the summary screen shows the total records extracted along with options to export to Google Sheets, schedule recurring runs, or download directly.
What the extracted data looks like
Below is a sample of two records extracted from the Flippa search page, with field names cleaned for readability.
[
{
"listing_id": "listing-12806224",
"business_age": "3 years",
"monthly_price": "USD $26,485 p/mo",
"total_price": "USD $572,072",
"profit_multiplier": "1.8x Profit",
"valuation_multiplier": "0.6x Revenue",
"thumbnail_image_src": "https://static2.flippa.com/blurred_thumbnail_407727b4.jpeg",
"watch_class": "watch_12806224"
},
{
"listing_id": "listing-12668272",
"business_age": "7 years",
"monthly_price": "USD $70,795 p/mo",
"total_price": "USD $3,000,000",
"profit_multiplier": "3.5x Profit",
"valuation_multiplier": "0.8x Revenue",
"thumbnail_image_src": "https://static2.flippa.com/blurred_thumbnail_006274cd.jpg",
"watch_class": "watch_12668272"
}
]The profit_multiplier and valuation_multiplier fields are particularly useful for comparative analysis across categories. A listing priced at 1.8x profit sits at a very different point in the market than one at 3.5x, and having both the profit and revenue multiples per record lets you filter and sort without any manual calculation.
Exporting and reusing the scraper
Once the job finishes, data can be exported as Excel, downloaded as JSON, or pushed directly to a Google Sheet. The scraper trained on the Flippa search page works across any filtered version of that URL, so you can reuse it for ecommerce-only listings, SaaS-only listings, or any other category filter Flippa supports without repeating the setup.
Scheduling is also available from the job summary screen. If you want a weekly snapshot of active listings in a specific category, you can set that up once and let it run automatically.
To get started, install the Minexa.ai Chrome extension and navigate to flippa.com/search with your preferred filters applied. The first extraction typically takes a few minutes from install to export.
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