Flippa Scraper — Businesses for Sale, Sold Comps & Valuation Multiples

Extract online businesses for sale from Flippa with everything their public listing page shows: asking price (and the price before a reduction), monthly revenue and profit, profit margin, profit and revenue multiples, business age, location, traffic, MRR, subscribers, churn, app ratings and downloads, data verification (Google Analytics, Stripe), Flippa vetting, the broker's track record, views and watchers. Get recently sold businesses as comparables (with the final price of sold auctions) and category snapshots: one row per business type or industry with median asking price, median profit multiple, revenue and margin, and an opportunity score.

No login, no account, no API key. Duplicate rows within a run and error rows are never charged.

This actor is not affiliated with, endorsed by, or connected to Flippa. It reads public pages only.

What you get

Output type	One row per	Highlights
listings (default)	business for sale matching your filters	104 columns, about 51 filled per listing (n = 6,081, 17 Oct 2026): asking price, original price and reduction, seller currency and exchange rate, monthly revenue and profit, margin, profit and revenue multiples, age and approximate founding date, location, sale method, bids, reserve and time left for auctions, page views, MRR, subscribers, churn, app rating/reviews/downloads, followers, domain data, newsletter rates, verification and vetting, broker company and track record, payment methods, views, watchers, comments, full "About the business" text
listing-details	Flippa link you paste	The same row for specific listings
sold	recently sold business	The same fields, the last asking price and, for sold auctions, the final price — valuation comparables
category-snapshot	business type or industry	Listings scanned and Flippa's total, median and quartile asking price, median and quartile profit multiple, median revenue multiple, median monthly revenue and profit, median margin and age, shares of verified, auction, price-reduced and brokered listings, top industries/types/countries, opportunity score

Filters

All filters can be combined: business type (18 Flippa categories, domains optional), industry (15), seller location (country codes or regions), asking price, monthly profit, monthly revenue, age, sale method (auction / classified), listed by (Flippa broker, partner broker, owner), keyword, profit multiple, revenue multiple, only listings with connected data, and sort order.

Flippa applies most filters itself, and the actor re-checks every result against your filters: a listing whose shown value breaks a filter is skipped without charge. Flippa's own multiple filters do not match the multiples it displays (measured on 17 Oct 2026: with "multiple at least 4", 88% of the results showed less), so profit and revenue multiples are checked by the actor on each search result before the listing page is opened; a run checks up to 100 search results per requested row (at least 2,000, at most 12,000) and says so in the status when it stops early.

Pasting listing links with the default output type runs listing-details for exactly those; filters are not applied to pasted links.

Keywords are plain words. Flippa's firewall refuses some searches that look like code or database commands (for example "union select"); the actor checks the same search without the keyword, and if that works it stops at once with an invalid-input row that is not charged, instead of retrying.

Data quality, measured

Measured with this actor on 17 Oct 2026: every business for sale in the 18 business types (6,081 listings, one full run on the Apify platform, 0 duplicates, 0 error rows) and every recently sold one listed by Flippa (1,115 sold listings, one full run on the Apify platform on 21 Oct 2026). "Filled" = not null and not empty.

Field	For sale (n = 6,081)	Sold (n = 1,115)
Asking price, age, location, business type, industry, views, watchers	100%	100%
Monthly profit	61%	72%
Monthly revenue	58%	70%
Profit margin	45%	61%
Profit multiple · revenue multiple	45% · 45%	59% · 55%
Final price of sold auctions	—	40%
"About the business" text	68%	31%
Flippa vetting label	49%	47%
Connected data (Google Analytics, Stripe…)	38%	35%
Page views per month	19%	21%
Broker name and track record	10%	7%
App Store rating and reviews (apps)	6% of all listings	6%

Filled fields per listing: average 50.7 of 104 (p10 40, median 52, p90 60; n = 6,081); sold: 51.1 (p10 41, median 53, p90 58; n = 1,115). The full run took 32 minutes for 6,081 listings with their pages.

Flippa keeps the monthly P&L, detailed traffic and seller notes behind an account; this actor never logs in, so those are not included (detailsLocked: true). Newer and smaller listings often omit revenue; broker-managed listings are the most complete.

How it compares

Compared with the other Flippa actors in the Store as declared in their own schemas, inputs and READMEs (17 Oct 2026):

	This actor	Market leader	Other Flippa actors
Price per listing	$0.001	$0.001	$0.0005–$0.016
Start fee	$0.00001	$0.007	up to $0.20
Needs your Flippa login for financials	never	asks for email, password or cookies	—
Sold businesses with final auction price	yes	not declared	2 small actors
Category snapshot with median multiples	yes	no	no
Multiple filters that match the shown multiple	yes	—	rely on Flippa's filter
Error rows charged	never	—	often (charged as dataset items)
Successful runs	50 of 50 test runs, plus 39 of 39 confirmation runs and an independent review (Oct 2026)	87.8% (361 of 411, last 30 days)	—

Pricing

Pay per event — you pay only for rows delivered:

Event	Price
Business for sale (listings, listing-details)	$0.001 ($1.00 per 1,000)
Sold business (sold)	$0.001
Category snapshot (one business type or industry)	$0.01
Actor start	$0.00001

Duplicates within a run, listings skipped by your filters, and error rows (not found, unavailable) are never charged. Rows reused from the shared cache cost the same as live rows and carry cachedAt. If you set a maximum cost per run, the actor stops cleanly when it is reached.

Opportunity score

Every category-snapshot row carries opportunityScore (0–100) and its four components, computed with a fixed, published formula — the same in every run and comparable between runs:

• Demand (40%): min(1, log10(1 + total monthly revenue of the group in USD) / 8) — 8 means $100M of monthly revenue listed in the group.
• Dissatisfaction (25%): share of items rated below 4 stars — not measurable on Flippa (listings have no ratings): its component is null and dissatisfactionMeasured is false.
• Staleness (20%): share of items not updated in two years — not measurable on Flippa (publication dates are not public): its component is null and stalenessMeasured is false. Components that cannot be measured are left out and the score is divided by the weights that remain.
• Openness (15%): 1 / (1 + populationCount / 50) — fewer listings means a less crowded market. populationCount is the group's real size on Flippa (its own count for your filters), so the score does not depend on how many rows you asked for; when Flippa only shows a cap ("10,000+", domains) the scanned count is used and opennessBasis says sample instead of category. Formula version v1.4 (in opportunityFormula).

With the multiple or verification filters, which the actor applies itself, populationCount is the number of listings that match them when the whole group was scanned (scanComplete: true).

medianDemand (the shared module's median of the demand signal) is the median monthly revenue of the listings that show one — listings without a published revenue are left out, not counted as $0 — so it equals medianMonthlyRevenueUsd; demandKnownCount says how many listings it covers.

So on Flippa the score is (0.40 × demand + 0.15 × openness) / 0.55, from 0 to 100 (measured on 22 Oct 2026: 46.5–70.4 across the 18 business types, 58.8–67.0 across the 15 industries), and ranks groups by listed revenue against crowding. Use it with the medians next to it: a group with high median revenue and a low median profit multiple is where businesses sell cheaply relative to what they earn.

Input examples

Default input {}: 20 listings in 4–7 seconds (measured on 17 Oct 2026, on the Apify platform, with listing pages from the shared cache; about 18 seconds fully live): the 20 newest businesses for sale with their listing pages. A market snapshot of every business type (about 6,080 listings scanned) takes about 1 minute; 300 listings with their pages, fetched live, about 2 minutes.

Profitable SaaS for sale under $100,000 at most 3x yearly profit:

{
  "propertyTypes": ["saas"],
  "maxPrice": 100000,
  "minMonthlyProfit": 1000,
  "maxProfitMultiple": 3,
  "maxItems": 100
}

US ecommerce businesses with connected data, sold recently (comparables):

{ "outputType": "sold", "propertyTypes": ["ecommerce"], "countries": ["us"], "dataVerifiedOnly": true, "maxItems": 200 }

Market snapshot by industry:

{ "outputType": "category-snapshot", "snapshotGroupBy": "industry" }

Specific listings:

{ "listingUrls": ["https://flippa.com/12000168", "11816135"] }

Output

One row type per run in the default dataset, with views for an overview, financials and valuation, traffic and apps, auctions, snapshots and errors. All money fields are in USD (currency: "USD"), the seller's own currency is kept next to them, dates are ISO 8601, and missing values are explicit null. The run also writes ERRORS (always present, empty when nothing failed) and RUN_SUMMARY (delivered, charged, duplicates, filtered, errors) to the key-value store.

Example (shortened):

{
  "_type": "listing",
  "id": "12000168",
  "url": "https://flippa.com/12000168-turnkey-ai-saas-with-12-3k-annual-revenue-71-margins-540-subscribers-0-churn-ios-android-apps-and-strong-growth-potential",
  "propertyType": "SaaS",
  "industry": "Internet",
  "country": "United Arab Emirates",
  "askingPriceUsd": 9999,
  "originalPriceUsd": 21740,
  "priceReducedPct": 54,
  "monthlyProfitUsd": 499,
  "profitMarginPct": 72,
  "profitMultiple": 1.7,
  "revenueMultiple": 1.2,
  "pageViewsPerMonth": 1511,
  "activeSubscribers": 546,
  "churnPct": 2,
  "integrations": ["Google Analytics", "Stripe"],
  "ageMonths": 24,
  "watchers": 311
}

Reliability

• Requests are paced per connection and spread across connections; a temporary block switches to a fresh connection at once (never the same one again), and long blocks pause briefly. Verification pages are never solved: the actor simply uses another connection.
• If something cannot be fetched, you get an explanatory error row that is not charged, and the run still finishes successfully. If a listing page cannot be read, the row still carries the search-result fields (detailFetched: false).
• Runs survive platform migrations without duplicating or re-charging rows.
• If Flippa changes its page format, the actor refuses to guess and returns a schema-changed error row instead of empty data.

Privacy

The actor returns only what Flippa publishes on public pages. Owners' identities are not shown by Flippa without an account, and are not returned. For brokers it returns their public professional profile (name, company, country, short bio, feedback and transaction totals). E-mail addresses and phone numbers are removed from every text and name. You are responsible for using the data in line with applicable law (e.g. GDPR) and Flippa's terms.

FAQ

Does it need a Flippa account? No. It reads public pages only, so the monthly P&L and seller notes that Flippa keeps behind an account are not included.

How fresh is the data? Search results are always live. Listing pages fetched by any run in the last 12 hours are reused from a shared cache (rows carry cachedAt; same price). Set maxCacheAgeHours to 0 to read every listing page live.

How many listings are there? About 6,080 businesses for sale across the 18 business types, plus 10,000+ domains, and about 1,120 recently sold (17 Oct 2026).

Why is monthly revenue rounded? Flippa's search results show revenue rounded to three significant digits (e.g. $160K); monthlyRevenueApprox says so. Monthly profit comes exact from the listing page, in the seller's currency and in USD.