Facebook Marketplace intelligence: resale pricing and comment sentiment
Facebook Marketplace is one of the largest resale and local-commerce platforms, and one of the least monitored. Most price-intelligence tools skip it entirely because it's local, listing-heavy, and constantly turning over.
Facebook Marketplace intelligence means combining public listing data with public comment data to price resale inventory, spot unauthorized brand resale, and read how a community actually reacts to a local post — instead of relying on manual browsing or reaction-emoji counts alone.
Quick answer
Use Facebook Marketplace Scraper to collect listing prices, condition, and seller data for a product category or location. Use Facebook Comments Scraper to pull public reactions on posts, whether that's a Marketplace listing, a local business page post, or a community group thread. Combine both to price resale inventory and read local sentiment in the same workflow.
The Marketplace intelligence stack
The stack uses two CrawlerBros Actors:
- Facebook Marketplace Scraper for listing data, pricing, and seller information.
- Facebook Comments Scraper for public comments on posts and videos with metadata.
The workflow:
\
Product category or location
-> Facebook Marketplace Scraper (listing prices, condition, seller)
-> Facebook Comments Scraper (reactions on flagged listings or posts)
-> resale price and sentiment dataset
\\
Key facts
- Location beats keyword. Marketplace is a local marketplace by design — a keyword search without a location filter returns inconsistent results.
- Condition explains price, not irrationality. Two identical-looking listings at different prices are usually explained by condition, not by one seller being unreasonable.
- Listing velocity beats a single price. A category with fast-turning listings at a given price point signals real demand better than a handful of stale unsold listings.
- "Public" has a specific meaning here. Marketplace listings and public post comments qualify; private group content doesn't.
Actor configuration that matters
For Facebook Marketplace Scraper, scope by category and location rather than a broad keyword search — Marketplace results are heavily location-weighted, so a keyword search without a location filter returns inconsistent results.
\json
{
"category": "furniture",
"location": "Austin, TX",
"maxItems": 150,
"sortBy": "date_listed"
}
\\
For Facebook Comments Scraper, feed it the specific post or listing URLs you want reactions on rather than trying to crawl comments broadly — Facebook's comment threading and pagination behave differently across post types, so targeted URL lists produce more reliable results than open-ended searches.
What the output looks like
The Marketplace row needs title, price, condition, location, seller name, listing date, and listing URL. Price and condition together are what make resale comps useful — a "good condition" $150 listing and a "like new" $150 listing aren't the same data point.
The Comments row needs comment text, author, timestamp, and post URL, so a spike in complaints or praise can be traced back to the specific listing or post that triggered it.
Use case 1: resale price benchmarking
A reseller flipping furniture, electronics, or collectibles can pull active listings for a category and location, then price new inventory against what's actually moving rather than guessing from a handful of manually checked listings.
Use case 2: gray-market and unauthorized resale monitoring
A brand that sells through authorized retailers can search Marketplace for its product names to spot bulk resale, counterfeit listings, or unauthorized liquidation stock showing up at prices well below MSRP — a pattern that's hard to catch by browsing manually.
Use case 3: local business post sentiment
A local business posting about a new menu item, event, or policy change can pull comments on that post to see the actual reaction, rather than relying on the reaction-emoji count alone. Comment text often reveals why a post landed well or badly, which the emoji summary doesn't.
Production notes
Scope by location first. Marketplace is a local marketplace by design — location filtering matters more here than on almost any other platform in this batch.
Track condition alongside price. Two identical-looking listings at different prices are often explained by condition, not by one seller being irrational.
Watch listing velocity, not just price. A category with fast-turning listings at a given price point tells you more about real demand than a handful of stale listings sitting unsold.
Respect what "public" means here. Only collect what's visible without logging in or joining a private group. Marketplace listings and public post comments qualify; private group content does not.
Price without condition confuses more than it clarifies. Price without condition and listing age produces comps that look contradictory. Adding condition and days-listed as required fields fixes most of the confusion.
Cost comparison
| Approach | Coverage | Local granularity | Sentiment data |
|---|---|---|---|
| Manual browsing | One category at a time | High, but slow | None |
| General resale-price tools | Limited or no Marketplace coverage | Low | None |
| Apify Marketplace + Comments pipeline | Category and location scoped | High | Yes |
Check the current Pricing tab on each Actor page before running at scale.
FAQ
Can Apify scrape Facebook Marketplace listings?
Yes. Facebook Marketplace Scraper extracts public listing data including price, condition, and seller information.
Can I pull comments from any Facebook post?
Facebook Comments Scraper works on public posts and videos; private group or profile content isn't accessible.
Is this useful outside of reselling?
Yes — brand protection, local sentiment tracking, and community research all use the same two-Actor pattern.
What's the fastest way to try this?
Pick one product category and city, run Facebook Marketplace Scraper for current listings, then run Facebook Comments Scraper on the most-commented listings.
Try it yourself
Pick one product category and one city. Run Facebook Marketplace Scraper for current listings, then run Facebook Comments Scraper on the two or three most-commented listings you find. The price spread plus the comment tone will tell you more about that local market than either dataset alone.
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