Facebook Marketplace : Listing Location Details Scraper

Facebook Marketplace Scraper — Listings, Prices and Location Data

Facebook Marketplace : Listing Location Details Scraper turns Facebook Marketplace searches into a geo-tagged dataset of listings, complete with title, price, condition, photos, and per-row latitude, longitude, city, state, region and distance from your search center. Point it at one coordinate and radius, or fan a single keyword out across dozens of cities in one run. Resellers use it to source underpriced inventory by territory, market researchers use it to map listing density and pricing by region, and analysts feed the output straight into spreadsheets or BI tools for territory-level comparisons.

What is Facebook Marketplace : Listing Location Details Scraper?

Facebook Marketplace : Listing Location Details Scraper is an Apify Actor that collects public Facebook Marketplace listings and enriches every row with location data: flat latitude/longitude/city/state/region fields plus a calculated distance from your search center. It runs logged out on residential proxies — no Facebook account, login, or API key is required. Results land in a structured Apify dataset you can export as JSON, CSV, or Excel, or read over the API.

• Discover listings by city (cities + searchQuery), by exact coordinate and radius (centerLatitude/centerLongitude/radiusKm), or by pasting ready-made Marketplace search URLs (areaSearchUrls).
• Returns core listing identity and content fields: id, listingTitle, description, condition, listingPrice, photos and video.
• Adds location columns not found in a standard search scrape: cityQueried, flat latitude/longitude, city/state/region, and distanceKm.
• Optional per-city aggregate rows (includeAreaSummary) summarize listing count, average, minimum and maximum price for each territory.
• Choose fast search-snippet rows or fullDetail mode, which visits each listing page for descriptions, attributes and exact coordinates.
• Exports to JSON, CSV, or Excel, or pull results directly via the Apify API/dataset endpoints.

What data can I extract with Facebook Marketplace : Listing Location Details Scraper?

Each row combines listing identity/content, price and condition, media, and location/targeting fields.

Field	Example Value	Use Case
id	"1234567890123456"	Unique listing ID for de-duplication and joins across runs
listingTitle	"Trek mountain bike, barely used"	Headline shown in search results
itemUrl	"https://www.facebook.com/marketplace/item/1234567890123456/"	Direct link back to the live listing
facebookUrl	"https://www.facebook.com/marketplace/seattle/search/?query=bike"	The search/area this row was collected from
description	{"text": "Selling my Trek..."}	Full listing description text (fullDetail mode)
condition	"Used - like new"	Buyer-facing condition label
listingPrice	{"amount": "180", "currency": "USD", "formatted_amount_zeros_stripped": "$180"}	Current asking price
strikethroughPrice	"$220"	Signals a markdown from an earlier price
listingAttributes	[{"attribute_name": "condition", "label": "Used - good"}]	Structured item attributes such as brand or size
deliveryTypes	["local_pick_up"]	Shipping/pickup options offered
listingPhotos / primaryListingPhoto	[{"image": {"uri": "https://scontent..."}}]	Images for gallery display
listingVideo	[]	Attached video, when the seller added one
timestamp	"2026-07-18T14:32:00.000Z"	ISO listing creation time
isSold / isPending / isLive / isHidden	false / false / true / false	Listing availability flags
listingCategoryId	"1626864784324722"	Marketplace category taxonomy ID
cityQueried	"seattle"	Which input city produced this row (null for areaSearchUrls rows)
latitude / longitude	47.6103 / -122.3341	Flat scalar coordinates ready for mapping tools
city / state / region	"Seattle" / "WA" / "Seattle, WA"	Reverse-geocoded place labels
distanceKm	3.42	Great-circle distance from your search center

Price and condition data

Each listing carries the price and condition signals that drive a buy/skip decision. listingPrice returns the current asking amount plus currency and a zero-stripped formatted string; strikethroughPrice appears when the seller has marked down from an earlier price, useful for flagging active discounts. condition is a human-readable label (e.g. "Used - like new") derived from Facebook's raw condition code, while listingAttributes exposes any additional structured attributes the seller filled in, such as brand or size. Resellers use these fields together to rank listings by margin: compare listingPrice against comparable resale value, then filter out anything below the condition threshold you're willing to relist.

Location and radius targeting data

cityQueried tags every row with the input city that produced it (null when the row came from a pasted areaSearchUrls link), so a multi-city run stays sortable by territory. latitude and longitude are flat scalars — ready to drop into a map or spreadsheet without parsing the nested location object. city, state and region give you a human place name for grouping and filtering. distanceKm is the great-circle distance from your centerLatitude/centerLongitude to the listing, letting you rank results by proximity or cut off anything outside a working radius.

Why not build this yourself?

Meta's Graph API does not expose Marketplace search — there is no public, documented endpoint for querying Marketplace listings by keyword, city, or coordinate, so there's no official API to compare against here. Building this in-house means reverse-engineering Facebook's internal GraphQL surface: locating the current CometMarketplaceSearchContentPaginationQuery doc_id inside minified JS bundles that Meta rotates, extracting a fresh lsd/fb_dtsg session token from the bootstrap HTML on every run, and paginating a cursor-based feed that returns 24 listings per page. On top of that, Marketplace blocks aggressively — 403/429 responses are common — so a homemade scraper needs residential IPs, a guest-session warmup, and retry logic across fresh IPs just to get results consistently.

This Actor already does all of that: it re-extracts the doc_id and session tokens per run, warms up a guest session, retries its bootstrap step on fresh residential IPs across several attempts, and finishes with an empty (not failed) dataset rather than crashing when Facebook blocks a run. Building and maintaining this yourself means owning that anti-bot arms race indefinitely; running the Actor means you only maintain your input parameters.

How to use data extracted from Facebook Marketplace?

Resellers and arbitrage sourcing

Resellers and dropshippers use cities + searchQuery to scan a shortlist of metros for one product line — e.g. "electric bike" across Seattle, Portland and Tacoma — then sort the resulting rows by listingPrice and condition to find underpriced, resellable inventory before it's gone. Pairing centerLatitude/centerLongitude/radiusKm narrows a single city down to a specific pickup catchment, so you're only looking at listings within reach of a warehouse or storefront. distanceKm on every row lets you rank by proximity, and strikethroughPrice flags sellers who already marked a listing down. The output — id, listingTitle, listingPrice, condition, itemUrl — drops straight into a spreadsheet or sourcing pipeline for repeat runs.

Local market research agencies

Agencies auditing a client's local market — a franchise considering a new territory, a reseller comparing metros — run the same searchQuery across a cities list and compare the per-city area_summary rows (listingCount, avgPrice, minPrice, maxPrice) side by side. Because every listing is tagged with cityQueried, city, state and region, an agency can benchmark listing density and price spread across several metros in one run instead of running separate manual searches, then hand the client a territory comparison built from the same fields every time.

Market and price research

Price researchers use includeAreaSummary to get one aggregate row per city — listingCount, avgPrice, minPrice, maxPrice — without touching a single listing row, which is enough to chart price distribution for a product category across regions. For deeper analysis, fullDetail mode adds condition and listingAttributes to every row, so a researcher can control for item condition before comparing prices, and distanceKm lets them weight results toward listings near a specific reference point rather than an entire metro.

AI agents and automated pipelines

Because every field is typed JSON with a stable schema, agent frameworks can call this Actor as a tool, pass in a city list and keyword, and reason over structured fields like listingPrice, condition and distanceKm instead of parsing HTML. A RAG pipeline can index listingTitle and description alongside the location fields to answer natural-language questions about what's for sale near a given point, and automated pipelines can schedule repeat runs and diff city/id pairs to alert on new listings within a radius.

🔼 Input sample

None of this Actor's parameters are required — every field has a sensible fallback or is simply skipped when empty.

Parameter	Required	Type	Description	Example Value
cities	No	array	One search per city — city name/slug (e.g. "seattle") or full Marketplace URL. Combined with searchQuery.	["seattle", "portland"]
searchQuery	No	string	Keyword searched in each city above. Ignored for entries in cities that are already full URLs.	"bike"
centerLatitude	No	number	Re-point every search to this latitude (overrides the city page's own center). Drives distanceKm.	47.6062
centerLongitude	No	number	Re-point every search to this longitude. Pairs with centerLatitude + radiusKm.	-122.3321
radiusKm	No	integer (1–500)	How far around the center to include listings, in kilometres.	40
includeAreaSummary	No	boolean (default false)	Append one aggregate row per city with listingCount, avgPrice, minPrice, maxPrice.	true
maxListingsPerArea	No	integer (min 0, default 50)	Cap on listings collected per city/URL; 0 = everything Facebook returns.	30
fullDetail	No	boolean (default true)	ON visits each listing page for description, attributes and exact coordinates; OFF returns fast search-snippet rows.	true
areaSearchUrls	No	array	Ready-made Facebook Marketplace search URLs to scan exact territories directly, instead of (or alongside) cities. Rows carry cityQueried: null.	["https://www.facebook.com/marketplace/108479165840750/search/?query=bike"]
proxyConfiguration	No	object	Apify Proxy preferences. This Actor always runs on residential IPs; region hints are honored where possible.	{"useApifyProxy": true, "apifyProxyGroups": ["RESIDENTIAL"]}

{
  "cities": ["seattle", "portland"],
  "searchQuery": "bike",
  "centerLatitude": 47.6062,
  "centerLongitude": -122.3321,
  "radiusKm": 40,
  "includeAreaSummary": true,
  "maxListingsPerArea": 30,
  "fullDetail": true
}

Common pitfall: cities accepts a plain city name or slug ("seattle", "los angeles") — not a "City, State" string. The Actor only lowercases and strips whitespace before building the URL, so an entry like "New York, NY" turns into an invalid path segment and that area returns zero rows. Use "newyork" or paste the full Marketplace URL instead, and put any state/keyword context into searchQuery.

🔽 Output sample

Every listing is pushed to the Actor's default dataset as one flat JSON object, exportable as JSON, CSV, or Excel, or readable via the Apify API. The record below is a fullDetail row (the Actor's default mode); with fullDetail: false the same location columns are added, but the listing fields come back in Facebook's original snake_case shape (listing_price, marketplace_listing_title, is_sold, etc.) instead of the shape shown here. Only listing rows are billed under the row_result event — if includeAreaSummary is on, the extra area_summary rows it adds are not charged; filter them out of a paid-row count or export with type != "area_summary".

{
  "facebookUrl": "https://www.facebook.com/marketplace/seattle/search/?query=bike",
  "id": "1234567890123456",
  "itemUrl": "https://www.facebook.com/marketplace/item/1234567890123456/",
  "listingAttributes": [
    { "attribute_name": "condition", "value": "used_like_new", "label": "Used - like new" }
  ],
  "condition": "Used - like new",
  "timestamp": "2026-07-18T14:32:00.000Z",
  "deliveryTypes": ["local_pick_up"],
  "listingPhotos": [
    {
      "__typename": "Photo",
      "accessibility_caption": "bike photo",
      "image": { "height": 720, "width": 960, "uri": "https://scontent.xx.fbcdn.net/photo1.jpg" },
      "id": "9988776655"
    }
  ],
  "listingPrice": {
    "formatted_amount_zeros_stripped": "$180",
    "amount_with_offset_in_currency": 18000,
    "amount": "180",
    "currency": "USD"
  },
  "location": { "latitude": 47.6103, "longitude": -122.3341 },
  "locationText": { "text": "Seattle, WA" },
  "listingTitle": "Trek mountain bike, barely used",
  "description": { "text": "Selling my Trek mountain bike, ridden a handful of times. Pickup only." },
  "isHidden": false,
  "isLive": true,
  "isPending": false,
  "isSold": false,
  "isViewerSeller": false,
  "listingVideo": [],
  "listingCategoryId": "1626864784324722",
  "strikethroughPrice": "$220",
  "primaryListingPhoto": {
    "__typename": "Photo",
    "id": "9988776655",
    "photo_image_url": "https://scontent.xx.fbcdn.net/photo1.jpg"
  },
  "customSubTitlesWithRenderingFlags": [],
  "cityQueried": "seattle",
  "latitude": 47.6103,
  "longitude": -122.3341,
  "city": "Seattle",
  "state": "WA",
  "region": "Seattle, WA",
  "distanceKm": 3.42
}

How do you filter and target specific listings?

This Actor gives you three independent ways to narrow down a huge, constantly-changing catalogue of listings.

Query approach: use cities + searchQuery when you want a broad, repeatable sweep — one keyword fanned out across as many metros as you list, each row tagged with cityQueried so you always know which city it came from. Use areaSearchUrls instead when you already have a specific Marketplace search link (a sub-category, a saved filter) you want scanned exactly as-is; those rows carry cityQueried: null since they bypass the city fan-out entirely.

Scope precision: centerLatitude, centerLongitude and radiusKm re-point a search to an exact coordinate and catchment radius (1–500 km), overriding whatever center Facebook's own city page would otherwise use. Every row then gets a distanceKm computed from that same center, so you can rank or cut off results by proximity instead of by city boundary alone.

Volume controls: maxListingsPerArea caps how many listings each city or URL contributes (0 removes the cap entirely), which matters because the Actor paginates Facebook's cursor-based feed 24 listings at a time per area until that cap or Facebook's own result set is exhausted. fullDetail trades speed for completeness — off returns fast search-snippet rows, on visits each listing page for description, attributes and exact coordinates.

Three real examples:

{
  "cities": ["seattle", "portland", "tacoma"],
  "searchQuery": "electric bike",
  "radiusKm": 25,
  "maxListingsPerArea": 40
}

{
  "cities": ["seattle"],
  "searchQuery": "sofa",
  "centerLatitude": 47.5301,
  "centerLongitude": -122.0326,
  "radiusKm": 15,
  "fullDetail": true
}

{
  "areaSearchUrls": ["https://www.facebook.com/marketplace/108479165840750/search/?query=bike"],
  "includeAreaSummary": true,
  "maxListingsPerArea": 0
}

▶️ Want to try other Facebook scrapers?

Scraper Name	What it extracts
Facebook Groups Search Scraper With Group Profile Details	Finds Facebook Groups by keyword and returns group profile details
Facebook Page Posts & Comments Scraper	Facebook Page posts plus their full comment threads
Facebook User Search Scraper: Related Profiles & Pages	Searches Facebook by name/keyword and returns related profiles, pages and groups
Facebook Ads Library Scraper: Creative & Ad Text Details	Competitor ad creatives and ad text from the Facebook Ads Library
eBay Scraper By Buyer Interest & Seller Feedback	eBay listings screened for buyer-interest signals and seller feedback
Zillow Search Scraper: Investment Analyzer	Zillow property search results with rental-yield/investment analysis

How to extract Facebook Marketplace data programmatically

Every Apify Actor run is a standard REST call: one endpoint, one Authorization header, structured JSON back — no scraping code, browser, or proxy management on your side.

Python

from apify_client import ApifyClient

client = ApifyClient("<APIFY_API_TOKEN>")

run = client.actor("SimpleAPI/facebook-marketplace-listing-location-details-scraper").call(
    run_input={
        "cities": ["seattle", "portland"],
        "searchQuery": "bike",
        "radiusKm": 40,
        "includeAreaSummary": True,
    }
)

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(item["listingTitle"], item["city"], item["distanceKm"])

MCP for AI agents

Apify exposes every Actor, including this one, through its Actors MCP Server, so an AI agent can call it as a tool instead of a REST client:

npx -y @apify/actors-mcp-server --actors SimpleAPI/facebook-marketplace-listing-location-details-scraper

A location-planning agent can use this to answer "what's listed for sale within 20 km of this address" by passing centerLatitude/centerLongitude/radiusKm straight through as tool arguments. Compatible with Claude Desktop, Claude Code, Cursor, and any other MCP-compatible client.

Export to spreadsheets or CRM

Every run's dataset has a built-in CSV/Excel export in the Apify Console, or you can pull it via the dataset API with format=csv. Map listingTitle to a "Product" column, listingPrice.amount to "Price", city/state/region to territory columns, and distanceKm to a "Distance" column for a ready-to-import sheet in Google Sheets, Airtable, or any CRM with a CSV importer.

Is it legal to scrape Facebook Marketplace?

Yes — scraping publicly available Facebook Marketplace listings (no login required to view them) for business or research use is generally permitted, and this Actor never accesses private, login-gated, or password-protected content. Marketplace listings exist to be found by buyers, so the data itself is public by design; the sensitivity here comes from the fact that a seller is often a private individual, not a registered business, and fields such as marketplace_listing_seller or a listing's coordinates can constitute personal data under GDPR/CCPA once you store and reuse it at scale. Treat any seller-identifying fields you keep with the same care as other personal data: have a lawful basis for storing it, and avoid republishing it. Consult legal counsel for commercial applications involving bulk storage of personal data.

❓ FAQ

What happens when a listing is sold or removed?

The dataset reflects listings as they existed on Facebook at request time — it doesn't retroactively update. In fullDetail mode, each row carries isSold, isPending, isLive and isHidden booleans straight from Facebook, so you can filter out anything already marked sold or hidden; if a listing has been fully removed since your last run, re-running the same cities/URLs is the only way to confirm it's gone.

Can I get price and condition data along with the location fields?

Yes. Every row includes listingPrice (or listing_price in fast mode), strikethroughPrice, and condition alongside the location columns — there's no separate mode you need to enable; fullDetail just adds richer attribute and description data on top.

How accurate is the location data?

The Actor returns exactly what Facebook has stored for that listing: the seller's own location, Facebook's reverse-geocoded city/state/region, and a distanceKm calculated from your search center. Accuracy depends on how precisely the seller set their location on Facebook — treat distanceKm as a filter, not a guarantee, and re-verify individual listings before acting on anything distance-sensitive.

How many listings can I get per run?

That's controlled by maxListingsPerArea, applied per city or URL — set it to 0 to remove the cap and collect everything Facebook returns for that search, or leave it at the default of 50 to keep runs quick. There's no separate actor-wide cap: a run across five cities with maxListingsPerArea: 100 can return up to 500 listings, plus optional area_summary rows.

How do I target an exact coordinate and radius instead of a whole city?

Set centerLatitude, centerLongitude and radiusKm (1–500 km). This overrides the search center Facebook's own city page would otherwise use, and every returned row gets a distanceKm measured from that same coordinate, so you can filter results down to an exact catchment area.

Does Facebook Marketplace : Listing Location Details Scraper work with Claude, ChatGPT, and AI agent frameworks?

Yes. It's reachable through Apify's Actors MCP Server for MCP-compatible clients (Claude Desktop, Claude Code, Cursor), and it's callable as a standard HTTP API endpoint by any agent framework that can make a REST call and read JSON back.

How does this compare to other Facebook Marketplace scrapers?

Most Marketplace scrapers return the listing fields Facebook already shows in search — title, price, photo, URL. This Actor adds a location layer on top: coordinate-and-radius targeting that overrides a city's default center, multi-city fan-out in a single run, flat latitude/longitude/city/state/region columns instead of a nested location object, a computed distanceKm per row, and optional per-city price/count aggregates — built for territory comparison rather than single-listing lookup.

Can I use this without a Facebook API key or developer account?

Yes. No Facebook login, API key, or developer account is required — the Actor collects public listings logged-out over a residential proxy. You only need an Apify account to run it and read the dataset.

Can I scan multiple cities in one run?

Yes — pass a cities array and the Actor runs one independent search per city, tagging every row with the cityQueried value so a multi-city dataset stays sortable by territory. You can mix cities with areaSearchUrls in the same run if you also want to scan specific pinned search links.

Conclusion

Facebook Marketplace : Listing Location Details Scraper turns a Facebook Marketplace keyword search into a structured, location-tagged dataset — coordinates, city/state/region, distance from your search center, and the price/condition fields that make each listing actionable. It's built for anyone who needs to compare a catalogue by territory rather than read it listing by listing: resellers sourcing by metro, agencies benchmarking markets for clients, and researchers mapping price and density across regions. No Facebook login is required, and every field is documented against the Actor's real output schema, so downstream pipelines don't break when Facebook changes its page layout. Open the Actor on Apify, add your cities and search term, and start your first run.