Facebook Marketplace Scraper

An Apify Actor that collects structured listing data from Facebook Marketplace based on keyword and location.

Overview

This scraper searches public Facebook Marketplace listings using a query and geographic coordinates. It returns clean, normalized JSON data suitable for analysis or integration into data pipelines. The actor handles retries, key rotation, and writes results in batches to the default Apify dataset.

Key Features

• Targeted Search: Query listings by keyword (query) and precise location (lat, lng).
• Configurable Filters: Optionally refine results by radius_km, min_price, max_price, and condition.
• Structured Output: Data is normalized into consistent fields (price, location, title, delivery types).
• Reliable Execution: Built with retry logic, request batching, and scalable dataset writes for production runs.

How to Use

Configure the actor run with the required input parameters. The scraper will execute the search, process the listings, and push all results to the dataset associated with the run.

Input Configuration

The following input fields are defined in input_schema.json:

• query (string, required): Search keyword (e.g., "car", "iPhone 14").
• lat (number, required): Latitude for the search center (e.g., 40.7128).
• lng (number, required): Longitude for the search center (e.g., -74.006).
• radius_km (number, optional): Search radius in kilometers.
• min_price (number, optional): Minimum price filter.
• max_price (number, optional): Maximum price filter.
• condition (string, optional): Filters by item condition. Must be one of: used_like_new, used_good, used_fair, new.

Example Input

{
  "query": "car",
  "lat": 40.7128,
  "lng": -74.006,
  "radius_km": 20,
  "min_price": 1000,
  "max_price": 10000,
  "condition": "used_good"
}

Input/Output

Output Schema

Each listing in the dataset is normalized into a consistent JSON object. A final summary item is also added at the end of the run.

Example Listing Output

{
  "source": "facebook_marketplace",
  "listing_id": "771476635286470",
  "category_id": "807311116002614",
  "title": "1984 Lincoln Town Car",
  "price_amount": 6600,
  "price_formatted": "$6,600",
  "strikethrough_amount": 15000,
  "image_url": "https://...jpg",
  "location_city": "Staten Island",
  "location_state": "NY",
  "city_display_name": "New York, New York",
  "is_live": true,
  "is_pending": false,
  "is_sold": false,
  "delivery_types": ["IN_PERSON"],
  "query": "car",
  "lat": 40.7128,
  "lng": -74.006,
  "fetched_at": "2025-01-01T12:34:56.000Z",
  "raw": {
    "...": "original item payload"
  }
}

Example Summary Output

{
  "success": true,
  "total_pushed": 42,
  "query": "car",
  "lat": 40.7128,
  "lng": -74.006,
  "fetched_at": "2025-01-01T12:34:56.000Z"
}

Best Practices

• Validate Inputs: Use specific keywords and ensure the latitude/longitude coordinates are accurate for your target area.
• Test First: Start with a small run or a low result limit to verify the output structure meets your needs.
• Respect Limits: Rely on the actor's built-in retry mechanisms and avoid excessive parallelism.