Blocket.se Classifieds Scraper (Sweden)
Pricing
from $1.00 / 1,000 results
Blocket.se Classifieds Scraper (Sweden)
Scrape Swedish classified ads from Blocket.se by search URL: title, price, location, brand, condition, images. Export JSON/CSV/Excel.
Pricing
from $1.00 / 1,000 results
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Developer
lysum
Maintained by CommunityActor stats
0
Bookmarked
2
Total users
1
Monthly active users
3 days ago
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🇸🇪 Blocket.se Classifieds Scraper
Scrape classified ads from Blocket.se (Sweden) by search URL — fast, reliable, and pay-per-result. Paste one or more Blocket.se search-result URLs and export clean structured data as JSON, CSV, or Excel, or pull it via the Apify API.
What it does
Give it a Blocket.se search URL (with whatever filters you set on the site) and it returns one row per listing with:
- title
- price (SEK)
- location
- brand
- condition
- age (relative, e.g. '1 dag')
- paid-placement flag
- image
- ad URL
It handles pagination automatically, deduplicates by listing URL, and runs behind an Apify residential Sweden proxy to avoid datacenter-IP blocking.
How to use it
- Go to Blocket.se, apply the filters you want (location, price, category, …).
- Copy the resulting search-result URL.
- Paste it into Search URLs, set Max listings, and click Start.
Example input
{"startUrls": [{ "url": "https://www.blocket.se/annonser/hela_sverige?q=iphone" }],"maxItems": 100}
Output
Each item is a flat JSON object with the fields listed above, plus the source search URL and a scrape timestamp — ready for spreadsheets, databases, market analysis, or lead generation.
Notes
- Age is a relative string ('1 dag'), and exact seller name + timestamps are detail-page only. Brand appears only when the seller set it.
- Keep the residential Sweden proxy enabled (prefilled). A run that suddenly returns 0 items usually means IP blocking, not broken selectors.
- This Actor collects only publicly available listing data. Use responsibly and in line with Blocket.se's terms and applicable law.
Pricing
Pay-per-result — you're charged per listing returned. Run a small maxItems first to preview the data before scaling up.