eBay Kleinanzeigen.de Ads Details Pages Scraper

A focused data extraction tool that collects rich details from individual eBay Kleinanzeigen ad pages. It helps developers, analysts, and founders turn classified listings into structured, usable datasets for analysis, automation, and product building.

     

Created by Bitbash, built to showcase our approach to Scraping and Automation!
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Introduction

This project extracts detailed information from eBay Kleinanzeigen ad detail pages and converts it into clean, structured data. It solves the problem of manually collecting scattered listing information by automating the process at scale. It’s built for developers, data teams, and entrepreneurs who need reliable marketplace data.

Why this project exists

• Converts unstructured ad pages into structured datasets
• Works with direct ad URLs for precise targeting
• Designed for high-volume data collection without manual effort
• Suitable for analysis, monitoring, and downstream automation

Features

Feature	Description
Ad detail extraction	Captures full listing data from individual ad pages
Rich seller insights	Extracts seller profile details and contact info when available
Media collection	Retrieves all listing images and media URLs
Attribute parsing	Collects category-specific attributes and metadata
Flexible export	Outputs data in formats ready for analysis and storage

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What Data This Scraper Extracts

Field Name	Field Description
title	The main title of the ad listing
description	Full textual description provided by the seller
price	Listed price and pricing type
images	Array of image URLs from the ad
category	Category and subcategory of the listing
attributes	Structured attributes specific to the item
seller_name	Name or username of the seller
seller_type	Private or professional seller indicator
rating	Seller rating when available
email	Seller email address if publicly available
phone	Seller phone number if publicly available
url	Original ad URL
posted_date	Date the ad was published

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Example Output

Example:

[
  {
    "title": "Used Wooden Desk",
    "price": "120 EUR",
    "description": "Solid wood desk in good condition, minor scratches.",
    "images": [
      "https://img.kleinanzeigen.de/desk1.jpg",
      "https://img.kleinanzeigen.de/desk2.jpg"
    ],
    "category": "Furniture",
    "attributes": {
      "condition": "Used",
      "material": "Wood"
    },
    "seller_name": "Max Müller",
    "seller_type": "Private",
    "rating": 4.8,
    "email": "seller@example.com",
    "phone": "+49 123 456789",
    "url": "https://www.kleinanzeigen.de/s-anzeige/example/1234567890",
    "posted_date": "2024-03-18"
  }
]

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Directory Structure Tree

eBay Kleinanzeigen.de Ads Details Pages Scraper/
├── src/
│   ├── runner.py
│   ├── extractors/
│   │   ├── ad_parser.py
│   │   └── seller_parser.py
│   ├── outputs/
│   │   └── exporters.py
│   └── config/
│       └── settings.example.json
├── data/
│   ├── inputs.sample.txt
│   └── sample_output.json
├── requirements.txt
└── README.md

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Use Cases

• Data analysts use it to analyze pricing trends, so they can understand market demand.
• Resellers use it to discover undervalued items, so they can source profitable inventory.
• Real estate researchers use it to monitor listings, so they can track regional activity.
• Product teams use it to feed recommendation systems, so users see more relevant listings.
• Founders use it to validate ideas, so they can build data-driven marketplace tools.

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FAQs

What type of URLs does this project support? It works with direct eBay Kleinanzeigen ad detail page URLs, ensuring accurate and targeted data extraction.

Does it collect private contact information? Only contact details that are publicly visible on the ad page are included.

Can it handle different categories? Yes, it dynamically adapts to category-specific attributes and listing structures.

Is the output suitable for analytics pipelines? Absolutely. The structured output is designed to plug directly into databases, dashboards, or ML workflows.

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Performance Benchmarks and Results

Primary Metric: Processes hundreds of ad pages per minute under normal network conditions.

Reliability Metric: Maintains a high success rate across diverse listing categories and layouts.

Efficiency Metric: Optimized parsing minimizes unnecessary requests and resource usage.

Quality Metric: Delivers highly complete records with consistent field coverage across listings.

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