Kleinanzeigen Reader — Universal Listing Analyzer

Extracts structured data from any kleinanzeigen.de listing using three built-in HTML data sources. No browser automation, no paid APIs, no external dependencies.

Quick Start

When a user shares a kleinanzeigen.de/s-anzeige/... URL:

1. Fetch the page → scripts/fetch.sh [URL] or inline curl (Step 1)
2. Extract all data → python3 scripts/extract.py listing.html
3. Identify category → auto-detect from ad_attributes or title
4. Apply category rules → load the relevant reference file
5. Output structured result with flags and recommendation

For multiple URLs → use compare mode (Step 5).

Step 1 — Fetch the Listing

curl -s -L \
  -H "User-Agent: Mozilla/5.0 (iPhone; CPU iPhone OS 17_4 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/17.4 Mobile/15E148 Safari/604.1" \
  -H "Accept-Language: de-DE,de;q=0.9" \
  -H "Referer: https://www.kleinanzeigen.de/" \
  --compressed \
  "[URL]" > /tmp/listing.html

Why iPhone UA: Kleinanzeigen blocks headless/server requests but allows mobile Safari — this is a well-documented characteristic of their bot filter.

Step 2 — Three Data Sources (Always Present)

Source A — Schema.org JSON-LD

SEO requirement. Always in <script type="application/ld+json">. Contains: title, description, asking price, main image URL.

Source B — ad_attributes GTM String

All structured attributes packed into a pipe-separated string for Google Tag Manager:

km:95000|aussenfarbe:schwarz|schaden:t|power:150|tuevdate:2026-08|...

→ See references/ad-attributes-fields.md for complete field list.

Source C — DFP Bidder Block

A second JS block with cleaner values for key fields: ExactPreis, Kilometerstand, Erstzulassung, HU_bis, Marke, Getriebe

Source D — Seller Type

• "li":"0" in GTM = Privater Anbieter
• "li":"1" in GTM = Gewerblicher Händler
• Confirmed by text badge: "Privater Nutzer" vs "Gewerblicher Anbieter"
• "lsc":["0"] = additional private/commercial signal

Step 3 — Dealer Detection

Extract and evaluate four dealer signals — combine for confidence score:

Output format:

Verkäufertyp: ✅ Privat  /  ⚠️ Gewerblich (Händler)

Why this matters:

• Dealers must provide 2-year statutory warranty (§ 475 BGB) — negotiating room is different
• Private sellers can exclude warranty entirely
• Dealer listings often have inflated prices vs. identical private listings
• Some "private" listings are actually undeclared commercial sellers (grey zone)

Undeclared dealer heuristics (flag if 2+ match):

• Title uses commercial language ("sofort verfügbar", "Händler", "Bestand")
• Multiple identical or very similar listings from same seller
• Price is at or above market rate with no personal story in description
• Description is structured/template-like (no typos, no personal details)
• Generic stock photos (no personal garage / street background)

Step 4 — Universal Category System

Auto-detect category from ad_attributes fields or listing title, then load the matching reference file for category-specific analysis.

If category is unclear → use generic output (Step 4b) without loading a reference file.

Step 4a — Category-Specific Flags

Fahrzeuge (see references/categories/fahrzeuge.md)

• schaden:t = declared damage → always highlight
• tuevdate expired = HU fällig → cost ~€80–150
• KM > 300k = high mileage note
• Motor identification from PS + build year (BMW N47/M47, VW TDI etc.)
• Duplicate detection: same car listed twice at different prices

Immobilien

• zimmer count vs. wohnflaeche ratio → abnormal if < 15m² per room
• kaltmiete vs. area median (flag if > 30% above)
• Missing fields: no floor plan, no energy certificate mentioned

Elektronik

• zustand field: neu, wie_neu, gut, akzeptabel, defekt
• defekt:t equivalent = listed under "Defekt / Bastlerware"
• Price vs. idealo/Amazon median (mention to check externally)
• Serial number fraud risk for high-value items (MacBooks, iPhones)

Möbel & Haushalt

• No structural red flags — focus on description quality and seller type
• Note if price > 60% of new price (rarely worth it for furniture)

Step 4b — Universal Output Format

Use this format for all categories:

## [Title]

**Kategorie:** [detected category]
**Verkäufertyp:** ✅ Privat  /  ⚠️ Gewerblich

| Feld          | Wert                          |
|---------------|-------------------------------|
| Preis         | X.XXX € [VB / Festpreis]      |
| Standort      | [PLZ City]                    |
| Datum         | [listing date]                |
| Zustand       | [if present]                  |
| [cat fields]  | [category-specific rows]      |

**Beschreibung:** [original text]

**⚠️ Flags:**
- [flag 1]
- [flag 2]

**Bilder:** [N] Fotos verfügbar
[image URLs listed]

**Gesamtbewertung:** ★★★☆☆  [short reasoning]

Step 5 — Comparison Mode

When 2+ URLs are given, fetch all listings then produce:

| Merkmal        | Inserat 1   | Inserat 2   | Inserat 3   |
|----------------|-------------|-------------|-------------|
| Preis          | ...         | ...         | ...         |
| Verkäufertyp   | Privat ✅   | Händler ⚠️  | ...         |
| [cat fields]   | ...         | ...         | ...         |
| Flags          | ...         | ...         | ...         |
| Bewertung      | ★★★☆☆      | ★★☆☆☆      | ...         |

Duplicate detection: If ≥5 fields match across listings AND location matches → flag as probable same item listed twice (common seller tactic to test prices).

Step 6 — Images

Download with:

curl -s -L \
  -H "Referer: https://www.kleinanzeigen.de/" \
  "[IMAGE_URL]" -o image.jpg

URL format: https://img.kleinanzeigen.de/api/v1/prod-ads/images/[2ch]/[UUID]?rule=X

Limitations

• kleinanzeigen.de web URLs only — convert app share links first
• ezdate / tuevdate = current month if seller left field blank
• Personal data in listing descriptions (phone numbers, names) — do not store or repeat
• Rate limit: add sleep 1 between requests for batch operations
• For research/personal use only — respect kleinanzeigen.de ToS

Reference Files

• references/ad-attributes-fields.md — All GTM field names + values
• references/categories/fahrzeuge.md — Vehicle-specific analysis rules
• references/categories/elektronik.md — Electronics flags + fraud indicators
• references/categories/immobilien.md — Real estate checks
• references/categories/moebelhaushalt.md — Furniture/household rules