Forem: Fatih İlhan

I Built 3 APIs for Turkey’s Used-Car Market with Apify

Fatih İlhan — Mon, 06 Apr 2026 08:37:59 +0000

Turkey’s used-car market is massive, fragmented, and surprisingly hard to work with if you want structured data.

Listings live across marketplaces, dealer pages are inconsistent, pricing changes fast, and even simple questions like “What is this car worth?” or “Which dealers dominate Istanbul for this brand?” are harder than they should be.

So I built three focused APIs on top of Apify to solve different layers of the problem:

A listing extraction API for Arabam
A valuation API for Arabam + Sahibinden
A dealer intelligence API for Arabam + Sahibinden

All three are built for developers, analysts, insurers, lenders, marketplaces, and automotive businesses that need clean Turkish vehicle data instead of brittle scraping scripts.

1. Arabam.com Vehicle Scraper API

The first API is the raw data layer.

It extracts structured used-car listings from Arabam.com, including title, make, model, year, price, mileage, fuel type, transmission, city, seller type, and optional detail-page enrichment like condition and seller metadata.

This is the API you use when you want the source-of-truth listing data itself.

Typical use cases:

building your own vehicle marketplace dataset
monitoring listing supply for a specific make/model
collecting comps for downstream analysis
feeding machine learning or BI pipelines

Example input:

{
  "searchUrls": [
    "https://www.arabam.com/ikinci-el/otomobil/volkswagen-passat"
  ],
  "maxListings": 5,
  "scrapeDetails": true
}

Example output fields:

{
  "listingId": "38718353",
  "title": "Galeriden Volkswagen Passat 1.6 TDi BlueMotion Business 2020 Model Mersin 124.000 km Füme",
  "make": "Volkswagen",
  "model": "Passat",
  "year": 2020,
  "price": {
    "amount": 2025000,
    "currency": "TRY"
  },
  "mileage": 124000,
  "fuelType": "dizel",
  "transmission": "yarı_otomatik",
  "city": "Yüksek Mh. Erdemli, Mersin",
  "sellerType": "galeri",
  "url": "https://www.arabam.com/ilan/..."
}

If you need listing-level data, this is the API to start with.

2. Turkish Auto Price Tracker API

The second API is the decision layer.

Instead of returning only listings, it turns listing data into valuation.

You provide a vehicle spec such as make, model, year range, fuel type, and transmission. The API searches Arabam and Sahibinden, then returns:

listing-level price records
median and average price
min/max range
percentiles
seller-type breakdown
mileage-bucket analysis
This is the API for questions like:
What is a 2020 diesel Passat actually worth today?
Is this asking price above market?
How do dealer prices compare to owner-listed prices?

Example input:

{
  "vehicles": [
    {
      "make": "Volkswagen",
      "model": "Passat",
      "yearMin": 2018,
      "yearMax": 2022,
      "fuelType": "dizel"
    }
  ],
  "platforms": ["arabam"],
  "maxListingsPerPlatform": 10
}

Example output includes:

PRICE_RECORD
PRICE_SUMMARY
RUN_SUMMARY

A summary record looks like this in practice:

{
  "type": "PRICE_SUMMARY",
  "totalListingsUsed": 10,
  "overall": {
    "averagePrice": 1839438,
    "medianPrice": 1795750,
    "minPrice": 1525000,
    "maxPrice": 2169000
  }
}

This is the most business-friendly API in the set because it converts raw marketplace noise into a usable market signal.

3. Turkish Auto Dealer Intelligence API

The third API is the market structure layer.

This one focuses on dealers rather than individual cars.

It can scrape direct dealer URLs or discover dealers by city and then return structured dealer profiles with:

dealer name
city and district
contact details
trust/profile signals
optional inventory analysis

That inventory mode is especially useful because it summarizes what a dealer is actually selling:

total listings
average price
median price
price range
fuel breakdown
inventory composition

Example input:

{
  "platforms": ["arabam"],
  "searchByCity": "istanbul",
  "maxDealers": 3,
  "includeInventory": false
}

Example dealer output:

{
  "type": "DEALER_PROFILE",
  "dealerName": "CM MOTORS",
  "city": "İstanbul",
  "district": "Fatih Oto Galeri",
  "phone": "(0539) 812 32 20",
  "dealerUrl": "https://www.arabam.com/galeri/cm-motors-istanbul"
}

And with inventory enabled:

{
  "inventory": {
    "totalListings": 6,
    "averagePrice": 573483,
    "medianPrice": 654000,
    "priceRange": {
      "min": 259000,
      "max": 789000
    }
  }
}

This is the API for:

dealer benchmarking
lead generation
market mapping by city
inventory strategy analysis
B2B automotive intelligence

Why split this into 3 APIs?

Because these are three different jobs.

Listing extraction is about raw data.
Price tracking is about market valuation.
Dealer intelligence is about competitive structure.

Trying to force all of that into one giant “automotive scraper” would make the product harder to understand, harder to price, and harder for developers to integrate.

Three narrower APIs make the value proposition much clearer.

One more important point: these APIs are open source, and contributions are welcome. If you want to improve marketplace coverage, harden parsers, expand normalization, add new output fields, improve dealer analytics, or help support more Turkish automotive workflows, you can jump in and contribute. I want this to become a practical open data tooling layer for the Turkish automotive ecosystem, not just a closed product.

Final thought

Turkey’s automotive market has a lot of publicly visible data, but not a lot of clean, reusable interfaces.

That’s the gap these APIs are designed to fill.

If you want listing-level data, use the vehicle scraper.
If you want valuation, use the price tracker.
If you want dealer-level market intelligence, use the dealer intelligence API.

That stack gives you a practical data layer for the Turkish used-car market without rebuilding the scraping and normalization work from scratch.

I Built 3 APIs for Turkish E-Commerce Intelligence on Apify

Fatih İlhan — Sat, 04 Apr 2026 19:50:15 +0000

If you want structured product, seller, and review data from Turkish marketplaces, you usually end up stitching together brittle scrapers, inconsistent schemas, and platform-specific quirks.

We decided to package that work into three production-ready Apify Actors that behave like APIs:

N11 Product Scraper
Turkish Marketplace Seller Intelligence
Turkish E-Commerce Review Aggregator

You send JSON input. You get clean, structured output. No custom parser per marketplace. No manual normalization step after the crawl. No guessing what the data shape will look like.

The problem with Turkish marketplace data
Turkish e-commerce is large, active, and fragmented.

The hard part is not just collecting pages. The hard part is turning marketplace data into something you can actually use for pricing analysis, seller evaluation, competitor tracking, or product research.

A few examples:

Product pages and listing pages expose different fields.
Seller profile pages vary wildly across Trendyol, Hepsiburada, and N11.
Review systems are inconsistent, especially once you try to normalize rating scales and optional metadata.
Even when you can scrape the page, the output is often too messy to plug into dashboards, alerts, or downstream AI workflows. That’s the gap these actors are meant to close.

What I built

N11 Product Scraper This actor extracts structured product data from N11 search results, category pages, and direct product URLs.

It returns records like:

product title
brand
current price and original price
rating and review count
seller name and seller URL
category breadcrumb path
image URLs
stock status
specifications
description when available A real output record looks like this in practice:

{
  "platform": "n11",
  "productId": "61465",
  "title": "Logitech MK270 Kablosuz USB Turkce Q Klavye Mouse Seti",
  "price": {
    "amount": 1329.9,
    "currency": "TRY"
  },
  "sellerName": "PETCOM",
  "sellerUrl": "https://www.n11.com/magaza/petcom",
  "inStock": true
}

That makes it useful for:

catalog intelligence
price monitoring
seller mapping
assortment comparison
marketplace research Pricing: $5 per 1,000 product records

Turkish Marketplace Seller Intelligence Products are only half the story. On marketplaces, the seller is often the real unit of analysis.

This actor normalizes seller and store profiles across:

Trendyol
Hepsiburada
N11
It extracts fields like:
seller name
seller URL
overall rating
total products
follower count
badges
member since
public business details when available
A sample output looks like:

{
  "platform": "n11",
  "sellerId": "petcom",
  "sellerName": "PETCOM",
  "sellerUrl": "https://www.n11.com/magaza/petcom",
  "overallRating": 5,
  "totalProducts": 20,
  "badges": [
    "Basarili Magaza",
    "Hizli Gonderim",
    "Ucretsiz Kargo"
  ]
}

This is the actor for teams doing:

supplier evaluation
marketplace seller scoring
brand monitoring
competitive intelligence
partner screening Pricing: $8 per 1,000 seller profiles

Turkish E-Commerce Review Aggregator Reviews are where marketplace data becomes operational.

This actor pulls reviews from Trendyol, Hepsiburada, and N11 into one unified schema and adds basic Turkish sentiment tagging.

Each review record includes:

product URL
product title
reviewer name
rating
title
review body
review date
helpful count
images
seller name
variant info
sentiment tag

Example:

{
  "platform": "n11",
  "productTitle": "Logitech MK270 Kablosuz USB Turkce Q Klavye Mouse Seti",
  "reviewerName": "M*** O***",
  "rating": 5,
  "body": "Iyiydi",
  "sentimentTag": "positive",
  "sellerName": "Techburada"
}

This is useful for:

sentiment analysis
product feedback monitoring
seller quality tracking
review mining
competitor product research Pricing: $3 per 1,000 reviews

Why these three actors work better together
Individually, each actor solves a clear problem.

Together, they give you a compact Turkish e-commerce intelligence stack.

A simple workflow looks like this:

Use the N11 Product Scraper to collect products in a category.
Extract seller URLs from those product records.
Pass those seller URLs into Seller Intelligence.
Pass the product URLs into Review Aggregator.
Join the outputs on product URL and seller URL. Now you can answer questions like:

Which sellers dominate a category?
Which sellers have strong trust signals but weak review sentiment?
Which products are priced aggressively but getting poor feedback?
Which brands are present across multiple sellers with inconsistent review patterns? That is much more useful than a raw HTML scraper.

Designed like APIs, not like hobby scripts
A lot of scrapers stop at “it works on my machine.”

These actors were built to behave more like production APIs:

input validation with clear English errors
normalized output schemas
progress logging
partial completion handling
final run summaries
deployment-ready on Apify
clean dataset outputs for downstream systems
That matters if you want to use them from:
internal tools
BI pipelines
LLM workflows
agent systems
scheduled monitoring jobs
enrichment pipelines
Why Apify
Apify is a strong fit for this category of product because it gives users a clean way to run, schedule, and consume data extraction jobs without managing crawler infrastructure.

That means users can treat these actors as ready-to-use APIs:

send input
run actor
read dataset output
plug into the next workflow And because output schemas are defined, the results are easier for both humans and AI agents to understand and chain.

Who these are for
These actors are a good fit for:

e-commerce operators in Turkey
agencies doing marketplace monitoring
brands tracking sellers and reviews
sourcing teams evaluating sellers
analysts building category intelligence dashboards
founders building vertical data products on top of Turkish commerce data The pitch, simply put Turkish marketplace data is valuable, but annoying to operationalize.

These three actors turn it into something you can actually use:

product records
seller profiles
normalized reviews with sentiment If you work on Turkish e-commerce intelligence, you should not have to rebuild this stack from scratch.

That is exactly why I built it.

Available now
N11 Product Scraper

Turkish Marketplace Seller Intelligence
Turkish E-Commerce Review Aggregator