How to scrape automotive data from CarTrade using Minexa.ai

Tracking used car prices across a city like Mumbai page by page is the kind of task that sounds manageable until you actually try it. CarTrade lists hundreds of second-hand vehicles with prices, mileage, fuel type, and photo galleries per listing. Getting all of that into a spreadsheet manually takes hours. This guide shows how to do it in minutes using the Minexa.ai Chrome extension.

Watch the full tutorial first

The video below walks through the entire extraction from opening CarTrade to exporting structured data. Watch it once before going through the steps below.

https://www.youtube.com/watch?v=1XE0yrH1nWk

What page do you start on?

Navigate to the CarTrade second-hand listings page for your target city. For Mumbai, that is cartrade.com/second-hand/mumbai/. This is the list page Minexa.ai will read. Once you are there, open the Minexa.ai extension from your Chrome toolbar.

After the extension opens, click the 'I'm on the right page' button to confirm you are on the correct starting URL. Minexa.ai then begins analysing the page structure automatically.

How does pagination get handled?

Minexa.ai detects the next-page button on CarTrade automatically. You will see the pagination method identified in the extension panel. Confirm it and click Continue. From that point, multi-page extraction runs without any further input from you.

Should you scrape just the list or go into each listing too?

After confirming pagination, Minexa.ai asks whether you want to extract only the data visible on the list page, or follow each listing link and pull detail data from individual pages as well. For most used car research tasks, the list data alone is already rich. Choose the option that fits your use case.

Next, select Simple scraping mode unless you need custom click interactions on the page. Simple mode covers the vast majority of CarTrade extraction scenarios.

What does Minexa.ai detect on the page?

Minexa.ai highlights the repeating container that holds each car listing. You confirm the selection, then click Create scraper. The extension identifies every data point within each container, including fields that are not visible to a human reader but are present in the page structure.

Once the scraper is created, a data preview shows all extracted columns with navigation arrows to browse each field. You can see the full set of data points before running the job at scale.

The job summary screen shows scheduling options and Google Sheets integration if you want the data to refresh automatically on a set interval.

After the job runs, the scraped data appears in a table. Export to Excel or JSON with one click.

What does the extracted data actually look like?

Here are two example rows from a real CarTrade extraction. Fields prefixed with meta__ are removed for clarity.

[
  {
    "car_model": "2020 BMW X7 xDrive40i M Sport (Petrol)",
    "car_price": "6500000",
    "car_id": "D7072010",
    "car_details": "make_year=2020|kilometers=42,000|fuel=Petrol|city_id=1|stock_id=zou7x0wi|car_price=6500000|photos=7",
    "buy_button_title": "Buy Used BMW X7 xDrive40i M Sport (Petrol) in Mumbai",
    "image_source_url": "https://imgd-ct.aeplcdn.com/640X480/vimages/202607/4670991_80138_1_1783861465263.jpg?q=80",
    "random_code": [{"value": "zou7x0wi", "tag": "button", "attribute": "data-stockid", "type": "attribute"}],
    "twitter_share_link": "https://twitter.com/share?text=Hey, Check out this 2020 BMW X7 on CarTrade.com"
  },
  {
    "car_model": "2022 Mercedes-Benz E-Class E 220d Exclusive [2021-2023]",
    "car_price": "5490000",
    "car_id": "D7076234",
    "car_details": "make_year=2022|kilometers=35,000|fuel=Diesel|city_id=1|stock_id=hqaj0vr3|car_price=5490000|photos=7",
    "buy_button_title": "Buy Used Mercedes-Benz E-Class E 220d Exclusive [2021-2023] in Mumbai",
    "image_source_url": "https://imgd-ct.aeplcdn.com/640X480/vimages/202607/4673224_54130_1_1784000619348.jpg?q=80",
    "random_code": [{"value": "hqaj0vr3", "tag": "button", "attribute": "data-stockid", "type": "attribute"}],
    "twitter_share_link": "https://twitter.com/share?text=Hey, Check out this 2022 Mercedes-Benz E-Class on CarTrade.com"
  }
]

The car_details field is particularly useful. It bundles make year, odometer reading, fuel type, city identifier, stock identifier, price, and photo count into a single pipe-delimited string per listing. That means you can reconstruct the full vehicle context for any row without making a secondary request to the listing page.

The random_code field returns a structured array encoding the stock ID as a data-stockid attribute value. This stock ID is stable across repeated scraping runs, which makes it reliable for deduplication when you schedule the job to run on a recurring basis.

The image_onclick_function field embeds a comma-separated sequence of CDN image URLs per listing. If you need the full photo gallery for a vehicle, every image URL is already present in this field without visiting the individual listing page.

The make_offer_description field contains a pre-formatted function call string that encodes both the display-formatted price (for example, 65,00,000) and the raw numeric price (6500000) alongside the listing identifiers. This means downstream price parsing requires no additional computation or formatting logic.

Ready to pull your first CarTrade dataset? Download the Minexa.ai Chrome extension and run the extraction yourself. The setup takes a few minutes and the scraper can be reused on any subsequent run without repeating configuration.

If you want to go deeper into what Minexa.ai can extract from automotive and listing sites, the post below covers related ground: Scraping book listings for publishers: a structured guide to extracting catalogue data without code.