Webmotors Scraper

Webmotors Scraper extracts detailed car and motorcycle listings from Brazil’s largest automotive marketplace into clean, structured datasets. It helps businesses and analysts track prices, inventory, and seller information efficiently. Designed for scalability, it turns complex vehicle listings into analysis-ready data.

     

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Introduction

Webmotors Scraper is built to collect comprehensive vehicle listings from Webmotors.com.br in a reliable and structured way. It solves the challenge of manually gathering pricing, specifications, and seller details from a fast-moving automotive marketplace. This project is ideal for data analysts, automotive businesses, researchers, and developers building vehicle data products.

Brazilian Automotive Market Data Extraction

• Collects structured vehicle data from cars and motorcycles listings
• Normalizes prices, specifications, and attributes for analysis
• Supports search, filtered, and location-based listing pages
• Handles pagination and large inventories automatically
• Produces clean datasets ready for dashboards or databases

Features

Feature	Description
Vehicle Listings Extraction	Collects cars and motorcycles with full specifications and pricing.
Seller Information	Extracts seller name, type, and location with optional detailed data.
Image Collection	Gathers multiple high-resolution images per vehicle.
Filtered Searches	Supports brand, model, year, price, and location filters.
Pagination Handling	Automatically crawls through all result pages.
Structured Output	Delivers normalized data suitable for analytics and storage.

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What Data This Scraper Extracts

Field Name	Field Description
id	Unique vehicle listing identifier.
url	Direct link to the vehicle listing page.
title	Full vehicle title as listed.
vehicle_type	Type of vehicle such as car or motorcycle.
make	Manufacturer or brand name.
model	Specific vehicle model.
version	Variant or trim level.
fabrication_year	Manufacturing year of the vehicle.
model_year	Market model year.
km	Mileage driven in kilometers.
transmission	Gearbox type.
fuel_type	Engine fuel category.
body_type	Vehicle body classification.
price	Listed selling price.
fipe_price	Reference market price.
color	Primary exterior color.
number_of_doors	Number of doors.
optionals	List of optional features and equipment.
attributes	Special listing attributes or tags.
photos	Array of vehicle image URLs.
seller	Seller profile and location details.

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Example Output

[
      {
        "id": 61932185,
        "url": "https://www.webmotors.com.br/comprar/audi/a3/20-40-tfsi-mhev-sportback-performance-black-s-tronic/4-portas/2023-2024/61932185",
        "title": "AUDI A3 2.0 40 TFSI MHEV SPORTBACK PERFORMANCE BLACK S-TRONIC",
        "vehicle_type": "car",
        "make": "AUDI",
        "model": "A3",
        "version": "2.0 40 TFSI MHEV SPORTBACK PERFORMANCE BLACK S-TRONIC",
        "fabrication_year": 2023,
        "model_year": 2024,
        "km": 29562,
        "transmission": "Automática",
        "fuel_type": "Gasolina",
        "body_type": "Hatchback",
        "price": 241900,
        "fipe_price": 247439,
        "color": "Cinza",
        "number_of_doors": 4,
        "optionals": ["Ar condicionado", "Teto solar", "Bancos em couro"],
        "photos": ["https://image.webmotors.com.br/example.jpg"],
        "seller": {
          "name": "Audi Center",
          "city": "São Paulo",
          "state": "SP"
        }
      }
    ]

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Directory Structure Tree

Webmotors Scraper/
├── src/
│   ├── main.py
│   ├── collectors/
│   │   ├── listings_collector.py
│   │   └── seller_collector.py
│   ├── parsers/
│   │   ├── vehicle_parser.py
│   │   └── images_parser.py
│   ├── utils/
│   │   ├── pagination.py
│   │   └── normalization.py
│   └── config/
│       └── settings.example.json
├── data/
│   ├── sample_input.txt
│   └── sample_output.json
├── requirements.txt
└── README.md

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Use Cases

• Automotive dealerships use it to monitor competitor inventory and pricing so they can adjust offers strategically.
• Market researchers use it to analyze trends in the Brazilian automotive market and identify demand patterns.
• Price comparison platforms use it to build up-to-date vehicle pricing databases for consumers.
• Investment analysts use it to evaluate market values and availability across regions.
• Fleet managers use it to research vehicle options and cost benchmarks efficiently.

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FAQs

Can this scraper handle large result sets? Yes, it is designed to process large inventories by automatically paginating through all available listing pages.

Does it support filtered searches? Yes, any filters applied on the marketplace such as brand, model, price range, or location are supported.

What formats can the output be used in? The structured data can be easily adapted for JSON, CSV, or spreadsheet-based workflows.

Is the data normalized for analysis? Yes, fields such as price, mileage, and specifications are cleaned and normalized for immediate use.

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Performance Benchmarks and Results

Primary Metric: Processes hundreds of vehicle listings per hour depending on filter scope and inventory size.

Reliability Metric: Maintains high extraction success rates across paginated and filtered searches.

Efficiency Metric: Optimized data collection minimizes redundant requests while maximizing throughput.

Quality Metric: Delivers consistently complete vehicle records with specifications, pricing, and media coverage.

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