Lot 59961766 · ON - TORONTO
2016 MERCEDES-BENZ GLC GLC300 Base-4M
VIN WDC0G4KB1GF053759
Price
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The vehicle
- Year 2016
- Make / model MERCEDES-BENZ GLC
- Trim level GLC300 Base-4M
- Body type Sport Utility Vehicle [SUV]/Multipurpose Vehicle [MPV]
- Condition PERMIT NO BRAND - UNFIT
- VIN WDC0G4KB1GF053759
- Horsepower 241 к.с.
- Exterior color white
- Odometer 119,970 mi (193,073 km)
- Listing valid until още не е насрочена
Notes
Този Mercedes-Benz GLC от 2016 г. е стилен и надежден SUV, готов за нови приключения.
Умерени щети по предната броня.
🚗 2016 Mercedes-Benz GLC джип
📋 Основни характеристики:
пробег: 193 073 км
конски сили: 241 к.с.
ℹ️ Допълнителна информация:
каросерия: джип
цвят: бял
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Damage assessment
Body zones
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Underbody
Condition assessment
The vehicle is listed by the auction with the condition designation “PERMIT NO BRAND – UNFIT” and a primary-damage code of “FRONT END.” Stage 1 reports that the visible front-end bumper damage supports the auction’s FRONT END classification. However, the provided photos do not establish the permit, branding, or roadworthiness status described by the UNFIT designation.
The front bumper cover is separated from the adjacent body and headlamp area, leaving a wide open gap and exposing dark inner components. The lower front bumper corner is also displaced, with a broken or separated lower edge and a black plastic component hanging beneath it. A front wheel-well liner is shown separately; it has multiple tears, deep abrasions, and distorted edges.
The VIN decodes as a 2016 Mercedes-Benz GLC-Class SUV, specifically a GLC300 Base-4M, manufactured in Germany. It is equipped with a turbocharged 2.0-liter, four-cylinder gasoline engine identified as the M274, with a decoded output of 241 horsepower. The available data does not specify the transmission or drivetrain beyond the trim designation.
The findings document moderate visible damage concentrated at the front bumper and front wheel-well area. Further physical inspection would be required to assess concealed damage, mounting points, lighting components, sensors, structural parts, mechanical condition, and the vehicle’s actual roadworthiness.
Generated from auction photos by AI vision, then written up by an AI model. Findings are qualitative, not a certified inspection.