DOI: 10.5281/zenodo.17643343
title: autoscout24_dataset_20251108.csv

description: 🚗 AutoScout24 Car Listings Dataset

🚗︎ Kaggle: AutoScout24 (2025 Snapshot) Car Listings Dataset: Derived from publicly available AutoScout24.

⚙️ Overview  

Structured dataset of vehicles listed on AutoScout24, one of Europe’s largest online car marketplaces.  Data was collected from public listings and processed using Pydantic and Pandas for validation and consistency.

Contains ~120K listings across multiple European markets, with attributes describing pricing, specifications, energy efficiency, and equipment.  Useful for modeling vehicle prices, market segmentation, and feature importance analysis.

🚗︎  Dataset Features

id – Unique listing ID.  description – Text description of the vehicle.  

Ratings:  - ratings_average – Average rating.  - ratings_count – Total ratings.  - ratings_recommend_percentage – Percentage of users recommending the seller.  

Pricing:  - price_currency – Currency of price (EUR default).  - price – Listed price.  - price_tax_deductible – VAT deductible flag.  - price_negotiable – Negotiable flag.  - price_net – Net price before VAT.  - price_vat_rate – VAT rate percent.  

Vehicle:  - vin – Vehicle identification number.  - make – Vehicle brand.  - model – Model name.  - model_version – Model or trim version.  - german_hsn_tsn – German HSN/TSN code.  - mileage_km_raw – Raw mileage value.  - mileage_km – Mileage (km).  - registration_date – First registration date.  - production_year – Year built.  - vehicle_type – Vehicle type (car, van, truck).  - body_type – Body style (sedan, SUV, etc.).  - nr_seats – Seat count.  - nr_doors – Door count.  - body_color – Exterior color.  - paint_type – Paint type (metallic, solid).  - body_color_original – Original factory color.  - upholstery – Upholstery type.  - upholstery_color – Upholstery color.  - power_kw – Power in kW.  - power_hp – Power in HP.  - transmission – Manual or automatic.  - gears – Number of gears.  - drive_train – Drive type (FWD, RWD, AWD).  - cylinders – Engine cylinders.  - cylinders_volume_cc – Engine displacement (cc).  - weight_kg – Vehicle weight.  

Fuel and Energy:  - has_particle_filter – Particle filter flag.  - fuel_category – Fuel category (Diesel, Petrol, Electric).  - primary_fuel – Primary fuel.  - electric_range_km – Electric range (km).  - electric_range_city_km – City electric range.  - fuel_cons_comb_l100_km – Combined fuel use (L/100 km).  - fuel_cons_city_l100_km – City fuel use.  - fuel_cons_highway_l100_km – Highway fuel use.  - co2_emission_grper_km – CO2 emissions (g/km).  - fuel_cons_comb_l100_wltp_km – WLTP combined fuel use.  - fuel_cons_electric_comb_l100_wltp_km – WLTP electric consumption.  - co2_emission_grper_wltp_km – WLTP CO2 emissions.  

Equipment / Features:  - equipment_comfort – Comfort features list.  - equipment_entertainment – Entertainment features list.  - equipment_extra – Extra features list.  - equipment_safety – Safety features list.  

Flags / Booleans:  - is_used – Used vehicle flag.  - is_new – New vehicle flag.  - is_preregistered – Preregistered vehicle flag.  - had_accident – Accident history flag.  - has_full_service_history – Full service history flag.  - non_smoking – Non-smoking vehicle flag.  - nr_prev_owners – Number of previous owners.  - is_rental – Rental vehicle flag.  

Miscellaneous:  - envir_standard – Emission standard (Euro class).  - original_market – Original sales market.  - offer_type – Offer type (sale, lease).  

Location:  - country_code – Country code.  - zip – Postal code.  - city – City name.  - street – Street name.  - latitude – Latitude coordinate.  - longitude – Longitude coordinate.  

Seller:  - seller_is_dealer – Dealer flag.  - seller_type – Seller type (private, dealer, company).  - seller_company_name – Company name if dealer.  - has_warranty – Warranty flag.  - warranty – Warranty description. 

🧩 AutoScout24 — Example Applications

🔍 Market Analysis

- Compare average prices by make, model, or region  - Identify popular car types or brands  - Explore price–mileage–age relationships

🧮 Machine Learning

- Regression for **price prediction**  - Feature selection and importance ranking  - Unsupervised clustering by technical specs or equipment

📊 Business Insights

- Forecast used car demand  - Analyze price elasticity by region  - Study adoption of electric and hybrid models

🧠 Example Analyses

- Depreciation Modeling – Price vs. Mileage or Production Year  - Fuel Efficiency Study – CO₂ emissions by fuel type  - Feature Popularity – Common comfort and safety equipment  - Regional Pricing – Average listing prices by city or country  

📂 Technical Details

- Format: CSV (UTF-8)  - Records: ~120K  - Language: English  - Currency: EUR  

✅ Summary

Suitable for:- Vehicle price prediction and feature-based ML models  - Automotive trend and market analysis  - Statistical research on vehicle attributes and resale value  - Dataset benchmarking for regression and clustering tasks

🔒 Licensing and Use

You are welcome to use this dataset for **research, educational, or analytical** purposes.  If you publish analyses, please include the citation below.

🧾 Citation

 

[apa7]: Çelik, M. (2025, Nov 8). "AutoScout24 Car Listings Dataset [Data set]". Kaggle. https://www.kaggle.com/datasets/clkmuhammed/autoscout24-car-listings-dataset

 

[apa]: çelik, muhammed. (2025). "AutoScout24 Car Listings Dataset [Data set]". Kaggle. https://doi.org/10.34740/KAGGLE/DS/8683897

 

[apa]: çelik, muhammed. (2025). "autoscout24_dataset_20251108.csv (1.0.0) [Data set]". Zenodo. https://doi.org/10.5281/zenodo.17643343

 

BibTex

 

```

@misc{muhammed_celik_2025,

title={AutoScout24 Car Listings Dataset},

url={https://www.kaggle.com/ds/8683897},

DOI={10.34740/KAGGLE/DS/8683897},

publisher={Kaggle},

author={muhammed celik},

year={2025}

}

```

Text generated and documentation written with the assistance of ChatGPT (OpenAI, GPT-5).