Georgian Vehicle Sales & Rental Data
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Georgia's online automotive market for 2024 is available, covering vehicle listings, features, pricing, and compatibility. This data also incorporates related entities such as sellers, categories, and statuses, offering a valuable resource for examining market trends, vehicle attributes, pricing strategies, and availability across various applications like sales, rentals, and auctions. The data's structured nature makes it suitable for data analysis, predictive modelling, and advanced machine learning techniques, including linear regression, multi-layer linear regression, classification, and clustering. Researchers and analysts can utilise this data to identify patterns, build predictive models for pricing or demand estimation, and improve decision-making within the automotive sector.
Columns
The dataset is organised across several interconnected tables, each detailing specific aspects of the automotive market.
Agreement
app_id
(bigint): The unique identifier for each record, representing a vehicle or listing.changeable
(boolean): Indicates if the agreement terms, such as price, are negotiable.auction
(boolean): Specifies if the vehicle can be purchased through an auction.for_rent
(boolean): Specifies if the vehicle is available for rental.rent_daily
(boolean): Indicates if daily rental is an option.rent_purchase
(boolean): Suggests a rent-to-own possibility for the vehicle.rent_insured
(boolean): States if rental agreements include insurance.
Applications
app_id
(bigint): A unique identifier for each application or listing.user_id
(bigint): The unique identifier for the user associated with the listing.status
(varchar): Represents the listing's current status (e.g., S-VIP, VIP, Standard).upload_date
(date): The date when the listing was added to the platform.vehicle_type
(varchar): The kind of vehicle listed (e.g., car, motorcycle, costume vehicle).category
(varchar): The vehicle's specific category (e.g., sedan, jeep, pickup).insert_date
(date): The date the record was inserted into the database.
Price
app_id
(bigint): The unique key linking to a specific vehicle listing.price
(float): The vehicle's advertised price.price_value
(float): The vehicle's price in Georgian Lari (GEL).has_predicted_price
(boolean): Denotes if a predicted price is available for the listing.predicted_price
(float): The calculated estimated price based on market factors.pred_first_breakpoint
(float): The initial threshold in the predicted price range.pred_second_breakpoint
(float): The subsequent threshold in the predicted price range.pred_min_price
(float): The lowest estimated price for the vehicle.pred_max_price
(float): The highest estimated price for the vehicle.
Primary Features
app_id
(bigint): The unique key linking to a specific vehicle listing.fuel_type_id
(smallint): References the vehicle's fuel type.gear_type_id
(smallint): References the vehicle's gear type.drive_type
(varchar): The vehicle's drive system (e.g., front, rear, 4X4).door_type
(varchar): The vehicle's door configuration (e.g., 4/5, 2/3, >5).color_id
(smallint): References the vehicle's exterior colour.saloon_color_id
(smallint): References the vehicle's interior saloon colour.man_id
(smallint): References the vehicle's manufacturer.model_id
(smallint): References the vehicle's specific model.location_id
(smallint): References the vehicle's geographical location.saloon_material
(varchar): The material used for the vehicle's interior saloon (e.g., "Leather").
Colors
id
(smallint): The unique key for each colour entry.color_code
(varchar): A coded representation of the colour.color_name
(varchar): The descriptive name of the colour (e.g., Red, Blue).
Comfort Features
app_id
(bigint): The unique key linking to a vehicle listing.feature_id
(smallint): The unique key referencing a specific comfort feature.
Depreciation
app_id
(bigint): The unique key linking to a vehicle listing.car_run_km
(bigint): The total distance the vehicle has travelled in kilometres.engine_volume
(bigint): The vehicle's engine capacity.prod_year
(smallint): The vehicle's production year.cylinders
(smallint): The number of cylinders in the vehicle's engine.airbags
(smallint): The quantity of airbags in the vehicle.
Extra Options
app_id
(bigint): The unique key linking to a vehicle listing.abs_break
(boolean): Presence of an anti-lock braking system.esd
(boolean): Presence of an electronic stability control system.el_windows
(boolean): Presence of electric windows.conditioner
(boolean): Presence of air conditioning.leather
(boolean): Presence of leather seats or interior.disks
(boolean): Presence of alloy or specialised disks.nav_system
(boolean): Presence of a navigation system.central_lock
(boolean): Presence of a central locking system.hatch
(boolean): Presence of a hatchback or liftgate.right_wheel
(boolean): Indicates if the vehicle is right-hand drive.alarm
(boolean): Presence of an anti-theft alarm system.board_comp
(boolean): Presence of an onboard computer system.hydraulics
(boolean): Presence of hydraulic suspension or systems.chair_warming
(boolean): Presence of heated seats.climat_control
(boolean): Presence of climate control.obstacle_indicator
(boolean): Presence of obstacle detection or parking sensors.customs_passed
(boolean): Indicates if the vehicle has cleared customs.tech_inspection
(boolean): Indicates if the vehicle has passed technical inspection.has_turbo
(boolean): Presence of a turbocharged engine.back_camera
(boolean): Presence of a rearview or backup camera.special_persons
(boolean): Indicates if the vehicle is adapted for individuals with special needs.start_stop
(boolean): Presence of an engine start-stop system.trunk
(boolean): Presence of a trunk or designated cargo area.windshield
(boolean): Presence of a windshield or specific windshield features.inspected_in_greenway
(boolean): Indicates if the vehicle was inspected by a specific vendor.has_catalyst
(boolean): Presence of a catalytic converter.has_vin
(boolean): Presence of a verified Vehicle Identification Number (VIN).
Features
id
(smallint): The unique key for each feature entry.parent_id
(smallint): References a parent feature, allowing hierarchical relationships (0 for top-level).feature
(varchar): The name or description of the feature.
Fuel
id
(smallint): The unique key for each fuel type entry.parent_id
(smallint): References a parent fuel type, allowing hierarchical relationships (0 for top-level).fuel_type
(varchar): The name or description of the fuel type.
Gear
id
(smallint): The unique key for each gear type entry.parent_id
(smallint): References a parent gear type, allowing hierarchical relationships (0 for top-level).gear_type
(varchar): The name or description of the gear type.
Locations
id
(smallint): The unique key for each location entry.parent_id
(smallint): References a parent location, allowing hierarchical relationships (0 for top-level).location_name
(varchar): The name of the location.
Mans (Manufacturers)
id
(smallint): The unique key for each manufacturer entry.is_car
(boolean): Indicates if the manufacturer produces cars.is_moto
(boolean): Indicates if the manufacturer produces motorcycles.is_spec
(boolean): Indicates if the manufacturer produces special-purpose vehicles.man_name
(varchar): The name of the manufacturer.
Models
id
(smallint): The unique key for each vehicle model entry.man_id
(smallint): References the manufacturer associated with the model.model_name
(varchar): The name of the vehicle model.is_car
(boolean): Indicates if the model is a car.is_moto
(boolean): Indicates if the model is a motorcycle.is_spec
(boolean): Indicates if the model is a special-purpose vehicle.
Distribution
The data is provided in a structured, tabular format, typically in CSV files. It offers a detailed schema, available for PostgreSQL on Drawsql, indicating a well-organised database structure. Specific total numbers for rows or records for the entire dataset are not explicitly provided in the available sources.
Usage
This data is perfectly suited for various analytical and machine learning tasks. Ideal applications include:
- Predictive Modelling: Building models for car price prediction, demand estimation, and market forecasting using techniques like linear regression, multi-layer linear regression, classification, and clustering.
- Market Analysis: Identifying trends in vehicle features, pricing strategies, and availability across different segments.
- Business Intelligence: Informing decision-making for sales, rental, and auction businesses within the automotive market.
- Research: Uncovering patterns and insights into vehicle depreciation, popular features, and regional market dynamics.
Coverage
The data focuses on Georgia's online automotive market in 2024. It covers various vehicle types, manufacturers, and features available for sale, rent, or auction. While the data captures listings and market activity for the specified year, specific notes on data availability for particular demographic groups or extended time ranges beyond 2024 are not detailed. The data is expected to be updated annually.
License
Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
Who Can Use It
The data is intended for:
- Researchers and Data Analysts: For studying automotive market dynamics, identifying patterns, and generating reports.
- Data Scientists and Machine Learning Engineers: For developing and training predictive models for car pricing, demand, and market segmentation.
- Automotive Businesses: Companies involved in vehicle sales, rentals, and auctions seeking to enhance pricing strategies, inventory management, and market understanding.
- Developers: Those building applications that require current or historical automotive market data from Georgia.
Dataset Name Suggestions
- Georgian Car Market 2024 Data
- Georgia Automotive Price Prediction
- 2024 Georgian Vehicle Listings
- Auto Market Insights Georgia
- Georgian Vehicle Sales & Rental Data
Attributes
Original Data Source: Georgian Vehicle Sales & Rental Data