Valuation Predictions
Generate market accurate predictions for vehicles.
Overview
The Valuation API can be used to determine the present retail & trade values, as well as the residual values of new vehicles.
This API requires authentication and an appropriate license attached to it.
To use the API, a Vehicle ID returned from the Vehicle Search API or Vehicle Facet API is required.
How is a valuation provided?
AutoGrab’s Valuation captures the asking prices from the past week for a specific vehicle’s year, make, model, and variant at a given mileage, sourced from private sellers and dealers within a selected state. It excludes government charges but includes GST, assuming the vehicles are in good condition and fitted with standard OEM accessories.
Leveraging advanced machine learning and updated weekly, our pricing integrates active listings and recently delisted data from public marketplaces. AutoGrab emphasises the most recent data to deliver comprehensive valuations that reflect current market trends.
Each estimate includes a confidence score, which indicates AutoGrab’s certainty in the valuation. Two key factors determine this score:
The number of vehicles listed in the past 365 days for the specific vehicle type
A detailed accuracy analysis of the pricing algorithm for that vehicle type
The confidence score ranges from 0 to 1, with 1 representing the highest confidence level
Value a vehicle using an AutoGrab ID
The AutoGrab Vehicle ID which corresponds to the vehicle that should be valued
The odometer reading of the vehicle. If no reading is provided, the average value will be subsituted
The registration plate of the vehicle, for reference purposes only
The registration state of the vehicle, if applicable
The VIN of the vehicle, for reference purposes only
Success
trueBad Request
Unauthorized
POST /v2/valuations/predict HTTP/1.1
Host: api.autograb.com.au
ApiKey: YOUR_API_KEY
Content-Type: application/json
Accept: */*
Content-Length: 142
{
"region": "au",
"vehicle_id": "text",
"kms": 1,
"rrp_overwrite": 1,
"rrp_adjustment": 1,
"condition_score": 1,
"rego": "text",
"state": "text",
"vin": "text"
}{
"success": true,
"prediction": {
"id": "text",
"vehicle_id": "text",
"created_at": "text",
"kms": 1,
"price": 1,
"score": 1,
"retail_price": 1,
"trade_price": 1,
"adjustment": {
"vehicle_id": "text",
"type": "account",
"enabled": true,
"trade_adjustment": {
"amount": 1,
"type": "fixed"
},
"retail_adjustment": {
"amount": 1,
"type": "fixed"
},
"overrides": [
{
"id": "text",
"min_kms": 1,
"max_kms": 1,
"trade_price": 1,
"retail_price": 1
}
]
}
},
"bounds": {
"retail": {
"lower": 1,
"upper": 1
},
"trade": {
"lower": 1,
"upper": 1
}
},
"max_offer": {
"reconditioning": 1,
"profit_margin": 1,
"lot": 1,
"transport": 1,
"admin": 1,
"price": 1
}
}Example
Request
Starting with a vehicle ID post it to /v2/valuations/predict
Response
Pricing ID
The payload returned by price prediction requests will include an ID, which you can use to refer to the pricing request in the future. The /v2/valuations/history/{PRICING_ID method will return the response from a previous pricing request, and you can also use the Pricing ID to track price changes with the Price Changes API, if licenced.
To get a paginated list of all your previous price predictions, you can use the /v2/valuations/history endpoint.
Condition Score
By supplying a condition score, you can manipulate the trade_price returned by the prediction endpoint. The condition score can be between 1 and 5. A condition of 1 being poor condition and a condition of 5 excellent condition.
Supplying any other numbers will return the default trade_price which assumes excellent condition.
If you're building a user interface where you allow the user to choose a condition it is recommended you follow the industry standard in the table below.
Poor
1
Fair
2
Average
3
Good
4
Excellent
5
Features
Positive Equity
The positive equity feature identifies if the vehicle is in positive equity and the current equity position. To add an equity calculation to the Predict call, use features=equity
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Valuation Bounds
If you require the upper and lower bounds used to calculate a prediction, you can use features=bounds.
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