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

post

Value a vehicle using an AutoGrab ID

Authorizations

ApiKeyAuth

ApiKeystringRequired

Body

application/json

regionstring · enumOptionalPossible values:

vehicle_idstringRequired

The AutoGrab Vehicle ID which corresponds to the vehicle that should be valued

kmsnumberOptional

The odometer reading of the vehicle. If no reading is provided, the average value will be subsituted

rrp_overwritenumberOptional

rrp_adjustmentnumberOptional

condition_scorenumberOptional

regostringOptional

The registration plate of the vehicle, for reference purposes only

statestringOptional

The registration state of the vehicle, if applicable

vinstringOptional

The VIN of the vehicle, for reference purposes only

Responses

200

Success

application/json

successbooleanOptionalDefault: true

400

Bad Request

application/json

401

Unauthorized

application/json

post

/v2/valuations/predict

HTTPcURLJavaScriptPython

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

To retrieve a Vehicle ID, use the Vehicle Search APIs.

Starting with a vehicle ID post it to /v2/valuations/predict

{
    "region": "uk",
    "vehicle_id": "5804870883868672"
    "kms": 30000,
    "condition_score": 2
}

The condition score is optional and can be used to further refine your pricing prediction.

The Catalogue field is optional and will default to 'autograb' and the vehicle ID for performing a valuation. If you have access to Jato catalogue information, 'jato' can be passed as the catalogue and a JATO ID as the vehicle_id. The valuation will then be performed using the JATO information.

Response

{
    "success": true,
    "prediction": {
        "id": "a2955915-9611-40ef-8b98-b827dad76ff4",
        "vehicle_id": "5804870883868672",
        "kms": 57306,
        "price": 20622,
        "score": 0.9239,
        "retail_price": 20622,
        "trade_price": 17122,
        "adjustment": null
    }
}

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.

Condition	Condition Score
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

Copy

{
    "success": true,
    "prediction": {
        "id": "599aff94-7c79-4b9c-a976-ff39c3892190",
        "vehicle_id": "4825547834130432",
        "kms": 20000,
        "price": 32669,
        "score": 0.8515,
        "retail_price": 32669,
        "trade_price": 27769,
        "adjustment": null
    },
    "equity": {
        "positive_equity": true,
        "equity_position": 15769
    }
}

Valuation Bounds

If you require the upper and lower bounds used to calculate a prediction, you can use features=bounds.

Copy

{
    "success": true,
    "prediction": {
        "id": "e2813b16-1012-41b5-9ea3-3b65675c50fd",
        "vehicle_id": "5804870883868672",
        "kms": 57306,
        "price": 20622,
        "score": 0.9239,
        "retail_price": 20622,
        "trade_price": 17122,
        "adjustment": null
    },
    "bounds": {
        "retail": {
            "lower": 19622,
            "upper": 21872
        },
        "trade": {
            "lower": 16122,
            "upper": 18372
        }
    }
}