# Valuation Predict
## Overview
The Valuation API can be used to determine new vehicles' present retail and trade values and their residual values.
This API requires [authentication](https://docs.autograb.com.au/guide/auth/) and an appropriate license attached to it.
A Vehicle ID returned from the Vehicle Search API or Vehicle Facet API is required to use the API.
### 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
{% openapi src="/files/TapIOjKDYdyzufcTwuea" path="/v2/valuations/predict" method="post" %}
[swagger\_fixed\_final.json](https://1154811857-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FUypmYo5OBsuC4nJIUOZk%2Fuploads%2FO1mxxRIRpDcKC6fr56tG%2Fswagger_fixed_final.json?alt=media\&token=956f728c-69a3-4540-b468-70d8d8e1068d)
{% endopenapi %}
## Example
### **Request**
{% hint style="info" %}
To retrieve a Vehicle ID, use the [Vehicle Search APIs](https://docs.autograb.com.au/guide/vehicle/).
{% endhint %}
Starting with a vehicle ID post it to /v2/valuations/predict
```json
{
"region": "au",
"catalogue": "autograb",
"vehicle_id": "5804870883868672",
"kms": 30000,
"condition_score": 2
}
```
{% hint style="info" %}
The [condition score](#condition-score) is optional and can be used to further refine your pricing prediction.
{% endhint %}
{% hint style="info" %}
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.
{% endhint %}
### **Response**
```json
{
"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**.
To get a paginated list of all your previous price predictions, you can use the `/v2/valuations/history` endpoint.
#### Condition Score
You can manipulate the valuation returned by the prediction endpoint by supplying a condition score. The condition score can be between `1` and `5`. A condition of 1 is poor, and 5 is excellent.
Supplying any other numbers will return the default trade\_price, which assumes excellent condition.
If you're building a user interface that allows the user to choose a condition, it is recommended that 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
```json
{
"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.
```json
{
"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
}
}
}
```
---
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