# Valuation Predictions ## 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](https://docs.autograb.com.au/guide/auth/) 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 {% openapi src="/files/bupYK34BudsRm0rodLbg" path="/v2/valuations/predict" method="post" %} [api.yaml](https://2006860467-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F5qLYvzu45gdlTAGQCN9d%2Fuploads%2FQNJ2oRfASYWNsu7VjJHE%2Fapi.yaml?alt=media\&token=58a3820d-1bba-4403-8808-4c1bbeab3f41) {% 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": "uk", "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**, 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.
ConditionCondition Score
Poor1
Fair2
Average3
Good4
Excellent5
### 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 ```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. Copy ```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 } } } ``` --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://devhub.autograb.com/uk-autograb-api-doc/valuation/valuation-predictions.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.