# La Centrale Market Analytics: Used-Car Prices (`axlymxp/lacentrale-market-analytics`) Actor

Used-car market data from La Centrale (lacentrale.fr): live inventory counts by make, model, fuel and region, plus price, mileage and year statistics (min, max, average, median, quartiles) for any make or model in France. No login. JSON, CSV or Excel.

- **URL**: https://apify.com/axlymxp/lacentrale-market-analytics.md
- **Developed by:** [axly](https://apify.com/axlymxp) (community)
- **Categories:** E-commerce, Business
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $2.00 / 1,000 dataset items

This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## La Centrale Market Analytics: used-car inventory and price statistics

Turn **La Centrale** (lacentrale.fr, a large French used-car marketplace) into market data. Instead of thousands of raw listings you get **inventory counts** (how many cars are for sale per make, model, fuel, gearbox, region, deal rating) and **price, mileage and year statistics** (min, max, average, median, quartiles) for any brand or model, computed from live listings. No login.

### Who it is for

- **Dealers and pricing teams:** benchmark your asking prices against the live market for the same model.
- **Analysts and investors:** size segments and watch supply by make, model, fuel or region over time.
- **Valuation, insurance and finance teams:** reference price and mileage distributions per model.
- **Journalists and researchers:** quantify the French used-car market.

### Two analysis modes

#### 1. Inventory counts (`analysisType: "inventory"`)

One row per facet bucket. Facets: `MAKE_MODEL` (make with a model sub-bucket), `ENERGY`, `GEARBOX`, `REGION`, `GOOD_DEAL_BADGE`, `EXTERNAL_COLOR`, `CUSTOMER_FAMILY_CODE`. Optionally scope the counts to a make or a make and model.

| Field | Description |
| --- | --- |
| `facet` | Facet name, e.g. `vehicle.make`, `vehicle.energy`, `location.visitPlace.region` |
| `key` | Bucket key, e.g. `RENAULT`, `DIESEL`, `FR-IDF` |
| `count` | Live listings in that bucket |
| `subKey`, `subCount` | Sub-bucket (a model under a make) and its count, otherwise null |
| `families` | Vehicle families analysed |

#### 2. Price and mileage statistics (`analysisType: "priceStats"`)

A single summary row for a make (and optional model, fuel type), computed from a sample of live listings.

| Field | Description |
| --- | --- |
| `make`, `model`, `families`, `energies` | What was analysed |
| `totalAvailable` | Live listings matching the filter |
| `sampleSize` | Listings used for the statistics |
| `priceEur`, `mileageKm`, `year` | `count`, `min`, `max`, `avg`, `median`, `p25`, `p75` |
| `dealBadgeCounts` | Good-deal ratings in the sample |
| `sellerTypeCounts` | Dealer (`PRO`) vs private (`PART`) split in the sample |

The sample is taken in La Centrale's default relevance order (not random), so it leans towards dealer listings. A larger `sampleSize` (up to 1000) gives a fuller picture; `totalAvailable` is always the full live count.

### Example input

```json
{
    "analysisType": "priceStats",
    "make": "PEUGEOT",
    "model": "208",
    "families": "AUTO",
    "sampleSize": 150
}
```

### Example output (priceStats)

```json
{
    "make": "PEUGEOT",
    "model": "208",
    "families": "AUTO",
    "energies": null,
    "totalAvailable": 8915,
    "sampleSize": 150,
    "priceEur": {
        "count": 150,
        "min": 4890,
        "max": 32290,
        "avg": 15990.4,
        "median": 15177,
        "p25": 12071.8,
        "p75": 19487.5
    },
    "mileageKm": {
        "count": 150,
        "min": 5,
        "max": 185434,
        "avg": 43128.2,
        "median": 32956,
        "p25": 10119.8,
        "p75": 61130
    },
    "year": {
        "count": 150,
        "min": 2012,
        "max": 2026,
        "avg": 2022.9,
        "median": 2023,
        "p25": 2022,
        "p75": 2025
    },
    "dealBadgeCounts": {
        "GOOD_DEAL": 68,
        "EQUITABLE_DEAL": 67,
        "VERY_GOOD_DEAL": 8,
        "BAD_DEAL": 5
    },
    "sellerTypeCounts": {
        "PRO": 150
    }
}
```

### Example output (inventory, MAKE_MODEL facet)

```json
[
    {
        "facet": "vehicle.make",
        "key": "ABARTH",
        "count": 448,
        "subKey": "124",
        "subCount": 8,
        "families": "AUTO,UTILITY"
    },
    {
        "facet": "vehicle.make",
        "key": "ABARTH",
        "count": 448,
        "subKey": "500",
        "subCount": 417,
        "families": "AUTO,UTILITY"
    },
    {
        "facet": "vehicle.make",
        "key": "ABARTH",
        "count": 448,
        "subKey": "850",
        "subCount": 1,
        "families": "AUTO,UTILITY"
    }
]
```

### Scheduling

Schedule daily or weekly runs to build a time series of prices and inventory for depreciation and supply tracking. Attach a webhook to push each snapshot to a warehouse or Google Sheet.

### FAQ

**Where do the numbers come from?** Live La Centrale inventory. Facet counts cover the whole market; price statistics come from the sample you size with `sampleSize`.

**Do I need a proxy or an account?** No. The actor works without either. If La Centrale blocks the server IP it retries through an Apify Residential (France) exit, billed to your account as proxy usage.

**How big is an inventory run?** A full `MAKE_MODEL` snapshot with no make filter returned 1,640 rows in a test run; scoping to one make returns a few dozen.

**Is this allowed?** The actor aggregates publicly visible listing data. Use the output in line with La Centrale's terms and applicable law.

# Actor input Schema

## `analysisType` (type: `string`):

inventory = facet counts (make/model, energy, …); priceStats = price & mileage stats for a make/model.

## `families` (type: `string`):

Which vehicle families to include in the analysis.

## `make` (type: `string`):

Brand in capitals, e.g. PEUGEOT. Optional for inventory (scopes the counts), required for priceStats.

## `model` (type: `string`):

Model in capitals, e.g. 208. Needs a make.

## `aggregations` (type: `string`):

Comma-separated facets for inventory mode: MAKE_MODEL, ENERGY, REGION, GEARBOX, GOOD_DEAL_BADGE, EXTERNAL_COLOR, CUSTOMER_FAMILY_CODE.

## `energies` (type: `string`):

Optional fuel filter for priceStats mode.

## `sampleSize` (type: `integer`):

How many live listings to sample for the statistics (10 to 1000). The sample follows La Centrale's default relevance order, so a larger sample gives a fuller picture.

## `proxyConfiguration` (type: `object`):

Optional. The actor works without a proxy. If La Centrale blocks the server IP it retries through Apify Residential proxy (France) automatically, billed to your account as proxy usage.

## Actor input object example

```json
{
  "analysisType": "inventory",
  "families": "AUTO,UTILITY",
  "make": "PEUGEOT",
  "model": "208",
  "aggregations": "MAKE_MODEL,ENERGY,GEARBOX,GOOD_DEAL_BADGE",
  "energies": "",
  "sampleSize": 150,
  "proxyConfiguration": {
    "useApifyProxy": false
  }
}
```

# Actor output Schema

## `results` (type: `string`):

No description

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "make": "PEUGEOT",
    "model": "208",
    "sampleSize": 150
};

// Run the Actor and wait for it to finish
const run = await client.actor("axlymxp/lacentrale-market-analytics").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {
    "make": "PEUGEOT",
    "model": "208",
    "sampleSize": 150,
}

# Run the Actor and wait for it to finish
run = client.actor("axlymxp/lacentrale-market-analytics").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "make": "PEUGEOT",
  "model": "208",
  "sampleSize": 150
}' |
apify call axlymxp/lacentrale-market-analytics --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,axlymxp/lacentrale-market-analytics"
        }
    }
}
```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/ECfie8er1w2embiWU/builds/Ma5hoUfdVabthaHpt/openapi.json