# France DVF Sold Prices & DPE Energy Ratings (MCP) (`amygraphics/france-property-data-scraper`) Actor

France property data: notarial sold prices (DVF, notaires) + ADEME DPE energy ratings — price, address, GPS, surfaces, energy class, consumption and cost. French sold prices and energy certificates. MCP-ready for AI agents.

- **URL**: https://apify.com/amygraphics/france-property-data-scraper.md
- **Developed by:** [Zakaria el bouami](https://apify.com/amygraphics) (community)
- **Categories:** Real estate
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $4.00 / 1,000 record extracteds

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.

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

## France DVF Sold Prices & DPE Energy Ratings (MCP)

Two **official French open-data sources** in one Actor — no browser, no proxy:

1. **DVF — every notarial sale in France** (price, exact date, address, GPS, surfaces, rooms, cadastral parcels), per department or commune, 2021 → 2025.
2. **ADEME DPE — 15.7 million energy certificates**: DPE/GES labels, consumption, estimated annual cost, surface, building type, construction period and heating energy.

Part of the *data franchise* series by the same developer: 🇲🇦 Morocco (Avito, Mubawab) • 🇫🇷 France (Paruvendu, Bien'ici, BODACC + SIRENE, Jobs, and now official property data).

### Mode 1 — `transactions` (DVF)

Every recorded sale, aggregated **one row per transaction**:

| Field | Example |
|---|---|
| `mutationId` / `date` / `nature` | 2024-1210538 / 2024-01-05 / Vente |
| `value` (€) + `pricePerSqm` | 1 042 000 / 9 806 €/m² |
| `address`, `postalCode`, `commune`, `department` | 14 RUE AMELOT, 75011, Paris 11e |
| `latitude`, `longitude` | 48.8565, 2.3694 |
| `builtSurface`, `landSurface`, `carrezSurface`, `rooms` | 106.2 / – / 96.4 / 4 |
| `lots[]` | type, surface, rooms, lot number, carrez per lot |
| `parcels[]`, `lotsCount` | cadastral references and number of lots |

Perfect for price-per-m² maps, valuation models, DPE-aware investment screening and market reports by street or district.

### Mode 2 — `dpe` (ADEME)

`dpeNumber`, `date`, `validUntil`, `energyClass` (A–G), `gesClass` (A–G), `energyConsumptionKwhM2`, `gesEmissionKgM2`, `estimatedAnnualEnergyCostEur`, `surfaceM2`, `buildingType`, `constructionPeriod`, `heatingEnergy`, `heatingInstallation`, full BAN address, INSEE, coordinates.

Filter by department, INSEE commune, **energy class (F/G = "passoires thermiques")**, GES class, building type and establishment dates.

### Quick start

Paris apartment sales above 500 000 €, 2024 and 2025:

```json
{ "mode": "transactions", "departments": ["75"], "years": ["2025", "2024"], "propertyTypes": ["Appartement"], "minValue": 500000, "maxResults": 500 }
```

Energy-poor dwellings in Lyon (DPE F and G), last 2 years:

```json
{ "mode": "dpe", "communes": ["69123"], "energyClasses": ["F", "G"], "dateFrom": "2024-01-01", "dateTo": "2025-12-31", "maxResults": 400 }
```

### Wo buys this

- 🏠 Estate agents, appraisers & proptech — real price-per-m² evidence by street
- 📈 Investors & analysts — yield, renovation and DPE-renovation screening
- ⚡ Energy renovation companies — DPE F/G target lists by area
- 🏛️ Journalists, researchers & public bodies — official, citable data

### Use from AI agents (MCP)

This Actor is exposed as a tool through **Apify's MCP server** — any MCP client (Claude, Cursor, VS Code / Copilot, Codex, custom agents) can discover and run it without writing code:

```json
{
  "mcpServers": {
    "apify": { "url": "https://mcp.apify.com?tools=amygraphics/france-property-data-scraper" }
  }
}
```

Then just ask: *"Use amygraphics/france-property-data-scraper to get me this data."* Agents can also pay autonomously — this Actor is **whitelisted for agentic payments** (Direct x402 / Apify AGI prepaid token), so no Apify account is required.

### Pricing

Pay-per-event: one **`record-extracted`** charge per successfully extracted record, plus a tiny **`apify-actor-start`** fee per run. Failed requests are never charged and the Actor stops cleanly when your spending limit is reached.

### Notes & limits

- DVF covers 2021 → 2025 (Etalab, updated twice a year; the current year is partial).
- Building-level DPE statistics can exceed the number of dwellings (collective buildings).
- Both sources are official French government open data.

***

### 🇫🇷 En français

Deux **sources officielles** dans un seul Actor : les **ventes notariales (DVF)** — prix, date exacte, adresse, coordonnées GPS, surfaces, pièces, parcelles cadastrales, une ligne par mutation — et les **DPE de l'ADEME** (15,7 M de diagnostics) avec étiquettes DPE/GES, consommation, coût annuel estimé, surface, type de bâtiment et période de construction.

Filtres : département, commune (INSEE), type de bien, budget, surface, nature de la mutation, étiquette énergétique (F/G = passoires thermiques), GES, type de bâtiment et plage de dates. Tarification au record extrait (`record-extracted`).

***

### Agents IA (MCP)

Cet Actor est exposé comme outil du serveur **MCP d'Apify** — branchez `https://mcp.apify.com?tools=amygraphics/france-property-data-scraper` dans Claude, Cursor, VS Code ou Codex, puis demandez vos données en langage naturel. Les agents peuvent payer seuls (x402 / AGI) : l'Actor est *whitelisted* pour les paiements agentiques.

# Actor input Schema

## `mode` (type: `string`):

`transactions` = official notarial sales (DVF, per department or commune, 2021-2025). `dpe` = ADEME energy-performance certificates (15.7 M records) with DPE/GES labels.

## `departments` (type: `array`):

French department codes, e.g. 75, 92, 13, 2A, 971. Used by both modes.

## `communes` (type: `array`):

Optional INSEE commune codes for precision, e.g. 75111 (Paris 11e), 33063 (Bordeaux). When set, departments are ignored for DVF.

## `years` (type: `array`):

Years to read for transactions: 2021, 2022, 2023, 2024 or 2025.

## `propertyTypes` (type: `array`):

Keep only mutations containing these property types, e.g. Appartement, Maison, Dépendance, Local industriel. commercial ou assimilé, Terrain.

## `natures` (type: `array`):

Optional exact filter on the mutation nature, e.g. 'Vente', 'Vente en bloc', 'Adjudication', 'Echange'.

## `minValue` (type: `integer`):

Keep only transactions at or above this price.

## `maxValue` (type: `integer`):

Keep only transactions at or below this price.

## `minSurface` (type: `integer`):

Minimum total built surface of the transaction.

## `maxSurface` (type: `integer`):

Maximum total built surface of the transaction.

## `energyClasses` (type: `array`):

Energy labels to keep: A to G. Use F, G to target poor-performing ('passoires thermiques') dwellings.

## `gesClasses` (type: `array`):

Greenhouse-gas labels to keep: A to G.

## `buildingTypes` (type: `array`):

ADEME building types: appartement, immeuble, maison.

## `dateFrom` (type: `string`):

Start date of the DPE establishment date range, format YYYY-MM-DD.

## `dateTo` (type: `string`):

End date of the DPE establishment date range, format YYYY-MM-DD.

## `maxResults` (type: `integer`):

Maximum number of records to extract in this run. One billable event per extracted record.

## `chargedEventName` (type: `string`):

Pay-per-event name charged for each successfully extracted record.

## Actor input object example

```json
{
  "mode": "transactions",
  "departments": [
    "75"
  ],
  "communes": [],
  "years": [
    "2024"
  ],
  "propertyTypes": [
    "Appartement",
    "Maison"
  ],
  "natures": [],
  "energyClasses": [],
  "gesClasses": [],
  "buildingTypes": [],
  "dateFrom": "",
  "dateTo": "",
  "maxResults": 10,
  "chargedEventName": "record-extracted"
}
```

# 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 = {
    "mode": "transactions",
    "departments": [
        "75"
    ],
    "years": [
        "2024"
    ],
    "maxResults": 10
};

// Run the Actor and wait for it to finish
const run = await client.actor("amygraphics/france-property-data-scraper").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 = {
    "mode": "transactions",
    "departments": ["75"],
    "years": ["2024"],
    "maxResults": 10,
}

# Run the Actor and wait for it to finish
run = client.actor("amygraphics/france-property-data-scraper").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 '{
  "mode": "transactions",
  "departments": [
    "75"
  ],
  "years": [
    "2024"
  ],
  "maxResults": 10
}' |
apify call amygraphics/france-property-data-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,amygraphics/france-property-data-scraper"
        }
    }
}
```

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/ekcIUaiKIZQDIbVYA/builds/7flfTuZSee5BWVsdE/openapi.json