# DVF France 🇫🇷 : Prix de Vente Officiels & Prix au m² (`tagadanar/french-real-estate-dvf`) Actor

Toutes les ventes immobilières officielles en France (données DVF, valeurs foncières) : prix, adresse, surface, pièces et prix au m² par commune, code postal ou code INSEE. Pour l'estimation immobilière, l'investissement et les agents IA.

- **URL**: https://apify.com/tagadanar/french-real-estate-dvf.md
- **Developed by:** [Tagada Data](https://apify.com/tagadanar) (community)
- **Categories:** Real estate, Lead generation, AI
- **Stats:** 2 total users, 0 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

Pay per event + usage

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/platform/actors/running/actors-in-store#pay-per-event

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
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.
Actors are written with capital "A".

## 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.
The best way to integrate Actors is as follows.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — the [REST API](https://docs.apify.com/api/v2.md).

For usage examples, see the [API](#api) section below.

For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).

# README

## French Real Estate Sales (DVF): Prices, Surfaces & €/m²

**Every real French property sale, straight from the official land registry.** Give it a commune name, postal code or INSEE code, and you get back structured transaction records: **sale price, address, property type, surface, rooms, land, and €/m²**, pulled from the government's *Demandes de Valeurs Foncières* (DVF) open data.

Built for **property valuation, real-estate market analysis, proptech, investment sourcing, and AI agents** (works via API and MCP).

### Why this actor

- ✅ **Official data.** This runs on the DGFiP/Etalab geo-DVF dataset, so every row is a real recorded sale, not an estimate or a listing.
- ✅ **Query how you think.** Search by commune name (`Bordeaux`), postal code (`75011`) or INSEE code (`33063`), and Paris, Lyon and Marseille get auto-expanded to their arrondissements.
- ✅ **€/m² computed for you**, along with a clean address, geocoordinates, surface, rooms and land area.
- ✅ **Real filters**: property type, price range, minimum surface, standard sales only, and year (2021 to 2025).
- ✅ **Fair pricing.** You pay per transaction returned.

### Use cases

- **Estimate a property's value** from comparable nearby sales (€/m² by type and area).
- **Market analysis** that tracks price trends and volumes by commune, arrondissement or department.
- **Investment sourcing** across multiple areas at once, filtered by budget and surface.
- **Enrichment**, attaching real transaction history to an address or parcel.
- **AI agents** can query it over MCP, asking Claude or Cursor things like *"average €/m² for 2-room flats sold in Bordeaux in 2024?"*

### Input

```json
{
    "locations": ["Bordeaux", "75011", "33063"],
    "years": ["2023", "2024"],
    "propertyTypes": ["Appartement", "Maison"],
    "minPrice": 100000,
    "minSurface": 30,
    "onlySales": true,
    "maxResults": 1000
}
```

`locations` accepts commune names, postal codes and INSEE codes, mixed freely.

### Output (one record per transaction line)

```json
{
    "source": "DVF",
    "date": "2024-01-04",
    "year": 2024,
    "nature": "Vente",
    "price": 557500,
    "currency": "EUR",
    "pricePerM2": 10519,
    "propertyType": "Appartement",
    "surfaceBuilt": 53,
    "rooms": 3,
    "landSurface": null,
    "address": "12 RUE DE LA ROQUETTE",
    "postalCode": "75011",
    "commune": "Paris 11e Arrondissement",
    "inseeCode": "75111",
    "department": "75",
    "parcelId": "75111000AB0123",
    "latitude": 48.856,
    "longitude": 2.376,
    "sourceRef": "files.data.gouv.fr/geo-dvf (Etalab / DGFiP)"
}
```

### Companion actors in the French real-estate suite

DVF gives you the **sold** prices (what buyers actually paid). Pair it with live **asking** prices from the listing scrapers to see the gap:

- [**SeLoger Scraper**](https://apify.com/tagadanar/french-real-estate-seloger), for asking prices from France's #1 portal, with seller phone plus particulier/agence flag.
- [**Logic-Immo Scraper**](https://apify.com/tagadanar/french-real-estate-logicimmo) pulls Logic-Immo asking prices, with the advertiser's phone number and agency/private flag.
- [**PAP Real Estate Listings**](https://apify.com/tagadanar/french-real-estate-pap) covers owner-direct (particulier) asking prices from PAP.
- [**Real Estate Deal Score**](https://apify.com/tagadanar/french-real-estate-deal-score) combines PAP asking prices with this DVF data to rank listings over or under market.

### Pricing

Pay per event, no subscription:

| Event | When |
|---|---|
| Actor start | Once per run |
| Transaction found | Per transaction returned |

### FAQ

**Where does the data come from?** The official geo-DVF dataset published by Etalab from DGFiP records (`files.data.gouv.fr/geo-dvf`). It covers metropolitan France and most of the DOM; Alsace-Moselle notary data is not in DVF.

**How recent is it?** DVF is published twice a year; this actor serves years 2021 through 2025. Sales can take a few months to appear after signing.

**Why several rows for one sale?** A single mutation can span multiple lots/parcels, so each line is one property component. Records are deduplicated by mutation, parcel, type and surface.

**Do Paris/Lyon/Marseille work?** Yes, a name or postal code for these cities gets automatically mapped to the correct arrondissement(s).

**Does it work with AI agents?** Yes, it's exposed via REST API and **MCP**.

***

### Something missing?

If you need an extra field, another source, or a different output, open an issue on this Actor and describe it. I read every request and small additions usually ship within days. More French real estate Actors (pige, DVF, deal score, DPE leads) are on [my profile](https://apify.com/tagadanar).

*Keywords: DVF, valeurs foncières, prix immobilier, real estate France, property prices, €/m², prix au mètre carré, estimation immobilière, transactions immobilières, land registry, proptech, DGFiP, Etalab.*

# Actor input Schema

## `locations` (type: `array`):

Where to fetch transactions. Accepts a commune name (e.g. <code>Bordeaux</code>), a 5-digit postal code (e.g. <code>75011</code>) or a 5-char INSEE code (e.g. <code>33063</code>). Postal codes may cover several communes — all are included.

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

Transaction years to include. Available: 2021–2025. Empty = the most recent year only.

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

Filter by property type. Empty = all.

## `minPrice` (type: `integer`):

Only transactions at or above this sale price.

## `maxPrice` (type: `integer`):

Only transactions at or below this sale price.

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

Only properties with at least this built surface.

## `onlySales` (type: `boolean`):

Exclude non-standard mutations (auctions, expropriations, exchanges…). Recommended for price analysis.

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

Cap the total number of transactions returned across all locations/years.

## Actor input object example

```json
{
  "locations": [
    "Bordeaux"
  ],
  "years": [
    "2024"
  ],
  "propertyTypes": [
    "Maison",
    "Appartement"
  ],
  "onlySales": true,
  "maxResults": 1000
}
```

# Actor output Schema

## `transactions` (type: `string`):

All property sale transactions (one item per transaction) in the default dataset.

# 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 = {
    "locations": [
        "Bordeaux"
    ],
    "years": [
        "2024"
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("tagadanar/french-real-estate-dvf").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 = {
    "locations": ["Bordeaux"],
    "years": ["2024"],
}

# Run the Actor and wait for it to finish
run = client.actor("tagadanar/french-real-estate-dvf").call(run_input=run_input)

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

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

```

## CLI example

```bash
echo '{
  "locations": [
    "Bordeaux"
  ],
  "years": [
    "2024"
  ]
}' |
apify call tagadanar/french-real-estate-dvf --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "command": "npx",
            "args": [
                "mcp-remote",
                "https://mcp.apify.com/?tools=tagadanar/french-real-estate-dvf",
                "--header",
                "Authorization: Bearer <YOUR_API_TOKEN>"
            ]
        }
    }
}

```

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/hjlctUxZCUZT5xw6P/builds/V9CmE8G0De01zhQwT/openapi.json