# French Real Estate Dvf (`tagadanar/french-real-estate-dvf`) Actor
Official French property sale prices from the DVF land registry, by commune, postal code or INSEE. Get price, address, type, surface, rooms, land and €/m² per transaction. Filter by type, price, surface and year (2021–2025). For valuation, market analysis, proptech and AI agents.
- **URL**: https://apify.com/tagadanar/french-real-estate-dvf.md
- **Developed by:** [Raphaël R](https://apify.com/tagadanar) (community)
- **Categories:** Real estate, Lead generation, AI
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet
## Pricing
Pay per event
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/platform/actors/running/actors-in-store#pay-per-event
## What's an Apify Actor?
Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases.
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.
In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md):
```bash
npm install apify-client
```
In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md):
```bash
pip install apify-client
```
In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md):
````bash
# MacOS / Linux
curl -fsSL https://apify.com/install-cli.sh | bash
# Windows
irm https://apify.com/install-cli.ps1 | iex
```bash
In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md).
If your project is in a different language, use 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, from the official land registry.** Give it a commune name, postal code or INSEE code and get structured transaction records — **sale price, address, property type, surface, rooms, land, and €/m²** — 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** — the DGFiP/Etalab geo-DVF dataset. Real recorded sales, not estimates or listings.
- ✅ **Query how you think** — by commune name (`Bordeaux`), postal code (`75011`) or INSEE code (`33063`). Paris, Lyon and Marseille are auto-expanded to their arrondissements.
- ✅ **€/m² computed for you** — plus clean address, geocoordinates, surface, rooms and land area.
- ✅ **Real filters** — property type, price range, minimum surface, standard-sales-only, and year (2021–2025).
- ✅ **Fair pricing** — pay per transaction returned.
### Use cases
- **Estimate a property's value** from comparable nearby sales (€/m² by type and area).
- **Market analysis** — price trends and volumes by commune, arrondissement or department.
- **Investment sourcing** — filter by budget and surface across multiple areas at once.
- **Enrichment** — attach real transaction history to an address or parcel.
- **AI agents** — ask Claude/Cursor via MCP: *"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)"
}
```
### 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 2021–2025. Sales can take a few months to appear after signing.
**Why several rows for one sale?** A single mutation can span multiple lots/parcels; each line is one property component. Records are deduplicated by mutation + parcel + type + surface.
**Do Paris/Lyon/Marseille work?** Yes — a name or postal code for these cities is automatically mapped to the correct arrondissement(s).
**Does it work with AI agents?** Yes — exposed via REST API and **MCP**.
***
*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. Bordeaux), a 5-digit postal code (e.g. 75011) or a 5-char INSEE code (e.g. 33063). 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 '' with your token
const client = new ApifyClient({
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 '' with your token.
client = ApifyClient("")
# 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 "
]
}
}
}
```
## OpenAPI specification
```json
{
"openapi": "3.0.1",
"info": {
"title": "French Real Estate Dvf",
"description": "Official French property sale prices from the DVF land registry, by commune, postal code or INSEE. Get price, address, type, surface, rooms, land and €/m² per transaction. Filter by type, price, surface and year (2021–2025). For valuation, market analysis, proptech and AI agents.",
"version": "0.1",
"x-build-id": "NaEkMaArX5Whzs1nL"
},
"servers": [
{
"url": "https://api.apify.com/v2"
}
],
"paths": {
"/acts/tagadanar~french-real-estate-dvf/run-sync-get-dataset-items": {
"post": {
"operationId": "run-sync-get-dataset-items-tagadanar-french-real-estate-dvf",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
},
"/acts/tagadanar~french-real-estate-dvf/runs": {
"post": {
"operationId": "runs-sync-tagadanar-french-real-estate-dvf",
"x-openai-isConsequential": false,
"summary": "Executes an Actor and returns information about the initiated run in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/runsResponseSchema"
}
}
}
}
}
}
},
"/acts/tagadanar~french-real-estate-dvf/run-sync": {
"post": {
"operationId": "run-sync-tagadanar-french-real-estate-dvf",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
}
},
"components": {
"schemas": {
"inputSchema": {
"type": "object",
"required": [
"locations"
],
"properties": {
"locations": {
"title": "Communes, postal codes or INSEE codes",
"type": "array",
"description": "Where to fetch transactions. Accepts a commune name (e.g. Bordeaux), a 5-digit postal code (e.g. 75011) or a 5-char INSEE code (e.g. 33063). Postal codes may cover several communes — all are included.",
"items": {
"type": "string"
}
},
"years": {
"title": "Years",
"type": "array",
"description": "Transaction years to include. Available: 2021–2025. Empty = the most recent year only.",
"items": {
"type": "string"
}
},
"propertyTypes": {
"title": "Property types",
"type": "array",
"description": "Filter by property type. Empty = all.",
"items": {
"type": "string",
"enum": [
"Maison",
"Appartement",
"Dépendance",
"Local industriel. commercial ou assimilé"
],
"enumTitles": [
"House",
"Apartment",
"Outbuilding",
"Commercial / industrial"
]
},
"default": [
"Maison",
"Appartement"
]
},
"minPrice": {
"title": "Minimum price (€)",
"minimum": 0,
"type": "integer",
"description": "Only transactions at or above this sale price."
},
"maxPrice": {
"title": "Maximum price (€)",
"minimum": 0,
"type": "integer",
"description": "Only transactions at or below this sale price."
},
"minSurface": {
"title": "Minimum built surface (m²)",
"minimum": 0,
"type": "integer",
"description": "Only properties with at least this built surface."
},
"onlySales": {
"title": "Only standard sales (Vente)",
"type": "boolean",
"description": "Exclude non-standard mutations (auctions, expropriations, exchanges…). Recommended for price analysis.",
"default": true
},
"maxResults": {
"title": "Max transactions",
"minimum": 1,
"maximum": 50000,
"type": "integer",
"description": "Cap the total number of transactions returned across all locations/years.",
"default": 1000
}
}
},
"runsResponseSchema": {
"type": "object",
"properties": {
"data": {
"type": "object",
"properties": {
"id": {
"type": "string"
},
"actId": {
"type": "string"
},
"userId": {
"type": "string"
},
"startedAt": {
"type": "string",
"format": "date-time",
"example": "2025-01-08T00:00:00.000Z"
},
"finishedAt": {
"type": "string",
"format": "date-time",
"example": "2025-01-08T00:00:00.000Z"
},
"status": {
"type": "string",
"example": "READY"
},
"meta": {
"type": "object",
"properties": {
"origin": {
"type": "string",
"example": "API"
},
"userAgent": {
"type": "string"
}
}
},
"stats": {
"type": "object",
"properties": {
"inputBodyLen": {
"type": "integer",
"example": 2000
},
"rebootCount": {
"type": "integer",
"example": 0
},
"restartCount": {
"type": "integer",
"example": 0
},
"resurrectCount": {
"type": "integer",
"example": 0
},
"computeUnits": {
"type": "integer",
"example": 0
}
}
},
"options": {
"type": "object",
"properties": {
"build": {
"type": "string",
"example": "latest"
},
"timeoutSecs": {
"type": "integer",
"example": 300
},
"memoryMbytes": {
"type": "integer",
"example": 1024
},
"diskMbytes": {
"type": "integer",
"example": 2048
}
}
},
"buildId": {
"type": "string"
},
"defaultKeyValueStoreId": {
"type": "string"
},
"defaultDatasetId": {
"type": "string"
},
"defaultRequestQueueId": {
"type": "string"
},
"buildNumber": {
"type": "string",
"example": "1.0.0"
},
"containerUrl": {
"type": "string"
},
"usage": {
"type": "object",
"properties": {
"ACTOR_COMPUTE_UNITS": {
"type": "integer",
"example": 0
},
"DATASET_READS": {
"type": "integer",
"example": 0
},
"DATASET_WRITES": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_READS": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_WRITES": {
"type": "integer",
"example": 1
},
"KEY_VALUE_STORE_LISTS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_READS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_WRITES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_INTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_EXTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_RESIDENTIAL_TRANSFER_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_SERPS": {
"type": "integer",
"example": 0
}
}
},
"usageTotalUsd": {
"type": "number",
"example": 0.00005
},
"usageUsd": {
"type": "object",
"properties": {
"ACTOR_COMPUTE_UNITS": {
"type": "integer",
"example": 0
},
"DATASET_READS": {
"type": "integer",
"example": 0
},
"DATASET_WRITES": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_READS": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_WRITES": {
"type": "number",
"example": 0.00005
},
"KEY_VALUE_STORE_LISTS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_READS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_WRITES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_INTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_EXTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_RESIDENTIAL_TRANSFER_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_SERPS": {
"type": "integer",
"example": 0
}
}
}
}
}
}
}
}
}
}
```