# Brandear Japan Luxury Buyback Price Database Scraper (`maximedupre/brandear`) Actor

Collect Brandear's public past-appraisal entries for luxury goods. Get brands, model or reference numbers, product names, yen amounts, dates, and source links when available. Choose a bounded run or collect until the public archive ends.

- **URL**: https://apify.com/maximedupre/brandear.md
- **Developed by:** [Maxime Dupré](https://apify.com/maximedupre) (community)
- **Categories:** E-commerce, Business, Developer tools
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

$0.90 / 1,000 appraisal entries

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 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

### 👜 Brandear luxury appraisal history

Researchers, resellers, and data teams can collect Brandear's public past-appraisal archive. Each saved row contains a published appraisal entry with its brand, model or reference number, product name, amount in Japanese yen, appraisal date, and source links when available. Use the rows to study buyback price history for Japanese luxury goods. This Actor does not authenticate items or create a new valuation.

- Compare published yen amounts over time with **[Japanese Luxury Goods Price History](https://apify.com/maximedupre/brandear/examples/japanese-luxury-goods-price-history)**.
- Browse dated Brandear entries with **[Japanese Luxury Appraisal History](https://apify.com/maximedupre/brandear/examples/japanese-luxury-appraisal-history)**.
- Track published amounts for one Brandear model with **[Brandear Model Price History](https://apify.com/maximedupre/brandear/examples/brandear-model-price-history)**.
- Collect Brandear appraisal prices with **[Brandear Appraisal Prices](https://apify.com/maximedupre/brandear/examples/brandear-appraisal-prices)**.
- Review dated Brandear entries with **[Brandear Appraisal History](https://apify.com/maximedupre/brandear/examples/brandear-appraisal-history)**.

#### 📚 Published Brandear appraisal rows

Each dataset row is one published appraisal entry. The same model can appear in several rows when Brandear publishes entries for different dates. Brand, model or reference number, product name, and appraisal date are required. Amount, source URL, and image URL are included when Brandear provides them.

#### ▶️ Collect Brandear archive entries

1. Choose **Bounded extraction** to set a maximum number of appraisal entries.
2. Leave **Maximum appraisal entries** empty to return all available results until the source is exhausted.
3. Choose **Archive-wide extraction** to run until the public archive ends.
4. Start the run and open the dataset link in the output.

To shorten the run, choose Bounded extraction and set a smaller maximum. The Actor adds eligible published entries to the dataset as it finds them.

#### ⚙️ Input

Choose a collection scope. Use the maximum only when you want to bound a run.

**Input fields**

| Field | Type | What it does |
| --- | --- | --- |
| `extractionScope` | string, required | Chooses **Bounded extraction** or **Archive-wide extraction**. |
| `maxItems` | integer, optional | Sets the maximum number of appraisal entries for Bounded extraction. Leave it empty to return all available results until the source is exhausted. Archive-wide extraction runs until the archive ends. |

**Successful default-input example**

The example below is the public input from the successful current-beta run.

```json
{
  "extractionScope": "bounded",
  "maxItems": 100
}
```

#### 🧾 Output

The run output contains a link to the dataset. The dataset contains one object for each saved published appraisal entry.

**Run output**

| Field | Type | What it does |
| --- | --- | --- |
| `dataset` | string (URL) | Opens the published appraisal entries in the dataset overview. |

The example below uses the dataset link from the successful current-beta run.

```json
{
  "dataset": "https://api.apify.com/v2/datasets/ffNc2Kdsn7DQZYEHY/items?view=overview"
}
```

**Dataset row**

| Field | Type | What it does |
| --- | --- | --- |
| `brand` | string, required | Luxury brand named in the published entry. |
| `modelReference` | string, required | Model or reference number listed for the item. |
| `productName` | string, required | Product name listed in the published entry. |
| `amountYen` | integer, when available | Published buyback or appraisal amount in Japanese yen. |
| `appraisalDate` | date string, required | Date shown for the published appraisal. |
| `sourceUrl` | string (URL), when available | Link to the published Brandear record. |
| `imageUrl` | string (URL), when available | Link to the published product image. |

The row below is a complete genuine row from the successful current-beta run.

```json
{
  "brand": "ルイヴィトン",
  "modelReference": "M80020",
  "productName": "1995年ヴィトンカップ ボストンバッグ",
  "appraisalDate": "2023-03-01",
  "amountYen": 31000,
  "sourceUrl": "https://brandear.jp/kakosatei/shouhin/36987296",
  "imageUrl": "https://image5.brandear.jp/image1/00230/36987296_1.jpg"
}
```

#### 💳 Pricing

This Actor uses pay-per-event pricing. The current price is `$0.0009` for each published appraisal entry saved to your dataset.

#### 🔌 Integrations

Run the Actor in Apify Console and use the dataset link in the output to read the rows through the Apify API. You can then use the structured entries in your own price history research.

https://www.youtube.com/watch?v=bNACk1\_S\_6w\&list=PLObrtcm1Kw6MUrlLNDbK9QRg8VDJg0gOW\&index=4

#### ❓ FAQ

##### Does this create a new appraisal or current market value?

No. It collects published Brandear appraisal entries. It does not create a new appraisal, authenticate an item, or calculate a current market estimate.

##### What does one dataset row represent?

One row represents one published appraisal entry. A model can have several rows when Brandear publishes entries for different dates.

##### What happens when I leave Maximum appraisal entries empty?

The run returns all available results until the source is exhausted. Use a number when you want a bounded run.

##### Should I use Bounded extraction or Archive-wide extraction?

Use Bounded extraction when you want to limit the number of entries. Use Archive-wide extraction when you want the Actor to continue until the public archive ends.

##### Can amount, source, or image fields be missing?

Yes. Brandear may not provide those values for every published entry, so the optional fields can be absent.

##### Do I need a Brandear login or source credentials?

No buyer-supplied source credential is part of the public input. The Actor reads Brandear's public past-appraisal archive.

##### Does it collect prices from other marketplaces?

No. The dataset contains Brandear's published buyback or appraisal amounts. It does not collect sell-side prices from unrelated marketplaces.

### 📝 Changelog

**0.0: Initial release**

### 🆘 Support

For issues, questions, or feature requests, [file a ticket](https://console.apify.com/actors/maximedupre~brandear/issues) and I'll fix or implement it in less than 24h 🫡

### 🔗 Related Actors

- [Brandear Japan Luxury Buyback Price Database Scraper](https://apify.com/jungle_synthesizer/brandear-japan-luxury-buyback-price-database-scraper) - Compare Brandear's published buyback data with another archive-focused Actor.
- [Vestiaire Collective Scraper | Luxury Resale Items](https://apify.com/parseforge/vestiaire-collective-scraper) - Compare published Brandear amounts with luxury resale listing prices.
- [Rebag Luxury Resale Listings Scraper](https://apify.com/jungle_synthesizer/rebag-luxury-resale-listings-scraper) - Compare Brandear appraisal amounts with Rebag luxury resale prices.
- [Ragtag Japan Designer Resale Listings Scraper](https://apify.com/jungle_synthesizer/ragtag-japan-designer-resale-listings-scraper) - Add Japanese designer resale listings and yen prices to a cross-market study.
- [Fashionphile Luxury Handbag Listings Scraper](https://apify.com/jungle_synthesizer/fashionphile-luxury-handbag-listings-scraper) - Compare Brandear entries with pre-owned luxury handbag prices and product details.

**Made with ❤️ by Maxime Dupré**

# Actor input Schema

## `extractionScope` (type: `string`):

Choose the part of Brandear's public past-appraisal archive to collect.

## `maxItems` (type: `integer`):

For Bounded extraction, set the maximum number of appraisal entries to collect. Leave it empty to return all available results until the source is exhausted. Archive-wide extraction runs until the archive ends.

## Actor input object example

```json
{
  "extractionScope": "bounded",
  "maxItems": 100
}
```

# Actor output Schema

## `dataset` (type: `string`):

Link to the published appraisal entries.

## `brand` (type: `string`):

Luxury brand named in the published entry.

## `modelReference` (type: `string`):

Model or reference number listed for the item.

## `productName` (type: `string`):

Product name listed in the published entry.

## `amountYen` (type: `string`):

Published buyback or appraisal amount in Japanese yen.

## `appraisalDate` (type: `string`):

Date shown for the published appraisal.

## `sourceUrl` (type: `string`):

Link to the published Brandear record, when provided.

## `imageUrl` (type: `string`):

Link to the published product image, when provided.

# 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 = {
    "extractionScope": "bounded",
    "maxItems": 100
};

// Run the Actor and wait for it to finish
const run = await client.actor("maximedupre/brandear").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 = {
    "extractionScope": "bounded",
    "maxItems": 100,
}

# Run the Actor and wait for it to finish
run = client.actor("maximedupre/brandear").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 '{
  "extractionScope": "bounded",
  "maxItems": 100
}' |
apify call maximedupre/brandear --silent --output-dataset

```

## MCP server setup

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

```

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/HdA3urhk7v986bjca/builds/4pSjYM6hdlD5frLDb/openapi.json