Brandear Japan Luxury Buyback Price Database Scraper

👜 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.
• Browse dated Brandear entries with Japanese Luxury Appraisal History.
• Track published amounts for one Brandear model with Brandear Model Price History.
• Collect Brandear appraisal prices with Brandear Appraisal Prices.
• Review dated Brandear entries with 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.

{
  "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.

{
  "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.

{
  "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.

❓ 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 and I'll fix or implement it in less than 24h 🫡

🔗 Related Actors

• Brandear Japan Luxury Buyback Price Database Scraper - Compare Brandear's published buyback data with another archive-focused Actor.
• Vestiaire Collective Scraper | Luxury Resale Items - Compare published Brandear amounts with luxury resale listing prices.
• Rebag Luxury Resale Listings Scraper - Compare Brandear appraisal amounts with Rebag luxury resale prices.
• Ragtag Japan Designer Resale Listings Scraper - Add Japanese designer resale listings and yen prices to a cross-market study.
• Fashionphile Luxury Handbag Listings Scraper - Compare Brandear entries with pre-owned luxury handbag prices and product details.

Made with ❤️ by Maxime Dupré