LibGuide draft: ALT/FNDATA

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Overview / About

ALT/FNDATA is a transaction-level pricing dataset covering the secondary market for physical luxury and collectible assets. It holds more than 10 million transaction records spanning roughly 7,000 brands, drawn from more than 100 auction houses and resale marketplaces worldwide, with history reaching back to the late 1990s. Categories include watches, jewelry and gems, handbags, fine art, works of art, wine and whisky, automobiles, motorcycles, aircraft, design, coins, and books.

Each record includes a pre-sale estimate range, the realized price, the sale date, the auction house or vendor, the designer or brand, the item title, a sold or unsold status, and a price normalized to US dollars. Many records also carry a stock ticker linking the brand to its parent company, useful for connecting secondary-market pricing to public equity research.

The dataset is aimed at students and researchers in finance, economics, business, art market studies, and data science, and it is accessible through a free, no-code sandbox that requires no coding background to start using.

Getting started

Most students and researchers only need the sandbox, and it takes about two minutes to set up.

  1. Go to sandbox.altfndata.com.
  2. Register with a work or school email address. There is no fee and no separate approval wait; work and school addresses are approved automatically.
  3. Once signed in, open the SQL editor tab to begin querying, or start with the coverage browser tab to see what brands and vendors are represented before writing a query.
  4. New to SQL? The sandbox includes sample datasets and pre-built charts, so you can explore visually before writing a query, and the example queries below can be copied in directly.

No API key is needed for the sandbox. If your research requires programmatic access beyond what the sandbox supports, see Access & licensing below.

What is in the data

Categories: Watches, jewelry and gems, handbags, fine art, works of art, wine and whisky, automobiles, motorcycles, aircraft, design, coins, and books.

Core fields on every record:

Field Description
designer Brand or maker name
model Model name or reference, where applicable
item_title Full lot title as listed by the auction house or marketplace
sale_date Date the lot sold or closed
usd_price_decimal Realized price, normalized to US dollars
sale_estimates_high_usd_price Pre-sale high estimate, normalized to US dollars
status Sold or unsold
vendor Auction house or marketplace
stock_ticker Parent company ticker, where the brand maps to a public or tracked private company

The full data dictionary and schema for every category is available inside the sandbox under the data dictionary and schema tabs, and also at docs.altfndata.com.

Coverage: More than 100 auction houses and resale marketplaces, with a tracked universe of up to roughly 850 houses, spanning North America, Europe, and Asia. Coverage reaches back to the late 1990s and is added to continuously.

The sandbox offers two main ways to explore the data: the coverage browser, for seeing what is available before you commit to a query, and the SQL editor, for querying records directly.

Coverage browser. Search by company or vendor name to see how many records, brands, and auction houses are represented before writing any SQL. This is the fastest way to check whether a particular brand or house has enough coverage to support a research question.

SQL editor. A standard SQL environment scoped to the sandbox's sample datasets. The examples below use only documented fields and can be pasted in directly.

Example query 1: realized prices for a brand

Returns sold lots for a given designer, most expensive first.

SELECT designer, item_title, sale_date, usd_price_decimal
FROM all_watches_data
WHERE designer LIKE '%Cartier%'
  AND status = 'sold'
ORDER BY usd_price_decimal DESC
LIMIT 25;

Example query 2: pricing power, realized price against high estimate

Pricing power is the ratio of realized price to the pre-sale high estimate. A ratio above 1.0 means lots sold above their high estimate on average, a common signal of strong demand.

SELECT designer,
       AVG(usd_price_decimal / sale_estimates_high_usd_price) AS avg_pricing_power
FROM all_jewels_gems_data
WHERE status = 'sold'
  AND sale_estimates_high_usd_price > 0
GROUP BY designer
ORDER BY avg_pricing_power DESC
LIMIT 20;

Example query 3: sell-through rate by vendor

Sell-through is the share of offered lots that actually sold, a standard measure of market demand at a given auction house.

SELECT vendor,
       COUNT(*) AS lots_offered,
       SUM(CASE WHEN status = 'sold' THEN 1 ELSE 0 END) AS lots_sold,
       CAST(SUM(CASE WHEN status = 'sold' THEN 1 ELSE 0 END) AS DOUBLE) / COUNT(*) AS sell_through_rate
FROM all_handbags_data
GROUP BY vendor
ORDER BY lots_offered DESC
LIMIT 20;

Example research questions by discipline

Finance and investments

  • How does the pricing power (realized price over high estimate) of a luxury brand compare to that of its publicly listed parent company's stock performance over the same period?
  • Do brands owned by the same conglomerate (for example, several Richemont-owned watch brands) show correlated pricing power over time?

Economics

  • What does the spread between realized price and pre-sale estimate suggest about how well auction houses forecast demand for a given category?
  • How does sell-through rate vary across categories, and what might that reveal about liquidity in different segments of the secondary luxury market?

Art market studies and art history

  • For a given artist or maker, how many sold lots are recorded, and what is the range between the lowest and highest realized price?
  • How does coverage of a given auction house compare across fine art and works of art categories?

Data science and statistics

  • Build a simple model of realized price as a function of pre-sale estimate and category. Where does the model perform worst, and what might explain it?
  • Using only the sandbox's export tools, construct a coverage profile of the dataset by vendor and category, and identify where recent-period coverage looks thin compared to historical coverage (see the note on data coverage below).

Export & cite

Exporting. Any query result in the SQL editor can be exported as CSV or JSON using the export button above the results grid. Pre-built charts can also be exported as images for use in papers, memos, or presentations.

Suggested citation format. There is no single mandated citation style for the dataset; adapt the elements below to your citation style of choice (APA, Chicago, MLA, or a discipline-specific format).

ALT/FNDATA (Oarbt Inc). [Query description or dataset name], accessed [date], sandbox.altfndata.com.

Example in a Chicago-style note:

ALT/FNDATA (Oarbt Inc), "Sold watch lots by designer, 1998 to present," accessed July 10, 2026, sandbox.altfndata.com.

Students should note the query or filters used (or attach the exported query as an appendix) alongside the citation, since the dataset is queried live rather than downloaded as a fixed file.

A note on data coverage

Like most continuously updated transactional datasets, the most recent quarters are still being backfilled as new auction and marketplace results are processed, so figures for the very latest period can understate true activity relative to older, fully settled periods. Historical counts, medians, and ratios such as pricing power and sell-through are stable and well suited to research. When comparing periods, it is good practice to check the coverage browser for the vendors and categories involved before drawing conclusions from a year-over-year comparison.

Tutorials & docs

  • Quickstart and task-based tutorials: docs.altfndata.com
  • Reusable Python client (altfndata_client.py) and a runnable notebook (altfndata_tutorials.ipynb): linked from docs.altfndata.com
  • Evaluation Guide, describing common use cases across disciplines: docs.altfndata.com/use-cases.html

Get help

For registration issues, access questions, or anything not covered on this page, contact info@altfndata.com.