Teaching with ALT/FNDATA

A one-page overview for professors, instructors, and librarians

What the dataset is

ALT/FNDATA is institutional-grade pricing data on the secondary market for physical luxury assets, the auctions and resale marketplaces where watches, jewelry, gems, handbags, fine art, works of art, wine and whisky, automobiles, and more actually change hands. It gathers more than 10 million transaction records spanning roughly 7,000 brands and over 100 auction houses, with history reaching back to the late 1990s. Every record carries the pieces a market analyst needs in one row: the pre-sale estimate, the realized price in US dollars, the sale date, the vendor, the brand and model, whether the lot sold or went unsold, and a stock ticker drawn from a company mapping that covers more than 2,000 public and private firms with S&P 500 sector labels. In short, it is a large, tidy, real-world market dataset that most students have never seen before and that connects directly to companies they already recognize.

Why it is pedagogically valuable

The dataset rewards teaching because it is real, it is legible, and it asks good questions of itself. Students meet genuine market microstructure rather than a toy CSV, yet the schema is simple enough to query in the first ten minutes of class. The same table supports a SQL exercise, an economics discussion about brand demand, and an equity-research argument about whether the saleroom and the public stock tell the same story. Three ideas travel especially well. Pricing power, the median of realized price over the high estimate, shows at a glance which brands buyers will overpay for, with a house like Van Cleef and Arpels clearing near 1.36 times its high estimate. Sell-through, sold lots over offered lots, measures demand without any modeling at all. And the brand-to-ticker mapping lets a student pull everything a listed group such as Richemont sold and lay it beside the equity. Because the material is concrete and a little glamorous, it holds attention while it teaches method.

How to get access

There are two doors, and students almost always use the first.

  • The no-code sandbox at sandbox.altfndata.com is the primary classroom tool. It runs in the browser with zero setup and no API key: an in-browser SQL editor, sample datasets, pre-built charts, a coverage browser by company and vendor, a data dictionary, schema tabs, and CSV or JSON export. Faculty and students self-register with a work or school email and are auto-approved, which means a whole class can be up and querying in one session.
  • The production API at api.altfndata.com is a REST service for the code track. It is not self-serve; API keys are issued manually by the ALT/FNDATA team on request, and an instructor can ask for a single shared class key to use across a course. Documentation, a reusable Python client, a runnable tutorials notebook, and an evaluation guide live at docs.altfndata.com.

For students, the message is simple: register for the sandbox with your school email and you are ready. For instructors who want the code track, write to us for a class key.

What support exists

This package is built to be taught as it stands. It includes six syllabus-ready course modules with timed session plans and sandbox-first demos, a two-track tutorial set that runs from first query to pricing power to a brand-to-ticker roll-up, a runnable student notebook that also works offline before a key arrives, full assessments with separate answer keys, and a dedicated librarian pack for catalog positioning and data-literacy sessions. Every example rests on stable measures such as pricing power, sell-through, counts, and medians rather than on recent-period totals, because the most recent quarters are still being backfilled and should not be read as a market trend. That caveat is itself a useful data-literacy lesson, and the materials treat it as one.

A note on responsible use of the numbers

Keep dataset figures conservative and consistent: more than 10 million records, roughly 7,000 brands, history to the late 1990s. Lean on ratios and medians for anything a student will present. Do not frame a fall-off in the latest quarter as the market cooling; it reflects ingestion coverage, not demand.

Getting started in one class

  1. Send students to sandbox.altfndata.com to register with their school email.
  2. Open the Schema and Data dictionary tabs together so the class sees the fields once.
  3. Run the first no-code tutorial (a single filtered query on a familiar brand) as a live demo.
  4. Assign the pricing-power and sell-through tutorials as the first homework.

Questions, a class API key, or a walkthrough: info@altfndata.com.

Sharon Obuobi, Founder, ALT/FNDATA