Hands-on lessons on querying real auction and resale data, from your first SQL query to pricing power, sell-through, and demand analysis.
Where auction and resale pricing fits in the alternative-data landscape.
Pricing power and sell-through as measures of brand demand.
Read secondary-market demand as a signal for listed luxury companies.
What forms an artwork's price: comparables, medium, provenance, and market.
How houses earn: premiums, guarantees, estimate accuracy, and take rates.
Auction house league tables, artist pricing power, and concentration.
Comparable sets, artist indices, and demand signals for art research.
Estimates as anchors, the winner's curse, and sell-through dynamics.
Query, filter, and build a demand index in the sandbox SQL editor.
Chart selection, honest visualization, and turning a metric into a clear figure.
Predict prices and classify sold or unsold, with point-in-time discipline.
Build a quarterly demand index and read it as method, not appreciation.
Discovery, the query API, pagination, and a small ETL with the client.
Coverage bias and the recency caveat as a case study in reading data.
Nine browser lessons: discovery, first query, pricing power, sell-through, export, and more. Zero setup, no key.
The same nine tasks with the reusable client and the raw REST API, ending in pandas.
A student notebook that runs offline on a sample first, then against the live API with a class key.
Every module is a drop-in session with objectives, a timed agenda, a no-code demo, discussion questions, and homework. Assessments and a librarian pack are ready to adopt.
