Facilitator guide: Module 14, the auction business model
A teacher-facing companion to Module 14. The module is the full lesson; this guide adds the facilitation layer: a preparation checklist, timing cues, guidance for the discussion questions, and notes on what to emphasize and where students get stuck. Slides and a printable version are below.
View slides (PDF) Download slides (.pptx) View printable guide (PDF)
At a glance
- Length: 90 minutes.
- Level: art business, auction studies, or arts administration course, and a natural extension for a business or finance elective examining intermediary and marketplace business models. Suitable for graduate students in art market or arts management programs, advanced undergraduates pairing art history or economics with business, and professional or continuing education students entering the trade.
- Access: sandbox-only, with an optional API/code extension for retrieving one house's sold lots programmatically and computing figures client-side.
- Goal: students leave able to describe the buyer's premium and seller's commission as the two revenue streams that fund an auction house, and to compute estimate accuracy, average lot value versus lot volume, and value concentration from documented fields on the fine art table.
Before class
- Register your own sandbox account and run all three guided queries end to end, so you have known-good results on screen.
- Open the coverage browser tab beforehand and confirm how a major auction house is written in the vendor field. Exact filters are spelling-sensitive.
- Load the slides and confirm the projector or screen share shows the sandbox clearly. The SQL editor text should be readable from the back of the room.
- Decide in advance which house you will use for the estimate-accuracy demo and which house for the average-lot-value demo, so the contrast between a high-value house and a high-volume house is concrete rather than hypothetical.
- If you plan to run the optional API extension live, request a class API key from info@altfndata.com well before class and confirm it works against the fine art table.
- Confirm students have taken Module 7 or read its session outline beforehand, since this session assumes the vocabulary of estimate, realized price, and sell-through.
Timed agenda with cues
| Time | Segment | What to do | Watch for |
|---|---|---|---|
| 0 to 15 | The auction house as a business | Introduce the buyer's premium and seller's commission as the two revenue streams, and explain why winning consignments, not running the sale itself, is the real competitive contest among houses. | Students assuming the house's revenue is visible in the data. Say plainly, before any query runs, that neither fee is a field. |
| 15 to 25 | Estimates, guarantees, and irrevocable bids | Explain how a pre-sale estimate is set, why the house has a commercial stake in getting it right, and how a guarantee or an irrevocable bid shifts risk before the sale even opens. | Students treating a guarantee and an irrevocable bid as the same mechanism. Have them name who is exposed in each case. |
| 25 to 45 | Sandbox orientation and guided demo, estimate accuracy | Confirm the documented fields on the fine art table, then build the ratio of realized price to high estimate for a chosen house, with the sale_estimates_high_usd_price > 0 guard. | Students omitting the guard and getting a distorted or undefined ratio. Run the query once without it to show why it matters. |
| 45 to 60 | Guided demo, average lot value versus lot volume | Contrast a high-value house against a high-volume house using total realized value divided by sold-lot count. | Students reading a higher average lot value alone as a stronger business, without yet weighing concentration. |
| 60 to 75 | Small-group exercise, value concentration | Students pick a different house and compute its value concentration among its own top lots. | Groups picking a house with too few sold lots, producing a concentration figure too noisy to interpret. Nudge them to check the row count first. |
| 75 to 85 | Class discussion | Groups report what estimate accuracy, average lot value, and concentration together suggest about the kind of business their house runs. | Reports that lean on one figure alone rather than triangulating all three. |
| 85 to 90 | Wrap-up and homework assignment | Restate the one-sentence takeaway and hand out the homework brief. | Leave the last few minutes for homework logistics and the grading criteria, not zero. |
Guidance for the discussion questions
Use these as the "what to listen for" behind each question in the module. They are talking points, not a graded key.
- What an analyst can infer about revenue without seeing the fee schedule. Listen for: an analyst cannot compute the buyer's premium or seller's commission directly, but can still infer relative commercial strength from total realized value, sold-lot count, and average lot value by house, since a house winning larger or more numerous consignments is presumably also collecting more in fees, even though the fee itself stays invisible.
- Why estimate accuracy matters commercially, beyond specialists looking good. Listen for: an estimate that consistently tracks the realized price signals to sellers that the house's specialists understand the market for their category, which helps win future consignments, and an estimate that is chronically too high or too low can either scare off bidders or leave money on the table for the consignor.
- Who absorbs the risk in a guarantee, and what the house gains. Listen for: the house, or a third-party guarantor, absorbs the downside if the lot fails to reach the guaranteed price, and in exchange the house wins a prized consignment away from a competitor and keeps the consignor's business, treating the guarantee as a cost of competing for the pipeline rather than a cost of the sale itself.
- How an irrevocable bid differs from a guarantee in who is exposed. Listen for: an irrevocable bid shifts the exposure to a third party who commits to buy at a set price if no one bids higher, rather than the house or the consignor, so a house may prefer it when it wants to protect its own balance sheet while still offering the consignor comparable downside protection.
- Two houses, similar total value, different average lot value and lot count. Listen for: one house is running a business built on a small number of expensive lots, which is more exposed to any single lot failing to sell or being withdrawn, and the other is running a high-volume, lower-average business that is more resilient to any one lot's outcome but depends on steady sourcing at scale.
- What top-twenty concentration implies, and what to check before concluding. Listen for: heavy concentration means the season's results depend on a small number of consignments succeeding, and before drawing a conclusion a student should ask whether this concentration repeats across seasons or was a one-time exceptional consignment, and who consigned those twenty lots.
- Evidence for or against consignments being the real competition. Listen for: a house with strong estimate accuracy, a favorable average lot value, and a resilient (not over-concentrated) spread of value across lots is consistent with a house that is winning good consignments consistently, while a house that only looks strong because of one or two lots undercuts the claim that it is winning the broader competition for consignments.
- What a complete picture would require that this dataset cannot provide. Listen for: the fee schedule itself (buyer's premium and commission rates), guarantee and irrevocable bid terms, private sale activity alongside public auction, and the house's own cost structure and margins, none of which are captured by secondary-market transaction records.
Teaching notes
- Most common misconception: students expect a query that computes buyer's premium, hammer price, or commission directly from the data. Fix: before any query runs, put usd_price_decimal on screen and say plainly that it is the realized price the buyer paid, not a component fee, and that no such fee field exists on the fine art table.
- Second misconception: students treat a high average lot value alone as evidence of a stronger business than a high-volume house, without weighing how concentrated that value is among a few lots. Fix: always show the average-lot-value result next to the concentration result for the same house before letting the class draw a conclusion.
- 60 minute variant: drop the small-group exercise and have the class watch you run the value-concentration query live for a single house instead; keep both guided demos intact, since estimate accuracy and average lot value are the load-bearing measures.
- Longer variant (105 to 120 minutes): after the class discussion, add the sell-through comparison from the Additional queries section, run for the same house used in the estimate-accuracy demo, and ask whether a conservative estimator also clears a higher share of its offered lots.
- If the sandbox is slow or blocked on the room network: fall back to the pre-run screenshots you captured in preparation, walk the class through the three queries and their results verbally, and have students run the queries themselves as part of the homework instead.
For the full lesson content, queries, and homework, see Module 14. Questions or a class API key: info@altfndata.com.