Hammer prices for art and antiques.
Lot, artist, estimate and final price for every sale. The history an appraiser needs to back a valuation with real market outcomes instead of reference catalogues.
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Lot, artist, estimate and final price for every sale. The history an appraiser needs to back a valuation with real market outcomes instead of reference catalogues.
Ansorena has been auctioning in Madrid since 1845 and Bukowskis is the Nordic reference. Different markets, identical fields.
The same field names across both houses. Load and compare without mapping.
| Field | Type | Example |
|---|---|---|
| lot_title | string | Oil on canvas, seascape |
| artist | string | Joaquín Sorolla |
| category | string | Painting |
| estimate_low | number | 18000 |
| estimate_high | number | 24000 |
| hammer_price | number | 31000 |
| currency | string | EUR |
| sold | boolean | true |
| auction_date | date | 2026-06-11 |
| lot_number | string | 142 |
| source | string | ansorena |
An appraiser receives an oil painting to value and needs to support the figure with comparable sales, not only reference catalogues. They search each house by artist, medium and period, copy estimates and prices into a spreadsheet and repeat the process with every commission. It is slow, and the result depends on which houses they had time to check.
With the Ansorena and Bukowskis lots in one schema, the query becomes a filter: artist, category, date range and currency. Each lot carries a low and high estimate, the hammer price, whether it sold and the sale date; comparing what was estimated with what was actually paid is one computed column.
A gallery or dealer does the reverse: after each sale they load the new lots and check which categories hammer above estimate. Because extraction runs per sale, the history builds up sale by sale without touching the original data, and it can be delivered as CSV or JSON for any analysis tool.
We do not publish a downloadable sample for these houses yet; you can request one from the datasets page. This table summarises what each field in a lot schema answers.
| Field | Question it answers | How it is used |
|---|---|---|
| artist · category | Who made it and what kind of work is it? | Filter comparable lots by artist and category |
| estimate_low · estimate_high | What did the house expect? | Measure the gap between estimate and result |
| hammer_price · sold | What was paid, and did it sell? | Separate sold lots from unsold ones |
| auction_date · lot_number | When and in which sale? | Build series by period |
| source | Which house sold it? | Compare Ansorena with Bukowskis |
Ansorena, in Madrid, and Bukowskis, based in Sweden and also active in Finland. Both are delivered with the same schema, so you can filter by house or analyse them together.
Yes. Each lot carries a low estimate, a high estimate, the hammer price, currency and a sold flag, plus artist, category, lot number and sale date.
Per auction sale. Each extraction is an independent run: launch it after every sale and chain the runs to accumulate history.
Each extraction returns whatever the house keeps published on its website at that moment. Accumulated history is built by chaining runs or, if you need it ready-made, we can request it as a dataset from the datasets page.
No. The schema describes lots: the artist as shown in the catalogue, category, estimate and result. It does not include buyer or seller data.
If you need more houses or a ready-made history, we can deliver it or build the extraction around your needs.
Pick your source and launch your first run. Pay per result.