Observed collector data
What do collectors report in Canada?
Toronto and Vancouver are repeatedly described as competitive; Montreal remains too thinly documented for a precise benchmark.
Status: active with caveat · Confidence: medium · Sample: limited · Reviewed on 2026-09-10 · Updated on 2026-10-03
MyQuotaBag is an independent app and is not affiliated with Hermès International or any of its affiliates. This page summarizes public reports from collectors and publishers. It is not verified fact and states no Hermès rule. Each boutique and country works differently and practices change. These are general observations shared without commitment. We do not claim that pre-spend exists or that any amount is required. Collectors describe bags going to established clients who buy over time, come back and build a relationship with their boutique. No outcome, offer or allocation is predicted or guaranteed.
The longer answer
Community material cites roughly 1.50:1 to 3.00:1 and 12 to 24 month waits in Toronto/Vancouver contexts, but MyQuotaBag lacks enough Montreal rows for rigorous ranking.
Ranges people report
- 1.50:1 to 3.00:1: community estimate for Toronto/Vancouver competitive contexts. This is a community estimate, not a MyQuotaBag benchmark. Confidence: medium.
- 12 to 24 months: commonly reported range in competitive Canadian/top-US contexts for popular 25/MK. This is a reported spread, not a median. Confidence: medium.
What MyQuotaBag observed
Toronto and Vancouver are repeatedly described as competitive, with community estimates around 1.50:1 to 3.00:1 and longer waits for popular small configurations; Montreal remains under-documented.
What Hermès officially says
No public Hermès numeric pre-spend threshold or formula was identified in the reviewed materials.
Where and when this was observed
- Geography
- Canada
- Period covered
- 2024-01 to 2026-09
- Research pack
- MQB-RP001-QUOTA-RATIO-2026-09
- Computed on
- 2026-10-03
- Reviewed on
- 2026-09-10
What collectors reported in this region
Reported Purchase Ratios: 23 reports, median 1.90:1, from 1.00:1 to 3.00:1.
| Reported Purchase Ratio | Reports |
|---|---|
| under 0.50:1 | 0 |
| 0.50:1 to 0.99:1 | 0 |
| 1.00:1 to 1.49:1 | 9 |
| 1.50:1 to 1.99:1 | 3 |
| 2.00:1 to 2.49:1 | 5 |
| 2.50:1 to 3.00:1 | 6 |
| above 3.00:1 | 0 |
Reported wait: 1 to 60 months.
Bags named in these reports: Birkin, Kelly, Mini Kelly, Constance and Lindy.
Recalculable from the records in the MyQuotaBag research dataset. These are values collectors published about themselves, counted together. They are not a MyQuotaBag benchmark and they are not an Hermès threshold.
Cities in this region
| City or boutique | Country | Reports | With a Purchase Ratio | Evidence |
|---|---|---|---|---|
| Montreal | Canada | 9 | 2 | moderate |
| Toronto | Canada | 17 | 11 | moderate |
| Vancouver | Canada | 19 | 7 | moderate |
| Calgary | Canada | 0 | 0 | collecting |
| No city given | 6 |
A city gets its own page once MyQuotaBag holds a dedicated topic for it, or at least three first-person reports. Below that it stays listed here with its real count and the page appears on its own once the reports exist. We do not open a page and fill it with figures borrowed from the next city over.
Sources reviewed for this topic
How this page was built
Every line above comes from a dated record in the MyQuotaBag research dataset and each record keeps a link down to the source it came from. Weak evidence is kept in the lower layers instead of being deleted and it is never moved up into a stronger claim without a source behind it.
Read the full method · Machine-readable version of this page
How to cite
MyQuotaBag Data, “What do collectors report in Canada?”, dataset version 2026.10.1, reviewed on 2026-09-10. https://myquotabag.com/myquotabag-data/canada/. The research behind the MyQuotaBag app for iPhone and iPad.
Community insights currently come from public research and community reports, not private MyQuotaBag user data. Any future use of user data will be anonymized, aggregated and controlled through the app's privacy settings. How this data is built