# When history fails you, borrow from geography

[Airbnb](https://yomu.fyi/company/airbnb) · Harrison Katz · Jun 2, 2026

## Summary

When historical travel data became uninformative during the asynchronous post-COVID recovery, Airbnb redesigned its demand forecasting to borrow signals across geographies rather than waiting for local data. Using a hierarchical Bayesian framework, the team propagated updated posterior estimates from early-recovering corridors as informative priors for structurally similar, later-recovering corridors. This allowed accurate corridor-level demand forecasting in near real time during periods of high disruption and uneven market reopening.

## Takeaways

- Posterior demand parameters from early-affected geographic corridors can serve as informative priors for structurally similar regions before local shock data accumulates.
- Information transfer between market corridors should be similarity-weighted based on structural attributes like traveler composition, domestic-versus-international mix, and accommodation types.
- The geographic prior propagation framework generalizes beyond crisis recovery to sequential product rollouts, regional policy changes, and macroeconomic shocks.

**Tags:** [Architecture](https://yomu.fyi/topic/architecture), [Data Pipelines](https://yomu.fyi/topic/data-pipelines), [Machine Learning](https://yomu.fyi/topic/machine-learning)

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