Our forecast error, published.
Every STR data vendor claims accuracy. None of them show their backtests. Here are ours: per market, our revenue forecast against Inside Airbnb's per-listing revenue estimates across the whole revenue distribution — including the markets where we're not good enough yet.
| Market | Median error | Worst single miss | Reality inside our range | Sample | State |
|---|---|---|---|---|---|
| Chicago | 26.6% | 82.9% | 69% | 16 listings | Validated · <30% |
| Nashville, TN | 27.7% | 147.5% | 50% | 16 listings | Validated · <30% |
| Denver | 29.1% | 74.9% | 38% | 16 listings | Validated · <30% |
| Broward County, FL | 34.0% | 79.8% | 38% | 16 listings | Wide band — improving |
| New Orleans, LA | 39.7% | 125.3% | 44% | 16 listings | Wide band — improving |
| San Diego, CA | 43.1% | 101.5% | 38% | 16 listings | Wide band — improving |
| Asheville | 43.9% | 84.8% | 50% | 16 listings | Wide band — improving |
| Austin | 60.9% | 95.9% | 31% | 16 listings | Wide band — improving |
Backtest: stratified sample across each market's revenue quartiles (not just top performers), forecast = median of nearby comparable listings, "realized" = Inside Airbnb's review-evidenced estimated_revenue_l365d per listing. "Reality inside our range" is how often the actual value landed within the p25–p75 band we showed — a calibrated band should be near 50%.
Comp-based STR forecasting carries ±20–30% error in liquid markets — that's the industry's open secret, and per-listing error is larger than the market-average numbers vendors quote. In November 2025 the category leader's revenue projections silently collapsed for paying customers when a comp-selection change went wrong. The fix for an industry built on unaudited point estimates is boring: show the backtest, every market, every model change.
- "Realized" here is Inside Airbnb's review-evidenced estimate, not owner bank statements. When owner-reported revenue lands, it replaces this baseline — same ledger, better source.
- Wide-band markets stay on the board. Hiding them would defeat the purpose.
- Numbers regenerate with every model change — they can get worse, and you'd see it here first.