Transparency & Trust
Our Methodology
How Chalet collects, processes, and presents short-term rental data — so you can invest with confidence, not guesswork.
500+
U.S. markets covered
6
Data sources
Monthly
Refresh cadence
Section 1
Data Sources
Chalet aggregates data from multiple independent sources to build a comprehensive and unbiased view of short-term rental markets. We deliberately avoid relying on competitor APIs or third-party data resellers that may carry their own analytical biases.
Airbnb Listings
Publicly available listing data including nightly rates, availability calendars, amenities, and historical booking signals.
Public Booking Data
Aggregated booking activity derived from publicly observable calendar and pricing signals across active listings.
County & Municipal Records
Property tax records, permit filings, and short-term rental licensing data obtained from county assessor databases and open-data portals.
What We Exclude
We do not purchase or ingest data from competitor analytics platforms (e.g. AirDNA, Mashvisor, Rabbu, or similar). All data is sourced independently.
On top of these public sources, Chalet maintains its own internal collection and processing pipelines that clean, deduplicate, and normalize raw signals before they reach the platform. The specifics of this infrastructure evolve continuously as we expand coverage and improve data quality.
Section 2
Sample Size & Coverage
Chalet currently tracks short-term rental performance across 500+ U.S. markets, spanning vacation destinations, urban metros, and emerging secondary markets.
500+
Markets tracked
1M+
Listings analyzed
50
States covered
A market becomes eligible for analytics once it has enough active listings to produce statistically meaningful aggregates. We prioritize markets with sustained short-term-rental activity, and we add new markets as inventory grows past that threshold. Markets with too few active listings to support reliable averages are held back until coverage is sufficient, so a sparse or seasonal area may not appear right away.
Coverage is currently Airbnb only. Listings on VRBO, Booking.com, or direct-booking sites are not reflected in market metrics. In markets where a meaningful share of supply lives on other platforms, our figures represent the Airbnb segment rather than the full short-term-rental market.
Section 3
Refresh Cadence
Data freshness directly affects the reliability of investment decisions. Below is the intended update schedule for each data type.
Different data types update on different schedules. Listing availability and pricing refresh most frequently, while aggregated market metrics and public records update on a slower cadence. “Lag” below is the typical delay between when data is collected and when it appears on the platform — so the figures you see reflect the market as it was a short window ago, not in real time.
| Data Type | Refresh Frequency | Lag |
|---|---|---|
| Listing availability & pricing | Weekly | 2–3 days |
| Market-level metrics (ADR, RevPAR, Occupancy) | Monthly | ~1 week |
| County & permit records | Monthly | 2–4 weeks |
| Competitor benchmarks | Monthly | ~1 week |
Section 4
Calculation Methodology
Each metric displayed on Chalet is computed using a defined formula applied consistently across all markets. Definitions for the core metrics are documented below.
ADR — Average Daily Rate
ADR = Total Revenue ÷ Number of Nights BookedExcludes cleaning fees and platform service charges.
ADR is based on nights actually booked, not nights available. To keep averages representative, listings with too little booking history to be reliable and entries with clearly anomalous rates are filtered out before the market average is computed.
Occupancy Rate
Occupancy = Nights Booked ÷ Nights Available × 100%Availability windows are inferred from calendar blocking patterns.
Occupancy is measured over a trailing 12-month window so seasonal peaks and troughs are captured in a single figure. Because public calendars do not label why a night is unavailable, we infer the difference between owner-blocked and booked nights from booking and pricing signals — an inference that is accurate in aggregate but imperfect for any single listing.
RevPAR — Revenue Per Available Room
RevPAR = ADR × Occupancy RateStandard hospitality metric adapted for STR market analysis.
Chalet uses the standard hospitality definition of RevPAR with no proprietary modification, so the figure is directly comparable to how the metric is used across the lodging industry.
Annual Revenue
Annual Revenue = ADR × (Occupancy Rate × 365)Represents projected gross revenue before expenses.
This figure is grounded in observed trailing-12-month performance rather than a forward-looking forecast. Because it is built from ADR and occupancy that already reflect a full year of booking activity, normal seasonality is baked into the underlying inputs. It represents gross revenue before operating expenses, debt service, and taxes.
The investment metrics in our calculators build on these market figures but are driven by the purchase and financing assumptions you enter:
NOI — Net Operating Income
Gross revenue less operating costs of ownership (management, cleaning, platform fees, insurance, property tax, and other recurring expenses). NOI excludes mortgage debt service.
Cap Rate
NOI ÷ Purchase Price, expressed as a percentage. It measures unlevered yield, so it ignores financing.
Cash-on-Cash Return
Net income after debt service ÷ Purchase Price, expressed as a percentage. Unlike cap rate, it accounts for mortgage payments.
Because these depend on inputs such as purchase price, down payment, and operating cost assumptions, they reflect the scenario you model rather than a single market-wide value.
Section 5
Known Limitations
No dataset is perfect. We document known gaps and caveats so investors can apply appropriate judgment when using Chalet data.
Calendar Ambiguity
Distinguishing between "owner blocked" and "booked" nights relies on inference from calendar patterns. This can introduce minor occupancy estimation error.
Platform Coverage
Market metrics reflect Airbnb listings only. Properties booked through VRBO, Booking.com, or direct channels are not included, so in markets where those platforms hold significant share our figures describe the Airbnb segment rather than total demand.
Low-Inventory Markets
In markets with few active listings, averages are computed from a small sample and can swing meaningfully as individual listings come on or off the platform. Treat metrics in thin markets as directional rather than precise.
Listing Turnover
Listings rotate on and off Airbnb over time. New listings, delisted properties, and seasonal hosts can shift a market’s composition between refreshes, which affects period-over-period comparisons.
Regulatory Data Freshness
Municipal STR regulations change frequently. County permit data may lag actual regulatory changes by 4–8 weeks.
Section 6
Update History
A log of material changes to Chalet's data sources, calculation methods, or coverage — so returning users know what has changed and when.
Each entry records the date, what changed — a data source, a calculation, or market coverage — and whether historical figures were retroactively restated.
Changelog
Initial publication
Methodology page published. Baseline documentation for all core metrics.
Questions about this methodology? Contact us . We review and update this page as our data infrastructure evolves.
