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.
Placeholder — Add any additional proprietary data sources, partnerships, or data ingestion pipelines specific to Chalet's infrastructure here.
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
Placeholder — Describe the market selection criteria — how are new markets added? What is the minimum listing count threshold for a market to appear in analytics? Are any geographies intentionally excluded?
Placeholder — Clarify whether coverage includes VRBO/Booking.com listings or is Airbnb-only.
Section 3
Refresh Cadence
Data freshness directly affects the reliability of investment decisions. Below is the intended update schedule for each data type.
Placeholder — Fill in the actual refresh cadence for each data type: listing data (daily? weekly?), market metrics aggregates (weekly? monthly?), county records (quarterly?). Also note any lag between data collection and when it appears on the platform.
| 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.
Placeholder — Confirm whether Chalet uses booked nights or available nights. Describe any outlier filtering (e.g. listings with <X reviews or abnormally high/low rates excluded).
Occupancy Rate
Occupancy = Nights Booked ÷ Nights Available × 100%Availability windows are inferred from calendar blocking patterns.
Placeholder — Clarify the lookback window used (trailing 12 months? rolling 90 days?). Describe how "blocked" vs "booked" nights are distinguished.
RevPAR — Revenue Per Available Room
RevPAR = ADR × Occupancy RateStandard hospitality metric adapted for STR market analysis.
Placeholder — Confirm if Chalet uses this standard formula or a modified version.
Annual Revenue
Annual Revenue = ADR × (Occupancy Rate × 365)Represents projected gross revenue before expenses.
Placeholder — Describe the estimation approach — is this based on observed trailing-12M data or a forward-looking model? What seasonality adjustments are applied?
Placeholder — Add any additional platform-specific metrics (e.g. Cash-on-Cash Return, Cap Rate, DSCR estimates) with their full calculation logic and any assumptions baked in.
Section 5
Known Limitations
No dataset is perfect. We document known gaps and caveats so investors can apply appropriate judgment when using Chalet data.
Placeholder — List the specific known limitations of Chalet's data. Examples to consider: seasonality bias in short lookback windows, sparse data in low-inventory markets (<20 listings), calendar blocking ambiguity, recency lag in county records, platform-specific behavior (Airbnb vs VRBO), listings that rotate on/off platform, etc.
Calendar Ambiguity
Distinguishing between "owner blocked" and "booked" nights relies on inference from calendar patterns. This can introduce minor occupancy estimation error.
Platform Coverage
[Placeholder — specify which OTAs are included. If only Airbnb, note that VRBO/Booking.com properties are not reflected in market metrics.]
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.
Placeholder — Maintain a versioned changelog here. Each entry should include: date, version number (optional), what changed (data source added/removed, formula change, new market coverage), and whether historical data was retroactively updated.
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.
