Airbnb Market Report — ADR & seasonality by city

Turn public Airbnb search data into a short-term rental market report for any city or area: median nightly rate (ADR), price percentiles, seasonality across future months, supply mix by room type, quality landscape, and transparent monthly revenue scenarios.

Built for people evaluating a short-term rental investment or pricing their own listing — the questions AirDNA answers for $40+/month, answered on demand from live public data, for the cost of one run.

What it does

1. Price-samples your market for several future months (one dated sample stay per month, starting next month) using public Airbnb search results.
2. Normalizes every listing to a comparable nightly rate (stay total ÷ nights) in your currency.
3. Outputs one market_report item plus the most-reviewed listings of the market (top_listing items).

The report contains

• ADR: median, P25/P75, min/max nightly rate across all sampled listings.
• Monthly table: ADR stats per sampled month — this is your seasonality curve (high/low season and spread %).
• Supply mix: share and median ADR per room type (entire home / private room / ...).
• Quality landscape: average guest rating, median review count, Superhost share — how hard the competition is.
• Revenue scenarios: median ADR × 30 nights × 50% / 65% / 80% occupancy. These are labeled scenarios, not measurements — occupancy is the one number public search data cannot prove, so the report never pretends otherwise.

Input example

{
  "location": "Lisbon, Portugal",
  "currency": "EUR",
  "sampleMonths": 3,
  "maxListingsPerSample": 60
}

The defaults work as-is: just set your city.

Output example (report item)

{
  "type": "market_report",
  "location": "Lisbon, Portugal",
  "currency": "EUR",
  "generatedFromListings": 142,
  "adr": { "adrMedian": 108.8, "adrP25": 84.5, "adrP75": 152.3, "adrMin": 38, "adrMax": 420 },
  "monthly": [
    { "month": "2026-08", "adrMedian": 131.0, "listings": 60 },
    { "month": "2026-09", "adrMedian": 112.4, "listings": 58 },
    { "month": "2026-10", "adrMedian": 96.1, "listings": 57 }
  ],
  "seasonality": { "highSeasonMonth": "2026-08", "lowSeasonMonth": "2026-10", "spreadPct": 36 },
  "supplyMix": { "Entire home/apt": { "count": 118, "sharePct": 83, "adrMedian": 115.2 } },
  "quality": { "avgRating": 4.82, "medianReviewsCount": 74, "superhostSharePct": 44 },
  "monthlyRevenueScenarios": { "occupancy50pct": 1632, "occupancy65pct": 2122, "occupancy80pct": 2611 }
}

How to use it

• Evaluating a market: run once per candidate city, compare adrMedian, spreadPct and superhostSharePct side by side.
• Pricing your listing: your room type's adrMedian in supplyMix is the market anchor; the monthly table tells you when to raise or drop.
• Tracking a market: schedule it monthly — each run re-samples the months ahead.

Fair use & data

• Uses only public Airbnb search results (no login).
• No host personal data in the output — host names, photos and profiles are deliberately dropped; only an aggregate Superhost share survives.
• Inputs are hard-capped (6 months, 200 listings per sample) so runs stay bounded and predictable.

FAQ

Is the occupancy real? No — and nobody's public number is. Occupancy scenarios are clearly labeled multipliers on measured ADR. What IS measured: dated prices, supply, ratings.

Why do monthly listing counts differ? Airbnb returns what is actually bookable for those dates — that variation is itself a market signal (availability tightens in high season).

Which months are sampled? Always the 10th of each future month, for nightsPerSample nights, so runs are comparable.