Airbnb Price Monitor · Nightly Rate Tracker

Track nightly prices across any Airbnb market. Paste a city search URL — get avg, min, max, and median rates per listing plus a market-level summary by month.

No login. No API key. No account required.

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Who is this for?

You are…	Typical goal	Suggested setup
STR revenue manager	Benchmark your listings vs the market for coming months	City search URL · 3 months ahead
Property management agency	Weekly pricing report for client portfolios	Listing URLs · schedule every Monday
Real estate investor	Validate projected revenue before purchase	City search URL · 6 months ahead
Short-term rental operator	Detect price undercutters in your segment	Search URL with your filters (guests, type…) · 1 month
Data analyst / BI team	Feed price time-series into a dashboard	API integration · flat-prices view · recurring schedule
Concierge / property manager	Know the going rate by season before quoting	City search URL · 2–3 months ahead

What you get by default — listing name, property type, ratings, avg/min/max/median nightly rate, price breakdown by calendar month (priceByMonth), discount detection for weekly and monthly stays, and a market-level summary (average price per month across all listings) in the run log.

For per-listing occupancy and full calendar data — use a listing URL directly. Calendar mode returns day-by-day prices, occupancy rate, and coverage rate over the full period.

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What it extracts

Per listing

Field	Description
listingId	Airbnb internal listing ID
listingUrl	Direct link to the listing
listingName	Listing title
propertyType	e.g. "Apartment in Paris"
city	City extracted from search results
rooms / beds / bathrooms	Capacity (numeric)
rating / reviewCount	Rating score and review count
currency	ISO currency code (e.g. EUR, USD)
dateFrom / dateTo	Date range covered by samples
minNights	Minimum stay requirement detected
priceSamples	Number of distinct date windows priced
avgPricePerNight	Average nightly rate across all samples
minPricePerNight	Cheapest sample in the period
maxPricePerNight	Most expensive sample
medianPricePerNight	Median nightly rate
weeklyDiscountPct	% discount for 7-night stays vs short stays
monthlyDiscountPct	% discount for 28-night stays vs short stays
pricingStrategy	fixed, dynamic, fixed-with-length-discounts, dynamic-with-length-discounts — only when ≥ 5 samples
priceByMonth	Array: avg/min/max per calendar month (deduplicated by check-in week)
coverageRate	% of bookable days with a price — calendar mode only
occupancyRate	% of days already booked — calendar mode only
prices[]	Array of all raw price points with checkin, checkout, pricePerNight, nights
scrapedAt	ISO timestamp of the run

Market summary (in run log)

At the end of every city scan, the run log prints:

─── Market Summary ──────────────────────────────────────────
  Listings with prices  : 2845/2859 (100%)
  Multi-date samples    : 913 listings have 2+ price points
  Sample distribution   : 0×→14, 1×→1932, 2×→528, 3×→182 …
  Avg market price/month:
    2026-06: avg 175 EUR/night (721 listings sampled)
    2026-07: avg 147 EUR/night (1211 listings sampled)
    2026-08: avg 166 EUR/night (1060 listings sampled)
─────────────────────────────────────────────────────────────

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Quick start

1. Open the Actor in Apify Console and click Try for free
2. Paste an Airbnb search URL (e.g. https://www.airbnb.com/s/Barcelona--Spain/homes) in the URLs field
3. Click Start — results appear in the Dataset tab within a few minutes
4. Use the Overview view to compare listings side by side, or Flat prices for a day-by-day spreadsheet

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Input

Parameter	Type	Default	Description
urls	array	—	Airbnb listing URLs (/rooms/…) or search URLs (/s/…). Mix freely.
monthsAhead	number	3	How many months of price data to collect
currency	string	auto	Override ISO currency code (e.g. "EUR")
verboseLogs	boolean	false	Log additional debug info (URLs, page counts)

Input examples

Scan an entire city market:

{
  "urls": ["https://www.airbnb.com/s/Lisbon--Portugal/homes"],
  "monthsAhead": 3
}

Monitor specific competitor listings:

{
  "urls": [
    "https://www.airbnb.com/rooms/12345678",
    "https://www.airbnb.com/rooms/87654321"
  ],
  "monthsAhead": 6
}

Mix search + direct listing in one run:

{
  "urls": [
    "https://www.airbnb.com/s/Nice--France/homes",
    "https://www.airbnb.com/rooms/99999999"
  ],
  "monthsAhead": 2,
  "currency": "EUR"
}

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How it works

City search URL (market scan)

1. URL normalisation — strips user locale, injects locale=en&currency=<selected> for consistent price strings
2. Multi-window discovery — runs 78 parallel search windows (2-night, 5-night, and 7-night stays × Monday + Friday check-ins × one window per week over the period). The three stay lengths matter: Airbnb only shows listings that are available and meet the minimum-nights requirement — a listing with minNights = 5 is invisible in 2-night searches.
3. Parallel page fetching — within each window, all result pages (up to 5) are fetched in parallel; windows run in bounded batches of 15 to control memory
4. Price extraction — nightly rate parsed from Airbnb's embedded JSON (pricePerNight from structured display); fallback computes price from total ÷ nights when the qualifier is "total"
5. Price deduplication — for listings found in multiple windows, the 2-night (baseline) price is preferred per check-in week to avoid inflating priceByMonth averages
6. Rate-limit retry — blocked windows are retried once after a 3-second pause
7. Stats computation — avg, min, max, median, monthly breakdown, discount detection, pricing strategy (when ≥ 5 samples)
8. Dataset push — one row per listing; market summary printed to run log

Direct listing URL (calendar mode)

1. Listing page fetch — extracts GPS coordinates and city from the listing HTML
2. Calendar API — calls PdpAvailabilityCalendar for each month in the period (10 listings in parallel), returns per-day price, availability, and minNights
3. Stats computation — same as above plus occupancyRate (% days already booked) and coverageRate (% bookable days with a price)

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Data quality — what "1 sample" means

In city scan mode, most listings have 1–3 price samples. This is not a limitation of the Actor — it reflects how Airbnb works: a listing only appears in a search window when it is available and meets the minimum stay for those specific dates. A listing that is fully booked for the summer appears in only one or two of the 78 windows.

priceSamples	Interpretation
0	Listing found but price display was non-standard (extremely rare, ~0.5%)
1	One confirmed price on one date — useful for comparison, not trend analysis
2–4	Enough for a seasonal comparison (e.g. June vs August)
5+	Reliable average and pricing strategy detection available

For deep per-listing analysis across all dates, use a direct listing URL — the calendar mode returns prices for every available day over the full period.

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Performance and cost

Scenario	Listings	Time (Apify cloud)	Proxy data
Single listing, 3 months	1	~5 s	< 1 MB
City scan, 2 months (Paris)	~2,900	~3 min	~150 MB
City scan, 6 months	~3,500	~5 min	~400 MB

Runs are purely HTTP (no browser) — low memory (256 MB default), high concurrency.

Proxy cost: $1.50 per city run with Apify Residential proxies ($10/GB). Without a proxy, large scans (200+ listings) may hit Airbnb rate-limiting. Add Apify Residential proxies in the Proxy section for reliable results.

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Output sample

City scan listing:

{
  "listingId": "25497233",
  "listingUrl": "https://www.airbnb.com/rooms/25497233",
  "listingName": "Charming studio near Montmartre",
  "propertyType": "Apartment in Paris",
  "city": "Paris",
  "rooms": 1, "beds": 1, "bathrooms": 1,
  "rating": 4.85, "reviewCount": 142,
  "currency": "EUR",
  "dateFrom": "2026-06-15",
  "dateTo": "2026-08-31",
  "priceSamples": 4,
  "avgPricePerNight": 118,
  "minPricePerNight": 95,
  "maxPricePerNight": 145,
  "medianPricePerNight": 112,
  "weeklyDiscountPct": 12,
  "pricingStrategy": "dynamic",
  "priceByMonth": [
    { "month": "2026-06", "avgPricePerNight": 95, "minPricePerNight": 95, "maxPricePerNight": 95, "pricedDays": 1 },
    { "month": "2026-07", "avgPricePerNight": 118, "minPricePerNight": 110, "maxPricePerNight": 125, "pricedDays": 2 },
    { "month": "2026-08", "avgPricePerNight": 145, "minPricePerNight": 145, "maxPricePerNight": 145, "pricedDays": 1 }
  ],
  "scrapedAt": "2026-06-01T16:00:00.000Z",
  "prices": [
    { "checkin": "2026-06-15", "checkout": "2026-06-17", "pricePerNight": 95, "nights": 2, "isBaseline": true },
    { "checkin": "2026-07-07", "checkout": "2026-07-09", "pricePerNight": 110, "nights": 2, "isBaseline": true }
  ]
}

Direct listing (calendar mode) — additional fields:

{
  "bookableDays": 72,
  "occupancyRate": 41,
  "coverageRate": 86,
  "priceSamples": 62
}

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Dataset views

View	Best for
Overview	Comparing listings side by side — avg/min/max/median at a glance
Flat prices	Day-level analysis, pivot tables, Excel / Sheets import
Full export	API integrations needing the complete prices[] and priceByMonth arrays

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Pricing

You pay only for listings where a price was successfully returned — not for the run itself.

What	Event	Price
City scan — listing with a price	airbnb-price-market-listing	$0.002 / listing
Direct listing URL — listing with calendar prices	airbnb-price-calendar-listing	$0.03 / listing
Listing found but no price returned	apify-default-dataset-item	$0
Run start	—	$0

Typical cost:

Scenario	Listings	Cost
Paris city scan, 3 months	~2,900	~$5.80
5 competitor listings (calendar mode)	5	$0.15
Rome city scan, 3 months	~2,200	~$4.40

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Local development

npm install
# Edit storage/key_value_stores/default/INPUT.json with your test URLs
apify run

Set HTTPS_PROXY in your shell (or use Apify's proxy URL) to avoid rate-limiting locally:

$HTTPS_PROXY="http://groups-RESIDENTIAL:TOKEN@proxy.apify.com:8000" apify run

Output is written to storage/datasets/default/ and a local storage/output.csv.

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Related Actors

• Airbnb Calendar Availability Scraper — occupancy rates, blocked/available days, and min/max nights for any market
• Airbnb Property Details Scraper — full listing pages: description, amenities, host info, GPS, photos
• Airbnb Pro Host Business Email Scraper — business emails and company data for professional Airbnb hosts

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Support

Custom scraper or tailored automation? Contact corentin@outreacher.fr.