Airbnb Full Year Price Tracker & Monthly Trends Scraper

Airbnb Price Tracker Scraper — Monthly & Seasonal Trends

Airbnb Price Tracker Scraper builds month-by-month and seasonal nightly-price trends for any Airbnb listing. Give it a room URL, listing ID, or search keyword, and it samples real check-in dates across up to twelve months, then rolls results into average, minimum, peak, and median nightly rates per month and season, alongside an occupancy signal. Every response is structured JSON, ready to pass to an LLM, load into a spreadsheet, or feed a monitoring pipeline. Run it before setting rates or before a trip to see how pricing moves.

What is Airbnb Price Tracker Scraper?

Airbnb Price Tracker Scraper is a query-driven Apify Actor that turns a listing URL, ID, or search keyword into a computed monthly and seasonal price-trend dataset — not a flat list of identical daily quotes. Its defining feature is server-side aggregation: every avgNightlyPrice, minNightlyPrice, peakNightlyPrice, and medianNightlyPrice value is calculated locally from real per-date price samples pulled from Airbnb, and paired with an occupancy signal derived from Airbnb's own availability calendar — no manual spreadsheet rollups required. It needs no Airbnb account, login, or session cookies; it authenticates requests with Airbnb's own public web API key and a browser-matched request fingerprint. Point it at one or more listings and get back trend rows ready for analysis.

Key capabilities:

• Accepts Airbnb room URLs, numeric listing IDs, or free-text search keywords (each keyword resolves to up to 5 matching listings)
• Builds 1–12 consecutive months of price-trend rows from a chosen start month
• Computes average, minimum, peak, and median nightly price per month from real sampled check-in dates
• Optionally adds seasonal (Winter / Spring / Summer / Autumn) roll-ups on top of the monthly rows
• Optionally computes an occupancy signal — occupancy rate, available/booked nights, and typical minimum-night rule — from the live availability calendar
• Prices every sampled quote in the requested guest party size (adults, children, pets) and currency

What data can you get with Airbnb Price Tracker Scraper?

Each run returns computed monthly price-trend rows, optional seasonal roll-up rows, and the listing's reference details attached to every row.

Result Type	Extracted Fields	Primary Use Case
Monthly price trend (type: "monthly_trend")	type, isChild, Listing ID, trendMonth, monthLabel, season, avgNightlyPrice, minNightlyPrice, peakNightlyPrice, medianNightlyPrice, priceSampleCount, trendCurrency, occupancyRate, availableNights, bookedNights, totalNights, typicalMinNights, cheapestMonth, peakMonth, rangeAvgNightlyPrice, rangeMinNightlyPrice, rangePeakNightlyPrice	Build a month-by-month pricing calendar and spot the cheapest and peak periods
Seasonal price trend (type: "seasonal_trend", isChild: true)	Same price/occupancy fields, aggregated per season	Compare four seasonal numbers instead of reading every monthly row
Listing reference (attached to every row)	Title, Room Rating, Location, Property Type, Listing URL, scrapedAt	Identify, filter, and timestamp which listing each trend row belongs to

The Actor also writes additional listing-detail fields to every row that aren't in the default dataset view table — including Host, Bedrooms, Beds, Bathrooms, Primary Price, Cleaning Fee, Service Fee, Taxes, Cancellation Policy Name, and per-category review ratings (accuracyRating, cleanlinessRating, locationRating, etc.). Open the dataset's API/JSON view, or add columns to the table view, to see them.

Monthly & seasonal price roll-ups

This is the Actor's core output, and the reason it exists: instead of handing you one price-per-day row that you have to average yourself, it samples several real check-in dates inside each month (samplesPerMonth, evenly spread), converts each quote to a per-night rate, and computes avg / min / peak / median locally with Python's statistics module. The same math runs again across each season's months when includeSeasonalRollups is on. A month with zero successful samples reports null prices rather than a guessed number — nothing here is estimated or interpolated. A single monthly row looks like:

{
  "type": "monthly_trend",
  "trendMonth": "2026-09",
  "monthLabel": "September 2026",
  "season": "Summer",
  "avgNightlyPrice": 268.1,
  "minNightlyPrice": 210.0,
  "peakNightlyPrice": 340.0,
  "medianNightlyPrice": 255.0,
  "priceSampleCount": 4
}

Occupancy signal

When computeOccupancySignal is on, the Actor fetches Airbnb's PdpAvailabilityCalendar and turns each day's available flag into per-month demand numbers: occupancyRate (share of nights already booked), availableNights, bookedNights, totalNights, and typicalMinNights (the most common minimum-stay rule seen that month). It pairs directly with the price roll-up for the same month, so a revenue manager can read "September: $268 avg/night, 55% booked" in one row instead of cross-referencing two datasets.

Why not build this yourself?

Airbnb does not publish a public API for third-party listing search or pricing data, so any DIY approach means reverse-engineering the same internal GraphQL endpoints this Actor uses (StaysPdpSections, PdpAvailabilityCalendar), keeping their persisted-query hashes current as Airbnb rotates them, and handling the Akamai bot defense that returns an HTML "Access Denied" page to any request without a real browser TLS fingerprint. This Actor already tracks a vetted set of operation-ID hashes with a dynamic-discovery fallback when Airbnb rotates them, impersonates a genuine Chrome TLS fingerprint on every request, and escalates from no proxy to datacenter to residential proxy only when a request is actually blocked — so a single blocked listing doesn't force you to pay for residential proxy traffic on every request. Building and maintaining that pipeline yourself is a standing engineering cost; running the Actor is not.

How to scrape Airbnb with Airbnb Price Tracker Scraper?

1. Open Airbnb Price Tracker Scraper on Apify and go to the Input tab.
2. Add one or more entries to trendListings — a room URL, a numeric listing ID, or a search keyword. No field is strictly required by the schema, but the run produces no rows without at least one entry here.
3. Set trendStartDate, analysisMonths, and samplesPerMonth to define the trend window and how densely each month is sampled; adjust partyAdults / partyChildren / partyPets and pricingCurrency to match the quote you want priced.
4. Click Start to run the Actor.
5. When the run finishes, open the Dataset tab and export the trend rows as JSON, CSV, Excel, or another supported format.

Example input for a single listing:

{
  "trendListings": ["https://www.airbnb.com/rooms/7431972"],
  "trendStartDate": "2026-08-01",
  "analysisMonths": 6,
  "samplesPerMonth": 4,
  "pricingCurrency": "USD"
}

How to run multiple queries in one job

Add more entries to the trendListings array — mix room URLs, numeric IDs, and search keywords freely in the same run; each keyword resolves to up to 5 listings, and every listing gets its own full monthly/seasonal trend build in the same dataset. The Actor does not expose a separate concurrency setting for this array; listings are processed one after another within the run.

⬇️ Input

Parameter	Required	Type	Description	Example Value
trendListings	No	array	Airbnb room URLs, numeric listing IDs, or search keywords to build price trends for. Keywords resolve to up to 5 listings each.	["https://www.airbnb.com/rooms/7431972"]
trendStartDate	No	string	First month to analyze (YYYY-MM-DD; only the month is used). Past dates are shifted to the next month automatically.	"2026-08-01"
analysisMonths	No	integer	How many consecutive months to build trends for, starting at the trend start month. Max 12.	6
nightsPerSample	No	integer	Length of stay used for each sampled quote; the total is normalized to a per-night rate.	3
samplesPerMonth	No	integer	How many real check-in dates to quote per month before rolling them up. Default 4, max 15.	4
includeSeasonalRollups	No	boolean	Also emit one extra row per season aggregating that season's months. Default true.	true
computeOccupancySignal	No	boolean	Fetch the availability calendar and derive occupancy rate, available/booked nights. Default true.	true
partyAdults	No	integer	Adult guests priced into every trend quote.	2
partyChildren	No	integer	Children included in the guest count for each quote.	0
partyPets	No	integer	Pets traveling with the guests, applied to every quote.	0
pricingCurrency	No	string (enum)	Currency every price figure is reported in: USD, GBP, EUR, CAD, or AUD.	"USD"
proxyConfiguration	No	object	Apify Proxy settings. The Actor starts direct and escalates to datacenter/residential only if Airbnb blocks a request.	{"useApifyProxy": false}

No parameter is required by the input schema — running with defaults trends the pre-filled example listing.

Common pitfall: leaving trendStartDate blank, or setting it to a date that has already passed, doesn't error — the Actor silently shifts the window to start next month instead. If your output's trendMonth values don't match the month you expected, check this field first.

⬆️ Output

Results are pushed to the Actor's dataset as typed, normalized JSON — one row per month (plus one per season, if enabled) per listing, with a consistent schema across runs. Export as JSON, CSV, Excel, HTML table, or any other format Apify's dataset export supports.

Scraped results

[
  {
    "type": "monthly_trend",
    "isChild": false,
    "Listing ID": "7431972",
    "Listing URL": "https://www.airbnb.com/rooms/7431972",
    "trendMonth": "2026-08",
    "monthLabel": "August 2026",
    "season": "Summer",
    "avgNightlyPrice": 312.5,
    "minNightlyPrice": 245.0,
    "peakNightlyPrice": 410.0,
    "medianNightlyPrice": 298.75,
    "priceSampleCount": 4,
    "trendCurrency": "USD",
    "occupancyRate": 0.68,
    "availableNights": 10,
    "bookedNights": 21,
    "totalNights": 31,
    "typicalMinNights": 2,
    "cheapestMonth": "2026-10",
    "peakMonth": "2026-08",
    "rangeAvgNightlyPrice": 275.4,
    "rangeMinNightlyPrice": 210.0,
    "rangePeakNightlyPrice": 410.0,
    "Title": "Oceanview Malibu Beach House",
    "Room Rating": 4.92,
    "Location": "Malibu, California, United States",
    "Property Type": "Entire home",
    "scrapedAt": "2026-07-26T14:02:11Z"
  },
  {
    "type": "monthly_trend",
    "isChild": false,
    "Listing ID": "7431972",
    "Listing URL": "https://www.airbnb.com/rooms/7431972",
    "trendMonth": "2026-09",
    "monthLabel": "September 2026",
    "season": "Summer",
    "avgNightlyPrice": 268.1,
    "minNightlyPrice": 210.0,
    "peakNightlyPrice": 340.0,
    "medianNightlyPrice": 255.0,
    "priceSampleCount": 4,
    "trendCurrency": "USD",
    "occupancyRate": 0.55,
    "availableNights": 14,
    "bookedNights": 16,
    "totalNights": 30,
    "typicalMinNights": 2,
    "cheapestMonth": "2026-10",
    "peakMonth": "2026-08",
    "rangeAvgNightlyPrice": 275.4,
    "rangeMinNightlyPrice": 210.0,
    "rangePeakNightlyPrice": 410.0,
    "Title": "Oceanview Malibu Beach House",
    "Room Rating": 4.92,
    "Location": "Malibu, California, United States",
    "Property Type": "Entire home",
    "scrapedAt": "2026-07-26T14:02:11Z"
  },
  {
    "type": "monthly_trend",
    "isChild": false,
    "Listing ID": "7431972",
    "Listing URL": "https://www.airbnb.com/rooms/7431972",
    "trendMonth": "2026-10",
    "monthLabel": "October 2026",
    "season": "Autumn",
    "avgNightlyPrice": 231.75,
    "minNightlyPrice": 189.0,
    "peakNightlyPrice": 275.0,
    "medianNightlyPrice": 228.0,
    "priceSampleCount": 4,
    "trendCurrency": "USD",
    "occupancyRate": 0.41,
    "availableNights": 18,
    "bookedNights": 13,
    "totalNights": 31,
    "typicalMinNights": 1,
    "cheapestMonth": "2026-10",
    "peakMonth": "2026-08",
    "rangeAvgNightlyPrice": 275.4,
    "rangeMinNightlyPrice": 210.0,
    "rangePeakNightlyPrice": 410.0,
    "Title": "Oceanview Malibu Beach House",
    "Room Rating": 4.92,
    "Location": "Malibu, California, United States",
    "Property Type": "Entire home",
    "scrapedAt": "2026-07-26T14:02:11Z"
  },
  {
    "type": "seasonal_trend",
    "isChild": true,
    "Listing ID": "7431972",
    "Listing URL": "https://www.airbnb.com/rooms/7431972",
    "trendMonth": null,
    "monthLabel": null,
    "season": "Summer",
    "seasonMonthsCovered": ["2026-08"],
    "avgNightlyPrice": 312.5,
    "minNightlyPrice": 245.0,
    "peakNightlyPrice": 410.0,
    "medianNightlyPrice": 298.75,
    "priceSampleCount": 4,
    "trendCurrency": "USD",
    "occupancyRate": 0.68,
    "availableNights": 10,
    "bookedNights": 21,
    "totalNights": 31,
    "Title": "Oceanview Malibu Beach House",
    "Room Rating": 4.92,
    "Location": "Malibu, California, United States",
    "Property Type": "Entire home",
    "scrapedAt": "2026-07-26T14:02:11Z"
  }
]

How can I use the data extracted with Airbnb Price Tracker Scraper?

• Revenue management and hosts: Compare avgNightlyPrice and occupancyRate across trendMonth rows for your own listing or nearby comparables to decide where to raise or drop rates ahead of peakMonth and cheapestMonth.
• Travel planning: Run the trend window across a shortlist of listings before booking to see which month has the lowest minNightlyPrice and medianNightlyPrice for the dates you're flexible on.
• AI engineers and LLM developers: Have an agent call the Actor with a listing or keyword, receive the monthly/seasonal JSON rows, and ground a pricing-advice or trip-planning response in real sampled data instead of a model guess.
• Market researchers: Run the same keyword across a region's listings on a schedule and compare rangeAvgNightlyPrice and occupancyRate month over month to track how a market's pricing and demand are shifting.

How do you monitor Airbnb monthly price trends over time?

Monitoring here means re-running the same listing or keyword set on a schedule and diffing the new monthly/seasonal rows against the previous run to see how pricing and demand actually moved, rather than reading a single snapshot. Each run's scrapedAt timestamp lets you keep every run's dataset separate, so you can align rows by Listing ID and trendMonth between two runs and compare.

The fields worth diffing between runs: avgNightlyPrice, minNightlyPrice, peakNightlyPrice, and medianNightlyPrice for rate movement; occupancyRate, availableNights, and bookedNights for demand movement; and cheapestMonth / peakMonth to see whether the best and worst months in the window have shifted. A widening gap between minNightlyPrice and peakNightlyPrice in a given month, for example, usually signals a host actively adjusting rates for weekday/weekend or event-driven demand within that month.

A practical workflow: schedule a run across your tracked listings or keyword at the start of each week or month, keep each run's dataset, and alert when avgNightlyPrice for a given trendMonth moves by more than the threshold that matters to you, or when occupancyRate for an upcoming month crosses a level that should trigger a rate change. Set this up with Apify's built-in Schedule feature on the Actor, and pull each run's dataset via the API to do the comparison in your own pipeline — the Actor itself only produces the per-run snapshot, not the diff.

Integrate Airbnb Price Tracker Scraper and automate your workflow

Airbnb Price Tracker Scraper works with any language or tool that can send an HTTP request.

REST API with Python

import requests

TOKEN = "YOUR_APIFY_API_TOKEN"
ACTOR = "scrapier~airbnb-full-year-price-tracker-monthly-trends-scraper"
url = f"https://api.apify.com/v2/acts/{ACTOR}/run-sync-get-dataset-items"

payload = {
    "trendListings": ["https://www.airbnb.com/rooms/7431972"],
    "trendStartDate": "2026-08-01",
    "analysisMonths": 6,
}

resp = requests.post(url, params={"token": TOKEN}, json=payload)
rows = resp.json()
for row in rows:
    print(row["trendMonth"], row["avgNightlyPrice"], row["occupancyRate"])

MCP for query-grounded AI agents

The Actor is reachable through Apify's MCP server without any custom integration code. Register it with https://mcp.apify.com?tools=scrapier/airbnb-full-year-price-tracker-monthly-trends-scraper, or via the Apify CLI: apify mcp install claude-code --tools scrapier/airbnb-full-year-price-tracker-monthly-trends-scraper. Once registered, a compatible client — Claude Desktop, Cursor, VS Code (Copilot agent mode), or another MCP client — can call the Actor as a tool: the agent passes a listing or keyword, receives the monthly/seasonal JSON, and grounds its answer in that real data.

Scheduled monitoring and delivery

Use Apify's built-in Schedule feature to run the Actor on a recurring cron schedule against a fixed listing or keyword set. Each scheduled run produces its own dataset; pull results per run via the API (or Apify's webhook-on-completion feature) to build the run-over-run comparison described above.

Is it legal to scrape Airbnb pricing data?

Scraping publicly viewable Airbnb listing and pricing data is generally legal in most jurisdictions — this Actor retrieves only prices, availability, and listing details that any visitor can see on a listing's public page, without logging in or accessing private account data. Because the data returned here is business/product data (nightly rates, availability, listing attributes) rather than personal data about the guests or hosts, the relevant framework is Airbnb's Terms of Service and general database-rights law rather than GDPR/CCPA — though listing pages do surface a host's display name, so treat any personal-name fields with care in downstream use. Scraping for internal monitoring or research carries a different risk profile than scraping to train or fine-tune a model at scale; the latter deserves closer legal review. Consult your legal team for commercial use cases involving bulk data storage.

Frequently asked questions

How are monthly and seasonal price trends actually calculated?

They're computed locally by the Actor, not read off a single Airbnb page. For each month it samples several real check-in dates (samplesPerMonth, default 4), converts each returned quote to a per-night rate, and computes the average, minimum, peak, and median with real statistics. Seasonal rows aggregate the same underlying samples across a season's months. A month with no successful samples reports null prices rather than an estimate.

Does Airbnb Price Tracker Scraper require an Airbnb account or login?

No. It authenticates with Airbnb's own public web API key and a browser-matched request fingerprint, the same way a logged-out visitor's browser would. You don't need to supply Airbnb credentials, cookies, or a session.

What currencies can I get prices in?

pricingCurrency supports USD, GBP, EUR, CAD, and AUD. Every avgNightlyPrice, minNightlyPrice, peakNightlyPrice, and medianNightlyPrice figure across all rows in the run is reported in the selected currency.

How do I control the trend window and sample density?

Use trendStartDate and analysisMonths (1–12) to set which months are covered, and samplesPerMonth (1–15) and nightsPerSample (1–28) to control how many check-in dates are quoted per month and how long each sampled stay is. More samples produce a more representative monthly average at the cost of more requests per listing.

How does the Actor handle Airbnb's anti-bot measures?

It impersonates a genuine Chrome TLS fingerprint on every request (so Airbnb's Akamai defense serves its normal JSON API instead of an HTML block page), keeps a vetted list of persisted-query hashes with dynamic re-discovery if Airbnb rotates them, and escalates proxying from none to datacenter to residential only after a request is actually detected as blocked.

Does it include an occupancy signal, not just price?

Yes, when computeOccupancySignal is on (default). It fetches Airbnb's availability calendar and reports occupancyRate, availableNights, bookedNights, totalNights, and typicalMinNights alongside the price fields for the same month or season.

How many rows does it return per listing?

One row per month in analysisMonths (up to 12), plus up to 4 additional seasonal rows if includeSeasonalRollups is on. A search keyword resolves to up to 5 listings, each producing its own full set of rows; there's no separate schema-level cap on how many URLs, IDs, or keywords you list in trendListings.

How do I monitor Airbnb price trends over time?

Schedule the Actor to run on a recurring basis against the same listing or keyword set using Apify's Schedule feature, keep each run's dataset (tagged by scrapedAt), and compare avgNightlyPrice, occupancyRate, and cheapestMonth/peakMonth between the current and previous run to see what changed.

Does Airbnb Price Tracker Scraper work with Claude, ChatGPT, and AI agent frameworks?

Yes. It's reachable through Apify's MCP server (register with https://mcp.apify.com?tools=scrapier/airbnb-full-year-price-tracker-monthly-trends-scraper) for MCP-compatible clients like Claude Desktop and Cursor, and callable as a plain HTTP endpoint by any agent framework that can make a REST call — which lets an agent retrieve real trend data before answering a pricing or travel question.

How does it compare to other Airbnb price scrapers?

As observed on the Apify Store on 2026-07-26, most comparable Airbnb price/calendar scrapers return one row per calendar day and leave averaging or trend-building to the user. Airbnb Price Tracker Scraper instead computes the monthly and seasonal average/min/peak/median roll-up and pairs it with an occupancy signal directly in the output, so no separate spreadsheet step is needed to read a month's pricing picture.

Can I use it without managing proxies or Airbnb credentials?

Yes. The Actor starts every request with no proxy and only escalates to Apify's datacenter, then residential, proxy tiers automatically if Airbnb blocks a request — you can leave proxyConfiguration at its default and supply no Airbnb login of any kind.

Your feedback

Found a bug, a missing field, or a listing type this Actor doesn't handle well? Let us know through the Issues tab on the Actor's Apify Console page — reports there go straight to the people maintaining it.