# Airbnb Price Monitor · Nightly Rate Tracker (`corent1robert/airbnb-price-monitor`) Actor Track nightly rates across any Airbnb market. Paste a city search URL — get avg, min, max, and median price per listing with full day-by-day breakdown. No login. No API key. - **URL**: https://apify.com/corent1robert/airbnb-price-monitor.md - **Developed by:** [Corentin Robert](https://apify.com/corent1robert) (community) - **Categories:** Travel, Automation, Developer tools - **Stats:** 2 total users, 1 monthly users, 33.3% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $2.00 / 1,000 market listings This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## 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.** --- ### 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. --- ### 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) ───────────────────────────────────────────────────────────── ```` --- ### 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 --- ### 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:** ```json { "urls": ["https://www.airbnb.com/s/Lisbon--Portugal/homes"], "monthsAhead": 3 } ```` **Monitor specific competitor listings:** ```json { "urls": [ "https://www.airbnb.com/rooms/12345678", "https://www.airbnb.com/rooms/87654321" ], "monthsAhead": 6 } ``` **Mix search + direct listing in one run:** ```json { "urls": [ "https://www.airbnb.com/s/Nice--France/homes", "https://www.airbnb.com/rooms/99999999" ], "monthsAhead": 2, "currency": "EUR" } ``` *** ### How it works #### City search URL (market scan) 1. **URL normalisation** — strips user locale, injects `locale=en¤cy=` 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) *** ### 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. *** ### 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. *** ### Output sample **City scan listing:** ```json { "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:** ```json { "bookableDays": 72, "occupancyRate": 41, "coverageRate": 86, "priceSamples": 62 } ``` *** ### 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 | *** ### 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 | *** ### Local development ```bash 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: ```bash 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`. *** ### Related Actors - **[Airbnb Calendar Availability Scraper](https://apify.com/corent1robert/airbnb-calendar-availability-scraper)** — occupancy rates, blocked/available days, and min/max nights for any market - **[Airbnb Property Details Scraper](https://apify.com/corent1robert/airbnb-property-details-scraper)** — full listing pages: description, amenities, host info, GPS, photos - **[Airbnb Pro Host Business Email Scraper](https://apify.com/corent1robert/airbnb-professional-host-scraper)** — business emails and company data for professional Airbnb hosts *** ### Support Custom scraper or tailored automation? Contact . # Actor input Schema ## `urls` (type: `array`): **What to paste here depends on your goal:** **I want to monitor a specific listing** (my property, a competitor…) Paste the listing URL: `https://www.airbnb.com/rooms/12345` → The actor fetches availability, checks prices across upcoming dates, and returns avg/min/max per night. **I want to scan an entire market** (a city, a neighbourhood…) Paste a search URL: `https://www.airbnb.com/s/Paris--France/homes` → To get one: search on Airbnb with your filters (city, guests, property type…), then copy the URL from your browser. → The actor collects all visible listings and their prices. You can mix both types freely. ## Actor input object example ```json { "urls": [ "https://www.airbnb.com/s/Paris--France/homes" ] } ``` # Actor output Schema ## `dataset` (type: `string`): One row per listing — price stats + nested prices array. ## `overview` (type: `string`): One row per listing — avg, min, max, median price per night, priced days, rating. No prices array. Best for comparing listings at a glance. ## `flatPrices` (type: `string`): One row per listing × date — listingId, date, price, available, minNights. Best for pivot tables, date lookups, and Excel/Sheets analysis. ## `runLog` (type: `string`): Progress log with listing counts and duration. # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "urls": [ "https://www.airbnb.com/s/Paris--France/homes" ] }; // Run the Actor and wait for it to finish const run = await client.actor("corent1robert/airbnb-price-monitor").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "urls": ["https://www.airbnb.com/s/Paris--France/homes"] } # Run the Actor and wait for it to finish run = client.actor("corent1robert/airbnb-price-monitor").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "urls": [ "https://www.airbnb.com/s/Paris--France/homes" ] }' | apify call corent1robert/airbnb-price-monitor --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=corent1robert/airbnb-price-monitor", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Airbnb Price Monitor · Nightly Rate Tracker", "description": "Track nightly rates across any Airbnb market. Paste a city search URL — get avg, min, max, and median price per listing with full day-by-day breakdown. No login. No API key.", "version": "1.2", "x-build-id": "HUoC2CrlOtBhyaQ4A" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/corent1robert~airbnb-price-monitor/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-corent1robert-airbnb-price-monitor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/corent1robert~airbnb-price-monitor/runs": { "post": { "operationId": "runs-sync-corent1robert-airbnb-price-monitor", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/corent1robert~airbnb-price-monitor/run-sync": { "post": { "operationId": "run-sync-corent1robert-airbnb-price-monitor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "urls" ], "properties": { "urls": { "title": "Listing or search URLs", "type": "array", "description": "**What to paste here depends on your goal:**\n\n**I want to monitor a specific listing** (my property, a competitor…)\nPaste the listing URL: `https://www.airbnb.com/rooms/12345`\n→ The actor fetches availability, checks prices across upcoming dates, and returns avg/min/max per night.\n\n**I want to scan an entire market** (a city, a neighbourhood…)\nPaste a search URL: `https://www.airbnb.com/s/Paris--France/homes`\n→ To get one: search on Airbnb with your filters (city, guests, property type…), then copy the URL from your browser.\n→ The actor collects all visible listings and their prices.\n\nYou can mix both types freely.", "items": { "type": "string" } } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```