# Airbnb Experiences Scraper - Low-costπŸ’²πŸ”₯✨ πŸ’¬ (`delectable_incubator/airbnb-experiences-scraper---low-cost`) Actor Scrape Airbnb Experiences metadata with this powerful scraper 🌍✨. Extract titles, average ratings, images, total price, latitude, longitude, and experience URLs by city, dates, and currency. Ideal for travel analytics, price comparison, and structured datasets for market research πŸ“ŠπŸš€ - **URL**: https://apify.com/delectable\_incubator/airbnb-experiences-scraper---low-cost.md - **Developed by:** [Prime Scrape](https://apify.com/delectable_incubator) (community) - **Categories:** Travel, Real estate, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $0.00005 / actor start This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. 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 Experiences Scraper - PrimeScrape

--- ### 🏑 Airbnb Experiences Scraper πŸ”βœ¨πŸŒ The Airbnb Experiences Scraper is a fast, reliable, and scalable automation tool designed to extract structured experience listing data directly from Airbnb search results. It converts raw experience listings into clean, structured datasets ready for market research, travel analysis, lead generation, and competitive intelligence. --- ### Airbnb Experiences Scraper πŸ‘πŸ”ŽπŸ“Š This scraper automatically extracts structured Airbnb experience listings based on location, dates, and currency preferences. Ideal for marketers, analysts, travel startups, and researchers. --- ### 🎯 What This Scraper Does Simply provide search parameters such as city, dates, and currency, and the scraper will automatically extract structured Airbnb experience data. βœ… Extracts Airbnb experience listings βœ… Supports location-based search βœ… Supports check-in / check-out filtering βœ… Handles pagination automatically βœ… Respects maxitems limits βœ… Cleans and structures listing data βœ… Ready for analytics and integrations --- ### πŸ“Š Data Extracted 🏑 Experience Fields | Field | Description | | ------------------------ | ------------------------------- | | πŸ“ `Title` | Experience title | | ⏱ `Duration` | Duration of the experience | | πŸ–Ό `Poster picture` | Experience image (if available) | | πŸ’² `Display price` | Price per person | | 🎯 `Activity type` | Category of the experience | | ⭐ `avg Rating Label` | Rating with review count | | ⭐ `avg Rating Localized` | Short rating format | | πŸ“ `Latitude` | Geographic latitude | | πŸ“ `Longitude` | Geographic longitude | | πŸ†” `ID` | Unique experience ID | | πŸ”— `Link_to` | Direct experience URL | --- ### πŸ›  How to Use 1️⃣ Configure Input Provide search parameters: ```` { "city": "Paris", "checkin": "2026-07-27", "checkout": "2026-07-30", "currency": "USD", "maxitems": 50 } ``` 2️⃣ Run the Actor The scraper will: β€’ Perform location-based search β€’ Apply date and currency filters β€’ Automatically paginate results β€’ Extract structured experience listings β€’ Stop when maxitems is reached 3️⃣ Export the Dataset Download your results in multiple formats: βœ… JSON βœ… CSV βœ… Excel βœ… XML βœ… HTML --- ### βš™οΈ Input Configuration #### πŸ“₯ Input Example ``` { "city": "Paris", "checkin": "2026-07-27", "checkout": "2026-07-30", "currency": "USD", "maxitems": 50 } ``` #### Input Fields | Field | Type | Description | | ---------- | ------- | ---------------------------------------- | | `city` | string | Target city or location | | `checkin` | string | Check-in date (YYYY-MM-DD) | | `checkout` | string | Check-out date (YYYY-MM-DD) | | `currency` | string | Selected currency (USD, EUR, etc.) | | `maxitems` | integer | Maximum number of experiences to extract | --- ### πŸ“€ Output Example ``` { "Title": "NO DIET CLUB - Best food tour in Le Marais", "Duration": "3 hours", "Poster picture": "No poster available", "Display price": "From $68 per person", "Activity type": "Food and drink in Paris", "avg Rating Label": "4.92 out of 5 average rating, 2,394 reviews", "avg Rating Localized": "4.92 (2,394)", "Latitude": 48.858038421686274, "Longitude": 2.359253228972538, "ID": "148925", "Link\_to": "https://airbnb.com/experiences/148925" } ```` --- ### πŸ“Š Output explanation | Field | Description | | ---------------------- | ----------------------- | | `Title` | Experience title | | `Duration` | Duration | | `Poster picture` | Image URL | | `Display price` | Price per person | | `Activity type` | Experience category | | `avg Rating Label` | Full rating description | | `avg Rating Localized` | Short rating | | `Latitude` | Latitude | | `Longitude` | Longitude | | `ID` | Experience ID | | `Link_to` | Experience URL | --- ### 🌍 Why Use This Scraper? 🏑 Market Research β€” Analyze experience-based businesses 🎯 Lead Generation β€” Identify hosts and opportunities πŸ“Š Competitive Analysis β€” Compare offerings and pricing πŸ“ˆ Travel Insights β€” Discover trends across locations πŸ€– Data & AI β€” Build structured datasets ⚑ Scalable β€” Designed for high-volume extraction --- ### ⚠️ Disclaimer This tool is an independent solution and is not affiliated with or endorsed by Airbnb. --- ### πŸ’Έ Pricing This scraper runs on a **pay per events subscription model**. You only pay for **successful runs**. πŸ’³ **Price:** $2.89 / 1000 results --- ### Related Actors If you're interested in other German πŸ‡©πŸ‡ͺ, Austrian πŸ‡¦πŸ‡Ή, Swiss πŸ‡¨πŸ‡­, or Dach or European πŸ‡ͺπŸ‡Ί scrapers, explore more scrapers across Europe πŸ‡©πŸ‡ͺπŸ‡ͺπŸ‡Ί (Coming soon) --- ### πŸ“¬ Support ⭐⭐⭐⭐⭐ Leave a 5-star rating if you like this tool --- ### 🌍 PrimeScrape Built for scalable web data extraction & automation Contact for custom scraping solutions or enterprise requests via Apify or by email. # Actor input Schema ## `city` (type: `string`): The name of the location ## `checkin` (type: `string`): The Date of checkin ## `checkout` (type: `string`): The Date of checkout ## `currency` (type: `string`): The selected currency ## `maxitems` (type: `integer`): Set the maximum number of experiences to scrape. The scraper will stop once this number is reached. ## Actor input object example ```json { "city": "Paris", "checkin": "2026-07-27", "checkout": "2026-07-30", "currency": "USD", "maxitems": 50 } ```` # Actor output Schema ## `overview` (type: `string`): No description # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "city": "Paris", "checkin": "2026-07-27", "checkout": "2026-07-30", "currency": "USD", "maxitems": 50 }; // Run the Actor and wait for it to finish const run = await client.actor("delectable_incubator/airbnb-experiences-scraper---low-cost").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`πŸ’Ύ Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // πŸ“š Want to learn more πŸ“–? Go to β†’ 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 = { "city": "Paris", "checkin": "2026-07-27", "checkout": "2026-07-30", "currency": "USD", "maxitems": 50, } # Run the Actor and wait for it to finish run = client.actor("delectable_incubator/airbnb-experiences-scraper---low-cost").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("πŸ’Ύ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # πŸ“š Want to learn more πŸ“–? Go to β†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "city": "Paris", "checkin": "2026-07-27", "checkout": "2026-07-30", "currency": "USD", "maxitems": 50 }' | apify call delectable_incubator/airbnb-experiences-scraper---low-cost --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=delectable_incubator/airbnb-experiences-scraper---low-cost", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Airbnb Experiences Scraper - Low-costπŸ’²πŸ”₯✨ πŸ’¬", "description": "Scrape Airbnb Experiences metadata with this powerful scraper 🌍✨. \n\nExtract titles, average ratings, images, total price, latitude, longitude, and experience URLs by city, dates, and currency. \n\nIdeal for travel analytics, price comparison, and structured datasets for market research πŸ“ŠπŸš€", "version": "0.0", "x-build-id": "1gtUc6qnHn3xHGp8m" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/delectable_incubator~airbnb-experiences-scraper---low-cost/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-delectable_incubator-airbnb-experiences-scraper---low-cost", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/delectable_incubator~airbnb-experiences-scraper---low-cost/runs": { "post": { "operationId": "runs-sync-delectable_incubator-airbnb-experiences-scraper---low-cost", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/delectable_incubator~airbnb-experiences-scraper---low-cost/run-sync": { "post": { "operationId": "run-sync-delectable_incubator-airbnb-experiences-scraper---low-cost", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "city" ], "properties": { "city": { "title": "City", "type": "string", "description": "The name of the location", "default": "Paris" }, "checkin": { "title": "Date of checkin", "type": "string", "description": "The Date of checkin", "default": "" }, "checkout": { "title": "Date of checkout", "type": "string", "description": "The Date of checkout", "default": "" }, "currency": { "title": "Selected Currency", "enum": [ "USD", "EUR", "GBP", "AED", "CNY", "QAR", "INR", "BHR", "CZK", "MYR", "COP", "THB", "TRY", "RUB", "CHF", "ZAR", "SGD", "KWD", "JOR", "JPY", "IDR", "BLR", "MXN", "SAR", "MAD", "CAD", "EGP", "AUD" ], "type": "string", "description": "The selected currency", "default": "USD" }, "maxitems": { "title": "Maximum Number of Experiences to Scrape πŸ“Š", "type": "integer", "description": "Set the maximum number of experiences to scrape. The scraper will stop once this number is reached.", "default": 50 } } }, "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 } } } } } } } } } } ```