# Booking Hotel Price Analyzer — Market Rates by City & Dates (`jdepablos/booking-hotel-price-analyzer`) Actor One call = one hotel market analyzed from Booking.com. Live price distribution, review-score mix and hotel sample for any city — structured for hotels, revenue managers and AI agents. - **URL**: https://apify.com/jdepablos/booking-hotel-price-analyzer.md - **Developed by:** [Juan de Pablos](https://apify.com/jdepablos) (community) - **Categories:** Travel, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $120.00 / 1,000 hotel market scans This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. 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 ## Booking Hotel Price Benchmark — Market Rates by City **One call = one city benchmarked from Booking.com.** Give it a city and get the local hotel market as structured data: indicative nightly price distribution (median, percentiles), review-score mix and a sample of the hotels behind the numbers. Built for hotels benchmarking their rates, revenue managers, OTAs and AI agents. ```json { "location": "Madrid", "resolvedCity": "Madrid, Madrid, Spain", "analysis": { "n": 12, "currency": "EUR", "priceBasis": "indicative-nightly", "median": 125, "p10": 74, "p25": 104, "p75": 180, "p90": 342, "reviewScoreAvg": null, "confidence": "medium" }, "hotels": [ { "name": "Catalonia Puerta del Sol", "price": 180, "currency": "EUR" } ], "meta": { "hotelsFound": 12, "hotelsAnalyzed": 12 } } ```` ### Why this is different Booking.com sits behind an anti-bot wall that blocks plain scrapers outright. This actor drives a real browser through Booking's own search flow and reads the **structured price data Booking ships to its own front-end** (not fragile HTML scraping), so every hotel comes with a clean numeric price and, where available, its review score. The result is a market benchmark, not a pile of listings: - **Indicative market prices.** Prices are Booking's "from" nightly rates per hotel — the going market rate for the city, ideal for benchmarking. Date-specific quotes are on the roadmap (v1.1). - **Robust to redesigns.** Prices come from Booking's data layer, not CSS classes that change weekly. ### Use cases - **Rate benchmarking**: a hotel checks where its price sits in the local `p25`–`p75` band. - **Revenue management**: schedule daily runs and track the city's median market rate over time (build your own rate history). - **Investor / OTA screening**: batch-run cities and compare `median` and `reviewScoreAvg`. ### Use with AI agents (MCP) ``` https://mcp.apify.com?tools=jdepablos/booking-hotel-price-analyzer ``` Your agent gets a "what do hotels cost in X on these dates?" tool with self-describing structured output — no pagination, no HTML, no anti-bot handling on your side. ### Pricing Pay per event: **$0.12 per hotel-market scan** (a full browser session against Booking's anti-bot wall, stats + hotel sample included). Scans that return no hotels are **never charged**. The price reflects the residential-browser cost of getting reliably past Booking's protection — still a fraction of a rate-shopping subscription. ### FAQ **Where do prices come from?** Booking.com's public city page — the "from" nightly rate per hotel. The output states the basis via `priceBasis` (`indicative-nightly`). **Which locales?** `booking.com` (English) and `booking.es` (Spanish). Inventory is the same; display currency follows the locale. **How many hotels per scan?** ~12 representative hotels from the city page — enough for a stable market median. **Is this affiliated with Booking.com?** No. Data comes from publicly visible search results. # Actor input Schema ## `location` (type: `string`): City to benchmark, e.g. "Madrid" or "Paris, France" (add a country after a comma to disambiguate). For several cities use `items`. ## `items` (type: `array`): Up to 10 cities per run: `[{"location": "Lisbon"}, {"location": "Rome, Italy"}]`. ## `domain` (type: `string`): Display locale/currency of the results. ## `maxHotels` (type: `integer`): How many individual hotels to include in the output (stats use the full parsed set). ## `proxy` (type: `object`): Residential proxy REQUIRED — Booking blocks datacenter IPs and plain HTTP. ## Actor input object example ```json { "location": "Madrid", "domain": "booking.com", "maxHotels": 25, "proxy": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } } ``` # 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 = { "location": "Madrid", "proxy": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } }; // Run the Actor and wait for it to finish const run = await client.actor("jdepablos/booking-hotel-price-analyzer").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 = { "location": "Madrid", "proxy": { "useApifyProxy": True, "apifyProxyGroups": ["RESIDENTIAL"], }, } # Run the Actor and wait for it to finish run = client.actor("jdepablos/booking-hotel-price-analyzer").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 '{ "location": "Madrid", "proxy": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } }' | apify call jdepablos/booking-hotel-price-analyzer --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=jdepablos/booking-hotel-price-analyzer", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Booking Hotel Price Analyzer — Market Rates by City & Dates", "description": "One call = one hotel market analyzed from Booking.com. Live price distribution, review-score mix and hotel sample for any city — structured for hotels, revenue managers and AI agents.", "version": "0.1", "x-build-id": "qs0O4kw41BSShYsha" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/jdepablos~booking-hotel-price-analyzer/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-jdepablos-booking-hotel-price-analyzer", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/jdepablos~booking-hotel-price-analyzer/runs": { "post": { "operationId": "runs-sync-jdepablos-booking-hotel-price-analyzer", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/jdepablos~booking-hotel-price-analyzer/run-sync": { "post": { "operationId": "run-sync-jdepablos-booking-hotel-price-analyzer", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "properties": { "location": { "title": "City", "type": "string", "description": "City to benchmark, e.g. \"Madrid\" or \"Paris, France\" (add a country after a comma to disambiguate). For several cities use `items`." }, "items": { "title": "Batch cities", "type": "array", "description": "Up to 10 cities per run: `[{\"location\": \"Lisbon\"}, {\"location\": \"Rome, Italy\"}]`." }, "domain": { "title": "Locale", "enum": [ "booking.com", "booking.es" ], "type": "string", "description": "Display locale/currency of the results.", "default": "booking.com" }, "maxHotels": { "title": "Hotels returned", "minimum": 0, "maximum": 40, "type": "integer", "description": "How many individual hotels to include in the output (stats use the full parsed set).", "default": 25 }, "proxy": { "title": "Proxy", "type": "object", "description": "Residential proxy REQUIRED — Booking blocks datacenter IPs and plain HTTP.", "default": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } } } }, "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 } } } } } } } } } } ```