# Hotels & Lodging Scraper – OpenStreetMap Hotel Data (`dataquarry/hotels-lodging`) Actor Extract hotels, hostels, guest houses, motels & apartments from OpenStreetMap by area, radius, or name. Get stars, room count, brand, operator, contact and address. No API key; open (ODbL) data. - **URL**: https://apify.com/dataquarry/hotels-lodging.md - **Developed by:** [Daniel Brenner](https://apify.com/dataquarry) (community) - **Categories:** Travel, Lead generation, Real estate - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $3.00 / 1,000 results 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 ## Hotels & Lodging Scraper – OpenStreetMap Hotel Data Extract **hotels, hostels, guest houses, motels and apartments from OpenStreetMap** by area, radius, or name — **no API key, no booking-site scraping, no anti-bot battles**. Open, legal (ODbL) data you can redistribute. Give it an area like `"Edinburgh, United Kingdom"` and get back tidy rows for every place to stay: name, lodging type, **star rating**, **room count**, brand, operator, internet access, full address, coordinates, contact, and the raw OpenStreetMap tags. You can also search **around a point** (every hotel within X km of a landmark) or **filter by name** (e.g. every `"Premier Inn"` or `"Hilton"`). ### Why use this scraper? - **Lodging-specific fields.** Not just a generic POI dump — `stars`, `rooms`, `lodging_type`, brand, operator and internet access are parsed out of the OpenStreetMap tags for you. - **Legal & open.** OpenStreetMap data is ODbL-licensed — redistributable with attribution. No booking-site Terms-of-Service issues, no login. - **No API key.** No hotel/OTA developer account, no per-call quota. - **Search by area, radius, or name.** A whole city/region, everything within X metres of a point, or every property of one brand. - **Filter by type.** Hotels only, or hostels + guest houses, etc. - **No duplicates.** When OpenStreetMap maps one property twice (a node and a building outline), you get a single, richer row. - **Honest data.** Every value comes straight from OpenStreetMap; anything not mapped is left empty (`null`) — never guessed. `stars`/`rooms` are read only from explicit tags. - **Global coverage & reliable.** Anywhere OSM has data; retries across multiple Overpass mirrors. ### Great for - Travel & hospitality datasets, hotel-finder apps, trip planners - Hotel market research, brand/coverage analysis, competitive mapping - Site selection and real-estate/hospitality analysis - Travel AI/RAG pipelines and dashboards ### Input | Field | Type | Description | |---|---|---| | `area` | string | Place to search within, e.g. `"Paris, France"`. Geocoded to a bounding box. | | `aroundLocation` | string | *(optional)* Address/place to search **around** within a radius. Pair with `radiusMeters`. | | `radiusMeters` | integer | *(optional)* Radius in metres for around-a-location search (default `3000`). | | `centerPoint` | object | *(advanced)* Explicit center `{ "lat":.., "lon":.. }` to search around. | | `boundingBox` | object | *(advanced)* Explicit `{ "south":.., "west":.., "north":.., "east":.. }`. Overrides `area`. | | `lodgingTypes` | array | *(optional)* Which types to include: `hotel`, `guest_house`, `hostel`, `motel`, `apartment`, `chalet`. Default: all. | | `searchTerm` | string | *(optional)* Only return places whose **name** contains this text (case-insensitive), e.g. `"Hilton"`. | | `maxResults` | integer | Maximum number of places to return (default `1000`). | #### Example input ```json { "area": "Edinburgh, United Kingdom", "lodgingTypes": ["hotel", "guest_house", "hostel"], "maxResults": 500 } ```` Every Premier Inn within 20 km of a point: ```json { "aroundLocation": "Manchester Piccadilly", "radiusMeters": 20000, "lodgingTypes": ["hotel"], "searchTerm": "Premier Inn" } ``` ### Output One row per lodging place: | Field | Description | |---|---| | `name` | Property name | | `lodging_type` | hotel / guest\_house / hostel / motel / apartment / chalet | | `stars` | Star rating (from OSM `stars`) | | `rooms` | Number of rooms (from OSM `rooms`) | | `brand`, `operator` | Brand / chain and operator | | `branch` | Branch / location name of a chain outlet (OSM `branch`), e.g. `Times Square` — disambiguates outlets of the same chain; `null` when not a tagged branch | | `brand_wikidata` | Wikidata id of the brand/chain (e.g. `Q598884`) — stable entity id for chain analysis, when tagged | | `brand_logo` | Official brand logo image URL from the brand's Wikidata entity (P154), when it has one; `null` otherwise — optional enrichment, never guessed | | `internet_access` | wlan / yes / no / … | | `wheelchair` | Accessibility | | `latitude`, `longitude` | Coordinates | | `street`, `housenumber`, `city`, `postcode`, `country` | Address | | `state` | State / province (OSM `addr:state` / `addr:province`) — common in US/CA/AU, `null` where not tagged | | `phone`, `email`, `website` | Contact | | `osm_id`, `osm_type`, `all_tags`, `source_url` | OpenStreetMap identifiers, raw tags, and link | | `full_address` | All present address parts in one string (e.g. `Main St 1, 10115 Berlin`) | | `map_url` | Google Maps link to the coordinates | #### Example output ```json { "name": "The Balmoral Hotel", "lodging_type": "hotel", "stars": 5, "rooms": 188, "brand": "Rocco Forte Hotels", "operator": "Rocco Forte Hotels", "internet_access": "wlan", "city": "Edinburgh", "postcode": "EH2 2EQ", "country": "GB", "website": "https://example.test", "latitude": 55.9521, "longitude": -3.1897, "osm_type": "way", "source_url": "https://www.openstreetmap.org/way/..." } ``` Any field is `null` when the property hasn't tagged it in OpenStreetMap — values are never guessed. ### FAQ **Do I need an API key or account?** No — give it an area (plus optional radius/name filters) and run. No data-provider key, no quota, no setup. **Is the data legal to use and redistribute?** Yes. It comes from OpenStreetMap under the Open Database License (ODbL): public data you can redistribute with attribution (© OpenStreetMap contributors). No logins, no Terms-of-Service violations. **How is this different from a Booking.com or Expedia scraper?** It uses open OpenStreetMap data instead of scraping a site behind anti-bot defenses and Terms of Service — so it's legal, needs no API key, and returns an honest `null` for anything OSM hasn't mapped instead of guessing. You get each property's own details (stars, rooms, brand, contact) rather than OTA listings or reviews. **How much does it cost?** Pay-per-result: **$3 per 1,000 results** — you only pay for the rows you actually get. **Which countries does it cover?** Worldwide — anywhere OpenStreetMap has data. **How fresh is the data?** It's pulled live from OpenStreetMap at run time, so it reflects the current map. ### Data source & license Data comes from **OpenStreetMap** via the public **Nominatim** (geocoding) and **Overpass** (querying) APIs. OpenStreetMap data is **© OpenStreetMap contributors**, licensed under the **Open Database License (ODbL)**. If you publish or redistribute results, attribute "© OpenStreetMap contributors". ### Notes - Coverage and tag richness vary by region — OSM is community-mapped, so cities are richer than rural areas. - `stars` reflects what is tagged in OSM, which may differ from official/marketing ratings. ### More OpenStreetMap data actors Part of **dataquarry**'s family of clean, ODbL OpenStreetMap extractors — same flexible **area / radius / bounding-box / name** search, same honest-null data (a field that isn't mapped is left empty, never guessed): - [OpenStreetMap Places Scraper](https://apify.com/dataquarry/osm-places-scraper) — POI & local business, 115+ categories - [EV Charging Stations Scraper](https://apify.com/dataquarry/ev-charging-stations) — socket types, power (kW), networks - [Hotels & Lodging Scraper](https://apify.com/dataquarry/hotels-lodging) — stars, rooms, brands - [Healthcare Facilities Scraper](https://apify.com/dataquarry/healthcare-facilities) — pharmacies, doctors, dentists, clinics - [Tourist Attractions & Museums Scraper](https://apify.com/dataquarry/tourist-attractions) — Wikidata & Wikipedia links - [Fuel Station Scraper](https://apify.com/dataquarry/fuel-stations) — fuel types, brands, payment methods - [Supermarket & Grocery Scraper](https://apify.com/dataquarry/supermarkets) — brands, organic, payment methods # Actor input Schema ## `area` (type: `string`): Place to search within, e.g. "Paris, France". Geocoded to a bounding box via Nominatim. Provide this OR a bounding box OR a center for radius search. ## `aroundLocation` (type: `string`): Optional. An address or place to search AROUND within a radius, e.g. "Eiffel Tower". Geocoded to a center point; set the radius with "radiusMeters". ## `radiusMeters` (type: `integer`): Radius in meters for the around-a-location search (used with Around Location or Center Point). Default 3000. ## `centerPoint` (type: `object`): Optional advanced alternative: an explicit center as {"lat":.., "lon":..} to search around. Overrides Around Location when set. ## `boundingBox` (type: `object`): Explicit bounding box as {"south":..,"west":..,"north":..,"east":..}. Overrides Area when set. ## `lodgingTypes` (type: `array`): Which lodging types to include. Supported: hotel, guest\_house, hostel, motel, apartment, chalet. Default: all of them. ## `searchTerm` (type: `string`): Optional. Only return places whose name contains this text (case-insensitive), e.g. "Hilton", "Premier Inn". ## `maxResults` (type: `integer`): Maximum number of lodging places to return. ## Actor input object example ```json { "area": "Edinburgh, United Kingdom", "radiusMeters": 3000, "lodgingTypes": [ "hotel", "guest_house", "hostel" ], "maxResults": 1000 } ``` # 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 = { "area": "Edinburgh, United Kingdom", "lodgingTypes": [ "hotel", "guest_house", "hostel" ] }; // Run the Actor and wait for it to finish const run = await client.actor("dataquarry/hotels-lodging").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 = { "area": "Edinburgh, United Kingdom", "lodgingTypes": [ "hotel", "guest_house", "hostel", ], } # Run the Actor and wait for it to finish run = client.actor("dataquarry/hotels-lodging").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 '{ "area": "Edinburgh, United Kingdom", "lodgingTypes": [ "hotel", "guest_house", "hostel" ] }' | apify call dataquarry/hotels-lodging --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=dataquarry/hotels-lodging", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Hotels & Lodging Scraper – OpenStreetMap Hotel Data", "description": "Extract hotels, hostels, guest houses, motels & apartments from OpenStreetMap by area, radius, or name. Get stars, room count, brand, operator, contact and address. No API key; open (ODbL) data.", "version": "0.0", "x-build-id": "HODgtPE8euCLVTuwf" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/dataquarry~hotels-lodging/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-dataquarry-hotels-lodging", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/dataquarry~hotels-lodging/runs": { "post": { "operationId": "runs-sync-dataquarry-hotels-lodging", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/dataquarry~hotels-lodging/run-sync": { "post": { "operationId": "run-sync-dataquarry-hotels-lodging", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "area": { "title": "Area", "type": "string", "description": "Place to search within, e.g. \"Paris, France\". Geocoded to a bounding box via Nominatim. Provide this OR a bounding box OR a center for radius search." }, "aroundLocation": { "title": "Search around a location (radius)", "type": "string", "description": "Optional. An address or place to search AROUND within a radius, e.g. \"Eiffel Tower\". Geocoded to a center point; set the radius with \"radiusMeters\"." }, "radiusMeters": { "title": "Radius (meters)", "minimum": 1, "type": "integer", "description": "Radius in meters for the around-a-location search (used with Around Location or Center Point). Default 3000.", "default": 3000 }, "centerPoint": { "title": "Center point (advanced radius)", "type": "object", "description": "Optional advanced alternative: an explicit center as {\"lat\":.., \"lon\":..} to search around. Overrides Around Location when set." }, "boundingBox": { "title": "Bounding box (advanced)", "type": "object", "description": "Explicit bounding box as {\"south\":..,\"west\":..,\"north\":..,\"east\":..}. Overrides Area when set." }, "lodgingTypes": { "title": "Lodging types", "type": "array", "description": "Which lodging types to include. Supported: hotel, guest_house, hostel, motel, apartment, chalet. Default: all of them.", "items": { "type": "string" } }, "searchTerm": { "title": "Filter by name (optional)", "type": "string", "description": "Optional. Only return places whose name contains this text (case-insensitive), e.g. \"Hilton\", \"Premier Inn\"." }, "maxResults": { "title": "Max results", "minimum": 1, "type": "integer", "description": "Maximum number of lodging places to return.", "default": 1000 } } }, "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 } } } } } } } } } } ```