# Boligsiden Property Search Scraper (`stealth_mode/boligsiden-property-search-scraper`) Actor Scrape property listings from Boligsiden.dk — Denmark's leading real estate portal. Extract prices, addresses, room counts, market duration, and 18+ fields per listing. Perfect for analysts, investors, and proptech developers. - **URL**: https://apify.com/stealth\_mode/boligsiden-property-search-scraper.md - **Developed by:** [Stealth mode](https://apify.com/stealth_mode) (community) - **Categories:** Automation, Developer tools, Real estate - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $2.50 / 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. 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 ## Boligsiden.dk Property Scraper: Extract Danish Real Estate Listings --- ### What Is Boligsiden.dk? Boligsiden.dk is one of Denmark's most comprehensive real estate platforms, aggregating residential property listings from agencies nationwide. It covers homes for sale, rental properties, and market statistics across Danish municipalities. For anyone conducting property market research, investment analysis, or competitive intelligence, manually collecting listing data is impractical at scale. The **Boligsiden.dk Property Scraper** automates this, delivering clean, structured records from search result pages. --- ### Overview The **Boligsiden Property Scraper** processes paginated search result URLs from Boligsiden.dk and extracts structured listing data for each property. It is suited for: - **Real estate investors** monitoring pricing trends by area - **Proptech developers** building Danish property aggregators - **Market analysts** tracking days-on-market and price change patterns - **Researchers** studying housing supply and demand across Danish regions --- ### Input Format The scraper accepts a JSON configuration object with three parameters: ```json { "urls": ["https://www.boligsiden.dk/tilsalg?page=2"], "ignore_url_failures": true, "max_items_per_url": 20 } ```` | Field | Type | Default | Description | |---|---|---|---| | `urls` | `array` | — | One or more Boligsiden.dk property search/list page URLs. Supports pagination URLs (e.g., `?page=2`). Add URLs one by one or via bulk edit. | | `max_items_per_url` | `integer` | `20` | Maximum number of property listings to collect per URL. Increase for broader datasets. | | `ignore_url_failures` | `boolean` | `true` | If `true`, the scraper continues running when individual URLs fail, rather than halting the entire run. Recommended for bulk jobs. | > **Tip:** To collect across multiple pages, add each paginated URL separately — e.g., `?page=1`, `?page=2`, `?page=3`. Filter by property type or municipality using Boligsiden's search filters before copying the URL. *** ### Output Format **Sample output** ```json { "address": { "_links": { "self": { "href": "/addresses/0a3f50b6-89b3-32b8-e044-0003ba298018" } }, "address_i_d": "0a3f50b6-89b3-32b8-e044-0003ba298018", "allow_new_valuation_info": false, "city": { "name": "Gram", "slug": "" }, "city_name": "Gram", "days_on_market": { "realtors": null }, "gstkvhx": "05100085__22_______", "has_multiple_cases": false, "house_number": "22", "is_on_market": false, "is_public": true, "municipality": { "municipality_code": 0, "name": "", "slug": "" }, "place": { "bbox": null, "coordinates": { "lat": 0, "lon": 0, "type": "" }, "id": 0, "name": "Arnum", "slug": "" }, "place_name": "Arnum", "province": { "name": "", "province_code": "", "region_code": 0, "slug": "" }, "road": { "municipality_code": 0, "name": "Birkevej", "road_code": 0, "road_i_d": "", "slug": "" }, "road_name": "Birkevej", "slug": "birkevej-22-6510-gram-05100085__22_______", "slug_address": "birkevej-22-6510-gram", "zip": { "name": "Gram", "slug": "", "zip_code": 6510 }, "zip_code": 6510 }, "address_type": "villa", "case_id": null, "coordinates": { "lat": 55.250797, "lon": 8.967432, "type": "EPSG4326" }, "days_listed": { "days": 0 }, "housing_area": 112, "image": { "image_sources": [ { "alt": "Et gult murstenshus med en skråttag står stille ved siden af en brolagt indkørsel. Omgivet af grønne buske og træer under en blå himmel.", "size": { "height": 400, "width": 600 }, "url": "https://images.boligsiden.dk/images/case/5e4d0d08-b38c-4036-a708-d4adcace571b/600x400/3e35cd93-e8f5-4e2c-a3c6-c55ec88e1741.webp" } ] }, "label": "new_on_boligsiden", "monthly_expense": 1030, "number_of_rooms": 4, "per_area_price": 5417, "price_cash": 650000, "price_change_percentage": 0, "realtor": { "_links": { "self": { "href": "/realtors/45d737eb-0a1d-49f7-bc28-6f35cacf931d" } }, "association_of_danish_realtors_member": false, "association_of_elo_member": false, "broadcast": { "target": 0 }, "contact_information": {}, "distinction": "realtor", "is_new": false, "name": "Mikkelsens Ejendomskontor", "preferred": false, "rating": {}, "ratings": { "buyer": {}, "seller": {} }, "realtor_branch": { "distinction": "", "logo": { "image_sources": [ { "size": { "height": 40, "width": 60 }, "url": "https://images.boligsiden.dk/images/realtor_branch/e19d6e83-f692-48a6-9541-6c8efddc555b/60x40/ebf6ab97-5c35-44d6-97c9-ce9977ded67b.gif" } ] }, "name": "", "realtor_branch_i_d": "" }, "realtor_i_d": "45d737eb-0a1d-49f7-bc28-6f35cacf931d", "slug": "mikkelsens-ejendomskontor-45d737eb0a1d49f7bc286f35cacf931d" }, "slug": "birkevej-22-6510-gram-05100085__22_______", "slug_address": "birkevej-22-6510-gram", "time_on_market": { "current": { "days": 0 }, "total": { "days": 0, "realtors": [ { "days": 0, "realtor_id": "45d737eb-0a1d-49f7-bc28-6f35cacf931d", "realtor_name": "Mikkelsens Ejendomskontor" } ] } }, "year_built": 1972 } ``` Each property listing returns a record with 18 fields: #### Location & Identity | Field | Meaning | |---|---| | `Address` | Full street address of the property | | `Address Type` | Classification of the address (e.g., road, place) | | `Slug` | URL-friendly identifier for the listing | | `Slug Address` | URL-friendly version of the property address | | `Coordinates` | Geographic coordinates (latitude/longitude) for mapping | | `Case ID` | Unique listing identifier assigned by Boligsiden | #### Property Characteristics | Field | Meaning | |---|---| | `Housing Area` | Living area in square metres (m²) | | `Number Of Rooms` | Total room count as listed | | `Year Built` | Year the property was originally constructed | | `Label` | Property type label (e.g., Villa, Rækkehus, Ejerlejlighed) | | `Image` | URL of the primary listing photo | #### Pricing & Financials | Field | Meaning | |---|---| | `Price Cash` | Asking price in DKK | | `Per Area Price` | Price per m² (DKK/m²) — useful for cross-listing comparison | | `Monthly Expense` | Estimated monthly ownership cost (e.g., HOA, utilities) | | `Price Change Percentage` | Percentage change from original asking price — indicates negotiation activity | #### Market Timing | Field | Meaning | |---|---| | `Days Listed` | Number of days since the listing was first published | | `Time On Market` | Human-readable representation of listing age (e.g., "3 months") | #### Agent Information | Field | Meaning | |---|---| | `Realtor` | Name of the agency or agent handling the sale | *** ### How to Use 1. **Find search URLs** — Go to [boligsiden.dk/tilsalg](https://www.boligsiden.dk/tilsalg), apply filters (region, property type, price range), then copy the resulting URL. 2. **Add pagination** — For larger datasets, copy URLs for multiple pages (e.g., `?page=1` through `?page=10`). 3. **Configure input** — Paste URLs into the `urls` array and set `max_items_per_url` to match your target volume. 4. **Run and export** — Start the scraper and download results as JSON, CSV, or Excel. **Common issues:** - Ensure URLs point to **list/search pages**, not individual property detail pages. - If results are unexpectedly low, check whether Boligsiden's filters are reflected in the URL (some filters may be session-based). *** ### Use Cases & Business Value - **Investment screening:** Identify underpriced listings using `Per Area Price` vs. local averages - **Market timing analysis:** Use `Days Listed` and `Price Change Percentage` to find motivated sellers - **Geographic intelligence:** Map listings using `Coordinates` for spatial analysis - **Agency benchmarking:** Track which realtors are most active in specific areas *** ### Conclusion The **Boligsiden.dk Property Scraper** turns Denmark's largest real estate portal into a structured data source — no manual copying, no browser automation setup required. With pricing, location, market timing, and property characteristics all in one record, it's a practical tool for anyone working with Danish real estate data. # Actor input Schema ## `urls` (type: `array`): Add the URLs of the tour list urls you want to scrape. You can paste URLs one by one, or use the Bulk edit section to add a prepared list. ## `ignore_url_failures` (type: `boolean`): If true, the scraper will continue running even if some URLs fail to be scraped. ## `max_items_per_url` (type: `integer`): The maximum number of items to scrape per URL. ## Actor input object example ```json { "urls": [ "https://www.boligsiden.dk/tilsalg?page=2" ], "ignore_url_failures": true, "max_items_per_url": 20 } ``` # 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.boligsiden.dk/tilsalg?page=2" ], "ignore_url_failures": true, "max_items_per_url": 20 }; // Run the Actor and wait for it to finish const run = await client.actor("stealth_mode/boligsiden-property-search-scraper").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.boligsiden.dk/tilsalg?page=2"], "ignore_url_failures": True, "max_items_per_url": 20, } # Run the Actor and wait for it to finish run = client.actor("stealth_mode/boligsiden-property-search-scraper").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.boligsiden.dk/tilsalg?page=2" ], "ignore_url_failures": true, "max_items_per_url": 20 }' | apify call stealth_mode/boligsiden-property-search-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=stealth_mode/boligsiden-property-search-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Boligsiden Property Search Scraper", "description": "Scrape property listings from Boligsiden.dk — Denmark's leading real estate portal. Extract prices, addresses, room counts, market duration, and 18+ fields per listing. Perfect for analysts, investors, and proptech developers.", "version": "0.0", "x-build-id": "CdXR0eqX5l4MQWrHJ" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/stealth_mode~boligsiden-property-search-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-stealth_mode-boligsiden-property-search-scraper", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/stealth_mode~boligsiden-property-search-scraper/runs": { "post": { "operationId": "runs-sync-stealth_mode-boligsiden-property-search-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/stealth_mode~boligsiden-property-search-scraper/run-sync": { "post": { "operationId": "run-sync-stealth_mode-boligsiden-property-search-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "urls": { "title": "URLs of the tour list urls to scrape", "type": "array", "description": "Add the URLs of the tour list urls you want to scrape. You can paste URLs one by one, or use the Bulk edit section to add a prepared list.", "items": { "type": "string" } }, "ignore_url_failures": { "title": "Continue running even if some URLs fail to be scraped", "type": "boolean", "description": "If true, the scraper will continue running even if some URLs fail to be scraped." }, "max_items_per_url": { "title": "Max items per URL", "type": "integer", "description": "The maximum number of items to scrape per URL." } } }, "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 } } } } } } } } } } ```