# Dh.hu Property Search Scraper (`stealth_mode/dh-hu-property-search-scraper`) Actor Scrape detailed property listings from DH.hu with 45+ data fields including price, location, property type, agent details, and market status. Perfect for real estate professionals, market analysts, and property aggregators seeking structured Hungarian property data at scale. - **URL**: https://apify.com/stealth\_mode/dh-hu-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, 0 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. 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 ## DH.hu Property Scraper: Extract Real Estate Listings Instantly --- ### What Is DH.hu? DH.hu is Hungary's leading online real estate marketplace, hosting thousands of property listings across residential, commercial, and investment categories. The platform aggregates offerings from agents, developers, and private sellers nationwide. Manually extracting property data from search results is tedious and error-prone — the **DH.hu Property Scraper** automates the workflow, delivering clean, machine-readable records from listing pages in seconds. --- ### Overview The **DH.hu Property Scraper** extracts comprehensive property records from DH.hu search and listing pages, transforming raw web data into 45 structured fields. It is designed for: - **Real estate agents** conducting market research and competitor analysis - **Property investors** identifying trends and portfolio opportunities - **Data analysts** building Hungarian real estate datasets - **Property aggregator platforms** feeding data into custom search engines - **Appraisers and valuators** gathering comparable sales data Key strengths include bulk URL support, configurable item limits, and fault tolerance via `ignore_url_failures` — allowing uninterrupted collection even if some pages fail. --- ### Input Format The scraper accepts a JSON configuration object: ```json { "urls": [ "https://dh.hu/property-for-sale/flat-house?page=2" ], "ignore_url_failures": true, "max_items_per_url": 200 } ```` | Field | Description | Example | |---|---|---| | `urls` | Array of DH.hu property list page URLs | `["https://dh.hu/property-for-sale/flat-house?page=1"]` | | `max_items_per_url` | Maximum properties extracted per URL | `200` (default: `20`) | | `ignore_url_failures` | If `true`, scraper continues even if some URLs fail | `true` | > **Tip:** Use filtered URLs (e.g., by city, property type, price range) to collect targeted datasets. Example: `https://dh.hu/property-for-sale/flat?city=budapest&price_from=50000000` *** ### Output Format **Sample output** ```json { "address": "2030 Érd Mátyás Király utca közelében", "city_name": "Érd", "district_name": null, "contract_type_name": "Eladó", "property_type_name": "Ház", "reference_number": "HZ042590", "hide_street": 1, "description": "Eladó Érd Tusculánum városrészében egy új építésű, összesen 175 négyzetméter belső hasznos alapterületű családi ház. A 2026-ban épülő ingatlan modern kialakítású, 4 szobával, nappalival és étkezővel rendelkezik, így ideális választás nagyobb családok számára.\r\nAz ingatlan Érd egyik csendes, aszfaltozott utcájában található, amely garantálja a nyugodt és zavartalan otthoni pihenést. A telek teljes közműhálózattal rendelkezik, ovaoda, iskola , tömegközlekedés gyalogosan is elérhető.\r\n\r\nA ház több szempontból is kiváló ajánlat: dupla garázsa biztosítja a kényelmes parkolást akár két autó számára. Az épülethez tartozó telek területe 1 062 négyzetméter, amely bőséges helyet biztosít kertrendezésre vagy akár további építésekhez is. Kulcsrakész ÁR: 169M\r\n", "alternative_address": "Mátyás Király utca közelében", "price": "169 000 000 Ft", "combined_target_price": "169000000.00", "combined_target_price_currency": 3040100000005, "combined_target_price_currency_text": "HUF", "area": "175", "alias": "/property/HZ042590/for-sale-house-pest-county-erd-matyas-kiraly-utca--kozeleben", "can_add_to_fav": 1, "newly": 0, "rooms": 5, "bathrooms": 0, "cover_image": "https://cdn2.fs2.matrixhu.com/storage/production/fileID/6a292f666d2edc35d15c3f43/fileVersionKey/NhZmQ1NmE1Njc5YjIzZG/fileVersionID/propertyListCard/fileVersionForceKey/b8037fa241cc9d024c2a0d729eaa0dfd", "lat": 47.383094574614, "lng": 18.901739672123, "is_exclusive": false, "is_price_drop": false, "contracts_id": 19410000259939, "is_new": true, "combined_target_price_m2_in_huf": null, "properties_is_osp": 0, "properties_property_type_id": 4080100000002, "agent_name": "Nyéki Zoltán", "is_newly": false, "is_sold": false, "is_popular": false, "is_coming_soon": false, "enabled_otthon_start": false, "images": [ "https://cdn2.fs2.matrixhu.com/storage/production/fileID/6a292f666d2edc35d15c3f43/fileVersionKey/NhZmQ1NmE1Njc5YjIzZG/fileVersionID/propertyListCard/fileVersionForceKey/b8037fa241cc9d024c2a0d729eaa0dfd", "https://cdn6.fs2.matrixhu.com/storage/production/fileID/6a292f6f6d2edc35d15c3f44/fileVersionKey/g4ZWQyMzI3ZDVjMmE4Ym/fileVersionID/propertyListCard/fileVersionForceKey/280c94355c7499027afdc49d6377a504", "https://cdn4.fs2.matrixhu.com/storage/production/fileID/6a292f746d2edc364970a9e3/fileVersionKey/E0OWRmNTMxNzI2YmE2MD/fileVersionID/propertyListCard/fileVersionForceKey/8b82e56557d55e34978526ac9d8ccd46", "https://cdn9.fs2.matrixhu.com/storage/production/fileID/6a292f7a6d2edc34475f3733/fileVersionKey/IyMDIxYWFmNGNmMjZjOD/fileVersionID/propertyListCard/fileVersionForceKey/d64b9debd48dda7d6554b189ccae02a8", "https://cdn4.fs2.matrixhu.com/storage/production/fileID/6a292f846d2edc323714dba5/fileVersionKey/NmMDQ4MDhlOTFmYTViMD/fileVersionID/propertyListCard/fileVersionForceKey/d856608694cfcfb4e7aaf404e22dc3c1" ], "city_filter": "Érd", "price_numeric": "169000000", "price_original": "169 000 000", "price_original_currency": "HUF", "favorite": 0, "phase_id": null, "min_price_orig": null, "max_price_orig": null, "number_of_properties": null, "planned_end_date": null, "properties_primary_market_phase_id": null, "city_id": null, "from_url": "https://dh.hu/property-for-sale/flat-house" } ``` Each scraped property returns a rich record with 45+ fields organized by category: #### Property Identification & Metadata | Field | Meaning | Use Case | |---|---|---| | `Reference Number` | Unique DH.hu property ID | Database key, linking records | | `Address` | Full street address | Geocoding, location verification | | `Alternative Address` | Secondary address format | Display flexibility | | `Hide Street` | Boolean: hides street in listing | Privacy flag for owners | | `Alias` | Property nickname or name | User-friendly reference | | `City Name` | Municipality (e.g., Budapest) | Regional filtering | | `City ID` | Internal city identifier | Database join field | | `District Name` | District/zone (e.g., District 5) | Neighborhood classification | | `City Filter` | Pre-formatted city filter for URLs | URL generation | #### Property Classification | Field | Meaning | Use Case | |---|---|---| | `Property Type Name` | Category (flat, house, land, etc.) | Segment analysis | | `Properties Property Type ID` | Type numeric ID | Data normalization | | `Contract Type Name` | Sale, rent, lease, investment | Market segment | | `Contracts ID` | Contract type numeric ID | Data indexing | #### Physical Characteristics | Field | Meaning | Use Case | |---|---|---| | `Rooms` | Number of bedrooms | Property filtering | | `Bathrooms` | Number of bathrooms | Amenity comparison | | `Area` | Total floor area in m² | Space analysis, price/m² calc | | `Combined Target Price M2 In HUF` | Price per square meter (HUF) | Valuation benchmarking | #### Pricing & Financial Data | Field | Meaning | Use Case | |---|---|---| | `Price` | Listing price (formatted) | Display, comparison | | `Price Numeric` | Price as number (no currency) | Calculations | | `Price Original` | Original listed price | Price change tracking | | `Price Original Currency` | Original price currency code | Multi-currency support | | `Combined Target Price` | Standardized price field | Unified pricing | | `Combined Target Price Currency` | Currency code (HUF, EUR, etc.) | Currency normalization | | `Combined Target Price Currency Text` | Currency label (e.g., "HUF") | Display text | | `Min Price Orig` | Minimum price in range | Price range analysis | | `Max Price Orig` | Maximum price in range | Portfolio pricing | | `Is Price Drop` | Boolean: price recently reduced | Deal alerts | #### Geographic & Spatial Data | Field | Meaning | Use Case | |---|---|---| | `Latitude` | GPS latitude coordinate | Map plotting, proximity search | | `Longitude` | GPS longitude coordinate | Geospatial analysis | #### Listing Status & Flags | Field | Meaning | Use Case | |---|---|---| | `Is New` | Boolean: newly listed property | New inventory alerts | | `Is Newly` | Boolean: newly built/renovated | Property age classification | | `Newly` | Additional new flag variant | Redundancy handling | | `Is Sold` | Boolean: property sold | Inventory status | | `Is Price Drop` | Boolean: price reduced recently | Deal identification | | `Is Exclusive` | Boolean: exclusive listing | Agent comparison | | `Is Popular` | Boolean: trending/popular property | Demand indicators | | `Is Coming Soon` | Boolean: pre-listing status | Future inventory | | `Enabled Otthon Start` | Boolean: featured promotion flag | Marketing tier | #### Media & Visual Content | Field | Meaning | Use Case | |---|---|---| | `Cover Image` | Primary listing photo URL | Thumbnail generation | | `Images` | Array of all property images | Gallery creation | #### Agent & Seller Information | Field | Meaning | Use Case | |---|---|---| | `Agent Name` | Real estate agent name | Agent performance tracking | #### Description & Content | Field | Meaning | Use Case | |---|---|---| | `Description` | Full property description text | Search indexing, SEO | #### Market & Project Data | Field | Meaning | Use Case | |---|---|---| | `Phase ID` | Development phase ID | New project tracking | | `Properties Primary Market Phase ID` | Phase identifier | Project segmentation | | `Planned End Date` | Project completion date | Timeline tracking | | `Properties Is OSP` | Boolean: off-plan property | Investment property flag | | `Number Of Properties` | Count in project | Portfolio size | #### User Interaction Flags | Field | Meaning | Use Case | |---|---|---| | `Can Add To Fav` | Boolean: favorites feature available | UI state | | `Favorite` | Boolean: user marked as favorite | Preference tracking | *** ### How to Use 1. **Prepare URLs** — Navigate DH.hu to your target search (e.g., flats for sale in Budapest). Copy the page URL. 2. **Add filters (optional)** — Use DH.hu's filters (price, rooms, area, district) to refine your dataset: ``` https://dh.hu/property-for-sale/flat?city=budapest&rooms=2&price_from=50000000&price_to=150000000 ``` 3. **Configure input** — Paste URLs into the `urls` array and set `max_items_per_url`: ```json { "urls": ["https://dh.hu/property-for-sale/flat?city=budapest&page=1"], "max_items_per_url": 200, "ignore_url_failures": true } ``` 4. **Run the scraper** — Start execution and monitor progress in the run log. 5. **Export results** — Download as JSON, CSV, or Excel for analysis or database import. **Best practices:** - Use pagination URLs (`?page=1`, `?page=2`) to collect multi-page datasets. - Set `ignore_url_failures: true` for large runs to prevent interruptions. - Filter by property type or price range to focus your dataset. **Common troubleshooting:** - If no properties appear, verify the URL is a *search results* page, not a single property detail page. - Ensure the page has loaded fully before copying the URL. *** ### Use Cases & Business Value - **Market analysis:** Track price trends, inventory levels, and regional demand across Hungary - **Investment intelligence:** Identify emerging neighborhoods and portfolio opportunities - **Competitor benchmarking:** Monitor agent listings and pricing strategies - **Data aggregation:** Feed DH.hu listings into custom property portals and search engines - **Valuation support:** Gather comparable sales data for appraisals and market analysis - **Lead generation:** Identify high-velocity listings and market-active agents The DH.hu Property Scraper eliminates hours of manual data gathering, delivering structured records ready for Excel, Python, or database integration. Analysts, investors, and real estate professionals save time while gaining the data advantage. *** ### Conclusion The **DH.hu Property Scraper** is the efficient solution for anyone needing structured Hungarian real estate data. With 45+ output fields covering location, pricing, property details, and market status, it transforms DH.hu's listing pages into actionable insights. Whether you're researching investment opportunities, conducting market analysis, or building a property aggregator, this scraper delivers the data infrastructure to succeed. # Actor input Schema ## `urls` (type: `array`): Add the URLs of the property 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://dh.hu/property-for-sale/flat-house?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://dh.hu/property-for-sale/flat-house?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/dh-hu-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://dh.hu/property-for-sale/flat-house?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/dh-hu-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://dh.hu/property-for-sale/flat-house?page=2" ], "ignore_url_failures": true, "max_items_per_url": 20 }' | apify call stealth_mode/dh-hu-property-search-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=stealth_mode/dh-hu-property-search-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Dh.hu Property Search Scraper", "description": "Scrape detailed property listings from DH.hu with 45+ data fields including price, location, property type, agent details, and market status. Perfect for real estate professionals, market analysts, and property aggregators seeking structured Hungarian property data at scale.", "version": "0.0", "x-build-id": "9b2gJpLZPJf94VcX6" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/stealth_mode~dh-hu-property-search-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-stealth_mode-dh-hu-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/stealth_mode~dh-hu-property-search-scraper/runs": { "post": { "operationId": "runs-sync-stealth_mode-dh-hu-property-search-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/stealth_mode~dh-hu-property-search-scraper/run-sync": { "post": { "operationId": "run-sync-stealth_mode-dh-hu-property-search-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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 property list urls to scrape", "type": "array", "description": "Add the URLs of the property 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 } } } } } } } } } } ```