# Belgrade Rental Market Monitor (`data_actors/belgrade-rental-monitor`) Actor Collects long-term apartment rental listings in Belgrade from multiple real estate platforms (Halo Oglasi, 4zida, Nekretnine.rs, CityExpert), normalizes data into a unified structure for market analysis, price tracking, and rental monitoring. - **URL**: https://apify.com/data\_actors/belgrade-rental-monitor.md - **Developed by:** [Victor](https://apify.com/data_actors) (community) - **Categories:** Real estate, Agents, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $0.0005 / actor start 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 ## Belgrade Rental Market Monitor Collect structured long-term rental listings from Belgrade's top real estate portals in one unified dataset. Built for agencies, analysts, developers, and anyone who needs reliable, normalized rental market data. --- ### What it does This Actor scrapes publicly available apartment rental listings from multiple Serbian real estate websites, normalizes all data into a single consistent schema, and saves results to an Apify Dataset. Each run produces a clean, analysis-ready table with price, area, location, rooms, floor, furnishing status, and more. **Currently supported sources:** | Source | URL | |---|---| | Halo Oglasi | halooglasi.com | --- ### Who it's for - **Real estate agencies** — monitor competitor pricing, find new listings faster - **Relocation services** — get fresh inventory for clients moving to Belgrade - **Market analysts & investors** — track price trends by district, rooms, and area - **Telegram bot developers** — power rental alert bots with fresh data - **Researchers & dashboards** — feed BI tools, notebooks, and reporting pipelines - **Individuals** — find apartments with exact criteria without clicking through multiple sites --- ### Input ```json { "city": "Belgrade", "districts": ["Novi Beograd", "Vračar"], "min_price": 400, "max_price": 1000, "min_area_m2": 40, "max_area_m2": 100, "rooms": ["2", "2.5", "3"], "max_results_per_source": 200, "scrape_details": true, "include_images": false, "proxy_enabled": true, "request_delay_ms": 1500 } ```` #### Input fields | Field | Type | Default | Description | |---|---|---|---| | `city` | string | `"Belgrade"` | City to search (only Belgrade currently) | | `districts` | array | `[]` | Filter by districts — empty = all. E.g. `["Novi Beograd", "Vračar"]` | | `min_price` | integer | null | Minimum rent in EUR | | `max_price` | integer | null | Maximum rent in EUR | | `min_area_m2` | integer | null | Minimum apartment size in m² | | `max_area_m2` | integer | null | Maximum apartment size in m² | | `rooms` | array | `[]` | Filter by rooms: `studio`, `1`, `1.5`, `2`, `2.5`, `3`, `4+` | | `max_results_per_source` | integer | `500` | Cap on listings per source (1–5000) | | `scrape_details` | boolean | `true` | Visit each listing's page for full description, heating, furnishing, etc. | | `include_images` | boolean | `false` | Collect image URLs (requires `scrape_details: true`) | | `proxy_enabled` | boolean | `true` | Use Apify proxy for requests | | `request_delay_ms` | integer | `1000` | Milliseconds between requests | **Supported districts:** Novi Beograd, Vračar, Stari Grad, Zemun, Savski Venac, Palilula, Voždovac, Zvezdara, Čukarica, Rakovica *** ### Output Each record in the dataset represents one rental listing. #### Example output record ```json { "source": "halo_oglasi", "external_id": "halo_oglasi_87654321", "listing_url": "https://www.halooglasi.com/nekretnine/izdavanje-stanova/dvoiposobni-stan/87654321", "title": "Dvoiposobni stan na Novom Beogradu, Blok 45", "description": "Namešteni dvoiposobni stan od 65 kvadrata na 4. spratu. Centralno grejanje, lift, parking...", "city": "Belgrade", "district": "Novi Beograd", "neighborhood": "Blok 45", "address": null, "price": 650, "currency": "EUR", "area_m2": 65.0, "rooms": "2.5", "floor": 4, "total_floors": 8, "heating": "Centralno grejanje", "furnished": true, "property_type": "apartment", "rent_type": "long_term", "advertiser_type": "agency", "agency_name": "ABC Nekretnine", "contact_name": null, "image_urls": [], "published_at": "2024-01-15", "updated_at": null, "scraped_at": "2024-01-20T14:30:00+00:00", "price_per_m2": 10.0, "normalized_title": "dvoiposobni stan na novom beogradu blok 45", "duplicate_key": "a1b2c3d4e5f67890", "possible_duplicate": false, "raw_data": null } ``` #### Output fields reference | Field | Type | Description | |---|---|---| | `source` | string | Data source: `halo_oglasi` / `4zida` / `nekretnine_rs` / `cityexpert` | | `external_id` | string | Stable unique ID: `{source}_{listing_id}` | | `listing_url` | string | Full URL to the original listing | | `title` | string | Listing title | | `description` | string | Full description (if `scrape_details: true`) | | `city` | string | City (always Belgrade in V1) | | `district` | string | Belgrade district / municipality | | `neighborhood` | string | Sub-district / neighborhood (if available) | | `address` | string | Street address (if publicly shown) | | `price` | integer | Monthly rent, numeric | | `currency` | string | `EUR` (most Belgrade listings) or `RSD` | | `area_m2` | float | Apartment area in m² | | `rooms` | string | Normalized rooms: `studio` / `1` / `1.5` / `2` / `2.5` / `3` / `4+` | | `floor` | integer | Floor number (0 = ground, -1 = basement) | | `total_floors` | integer | Total floors in building | | `heating` | string | Heating type | | `furnished` | boolean | `true` / `false` / `null` | | `property_type` | string | `apartment` / `studio` / `room` / `house` | | `rent_type` | string | Always `long_term` in V1 | | `advertiser_type` | string | `agency` / `private` / `unknown` | | `agency_name` | string | Real estate agency name | | `contact_name` | string | Contact person name (if publicly available) | | `image_urls` | array | Photo URLs (populated when `include_images: true`) | | `published_at` | string | Date listing was published (ISO 8601) | | `updated_at` | string | Date listing was last updated | | `scraped_at` | string | Timestamp when this record was collected | | `price_per_m2` | float | `price / area_m2`, rounded to 2 decimals | | `normalized_title` | string | Lowercase title, stripped for deduplication | | `duplicate_key` | string | Hash used for cross-source fuzzy deduplication (V2) | | `possible_duplicate` | boolean | `false` in V1; V2 will flag cross-run duplicates | | `raw_data` | object | Original scraped fields for debugging (debug builds only) | *** ### Use cases #### Rental price monitoring Run on a schedule (daily or weekly). Compare `price` and `scraped_at` across runs to track price changes by district and room count. #### Relocation services Filter by `district`, `rooms`, `max_price`, and `furnished` to build a curated shortlist for clients. Export to CSV or Google Sheets. #### Real estate agency competitive analysis Identify new listings before they go viral. Track how long listings stay on the market (coming in V2 with `first_seen_at` / `days_on_market`). #### Telegram bot / email alerts Trigger on new listings matching criteria. The `external_id` field makes it easy to track which listings you've already seen. #### Research & dashboards Feed directly into Jupyter notebooks, Metabase, Grafana, or any BI tool via Apify's dataset API. Calculate median rent by district, price per m² trends, etc. #### Price analytics Use `price_per_m2` for comparable analysis across differently-sized apartments. Group by `district` + `rooms` for market benchmarks. *** ### Limitations - Data depends on source site availability and HTML structure. If a site changes its layout, the scraper may need an update. - Some fields (`address`, `contact_name`, `heating`, `floor`) may be `null` — not all listings show all details. - Listings may appear on multiple sources. Cross-source deduplication is planned for V2 (`possible_duplicate` field). - This scraper collects only publicly visible data — no paywalled content, no login-gated listings. - Prices are as listed by the advertiser — not verified or adjusted for utilities. - Images are only collected when `include_images: true`. Large image counts increase run time. *** ### Roadmap #### V2 — Price Tracking Fields: `first_seen_at`, `last_seen_at`, `is_new_listing`, `is_removed`, `old_price`, `new_price`, `price_change_pct`, `days_on_market` Store listing history across runs. Enable alerts on price drops and new listings. #### V3 — Multi-city Novi Sad, Niš, Kragujevac, Subotica. Same schema, new city selector. #### V4 — Sale listings Track apartments for sale. Calculate rent vs. buy ratios. Price per m² by district for both categories. #### V5 — Analytics summary Push aggregate stats as a separate dataset: `median_price_by_district`, `median_price_per_m2`, `listings_count_by_district`, `new_listings_count`, `price_drop_count`. *** ### Support Found a bug or selector that stopped working? Open an issue on GitHub or contact via Apify. # Actor input Schema ## `sources` (type: `array`): Real estate platforms to scrape. Currently only 'halo\_oglasi' is fully implemented. ## `city` (type: `string`): City to search in. Currently only Belgrade is supported. ## `districts` (type: `array`): Belgrade districts to filter by. Leave empty to scrape all districts. Examples: Novi Beograd, Vračar, Stari Grad, Zemun, Savski Venac, Palilula, Voždovac, Zvezdara, Čukarica, Rakovica. ## `min_price` (type: `integer`): Minimum monthly rent in EUR. Leave empty for no lower bound. ## `max_price` (type: `integer`): Maximum monthly rent in EUR. Leave empty for no upper bound. ## `min_area_m2` (type: `integer`): Minimum apartment area in square meters. ## `max_area_m2` (type: `integer`): Maximum apartment area in square meters. ## `rooms` (type: `array`): Filter by number of rooms. Leave empty for all. Valid values: studio, 1, 1.5, 2, 2.5, 3, 4+ ## `max_results_per_source` (type: `integer`): Maximum number of listings to collect from each source. ## `include_images` (type: `boolean`): Collect image URLs for each listing. Requires scrape\_details=true and increases run time. ## `scrape_details` (type: `boolean`): Visit each listing's detail page for full description, floor info, heating, furnishing, etc. Disabling this is faster but produces less data. ## `proxy_enabled` (type: `boolean`): Route requests through Apify proxy. Recommended for production runs. ## `request_delay_ms` (type: `integer`): Delay between HTTP requests in milliseconds. Increase to reduce server load. ## Actor input object example ```json { "sources": [ "halo_oglasi" ], "city": "Belgrade", "districts": [], "rooms": [], "max_results_per_source": 500, "include_images": false, "scrape_details": true, "proxy_enabled": true, "request_delay_ms": 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 = {}; // Run the Actor and wait for it to finish const run = await client.actor("data_actors/belgrade-rental-monitor").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 = {} # Run the Actor and wait for it to finish run = client.actor("data_actors/belgrade-rental-monitor").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 '{}' | apify call data_actors/belgrade-rental-monitor --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=data_actors/belgrade-rental-monitor", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Belgrade Rental Market Monitor", "description": "Collects long-term apartment rental listings in Belgrade from multiple real estate platforms (Halo Oglasi, 4zida, Nekretnine.rs, CityExpert), normalizes data into a unified structure for market analysis, price tracking, and rental monitoring.", "version": "0.1", "x-build-id": "DD2dDBk6nn4sDqAfC" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/data_actors~belgrade-rental-monitor/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-data_actors-belgrade-rental-monitor", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/data_actors~belgrade-rental-monitor/runs": { "post": { "operationId": "runs-sync-data_actors-belgrade-rental-monitor", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/data_actors~belgrade-rental-monitor/run-sync": { "post": { "operationId": "run-sync-data_actors-belgrade-rental-monitor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "sources" ], "properties": { "sources": { "title": "Sources", "type": "array", "description": "Real estate platforms to scrape. Currently only 'halo_oglasi' is fully implemented.", "items": { "type": "string", "enum": [ "halo_oglasi", "4zida", "nekretnine_rs", "cityexpert" ] }, "default": [ "halo_oglasi" ] }, "city": { "title": "City", "type": "string", "description": "City to search in. Currently only Belgrade is supported.", "default": "Belgrade" }, "districts": { "title": "Districts", "type": "array", "description": "Belgrade districts to filter by. Leave empty to scrape all districts. Examples: Novi Beograd, Vračar, Stari Grad, Zemun, Savski Venac, Palilula, Voždovac, Zvezdara, Čukarica, Rakovica.", "items": { "type": "string" }, "default": [] }, "min_price": { "title": "Minimum Price (EUR)", "minimum": 0, "type": "integer", "description": "Minimum monthly rent in EUR. Leave empty for no lower bound." }, "max_price": { "title": "Maximum Price (EUR)", "minimum": 0, "type": "integer", "description": "Maximum monthly rent in EUR. Leave empty for no upper bound." }, "min_area_m2": { "title": "Minimum Area (m²)", "minimum": 0, "type": "integer", "description": "Minimum apartment area in square meters." }, "max_area_m2": { "title": "Maximum Area (m²)", "minimum": 0, "type": "integer", "description": "Maximum apartment area in square meters." }, "rooms": { "title": "Number of Rooms", "type": "array", "description": "Filter by number of rooms. Leave empty for all. Valid values: studio, 1, 1.5, 2, 2.5, 3, 4+", "items": { "type": "string" }, "default": [] }, "max_results_per_source": { "title": "Max Results Per Source", "minimum": 1, "maximum": 5000, "type": "integer", "description": "Maximum number of listings to collect from each source.", "default": 500 }, "include_images": { "title": "Include Images", "type": "boolean", "description": "Collect image URLs for each listing. Requires scrape_details=true and increases run time.", "default": false }, "scrape_details": { "title": "Scrape Detail Pages", "type": "boolean", "description": "Visit each listing's detail page for full description, floor info, heating, furnishing, etc. Disabling this is faster but produces less data.", "default": true }, "proxy_enabled": { "title": "Use Proxy", "type": "boolean", "description": "Route requests through Apify proxy. Recommended for production runs.", "default": true }, "request_delay_ms": { "title": "Request Delay (ms)", "minimum": 0, "maximum": 10000, "type": "integer", "description": "Delay between HTTP requests in milliseconds. Increase to reduce server load.", "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 } } } } } } } } } } ```