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.

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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

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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

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Input

{
  "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

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Output

Each record in the dataset represents one rental listing.

Example output record

{
  "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)

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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.

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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.

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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.

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Support

Found a bug or selector that stopped working? Open an issue on GitHub or contact via Apify.