Bezrealitky Scraper is a focused data extraction tool designed to collect structured real estate listings from the Bezrealitky platform. It helps users analyze property offers across regions, transaction types, and estate categories with clean, consistent outputs suitable for analytics and reporting.
The project delivers reliable access to housing market data, making it easier to compare listings, track trends, and build downstream applications using real-world property information.
Created by Bitbash, built to showcase our approach to Scraping and Automation!
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This project collects and structures real estate listings based on user-defined geographic and property filters. It solves the problem of manually browsing and comparing hundreds of listings by providing normalized data in a machine-friendly format.
It is built for analysts, developers, investors, and businesses that need accurate and filterable property data for decision-making.
- Supports multiple countries and administrative regions
- Filters by transaction intent such as sale or rent
- Handles different estate categories like flats and houses
- Produces consistent, structured records ready for analysis
- Designed for repeatable and scalable data collection workflows
| Feature | Description |
|---|---|
| Region-Based Filtering | Collect listings from specific countries, provinces, or states. |
| Transaction Type Control | Narrow results by sale or rental offers for precise targeting. |
| Estate Category Selection | Focus on flats, houses, or multiple property types at once. |
| Structured Output | Delivers clean, predictable fields for easy integration. |
| Scalable Queries | Designed to handle multiple regions and filters efficiently. |
| Field Name | Field Description |
|---|---|
| listingId | Unique identifier for the property listing. |
| title | Short headline describing the property. |
| price | Listed price of the property. |
| currency | Currency associated with the price. |
| transactionType | Indicates sale or rental listing. |
| estateType | Property category such as flat or house. |
| region | Geographic region or province of the property. |
| locality | City or local area name. |
| size | Property size, typically in square meters. |
| rooms | Number of rooms in the property. |
| url | Direct link to the listing detail page. |
[
{
"listingId": "BR-948271",
"title": "Modern 2-room apartment",
"price": 5200000,
"currency": "CZK",
"transactionType": "PRODEJ",
"estateType": "BYT",
"region": "praha",
"locality": "Prague 7",
"size": 54,
"rooms": 2,
"url": "https://www.bezrealitky.cz/nemovitosti/948271"
}
]
Bezrealitky/
├── src/
│ ├── main.py
│ ├── client/
│ │ ├── request_handler.py
│ │ └── response_parser.py
│ ├── filters/
│ │ ├── regions.py
│ │ ├── transaction_types.py
│ │ └── estate_types.py
│ ├── models/
│ │ └── listing.py
│ └── utils/
│ └── validators.py
├── data/
│ ├── sample_input.json
│ └── sample_output.json
├── config/
│ └── settings.example.json
├── requirements.txt
└── README.md
- Real estate analysts use it to compare regional price trends, so they can identify high-growth areas.
- Property investors use it to monitor sale listings, so they can spot undervalued opportunities.
- Rental platforms use it to aggregate rental data, so they can benchmark market rates.
- Developers use it to feed property data into dashboards, so they can build housing market tools.
- Researchers use it to study housing availability, so they can produce data-backed insights.
What regions are supported by this project? The project supports multiple countries and their administrative regions, allowing users to target specific provinces or states depending on their data needs.
Can I filter results by both transaction and estate type? Yes, transaction intent (sale or rent) and estate categories (such as flats or houses) can be combined for highly specific queries.
Is the output suitable for analytics tools? The data is structured and normalized, making it easy to import into spreadsheets, databases, or visualization platforms.
How flexible is the input configuration? Inputs are fully configurable through structured files, allowing quick adjustments without code changes.
Primary Metric: Processes hundreds of listings per region in a single execution cycle.
Reliability Metric: Consistently returns complete records with stable field coverage across regions.
Efficiency Metric: Optimized request handling minimizes redundant data processing and resource usage.
Quality Metric: High data completeness with validated fields for pricing, location, and property attributes.
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