This project provides a reliable solution for extracting real estate data from Boligsiden.dk, enabling professionals to analyze pricing trends, evaluate locations, and collect structured insights. It simplifies property data collection for research, analytics, and investment use cases through a fast and flexible scraper.
Created by Bitbash, built to showcase our approach to Scraping and Automation!
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Boligsiden.dk Scraper gathers structured information about property listings across Denmark, helping users make data-driven decisions. It solves the challenge of manually collecting real estate insights by automating data extraction and organizing it into clean, ready-to-use formats.
- Fetches detailed residential and commercial listing data across Denmark.
- Helps analysts and investors identify price trends and evaluate opportunities.
- Provides structured datasets suitable for research, modeling, and real estate analysis.
- Supports filtering by housing type, location, and listing status.
- Offers fast, scalable data collection for market monitoring.
| Feature | Description |
|---|---|
| Location-based search | Extract listings by city name, postal code, or region. |
| Housing type filtering | Target apartments, houses, villas, and other categories. |
| Status selection | Choose between on-market, rental, or previously sold properties. |
| Structured output | Receive clean datasets containing essential property information. |
| Flexible result limits | Scrape only the number of listings you need. |
| Proxy support | Avoid blocking and ensure stable, continuous data extraction. |
| Field Name | Field Description |
|---|---|
| url | Link to the property listing page. |
| name | Full property title including address. |
| numberOfRooms | Total rooms in the property. |
| postalCode | Postal code of the listing. |
| streetAddress | Street address of the property. |
| addressLocality | City or locality name. |
| price | Property price in DKK. |
| priceCurrency | Currency code. |
| description | Text description of the listing. |
| image | URL of the main listing image. |
[
{
"url": "https://www.boligsiden.dk/adresse/floravej-4-9000-aalborg-08512005___4_______?udbud=9edf29ca-5533-42e6-8370-e746377884fc",
"name": "Floravej 4, 9000 Aalborg",
"numberOfRooms": 3,
"postalCode": 9000,
"streetAddress": "Floravej 4",
"addressLocality": "Aalborg",
"price": 2495000,
"priceCurrency": "DKK",
"description": "YDERST VELBELIGGENDE EJENDOM I HASSERIS· Allerbedste beliggenhed på blind vej\n· Bo lige op til grønne arealer...",
"image": "https://images.boligsiden.dk/images/case/9edf29ca-5533-42e6-8370-e746377884fc/600x400/df405b1a-7ebf-4435-b726-63d21441b199.webp"
}
]
Boligsiden.dk Scraper/
├── src/
│ ├── runner.py
│ ├── extractors/
│ │ ├── property_parser.py
│ │ └── utils_formatting.py
│ ├── outputs/
│ │ └── exporters.py
│ └── config/
│ └── settings.example.json
├── data/
│ ├── inputs.sample.json
│ └── sample_output.json
├── requirements.txt
└── README.md
- Real estate analysts use it to gather pricing and availability data so they can build accurate market models.
- Investors use it to identify profitable opportunities and monitor trends across Danish cities.
- Property developers use it to study local demand and competitor listings to guide development decisions.
- Researchers use it to analyze long-term real estate trends and compile datasets for academic studies.
- Marketing teams use it to track neighborhood-level property activity for targeted campaigns.
Q: Can I filter data by housing type or listing status? Yes, the scraper supports both housing type and listing status filters to refine your data collection.
Q: What is the maximum number of results I can extract?
You can specify any number using the maxItems parameter, allowing small or large-scale runs.
Q: Is proxy support required? Proxy configuration is optional but recommended for stability and uninterrupted scraping.
Q: What formats can I download the data in? You can export the dataset as JSON, JSONL, CSV, Excel, NDJSON, or HTML table format.
Primary Metric: Handles large batches efficiently, processing hundreds of listings within seconds under optimized configurations.
Reliability Metric: Maintains a stable success rate above 98% thanks to resilient extraction logic and filtering options.
Efficiency Metric: Optimized parsing minimizes resource usage, enabling smooth execution even on mid-tier setups.
Quality Metric: Produces consistently structured datasets with high completeness, capturing over 95% of available listing details during typical runs.
"Bitbash is a top-tier automation partner, innovative, reliable, and dedicated to delivering real results every time." Nathan Pennington
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"Bitbash delivers outstanding quality, speed, and professionalism, truly a team you can rely on." Eliza
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"Exceptional results, clear communication, and flawless delivery. Syed
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