A production-ready scraper that collects structured property listings from Argenprop search result pages. It helps real estate professionals, analysts, and investors transform large volumes of listings into clean, analysis-ready data for smarter decisions.
Created by Bitbash, built to showcase our approach to Scraping and Automation!
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The Argenprop Property Search Scraper extracts comprehensive real estate listing data from Argenprop search result pages and delivers it in a structured format. It eliminates the need for manual browsing and copy-paste workflows across thousands of listings. This project is designed for real estate agencies, investors, analysts, and researchers who rely on accurate market data.
- Processes multiple search result pages in one run
- Handles pagination automatically for large result sets
- Normalizes listing data for analysis and storage
- Designed for scalability across cities and property types
- Focused on reliability and data completeness
| Feature | Description |
|---|---|
| Bulk search scraping | Extracts data from multiple search result URLs in a single run. |
| Filter-based discovery | Supports location, property type, price, area, and room filters. |
| Pagination handling | Automatically navigates paginated result pages. |
| Image collection | Gathers all available listing images per property. |
| Retry logic | Improves reliability when pages fail to load. |
| Configurable limits | Controls how many listings are collected per run. |
| Field Name | Field Description |
|---|---|
| id | Unique identifier of the property listing. |
| title | Listing headline describing the property. |
| url | Direct link to the property detail page. |
| price | Price text including currency and conditions. |
| address | Location or street information of the property. |
| details | Structured specs such as area, rooms, and bathrooms. |
| description | Full textual description provided by the seller or agent. |
| images | Array of image URLs associated with the listing. |
| from_url | Search page URL where the property was found. |
[
{
"id": "18205265",
"title": "Departamento en Alquiler en Palermo, Capital Federal",
"url": "https://www.argenprop.com/departamento-en-alquiler-en-palermo-2-ambientes--18205265",
"price": "$ 630.000 + $149.000 expensas",
"address": "Borges 2400",
"details": [
"42 m² cubie.",
"1 dorm.",
"1 baño"
],
"description": "Alquiler de departamento 2 ambientes con balcón en Palermo.",
"images": [
"https://www.argenprop.com/static-content/example_1.jpg",
"https://www.argenprop.com/static-content/example_2.jpg"
],
"from_url": "https://www.argenprop.com/departamentos/alquiler?pagina-2"
}
]
Argenprop Property Search Scraper/
├── src/
│ ├── runner.py
│ ├── fetcher/
│ │ └── search_pages.py
│ ├── parsers/
│ │ ├── listing_parser.py
│ │ └── image_parser.py
│ ├── config/
│ │ └── settings.example.json
│ └── utils/
│ └── retry.py
├── data/
│ ├── sample_input.json
│ └── sample_output.json
├── requirements.txt
└── README.md
- Real estate agencies use it to monitor competitor listings so they can adjust pricing strategies.
- Property investors use it to analyze price-per-square-meter trends to identify undervalued assets.
- Market analysts use it to study regional housing supply and demand patterns.
- Proptech platforms use it to aggregate listings and power comparison tools.
- Researchers use it to analyze urban development and housing dynamics.
Does this scraper work with individual property URLs? No, it is designed to work with search result pages that contain multiple listings.
Can I filter properties by location and price? Yes, location, price range, area range, and property characteristics can be configured before scraping.
How do I avoid duplicate listings? Each property includes a unique id field that can be used for deduplication.
What happens if a page fails to load? The scraper retries failed requests automatically based on the configured retry limit.
Primary Metric: Average extraction of 20–30 listings per minute per search page.
Reliability Metric: Over 97% successful page processing across multi-page searches.
Efficiency Metric: Low memory footprint with linear scaling as result pages increase.
Quality Metric: High data completeness with consistent fields across listings.
"Bitbash is a top-tier automation partner, innovative, reliable, and dedicated to delivering real results every time." Nathan Pennington
|
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