Airbnb Properties Scraper

Airbnb Properties Scraper collects detailed rental and property information from Airbnb listings at scale. It helps teams and researchers gather structured property data, pricing insights, and guest-related details efficiently. Built for reliability, it supports large datasets and flexible filtering for real-world analysis.

     

Created by Bitbash, built to showcase our approach to Scraping and Automation!
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Introduction

This project extracts comprehensive property and rental listing data from Airbnb in a structured and analysis-ready format. It solves the challenge of manually collecting large volumes of listing information by automating data retrieval with configurable filters. It’s designed for data analysts, real estate professionals, travel platforms, and developers who need consistent Airbnb property data.

Flexible Property Discovery

• Collects thousands of property records in a single run
• Supports advanced filters for precise targeting
• Outputs clean, structured data formats
• Handles large-scale data extraction reliably

Features

Feature	Description
Advanced Filters	Narrow results by location, price, dates, and guest count.
Property Details	Extracts room types, bathrooms, bedrooms, and amenities.
Pricing Insights	Captures base price, discounts, and availability ranges.
Guest Capacity	Retrieves supported adults, children, infants, and pets.
Review Data	Collects review counts and related feedback metadata.
Scalable Output	Supports large datasets with tens of thousands of records.

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What Data This Scraper Extracts

Field Name	Field Description
location	Geographic area or city of the property.
price	Base listing price per stay or per night.
discounted_price	Reduced price when discounts apply.
rooms	Room configuration and room types.
bathrooms	Number of bathrooms available.
bedrooms	Number of bedrooms in the property.
beds	Total number of beds offered.
guests	Maximum number of allowed guests.
pets_allowed	Indicates if pets are permitted.
reviews	Review count and rating-related data.
availability_dates	Start and end dates for booking.

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Example Output

[
  {
    "location": "Paris, France",
    "price": 180,
    "discounted_price": 150,
    "rooms": "Entire apartment",
    "bedrooms": 2,
    "bathrooms": 1,
    "beds": 3,
    "guests": 4,
    "pets_allowed": false,
    "reviews": 128,
    "availability_dates": {
      "start": "2025-03-01",
      "end": "2025-03-31"
    }
  }
]

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Directory Structure Tree

Airbnb Properties Actor/
├── src/
│   ├── main.py
│   ├── scrapers/
│   │   ├── listings_scraper.py
│   │   └── reviews_scraper.py
│   ├── filters/
│   │   └── search_filters.py
│   ├── utils/
│   │   └── helpers.py
│   └── config/
│       └── settings.example.json
├── data/
│   ├── sample_input.json
│   └── sample_output.json
├── requirements.txt
└── README.md

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Use Cases

• Market analysts use it to collect Airbnb listing data, so they can evaluate pricing trends across regions.
• Real estate investors use it to compare property features, helping them identify profitable locations.
• Travel platforms use it to aggregate rental data, enabling better recommendations for users.
• Data scientists use it to build datasets, so they can train pricing or demand prediction models.

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FAQs

Does this scraper support large-scale data extraction? Yes, it’s built to handle high-volume runs and has been used for datasets exceeding tens of thousands of listings.

Can I filter listings by price and guest count? Absolutely. Minimum and maximum price, guest numbers, and other parameters are fully configurable.

What output formats are supported? The scraper produces structured data suitable for JSON-based workflows and easy conversion to other formats.

Is proxy support available? Custom networking and proxy configurations can be added for advanced usage scenarios.

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Performance Benchmarks and Results

Primary Metric: Average extraction rate of several hundred listings per minute, depending on filter complexity.

Reliability Metric: Consistent completion rate above 95% across large data collection runs.

Efficiency Metric: Optimized data requests minimize redundant calls and reduce processing overhead.

Quality Metric: High data completeness with full property, pricing, and guest-related fields captured.

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