Hostelworld Scraper is a data extraction tool that collects detailed hostel listings, prices, ratings, and facilities from Hostelworld. It helps teams turn raw accommodation data into structured insights for analysis, comparison, and decision-making in the travel space.
Created by Bitbash, built to showcase our approach to Scraping and Automation!
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This project extracts comprehensive hostel information from Hostelworld search results and property pages. It solves the problem of manually gathering scattered accommodation data by delivering clean, structured datasets ready for analysis. It’s built for developers, analysts, and travel-focused businesses that need reliable hostel data at scale.
- Collects structured hostel data from multiple search URLs in one run
- Normalizes pricing, ratings, and facilities into consistent fields
- Supports both dormitory and private room listings
- Designed for scalable, repeatable data collection workflows
| Feature | Description |
|---|---|
| Detailed property extraction | Captures hostel name, type, status, and full descriptive overview. |
| Pricing intelligence | Extracts current prices, averages, and promotional discounts. |
| Rating breakdowns | Includes overall scores and category-level ratings like security and cleanliness. |
| Room-level data | Separates dorms and private rooms with capacity and pricing details. |
| Facilities categorization | Groups amenities into clear, user-friendly categories. |
| Location precision | Provides latitude, longitude, and structured address fields. |
| Flexible inputs | Supports multiple search URLs with optional item limits. |
| Stable collection | Designed for efficient, large-scale data runs. |
| Field Name | Field Description |
|---|---|
| searchUrl | Source search URL used to collect the listing. |
| id | Unique property identifier. |
| name | Hostel or property name. |
| starRating | Star classification if available. |
| overallRating | Overall guest rating and total review count. |
| ratingBreakdown | Detailed scores for security, staff, location, and more. |
| location | Geolocation coordinates and address details. |
| propertyInfo | Property type, promotion status, and descriptive overview. |
| pricing | Lowest available prices and applied promotions. |
| rooms | Available dormitory and private room configurations. |
| facilities | Categorized list of amenities and services. |
| cancellation | Cancellation rules and eligibility details. |
[
{
"searchUrl": "https://www.hostelworld.com/pwa/wds/s?q=Tokyo,Japan",
"id": 265421,
"name": "UNPLAN Kagurazaka",
"overallRating": {
"overall": 93,
"numberOfRatings": "1294"
},
"location": {
"latitude": 35.7050654,
"longitude": 139.7313228,
"district": "Shinjuku City"
},
"pricing": {
"lowestPricePerNight": {
"value": "229.70",
"currency": "CNY"
}
}
}
]
Hostelworld Scraper/
├── src/
│ ├── main.js
│ ├── extractors/
│ │ ├── searchParser.js
│ │ ├── propertyParser.js
│ │ └── ratingParser.js
│ ├── utils/
│ │ ├── helpers.js
│ │ └── validators.js
│ └── config/
│ └── settings.example.json
├── data/
│ ├── input.sample.json
│ └── output.sample.json
├── package.json
└── README.md
- Travel analysts use it to monitor hostel pricing trends, so they can identify seasonal demand shifts.
- Accommodation platforms use it to compare hostel offerings, so they can build competitive comparison tools.
- Market researchers use it to analyze ratings and reviews, so they can assess service quality across regions.
- Developers use it to power travel dashboards, so users get up-to-date hostel insights.
Does this scraper support multiple cities or countries? Yes. You can pass multiple search URLs covering different cities or regions in a single run.
What formats can the extracted data be used in? The output is structured JSON, making it easy to convert into CSV, spreadsheets, or database imports.
Are both dorm and private room prices included? Yes. The scraper separates dormitory and private room data, including capacity and average pricing.
How does it handle incomplete listings? If certain fields are unavailable, the scraper still returns the remaining structured data without breaking the dataset.
Primary Metric: Processes dozens of hostel listings per minute depending on listing depth.
Reliability Metric: Maintains a high success rate across repeated runs with consistent data structures.
Efficiency Metric: Optimized extraction flow minimizes redundant requests and memory usage.
Quality Metric: Produces highly complete records, capturing core property, pricing, and facility data in most listings.
"Bitbash is a top-tier automation partner, innovative, reliable, and dedicated to delivering real results every time." Nathan Pennington
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"Exceptional results, clear communication, and flawless delivery. Syed
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