Australia Cloudbeds Properties Data Mining
Budget: $250 – $750 AUD
1. Project Overview
Objective: To build a comprehensive database of all accommodation properties in Australia that utilise the Cloudbeds Property Management System (PMS).
Background: Cloudbeds properties share a distinct public-facing booking URL pattern. We have manually identified a sample set of 26 properties. The goal of this project is to automate the discovery process to find the remaining properties across Australia (estimated to be around 700.
2. Technical Identification Logic
The target properties can be identified using the following footprint:
Domain: https://hotels.cloudbeds.com
URL Pattern: https://hotels.cloudbeds.com/reservation/{unique_id}
e.g.
https://www.hotels.cloudbeds.com/reservation/jt0MvA
https://hotels.cloudbeds.com/reservation/muXJte
https://hotels.cloudbeds.com/reservation/ED1kuO
Keyword Association: The text "Australia" or specific Australian state abbreviations (NSW, VIC, QLD, WA, SA, TAS, ACT, NT) usually appears in the address section or page metadata.
3. Scope of Work
Phase 1: Discovery & Scraping
The developer needs to build a script/bot to discover these URLs. To ensure complete coverage of the Australian market, the strategy must be iterative.
Phase 2: Data Extraction & Validation
For every URL found, the script must visit the page and extract the following data points. It must also validate that the property is physically located in Australia.
Required Data Fields:
Property Name (e.g., "Space Hotel")
City (e.g., "Melbourne")
State (e.g., "VIC")
Full Booking URL (e.g., https://hotels.cloudbeds.com/reservation/zXs2Dr)
Optional/Bonus Fields:
Property Address
Phone Number (with area code)
Email Address
Phase 3: Data Cleaning
Deduplication: Ensure no property is listed twice (dedupe based on the Cloudbeds ID in the URL).
4. Deliverables
The Australian Dataset: A .csv file containing the clean data.
Format: Property Name, City, State, Booking URL, and the Optional Fields
Source Code: The Python/Node.js scripts used to generate the data.
Documentation: A simple README.md explaining how to run the script and any dependencies.
5. Input Data (Sample)
We are providing a sample CSV (cloudbeds_australia_sample.csv) containing some valid entries easily uncovered by chatgpt to demonstrate the expected output format and valid URL structures.
6. Timeline & Budget
Timeline: 1 week
Budget: $250 to $750
Objective: To build a comprehensive database of all accommodation properties in Australia that utilise the Cloudbeds Property Management System (PMS).
Background: Cloudbeds properties share a distinct public-facing booking URL pattern. We have manually identified a sample set of 26 properties. The goal of this project is to automate the discovery process to find the remaining properties across Australia (estimated to be around 700.
2. Technical Identification Logic
The target properties can be identified using the following footprint:
Domain: https://hotels.cloudbeds.com
URL Pattern: https://hotels.cloudbeds.com/reservation/{unique_id}
e.g.
https://www.hotels.cloudbeds.com/reservation/jt0MvA
https://hotels.cloudbeds.com/reservation/muXJte
https://hotels.cloudbeds.com/reservation/ED1kuO
Keyword Association: The text "Australia" or specific Australian state abbreviations (NSW, VIC, QLD, WA, SA, TAS, ACT, NT) usually appears in the address section or page metadata.
3. Scope of Work
Phase 1: Discovery & Scraping
The developer needs to build a script/bot to discover these URLs. To ensure complete coverage of the Australian market, the strategy must be iterative.
Phase 2: Data Extraction & Validation
For every URL found, the script must visit the page and extract the following data points. It must also validate that the property is physically located in Australia.
Required Data Fields:
Property Name (e.g., "Space Hotel")
City (e.g., "Melbourne")
State (e.g., "VIC")
Full Booking URL (e.g., https://hotels.cloudbeds.com/reservation/zXs2Dr)
Optional/Bonus Fields:
Property Address
Phone Number (with area code)
Email Address
Phase 3: Data Cleaning
Deduplication: Ensure no property is listed twice (dedupe based on the Cloudbeds ID in the URL).
4. Deliverables
The Australian Dataset: A .csv file containing the clean data.
Format: Property Name, City, State, Booking URL, and the Optional Fields
Source Code: The Python/Node.js scripts used to generate the data.
Documentation: A simple README.md explaining how to run the script and any dependencies.
5. Input Data (Sample)
We are providing a sample CSV (cloudbeds_australia_sample.csv) containing some valid entries easily uncovered by chatgpt to demonstrate the expected output format and valid URL structures.
6. Timeline & Budget
Timeline: 1 week
Budget: $250 to $750