Orange NSW Accommodation Rate Tracker
Budget: $2 – $8 AUD
Objective
To collect and monitor daily hotel and short-stay accommodation room rates in Orange, NSW to identify price trends, peak periods, and market volatility. This data will support feasibility studies, investment planning, and pricing strategy for development and tourism-related initiatives.
Frequency
• Daily scrape (early morning, e.g. 5am AEST) to capture consistent daily rate trends and detect fluctuations during weekends, events, or holidays.
Target Platforms
Scrape room availability and pricing data from the following platforms:
1. Booking.com – for hotels, motels, serviced apartments
2. wotif.com – for short-stay homes and private rentals
3. Expedia.com.au / Hotels.com – for comparison and rate validation
5. Direct websites of top-performing hotels in Orange (e.g., Mercure, Quest, Oriana Hotel, The Remington, de Russie, Yallungah Boutique Hotel, Central Caleula Motel and Duntry Leagues)
Data Fields to Capture
For each listing, capture the following:
• Hotel/property name
• Platform (e.g., Booking.com, Airbnb)
• Date of scrape
• Date of stay
• Room type - All rooms(Standard, Deluxe, Suites etc.)
• Number of guests (2 guests)
• Room rate (AUD)
• Taxes & fees (if listed separately)
• Minimum stay requirement (if any)
• Cancellation policy (flexible, non-refundable)
• Availability (yes/no)
• Star rating (if applicable)
• Guest rating (e.g., 4.2/5)
• Address or coordinates (if available)
Smart Logic Requirements
• Capture rates for a rolling window: next fortnight
• Ensure consistent time of scrape daily for accurate comparisons
• Flag missing listings or rate changes >20% compared to previous scrape
• Maintain historical data in time-series format for analysis
Output Format
• Store in CSV or database (e.g., MySQL, PostgreSQL, or Google BigQuery)
• Suggested table name: orange_room_rates
• File naming convention: hotel_rates_YYYYMMDD.csv
Access, Ethics & Compliance
• Ensure scraper adheres to each platform’s robots.txt and terms of service
• Use rotating proxies, user-agent spoofing, and rate-limiting to avoid IP bans
• Avoid scraping logged-in content or bypassing CAPTCHAs unethically
⸻
Success Criteria
• 90% of major accommodation listings in Orange captured
• Scrape completes without failure daily
• Room rate trends can be visualised by lead time and booking date
To collect and monitor daily hotel and short-stay accommodation room rates in Orange, NSW to identify price trends, peak periods, and market volatility. This data will support feasibility studies, investment planning, and pricing strategy for development and tourism-related initiatives.
Frequency
• Daily scrape (early morning, e.g. 5am AEST) to capture consistent daily rate trends and detect fluctuations during weekends, events, or holidays.
Target Platforms
Scrape room availability and pricing data from the following platforms:
1. Booking.com – for hotels, motels, serviced apartments
2. wotif.com – for short-stay homes and private rentals
3. Expedia.com.au / Hotels.com – for comparison and rate validation
5. Direct websites of top-performing hotels in Orange (e.g., Mercure, Quest, Oriana Hotel, The Remington, de Russie, Yallungah Boutique Hotel, Central Caleula Motel and Duntry Leagues)
Data Fields to Capture
For each listing, capture the following:
• Hotel/property name
• Platform (e.g., Booking.com, Airbnb)
• Date of scrape
• Date of stay
• Room type - All rooms(Standard, Deluxe, Suites etc.)
• Number of guests (2 guests)
• Room rate (AUD)
• Taxes & fees (if listed separately)
• Minimum stay requirement (if any)
• Cancellation policy (flexible, non-refundable)
• Availability (yes/no)
• Star rating (if applicable)
• Guest rating (e.g., 4.2/5)
• Address or coordinates (if available)
Smart Logic Requirements
• Capture rates for a rolling window: next fortnight
• Ensure consistent time of scrape daily for accurate comparisons
• Flag missing listings or rate changes >20% compared to previous scrape
• Maintain historical data in time-series format for analysis
Output Format
• Store in CSV or database (e.g., MySQL, PostgreSQL, or Google BigQuery)
• Suggested table name: orange_room_rates
• File naming convention: hotel_rates_YYYYMMDD.csv
Access, Ethics & Compliance
• Ensure scraper adheres to each platform’s robots.txt and terms of service
• Use rotating proxies, user-agent spoofing, and rate-limiting to avoid IP bans
• Avoid scraping logged-in content or bypassing CAPTCHAs unethically
⸻
Success Criteria
• 90% of major accommodation listings in Orange captured
• Scrape completes without failure daily
• Room rate trends can be visualised by lead time and booking date