Hotel Rates vs Airbnb Rentals After the Games | Data Scraping
Introduction
When a mega sporting event ends, the real story often begins. The crowd leaves, flights normalize, and pricing teams quietly reassess what just happened.
That post-event phase is what we call the Olympics hangover.
For the Milan–Cortina 2026 Winter Olympics, accommodation pricing across northern Italy went through three distinct phases:
Pre-event build-up
Event-time surge
Post-event correction
This article focuses on phase three and shows how scraping hotel rates and Airbnb listings reveals patterns that traditional reports usually miss.
Why the Post-Olympics Period Matters More Than the Event
Most analysts obsess over peak pricing during the Games. That data is useful, but incomplete.
The real business questions start after the closing ceremony:
How fast do hotel prices fall back to normal?
Do Airbnb hosts reduce rates or keep them inflated?
Which cities normalize faster: Milan or Cortina?
Are there lingering “Olympics premiums” weeks later?
Answering these requires daily, listing-level data, not monthly averages.
Hotels vs Airbnb: Two Very Different Pricing Behaviors
From a data perspective, hotels and short-term rentals behave very differently after major events.
Hotels
Centralized revenue management systems
Faster reaction to demand drops
Aggressive discounting to recover occupancy
Clear weekday vs weekend corrections
Airbnb Rentals
Individual host decision-making
Slower price correction
Emotional pricing (“the Olympics just ended, demand will come back”)
High variance across similar properties
These differences only become obvious when you track prices day by day.
What Data Needs to Be Scraped
To analyze the Olympics hangover properly, raw availability data is not enough. At Actowiz, we focus on pricing context, not just numbers.
Hotel Data Points
Hotel name
City and neighborhood
Star rating
Room type
Nightly price
Taxes and fees
Minimum stay rules
Availability status
Booking date vs stay date
Airbnb Data Points
Listing ID
Property type
Host type (individual vs professional)
Nightly price
Cleaning fee
Minimum nights
Occupancy calendar
Review count and rating
All data is collected daily to capture adjustment speed, not just price levels.
Sample Post-Olympics Pricing Snapshot
Below is an illustrative view of what scraped data looks like two weeks after the Games.
Sample Hotel Data (Milan)
City Center Hotel A
Stars: 4★
Date: Feb 25
Nightly Price: €142
Change vs Olympics: −38%
Business Hotel B
Stars: 3★
Date: Feb 25
Nightly Price: €98
Change vs Olympics: −41%
Luxury Hotel C
Stars: 5★
Date: Feb 25
Nightly Price: €265
Change vs Olympics: −29%
Sample Airbnb Data (Milan)
Studio Apartment
Date: Feb 25
Nightly Price: €135
Change vs Olympics: −12%
1-Bedroom
Date: Feb 25
Nightly Price: €162
Change vs Olympics: −9%
Premium Loft
Date: Feb 25
Nightly Price: €210
Change vs Olympics: −6%
.
Hotels corrected sharply. Airbnb prices barely moved.
This divergence is exactly what post-event scraping exposes.
Key Insights From Olympics Hangover Analysis
Based on similar event studies and early 2026 data patterns, several insights consistently emerge.
1. Hotels Normalize Faster Than Airbnb
Most hotels return to baseline pricing within 10–15 days. Airbnb listings often take 30+ days.
2. Secondary Cities Correct First
Cortina and nearby towns drop prices faster than Milan, which has year-round business demand.
3. Minimum Stay Rules Linger
Even after prices drop, restrictive minimum stays remain active on Airbnb, suppressing demand.
4. Professional Hosts React Faster
Multi-listing Airbnb operators behave more like hotels than individual hosts.
Why Manual Tracking Fails Here
Manually checking prices once or twice tells you almost nothing.
Post-event pricing is about:
Speed of change
Consistency across listings
Variance between similar properties
Lag between demand and price correction
Only automated scraping can capture that motion.
Actowiz Approach to Post-Event Accommodation Scraping
Our scraping workflows are designed for longitudinal analysis, not one-off snapshots.
How We Do It
City-level and neighborhood-level targeting
Fixed stay dates with rolling booking dates
Daily re-scraping of the same listings
Change detection at listing level
Normalization across currencies and fees
Output Formats
CSV for analysts
JSON for data science teams
Dashboards for revenue and pricing teams
Historical price curves for forecasting models
Who Uses This Data
Post-event accommodation scraping is used by:
Hotel revenue management teams
Investment and real estate analysts
Tourism boards evaluating event impact
AI pricing and demand-forecasting platforms
The Olympics are just one example. The same approach applies to expos, world cups, festivals, and large conventions.
Final Thoughts
The story of the Olympics does not end on closing night.
Pricing behavior in the weeks that follow reveals:
How rational the market really is
Who reacts to demand and who guesses
Where pricing power actually lives
Scraping hotel rates and Airbnb rentals during the Olympics hangover turns short-term chaos into long-term insight.
If your team wants to understand travel demand beyond headlines and hype, this is where the real data starts.
You can also reach us for all your mobile app scraping, data collection, web scraping , and instant data scraper service requirements!By leveraging Actowiz Solutions, your business stays ahead of the competition, armed with actionable insights from every marketplace.
Learn More >>
https://www.actowizsolutions.com/scraping-hotel-rates-vs-airbnb-rentals-after-the-games.php
Comments
Post a Comment