Flight Fare Volatility: Web Scraping DEL–BOM Airfares to See How Prices Change in 24 Hours

By Travel Scrape · Route: Delhi–Mumbai (DEL–BOM) · 10 min read

Report summary. This Travel Scrape report measures flight fare volatility on India’s busiest route, Delhi–Mumbai (DEL–BOM), by web scraping fares continuously over 24-hour windows. The finding: airfares on this route change multiple times a day, with swings large enough that the time you check can matter as much as the day you fly. All figures here are illustrative pending your real dataset.

Why flight fare volatility matters

Travellers assume a fare is “the price.” In reality, airfares are among the most volatile consumer prices anywhere — adjusted by algorithms reacting to demand, seat inventory, competitor moves and time of day. For OTAs, metasearch products and corporate travel teams, understanding flight fare volatility is the difference between selling at the right moment and losing the booking. The only way to measure it accurately is to scrape fares repeatedly and timestamp every observation.

Methodology

• Route. DEL–BOM, India’s highest-traffic domestic corridor.
• Method. Continuous flight data scraping across major OTAs and metasearch sources.
• Cadence. Fares captured at short intervals across full 24-hour windows.
• Fields. Lowest fare, carrier, seats remaining, capture timestamp.
• Validation. All observations cleaned, deduplicated and timestamped in UTC.

Key findings (illustrative)

• Fares changed 6–9 times per day on average across carriers. [illustrative]
• Intra-day swings of ±18% were common, with larger moves as seats sold out. [illustrative]
• Late-night and early-morning often showed the lowest fares; midday spikes were frequent. [illustrative]
• The final 48 hours before departure saw the sharpest increases. [illustrative]

How fares moved across one 24-hour window (illustrative)

Replace with your aggregated capture data. Structure shown for presentation.

What drives the swings

Three forces dominate intra-day flight fare volatility: seat inventory (fares rise as cheaper buckets sell out), demand timing (search and booking surges push prices up), and competitive response (carriers and OTAs adjust against each other). Because these interact continuously, only frequent web scraping captures the true picture — a once-a-day check would have missed most of the movement above.

What this means

For OTAs & metasearch

Real-time fare data is essential to display accurate prices and power “price will rise/fall” predictions that build user trust.

For corporate travel & TMCs

Knowing intra-day patterns helps time bookings and set smarter fare-cap policies.

For revenue & analytics teams

Volatility itself is a demand signal — rising change-frequency often precedes sell-out.

About the data

This report is produced by Travel Scrape from public fare data via compliance-minded flight data scraping. Travel Scrape collects only public, non-personal data and respects rate limits. Custom route-level volatility datasets — any origin–destination pair, any window — are available on request.

Frequently asked questions

What is flight fare volatility?

It is how much and how often airfares change over a period. On busy routes like DEL–BOM, fares can change several times within 24 hours. Travel Scrape measures it through continuous flight data scraping.

How often do DEL–BOM fares change in a day?

In this illustrative analysis, fares changed an estimated 6–9 times per day, with intra-day swings around ±18%. Replace with your measured figures.

How is flight fare data collected?

Through Travel Scrape’s managed scraping of public OTA and metasearch fares, captured at short intervals and timestamped for accurate volatility measurement.

Can I get fare volatility data for other routes?

Yes. Travel Scrape provides route-level fare datasets for any origin–destination pair as CSV, JSON or API.

Want fare volatility data for your routes?

Tell Travel Scrape which routes and windows you need. Get a free sample fare dataset within 24 hours.