India Hotel Price Index 2026: 50-city Rate Report | Travelscrape

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Report summary. The India Hotel Price Index 2026 from Travel Scrape tracks hotel room rates and rate parity across 50 Indian cities, built from millions of price observations scraped from 8 major OTAs. This edition measures how rates moved by city, season and hotel tier — and how often the same room was sold at inconsistent prices across channels.
This report is built entirely from publicly available OTA pricing data collected through Travel Scrape’s hotel rate scraping pipeline. All figures below are illustrative placeholders pending your real dataset.
Key findings at a glance
National average daily rate (ADR) rose an estimated 12% year on year across the 50 cities indexed. [illustrative]
Tier-1 metros led growth, with the steepest increases around festival and event periods. [illustrative]
Rate parity violations appeared in roughly 1 in 7 rate checks — the same room priced differently across OTAs. [illustrative]
Peak-season surges reached 40–50% above baseline in leisure destinations like Goa during December. [illustrative]
Budget chains showed the most volatile pricing, adjusting ...
... rates most frequently within a day. [illustrative]
Why an India hotel price index matters

India’s hotel market moves fast and unevenly. Rates swing with festivals, cricket fixtures, weddings and weather, and the same property is often priced differently across Booking.com, Agoda, MakeMyTrip and others at the same moment. A consistent, data-driven India Hotel Price Index gives revenue managers, investors and analysts a shared benchmark — instead of anecdotes — for how the market is actually priced.
Travel Scrape produces this index from large-scale hotel rate scraping rather than surveys, so it reflects live market behaviour, not reported intentions.
Methodology: how the index is built
The index is constructed from public OTA data collected through Travel Scrape’s managed hotel data scraping pipeline. The approach is designed to be transparent and repeatable each edition.
Coverage. 50 Indian cities across Tier-1, Tier-2 and Tier-3 markets.
Sources. 8 major OTAs plus direct hotel sites, collected with geo-targeting to reflect true local pricing.
Volume. Millions of timestamped rate observations across the measurement window.
Metrics. ADR, rate change vs prior period, intra-day volatility, and rate parity violation rate.
Validation. All records are cleaned, deduplicated and validated before aggregation.
City-tier price movement (illustrative)
Replace the values below with your aggregated figures. The structure mirrors how the data should be presented.
Tier-1 Metros
Average Daily Rate (ADR): ₹8,900
Year-over-Year Growth: ▲ 14%
Pricing Volatility: High
Major business and tourism hubs experience frequent rate fluctuations driven by demand, events, and occupancy levels.
Tier-2 Cities
Average Daily Rate (ADR): ₹5,400
Year-over-Year Growth: ▲ 11%
Pricing Volatility: Medium
Growing corporate travel and domestic tourism continue to support steady rate growth.
Tier-3 Cities
Average Daily Rate (ADR): ₹3,200
Year-over-Year Growth: ▲ 8%
Pricing Volatility: Low–Medium
More stable pricing environment with lower demand fluctuations compared to larger markets.
Leisure Destinations (Peak Season)
Average Daily Rate (ADR): ₹11,200
Year-over-Year Growth: ▲ 22%
Pricing Volatility: Very High
Strong seasonal demand, holidays, and event-driven travel create significant pricing spikes.
Rate parity findings
One of the most valuable outputs of hotel rate scraping is measuring rate parity — whether a hotel’s room is priced consistently across channels. In this edition, an estimated 1 in 7 checks found a discrepancy [illustrative], with the cheapest channel often carrying the highest commission. For hotels, every such gap quietly leaks margin; our companion playbook on competitor price tracking explains how to close them.
OTA A
Parity Violation Rate: ~9%
Typical Price Gap: 2–4% below direct hotel rates
Generally maintains strong rate parity compliance, with occasional discounting through promotions or member-only offers.
OTA B
Parity Violation Rate: ~15% (highest)
Typical Price Gap: 3–6% below direct rates
Most aggressive pricing behavior among the compared channels, creating greater potential for rate leakage and parity breaches.
OTA C
Parity Violation Rate: ~12%
Typical Price Gap: 2–5% below direct rates
Moderate parity compliance with periodic undercutting of direct booking prices.
Seasonal & event-driven patterns

Indian hotel pricing is highly event-sensitive. The data consistently shows sharp surges around major demand windows — Diwali and the festive season, the IPL cricket calendar, wedding season, and long weekends. Leisure destinations spike hardest; business hubs show steadier, demand-led movement. Tracking these patterns lets revenue teams raise rates ahead of demand instead of reacting after rooms sell out cheap.
What this means for the industry
For hotels and chains
Benchmark your rates against your true city set, and watch parity continuously — the data shows leakage is common and costly.
For investors and analysts
Rate and volatility trends act as a leading indicator of demand and occupancy, useful for valuing assets and timing market entry.
For OTAs and travel tech
City-level pricing baselines help calibrate competitiveness and detect undercutting across the market.
About the data
The India Hotel Price Index is produced by Travel Scrape from public OTA data via large-scale, compliance-minded hotel rate scraping. Travel Scrape collects only public, non-personal pricing data and respects reasonable rate limits. Custom city-level or chain-level cuts of this dataset are available on request.
Frequently asked questions
What is the India Hotel Price Index?
It is a Travel Scrape research index tracking hotel room rates and rate parity across 50 Indian cities, built from millions of OTA price observations collected through hotel rate scraping.
How is the index data collected?
Through Travel Scrape’s managed hotel data scraping pipeline, which gathers public rates from 8 major OTAs with geo-targeting, then cleans, deduplicates and validates the records.
How often is the index updated?
It is published as a recurring edition. Custom or more frequent cuts (monthly, by city or chain) are available on request.
Can I get the underlying hotel price data for India?
Yes. Travel Scrape provides city-level India hotel price datasets and custom research cuts as CSV, JSON or API.

Source : https://www.travelscrape.com/india-hotel-price-index.php

Originally published at https://www.travelscrape.com.

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