Platform · IHG

IHG Data Scraping

Q: Can you tell which properties are franchised?

Where the group publishes it, yes. Where it does not, ownership_model is unstated rather than assumed. That is a corporate arrangement rather than something a booking page establishes.

Most of the estate is franchised. The parent sets brand standards; the franchisee sets the rate.

IHG data scraping collects rates, room types, plan conditions and availability across the group's brands. The distinguishing feature: a very high share of properties are franchised, and franchisees price with substantial latitude. So a group-level rate view attributes to the parent decisions the parent does not make, and property-level variation within a brand is wider than the brand positioning suggests.

The group page says property is the unit. On this group, understanding why is the point — it is not a granularity preference, it is who is actually setting the number.

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ihg.jsonl LIVE FEED

{"group":"ihg","brand":"brand-midscale", "property_id":"ig-44120","city":"Example city", "ownership_model":"not_published", "rate_public":112.00,"promo_participating":true} {"property_id":"ig-44188","city":"Example city", "rate_public":149.00,"promo_participating":false, "note":"same brand, same market, 33% apart. franchisee decisions, not brand positioning"} {"rate_spread_pct":36.4, "owner_identity":"not_collected", "caution":"we measure the PROPERTY. not who owns it"}

3 of 4,204,880 property-plan rows · globalspread within brand is the output, not the caveat · schema v1.0

Independence and trademarks. Actowiz Solutions is not affiliated with, endorsed by or connected to IHG or its owners. IHG and related marks belong to their respective owners, used here only to name the publicly accessible source this service collects from.

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IHG at a glance

How we handle IHG specifically

Platform-specific handling, not a generic retail template pointed at a different domain.

Group: IHG — global, wide portfolio
The point: Very high franchise share
Consequence: Franchisees set rates, within brand standards
So: Within-brand spread is wide
Ownership: Recorded where published, unstated where not
Member rates: Below what OTAs may show. Gated share reported
Brands: Span economy to luxury. Tier is a dimension
Refresh: Daily per stay date; lead time matters

Platform specifics

Who sets the rate, and what that does to a brand view

These are the reasons a IHG dataset needs its own handling rather than a shared retail schema.

Franchise latitude widens within-brand spread

A franchised hotel operates to brand standards on product and service. Rate is largely the franchisee's decision, subject to those standards and to any parent-set floor or programme participation.

Two properties of the same brand in one market can differ materially, driven by owner strategy rather than by brand positioning.
A brand-level average therefore blends independent commercial decisions.
Promotional participation varies, so a group campaign is not uniformly executed.
Which means a group figure attributes to the parent decisions the parent did not make.

We record property_id as the unit, ownership_model where the group publishes it and unstated where it does not, and we report rate_spread_pct within brand and market — which on this group is the informative output rather than a caveat.

Which makes panel design matter

A property panel in a franchised estate must be fixed and versioned, or the series moves when the panel does. That is the argument on our panel design page, and it applies with more force where properties price independently.

Everything else is the group page, and we defer to it

Rather than restating: member rates gated below OTA-displayable levels, points not convertible, rate plans as the record, brand tier as a dimension, property as the unit. All set out in full on our hotel chain direct page.

What is worth repeating once

gated_share is reported per brand and tier before quoting, and the public rate is never substituted for a gated member rate. On parity work that substitution does not distort the finding — it reverses it.

What we do not collect

Franchisee or owner identities. The property is a commercial entity; its owner is a party we do not profile.
Management agreements or franchise terms. Corporate structure, not rate data.
Occupancy, bookings or guest data. Not published.
Loyalty account data. No accounts created, in any market.

The first point matters here because franchise latitude is the finding — and it would be easy to slide from measuring a property's rates to identifying who owns it. We measure the property.

Scope

What we collect on IHG, and what we do not

The right column matters more than the left. Anyone can list fields; the limits are what tell you whether the dataset will hold up.

✅ What we collect

property_id as the unit, with rate_spread_pct within brand and market
ownership_model where published, unstated where not
brand and brand_tier as dimensions
A fixed versioned property panel
Publicly visible rates, with gated ones null and a reason
gated_share reported per brand and tier before quoting
One record per rate plan, with conditions structured
Point cost as displayed, never converted
Promotional participation recorded per property

❌ What we do not, and why

A brand average presented as the brand's pricing
An ownership model assumed where not published
Franchisee or owner identities
A public rate substituted for a gated member rate
Occupancy, bookings, guest or loyalty account data

Core IHG fields

The full dictionary is agreed during scoping. These are the fields specific to this platform.

Field	What it is on this platform
group / brand / brand_tier	Dimensions
property_id / city / country	The unit
ownership_model	Where published. Unstated where not
rate_spread_pct	Within brand and market. The informative output here
rate_plan_id / rate_plan_type	The record is the plan
rate_public / rate_member / gated_reason / gated_share	Visible, gated, and the share
stay_date / observed_at / lead_time_days	Both dates and the derived axis
promo_participating	Execution varies by property
points_cost / points_cash_equivalent	As displayed, never computed
property_count_observed / panel_version	Rollup basis and panel integrity
availability_state	Available, sold out or not listed

Use cases

What teams do with IHG data

Within-brand rate variation

Spread reported within brand and market, which on a heavily franchised estate is the informative output rather than a caveat on the average.

Campaign execution measurement

Participation recorded per property, since a group promotion in a franchised estate is not uniformly executed.

Tier-matched benchmarking

Brand tier as a dimension, so comparisons are made within a tier rather than across a portfolio spanning economy to luxury.

Parity monitoring with the hole stated

Gated share per brand and tier, so a parity study states what proportion of the direct channel it could not see.

The 24-hour sample — run on your sources, not ours

Send us a IHG item or category list. We run real collection against it and return the output within 24 hours, with the platform-specific fields populated so you can check them yourself rather than take our word for it.

Real extraction from your actual sources
Returned within 24 hours
Coverage and QA note included
You keep the data either way
No card, no trial clock
Named engineer on the call

Get my free sample Book a 20-min scoping call Reply within one business day. Reference calls available under NDA.

How we engage

Three ways to engage us

Same collection pipeline and QA underneath. The difference is who holds the schedule and how the data reaches you.

Managed service (most common)

We own the collection, the QA and the delivery. You receive clean data on a schedule and never touch a scraper.

Dedicated engineer assigned to your account
Site changes fixed by us, not reported to you
Scheduled delivery to your warehouse or S3
Named contact on Slack or email

Best fit: Teams who need the data, not the infrastructure.

API access

The same collection pipeline exposed as an authenticated REST endpoint your systems query directly.

On-demand and scheduled endpoints
Rate limits agreed to your load profile
Sandbox keys for integration testing
Versioned schema with deprecation notice

Best fit: Product and engineering teams building on live data.

One-time or project extraction

A defined pull for a specific question — market sizing, diligence, a pitch, a one-off audit.

Fixed scope agreed in writing upfront
Single delivery with full QA report
Methodology documented for your records
Converts to managed if you want continuity

Best fit: Research, strategy and diligence work with a deadline.

Pricing

Every engagement is quoted individually, because the honest answer depends on your scope: how many sources, how many records, how often, and how the data reaches you. We scope it with you, run a free pilot on your own sources, and then quote a fixed monthly figure — no per-request metering and no overage billing when volumes move. Request a quote and you will have a number after one call.

IHG is usually collected alongside its competitors

Almost nobody buys a single platform in isolation. IHG data becomes useful when it sits next to the competitor set on one schema, refreshed on one schedule, so a price index or availability comparison is genuinely like-for-like.

That is what travel & hospitality data covers, and a IHG-only engagement can be expanded into it without rebuilding. If you already know you need several platforms, start there instead — it is the same pipeline and usually the better scoping conversation.

FAQ

IHG data scraping: frequently asked questions

Platform-specific questions, including what cannot be collected here.

Because a franchised hotel operates to brand standards on product and service while rate is largely the franchisee's decision.

So two properties of the same brand in one market can differ materially for owner-strategy reasons, and a group figure attributes to the parent decisions the parent did not make.

Where the group publishes it, yes. Where it does not, ownership_model is unstated rather than assumed.

That is a corporate arrangement rather than something a booking page establishes.

No, and we would not. The property is a commercial entity; its owner is a party we do not profile.

This matters here because franchise latitude is the finding, and it would be easy to slide from measuring a property's rates to identifying who owns it. We measure the property.

Not necessarily. Participation varies in a franchised estate, so we record it per property rather than assuming uniform execution.

Because on parity work that does not distort the finding — it reverses it. A member rate sits below what OTAs may display, so substituting the public rate shows parity where a shopper sees none.

We report the gated share instead.

We quote individually on properties times stay dates times rate plans times observations. Property count is the driver, and a franchised estate rewards a wider panel because the variation lives there.

One scoping call, a free pilot within 24 hours, then a fixed monthly quote. Request a quote.

See real IHG data before you commit to anything

Send us an item or category list. We return the output within 24 hours with the platform-specific fields populated.

Get a free sample Talk to an engineer

Free pilot, no card, no obligation. If we cannot collect a field you need on this platform, the sample shows you that too.

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