Platform · Wyndham

Wyndham Data Scraping

More properties than any other group here, at the lowest rates, almost all franchised. Three things that compound.

Wyndham data scraping collects rates, room types, plan conditions and availability across the group's brands. Three characteristics compound here: the largest property count of the groups in this set, an estate weighted toward economy and midscale, and a very high franchise share. Together they produce the widest property-level rate variation of any group we cover.

Large estate, low rates, franchised operators. Each on its own widens variation; together they make the property-level view not optional.

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wyndham.jsonl LIVE FEED

{"group":"wyndham","brand_tier":"economy", "property_id":"wy-44120","city":"Example city", "rate_public":62.00,"currency":"USD", "rate_spread_absolute":21.00,"rate_spread_pct":33.9, "base_stated":"same brand, same market"} {"property_id":"wy-88120","brand_changed_at":"2026-07-11", "caution":"a brand conversion looks like a rate change in a brand series if undated"} {"panel_version":3,"property_exit_reason":"left_brand", "panel_topped_up":false, "note":"21 dollars is 34% at this rate level. use absolutes across tiers"}

3 of 6,884,110 property-plan rows · globalwidest spread in our hotel set · brand changes dated · schema v1.0

Independence and trademarks. Actowiz Solutions is not affiliated with, endorsed by or connected to Wyndham or its owners. Wyndham 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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Wyndham at a glance

How we handle Wyndham specifically

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

Group: Wyndham — largest property count in this set
Positioning: Economy and midscale weighted
Ownership: Very high franchise share
Result: Widest property-level variation of any group here
Consequence: Property-level collection is not optional
Panel: Large, so panel design and versioning matter
Member rates: Gated. Share reported
Refresh: Daily per stay date; lead time matters

Platform specifics

Three characteristics that compound

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

Scale, tier and franchise together widen the spread

Each of these widens rate variation on its own. Together they produce the widest spread in our hotel coverage.

Scale. A large estate spans many more local market conditions than a small one.
Economy and midscale positioning. These tiers are more exposed to local supply and demand, and to roadside and seasonal demand patterns.
Franchise share. Operators price with substantial latitude, so two properties of one brand in one market reflect independent commercial decisions.

So property_id is the unit, rate_spread_pct within brand and market is a primary output, and a brand or group average is a rollup with property_count_observed stated.

Panel design matters more at this scale

A large franchised estate has meaningful property turnover — properties join and leave brands. A panel that changes membership produces movement that looks like rate change.

panel_version, property_entered_at and departures retained with reasons, as our panel design page requires. We do not top the panel up to keep counts steady.

Low rate level, and the group mechanics

Low rates change what a spread means

At economy rate levels, a small absolute difference is a large percentage. A property twenty units above another can be twenty per cent more expensive.

So percentage spreads look dramatic in this tier and absolute spreads look small. We deliver both, with the base stated, and recommend absolute figures for cross-tier comparison and percentage for within-tier.

Group mechanics

Member rates gated with the share reported, points captured and never converted, rate plans as the record, brand tier as a dimension. All on our hotel chain direct page.

What we do not collect

Franchisee or owner identities. The property is a commercial entity.
Franchise agreements or brand conversion terms. Corporate structure.
Occupancy, bookings or guest data. Not published.
Loyalty account data. No accounts created.

On brand conversions

Properties move between brands in a large franchised estate. We record brand_changed_at where our series contains the transition, and flag it — because a property changing brand looks like a rate change in a brand-level series if it is not dated.

Scope

What we collect on Wyndham, 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 as a primary output
Brand and group figures as rollups with property_count_observed
panel_version and property_entered_at, with departures retained
brand_changed_at where our series contains the transition
Absolute and percentage spreads both delivered, with the base stated
Publicly visible rates, with gated ones null and a reason
gated_share reported per brand before quoting
One record per rate plan, with conditions structured
Point cost as displayed, never converted

❌ What we do not, and why

A brand average presented as the brand's pricing
A panel topped up to keep property counts steady
A brand conversion left undated in a brand-level series
A public rate substituted for a gated member rate
Franchisee identities, occupancy, bookings or guest data

Core Wyndham 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. Not optional here
rate_spread_pct / rate_spread_absolute	Both, with the base stated
ownership_model	Where published. Unstated where not
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
brand_changed_at	Where our series contains the transition
panel_version / property_entered_at / property_exit_reason	Panel integrity
points_cost / points_cash_equivalent	As displayed, never computed
property_count_observed	Stated on any rollup

Use cases

What teams do with Wyndham data

Widest-spread property analysis

Property-level records with spread within brand and market, on the group where scale, tier and franchise share compound into the widest variation in our hotel coverage.

Economy tier benchmarking

Absolute and percentage spreads both delivered, since at economy rate levels a small absolute difference reads as a large percentage.

Brand conversion tracking

Brand changes dated where our series contains the transition, so a property moving between brands does not read as a rate change in a brand series.

Large panel integrity

Panel versioning with entries and exits retained, so a series in an estate with real property turnover is not moved by the panel.

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

Send us a Wyndham 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.

Wyndham is usually collected alongside its competitors

Almost nobody buys a single platform in isolation. Wyndham 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 Wyndham-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

Wyndham data scraping: frequently asked questions

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

Because three things compound. Scale spans more local market conditions; economy and midscale positioning is more exposed to local supply, demand and seasonal patterns; and a very high franchise share means operators price with substantial latitude.

Each widens variation on its own. Together they produce the widest spread in our hotel coverage.

Depends on the comparison. At economy rate levels a small absolute difference is a large percentage — a property twenty units above another can be twenty per cent more expensive.

We deliver both with the base stated, and recommend absolutes for cross-tier comparison and percentages for within-tier.

We date it where our series contains the transition and flag it. A property moving between brands looks like a rate change in a brand-level series if it is not dated.

In a large franchised estate that happens often enough to matter.

No. Properties join and leave brands, and topping the panel up to keep counts steady would hide real movement.

We version the panel, record entry dates and retain departures with reasons.

No. The property is a commercial entity; its operator is a party we do not profile.

Franchise latitude is the finding here, and it would be easy to slide from measuring rates to identifying who sets them. We measure the property.

We quote individually, and property count is the dominant driver since this is the largest estate in the set and the value is entirely at property level.

One scoping call, a free pilot within 24 hours including spread by brand and market, then a fixed monthly quote. Request a quote.

See real Wyndham 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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