Do Hotels Use Schema.org?
A 121,425-Property Study Across 7 Countries
We crawled 121,425 hotel homepages to find out how many implement structured data — and how complete it is.36.3% have no structured data at all. Among those that do, 41% use the wrong schema type (Organization instead of Hotel). Only 10.6% have what we'd consider a good implementation.
TL;DR
We crawled 121,425 hotel homepages across 7 countries. 36.3% have no structured data at all. Among the 55.8% that have JSON-LD, 41.1% use the wrong schema type (Organization or LocalBusiness instead of Hotel). Only 10.6% of hotels have what we'd consider a good implementation. Critical fields like aggregateRating (12.5% adoption), amenityFeature (7.7%), and geo (18.8%) are rarely implemented. In the AI era, this is a massive missed opportunity — and an easy competitive advantage for hotels willing to invest.
Executive Summary
Schema.org markup is the language search engines and AI systems use to understand hotel websites. It's how Google knows your star rating, your amenities, your check-in time. It's what powers rich snippets in search results. And increasingly, it's what AI models like ChatGPT and Gemini parse when deciding which hotels to recommend.
Yet our analysis of 121,425 hotel homepages across Italy, Germany, France, Spain, the US, the UK, and the Netherlands reveals a sobering reality: half of all reachable hotels score exactly zero. Even among hotels with JSON-LD, the most common schema type is Organization (34.7%) — not Hotel (28.3%). The average score across all reachable properties is just 14.3 out of 100.
Organization to Hotel is a one-line fix.Structured Data Adoption
How many hotel homepages have any form of structured data? (n=105,002 reachable hotels)
Structured data format adoption
How We Score Hotels
Throughout this study we reference a "schema score" out of 100. Here's how it works.
We evaluate 15 key Schema.org properties across three tiers. Hotels must use a correct lodging type (Hotel, LodgingBusiness, Resort, etc.) to receive any points. Hotels using Organization or LocalBusiness score 0 regardless of what fields they include — because the wrong type negates the semantic value.
Tier 1 — Critical
35 pts- name (7 pts)
- description (7 pts)
- address (7 pts)
- telephone (7 pts)
- url (7 pts)
Tier 2 — High Impact
40 pts- geo / coordinates (8 pts)
- starRating (8 pts)
- priceRange (8 pts)
- aggregateRating (8 pts)
- amenityFeature (8 pts)
Tier 3 — Medium
25 pts- image (5 pts)
- checkInTime (5 pts)
- checkOutTime (5 pts)
- numberOfRooms (5 pts)
- review (5 pts)
Each field is binary: present = full points, absent = 0. We don't evaluate the quality of the content — just whether it exists.
Schema Types Used
Among the 58,625 hotels with JSON-LD, which @type do they use?
The Schema.org Hierarchy
Only 32.4% of hotels with JSON-LD use a correct lodging type. The most common type is Organization (34.7%), which provides no hotel-specific semantic value.
Primary @type distribution (hotels with JSON-LD)
Schema @type usage breakdown
| Schema Type | % of Hotels | Count |
|---|---|---|
| Organization | 34.7% | 20,325 |
| Hotel | 28.3% | 16,567 |
| None / unidentifiable | 26.4% | 15,489 |
| LocalBusiness | 6% | 3,510 |
| LodgingBusiness | 3% | 1,769 |
| Resort | 0.4% | 259 |
Hotel instead of Organization or LocalBusiness unlocks hotel-specific properties (starRating, amenityFeature, checkinTime) and gives AI models a clearer signal. This is a one-line code change.Adoption by Country
Schema.org adoption varies significantly across the 7 markets in our dataset.
Schema quality by country
Schema adoption by country
| Country | Hotels | JSON-LD % | Correct Type % | Avg Score | Median |
|---|---|---|---|---|---|
| France | 17,634 | 67.1% | 27.9% | 21/100 | 3 |
| United States | 7,445 | 63.5% | 32.6% | 20/100 | 8 |
| United Kingdom | 10,547 | 63.4% | 25.3% | 18.8/100 | 3 |
| Netherlands | 2,891 | 62.3% | 18.4% | 14.6/100 | 3 |
| Spain | 16,411 | 54.9% | 15.5% | 13/100 | 3 |
| Italy | 27,319 | 51.8% | 11.4% | 10.8/100 | 0 |
Top Cities by JSON-LD Adoption
60 cities have 30+ hotels in our dataset: 30 in France, 14 in the UK, 8 in the US, 5 in Spain, 2 in Italy, 1 in Germany. Here are the top 22 by JSON-LD adoption rate.
Top cities by JSON-LD adoption (30+ hotels)
| City | Country | Hotels | JSON-LD % | Avg Score |
|---|---|---|---|---|
| Grenoble | FR | 31 | 93.5% | 35.8 |
| Rouen | FR | 39 | 84.6% | 42.3 |
| Sant Josep de sa Talaia | ES | 34 | 82.4% | 48.6 |
| Saint-Raphaël | FR | 32 | 81.2% | 26.4 |
| Rennes | FR | 41 | 80.5% | 33.5 |
| La Rochelle | FR | 56 | 80.4% | 28.5 |
Adoption by Star Classification
Higher-star hotels invest more in structured data — but even 5-star properties average only 21/100.
Schema adoption by star classification
Schema adoption by star classification
| Stars | Hotels | JSON-LD % | Correct Type % | Avg Score | Median |
|---|---|---|---|---|---|
| 1-star | 2,699 | 52.5% | 12.1% | 11.3/100 | 0 |
| 2-star | 10,222 | 56.2% | 22.5% | 15.5/100 | 3 |
| 3-star | 30,199 | 56.4% | 19.3% | 14.7/100 | 3 |
| 4-star | 16,548 | 61.2% | 24.3% | 17.9/100 | 3 |
| 5-star | 2,062 | 65.7% | 29.9% | 21/100 | 11 |
| Unclassified | 43,272 | 53% | 13.7% | 12.2/100 | 0 |
Property Coverage
Among hotels with JSON-LD, which properties do they actually include?
Key property adoption rate (among hotels with JSON-LD)
What Each Property Signals to AI
aggregateRating12.5% adoptionTrust & quality signal — directly influences AI ranking
amenityFeature7.7% adoptionEnables AI to match hotels to specific user needs (pool, spa, gym)
geo18.8% adoptionLocation precision — helps AI with "near X" and proximity queries
starRating10% adoptionCategory classification — "luxury", "budget", "5-star"
priceRange14.1% adoptionBudget matching — "affordable hotels in Paris"
description21.8% adoptionThe most basic identity field — yet 78.2% don't include it
Schema Completeness Score
We scored each hotel on a 100-point scale based on the presence of 15 key properties across three tiers. Here's the distribution.
Schema completeness score distribution
Most Common Errors
Among the 58,625 hotels with JSON-LD, these are the most frequent issues.
Common schema errors (% of hotels with JSON-LD)
Common schema implementation errors
| Error | % of Hotels | Count |
|---|---|---|
| Missing amenityFeature | 92.3% | 54,116 |
| Missing aggregateRating | 87.5% | 51,280 |
| Missing priceRange | 85.9% | 50,340 |
| Missing geo/coordinates | 81.2% | 47,631 |
| Missing description | 78.2% | 45,828 |
| Missing address | 62.4% | 36,596 |
Highest-Impact Fixes
Change the wrong type (41.1% — 24,119 hotels)
Replace @type: "LocalBusiness" or @type: "Organization" with @type: "Hotel". One-line fix that unlocks all hotel-specific field scoring.
Add geo coordinates (81.2% missing)
Latitude and longitude are easy to add and critical for location-based queries. Without geo, AI models can't answer "hotels near [landmark]" accurately.
Add a description (78.2% missing)
A text description is trivial to provide and helps AI models understand what makes your property unique. Without it, AI has to infer from other sources.
Bonus: What Predicts Schema Quality?
A fun detour — we correlated schema scores with Google Maps data. The short answer: it's mostly about whether a hotel invested in a proper website (probably with an agency).
Review Count
Strongest predictor (r=0.22)Schema score by Google review count
| Reviews | Hotels | Avg Score |
|---|---|---|
| < 50 | 12,640 | 9.0 |
| 50–99 | 11,011 | 9.5 |
| 100–249 | 24,932 | 10.8 |
| 250–499 | 23,563 | 13.5 |
| 500–999 | 19,162 | 18.8 |
| 1K–5K | 13,111 | 24.2 |
Google Rating
No correlation (r=-0.05)Schema score by Google rating
| Rating | Hotels | Avg Score |
|---|---|---|
| 2.0 | 247 | 9.4 |
| 3.0 | 3,955 | 14.1 |
| 3.5 | 13,613 | 16.5 |
| 4.0 | 42,105 | 15.6 |
| 4.5 | 41,641 | 12.5 |
What This Means for AI Visibility
Schema.org is not just about Google rich snippets anymore.
Traditional Search
- Rich snippets (stars, price, reviews)
- Knowledge panel data
- Google AI Overviews source data
- Hotel pack eligibility
AI Search (ChatGPT, Gemini, Perplexity)
- Entity resolution (matching hotel to queries)
- Attribute extraction (amenities, ratings)
- Structured facts for recommendation
- Disambiguation (which "Grand Hotel"?)
Opportunity Sizing
89.4% of hotels have significant room for improvement. Here's how the market breaks down:
The Competitive Edge
If 89.4% of hotels have poor or no schema, implementing comprehensive structured data immediately puts you ahead of the vast majority. It's one of the few AI visibility levers that is entirely within a hotel's control, requires no ongoing content production, and can be implemented in a single technical sprint. The largest quick-win: 24,119 hotels already have JSON-LD but use the wrong type — changing to Hotel is a one-line fix that would immediately make their fields semantically meaningful.
Frequently Asked Questions
Methodology
Data Collection
- Source: Google Maps data, filtered for category "hotel", with a website, and 10+ Google reviews
- 121,425 hotel homepages across 7 countries: IT (29K), DE (24K), FR (20K), ES (19K), US (12K), UK (12K), NL (3K)
- 105,002 reachable (86.5% success rate)
- Each hotel's homepage fetched with Chrome-like user agent, 15-second timeout
- HTML parsed for JSON-LD, Microdata, RDFa, and Open Graph tags
- Hotel metadata (Google rating, review count, star classification) joined from Google Maps
Scoring Method
- 3-tier system, max 100 points
- Tier 1 (Critical): 35 pts — name, description, address, phone, URL
- Tier 2 (High Impact): 40 pts — geo, starRating, priceRange, aggregateRating, amenityFeature
- Tier 3 (Medium): 25 pts — image, checkInTime, checkOutTime, numberOfRooms, review
- Must use correct lodging type to receive any points
- Each field: present = full points, absent = 0
Reachability
Top unreachability reasons: HTTP 403 (9,597), timeout/unreachable (4,648), HTTP 404 (1,032).
Continue Reading
Explore more Nicolas Sitter research on AI hotel search.