{"@context":"https://schema.org","@type":"BlogPosting","headline":"Hotel Schema.org Adoption Study 2026: Do Hotels Use Structured Data?","description":"121,425 hotel homepages scanned across 7 countries. Only 10.6% score 50+/100. Median score: 0/100.","datePublished":"2026-03-10","dateModified":"2026-03-10","url":"https://nicolassitter.com/research/hotel-schema-adoption-study-2026","category":"research","keywords":["hotel schema.org","structured data hotels","schema adoption","hotel SEO"],"articleSection":"Research","wordCount":5000,"readTime":"20 min","articleBody":"Nicolas Sitter ResearchMarch 2026\n\n# Do Hotels Use Schema.org?\n\nA 121,425-Property Study Across 7 Countries\n\nWe 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.\n\n121,425\n\nHotels Scanned\n\n7\n\nCountries\n\n36.3%\n\nHave Zero Schema\n\n41%\n\nUse Wrong Type\n\n[Summary](#executive-summary)[Adoption](#adoption-rate)[Schema Types](#schema-types)[By Country](#by-country)[By Stars](#by-stars)[Properties](#property-coverage)[Score](#completeness-score)[Errors](#common-errors)[AI Impact](#ai-implications)[FAQ](#faq)[Methodology](#methodology)\n\n## TL;DR\n\nWe 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.\n\n## Executive Summary\n\nSchema.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.\n\nYet 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.\n\n36.3%\n\nNo structured data at all\n\n41.1%\n\nWrong schema type\n\nAmong hotels with JSON-LD\n\n10.6%\n\nGood implementation (50+)\n\n**The opportunity:** 89.4% of hotels have significant room for improvement in their schema markup. The largest quick win: 24,119 hotels already have JSON-LD but use the wrong type. Changing `Organization` to `Hotel` is a one-line fix.\n\n## Structured Data Adoption\n\nHow many hotel homepages have any form of structured data? (n=105,002 reachable hotels)\n\n55.8%\n\nJSON-LD\n\n58,625 hotels\n\n68.2%\n\nOpen Graph\n\n71,591 hotels\n\n21.6%\n\nMicrodata\n\n22,716 hotels\n\n36.3%\n\nNo Data\n\n38,143 hotels\n\nStructured data format adoption\n\n**More than a third of hotel websites have zero structured data.** While 55.8% have JSON-LD and 68.2% have Open Graph tags, the quality and specificity of these implementations varies enormously — as the following sections show.\n\n## How We Score Hotels\n\nThroughout this study we reference a \"schema score\" out of 100. Here's how it works.\n\nWe 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.\n\n#### Tier 1 — Critical\n\n35 pts\n\n-   name (7 pts)\n-   description (7 pts)\n-   address (7 pts)\n-   telephone (7 pts)\n-   url (7 pts)\n\n#### Tier 2 — High Impact\n\n40 pts\n\n-   geo / coordinates (8 pts)\n-   starRating (8 pts)\n-   priceRange (8 pts)\n-   aggregateRating (8 pts)\n-   amenityFeature (8 pts)\n\n#### Tier 3 — Medium\n\n25 pts\n\n-   image (5 pts)\n-   checkInTime (5 pts)\n-   checkOutTime (5 pts)\n-   numberOfRooms (5 pts)\n-   review (5 pts)\n\nEach field is binary: present = full points, absent = 0. We don't evaluate the quality of the content — just whether it exists.\n\n## Schema Types Used\n\nAmong the 58,625 hotels with JSON-LD, which `@type` do they use?\n\n### The Schema.org Hierarchy\n\nThing\n\n└─ Organization 34.7% use this\n\n└─ Place\n\n└─ LocalBusiness 6.0% use this\n\n└─ LodgingBusiness 3.0%\n\n└─ Hotel 28.3%\n\n└─ Resort 0.4%\n\n└─ BedAndBreakfast 0.3%\n\n└─ Hostel 0.2%\n\nOnly **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.\n\nPrimary @type distribution (hotels with JSON-LD)\n\nSchema @type usage breakdown\n\nSchema Type\n\n% of Hotels\n\nCount\n\nOrganization\n\n34.7%\n\n20,325\n\nHotel\n\n28.3%\n\n16,567\n\nNone / unidentifiable\n\n26.4%\n\n15,489\n\nLocalBusiness\n\n6%\n\n3,510\n\nLodgingBusiness\n\n3%\n\n1,769\n\nResort\n\n0.4%\n\n259\n\n**41.1% of hotels with JSON-LD misrepresent their business to search engines.** Using `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.\n\n## Adoption by Country\n\nSchema.org adoption varies significantly across the 7 markets in our dataset.\n\nSchema quality by country\n\nSchema adoption by country\n\nCountry\n\nHotels\n\nJSON-LD %\n\nCorrect Type %\n\nAvg Score\n\nMedian\n\nFrance\n\n17,634\n\n67.1%\n\n27.9%\n\n21/100\n\n3\n\nUnited States\n\n7,445\n\n63.5%\n\n32.6%\n\n20/100\n\n8\n\nUnited Kingdom\n\n10,547\n\n63.4%\n\n25.3%\n\n18.8/100\n\n3\n\nNetherlands\n\n2,891\n\n62.3%\n\n18.4%\n\n14.6/100\n\n3\n\nSpain\n\n16,411\n\n54.9%\n\n15.5%\n\n13/100\n\n3\n\nItaly\n\n27,319\n\n51.8%\n\n11.4%\n\n10.8/100\n\n0\n\n**The US has the highest rate of correct schema types (32.6%)**, but caveat: our US dataset only covers major tourism cities (New York, LA, Miami, San Diego, etc.) — not the full country. These are hotels more likely to have worked with agencies. France leads in overall JSON-LD presence (67.1%) and average score (21.0). Germany's 45.6% JSON-LD adoption is the lowest among major markets.\n\n### Top Cities by JSON-LD Adoption\n\n60 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.\n\nTop cities by JSON-LD adoption (30+ hotels)\n\nCity\n\nCountry\n\nHotels\n\nJSON-LD %\n\nAvg Score\n\nGrenoble\n\nFR\n\n31\n\n93.5%\n\n35.8\n\nRouen\n\nFR\n\n39\n\n84.6%\n\n42.3\n\nSant Josep de sa Talaia\n\nES\n\n34\n\n82.4%\n\n48.6\n\nSaint-Raphaël\n\nFR\n\n32\n\n81.2%\n\n26.4\n\nRennes\n\nFR\n\n41\n\n80.5%\n\n33.5\n\nLa Rochelle\n\nFR\n\n56\n\n80.4%\n\n28.5\n\n**French cities completely dominate.** 15 of the top 22 cities are French — Grenoble leads at 93.5% JSON-LD adoption. The Balearic Islands (Sant Josep, Sant Antoni) punch above their weight, likely driven by tourism-focused agencies. The first US city is Panama City Beach at #9, and the first UK city is Southampton at #21.\n\n## Adoption by Star Classification\n\nHigher-star hotels invest more in structured data — but even 5-star properties average only 21/100.\n\nSchema adoption by star classification\n\nSchema adoption by star classification\n\nStars\n\nHotels\n\nJSON-LD %\n\nCorrect Type %\n\nAvg Score\n\nMedian\n\n1-star\n\n2,699\n\n52.5%\n\n12.1%\n\n11.3/100\n\n0\n\n2-star\n\n10,222\n\n56.2%\n\n22.5%\n\n15.5/100\n\n3\n\n3-star\n\n30,199\n\n56.4%\n\n19.3%\n\n14.7/100\n\n3\n\n4-star\n\n16,548\n\n61.2%\n\n24.3%\n\n17.9/100\n\n3\n\n5-star\n\n2,062\n\n65.7%\n\n29.9%\n\n21/100\n\n11\n\nUnclassified\n\n43,272\n\n53%\n\n13.7%\n\n12.2/100\n\n0\n\n**5-star hotels score nearly double 1-star properties** (21.0 vs 11.3), but even they have a median of only 11/100 — meaning more than half of 5-star hotels score below that. The 43,272 unclassified hotels (the largest group) have the second-lowest average at 12.2.\n\n## Property Coverage\n\nAmong hotels with JSON-LD, which properties do they actually include?\n\nKey property adoption rate (among hotels with JSON-LD)\n\n### What Each Property Signals to AI\n\n`aggregateRating`12.5% adoption\n\nTrust & quality signal — directly influences AI ranking\n\n`amenityFeature`7.7% adoption\n\nEnables AI to match hotels to specific user needs (pool, spa, gym)\n\n`geo`18.8% adoption\n\nLocation precision — helps AI with \"near X\" and proximity queries\n\n`starRating`10% adoption\n\nCategory classification — \"luxury\", \"budget\", \"5-star\"\n\n`priceRange`14.1% adoption\n\nBudget matching — \"affordable hotels in Paris\"\n\n`description`21.8% adoption\n\nThe most basic identity field — yet 78.2% don't include it\n\n**The gap between having schema and having useful schema is enormous.** 71% include a name. But only 12.5% include aggregateRating, 7.7% include amenityFeature, and just 2.4% specify numberOfRooms. The properties that actually drive AI recommendations are the least implemented.\n\n## Schema Completeness Score\n\nWe scored each hotel on a 100-point scale based on the presence of 15 key properties across three tiers. Here's the distribution.\n\n14.3\n\nMean Score\n\nOut of 100\n\n0\n\nMedian Score\n\nHalf score exactly zero\n\n97\n\nMaximum Score\n\nBest implementation found\n\nSchema completeness score distribution\n\n50.4%\n\nZero\n\n0\n\n52,945\n\n9.7%\n\nMinimal\n\n1–10\n\n10,192\n\n19.8%\n\nBasic\n\n11–25\n\n20,745\n\n10.4%\n\nModerate\n\n26–50\n\n10,893\n\n7%\n\nGood\n\n51–75\n\n7,383\n\n2.7%\n\nExcellent\n\n76–100\n\n2,844\n\n**Half of all reachable hotels score exactly zero.** Only 2.7% (2,844 hotels) score above 75, which represents a reasonably complete implementation. Scoring above 50 puts a hotel in the top 10.6% — a remarkably low bar.\n\n## Most Common Errors\n\nAmong the 58,625 hotels with JSON-LD, these are the most frequent issues.\n\nCommon schema errors (% of hotels with JSON-LD)\n\nCommon schema implementation errors\n\nError\n\n% of Hotels\n\nCount\n\nMissing amenityFeature\n\n92.3%\n\n54,116\n\nMissing aggregateRating\n\n87.5%\n\n51,280\n\nMissing priceRange\n\n85.9%\n\n50,340\n\nMissing geo/coordinates\n\n81.2%\n\n47,631\n\nMissing description\n\n78.2%\n\n45,828\n\nMissing address\n\n62.4%\n\n36,596\n\n### Highest-Impact Fixes\n\n1\n\n#### Change the wrong type (41.1% — 24,119 hotels)\n\nReplace `@type: \"LocalBusiness\"` or `@type: \"Organization\"` with `@type: \"Hotel\"`. One-line fix that unlocks all hotel-specific field scoring.\n\n2\n\n#### Add geo coordinates (81.2% missing)\n\nLatitude and longitude are easy to add and critical for location-based queries. Without geo, AI models can't answer \"hotels near \\[landmark\\]\" accurately.\n\n3\n\n#### Add a description (78.2% missing)\n\nA 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.\n\n## Bonus: What Predicts Schema Quality?\n\nA 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).\n\n### Review Count\n\nStrongest predictor (r=0.22)\n\nSchema score by Google review count\n\nReviews\n\nHotels\n\nAvg Score\n\n< 50\n\n12,640\n\n9.0\n\n50–99\n\n11,011\n\n9.5\n\n100–249\n\n24,932\n\n10.8\n\n250–499\n\n23,563\n\n13.5\n\n500–999\n\n19,162\n\n18.8\n\n1K–5K\n\n13,111\n\n24.2\n\n### Google Rating\n\nNo correlation (r=-0.05)\n\nSchema score by Google rating\n\nRating\n\nHotels\n\nAvg Score\n\n2.0\n\n247\n\n9.4\n\n3.0\n\n3,955\n\n14.1\n\n3.5\n\n13,613\n\n16.5\n\n4.0\n\n42,105\n\n15.6\n\n4.5\n\n41,641\n\n12.5\n\n**Bigger hotels have better schema — but it's not about quality.** Hotels with 1K+ Google reviews score nearly 3x higher than those with fewer than 50. This probably just means bigger properties are more likely to have worked with a web agency that included structured data. Guest rating? No correlation at all. Hotels rated 3.5 actually score higher than 4.5-rated ones.\n\n## What This Means for AI Visibility\n\nSchema.org is not just about Google rich snippets anymore.\n\n### Traditional Search\n\n-   Rich snippets (stars, price, reviews)\n-   Knowledge panel data\n-   Google AI Overviews source data\n-   Hotel pack eligibility\n\n### AI Search (ChatGPT, Gemini, Perplexity)\n\n-   Entity resolution (matching hotel to queries)\n-   Attribute extraction (amenities, ratings)\n-   Structured facts for recommendation\n-   Disambiguation (which \"Grand Hotel\"?)\n\n### Opportunity Sizing\n\n**89.4% of hotels have significant room for improvement.** Here's how the market breaks down:\n\n36.7%\n\nNo structured data\n\n38,524\n\n23%\n\nWrong type (easy fix)\n\n24,119\n\n29%\n\nPartial (score 1–24)\n\n30,460\n\n9.9%\n\nModerate (score 25–49)\n\n10,442\n\n10.6%\n\nGood (score 50+)\n\n11,155\n\n### The Competitive Edge\n\nIf 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.\n\n[Read our Schema.org Implementation Guide for Hotels](/learn/schema-markup-hotels)\n\n## Frequently Asked Questions\n\n## Methodology\n\n### Data Collection\n\n-   **Source:** Google Maps data, filtered for category \"hotel\", with a website, and 10+ Google reviews\n-   121,425 hotel homepages across 7 countries: IT (29K), DE (24K), FR (20K), ES (19K), US (12K), UK (12K), NL (3K)\n-   105,002 reachable (86.5% success rate)\n-   Each hotel's homepage fetched with Chrome-like user agent, 15-second timeout\n-   HTML parsed for JSON-LD, Microdata, RDFa, and Open Graph tags\n-   Hotel metadata (Google rating, review count, star classification) joined from Google Maps\n\n### Scoring Method\n\n-   3-tier system, max 100 points\n-   Tier 1 (Critical): 35 pts — name, description, address, phone, URL\n-   Tier 2 (High Impact): 40 pts — geo, starRating, priceRange, aggregateRating, amenityFeature\n-   Tier 3 (Medium): 25 pts — image, checkInTime, checkOutTime, numberOfRooms, review\n-   Must use correct lodging type to receive any points\n-   Each field: present = full points, absent = 0\n\n### Reachability\n\n121,425\n\nTotal hotels\n\n105,002\n\nReachable (86.5%)\n\n16,423\n\nUnreachable (13.5%)\n\nTop unreachability reasons: HTTP 403 (9,597), timeout/unreachable (4,648), HTTP 404 (1,032).\n\n## Continue Reading\n\nExplore more Nicolas Sitter research on AI hotel search.\n\n[AI Hotel Landscape 2026](/research/ai-hotel-landscape-2026)\n\n[Schema.org Guide](/learn/schema-markup-hotels)[Anatomy of ChatGPT Search](/research/anatomy-chatgpt-hotel-search-2026)[Google AI Mode Study](/research/google-ai-mode-hotel-study-2026)[Hotel Blog Study](/research/french-hotel-blog-study-2026)[All Research](/research)","author":{"@type":"Person","name":"Nicolas Sitter","url":"https://nicolassitter.com/about","sameAs":["https://www.linkedin.com/in/nicolassitternolleau/","https://github.com/Nicositter88","https://hotelrank.ai"]},"publisher":{"@type":"Person","name":"Nicolas Sitter","url":"https://nicolassitter.com"},"image":"https://nicolassitter.com/api/og/hotel-schema-adoption-study-2026","mainEntityOfPage":{"@type":"WebPage","@id":"https://nicolassitter.com/research/hotel-schema-adoption-study-2026"},"tags":["Schema.org","Structured Data","Hotel SEO","AI Visibility"],"sameAs":["https://hotelrank.ai/research/hotel-schema-adoption-study-2026"],"alternateFormat":{"html":"https://nicolassitter.com/research/hotel-schema-adoption-study-2026","json":"https://nicolassitter.com/api/post/hotel-schema-adoption-study-2026","rss":"https://nicolassitter.com/rss.xml"},"datasets":[{"name":"summary","contentUrl":"https://nicolassitter.com/data/hotel-schema-adoption-study-2026/summary.csv","encodingFormat":"text/csv"}]}