Introduction

With over 168 Marriott Autograph Collection hotels spread across 38 U.S. states and territories, understanding where these properties are located — and what that data looks like at scale — matters enormously for businesses making location-based decisions. Whether you’re a travel platform, a market researcher, or a hospitality intelligence team, having accurate, structured location data is no longer optional. It’s a competitive necessity.

What Is the Marriott Autograph Collection?

The Autograph Collection is Marriott International’s portfolio of independent, character-driven hotels — each one chosen for its distinct personality rather than brand conformity. Unlike standardized hotel chains, every Autograph Collection property reflects the architectural identity and local culture of its destination, making the collection one of the most geographically and stylistically varied hotel portfolios in the country.

That distinctiveness makes it appealing to premium travelers, but it also makes location data more complex to collect, organize, and analyze. Properties are not cookie-cutter outposts in predictable corridors — they’re spread across downtown business districts, coastal resort towns, historic neighborhoods, and mid-sized markets that many data sources overlook entirely.

Marriott Autograph Collection Hotel Locations Across the USA

As of 2026, there are 168 Marriott Autograph Collection hotels operating across the United States, spanning 38 states and territories. Florida leads all states with 26 properties — roughly 15% of the total national footprint — driven by its strong tourism economy and year-round demand for premium hospitality.

California follows as the second-largest market, with approximately 12% of total U.S. locations, reflecting demand concentrated in cities like Los Angeles, San Francisco, and San Diego. Texas holds around 10% of all locations, with notable properties including The Adolphus and Hotel Drover in Dallas and The Ben in Fort Worth.

Beyond the top three states, the collection reaches into markets that often surprise people — the Midwest, the Mountain West, the Southeast, and the Pacific Northwest all have meaningful representation. Properties like The Brown Palace Hotel and Spa in Denver, Hotel EMC2 in Chicago, and The Farnam in Omaha speak to how deliberately Marriott has expanded this brand into markets that reward distinctiveness over volume.

Key U.S. cities with Autograph Collection presence include:

• New York City — The Lexington Hotel and The Algonquin Hotel Times Square, Autograph Collection
• New Orleans — The Saint Hotel, French Quarter and Q&C Hotel and Bar
• Dallas — HALL Arts Hotel and The Adolphus
• Denver — The Brown Palace Hotel and Spa and The Jacquard
• San Francisco — The Jay, Autograph Collection (360-room property in the Embarcadero/Financial District)
• Charlotte — Grand Bohemian Charlotte
• Fort Worth — Hotel Drover and The Ben
• Chicago — Hotel EMC2

The collection’s reach across 38 states means that decision-makers relying on manual research to track this footprint are already working at a disadvantage. Properties open, rebrand, change contact details, and update their operational hours. Location datasets go stale fast in a brand this dynamic.

Why Businesses Need Accurate Location Data on Hotel Chains

Understanding where hotels like the Autograph Collection operate is valuable across a wide range of business functions — and the need for that data goes well beyond simple curiosity about where to book a room.

Travel and booking platforms need complete, geocoded property lists to power search results, map interfaces, and availability tools. Missing or inaccurate property data means users hit dead ends or see outdated listings — both of which drive churn.

Market researchers and consultants analyzing hospitality trends, luxury hotel distribution, or regional tourism density rely on structured location data to identify patterns, gaps, and growth opportunities. A raw, unstructured list of property names provides little analytical value without address-level geocoding, state-level categorization, and consistent formatting.

Real estate developers and investment firms evaluating hotel market saturation or opportunity zones in specific MSAs (Metropolitan Statistical Areas) need reliable point-of-interest data to inform site selection and competitive mapping.

Sales and B2B intelligence teams targeting hospitality decision-makers need verified, current contact and location records to build outreach lists that are actually accurate.

Retail and proximity-based businesses — from luxury retail to corporate catering — use hotel location data to understand their audience density in a given market.

In every one of these cases, the problem is the same: the data exists on the web, but it isn’t structured, it isn’t current, and it isn’t readily exportable into the formats that drive business decisions.

The Challenge of Collecting Hotel Location Data at Scale

Manually researching even a single hotel brand across 38 states is time-intensive and error-prone. A researcher visiting Marriott’s official site, cross-referencing regional property pages, and checking third-party directories would spend days producing a dataset that still might be incomplete or inconsistently formatted by the time it’s usable.

The real challenge scales exponentially when you factor in the data points that matter beyond just a property name and city — geocoded coordinates, phone numbers, operating hours, brand tier, room count, meeting space availability, and amenity flags. These are the fields that turn a list into an actionable dataset.

The other complication is freshness. Hotel brands update their portfolios regularly. New properties join the Autograph Collection. Others exit the portfolio or rebrand. Properties update contact details, ownership, and operational status. A dataset built manually six months ago may already be significantly outdated.

Web scraping addresses this directly by automating the collection, structuring, and regular refresh of location data from authoritative sources.

How Web Scraping Delivers Structured Hotel Location Data

Web scraping is the process of automatically extracting publicly available data from websites and converting it into structured, machine-readable formats — typically CSV, Excel, JSON, or database-ready files. For hotel location data like the Marriott Autograph Collection, professional web scraping services can extract and deliver fields including:

• Property name and brand tier
• Full street address and city
• State and ZIP code
• Geographic coordinates (latitude and longitude)
• Phone number and contact details
• Operating hours
• Number of rooms and suites
• Meeting and event space availability
• Nearby points of interest and regional metadata

When this data is properly structured, validated, and geocoded, it becomes the foundation for competitive analysis, market mapping, CRM enrichment, pricing intelligence, and investment research.

Critically, web scraping enables regular refresh cycles — whether daily, weekly, or monthly — so that location datasets reflect the current state of a brand’s portfolio rather than a historical snapshot. For a collection as dynamic as Marriott’s Autograph brand, that currency is directly tied to data quality.

How Web Scrape Supports Hospitality Data Extraction

Web Scrape is a professional web scraping and data extraction service built for businesses that need clean, structured, and scalable data without the overhead of building and maintaining their own scraping infrastructure.

For businesses researching hotel portfolios like the Marriott Autograph Collection, Web Scrape provides fully managed data extraction that handles the technical complexity — including JavaScript-rendered pages, proxy rotation, anti-bot navigation, and multi-page crawling — so that clients receive ready-to-use datasets rather than raw, unprocessed output.

Web Scrape’s capabilities are particularly relevant for hospitality and travel intelligence use cases in the USA, where data needs often span multiple states, property types, and source formats. Its service covers location data extraction with geocoded addresses, contact details, and operational fields delivered in CSV, Excel, or JSON formats — the exact structures that analytics, CRM, and mapping tools require.

For organizations that need to track hotel portfolios, monitor brand expansions, or build location-based market intelligence, Web Scrape delivers the data as a managed service — removing the need for in-house engineering resources while maintaining consistent data quality and delivery schedules. Its Data as a Service model means businesses receive structured output on a defined cadence, making it practical for teams that need hospitality data as an ongoing intelligence input rather than a one-time project.

Whether you’re building a travel platform, conducting investment due diligence, or enriching a B2B sales dataset with verified hospitality contacts, Web Scrape’s extraction infrastructure is designed to deliver the accuracy, completeness, and freshness that location-dependent decisions require.

Practical Use Cases: Autograph Collection Data in Action

Understanding the geographic distribution of Autograph Collection hotels is directly useful in several business scenarios.

A travel technology company building a luxury hotel discovery app needs a complete, geocoded property list — not just the 10 most well-known properties, but all 168, with accurate addresses and coordinates. That dataset powers the search layer, the map interface, and the filtering logic.

A consultancy advising a hospitality investor on market concentration would use state-level distribution data to assess whether certain markets are saturated or underserved relative to premium demand indicators.

A corporate travel management firm looking to negotiate preferred rates with an independent hotel network would start with structured property data to identify which Autograph Collection hotels exist within their most frequently traveled corridors.

A regional tourism board analyzing visitor accommodation patterns in Florida — the state with the highest Autograph Collection density — would need structured data to map premium hotel supply against demand zones and infrastructure investment priorities.

In each case, the underlying requirement is the same: accurate, structured, geocoded location data, delivered in a format that plugs directly into the tools and workflows where decisions get made.

Frequently Asked Questions

How many Marriott Autograph Collection hotels are there in the USA?

As of April 2026, there are 168 Marriott Autograph Collection hotels operating across the United States, spread across 38 states and territories.

Which state has the most Marriott Autograph Collection hotels?

Florida has the highest concentration, with 26 properties — approximately 15% of the total U.S. footprint. California and Texas follow as the second and third largest markets respectively.

What data fields are typically available in a hotel location dataset?

A professionally structured hotel location dataset typically includes property name, full address, city, state, ZIP code, geocoded coordinates, phone number, brand tier, operating hours, and in many cases room count and meeting space details. The exact fields depend on the data source and extraction methodology.

Why is web scraping used to collect hotel location data?

Manual research is too slow and error-prone to maintain accurate, complete location data for large hotel portfolios at scale. Web scraping automates the extraction process, handles dynamic content and multi-page sources, and enables regular dataset refresh to maintain accuracy as portfolios change.

Can Web Scrape provide Marriott Autograph Collection location data for the USA?

Yes. Web Scrape offers fully managed hotel location data extraction for U.S.-based hospitality brands, including geocoded addresses, contact information, and structured output in formats ready for analytics, CRM, and mapping applications.

How often should hotel location datasets be updated?

For brands with active portfolio growth or regular property changes, monthly refresh cycles are generally recommended as a minimum. For use cases requiring higher data currency — such as live booking integrations or real-time competitive monitoring — weekly or more frequent extraction is appropriate.

Conclusion

The Marriott Autograph Collection’s 168 U.S. locations represent one of the most geographically diverse premium hotel portfolios in the country, spanning 38 states from Florida’s resort markets to the Pacific Coast and the Mountain West. For businesses that need to understand, analyze, or act on that footprint, structured location data is the starting point. Web scraping makes it possible to collect, standardize, and regularly refresh that data at scale — without the manual overhead that makes large-scale location research impractical. Web Scrape provides the extraction infrastructure and managed delivery that turns publicly available hotel data into business-ready intelligence, supporting travel platforms, researchers, investors, and commercial teams that depend on accuracy and completeness to make informed decisions.