Hospitality Economic Impact & State Facts Dashboard

AHLA - Hospitality Economic Impact & State Facts Dashboard is the American Hotel & Lodging Association's official accounting of what the hotel business contributes to the US economy, geography by geography. It covers all 50 states plus the District of Columbia and reaches down into every congressional district - 435 seats plus the at-large entries.

The dashboard opens on the national picture: hotels support more than 9.1 million jobs and contribute more than $894 billion to US GDP, with AHLA itself counting more than 30,000 members. From there, each geography offers three views - an Industry Snapshot, Total Hotel Industry Impact and Hotel Operations Impact - covering guest spending, guestroom and property counts, employment supported, wages, GDP contribution and the full stack of taxes generated.

The numbers carry their pedigree on their sleeve. The methodology is attributed to Oxford Economics, drawing on CoStar/STR hotel inventory and performance data plus IMPLAN, Bureau of Economic Analysis and US Census Bureau statistics, with 2024 as the reference year. One definitional boundary is worth keeping in mind: the hotel industry here means hotels, motels and B&Bs, with short-term rentals explicitly excluded. Datadory delivers all of it as typed rows - 52 at state level, 436 at district level, fourteen fields each.

Three states exactly as delivered, reference year 2024:

# state industry snapshot - reference year 2024, one row per geography
state                          : CA
guest_spending_dollars         : $85,683,908,046.95
guestrooms                     : 571,794
properties                     : 6,778
share_of_total_jobs_supported  : 3.95%

state                          : AL
guest_spending_dollars         : $5,477,527,420.80
guestrooms                     : 80,626
properties                     : 1,031
share_of_total_jobs_supported  : 2.95%

state                          : AK
guest_spending_dollars         : $3,873,291,283.78
guestrooms                     : 20,868
properties                     : 273
share_of_total_jobs_supported  : 8.33%

Read them as scale versus dependence. California dwarfs everyone on absolute dollars - $85.68 billion in guest spending across 6,778 properties - yet hotels support 3.95% of its jobs. Alaska books barely a twentieth of California's spending but leans on hotels for 8.33% of its employment, the highest share in the set. Alabama sits between on dollars and lowest on dependence. The remaining 47 states and DC repeat the identical row shape, and the congressional-district table repeats it again at finer grain - so the entire country loads as flat, uniform rows rather than fifty bespoke reports.

Fourteen fields carry the dataset, and they divide neatly into four jobs. Scale: Hotel_Guest_Spending_Dollars, Hotel_Guestrooms_Number_of_Rooms and Properties_Number_of_Hotels describe how big the lodging stock is and how much guests spend through it. Employment: Employment_Number_of_Jobs, Share_of_Total_Jobs_Supported_by_Hotel_Industry and Total_Jobs_Supported_by_Hotel_Industry_per_100_Households measure how much of a local economy's work the industry props up. Money flows: GDP_Dollars, Wages_and_Salaries_Dollars and Business_Sales_Output_Dollars capture the output story. Government take: Federal_Taxes_Dollars, State_and_Local_Taxes_Per_Household_Dollars, Total_Taxes_Per_Household_Dollars and Taxes_on_Lodging_Dollars itemize the revenue side, several of them already normalized per household so small and large states compare fairly.

The State column anchors it all - a two-letter USPS code at state level, a district key such as 'AK-At Large' or 'CA-1' in the district table. Every definition was verified during the August 2026 review; the example values above print where the review captured live rows, and the rest arrive with real data in your sample rather than fabricated placeholders.

• Geography - complete national coverage by design: all 50 states, the District of Columbia, and every congressional district including the at-large seats. Nothing is sampled; every geography gets a row.
• Temporal - figures reference 2024, stated in the dashboard's own methodology footnote alongside the Oxford Economics attribution. This is a studied annual picture of the industry's impact, not a live counter.
• Granularity - one row per geography: 52 state-level rows and 436 district-level rows, held across three views (snapshot, total industry impact, direct hotel operations) that share one field dictionary.

The combination is rarer than it sounds. Advocacy material usually stops at state lines; this dataset continues down to the district, which is exactly the grain at which tax policy and political attention operate.

API, files, or your warehouse. Daily, weekly, or hourly.

You pick the channel and the cadence; the fourteen-field dictionary travels unchanged through all of them. Rows land flattened and typed - dollar amounts as currency values, room and property counts as integers, shares as fractions - so a state comparison table joins to your own geography dimension on the first attempt. Cadence is a setting, not a migration: a quarterly research pull and an hourly desk feed read the same schema. A sample scoped to your named states or districts proves the shape before anything broader ships.

• Market sizing - guest spending and GDP contribution per state turn 'how big is lodging there?' into one sorted column: California at $85.68 billion sets the top of the range.
• Tax analysis - federal, state-and-local and lodging-specific taxes, several normalized per household, quantify what occupancy taxation actually raises and where.
• Dependence measurement - the share-of-jobs column ranks economies by how structurally they rely on hotels: Alaska at 8.33% against Alabama at 2.94%.
• Advocacy preparation - the district table arms a briefing with the recipient's own constituents' numbers instead of a state-wide average.
• Portfolio context - property and guestroom counts per state give a hotel REIT thesis its physical footprint before market metrics arrive.
• Pipeline seeding - clean state and district keys make these rows the join target for anything else lodging-shaped.

Investors and quants get demand-side fundamentals for lodging exposure, state by state, to pair against tickers and filings (investors and quants). Data scientists get a compact, typed feature table keyed on standard geography codes (data scientists). Journalists and academics get quotable, attributed figures - more than 9.1 million jobs, more than $894 billion in GDP - with a published methodology standing behind them (journalists and academics). Developers get a tiny stable schema that seeds any hospitality pipeline in minutes (developers and builders).

This dataset covers the demand-and-impact side of lodging. On the supply side, Booking.com hotel listings observes individual properties as travelers see them, and Hugging Face hotel search datasets add query-level behavior. On the capital side, the Nareit Lodging/Resorts Sector Overview enumerates the listed lodging REITs, Yahoo Finance Quote Pages (Hotel REITs) attach twenty-one market metrics per ticker, and SEC XBRL Company Facts supplies filing-grade fundamentals. For macro context around travel spending, BEA Travel and Tourism Satellite Account works at national scale.

The natural sequence: size the markets here, enumerate who owns them in the sector overview, then go deep per issuer in the quotes and filings - each hop keyed on geography or ticker.

Browse the full pool in the hotel & resort REITs data hub, or see how the sources compare in Nareit Lodging/Resorts vs Yahoo Finance Hotel REIT Quotes.