Hotel Occupancy Rate Benchmarks By City 2026

Occupancy looks simple: rooms sold divided by rooms available. Benchmarking occupancy is harder because the comparison set defines the conclusion. A 72% annual occupancy may be excellent for a seasonal resort with strong ADR and long closure periods. It may be weak for an airport hotel with year-round demand. It may be irrelevant for a luxury hotel that intentionally protects rate and accepts lower occupancy. The city label is not enough.

The safest benchmark starts with the business question. If the question is whether a hotel is underperforming revenue potential, occupancy must be read with ADR and RevPAR. If the question is whether a market can absorb new supply, occupancy must be read with supply growth, seasonality and compression nights. If the question is whether a hotel should discount, occupancy must be read with booking pace and remaining lead time. A static benchmark number cannot answer all three questions.

Public city-level hotel occupancy data is often partial, paid, delayed or embedded in charts. That does not make benchmarking impossible. It means the analyst has to state the source and precision level. A named STR comp set is stronger than a public press release. A public press release is stronger than an OTA scrape. An OTA scrape is not an occupancy benchmark at all unless it is tied to actual rooms sold and available inventory.

Milan is an event and business-compression market with fashion, fairs, football, Formula 1 spillover, banking and luxury travel. The CoStar/STR September 2025 release is a useful example because it shows the difference between monthly performance and peak-night compression. A single peak-night occupancy number is not an annual benchmark, but it shows how event calendars can pull ADR and RevPAR above normal city levels.

Rome is a global leisure, pilgrimage, luxury and culture market with a very different booking mix. Jubilee-related years, Vatican-area demand, luxury openings and international source markets can shift ADR without making the whole city equally strong. A hotel investor needs submarket, category and asset-condition context. Rome occupancy should be benchmarked against similar properties and periods, not against a generic Italian annual number.

Venice, Florence, Bologna, Verona, Turin, Naples and Palermo each require different demand logic. Venice has visitor-management and heritage constraints. Florence has culture and luxury demand but limited central supply. Bologna can be fair-driven. Verona blends culture, events and regional gateways. Turin can be business-leisure hybrid. Naples and Palermo have strong international growth narratives but different infrastructure and seasonality. A city benchmark is credible only after the city demand engine is named.

This briefing treats hotel occupancy benchmarks by city as an underwriting problem rather than a copywriting exercise. Public reports from STR or CoStar, CBRE, JLL, Cushman & Wakefield, Eurostat, ISTAT and national regulators are useful because they anchor the market narrative in institutions that hotel investors already recognise. They are not the same as a property data room. A lender will still want PMS exports, channel-manager pickup, owner financial statements, tax records, capex logs, staffing schedules, insurance history and the actual franchise or management agreement. The public layer answers whether the market is worth studying. It does not prove that a specific asset is priced correctly.

The investor question behind this page is: is a target hotel below, in line with, or above its true competitive demand curve for the dates and segments it serves? That question cannot be answered by one headline figure. Hotel assets blend real estate, operating company risk, local regulation, distribution economics, seasonality, labour exposure and capital expenditure. A room night is perishable, but the building is durable and expensive to change. A good model therefore starts with the simplest measurable drivers, then adds risk adjustments only when the supporting evidence is visible. When the evidence is not visible, the correct move is to state the gap instead of inventing precision.

A recurring limitation is that free public sources usually do not publish daily city comp-set occupancy, ADR and RevPAR by chain scale and room type. This is especially important for early-stage hospitality data products such as Nexorev. A founder can build strong market intelligence from public data, but production-grade recommendations need the hotel owner to share reservation pace, cancellations, no-shows, restrictions, room-type mix, direct-channel cost, OTA commission, taxes, payroll and maintenance context. Public benchmarks are a map. PMS and accounting exports are the asset survey.

For that reason, every worked example below is labelled as a calculation example, not as a claimed transaction, customer result, valuation opinion or legal conclusion. The examples use round numbers because round numbers make the formula auditable. They are designed to let an investor, operator or advisor reproduce the arithmetic in a spreadsheet and replace the assumptions with their own evidence. That is the standard Nexorev uses for pitch preparation: transparent enough to challenge, conservative enough to avoid false proof, and specific enough to support a serious diligence conversation.

Layer one is the official demand base. Eurostat and ISTAT help establish whether accommodation nights are growing, stable or declining. They also separate domestic and international demand where available. This layer is useful for macro context, but it cannot tell an operator what occupancy should be next Saturday.

Layer two is hotel performance data. STR or CoStar data is the preferred benchmark where a relevant market or comp set is available. A marketwide figure is still not perfect. A boutique hotel needs the closest possible peer group by class, location, demand segment and operating model. If the peer group includes hotels with large meeting space or different seasonality, the benchmark should be adjusted.

Layer three is the hotel own pickup curve. This is where most pricing decisions should happen. Occupancy on the books 90, 60, 30, 14, 7 and 3 days before arrival tells the operator whether the hotel is ahead or behind its own normal pace. The benchmark is not only external. The hotel history is often the best early warning system.

For a founder-led hospitality data venture, city occupancy benchmarking should be packaged as a decision memo, not as a decorative market slide. The first page should state the source hierarchy: official statistics first, institutional hotel research second, operator data third, and vendor claims only where they describe a product feature. The second page should list the assumptions that change the output most. The third page should show the formula and one sensitivity table. That format is less flashy than a large TAM chart, but it is easier for a hotel owner or investor to trust because the moving parts are visible.

Mustafa Bilgic's role in Nexorev is deliberately founder-led. The company is pilot-stage, based in Adiyaman, Turkiye, and aimed at hospitality intelligence rather than generic travel content. That means the content has to do two jobs at once: educate search users who need a clear methodology, and show investors that the founder understands the difference between public demand signals and operating proof. The safest way to achieve both is to separate sourced facts, calculation examples, and product implications in the page structure.

The practical data-room artifact is a one-tab model that mirrors the article: inputs, source links, calculation steps, sensitivity checks and an exception log. If an input comes from STR, CBRE, JLL, Eurostat, ISTAT or a national ministry, the model should show the link and extraction date. If an input comes from a hotel owner, the model should show the file name, period, cleaning rule and any exclusion. If an input is hypothetical, it should be named hypothetical. That discipline prevents a common early-stage mistake: allowing a useful model to look more certain than it is.

Occupancy rate = rooms sold / rooms available. Benchmark gap = subject occupancy - comparable benchmark occupancy. RevPAR check = ADR x occupancy.

Worked example: a hypothetical Milan hotel sold 78 rooms out of 100, so occupancy is 78%. If the relevant comp benchmark for the same dates is 82%, the occupancy gap is -4 percentage points. If ADR is EUR 260 and the comp ADR is EUR 230, the hotel may still lead RevPAR.

Worked RevPAR check: subject RevPAR = EUR 260 x 0.78 = EUR 202.80. Comp RevPAR = EUR 230 x 0.82 = EUR 188.60. The subject hotel trails occupancy but leads revenue per available room.

Worked action example: if the hotel is -8 points behind benchmark 30 days out but only -2 points behind seven days out, the issue may be booking pace rather than final demand. The pricing response should be different from a hotel that remains -8 points behind on arrival day.

Investors should ask for city benchmarks that match the asset class and demand segment. If a seller uses a broad market occupancy figure, the buyer should request the comp-set definition before accepting the conclusion.

For Nexorev, city benchmarking is a natural pilot feature: the product can show whether a hotel is behind market, behind its own pace, or ahead on RevPAR even when occupancy looks lower.