Aspen $582, Vegas $127: There Is No National Airbnb Price

We built airbnb-scraper to scrape a city's public Airbnb listings and rank entire-home listings by comp-set pricing position. To stress-test how comparable an "Airbnb price" actually is across the US, we ran it in compare mode across 20 short-term-rental markets, entire-home only, on 22 May 2026. The 4.6× spread between the most expensive market (Aspen, CO at $582/night median) and the cheapest (Las Vegas, NV at $127/night) is the data this post documents.

The problem: The 2026 short-term-rental press is fixated on oversaturation and RevPAR collapse - the gap between top-decile and bottom-quartile operators, the "Airbnbust" headline that recycles every quarter. That's real, but it skips a more basic fact almost nobody reports: there is no national "Airbnb price" at all. The same product, an entire home, costs 4.6× more depending on which US market you book in. And even inside one market, comparable units of the same bedroom count routinely span about 2×. "The average Airbnb costs $X" is a meaningless number. Price is almost entirely a function of market, then position within that market.

This post is a documentary audit of entire-home Airbnb pricing across 20 US markets. Every number below is derivable from the public listing data captured on 22 May 2026, and the downloadable leaderboard CSV is linked from the source-actor banner above. The data is real, the dispersion is real, and the "average Airbnb" framing journalists usually slap on top is wrong.

What is short-term-rental price dispersion? Price dispersion is the spread of prices for a comparable product across sellers or markets. For Airbnb, it means the same thing - an entire home for a given number of guests - does not have one price; it has a wide distribution that depends on market and listing position. See the economics framing on price dispersion.

Why it matters: A single "average nightly rate" headline hides the only two variables that actually move price: which market, and where a listing sits inside that market's distribution. Across 20 US markets, the between-market gap is 4.6× and the within-market, same-bedroom spread is routinely ~2×. Industry data from sources like AirDNA's US market reporting consistently shows resort and mountain markets at the top and big-supply cities at the bottom - the directional pattern this audit confirms.

Use it when: sizing a vacation-rental investment against the right comp set, sanity-checking a "national Airbnb average" stat in a story, briefing a host on where their nightly rate sits versus same-city peers, or sourcing a named-market quantitative anchor for a feature on short-term-rental economics.

Key findings

• Median entire-home ADR ranges 4.6× across 20 US markets - from $582/night in Aspen, CO to $127/night in Las Vegas, NV. No single national figure represents that range.
• Aspen sits 62% above the #2 market. Aspen's $582 median is 1.6× Key West ($359) and 4.6× Las Vegas. The top of the table is mountain-luxury and beach/resort; the bottom is big-supply cities plus value markets.
• Las Vegas is the cheapest entire-home market of all 20 ($127 median), despite its big-spender reputation. Off-Strip condo and home supply plus casino-hotel competition pulls the entire-home median down.
• Same-city, same-bedroom comparable homes routinely vary ~2×. One-bedroom P90/P10 spreads cluster around 2× across markets - and reach 5.54× in Sedona ($146 to $810 for one-bedrooms).
• Loosest comparable pricing: Sedona 5.54×, Park City 3.76×, Asheville 3.27×. Tightest: Scottsdale 1.55× (thin, n=9), Savannah 1.84×, Las Vegas 1.87×.
• 760 entire-home listings across 20 markets were captured in a single snapshot on 22 May 2026 - a representative first-page slice per market, not a census. This crosses over with the same "list price hides the real story" theme in our SaaS pricing time machine.

In this article: The leaderboard · Aspen at the top · Vegas at the bottom · The dispersion layer · What Airbnb hides · Cross-market aggregations · What coverage gets wrong · Methodology · Caveats · FAQ

The 20-market leaderboard - top to bottom by median ADR

Median entire-home nightly ADR, ranked descending, across 20 US short-term-rental markets captured 22 May 2026. The 1-bed columns isolate the one-bedroom cohort within each market so the within-city spread is comparable across rows.

Source: public Airbnb search and room pages for each market, captured 22 May 2026 via the airbnb-scraper Apify actor in compare mode, entire-home only, one search page per market. Medians are over each market's full entire-home cohort; the 1-bed spread is P90/P10 of that market's one-bedroom listings.

The top-5 vs bottom-5 cut

The top five markets - Aspen, Key West, Destin, Gatlinburg, Sedona - all sit at $288 or above and are mountain-luxury or beach/resort destinations. The bottom five - Branson, Las Vegas, New Orleans, Austin, Savannah - sit at $167 or below and are either supply-heavy major cities or value-positioned tourist towns. The top-five median floor ($288) is 1.7× the bottom-five median ceiling ($167). The product is the same. The market is the whole story.

Story A - Aspen, the $582 outlier

Aspen's $582 median entire-home nightly rate is more than 1.6× the second-ranked market, Key West at $359, and 4.6× the cheapest, Las Vegas at $127. It is the clearest single-market outlier in the sample.

The shape behind that number is constrained luxury supply. Aspen is a small mountain market with tight zoning, a short build envelope, and a buyer base that treats nightly rate as secondary to access. Even Aspen's one-bedroom cohort - the smallest, theoretically cheapest unit class - carries a $399 median and a $338–$726 range. The cheap end of Aspen one-bedrooms ($338) is still above the median entire home in 18 of the other 19 markets. There is no budget tier here; the floor is the luxury floor. Aspen's filing of high rates across every bedroom class is the textbook signature of a supply-constrained resort market, not a pricing anomaly.

Story B - Las Vegas, the cheapest market in the sample

Las Vegas posts the lowest entire-home median of all 20 markets at $127/night, despite a national reputation as a big-spender destination. The reputation and the median point in opposite directions.

The mechanism is supply and substitution. Las Vegas carries an enormous off-Strip inventory of condos and single-family homes, and it competes directly against a deep, heavily discounted casino-hotel room market that an entire-home Airbnb has to undercut to win a booking. That compresses the median hard. The high tail still exists - Las Vegas one-bedrooms reach $154 at P90 - but the bulk of entire-home supply sits low. This is the cleanest example in the dataset of why a city's brand tells you nothing about its Airbnb median: the "expensive city" is the cheapest entire-home market here, and the "average Airbnb" framing would have predicted exactly the wrong answer.

Story C - the destination premium is a band, not a number

Even within one bedroom class in the same city, comparable entire homes routinely vary about 2×, and up to 5.54× in Sedona, where one-bedroom listings span $146 to $810. The destination premium is a wide distribution, not a single sticker price.

Across the 20 markets, the one-bedroom P90/P10 spread clusters around 2× - Nashville 2.03×, Gatlinburg 2.16×, New Orleans 2.16×, Aspen 2.15×. The loose end is wide: Sedona 5.54×, Park City 3.76×, Asheville 3.27×, San Diego 2.70×. The tight end is narrow: Scottsdale 1.55× (on a thin n=9), Savannah 1.84×, Las Vegas 1.87×, Myrtle Beach 1.91×. So even after you fix the market and fix the bedroom count, you still cannot quote one price for a comparable home - the realistic answer is a 2× band, and in scenic markets like Sedona it widens to more than 5×. The single number people want does not exist at any level of the data.

Story D - what Airbnb hides from the comp set

The single biggest limit on comparing two listings is what Airbnb does not publish. roomType and exact guest capacity are absent from Airbnb's public search payload, so even a careful comp set can only match on bedroom count - never on the variables that actually explain why two same-size homes differ 2×.

Exact location within a market, view, floor, amenity tier, renovation quality: those are the factors that move price, and they are exactly what Airbnb withholds from the public listing grid. So hosts price into the same fog buyers see. The within-city 2× spread in Story C is not noise - it's real price difference driven by attributes the public data can't see. This is the negative-space finding most coverage misses: the reason there's no clean "Airbnb price" isn't messy data, it's that the platform structurally hides the attributes that would let anyone build a true like-for-like comp. The audit can rank markets and bound the spread; it cannot close the gap, because the closing data isn't public.

Cross-market aggregations

Grouping the 20 markets by destination type makes the pattern explicit. The resort and mountain cluster (Aspen, Park City, Sedona, plus the beach markets Key West, Destin, Gatlinburg, Myrtle Beach, Panama City Beach, Galveston) carries the high medians. The major-metro and value cluster (Las Vegas, New Orleans, Austin, Nashville, Miami, San Diego, Scottsdale, Savannah, Charleston, Asheville, Branson) carries the lower ones, with a few scenic-metro exceptions like San Diego and Asheville sitting mid-table.

One summary stat captures the whole audit: the median entire-home nightly rate across the 20 markets spans from $127 to $582, a 4.6× range, with no market median repeating the national-average framing that press coverage tends to quote. The middle of the table is densely clustered between $160 and $230, which is where sampling noise matters most - see the caveats.

What most coverage gets wrong about Airbnb pricing

Short-term-rental coverage repeats a few framings the data here does not support:

• "The average Airbnb costs $X." There is no national average worth quoting. The same product spans 4.6× across markets and ~2× within a single market and bedroom class. An average across that distribution describes nothing real.
• "Expensive cities have expensive Airbnbs." Las Vegas is the cheapest entire-home market in the sample. Brand and median diverge; supply depth and substitute competition set the median, not the city's spending reputation.
• "Comparable homes should cost about the same." Comparable same-bedroom homes in the same city routinely differ 2×, because the attributes that justify the difference (location, view, tier) aren't in the public data. A 2× band is the honest answer, not a point estimate.
• "The Airbnbust means rates are collapsing everywhere." Oversaturation and RevPAR pressure are real in supply-heavy markets, but they don't generalize. Constrained resort markets like Aspen show no budget tier at all - the floor is still luxury.
• "More listings means more accurate averages." Larger samples tighten the median, not the dispersion. Even with thousands of listings, the within-market spread stays wide because it's structural, not a sampling artifact.

Methodology

• Tool: airbnb-scraper Apify actor, run in compare mode with roomType = entire_home (an Airbnb-side filter), persona = investor, and enableMarketMemory = false. The actor reads public Airbnb search and room pages over residential proxies and returns each entire-home listing's nightly rate and its comp-set pricing position within the market.
• Sample queried: 20 US short-term-rental markets - Aspen CO, Key West FL, Destin FL, Gatlinburg TN, Sedona AZ, Myrtle Beach SC, San Diego CA, Charleston SC, Asheville NC, Panama City Beach FL, Park City UT, Galveston TX, Miami FL, Nashville TN, Scottsdale AZ, Savannah GA, Austin TX, New Orleans LA, Branson MO, Las Vegas NV.
• Date captured: single-day snapshot, 22 May 2026.
• Field filters: entire-home listings only, applied as an Airbnb-side search filter. One Airbnb search page per market (the actor's pagination returns roughly one page), yielding ~28–55 entire homes each.
• Total records captured: 760 entire-home listings across 20 markets.
• Aggregation rule: per market, median ADR is computed over all entire-home nightly rates in that market's page. The 1-bed spread is the P90/P10 ratio of that market's one-bedroom cohort. Nightly rates are parsed per-night from a 5-night quote - not a single-night rate and not fee-inclusive.
• What was deliberately not reported: the actor's underpriced/overpriced labels are percentile-defined, so a "% of listings underpriced" figure would be a mechanical artifact (~25% in any market) rather than a finding. It is excluded on purpose.
• Known gaps: single search page per market; roomType and exact guest capacity are not exposed by Airbnb in search results, so comparability is bedroom-matched only; revenue and occupancy are not estimated in compare mode.
• Cross-reference: the ranking is directionally consistent with published third-party short-term-rental datasets - for example, AirROI's 2026 US market data and AirDNA both show resort and mountain markets commanding the highest ADR and big-supply cities the lowest. This audit uses those sources only as a directional sanity check, not for specific figures.

Caveats and what this data does not say

• Single-day snapshot. Everything here was captured on 22 May 2026. Airbnb pricing moves with season, day-of-week, and events; a different capture date would shift absolute rates.
• One search page per market, not a census. Each market is represented by ~28–55 entire homes - a slice of first-page inventory, not the full market. Medians from this slice are stable, but they're not the market's true population median.
• Treat the exact ordering as directional. The robust claims are the extremes - Aspen at the top, Las Vegas and Branson at the bottom, and the 4.6× top-to-bottom gap. Adjacent ranks in the clustered $160–$230 middle of the table are within sampling noise; a re-run on a different day moved some middle medians.
• Comparability is bedroom-matched only. Because Airbnb doesn't expose roomType or exact capacity in search results, the comp set can match on bedroom count but not on size, layout, or guest cap.
• Thin 1-bed cells need caution. The one-bedroom cohorts range n=9–37. Spreads are directional, and the thinnest cells - Scottsdale n=9, Aspen n=11, Park City n=13, Sedona n=16 - should be read carefully. Sedona's 5.54× rests on a single high outlier ($810).
• Prices are per-night from a 5-night quote. Not a single-night rate and not fee-inclusive, so absolute figures will differ from a one-night booking total.
• No occupancy or revenue. Compare mode reports asking rates and pricing position, not realized bookings. A high ADR market is not automatically a high-revenue market.

Press lift-out for journalists

There is no national "Airbnb price." A 2026 ApifyForge analysis of 760 entire-home Airbnb listings across 20 US markets found the median nightly rate ranges 4.6× - from $582 in Aspen, Colorado down to $127 in Las Vegas, Nevada - and even comparable same-bedroom homes inside a single market routinely differ about 2×, reaching 5.5× in Sedona, Arizona.

Source: airbnb-scraper Apify actor, compare mode, entire-home only, reading public Airbnb search and room pages; 760 listings across 20 US markets captured 22 May 2026. Directionally consistent with third-party short-term-rental datasets (AirROI, AirDNA).

Embeddable visuals

Chart 1 - Ranked median ADR by market

A horizontal bar chart, one bar per market, sorted descending from Aspen ($582) at the top to Las Vegas ($127) at the bottom. Bars labeled with the dollar median. Color the top five (Aspen, Key West, Destin, Gatlinburg, Sedona) in a warm resort tone and the bottom five (Branson, Las Vegas, New Orleans, Austin, Savannah) in a cool tone to make the 4.6× gap visually obvious. Source line: ApifyForge analysis of 760 entire-home Airbnb listings across 20 US markets, captured 22 May 2026.

Chart 2 - Within-market comparable spread

A horizontal bar chart of the one-bedroom P90/P10 spread per market, sorted descending. Sedona's 5.54× bar should be visually isolated and annotated as a single-outlier-driven figure (n=16). Draw a reference line at 2× to show how tightly most markets cluster around the typical comparable spread, with Scottsdale (1.55×, n=9) at the low end. Source line: ApifyForge analysis of one-bedroom entire-home Airbnb listings across 20 US markets, captured 22 May 2026.

Chart 3 - Top-5 vs bottom-5 medians

A grouped comparison showing the five highest-median markets against the five lowest, with the top-five median floor ($288) and bottom-five median ceiling ($167) marked. A bracket spanning the two groups labeled "1.7× floor-to-ceiling" makes the destination effect legible at a glance. Source line: ApifyForge analysis of 760 entire-home Airbnb listings across 20 US markets, captured 22 May 2026.

Frequently asked questions

What is the most expensive US Airbnb market in 2026?

In this 20-market audit captured on 22 May 2026, Aspen, Colorado is the most expensive, with a median entire-home nightly rate of $582. That's more than 1.6× the second-ranked market, Key West, Florida ($359), and 4.6× the cheapest market, Las Vegas, Nevada ($127). The ranking reflects a single first-page sample per market, so treat Aspen's top position as robust and the clustered middle ranks as directional.

Why is Las Vegas the cheapest Airbnb market in the study?

Las Vegas posts the lowest entire-home median ($127) because of supply and substitution. The city carries a large off-Strip inventory of condos and homes, and entire-home listings compete directly against a deep, discounted casino-hotel market they have to undercut to win bookings. That compresses the median, even though the high tail still reaches $154 at the 90th percentile for one-bedrooms.

Is there a single average price for an Airbnb?

No. Across 20 US markets the median entire-home rate spans 4.6× ($127 to $582), and even within one market and one bedroom class, comparable homes routinely differ about 2×, reaching 5.5× in Sedona. A national average across that distribution describes nothing real. Price is a function of market first, then position within that market, not a single quotable number.

How much do comparable Airbnb homes vary within the same city?

Within a single market and the same bedroom count, comparable entire homes typically span about 2× from the 10th to 90th percentile. The tightest market in the sample is Scottsdale at 1.55× (thin sample, n=9); the widest is Sedona at 5.54×, driven by a single high outlier. So even after fixing market and size, the realistic answer is a price band, not a point estimate.

Where can I download the underlying Airbnb data myself?

The full 20-market leaderboard is available as a downloadable CSV from the source-actor banner at the top of this page, and the data was produced by the airbnb-scraper Apify actor. The actor reads public Airbnb search and room pages and returns entire-home nightly rates with comp-set pricing position; running it yourself reproduces the leaderboard for any set of markets.

How does this compare to other short-term-rental datasets?

The ranking is directionally consistent with published third-party datasets like AirROI and AirDNA: resort and mountain markets command the highest ADR, and big-supply cities sit lowest. This audit is a single-day, single-page snapshot rather than a continuous market index, so its strength is the cross-market comparison and the dispersion finding, not absolute revenue or occupancy estimates.

Why does this study only match listings on bedroom count?

Because Airbnb doesn't expose roomType or exact guest capacity in its public search payload. The comp set can match on bedroom count but not on size, view, location within the market, or amenity tier - which are exactly the attributes that explain why two same-size homes differ 2×. That structural gap is the reason a clean like-for-like Airbnb comparison isn't possible from public data alone.

Ryan Clinton publishes Apify actors and MCP servers as ryanclinton and builds developer tools at ApifyForge. The leaderboard above was produced via the airbnb-scraper actor across 20 market queries against public Airbnb search and room pages; the methodology, analysis, and framing are independent of any product positioning. This audit shares the "list price hides the real story" theme with our SaaS pricing time machine and the multi-entity leaderboard method of the Trustpilot two-tier trust index.

Last updated: May 2026