Airbnb data by city: 6 US markets, one schema

Most Airbnb datasets force a choice: one giant multi-city file where you filter everything yourself, or a scatter of per-city downloads with different column names. This catalog does neither. You get one file per city, all six in the same 12-column schema, so a query written for Austin runs unchanged against San Diego. A 6-row summary (cities-index.csv) sits on top with row counts, median nightly prices, and room-type mix per market.

The table below is that summary. Pick a city, see its size and its median, then take one of the copy-paste queries further down to open just that market.

The 6 cities at a glance

Numbers copied from cities-index.csv, the per-city index that ships free with the sample files.

Three things the table says. New York City carries the most rows (30,234) and the lowest median (175), and that combination comes from its room mix: 42% private rooms, the highest share of the six markets, pulls the typical price down. San Diego is the priciest typical listing at a median of 323 with a conventional mix (83% entire homes). Denver is the smallest market at 4,939 listings, so treat any Denver split with wider error bars.

Every city keeps the same columns: id, name, neighbourhood (a ZIP code area), latitude, longitude, room_type, price, minimum_nights, availability_365, number_of_reviews, reviews_per_month, last_review. The Airbnb data guide explains each one.

One query per city

The dataset-mcp server queries the full released files without any download. Start it, then pass a file argument to pick the city: each market is its own file (airbnb-austin.csv, airbnb-nashville.csv, airbnb-denver.csv, airbnb-new-york-city.csv, airbnb-clark-county-nv.csv, airbnb-san-diego.csv), 100 rows per call.

npx --allow-git=all -y github:jayjex/dataset-mcp
# plain form works on npm 10/11: npx -y github:jayjex/dataset-mcp

Austin, TX

Five listings in downtown Austin, ZIP 78701:

query_dataset("airbnb-six-cities", {file: "airbnb-austin.csv", where: [{column: "neighbourhood", op: "=", value: "78701"}], limit: 5})

Nashville, TN

Nashville runs 90% entire homes, the most investor-shaped mix of the six. The whole market in one filter:

query_dataset("airbnb-six-cities", {file: "airbnb-nashville.csv", where: [{column: "room_type", op: "=", value: "Entire home/apt"}], limit: 5})

Denver, CO

Budget stays well under the city median of 176: cheap nightly rates on short minimum nights:

query_dataset("airbnb-six-cities", {file: "airbnb-denver.csv", where: [{column: "price", op: "lt", value: 80}, {column: "minimum_nights", op: "lt", value: 7}], limit: 5})

New York City, NY

Private rooms below the city median of 175, the segment that dominates NYC supply:

query_dataset("airbnb-six-cities", {file: "airbnb-new-york-city.csv", where: [{column: "room_type", op: "=", value: "Private room"}, {column: "price", op: "lt", value: 175}], limit: 5})

Las Vegas (Clark County), NV

Vegas ZIP codes start with 891, so a contains filter pulls the strip-adjacent market:

query_dataset("airbnb-six-cities", {file: "airbnb-clark-county-nv.csv", where: [{column: "neighbourhood", op: "contains", value: "891"}], limit: 5})

San Diego, CA

Active listings: wide-open calendar plus real review history, useful for gauging demand in the priciest market:

query_dataset("airbnb-six-cities", {file: "airbnb-san-diego.csv", where: [{column: "availability_365", op: "gt", value: 180}, {column: "number_of_reviews", op: "gt", value: 50}], limit: 5})

Operators are =, contains, gt, and lt; get_stats returns counts, min/max, and top values for any column without paging rows. To compare markets instead of opening one, the per-city index is the default file of the dataset, and its median_price_usd column filters straight to the expensive half:

query_dataset("airbnb-six-cities", {where: [{column: "median_price_usd", op: "gt", value: 200}], limit: 6})

Setup steps and worked examples for all five datasets: For agents.

FAQ

How do I get Airbnb data for one specific city?

Three routes. The free 20-row sample CSV shows the schema on Austin rows. The full pack ships one CSV per city (airbnb-austin.csv, airbnb-nashville.csv, airbnb-denver.csv, airbnb-new-york-city.csv, airbnb-clark-county-nv.csv, airbnb-san-diego.csv), 90,169 rows in total. Or query a single city file over MCP without downloading anything, as in the examples above.

Which cities does the Airbnb 6-city dataset cover?

Austin, TX (11,295 listings, snapshot 2026-06-22), Nashville, TN (10,242, 2026-06-26), Denver, CO (4,939, 2026-06-30), New York City, NY (30,234, 2026-08-10), Las Vegas Clark County, NV (20,234, 2026-06-27), and San Diego, CA (13,225, 2026-06-27). That is 90,169 listings in one shared 12-column schema, built from Inside Airbnb summary data.

How do I compare Airbnb prices across these cities?

Use cities-index.csv, the 6-row per-city summary that ships with the free sample: it carries row counts, median nightly prices (175 to 323 USD), and room-type mix for all six markets. Medians travel better than means because a few very expensive listings skew averages. One caveat: each city was captured on its own snapshot date, June through August 2026, so treat the comparison as a screen, not a same-day quote.

Get all six cities in one pack

90,169 rows across 6 city files plus cities-index.csv, data dictionary, methodology, and source checksums. Checkout and download run through Getly.

Keep reading

• Airbnb data download guide: the 12 columns explained, snapshot dates, MCP queries
• Airbnb occupancy data: what a listings snapshot can and cannot tell you about occupancy, and the proxies that work
• Airbnb dataset sample: 20 free rows to test your import against
• Airbnb rental analysis: a worked market screen using this same per-city index
• Inside Airbnb alternative: what the 6-city pack adds over the raw per-city files
• Airbnb Listings, 6 US Cities: dataset page with preview and stats