North America short-term rental supply intelligence (quarterly)

Name: North America short-term rental supply intelligence (quarterly)
Creator: Inside Airbnb
License: https://creativecommons.org/licenses/by/4.0/
Keywords: tourism, travel, rental, housing, demand-forecasting, united-states, canada, quarterly

Commercial use OKFreshLicense: CC-BY-4.0

Query in workbench

The full snapshot isn't published yet — check back after the next refresh.Download sample CSV Download sample JSONSample rows only (up to 20) — not the complete dataset.

Quarterly short-term-rental supply intelligence for 24 major US and Canadian cities, built from Inside Airbnb's public city listing files (CC-BY 4.0, keyless download): per-city listing counts, room-type mix, median nightly price in local currency and USD (CAD converted with the FRED DEXCAUS monthly average), availability distribution, review-velocity demand signals, professional-host concentration, license coverage, and listings per 100k residents. A documented absolute 0-100 demand-pressure score (40% inverse availability, 30% recent-review activity, 30% low-availability share) with hot/active/balanced/soft tiers summarizes tourism demand pressure per city. Scrape months follow Inside Airbnb's quarterly releases (current panel: June 2026). Caveats: one scrape per city per quarter and counts include inactive listings; prices are host-posted nightly rates before fees; Canadian USD prices rest on a monthly-average FX rate; city populations are approximate 2024 reference values. Source: Inside Airbnb (CC-BY 4.0).

Source: Inside Airbnb
Rows: 24
Columns: 31
Source cadence: Quarterly
Last refreshed: Oct 1, 2026
Theme: economy

Column	Type	Description
scrape_month	string	Reference month: first day of the scrape month, ISO date, derived from the Inside Airbnb publish-path date. The panel follows Inside Airbnb's quarterly releases (current: 2026-06). (unit: ISO date)
country_code	string	ISO alpha-3 country code (USA or CAN). (unit: ISO 3166-1 alpha-3)
region_code	string	US state postal code or Canadian province code, lowercase (e.g. ny, on, bc).
city_slug	string	Stable city code (e.g. toronto, new-york-city, clark-county-nv). Join key with scrape_month.
city_name	string	Display name of the city / county / metro area.
geo_level	string	Geographic level of the Inside Airbnb extract: city, county (Clark County NV = Las Vegas metro) or msa (Twin Cities).
currency	string	Local price currency: USD for US cities, CAD for Canadian cities.
listings_total	integer	Listing count in the city's scrape file. Includes inactive listings — see share_avail_ge300. (unit: listings)
entire_home_share	float	Share of listings that are entire homes/apartments. (unit: share 0-1)
private_room_share	float	Share of listings that are private rooms. (unit: share 0-1)
shared_room_share	float	Share of listings that are shared rooms. (unit: share 0-1)
hotel_room_share	float	Share of listings that are hotel rooms. (unit: share 0-1)
median_price_local	float	Median host-posted nightly price in local currency (before fees and taxes). (unit: currency / night)
median_price_usd	float	Median nightly price converted to USD (Canadian cities via the FRED DEXCAUS monthly average). (unit: USD / night)
fx_cad_per_usd	float	CAD-per-USD rate used for the conversion (1.0 on USD rows). (unit: CAD per USD)
fx_month	string	Month the FX rate applies to (YYYY-MM).
fx_source	string	FX provenance: 'FRED DEXCAUS <month> monthly avg', the pinned fallback, or 'n/a (USD)'.
median_availability_365	float	Median open nights in the next 365 days across listings. (unit: nights)
share_avail_le90	float	Share of listings with <= 90 open nights in the next year: strong demand signal. (unit: share 0-1)
share_avail_ge300	float	Share of listings with >= 300 open nights: likely inactive or seasonally blocked supply. (unit: share 0-1)
share_reviews_ltm_gt0	float	Share of listings with at least one review in the last twelve months: active-demand signal. (unit: share 0-1)
median_reviews_per_month	float	Median review velocity across listings. (unit: reviews / month)
share_pro_host	float	Share of listings whose host holds >= 3 listings: professionalization gauge. (unit: share 0-1)
top10_host_share	float	Share of the city's listings held by its ten largest hosts: concentration gauge. (unit: share 0-1)
share_licensed	float	Share of listings with a non-empty license field. Licensing regimes differ by city — descriptive, not a quality rank. (unit: share 0-1)
listings_per_100k	float	Listings per 100,000 residents (approximate 2024 reference population). (unit: listings / 100k)
demand_pressure_score	float	Documented absolute 0-100 composite: 100 * (0.40 * (1 - median_availability_365 / 365) + 0.30 * share_reviews_ltm_gt0 + 0.30 * share_avail_le90). Higher = hotter tourism demand pressure versus available supply. Absolute so quarters compare. (unit: 0-100 score)
demand_tier	string	Score tier: hot (>= 60), active (>= 45), balanced >= 30), soft (< 30).
population_ref	integer	Approximate 2024 reference population used only for listings_per_100k (US Census Vintage 2024 / StatCan 2024 municipal estimates). (unit: persons)
source_url	string	Exact Inside Airbnb CSV URL behind the row.
row_hash	string	Deterministic 16-hex row hash of city_slug + scrape_month (idempotency).

First 10 sample rows — a preview, not the complete dataset.

scrape_month	country_code	region_code	city_slug	city_name	geo_level	currency	listings_total	entire_home_share	private_room_share	shared_room_share	hotel_room_share	median_price_local	median_price_usd	fx_cad_per_usd	fx_month	fx_source	median_availability_365	share_avail_le90	share_avail_ge300	share_reviews_ltm_gt0	median_reviews_per_month	share_pro_host	top10_host_share	share_licensed	listings_per_100k	demand_pressure_score	demand_tier	population_ref	source_url	row_hash
2026-06-01	CAN	qc	montreal	Montreal	city	CAD	10,677	0.817	0.175	0.003	0.005	163	116.147	1.403	2026-06	pinned fallback 2026-06 (FRED 2026-10-01)	210	0.25	0.25	0.658	0.2	0.667	0.09	0.529	593.167	44.21	balanced	1,800,000	https://data.insideairbnb.com/canada/qc/montreal/2026-06-15/visualisations/listings.csv	b9b47a9424abe823
2026-06-01	CAN	on	ottawa	Ottawa	city	CAD	2,727	0.61	0.384	0.001	0.006	119	84.794	1.403	2026-06	pinned fallback 2026-06 (FRED 2026-10-01)	254	0.225	0.348	0.651	—	0.406	0.084	0.592	254.86	38.424	balanced	1,070,000	https://data.insideairbnb.com/canada/on/ottawa/2026-06-24/visualisations/listings.csv	eef19df070111df9
2026-06-01	CAN	qc	quebec-city	Quebec City	city	CAD	2,211	0.849	0.147	0	0.004	215	153.199	1.403	2026-06	pinned fallback 2026-06 (FRED 2026-10-01)	254	0.13	0.305	0.788	0.63	0.575	0.158	0.748	374.746	39.722	balanced	590,000	https://data.insideairbnb.com/canada/qc/quebec-city/2026-06-19/visualisations/listings.csv	0de7144bfc063cba
2026-06-01	CAN	on	toronto	Toronto	city	CAD	22,226	0.685	0.311	0.001	0.003	185	131.823	1.403	2026-06	pinned fallback 2026-06 (FRED 2026-10-01)	185	0.331	0.277	0.55	1.045	0.323	0.029	0.541	734.744	46.165	active	3,025,000	https://data.insideairbnb.com/canada/on/toronto/2026-06-15/visualisations/listings.csv	6751a6e5b41316dc
2026-06-01	CAN	bc	vancouver	Vancouver	city	CAD	6,525	0.816	0.181	0.002	0	319	227.305	1.403	2026-06	pinned fallback 2026-06 (FRED 2026-10-01)	162	0.347	0.216	0.672	—	0.29	0.075	0.823	959.559	52.83	active	680,000	https://data.insideairbnb.com/canada/bc/vancouver/2026-06-15/visualisations/listings.csv	36a823dab01b8905
2026-06-01	CAN	bc	victoria	Victoria	city	CAD	3,531	0.863	0.135	0.003	0	245	174.576	1.403	2026-06	pinned fallback 2026-06 (FRED 2026-10-01)	199	0.272	0.236	0.729	1.3	0.285	0.086	0.791	3,531	48.234	active	100,000	https://data.insideairbnb.com/canada/bc/victoria/2026-06-29/visualisations/listings.csv	81a228d6e3db2950
2026-06-01	CAN	mb	winnipeg	Winnipeg	city	CAD	1,790	0.749	0.238	0	0.013	136	96.908	1.403	2026-06	pinned fallback 2026-06 (FRED 2026-10-01)	284	0.169	0.452	0.772	—	0.445	0.119	0.429	232.468	37.117	balanced	770,000	https://data.insideairbnb.com/canada/mb/winnipeg/2026-06-22/visualisations/listings.csv	c18348559f72052a
2026-06-01	USA	tx	austin	Austin	city	USD	11,320	0.826	0.149	0.007	0.018	217	217	1	2026-06	n/a (USD)	261	0.216	0.406	0.725	—	0.509	0.07	0.272	1,143.434	39.632	balanced	990,000	https://data.insideairbnb.com/united-states/tx/austin/2026-06-22/visualisations/listings.csv	0da21b665cf280c6
2026-06-01	USA	ma	boston	Boston	city	USD	4,414	0.68	0.297	0.001	0.022	279	279	1	2026-06	n/a (USD)	249	0.21	0.341	0.626	0.5	0.71	0.189	0.714	679.077	37.798	balanced	650,000	https://data.insideairbnb.com/united-states/ma/boston/2026-06-15/visualisations/listings.csv	49ad6e64f7fad35f
2026-06-01	USA	il	chicago	Chicago	city	USD	8,704	0.766	0.223	0.004	0.008	223	223	1	2026-06	n/a (USD)	250	0.186	0.357	0.721	—	0.593	0.123	0.662	334.769	39.828	balanced	2,600,000	https://data.insideairbnb.com/united-states/il/chicago/2026-06-24/visualisations/listings.csv	fc06d0c3e0d079b3

Profiled Oct 1, 2026 from snapshot 20261001T153602Z-b4d7eb3b3c52

Measured

Completeness: 98.9%
Rows: 24
Columns: 31
Columns with gaps: 1

Column	Missing	Distinct	Range	Distribution
scrape_monthvarchar	0%	1	—	2026-06-0124
country_codevarchar	0%	2	—	USA17 CAN7
region_codevarchar	0%	17	—	ca3 bc2 on2
city_slugvarchar	0%	23	—	austin1 boston1 chicago1
city_namevarchar	0%	26	—	Austin1 Boston1 Chicago1
geo_levelvarchar	0%	3	—	city22 county1 msa1
currencyvarchar	0%	2	—	USD17 CAD7
listings_totalbigint	0%	24	1,790 → 43,932median 7,125	2 outside 1st–99th percentile
entire_home_sharedouble	0%	25	0.5501 → 0.9036median 0.8028	2 outside 1st–99th percentile
private_room_sharedouble	0%	24	0.0883 → 0.4258median 0.1823	2 outside 1st–99th percentile
shared_room_sharedouble	0%	24	0 → 0.0105median 0.0025	1 outside 1st–99th percentile
hotel_room_sharedouble	0%	24	0.0003 → 0.0364median 0.0098	2 outside 1st–99th percentile
median_price_localdouble	0%	28	119 → 322median 220.5	2 outside 1st–99th percentile
median_price_usddouble	0%	27	84.79 → 322median 218.5	2 outside 1st–99th percentile
fx_cad_per_usddouble	0%	2	1 → 1.4median 1
fx_monthvarchar	0%	1	—	2026-0624
fx_sourcevarchar	0%	2	—	n/a (USD)17 pinned fallback 2026-06 (FRED 2026-10-01)7
median_availability_365double	0%	21	162 → 317median 252	2 outside 1st–99th percentile
share_avail_le90double	0%	23	0.1303 → 0.3473median 0.2221	2 outside 1st–99th percentile
share_avail_ge300double	0%	24	0.2162 → 0.5529median 0.3445	2 outside 1st–99th percentile
share_reviews_ltm_gt0double	0%	24	0.3505 → 0.8013median 0.7116	2 outside 1st–99th percentile
median_reviews_per_monthdouble	33.3%	18	0.03 → 6.66median 0.735	2 outside 1st–99th percentile
share_pro_hostdouble	0%	24	0.2849 → 0.71median 0.5206	2 outside 1st–99th percentile
top10_host_sharedouble	0%	25	0.0291 → 0.1894median 0.0969	2 outside 1st–99th percentile
share_licenseddouble	0%	24	0 → 0.8427median 0.6473	2 outside 1st–99th percentile
listings_per_100kdouble	0%	23	145.07 → 3,531median 713.46	2 outside 1st–99th percentile
demand_pressure_scoredouble	0%	24	31.23 → 52.83median 39.78	2 outside 1st–99th percentile
demand_tiervarchar	0%	2	—	balanced19 active5
population_refbigint	0%	27	100,000 → 8,260,000median 860,000	2 outside 1st–99th percentile
source_urlvarchar	0%	24	—	https://data.insideairbnb.com/canada/bc/vancouver/2026-06-15/visualisations/listings.csv1 https://data.insideairbnb.com/canada/bc/victoria/2026-06-29/visualisations/listings.csv1 https://data.insideairbnb.com/canada/mb/winnipeg/2026-06-22/visualisations/listings.csv1
row_hashvarchar	0%	24	—	04eb810e5043ab8f1 09823706c6857ead1 0da21b665cf280c61

Point any LLM at the metadata endpoint — the documentation above is machine-readable too (JSON-LD + Croissant).

cURL

curl "https://datazimuts.com/v1/datasets/inside_airbnb_intel/na_str_supply_quarterly" | jq '{title, rows, columns_count, license}'

Python

import requests

ds = requests.get("https://datazimuts.com/v1/datasets/inside_airbnb_intel/na_str_supply_quarterly").json()
print(ds["title"], ds["rows"], "rows")

# Sample rows for an LLM context window
for row in ds.get("sample_rows", [])[:5]:
    print(row)

API endpoint: https://datazimuts.com/v1/datasets/inside_airbnb_intel/na_str_supply_quarterly

Tip: fetch /llms.txt for the full machine-readable catalog.

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Pinned to snapshot 20261001T153602Z-b4d7eb3b3c52 and its content hash, so readers get exactly the data you used.

APA

Inside Airbnb. (2026). North America short-term rental supply intelligence (quarterly) [Data set, snapshot 20261001T153602Z-b4d7eb3b3c52, sha256 b4d7eb3b3c52]. Datazimuts. Retrieved 2026-10-02, from https://datazimuts.com/en/datasets/inside_airbnb_intel/na_str_supply_quarterly?snapshot=20261001T153602Z-b4d7eb3b3c52

BibTeX

@misc{dz_inside_airbnb_intel_na_str_supply_quarte_b4d7eb3b,
  title        = {{North America short-term rental supply intelligence (quarterly)}},
  author       = {{Inside Airbnb}},
  year         = {2026},
  publisher    = {Datazimuts},
  howpublished = {\url{https://datazimuts.com/en/datasets/inside_airbnb_intel/na_str_supply_quarterly?snapshot=20261001T153602Z-b4d7eb3b3c52}},
  note         = {Snapshot 20261001T153602Z-b4d7eb3b3c52, sha256 b4d7eb3b3c52eb576054f94618503b7144b5e14ed4644c815b24db70a9c27f39; accessed 2026-10-02}
}

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