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

Usage commercial OKÀ jourLicence: CC-BY-4.0

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Le jeu complet n’est pas encore publié — revenez après la prochaine actualisation.Télécharger l'exemple CSV Télécharger l'exemple JSONLignes d’exemple seulement (jusqu’à 20) — pas le jeu de données complet.

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).

Les titres et les descriptions proviennent des sources de données, en anglais.

Source: Inside Airbnb
Lignes: 24
Colonnes: 31
Cadence de la source: Trimestrielle
Dernière actualisation: 1 oct. 2026
Thème: economy

Colonne	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).

10 premières lignes d’exemple — un aperçu, pas le jeu de données complet.

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

Profilé le 1 oct. 2026 à partir de l’instantané 20261001T153602Z-b4d7eb3b3c52

Mesuré

Complétude: 98,9 %
Lignes: 24
Colonnes: 31
Colonnes incomplètes: 1

Colonne	Manquant	Distinctes	Plage	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 932médiane 7 125	2 hors du 1er–99e centile
entire_home_sharedouble	0 %	25	0,5501 → 0,9036médiane 0,8028	2 hors du 1er–99e centile
private_room_sharedouble	0 %	24	0,0883 → 0,4258médiane 0,1823	2 hors du 1er–99e centile
shared_room_sharedouble	0 %	24	0 → 0,0105médiane 0,0025	1 hors du 1er–99e centile
hotel_room_sharedouble	0 %	24	0,0003 → 0,0364médiane 0,0098	2 hors du 1er–99e centile
median_price_localdouble	0 %	28	119 → 322médiane 220,5	2 hors du 1er–99e centile
median_price_usddouble	0 %	27	84,79 → 322médiane 218,5	2 hors du 1er–99e centile
fx_cad_per_usddouble	0 %	2	1 → 1,4médiane 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 → 317médiane 252	2 hors du 1er–99e centile
share_avail_le90double	0 %	23	0,1303 → 0,3473médiane 0,2221	2 hors du 1er–99e centile
share_avail_ge300double	0 %	24	0,2162 → 0,5529médiane 0,3445	2 hors du 1er–99e centile
share_reviews_ltm_gt0double	0 %	24	0,3505 → 0,8013médiane 0,7116	2 hors du 1er–99e centile
median_reviews_per_monthdouble	33,3 %	18	0,03 → 6,66médiane 0,735	2 hors du 1er–99e centile
share_pro_hostdouble	0 %	24	0,2849 → 0,71médiane 0,5206	2 hors du 1er–99e centile
top10_host_sharedouble	0 %	25	0,0291 → 0,1894médiane 0,0969	2 hors du 1er–99e centile
share_licenseddouble	0 %	24	0 → 0,8427médiane 0,6473	2 hors du 1er–99e centile
listings_per_100kdouble	0 %	23	145,07 → 3 531médiane 713,46	2 hors du 1er–99e centile
demand_pressure_scoredouble	0 %	24	31,23 → 52,83médiane 39,78	2 hors du 1er–99e centile
demand_tiervarchar	0 %	2	—	balanced19 active5
population_refbigint	0 %	27	100 000 → 8 260 000médiane 860 000	2 hors du 1er–99e centile
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

Dirigez n’importe quel LLM vers le point d’accès des métadonnées — la documentation ci-dessus est aussi lisible par machine (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)

Point d’accès API : https://datazimuts.com/v1/datasets/inside_airbnb_intel/na_str_supply_quarterly

Astuce : récupérez /llms.txt pour le catalogue complet lisible par machine.

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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/fr/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/fr/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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