US metro housing-supply heat (monthly, Realtor.com)
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Monthly US metro housing-supply intelligence from Realtor.com Economic Research's Inventory Core Metrics (keyless public CSV, 925 CBSAs metro + micro, 2016-07 -> present: median listing price, active/new/pending listings, median days on market, price-cut share, pending ratio). Per (month, metro): 12-month changes of price, inventory, new/pending supply and days on market; trailing-12-month z-scores of inventory scarcity, sales velocity, price-cut pressure and absorption; a documented 0-100 seller-heat index (thin inventory + fast sales + rising prices + strong absorption) with heat tiers and per-month cross-sectional percentiles; seller's/buyer's market flags; inventory-surge, price-cut-wave and stale-market flags. Upstream gaps fail the ingest loud, never imputed. The supply-side complement to the ZHVI value-momentum and ZORI rent panels: hot seller's markets read as confident household balance sheets (Shopify demand, HubSpot territory heat), loosening markets as affordability stress and churn risk (Stripe) — joinable on ISO month + metro. Realtor.com data reused with required attribution; commercial_use=no.
Les titres et les descriptions proviennent des sources de données, en anglais.
- Lignes
- 113 775
- Colonnes
- 38
- Cadence de la source
- Mensuelle
- Dernière actualisation
- 1 oct. 2026
- Thème
- housing
| Colonne | Type | Description |
|---|---|---|
| month | string | Reference month as month-end ISO date (e.g. 2026-09-30 for the file's 202609 vintage). Hub convention. (unit: ISO date (month-end)) |
| country_code | string | ISO alpha-3 country code (USA for every row). (unit: ISO 3166-1 alpha-3) |
| metro_key | string | Stable metro join key: slug of the Realtor.com CBSA title (e.g. 'new-york-newark-jersey-city-ny-nj'). Primary join key with month. (unit: code) |
| metro_name | string | Realtor.com CBSA title as published. (unit: name) |
| cbsa_code | string | 5-digit CBSA code as published (stable numeric id). (unit: code) |
| state_codes | string | Comma-joined 2-letter state codes parsed from the CBSA title suffix ('' when unparseable). (unit: code) |
| household_rank | integer | Realtor.com's household-count rank of the CBSA (1 = New York-Newark-Jersey City). (unit: rank) |
| median_listing_price_usd | float | Median listing price of active inventory, dollars. (unit: USD) |
| active_listing_count | float | Count of active listings. (unit: count) |
| median_days_on_market | float | Median days on market of active inventory (null where not published). (unit: days) |
| new_listing_count | float | Count of new listings entering the market that month. (unit: count) |
| pending_listing_count | float | Count of pending listings (under contract; null where not published — observed for small/micro CBSAs). (unit: count) |
| price_reduced_share | float | Share of active listings with a price reduction, percent (as reported by Realtor.com; null where not published). (unit: share) |
| median_listing_price_psf_usd | float | Median listing price per square foot, dollars (null where not published). (unit: USD/sqft) |
| total_listing_count | float | Total listing count (active + pending) as published (null where not published). (unit: count) |
| pending_ratio | float | Pending listings as a share of total listings, as published (null where not published). (unit: share) |
| price_increased_share | float | Share of active listings with a price increase, percent (as reported by Realtor.com; null where not published). (unit: share) |
| quality_flag | float | Realtor.com's own per-row data-quality flag as published (0.0/1.0; null where not computed). Rows are kept regardless; consumers may filter. (unit: flag) |
| price_yoy_pct | float | 12-month percent change of median_listing_price_usd; null for the first 12 months of each metro. (unit: percent) |
| active_yoy_pct | float | 12-month percent change of active_listing_count. (unit: percent) |
| new_yoy_pct | float | 12-month percent change of new_listing_count. (unit: percent) |
| pending_yoy_pct | float | 12-month percent change of pending_listing_count. (unit: percent) |
| dom_yoy_pct | float | 12-month percent change of median_days_on_market. (unit: percent) |
| z12_active | float | Trailing-12-month z-score of active_listing_count (window includes the current month; sample std; null when the window is short or std is zero). Negative = unusually thin supply. (unit: z-score) |
| z12_dom | float | Trailing-12-month z-score of median_days_on_market. Negative = unusually fast sales. (unit: z-score) |
| z12_price_reduced_share | float | Trailing-12-month z-score of price_reduced_share. (unit: z-score) |
| z12_pending_ratio | float | Trailing-12-month z-score of pending_ratio (absorption). (unit: z-score) |
| seller_heat_index | float | Documented 0-100 composite: 100*(0.35*minmax(clip(-z12_active,-3,3)) + 0.25*minmax(clip(-z12_dom,-3,3)) + 0.25*minmax(clip(price_yoy_pct,-15,15)) + 0.15*minmax(clip(z12_pending_ratio,-3,3))). High = thin inventory AND fast sales AND rising prices AND strong absorption (a seller's market). Null when any input is null. (unit: 0-100) |
| heat_tier | string | Bucket of seller_heat_index: very_high >= 80, high >= 65, moderate >= 45, low >= 30, very_low < 30. (unit: categorical) |
| heat_percentile | float | Per-month cross-sectional percentile of seller_heat_index across scored metros: 1-(rank-1)/n with dense rank descending (1.0 = hottest market that month). Null when the month has < 10 scored metros. (unit: 0-1) |
| sellers_market_flag | integer | 1 when seller_heat_index >= 70. (unit: 0/1) |
| buyers_market_flag | integer | 1 when seller_heat_index <= 30. (unit: 0/1) |
| inventory_surge_flag | integer | 1 when active_listing_count equals its trailing-12-month maximum (a supply wave); null until 12 observations exist. (unit: 0/1) |
| price_cut_wave_flag | integer | 1 when price_reduced_share is at or above its trailing-12-month 75th percentile (sellers capitulating); null until 12 observations exist. (unit: 0/1) |
| stale_market_flag | integer | 1 when median_days_on_market is at or above its trailing-12-month 90th percentile (demand stalling); null until 12 observations exist. (unit: 0/1) |
| as_of | string | Latest month in the fetched panel (YYYY-MM-DD, month-end), identical across rows and across runs on the same vintage (ingest idempotency anchor). (unit: ISO date) |
| source_file | string | Upstream filename the row was parsed from. (unit: filename) |
| row_hash | string | Deterministic 16-hex row id: sha256('REALTORINV|<metro_key>|<month>'). (unit: hash) |
10 premières lignes d’exemple — un aperçu, pas le jeu de données complet.
| month | country_code | metro_key | metro_name | cbsa_code | state_codes | household_rank | median_listing_price_usd | active_listing_count | median_days_on_market | new_listing_count | pending_listing_count | price_reduced_share | median_listing_price_psf_usd | total_listing_count | pending_ratio | price_increased_share | quality_flag | price_yoy_pct | active_yoy_pct | new_yoy_pct | pending_yoy_pct | dom_yoy_pct | z12_active | z12_dom | z12_price_reduced_share | z12_pending_ratio | seller_heat_index | heat_tier | heat_percentile | sellers_market_flag | buyers_market_flag | inventory_surge_flag | price_cut_wave_flag | stale_market_flag | as_of | source_file | row_hash |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2016-07-31 | USA | aberdeen-sd | Aberdeen, SD | 10100 | SD | 680 | 190 650 | 173 | 59 | 48 | — | 0,214 | 79 | 173 | — | 0 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | 2026-09-30 | RDC_Inventory_Core_Metrics_Metro_History.csv | b778d04edc92e9f9 |
| 2016-08-31 | USA | aberdeen-sd | Aberdeen, SD | 10100 | SD | 680 | 194 425 | 164 | 65 | 44 | — | 0,193 | 80 | 164 | — | 0 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | 2026-09-30 | RDC_Inventory_Core_Metrics_Metro_History.csv | 91082fa63e9435e6 |
| 2016-09-30 | USA | aberdeen-sd | Aberdeen, SD | 10100 | SD | 680 | 192 925 | 154 | 79 | 48 | — | 0,176 | 79 | 154 | — | 0 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | 2026-09-30 | RDC_Inventory_Core_Metrics_Metro_History.csv | 38fd3ccf2aca8cd0 |
| 2016-10-31 | USA | aberdeen-sd | Aberdeen, SD | 10100 | SD | 680 | 189 900 | 147 | 85 | 36 | — | 0,218 | 80 | 147 | — | 0,021 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | 2026-09-30 | RDC_Inventory_Core_Metrics_Metro_History.csv | 3eff0b464dceb515 |
| 2016-11-30 | USA | aberdeen-sd | Aberdeen, SD | 10100 | SD | 680 | 189 900 | 139 | 91 | 34 | — | 0,081 | 82 | 139 | — | 0 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | 2026-09-30 | RDC_Inventory_Core_Metrics_Metro_History.csv | 1b700f98a5b65ebf |
| 2016-12-31 | USA | aberdeen-sd | Aberdeen, SD | 10100 | SD | 680 | 189 900 | 138 | 97 | 36 | — | 0,052 | 82 | 138 | — | 0 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | 2026-09-30 | RDC_Inventory_Core_Metrics_Metro_History.csv | 505d168ae10b1545 |
| 2017-01-31 | USA | aberdeen-sd | Aberdeen, SD | 10100 | SD | 680 | 192 400 | 134 | 98 | 16 | — | 0,143 | 85 | 134 | — | 0 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | 2026-09-30 | RDC_Inventory_Core_Metrics_Metro_History.csv | 666e3ac80171d86a |
| 2017-02-28 | USA | aberdeen-sd | Aberdeen, SD | 10100 | SD | 680 | 189 700 | 128 | 106 | 34 | — | 0,086 | 83 | 128 | — | 0 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | 2026-09-30 | RDC_Inventory_Core_Metrics_Metro_History.csv | 95cb6ed7b77fe333 |
| 2017-03-31 | USA | aberdeen-sd | Aberdeen, SD | 10100 | SD | 680 | 186 400 | 125 | 108 | 28 | — | 0,092 | 82 | 125 | — | 0 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | 2026-09-30 | RDC_Inventory_Core_Metrics_Metro_History.csv | 2b9e59e67d90b5fc |
| 2017-04-30 | USA | aberdeen-sd | Aberdeen, SD | 10100 | SD | 680 | 184 900 | 129 | 84 | 64 | — | 0,162 | 82 | 129 | — | 0 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | 2026-09-30 | RDC_Inventory_Core_Metrics_Metro_History.csv | e78053d3d892401d |
Profilé le 1 oct. 2026 à partir de l’instantané 20261001T045509Z-265b80586bbc
Mesuré- Complétude
- 94,7 %
- Lignes
- 113 775
- Colonnes
- 38
- Colonnes incomplètes
- 22
20162026
| Colonne | Manquant | Distinctes | Plage | Distribution |
|---|---|---|---|---|
| monthvarchar | 0 % | 116 | — |
|
| country_codevarchar | 0 % | 1 | — |
|
| metro_keyvarchar | 0 % | 776 | — |
|
| metro_namevarchar | 0 % | 987 | — |
|
| cbsa_codevarchar | 0 % | 914 | — |
|
| state_codesvarchar | 0 % | 109 | — |
|
| household_rankbigint | 0 % | 1 097 | 1 → 925médiane 463 | 2 214 hors du 1er–99e centile |
| median_listing_price_usddouble | 0 % | 24 270 | 19 900 → 5 972 500médiane 253 225 | 2 270 hors du 1er–99e centile |
| active_listing_countdouble | 0 % | 7 522 | 0 → 69 708médiane 226 | 2 261 hors du 1er–99e centile |
| median_days_on_marketdouble | 0,01 % | 225 | 3 → 322médiane 62 | 2 026 hors du 1er–99e centile |
| new_listing_countdouble | 0 % | 3 784 | 0 → 27 262médiane 86 | 2 166 hors du 1er–99e centile |
| pending_listing_countdouble | 2,4 % | 6 187 | 0 → 28 578médiane 79 | 1 550 hors du 1er–99e centile |
| price_reduced_sharedouble | 0 % | 3 175 | 0 → 0,6667médiane 0,128 | 2 271 hors du 1er–99e centile |
| median_listing_price_psf_usddouble | 0 % | 1 084 | 22 → 1 912médiane 134 | 2 199 hors du 1er–99e centile |
| total_listing_countdouble | 0 % | 9 597 | 1 → 83 124médiane 321 | 2 246 hors du 1er–99e centile |
| pending_ratiodouble | 2,4 % | 38 433 | 0 → 2médiane 0,2962 | 2 220 hors du 1er–99e centile |
| price_increased_sharedouble | 0 % | 1 195 | 0 → 0,3815médiane 0,0013 | 1 137 hors du 1er–99e centile |
| quality_flagdouble | 9,8 % | 2 | 0 → 1médiane 0 | |
| price_yoy_pctdouble | 9,8 % | 107 740 | -74,18 → 483,08médiane 5,15 | 2 054 hors du 1er–99e centile |
| active_yoy_pctdouble | 9,8 % | 51 007 | -100 → 9 200médiane -1,53 | 2 053 hors du 1er–99e centile |
| new_yoy_pctdouble | 9,9 % | 17 438 | -100 → 3 700médiane 0 | 2 084 hors du 1er–99e centile |
| pending_yoy_pctdouble | 13 % | 33 524 | -100 → 476 000médiane 0,7634 | 1 929 hors du 1er–99e centile |
| dom_yoy_pctdouble | 9,8 % | 5 308 | -87,96 → 747,37médiane -1,06 | 2 055 hors du 1er–99e centile |
| z12_activedouble | 8,9 % | 132 980 | -3,12 → 3,17médiane -0,1028 | 2 072 hors du 1er–99e centile |
| z12_domdouble | 9 % | 121 654 | -3,02 → 2,95médiane -0,2054 | 2 072 hors du 1er–99e centile |
| z12_price_reduced_sharedouble | 9 % | 82 750 | -2,98 → 3,18médiane -0 | 2 072 hors du 1er–99e centile |
| z12_pending_ratiodouble | 13,2 % | 102 710 | -3,15 → 3,18médiane 0,0699 | 1 978 hors du 1er–99e centile |
| seller_heat_indexdouble | 13,8 % | 813 | 9 → 91,7médiane 54,4 | 1 933 hors du 1er–99e centile |
| heat_tiervarchar | 13,8 % | 4 | — |
|
| heat_percentiledouble | 13,8 % | 15 903 | 0,5624 → 1médiane 0,8165 | 1 978 hors du 1er–99e centile |
| sellers_market_flagbigint | 13,8 % | 2 | 0 → 1médiane 0 | |
| buyers_market_flagbigint | 13,8 % | 2 | 0 → 1médiane 0 | |
| inventory_surge_flagbigint | 8,9 % | 2 | 0 → 1médiane 0 | |
| price_cut_wave_flagbigint | 8,9 % | 2 | 0 → 1médiane 0 | |
| stale_market_flagbigint | 9 % | 2 | 0 → 1médiane 0 | |
| as_ofvarchar | 0 % | 1 | — |
|
| source_filevarchar | 0 % | 1 | — |
|
| row_hashvarchar | 0 % | 113 229 | — |
|
- Actuelle
20261001T045509Z-265b80586bbc · sha256 265b80586bbc…
113 775 lignes · premier instantané
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/realtor_metro_inventory_intel/us_metro_housing_supply_monthly" | jq '{title, rows, columns_count, license}'Python
import requests
ds = requests.get("https://datazimuts.com/v1/datasets/realtor_metro_inventory_intel/us_metro_housing_supply_monthly").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/realtor_metro_inventory_intel/us_metro_housing_supply_monthly
Astuce : récupérez /llms.txt pour le catalogue complet lisible par machine.
D’où viennent ces données et ce qui en a été fait. Le travail des autres apparaît sous forme de décomptes ; seuls les projets partagés sont nommés.
Citer cet instantané
Épinglé à l’instantané 20261001T045509Z-265b80586bbc et à son empreinte, pour que vos lecteurs obtiennent exactement les données utilisées.
APA
Realtor.com Economic Research. (2026). US metro housing-supply heat (monthly, Realtor.com) [Data set, snapshot 20261001T045509Z-265b80586bbc, sha256 265b80586bbc]. Datazimuts. Retrieved 2026-10-01, from https://datazimuts.com/fr/datasets/realtor_metro_inventory_intel/us_metro_housing_supply_monthly?snapshot=20261001T045509Z-265b80586bbc
BibTeX
@misc{dz_realtor_metro_inventory_intel_us_metro_h_265b8058,
title = {{US metro housing-supply heat (monthly, Realtor.com)}},
author = {{Realtor.com Economic Research}},
year = {2026},
publisher = {Datazimuts},
howpublished = {\url{https://datazimuts.com/fr/datasets/realtor_metro_inventory_intel/us_metro_housing_supply_monthly?snapshot=20261001T045509Z-265b80586bbc}},
note = {Snapshot 20261001T045509Z-265b80586bbc, sha256 265b80586bbcbe6c59165d121635ee1319781f5e9db30b36c669d76dd1b5eaa8; accessed 2026-10-01}
}Intégrer un tableau ou un graphique
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HTML
<iframe src="https://datazimuts.com/embed/chart?dataset=realtor_metro_inventory_intel%2Fus_metro_housing_supply_monthly&lang=fr&theme=auto&snapshot=20261001T045509Z-265b80586bbc&x=month&y=household_rank&agg=avg" title="US metro housing-supply heat (monthly, Realtor.com)" width="100%" height="380" style="border:0" loading="lazy"></iframe>
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