US county housing-velocity intelligence (monthly)
Monthly US county housing-velocity intelligence from Redfin's Data Center Market Tracker county bulk feed (keyless download): ~3,100 counties, 2012 -> present, 'All Residential'. Per (month, county): median sale/list price and price per sq ft, homes sold, pending sales, new listings, inventory, months of supply, median days on market, sale-to-list ratio, share sold above list, price-drop share and off-market-in-two-weeks share (all with published YoY changes), plus a documented 0-100 housing-demand heat score, seller's/balanced/buyer's market tiers, price-momentum tiers, and sale-price YoY vs the panel-median national benchmark. County names resolved to 5-digit FIPS via Census gazetteers (deterministic, fail-loud above 1% unmatched). The velocity complement to the FHFA price panels: pending sales and price drops move months before prices do. Redfin data reuse is governed by Redfin's Terms of Use (commercial redistribution not explicitly granted); attribution required.
Les titres et les descriptions proviennent des sources de données, en anglais.
- Source
- Redfin
- Lignes
- 459 648
- Colonnes
- 32
- Cadence de la source
- Mensuelle
- Dernière actualisation
- 29 sept. 2026
- Thème
- housing
| Colonne | Type | Description |
|---|---|---|
| month | string | Reference month (first day's YYYY-MM). (unit: ISO month) |
| country_code | string | ISO alpha-3 country code (USA for every row). (unit: ISO 3166-1 alpha-3) |
| county_fips | string | 5-digit county FIPS code, resolved from Redfin's county name via Census gazetteers. Primary join key with month. (unit: FIPS code) |
| county_name | string | County name as published by Redfin (without the state suffix). |
| state_code | string | USPS 2-letter state abbreviation. (unit: USPS code) |
| state_name | string | State name as published by Redfin. |
| parent_metro_code | string | CBSA metro code of the parent metro region, where Redfin assigns one; null for non-metro counties. (unit: CBSA code) |
| parent_metro_name | string | Parent metro region name as published by Redfin; null for non-metro counties. |
| median_sale_price | float | Median closed sale price for the month, as published by Redfin. Null where upstream publishes no value (never zero-filled). (unit: USD) |
| median_sale_price_yoy | float | Year-over-year percent change in median sale price, as published by Redfin. (unit: percent) |
| sale_price_yoy_vs_national | float | County median-sale-price YoY minus the panel-median YoY that month: the county's price momentum vs the national benchmark. (unit: percentage points) |
| national_median_sale_price_yoy | float | Panel-median (across counties) median-sale-price YoY for the month: the internal national benchmark, as_of-stamped. (unit: percent) |
| median_list_price | float | Median list price for the month, as published by Redfin. (unit: USD) |
| median_ppsf | float | Median sale price per square foot, as published by Redfin. (unit: USD per sq ft) |
| homes_sold | float | Closed home sales in the month, as published by Redfin. (unit: count) |
| homes_sold_yoy | float | Year-over-year percent change in closed sales, as published by Redfin. (unit: percent) |
| pending_sales | float | Homes that went under contract in the month: the leading demand gauge. (unit: count) |
| pending_sales_yoy | float | Year-over-year percent change in pending sales, as published by Redfin. (unit: percent) |
| new_listings | float | New listings in the month: the supply-flow gauge. (unit: count) |
| new_listings_yoy | float | Year-over-year percent change in new listings, as published by Redfin. (unit: percent) |
| inventory | float | Active listings (supply stock) in the month. (unit: count) |
| months_of_supply | float | Months of supply: inventory divided by the sales pace. Below 4 = seller's market, 4-6 = balanced, above 6 = buyer's market. (unit: months) |
| median_dom | float | Median days on market before going under contract. (unit: days) |
| avg_sale_to_list | float | Average sale-to-list price ratio. Above 100 = homes selling over asking. (unit: percent) |
| sold_above_list | float | Share of homes sold above list price. (unit: percent) |
| price_drops | float | Share of active listings with a price drop: the seller-distress gauge. (unit: percent) |
| off_market_in_two_weeks | float | Share of homes going off market within two weeks of listing: the velocity gauge. (unit: percent) |
| demand_heat_score | float | Documented 0-100 composite: 100*(0.30*mm(pending_sales_yoy,-50,50) + 0.25*mm(avg_sale_to_list-100,-5,5) + 0.20*(1-mm(months_of_supply,0,10)) + 0.15*(1-mm(price_drops,0,50)) + 0.10*mm(off_market_in_two_weeks,0,60)), mm = clipped min-max. Null when any input is null. High = hot seller's market. (unit: 0-100 score) |
| market_tier | string | Market balance tier from months_of_supply: sellers_market (<4), balanced (4-6), buyers_market (>=6). (unit: categorical) |
| price_momentum_tier | string | Price momentum tier from median_sale_price_yoy: surging (>10), rising (>3), flat (>= -3), cooling (> -10), falling (<= -10). (unit: categorical) |
| as_of | string | Latest month in the fetched panel (YYYY-MM); identical across rows and across runs on the same vintage. (unit: ISO month) |
| row_hash | string | Deterministic 16-hex row id: sha256('REDFIN|<county_fips>|<month>'). (unit: hash) |
10 premières lignes d’exemple — un aperçu, pas le jeu de données complet.
| month | country_code | county_fips | county_name | state_code | state_name | parent_metro_code | parent_metro_name | median_sale_price | median_sale_price_yoy | sale_price_yoy_vs_national | national_median_sale_price_yoy | median_list_price | median_ppsf | homes_sold | homes_sold_yoy | pending_sales | pending_sales_yoy | new_listings | new_listings_yoy | inventory | months_of_supply | median_dom | avg_sale_to_list | sold_above_list | price_drops | off_market_in_two_weeks | demand_heat_score | market_tier | price_momentum_tier | as_of | row_hash |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2012-01 | USA | 01001 | Autauga County | AL | Alabama | 33860 | Montgomery, AL | 133 250 | -22,145 | -18,39 | -3,752 | 157 450 | 79,51 | 20 | -9,091 | 20 | 0 | 54 | -28 | 293 | 14,7 | 158 | 94,331 | 5 | — | 0 | — | buyers_market | falling | 2026-05 | 32bdd7e03c9f8827 |
| 2012-01 | USA | 01003 | Baldwin County | AL | Alabama | 19300 | Daphne, AL | 162 500 | -2,108 | 1,64 | -3,752 | 216 200 | 86,918 | 176 | -14,146 | 175 | -13,793 | 298 | 4,196 | 1 371 | 7,8 | 127 | 94,976 | 13,068 | — | 5,143 | — | buyers_market | flat | 2026-05 | 18b3c1ef8277b38d |
| 2012-01 | USA | 01009 | Blount County | AL | Alabama | 13820 | Birmingham, AL | 68 876 | — | — | -3,752 | 132 000 | 46,624 | 19 | — | 18 | — | 17 | — | 131 | 6,9 | 105 | 93,155 | 10,526 | — | 0 | — | buyers_market | falling | 2026-05 | 3ff90b442ef2068e |
| 2012-01 | USA | 01013 | Butler County | AL | Alabama | NA | Alabama nonmetropolitan area | 29 900 | -54,868 | -51,12 | -3,752 | 120 000 | 17,609 | 3 | 50 | 3 | 50 | 21 | 250 | 72 | 24 | 209 | 92,895 | 0 | — | 0 | — | buyers_market | falling | 2026-05 | 948ee7900e5695f3 |
| 2012-01 | USA | 01015 | Calhoun County | AL | Alabama | 11500 | Anniston, AL | 93 000 | — | — | -3,752 | 92 200 | 49,463 | 44 | — | 44 | — | 61 | — | 296 | 6,7 | 88 | 91,216 | 9,091 | — | 2,273 | — | buyers_market | falling | 2026-05 | 91d8abb5a37f916b |
| 2012-01 | USA | 01017 | Chambers County | AL | Alabama | 29300 | LaGrange, GA | 53 750 | — | — | -3,752 | 69 900 | 41,315 | 6 | — | 6 | — | 12 | — | 114 | 19 | 131 | 91,411 | 16,667 | — | 0 | — | buyers_market | falling | 2026-05 | aba92fb67f0c3f64 |
| 2012-01 | USA | 01019 | Cherokee County | AL | Alabama | NA | Alabama nonmetropolitan area | 112 250 | — | — | -3,752 | 89 800 | 65,074 | 8 | — | 8 | — | 13 | — | 63 | 7,9 | 129 | 103,109 | 37,5 | — | 0 | — | buyers_market | falling | 2026-05 | c3a9fb94e373811c |
| 2012-01 | USA | 01021 | Chilton County | AL | Alabama | 13820 | Birmingham, AL | 128 900 | 112,181 | 115,93 | -3,752 | 111 200 | 61,92 | 9 | 12,5 | 9 | 12,5 | 28 | 12 | 165 | 18,3 | 313 | 89,651 | 0 | — | 0 | — | buyers_market | surging | 2026-05 | 1eb216fd4757172c |
| 2012-01 | USA | 01027 | Clay County | AL | Alabama | NA | Alabama nonmetropolitan area | 7 000 | — | — | -3,752 | 125 000 | 6,034 | 1 | — | 1 | — | 3 | — | 8 | 8 | 63 | 70,707 | 0 | — | 0 | — | buyers_market | falling | 2026-05 | 576135832d415d3d |
| 2012-01 | USA | 01029 | Cleburne County | AL | Alabama | NA | Alabama nonmetropolitan area | 53 200 | — | — | -3,752 | 122 000 | 33,333 | 4 | — | 4 | — | 2 | — | 19 | 4,8 | 219 | 80,288 | 25 | — | 0 | — | balanced | falling | 2026-05 | f1659cfe134e7cc1 |
- Actuelle
20260929T170823Z-de645ca04416 · sha256 de645ca04416…
459 648 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 "https://datazimuts.com/v1/datasets/redfin_county_housing_velocity_intel/us_county_housing_velocity_monthly" | jq '{title, rows, columns_count, license}'import requests
ds = requests.get("https://datazimuts.com/v1/datasets/redfin_county_housing_velocity_intel/us_county_housing_velocity_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/redfin_county_housing_velocity_intel/us_county_housing_velocity_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 20260929T170823Z-de645ca04416 et à son empreinte, pour que vos lecteurs obtiennent exactement les données utilisées.
Redfin. (2026). US county housing-velocity intelligence (monthly) [Data set, snapshot 20260929T170823Z-de645ca04416, sha256 de645ca04416]. Datazimuts. Retrieved 2026-09-30, from https://datazimuts.com/fr/datasets/redfin_county_housing_velocity_intel/us_county_housing_velocity_monthly?snapshot=20260929T170823Z-de645ca04416
@misc{dz_redfin_county_housing_velocity_intel_us__de645ca0,
title = {{US county housing-velocity intelligence (monthly)}},
author = {{Redfin}},
year = {2026},
publisher = {Datazimuts},
howpublished = {\url{https://datazimuts.com/fr/datasets/redfin_county_housing_velocity_intel/us_county_housing_velocity_monthly?snapshot=20260929T170823Z-de645ca04416}},
note = {Snapshot 20260929T170823Z-de645ca04416, sha256 de645ca044161bb6efeed0a8ddbc110b1852604fd8d88d71a65d433f2f6c8b16; accessed 2026-09-30}
}Intégrer un tableau ou un graphique
Collez ce code dans n’importe quelle page. L’intégration est épinglée au même instantané, suit le thème clair ou sombre du lecteur et affiche toujours la source, la licence et un lien de retour.
<iframe src="https://datazimuts.com/embed/chart?dataset=redfin_county_housing_velocity_intel%2Fus_county_housing_velocity_monthly&lang=fr&theme=auto&snapshot=20260929T170823Z-de645ca04416&x=month&y=median_sale_price&agg=avg" title="US county housing-velocity intelligence (monthly)" width="100%" height="380" style="border:0" loading="lazy"></iframe>
Posez une question sur ce jeu de données. Les réponses viennent uniquement de sa fiche, de son profil mesuré et de son historique, et citent les faits utilisés.