US metro home-value momentum (monthly, Zillow ZHVI)
Monthly US metro home-value momentum intelligence from Zillow Research's Zillow Home Value Index (ZHVI), metro grain (keyless public CSV, 894 US metros + national reference, 2000-01 -> present, smoothed seasonally-adjusted typical home values in $). Per (month, metro): home-value level, month-over-month, 12-month and 3-month-annualized momentum, acceleration in percentage points, deviation from the national home value, per-month cross-sectional dense rank / percentile / z-score of appreciation, a documented 0-100 home-value heat score (fast vs peers + expensive vs nation + accelerating) with heat tiers, and hot/cold/cooling/36-month-record flags. Upstream coverage gaps stay null, never imputed. The owner's wealth complement to the ZORI rent-pressure panel: appreciating metros expand household balance sheets (Shopify demand), cooling prints flag churn/pause risk (Stripe subscriptions), and the ranks heat-map sales territories (HubSpot) — joinable on ISO month + metro. Zillow aggregate data reused under the Zillow Terms of Use (non-personal market-analysis use of aggregate data with "Data Provided by Zillow Group" attribution); source: Zillow Research.
Les titres et les descriptions proviennent des sources de données, en anglais.
- Source
- Zillow Research
- Lignes
- 237 134
- Colonnes
- 24
- Cadence de la source
- Mensuelle
- Dernière actualisation
- 1 oct. 2026
- Thème
- housing
| Colonne | Type | Description |
|---|---|---|
| month | string | Reference month: the month-end date of the ZHVI monthly column as published by Zillow (e.g. 2026-08-31). ISO date. (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 Zillow RegionName (e.g. 'new-york-ny' for 'New York, NY'). Primary join key with month. (unit: code) |
| metro_name | string | Zillow RegionName for the metro (e.g. 'New York, NY'); 'United States' on the national reference row. (unit: name) |
| state_code | string | Two-letter US state/territory code from Zillow's StateName (empty on the national row). (unit: code) |
| entity_type | string | Entity kind: 'metro' (894 metropolitan statistical areas) or 'national' (the United States reference row used for deviation arithmetic). (unit: categorical) |
| zhvi_usd | float | Zillow Home Value Index: smoothed, seasonally adjusted typical home value for the middle market tier (single-family + condo), dollars. (unit: USD) |
| yoy_pct | float | 12-month percent change of zhvi_usd; null for the first 12 months of each metro (warm-up) and across upstream coverage gaps. (unit: percent) |
| mom_pct | float | Month-over-month percent change of zhvi_usd. (unit: percent) |
| momentum_3m_ann_pct | float | 3-month change of zhvi_usd, annualized: 100*((zhvi/zhvi_lag3)^4 - 1). (unit: percent) |
| acceleration_pp | float | Change in the monthly pace: mom_pct minus mom_pct lagged 3 months, in percentage points. (unit: percentage points) |
| vs_national_pct | float | The metro's zhvi_usd minus the national zhvi_usd for the same month, as a percent of the national value (0.0 on the national row). (unit: percent) |
| yoy_pct_rank | integer | Per-month dense rank of yoy_pct across metros with non-null yoy, descending (1 = fastest appreciation). (unit: rank) |
| yoy_pct_percentile | float | Per-month percentile of yoy_pct: 1 - (rank-1)/n (1.0 = fastest appreciation that month). (unit: 0-1) |
| z_yoy | float | Per-month cross-sectional z-score of yoy_pct across metros (null when the month has < 10 metros or zero dispersion). (unit: z-score) |
| homevalue_heat_score | float | Documented 0-100 composite: 100*(0.5*minmax(clip(z_yoy,-3,3)) + 0.3*minmax(clip(vs_national_pct,-100,100)) + 0.2*minmax(clip(acceleration_pp,-3,3))). High = appreciating fast vs peers AND expensive vs the nation AND accelerating. Null on the national row. (unit: 0-100) |
| heat_tier | string | Bucket of homevalue_heat_score: very_high >= 80, high >= 65, moderate >= 45, low >= 30, very_low < 30. (unit: categorical) |
| hot_flag | integer | 1 when yoy_pct_percentile >= 0.90 (top-decile appreciation that month). (unit: 0/1) |
| cold_flag | integer | 1 when yoy_pct_percentile <= 0.10 (bottom-decile appreciation that month). (unit: 0/1) |
| cooling_flag | integer | 1 when yoy_pct < 0 (nominal home values falling year over year). (unit: 0/1) |
| record_36m_flag | integer | 1 when zhvi_usd equals the maximum over the trailing 36 non-null monthly observations (inclusive); null until 12 observations exist. (unit: 0/1) |
| as_of | string | Latest month in the fetched panel (YYYY-MM-DD), 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('ZILLOWVALUE|<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 | state_code | entity_type | zhvi_usd | yoy_pct | mom_pct | momentum_3m_ann_pct | acceleration_pp | vs_national_pct | yoy_pct_rank | yoy_pct_percentile | z_yoy | homevalue_heat_score | heat_tier | hot_flag | cold_flag | cooling_flag | record_36m_flag | as_of | source_file | row_hash |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2009-02-28 | USA | aberdeen-sd | Aberdeen, SD | SD | metro | 130 512,411 | — | — | — | — | -29,118 | — | — | — | — | — | — | — | — | — | 2026-08-31 | Metro_zhvi_uc_sfrcondo_tier_0.33_0.67_sm_sa_month.csv | c60fb846cddc9188 |
| 2009-03-31 | USA | aberdeen-sd | Aberdeen, SD | SD | metro | 130 525,69 | — | 0,01 | — | — | -28,574 | — | — | — | — | — | — | — | — | — | 2026-08-31 | Metro_zhvi_uc_sfrcondo_tier_0.33_0.67_sm_sa_month.csv | a9e0ab612f3474a2 |
| 2009-04-30 | USA | aberdeen-sd | Aberdeen, SD | SD | metro | 130 327,016 | — | -0,152 | — | — | -28,171 | — | — | — | — | — | — | — | — | — | 2026-08-31 | Metro_zhvi_uc_sfrcondo_tier_0.33_0.67_sm_sa_month.csv | 897e423791f0e387 |
| 2009-05-31 | USA | aberdeen-sd | Aberdeen, SD | SD | metro | 130 214,536 | — | -0,086 | -0,91 | — | -27,708 | — | — | — | — | — | — | — | — | — | 2026-08-31 | Metro_zhvi_uc_sfrcondo_tier_0.33_0.67_sm_sa_month.csv | dafa57dbe6f50fa2 |
| 2009-06-30 | USA | aberdeen-sd | Aberdeen, SD | SD | metro | 130 018,411 | — | -0,151 | -1,546 | -0,161 | -27,316 | — | — | — | — | — | — | — | — | — | 2026-08-31 | Metro_zhvi_uc_sfrcondo_tier_0.33_0.67_sm_sa_month.csv | f311b5f5984d2c28 |
| 2009-07-31 | USA | aberdeen-sd | Aberdeen, SD | SD | metro | 129 879,859 | — | -0,107 | -1,365 | 0,046 | -26,934 | — | — | — | — | — | — | — | — | — | 2026-08-31 | Metro_zhvi_uc_sfrcondo_tier_0.33_0.67_sm_sa_month.csv | dc4ed932a9a54f64 |
| 2009-08-31 | USA | aberdeen-sd | Aberdeen, SD | SD | metro | 129 661,596 | — | -0,168 | -1,688 | -0,082 | -26,627 | — | — | — | — | — | — | — | — | — | 2026-08-31 | Metro_zhvi_uc_sfrcondo_tier_0.33_0.67_sm_sa_month.csv | 59dad59c1639caf1 |
| 2009-09-30 | USA | aberdeen-sd | Aberdeen, SD | SD | metro | 129 441,766 | — | -0,17 | -1,762 | -0,019 | -26,354 | — | — | — | — | — | — | — | — | — | 2026-08-31 | Metro_zhvi_uc_sfrcondo_tier_0.33_0.67_sm_sa_month.csv | 795e09a182274cad |
| 2009-10-31 | USA | aberdeen-sd | Aberdeen, SD | SD | metro | 129 346,677 | — | -0,073 | -1,632 | 0,033 | -26,079 | — | — | — | — | — | — | — | — | — | 2026-08-31 | Metro_zhvi_uc_sfrcondo_tier_0.33_0.67_sm_sa_month.csv | 63518143b2f1f368 |
| 2009-11-30 | USA | aberdeen-sd | Aberdeen, SD | SD | metro | 129 288,991 | — | -0,045 | -1,145 | 0,123 | -25,926 | — | — | — | — | — | — | — | — | — | 2026-08-31 | Metro_zhvi_uc_sfrcondo_tier_0.33_0.67_sm_sa_month.csv | 9edfadde48bf0c6d |
Profilé le 1 oct. 2026 à partir de l’instantané 20261001T001126Z-453fe1a433ad
Mesuré- Complétude
- 97,9 %
- Lignes
- 237 134
- Colonnes
- 24
- Colonnes incomplètes
- 13
| Colonne | Manquant | Distinctes | Plage | Distribution |
|---|---|---|---|---|
| monthvarchar | 0 % | 344 | — |
|
| country_codevarchar | 0 % | 1 | — |
|
| metro_keyvarchar | 0 % | 855 | — |
|
| metro_namevarchar | 0 % | 1 103 | — |
|
| state_codevarchar | 0 % | 57 | — |
|
| entity_typevarchar | 0 % | 2 | — |
|
| zhvi_usddouble | 0 % | 226 110 | 48 084 → 1 601 701médiane 156 331 | 4 744 hors du 1er–99e centile |
| yoy_pctdouble | 4,7 % | 268 286 | -45,99 → 59,9médiane 3,82 | 4 520 hors du 1er–99e centile |
| mom_pctdouble | 0,46 % | 217 935 | -8,21 → 7,29médiane 0,2977 | 4 722 hors du 1er–99e centile |
| momentum_3m_ann_pctdouble | 1,3 % | 281 204 | -61,61 → 127,57médiane 3,68 | 4 682 hors du 1er–99e centile |
| acceleration_ppdouble | 1,8 % | 277 166 | -9,83 → 7,03médiane 0,0035 | 4 658 hors du 1er–99e centile |
| vs_national_pctdouble | 0 % | 320 191 | -86,38 → 402,04médiane -28,82 | 4 744 hors du 1er–99e centile |
| yoy_pct_rankbigint | 4,8 % | 1 051 | 1 → 894médiane 367 | 4 381 hors du 1er–99e centile |
| yoy_pct_percentiledouble | 4,8 % | 79 351 | 0,0011 → 1médiane 0,5006 | 4 513 hors du 1er–99e centile |
| z_yoydouble | 4,8 % | 337 711 | -9,45 → 13,14médiane -0,0258 | 4 514 hors du 1er–99e centile |
| homevalue_heat_scoredouble | 5,1 % | 988 | 2,6 → 98,8médiane 46,5 | 4 452 hors du 1er–99e centile |
| heat_tiervarchar | 5,1 % | 4 | — |
|
| hot_flagbigint | 4,8 % | 2 | 0 → 1médiane 0 | |
| cold_flagbigint | 4,8 % | 2 | 0 → 1médiane 0 | |
| cooling_flagbigint | 4,7 % | 2 | 0 → 1médiane 0 | |
| record_36m_flagbigint | 4,2 % | 2 | 0 → 1médiane 1 | |
| as_ofvarchar | 0 % | 1 | — |
|
| source_filevarchar | 0 % | 1 | — |
|
| row_hashvarchar | 0 % | 253 927 | — |
|
- Actuelle
20261001T001126Z-453fe1a433ad · sha256 453fe1a433ad…
237 134 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/zillow_metro_homevalue_intel/us_metro_homevalue_momentum_monthly" | jq '{title, rows, columns_count, license}'import requests
ds = requests.get("https://datazimuts.com/v1/datasets/zillow_metro_homevalue_intel/us_metro_homevalue_momentum_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/zillow_metro_homevalue_intel/us_metro_homevalue_momentum_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 20261001T001126Z-453fe1a433ad et à son empreinte, pour que vos lecteurs obtiennent exactement les données utilisées.
Zillow Research. (2026). US metro home-value momentum (monthly, Zillow ZHVI) [Data set, snapshot 20261001T001126Z-453fe1a433ad, sha256 453fe1a433ad]. Datazimuts. Retrieved 2026-10-01, from https://datazimuts.com/fr/datasets/zillow_metro_homevalue_intel/us_metro_homevalue_momentum_monthly?snapshot=20261001T001126Z-453fe1a433ad
@misc{dz_zillow_metro_homevalue_intel_us_metro_ho_453fe1a4,
title = {{US metro home-value momentum (monthly, Zillow ZHVI)}},
author = {{Zillow Research}},
year = {2026},
publisher = {Datazimuts},
howpublished = {\url{https://datazimuts.com/fr/datasets/zillow_metro_homevalue_intel/us_metro_homevalue_momentum_monthly?snapshot=20261001T001126Z-453fe1a433ad}},
note = {Snapshot 20261001T001126Z-453fe1a433ad, sha256 453fe1a433adf22ce71cb5728b44b7cb2be3aa6b2db77466e3616d9b7311e952; accessed 2026-10-01}
}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=zillow_metro_homevalue_intel%2Fus_metro_homevalue_momentum_monthly&lang=fr&theme=auto&snapshot=20261001T001126Z-453fe1a433ad&x=month&y=zhvi_usd&agg=avg" title="US metro home-value momentum (monthly, Zillow ZHVI)" width="100%" height="380" style="border:0" loading="lazy"></iframe>
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