US metro rent-pressure intelligence (monthly, Zillow ZORI)
Monthly US metro rent-pressure intelligence from Zillow Research's Zillow Observed Rent Index (ZORI), metro grain (keyless public CSV, 749 US metros + national reference, 2015-01 -> present, smoothed typical asking rents in $/mo). Per (month, metro): asking-rent level, month-over-month, 12-month and 3-month-annualized momentum, acceleration in percentage points, deviation from the national asking rent, per-month cross-sectional dense rank / percentile / z-score of rent growth, a documented 0-100 rent-pressure score (fast vs peers + expensive vs nation + accelerating) with pressure tiers, and hot/cold/cooling/36-month-record flags. Upstream coverage gaps stay null, never imputed. The rent-burden complement to the housing-price panels: rising asking rents squeeze household discretionary spend (Shopify demand), flag churn/pause risk (Stripe subscriptions), and 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.
- Source
- Zillow Research
- Rows
- 53,449
- Columns
- 24
- Source cadence
- Monthly
- Last refreshed
- Sep 30, 2026
- Theme
- housing
| Column | Type | Description |
|---|---|---|
| month | string | Reference month: the month-end date of the ZORI 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' (749 metropolitan statistical areas) or 'national' (the United States reference row used for deviation arithmetic). (unit: categorical) |
| zori_usd | float | Zillow Observed Rent Index: smoothed typical asking rent (single-family + condo + multifamily) for the metro and month, dollars per month, not seasonally adjusted. (unit: USD/month) |
| yoy_pct | float | 12-month percent change of zori_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 zori_usd. (unit: percent) |
| momentum_3m_ann_pct | float | 3-month change of zori_usd, annualized: 100*((zori/zori_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 zori_usd minus the national zori_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 rent growth). (unit: rank) |
| yoy_pct_percentile | float | Per-month percentile of yoy_pct: 1 - (rank-1)/n (1.0 = fastest rent growth 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) |
| rent_pressure_score | float | Documented 0-100 composite: 100*(0.5*minmax(clip(z_yoy,-3,3)) + 0.3*minmax(clip(vs_national_pct,-60,60)) + 0.2*minmax(clip(acceleration_pp,-3,3))). High = growing fast vs peers AND expensive vs the nation AND accelerating. Null on the national row. (unit: 0-100) |
| pressure_tier | string | Bucket of rent_pressure_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 rent growth that month). (unit: 0/1) |
| cold_flag | integer | 1 when yoy_pct_percentile <= 0.10 (bottom-decile rent growth that month). (unit: 0/1) |
| cooling_flag | integer | 1 when yoy_pct < 0 (nominal asking rents falling year over year). (unit: 0/1) |
| record_36m_flag | integer | 1 when zori_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('ZILLOWRENT|<metro_key>|<month>'). (unit: hash) |
First 10 sample rows — a preview, not the complete dataset.
| month | country_code | metro_key | metro_name | state_code | entity_type | zori_usd | yoy_pct | mom_pct | momentum_3m_ann_pct | acceleration_pp | vs_national_pct | yoy_pct_rank | yoy_pct_percentile | z_yoy | rent_pressure_score | pressure_tier | hot_flag | cold_flag | cooling_flag | record_36m_flag | as_of | source_file | row_hash |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2025-05-31 | USA | aberdeen-sd | Aberdeen, SD | SD | metro | 921.539 | — | — | — | — | -51.318 | — | — | — | — | — | — | — | — | — | 2026-08-31 | Metro_zori_uc_sfrcondomfr_sm_month.csv | 115bc4fbaf298d9f |
| 2025-06-30 | USA | aberdeen-sd | Aberdeen, SD | SD | metro | 939.325 | — | 1.93 | — | — | -50.505 | — | — | — | — | — | — | — | — | — | 2026-08-31 | Metro_zori_uc_sfrcondomfr_sm_month.csv | ace2c8c83502216b |
| 2025-07-31 | USA | aberdeen-sd | Aberdeen, SD | SD | metro | 966.942 | — | 2.94 | — | — | -49.116 | — | — | — | — | — | — | — | — | — | 2026-08-31 | Metro_zori_uc_sfrcondomfr_sm_month.csv | 5a2780687bb981cf |
| 2025-08-31 | USA | aberdeen-sd | Aberdeen, SD | SD | metro | 977.701 | — | 1.113 | 26.698 | — | -48.562 | — | — | — | — | — | — | — | — | — | 2026-08-31 | Metro_zori_uc_sfrcondomfr_sm_month.csv | fde09a881333ae96 |
| 2025-09-30 | USA | aberdeen-sd | Aberdeen, SD | SD | metro | 962.372 | — | -1.568 | 10.181 | -3.498 | -49.341 | — | — | — | — | — | — | — | — | — | 2026-08-31 | Metro_zori_uc_sfrcondomfr_sm_month.csv | dbc698e2e7b9df41 |
| 2025-10-31 | USA | aberdeen-sd | Aberdeen, SD | SD | metro | 948.711 | — | -1.419 | -7.331 | -4.36 | -50.003 | — | — | — | — | — | — | — | — | — | 2026-08-31 | Metro_zori_uc_sfrcondomfr_sm_month.csv | 7ab6cc4e33ff356f |
| 2025-11-30 | USA | aberdeen-sd | Aberdeen, SD | SD | metro | 995.782 | — | 4.962 | 7.605 | 3.849 | -47.429 | — | — | — | — | — | — | — | — | — | 2026-08-31 | Metro_zori_uc_sfrcondomfr_sm_month.csv | f3c5894083fb8953 |
| 2025-12-31 | USA | aberdeen-sd | Aberdeen, SD | SD | metro | 1,042.66 | — | 4.708 | 37.784 | 6.276 | -44.879 | — | — | — | — | — | — | — | — | — | 2026-08-31 | Metro_zori_uc_sfrcondomfr_sm_month.csv | e740bc777e6a5cb2 |
| 2026-01-31 | USA | aberdeen-sd | Aberdeen, SD | SD | metro | 1,095.289 | — | 5.048 | 77.655 | 6.467 | -42.16 | — | — | — | — | — | — | — | — | — | 2026-08-31 | Metro_zori_uc_sfrcondomfr_sm_month.csv | 184dca3b93dbbfba |
| 2026-02-28 | USA | aberdeen-sd | Aberdeen, SD | SD | metro | 1,057.477 | — | -3.452 | 27.182 | -8.414 | -44.366 | — | — | — | — | — | — | — | — | — | 2026-08-31 | Metro_zori_uc_sfrcondomfr_sm_month.csv | d02094137de8e6d5 |
- Current
20260930T025714Z-c4aa0576aa2c · sha256 c4aa0576aa2c…
53,449 rows · first snapshot
Point any LLM at the metadata endpoint — the documentation above is machine-readable too (JSON-LD + Croissant).
curl "https://datazimuts.com/v1/datasets/zillow_metro_rent_intel/us_metro_rent_pressure_monthly" | jq '{title, rows, columns_count, license}'import requests
ds = requests.get("https://datazimuts.com/v1/datasets/zillow_metro_rent_intel/us_metro_rent_pressure_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)API endpoint: https://datazimuts.com/v1/datasets/zillow_metro_rent_intel/us_metro_rent_pressure_monthly
Tip: fetch /llms.txt for the full machine-readable catalog.
Where this data comes from and what was made from it. Other people's work shows as counts; only shared projects are named.
Cite this snapshot
Pinned to snapshot 20260930T025714Z-c4aa0576aa2c and its content hash, so readers get exactly the data you used.
Zillow Research. (2026). US metro rent-pressure intelligence (monthly, Zillow ZORI) [Data set, snapshot 20260930T025714Z-c4aa0576aa2c, sha256 c4aa0576aa2c]. Datazimuts. Retrieved 2026-09-30, from https://datazimuts.com/en/datasets/zillow_metro_rent_intel/us_metro_rent_pressure_monthly?snapshot=20260930T025714Z-c4aa0576aa2c
@misc{dz_zillow_metro_rent_intel_us_metro_rent_pr_c4aa0576,
title = {{US metro rent-pressure intelligence (monthly, Zillow ZORI)}},
author = {{Zillow Research}},
year = {2026},
publisher = {Datazimuts},
howpublished = {\url{https://datazimuts.com/en/datasets/zillow_metro_rent_intel/us_metro_rent_pressure_monthly?snapshot=20260930T025714Z-c4aa0576aa2c}},
note = {Snapshot 20260930T025714Z-c4aa0576aa2c, sha256 c4aa0576aa2c8343e4d021bc4994de08851168238fa6758afdb1304036dee033; accessed 2026-09-30}
}Embed a table or a chart
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<iframe src="https://datazimuts.com/embed/chart?dataset=zillow_metro_rent_intel%2Fus_metro_rent_pressure_monthly&lang=en&theme=auto&snapshot=20260930T025714Z-c4aa0576aa2c&x=month&y=zori_usd&agg=avg" title="US metro rent-pressure intelligence (monthly, Zillow ZORI)" width="100%" height="380" style="border:0" loading="lazy"></iframe>
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