Zillow Research

Coverage

Licensing & provenance

License

zillow-research

Required attribution

Data Provided by Zillow Group

Redistribution

Permitted (served here)

Commercial use

Yes

Modification

Permitted

How to cite

Zillow Research (ZHVI / ZORI). Retrieved from https://www.zillow.com/research/data. Compiled and redistributed by the Elkassabgi Data Library.

Processing: Retrieved from the official source, normalized to a long {series_key, obs_date, value} schema (period-start dates), de-duplicated, and stored as zstd Parquet. Compiled and redistributed by the Elkassabgi Data Library.

Access & mirrors

Download

Select & download zillow series as CSV →

API

Get a free API key, then GET /v1/series/<id>.csv

Canonical landing

https://econdatalibrary.com/zillow.html

Update cadence

monthly

Update strategy

bulk snapshot if changed

Storage layout

~206 grouped 'cubes' under data/clean_full/zillow/: per dataset <name>.parquet (dataset, series_key, obs_date date32, value), <name>__series.parquet sidecar (RegionID + all metadata cols), <name>.meta.json (verify stats); plus top-level zillow.meta.json. Catalog of URLs in data/_zillow_files.json; raw CSVs cached in data/raw/zillow/. ~107M obs total.

Full description

Full-coverage ingest of the ENTIRE Zillow Research public CSV catalog. Source page: https://www.zillow.com/research/data/ (the page embeds a JS `var data` object mapping SET -> TYPE -> GEOGRAPHY -> direct CSV URL on files.zillowstatic.com). License class: zillow-research (re-serveable). Attribution MUST read exactly "Data Provided by Zillow Group". What we pull ------------ EVERY metric x EVERY geography x EVERY frequency Zillow publishes: Home Values (ZHVI, all tiers/home-types), Home-Value Forecast (ZHVF), Rentals (ZORI/ZORDI), Rental Forecast (ZORF), For-Sale Listings (inventory, median list price, new listings, new pending, share price-cut...), Sales (median/mean sale price, sales count, sale-to-list, days-to-close, total transaction value...), Days-on-Market & Price-Cuts, Market Heat Index, New Construction, and Affordability. Across ~206 live CSVs spanning geographies National / Metro / State / County / City / Zip / Neighborhood, monthly + weekly. GROUPED storage (anti-bloat) ---------------------------- ONE Parquet "cube" per source CSV (the dataset), each holding MANY region-level series in long form -- never one-file-per-series. ~206 cubes total. Per cube we write under data/clean_full/zillow/ : <dataset>.parquet long obs: dataset, series_key, obs_date, value <dataset>__series.parquet one row per region series + all ID/metadata columns <dataset>.meta.json verification stats (re-read from disk) And a top-level zillow.meta.json aggregating the run. Each wide Zillow CSV is region rows x date columns. Leading non-date columns (RegionID, SizeRank, RegionName, RegionType, StateName, and -- for finer geos -- State, City, Metro, CountyName, FIPS, plus BaseDate on forecasts) are metadata; they go in the __series sidecar. Every YYYY-MM-DD column becomes a long observation (obs_date = that date, value = the cell). series_key format: zillow:<metric>:<geo>:<RegionID> e.g. zillow:zhvi:Metro:394913 Usage ----- python jobs/ingest_zillow.py --refresh # re-fetch the research page + re-enumerate python jobs/ingest_zillow.py --dry 5 # parse 5 cubes to memory, print, no writes python jobs/ingest_zillow.py # FULL run (download + parse + write + verify) python jobs/ingest_zillow.py --catalog # also upsert into catalog.db (off by default)

Structured metadata

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