US House Price Index (Case-Shiller)

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Files:3
Size:187 kB
Formats:csv
License:ODC-PDDL-1.0

Case-Shiller Index of US residential house prices. Data comes from S&P Case-Shiller data and includes both the national index and the indices for 20 metropolitan regions. The indices are created using...

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Dataset Files

Each file has a stable URL (r-link) that you can use directly in scripts, apps, or AI agents. These URLs are permanent and safe to hardcode.

/core/house-prices-us/
https://datahub.io/core/house-prices-us/_r/-/.gitattributes
https://datahub.io/core/house-prices-us/_r/-/.gitignore
https://datahub.io/core/house-prices-us/_r/-/Makefile
https://datahub.io/core/house-prices-us/_r/-/README.md
https://datahub.io/core/house-prices-us/_r/-/archive/10-City Composite-NSA.csv
https://datahub.io/core/house-prices-us/_r/-/archive/10-City Composite-SA.csv
https://datahub.io/core/house-prices-us/_r/-/archive/20-City Composite-NSA.csv
https://datahub.io/core/house-prices-us/_r/-/archive/20-City Composite-SA.csv
https://datahub.io/core/house-prices-us/_r/-/archive/Atlanta , GA-NSA.csv
https://datahub.io/core/house-prices-us/_r/-/archive/Atlanta, GA-SA.csv
https://datahub.io/core/house-prices-us/_r/-/archive/Boston , MA-NSA.csv
https://datahub.io/core/house-prices-us/_r/-/archive/Boston, MA-SA.csv
https://datahub.io/core/house-prices-us/_r/-/archive/Charlotte , NC-NSA.csv
https://datahub.io/core/house-prices-us/_r/-/archive/Charlotte, NC-SA.csv
https://datahub.io/core/house-prices-us/_r/-/archive/Chicago , IL-NSA.csv
https://datahub.io/core/house-prices-us/_r/-/archive/Chicago, IL-SA.csv
https://datahub.io/core/house-prices-us/_r/-/archive/Cleveland , OH-NSA.csv
https://datahub.io/core/house-prices-us/_r/-/archive/Cleveland , OH-SA.csv
https://datahub.io/core/house-prices-us/_r/-/archive/Dallas , TX-NSA.csv
https://datahub.io/core/house-prices-us/_r/-/archive/Dallas , TX-SA.csv
https://datahub.io/core/house-prices-us/_r/-/archive/Denver , CO-NSA.csv
https://datahub.io/core/house-prices-us/_r/-/archive/Denver , CO-SA.csv
https://datahub.io/core/house-prices-us/_r/-/archive/Detroit , MI-NSA.csv
https://datahub.io/core/house-prices-us/_r/-/archive/Detroit , MI-SA.csv
https://datahub.io/core/house-prices-us/_r/-/archive/Las Vegas , NV-NSA.csv
https://datahub.io/core/house-prices-us/_r/-/archive/Las Vegas , NV-SA.csv
https://datahub.io/core/house-prices-us/_r/-/archive/Los Angeles , CA-NSA.csv
https://datahub.io/core/house-prices-us/_r/-/archive/Los Angeles , CA-SA.csv
https://datahub.io/core/house-prices-us/_r/-/archive/Miami , FL-NSA.csv
https://datahub.io/core/house-prices-us/_r/-/archive/Miami , FL-SA.csv
https://datahub.io/core/house-prices-us/_r/-/archive/Minneapolis , MN-NSA.csv
https://datahub.io/core/house-prices-us/_r/-/archive/Minneapolis , MN-SA.csv
https://datahub.io/core/house-prices-us/_r/-/archive/New York , NY-NSA.csv
https://datahub.io/core/house-prices-us/_r/-/archive/New York , NY-SA.csv
https://datahub.io/core/house-prices-us/_r/-/archive/Phoenix , AZ-NSA.csv
https://datahub.io/core/house-prices-us/_r/-/archive/Phoenix , AZ-SA.csv
https://datahub.io/core/house-prices-us/_r/-/archive/Portland , OR-NSA.csv
https://datahub.io/core/house-prices-us/_r/-/archive/Portland , OR-SA.csv
https://datahub.io/core/house-prices-us/_r/-/archive/San Diego , CA-NSA.csv
https://datahub.io/core/house-prices-us/_r/-/archive/San Diego , CA-SA.csv
https://datahub.io/core/house-prices-us/_r/-/archive/San Francisco , CA-NSA.csv
https://datahub.io/core/house-prices-us/_r/-/archive/San Francisco , CA-SA.csv
https://datahub.io/core/house-prices-us/_r/-/archive/Seattle , WA-NSA.csv
https://datahub.io/core/house-prices-us/_r/-/archive/Seattle , WA-SA.csv
https://datahub.io/core/house-prices-us/_r/-/archive/Tampa , FL-NSA.csv
https://datahub.io/core/house-prices-us/_r/-/archive/Tampa , FL-SA.csv
https://datahub.io/core/house-prices-us/_r/-/archive/U.S. National-NSA.csv
https://datahub.io/core/house-prices-us/_r/-/archive/U.S. National-SA.csv
https://datahub.io/core/house-prices-us/_r/-/archive/Washington , DC-NSA.csv
https://datahub.io/core/house-prices-us/_r/-/archive/Washington , DC-SA.csv
https://datahub.io/core/house-prices-us/_r/-/data/cities-month-NSA.csv
https://datahub.io/core/house-prices-us/_r/-/data/cities-month-SA.csv
https://datahub.io/core/house-prices-us/_r/-/data/national-month.csv
https://datahub.io/core/house-prices-us/_r/-/datapackage.json
Key Files

Start with these files — they give you everything you need to understand and access the dataset.

datapackage.jsonmetadata & schema
https://datahub.io/core/house-prices-us/_r/-/datapackage.json
README.mddocumentation
https://datahub.io/core/house-prices-us/_r/-/README.md
Typical Usage
  1. 1. Fetch datapackage.json to inspect schema and resources
  2. 2. Download data resources listed in datapackage.json
  3. 3. Read README.md for full context

Data Views

United States National Housing Price Indices

Data Files

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cities-sa

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About

Case-Shiller US home price index levels at national and city level. Monthly. Seasonally adjusted.
Last updated
22 May 2026
Total rows
...
Format
CSV
File size
85.3 kB

cities-nsa

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About

Case-Shiller US home price index levels at national and city level. Monthly. Not seasonally adjusted. Note: MA-Boston values are 0 for months before the series began (early data gaps from FRED); CA-San Diego has blank values in early months.
Last updated
22 May 2026
Total rows
...
Format
CSV
File size
85.5 kB

national

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Download CSV

About

United States National Housing Price Indices. Monthly data from 1975 onwards, with both seasonally adjusted and not seasonally adjusted series.
Last updated
22 May 2026
Total rows
...
Format
CSV
File size
16 kB

About this dataset

badge

Case-Shiller Index of US residential house prices. Data comes from S&P Case-Shiller data and includes both the national index and the indices for 20 metropolitan regions. The indices are created using a repeat-sales methodology.

Data

As per the home page for Indices on S&P website (now hosted at spglobal.com):

The S&P/Case-Shiller U.S. National Home Price Index is a composite of
single-family home price indices for the nine U.S. Census divisions and is
calculated monthly. It is included in the S&P/Case-Shiller Home Price Index
Series which seeks to measure changes in the total value of all existing
single-family housing stock.

Documentation of the methodology can be found on the S&P DJI methodology page.

Key points are (excerpted from methodology):

  • The indices use the "repeat sales method" of index calculation which uses data on properties that have sold at least twice, in order to capture the true appreciated value of each specific sales unit.
  • The quarterly S&P/Case-Shiller U.S. National Home Price Index aggregates nine quarterly U.S. Census division repeat sales indices using a base period a nd estimates of the aggregate value of single family housing stock for those periods.
  • The S&P/Case - Shiller Home Price Indices originated in the 1980s by Case Shiller Weiss's research principals, Karl E. Case and Robert J. Shiller. At the time, Case and Shiller developed the repeat sales pricing technique. This methodology is recognized as the most reliable means to measure housing price movements and is used by other home price ind ex publishers, including the Office of Federal Housing Enterprise Oversight (OFHEO)

Preparation

To download and process the data, set the API_KEY environment variable to a valid FRED API key and run:

make

This runs scripts/data_fetch_and_process.py (fetches per-series data from the FRED API into archive/) followed by scripts/convert_to_final_data.py (combines the archive files into the final CSVs in data/).

Data quirks

  • MA-Boston (NSA): values are 0 for months in the early part of the series where FRED reports no observation rather than a null.
  • CA-San Diego (SA): blank values appear in the earliest months before the series begins.
  • All dates are set to the first day of the month (YYYY-MM-01).

This product uses the FRED® API but is not endorsed or certified by the Federal Reserve Bank of St. Louis.

License

Any rights of the maintainer are licensed under the PDDL. Exact legal status of source data (and hence of resulting processe data) is unclear but could have a presumption of public domain given its factual nature and US provenance. However, the current application of PDDL is indicative of maintainers best-guess (and comes with no warranty).