Federal Reserve Economic Data

The FRED Blog has discussed how economists use the consumer price index (CPI) to adjust the dollar value of retail sales, credit card debt, and state tax revenue to account for rising prices over time.

Today, we discuss how the Social Security Administration uses a version of the CPI to calculate cost of living adjustments, which affect the benefits recipients receive.

Another way to put it: how SSA uses CPI-W to calculate COLAs.

The FRED graph above shows the annual percent growth in both the CPI and the related CPI-W (from the US Bureau of Labor Statistics) from 2014 through 2024:

• Blue bars represent the all-items price inflation for all households living in cities and urban settings. This CPI inflation metric covers over 90% of the total population, regardless of employment status.
• Green bars represent the all-items price inflation for all urban wage earners and clerical workers,* CPI-W. Those households amount to approximately 30% of the total US population.

As we might expect, the CPI and CPI-W annual inflation rates are very similar. But it’s important to note that neither is consistently higher or lower than the other. Also, in times of higher inflation, the differences are more marked than in times of lower inflation. Different spending patterns among the populations covered by each price index can help explain that.

Since 1975, the dollar value of the Social Security’s general benefits has increased by the average July-to-September annual growth rate of the CPI-W. The amount of adjustment is announced in October. Last year, it was 2.5%. More details are available here.

*NOTE: Urban wage earners and clerical workers are defined as “households in which at least one of the members has been employed for 37 weeks or more during the previous 12 months in an eligible occupation and for which 50 percent or more of the household income must come from wage earnings associated with an eligible occupation.”

How this graph was created: Search FRED for and select “Consumer Price Index for All Urban Consumers: All Items in U.S. City Average.” From the “Edit Graph” panel, use the “Add Line” tab to search for and select “Consumer Price Index for All Urban Wage Earners and Clerical Workers: All Items in U.S. City Average.” Be sure to click “Add data series.” Next, use the “Edit Lines” tab to select each of the two graph lines from the dropdown menu and change the units to “Percent Change from Year Ago” and modify the frequency to “Annual.” Lastly, use the “Format” tab to change the graph type to “Bar.”

Suggested by Nylah Martinez and Diego Mendez-Carbajo.

Life insurance provides a benefit, typically a one-time payout, at the time of death of the insured person. As such, it could be called death assurance. This FRED Blog post seeks to determine the scope of such insurance in the US.

One thing we could look at is the total amount of promised benefits. But imagine if all the promised benefits of all the life insurance policies had to be paid out immediately. How much life insurance companies could pay would be limited by the assets they hold. So that is what our FRED graph above shows. It’s a big number that’s been growing over time, but it’s not a complete picture of what we’re trying to find out.

In our second FRED graph, above, we represent those same life insurance company assets, but this time as a percentage of total yearly US personal income.

We can see that life insurance assets hit a low point in the early 1980s, at 19% of total yearly US personal income; a high point right after the pandemic, at 48%; and it seems to have now settled around 41%. This means that the life insurance industry would now be able to provide the equivalent of a little less than five months of income if everyone had an insurance policy.

Given about 51% of Americans have life insurance, that would amount to an average of about ten months of income as benefits to the insured.

How these graphs were created: Search FRED for “life insurance assets.” The first choice should be our first graph. For the second graph, click on “Edit Graph”; search for “personal income”; take the nominal series, not the real one; apply the formula a/b/10 to get percentages, given the units. Note that this ratio is not perfect, as we divide a series that is not seasonally adjusted by one that is. For looking at longer trends, this is sufficient.

Addendum: Today, our editor George Fortier is celebrating 25 years at the Federal Reserve Bank of St. Louis. He is the one who is giving this blog its special je ne sais quoi while getting credit for very few blog posts. A big thank you for your dedication, and we are looking forward to many more posts.

Suggested by Christian Zimmermann.

On September 30, the Federal Housing Finance Agency (FHFA) released estimates of house price growth for the second quarter of 2025. Their index is estimated using actual sales prices as well as appraisal data, but the data are not seasonally adjusted.

Because home prices can fluctuate over the year, with a cooler market in winter months and a pick-up in the spring, it’s best to measure home prices relative to the same period a year ago. And that’s what our FRED graph above does.

• Since 1976, housing prices have increased an average of 5.1% per year, while the consumer price index (CPI) inflation rate has increased an average of 3.7% per year.
• In the second quarter of 2022, coming out of the COVID-19 pandemic, house price growth and CPI inflation peaked at 20.5% and 8.6%, respectively.
• In the second quarter of 2025, the most recent data available indicate house price growth and CPI inflation increased 3.8% and 2.5%, respectively.

While broader CPI inflation trends are relatively consistent across the nation, there’s considerable variation in house prices. (FRED also has CPI data for US metro areas.)

The FRED map below shows second-quarter house price growth for all 50 states and Washington, DC. Prices increased in all states, but declined in DC. Outside of the nation’s capital, house price growth was the slowest in Colorado (0.9%) and Florida (1.0%). The fastest house price growth was in the Northeastern portion of the US, with Connecticut and New York home prices both increasing 7.5% from one year ago.   

For more geographic detail, check out FRED’s FHFA house price data for 337 US metro areas, 24 of which had price declines relative to one year ago. The steepest drop in prices occurred in Punta Gorda, Florida, at –7.4%, followed by Cape Coral-Fort Myers, Florida, at –6.6%. Sumter, South Carolina, had the fastest growth at 18.0%, followed by Auburn-Opelika, Alabama, at 11.8%.

How this graph and map were created: Graph: Search FRED for and select “All-Transactions House Price Index for the United States.” The series you want should be at the top of the results. Change the time frame to 1976-01-01 to the Most Recent data release. From the “Edit Graph” section, change the units to “Percent Change from a Year Ago.” Click “Add Line” and search for “Consumer Price Index for All Urban Consumers: All Items in U.S City Average” and click “Add data series.” Make sure the units are also “Percent Change from Year Ago.” Map: Search FRED for and select “All-Transactions House Price Index for Missouri.” Click the “View Map” button and then “Edit Map”; change units to “Percent Change from Year Ago” and the “Data grouped by” to “User Defined Method” with intervals of 0, 2, 4, 6, and 8. Change the <0 to the color red to identify negative values.

Suggested by John Fuller and Charles Gascon.