Federal Reserve Economic Data

Intellectual property products (IPP) such as software, patents, and original artwork have become a much larger share of the capital stock over time. Our FRED graph above shows that the IPP share of the current-cost capital stock rose from 5.6% in 1980 to 14.5% in 2023.

A unique feature of IPP is that it depreciates much faster than traditional capital. IPP isn’t usually subject to the physical depreciation from wear and tear that affects buildings, computers, and equipment. But it is subject to obsolescence from new technological innovations. A competitor’s innovation can quickly render a patent or algorithm irrelevant.

The Bureau of Economic Analysis (BEA) assumes that software, for example, depreciates at a rate of 33% per year, which is much faster than physical wear and tear.

Our second FRED graph above shows annual depreciation for equipment, structures, and IPP as a share of the current capital stock. Depreciation of structures (orange dashed line) and equipment (blue solid line) have remained stable over time, at around 3% and 13%, respectively. Depreciation of IPP (green dotted line) was 24% in 2023.

In short, this faster pace of growth and depreciation of IPP implies that more investment is necessary to maintain the current capital stock.

How these graphs were created: All these series are in millions of dollars, not seasonally adjusted. First graph: Search FRED for and select “Current-Cost Net Stock of Fixed Assets: Private: Intellectual property products.” Click “Edit Graph,” use “Customize data” to search for “Current-Cost Net Stock of Fixed Assets: Private: Nonresidential,” and click “Add.” Input the formula a/b and click “Apply.” Second graph: Search for and select “Current-Cost Depreciation of Fixed Assets: Private: Nonresidential: Equipment” and follow the same steps as above, dividing by “Current-Cost Net Stock of Fixed Assets: Private: Nonresidential: Equipment.” Use the “Add Line” tab to add the next two series and their divisors: “Current-Cost Depreciation of Fixed Assets: Private: Intellectual property products” / “Current-Cost Net Stock of Fixed Assets: Private: Intellectual property products” and “Current-Cost Depreciation of Fixed Assets: Private: Nonresidential: Structures” / “Current-Cost Net Stock of Fixed Assets: Private: Nonresidential: Structures.”

Suggested by Cassandra Marks and Hannah Rubinton.

FRED has many data series on closely related variables, such as the various measures of U.S. price levels. These include the PCE, CPI, PPI, and GDP deflator and their variants, such as core PCE. Comparing similar series can reveal subtle but important differences.

Today we look at another set of series that move together but have important distinctions: expected asset price volatility, also called implied volatility. These series are derived from the prices of options, many of which are traded on futures exchanges such as the Chicago Board of Options Exchange. Because option prices rise and fall with the expectations of market participants, they provide information about how much volatility is expected in the underlying assets.

Our two FRED graphs show four of these forward-looking measures of implied volatility over different time periods. These measures reach their highest levels during times of financial market uncertainty, such as the Great Financial Crisis (October-November 2008) and the COVID-19-pandemic shutdown (March 2020). In short, expected stock market volatility rises during crises.

Defining the measures

These four measures clearly move together, and their ranking from highest to lowest volatility is fairly consistent:

• the Russell 2000 (red)
• the 3-month-ahead S&P 500 (green)
• the 1-month-ahead S&P 500 (blue, labeled “VIX”)
• the DJIA (purple)

These measures differ because they describe expected volatility for different markets over different horizons and because the indexes themselves are weighted differently.

The Russell 2000 Index is the capitalization-weighted average of their smallest 2,000 stocks. Prices of small firms tend to be more volatile than those of large firms, other things equal.

The price indexes for the S&P 500 are also capitalization weighted, so these volatility measures can be heavily influenced by a few very large, high-tech firms whose stock prices can be sensitive to technical advances.

The DJIA index is computed from the price-weighted index of 30 well-established, large companies. Stock prices of well-established industrial firms are relatively stable compared with those of smaller or more high-tech companies.

The impact of time

The graphs show that the 3-month S&P 500 measure is usually greater than the 1-month (“VIX”) measure. This is likely because there’s greater financial risk over longer horizons and market participants must pay more to insure against losses caused by rising volatility further into the future.

But the 3-month index isn’t always greater than the 1-month index. For example, in August 2024 and April 2025, the 1-month index was often higher than the 3-month index because near-term volatility was expected to be higher than long-term volatility. In August 2024, this was likely due to a negative unemployment report stoking fears of a near-term recession. And in April 2025, the high near-term volatility was very likely due to fears of a trade war sparking recession.

Takeaways

FRED contains many sets of related series on inflation, output, asset prices, volatility, and more. Comparing similar series and considering why they may differ can help us better understand the data, why they behave as they do, and why one series may be better suited for some specific purpose.

How these graphs were created: Search FRED for “volatility” and select the “CBOE Volatility Index: VIX.” Click on “Edit Graph” in the upper right corner and select “Add Line”: Search again for “volatility” and select “CBOE S&P 500 3-Month Volatility Index,” “CBOE Russell 2000 Volatility Index,” and “CBOE DJIA Volatility Index.” Go to the “Format” tab to adjust line colors, line styles, and the order of the series. Finally, set the dates you want with the date picker above the graph.

Suggested by Anna Cole and Christopher Neely.

St. Louis is the home of FRED and the FRED Blog and part of the Federal Reserve’s Eighth District. In the past few years, the housing markets in the seven states of our District have cooled and are slowly returning to their previous trend.

The FRED graph above plots the Federal Housing Finance Agency’s house price index for these states over the past five years: House price appreciation has moderated since its peak in 2022 and is now back to pre-pandemic levels, with prices increasing between 4% and 5% annually.

The second graph above shows the number of active house listings for each state relative to the number of listings in May 2017. Despite the normalization of price growth, the number of houses in most of these markets remains well below pre-pandemic levels. House inventories range from only 33% in Illinois to 99% in Tennessee, compared with the number in 2017.

It’s worth noting the positive relationship between the speed of house price appreciation and the increase in the supply of housing, seen most clearly here in Tennessee: Compared with the other states in our District, Tennessee had the fastest pace of price growth and also the fastest increase in the supply of housing.

How these graphs were created: First graph: Search FRED for “All Transactions House Price Index for Arkansas” and click on the first link. Click “Edit Graph” in the top right corner and change the units to “Percent Change from a Year Ago.” To quickly add the other states’ data, use this pattern of series IDs in the “Add Line” tab: “ARSTHPI” where the “AR” is for Arkansas, “ILSTHPI” where the “IL” is for Illinois, etc. Change the first date of the time frame to May 2017. Second graph: Search for “Housing Inventory: Active Listing Count in Arkansas” and click on the first link. Click “Edit Graph” to change the units to “Index (Scale Value to 100 for a chosen date),” choosing 2017-05-01. Add the series IDs in the same way as above, except the two letters that identify the state are at the end of the series IDs: “ACTLISCOUAR,” ACTLISCOUIL,” etc.

Suggested by John Fuller and Violeta Gutkowski.