Federal Reserve Economic Data

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The St. Louis Fed’s Financial Stress Index, version 4

The FRED graph above depicts the St. Louis Fed’s Financial Stress Index (STLFSI). This data series in FRED was created in 2010 to measure changes in U.S. financial market conditions in response to a broad array of macroeconomic and financial developments. In particular, the STLFSI is designed to quantify financial market stress. There’s no specific definition for financial market stress, but periods of stress have historically been characterized by increased volatility of asset prices, reduced market liquidity conditions, or the narrowing or widening of key interest rate spreads. The STLFSI is constructed using 18 key indicators of financial market conditions—7 interest rates, 6 yield spreads, and 5 other indicators.

In late 2021, some Federal Reserve officials encouraged financial market participants and others to consider using an alternative short-term interest rate benchmark because of concerns about the eventual retirement of the London interbank offered rate (LIBOR). Since the STLFSI had two yield spreads based on the LIBOR, we replaced the LIBOR rate with the secured overnight financing rate (SOFR). Specifically, we shifted to the 90-day average SOFR. This rate measures the compounded average of the SOFR over a rolling 90-day period. In other words, it’s a backward-looking measure. We showed that the correlation between the previous version (STLFSI2) and the new version (STLFSI3) was 0.99 over the sample period dating back to December 1993. Click here for details and more information about this switch.

In 2022, we received numerous inquiries about the behavior of the STLFSI during the year. Most asked why the STLFSI was continuing to indicate lower-than-average levels of financial market stress, while other measures showed a “tightening” in financial market conditions. The divergence between the STLFSI and other indexes occurred more or less at the time when the Federal Open Market Committee (FOMC) began to signal its intent to raise its federal funds rate target in March 2022 and, importantly, subsequently signaled that further increases in the policy rate were likely in 2022—and perhaps in 2023.

Our analysis showed that instead of using the 90-day backward-looking SOFR rate, we should have used the 90-day forward-looking SOFR rate. In our view, using the forward-looking SOFR better captures financial market expectations in response to expected changes in the federal funds rate and its attendant effects on other asset prices and yields.

The second FRED graph plots the STLFSI4 and the STLFSI3 since early January 2020—just prior to the financial market turmoil and deep recession spawned by business and government actions designed to counteract the COVID-19 virus. In the graph, the two versions track each other closely over most of this period. But the close comovement began to erode in early February 2022, as it became clear that the FOMC was poised to begin raising its policy rate to combat an inflation rate that was the highest in 40 years. For example, the correlation between STLFSI3 and STLFSI4 was 0.993 from the week ending December 31, 1993, to the week ending January 28, 2022. Since the week ending February 4, 2022, the correlation has declined to 0.526.

A final takeaway from this second graph is that the new measure of the STLFSI shows that financial market stresses during the current Fed tightening episode are moderately higher compared with the previous version. Still, levels of financial market stress are currently near their historical levels. (In the index, zero is designed to be an “average” level of stress.) Moreover, the current Fed tightening episode has not triggered the kind of financial market stress seen during the heights of the pandemic-spawned shutdowns in the economy.

How these graphs were created: Search FRED for “Financial Stress Index” and make sure to take version 4. For the second graph, take the first, click on “edit graph,” open the “add line” tab, and search for “Financial Stress Index,” making sure to take version 3.

Suggested by Cassandra Marks, Kevin Kliesen, and Michael McCracken.

The new disconnect between mortgages and house equity

Our FRED graph above looks at what U.S. households own and owe in terms of real estate: The blue line represents households’ total equity in real estate as a share of GDP, and the red line represents households’ total mortgage debt as a share of GDP over the same time period.

Household equity and mortgage debt generally moved in tandem before 2007. However, this comovement breaks down after the 2007-2009 financial crisis. Right before the financial crisis, property values began to fall; and, for a few years, total real estate equity fell below total real estate debt. The fall in housing equity reversed after 2012 and has been continuing on its rising trend even as we write this post. Total mortgage debt, on the other hand, has fallen consistently since the 2007-2009 financial crisis.

The graph above shows the ratio of equity to mortgage debt: From 1993 to 2005, the ratio of equity to mortgage debt was around 1.5 on average. After the housing crisis, this ratio bottomed out at 0.83 in 2012 before surging to 2.34 as of 2022, a level not seen since 1960.

This phenomenon may have arisen from changes in the financial sector’s lending capacity, whether from regulation or risk attitude. It could also indicate a change in the ownership structure of houses: It may be that houses were accessible only—or mostly—to people who took out a mortgage, but now they can be owned by people who have enough equity to bypass external financing.

How this graph was created: Search FRED for “Owners’ equity in real estate” and select “Households; Owners’ Equity in Real Estate, Level.” Go to the “Edit Graph” panel in the upper right corner to open the “Edit Line” box. Scroll down to “Customize data.” In the text box, search for “gdp” and select “Gross Domestic Product.” Click “Add” next to the text box. Below this section, in the “Formula” space, enter a/b and click “Apply.” Next click the gray “ADD LINE” box at the top. In that search box, search for “Household Mortgages” and select “Households and Nonprofit Organizations; Total Mortgages; Liability, Level.“ Scroll down to “Customize data”: Search for “gdp” and select “Gross Domestic Product.” Click “Add” next to the text box. Below this section, in the “Formula” space, enter (a/1000)/b and click “Apply.”

Suggested by Yu-Ting Chiang and Jesse LaBelle.

FRED gets real, unless you want to keep it nominal

Oil prices vs. oil prices deflated by the CPI

Let’s start with nominal. Economic variables are often quoted in nominal terms—that is, terms that are not adjusted for changes in prices over time. For example, it’s easy to find nominal oil prices in FRED.

In the FRED graph above, the blue line (left scale) depicts the end-of-month prices for West Texas Intermediate crude, an important oil market. This series is not adjusted for changes in the general price level. So, if one wants to know how much consumption of other goods one has to give up to buy a barrel of oil, then one needs to “deflate” the price of that barrel of oil by a price level that corresponds to a relevant basket of goods.

Now let’s get real. Fortunately, FRED allows us to construct and graph the real price of oil by deflating the nominal price by just such a price level. The red line (right scale) in the graph shows a real oil price series, after dividing the nominal price series by the consumer price index (CPI). The series is also normalized to equal 100 in January 1986, making it easy to calculate percentage changes from that date.

Comparing nominal and real prices. Placing the blue nominal price series next to the red real price series in the same graph provides a new perspective on recent oil price movements. Nominal oil prices rose to near-record levels in the first half of 2022, surpassed only by prices in 2008. This rise was associated with the Russian invasion of Ukraine: The green vertical line denotes March 2022, the first full month of the invasion. But deflating the nominal price by the CPI shows that real oil prices in early 2022 were not as high as the nominal series might suggest. In March and April 2022, real prices were 130% higher than in January 1986 but lower than they were for most of the 2010-2015 period.

Of course, the CPI isn’t the only price level or even necessarily the best price level to use as a deflator. For example, one could deflate by the personal consumption expenditures price index (PCE), which is the Fed’s favored inflation measure. Because PCE inflation tends to be lower than CPI inflation, using PCE inflation would produce a real oil price series that does not deviate quite as far from the nominal series as our graph above shows.

How this graph was created: This graph employs three features of FRED that will help you illustrate features of the data more effectively: formulas, 2-scale graphs, and marking of dates with vertical lines.

  1. Open FRED and type “oil prices” in the search window. Select “Crude oil Price: West Texas Intermediate,” which will likely be the second choice in the search results.
  2. Select “Edit Graph,” change the frequency from daily to monthly, and select “End of period” as the aggregation method.
  3. From the same “Edit Graph” panel, select “Add Line” and search for “oil prices” in the search window and select “Crude oil Price: West Texas Intermediate.” Click “Add data series.” Again, change the frequency from daily to monthly and select “End of period” as the aggregation method.
  4. Select “Edit lines” and choose “Edit line 2”: Go down to the “Customize data:” section and type “cpi” in the search box. Select “Consumer Price Index for all urban consumers: All items in the US city average” and click the “Add” button to the right.
  5. Within the “Formula” box under “Customize data,” type “a/b” to create a real oil price series (that is, monthly oil prices deflated by the CPI price level). Click “Apply” to the right of the formula box. Select “Index (Scale value to 100 for chosen date”) under the “Units” window.
  6. Select the “Format” tab at the top of the editing box and select “Right” under “Y axis position” for “Line 2.”
  7. One can also mark chosen dates with vertical lines by selecting “Add line” in the “Edit Graph” panel: Click “Create user-defined line? [+]” and then “Create line.” For example, one can mark the approximate date of the early weeks of the Russian invasion of Ukraine —an important event in oil markets —by defining both the start and ending dates as “2022-03-01” and the “values start/end” as “0” to “140.”
  8. Close the editing box. On the main graph page, select “Max” as the date range.

Suggested by Christopher Neely.



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