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The Cass freight index

FRED absolutely, positively has the data on shipping

The volume and value of transportation services can serve as an indicator of overall economic activity. Goods produced in any one part of the country are consumed all over the U.S., so producers and consumers are connected by freight shippers. The business activity of those domestic freight shippers broadly reflects the buying and selling of goods in the economy.

The FRED graph above shows the percent change from a year ago in the volume of shipments (in blue) and in the value of their related expenditures (in red). The data are reported by Cass Information Systems, Inc., in the form of an index going back to January 1991.

Shipments and expenditures generally increase and decrease at approximately the same time during economic expansions and recessions. That’s to be expected, because freight movements reflect overall economic activity and that activity changes during the business cycle. However, there are multiple occasions during economic expansions when the shipping index remains constant or even declines. Perhaps more interestingly, there are extended periods of time when the expenditures index grows at a noticeably faster rate than the shipping index does.

Between May 2020 and the time of this writing, the expenditures index doubled in value while the shipment index increased by a little more than a third. In fact, since May 2021, the shipment index experienced almost no growth while the expenditures index kept on rising. Congestion of freight services and rising fuel costs may deliver the explanation here.

How this graph was created: In FRED, search for “Cass Freight Index: Shipments.” Next, click “Edit Graph” at the top right corner and use the “Add Line” tab to search for “Cass Freight Index: Expenditures” and click “Add data series.” Edit Line 1 by using the “Units” dropdown menu to select “Percent Change from Year Ago” and click “Copy to all.”

Suggested by Diego Mendez-Carbajo.

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.

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