Federal Reserve Economic Data

Buckle up and come along on a joyful ride with FRED. Like a three-point turn, this post covers the average maturity of new and used car loans by making three maneuvers.

First, we define the terms. The maturity of a car loan is the target date for full repayment of the borrowed amount. It can be reported in years or months. A value-weighted maturity refers to assigning more importance to loans that are for a larger percentage of the vehicle’s value.

Second, we describe the data. The FRED graph above shows data from the Board of Governors of the Federal Reserve System on the value-weighted average maturity of car loans. Both the blue line tracking new cars and the green line tracking used cars reveal a rising trend since 2009. Also, the value-weighted average maturity of loans for used cars had been consistently lower than that for new cars. But since 2022, both weighted-maturity averages are effectively the same. What gives?

Third, we analyze the data. The rising average value-weighted maturity of loans for used cars can be the result of two different factors:

(a) Used car owners may be taking on loans with longer maturities and repayment schedules.

(b) They may be financing a rising proportion of the vehicle’s value, as high as that financed by new car owners.

Research by Robert Adams, Vitaly Bord, and Haja Sannoh suggests it’s b that’s driving the recent trend in these data.

How this graph was created: Search FRED for and select “Average Maturity of New Car Loans at Finance Companies, Amount of Finance Weighted.” From the “Edit Graph” panel, use the “Add Line” tab to search for and select “Average Maturity of Used Car Loans at Finance Companies, Amount of Finance Weighted.”

Suggested by Diego Mendez-Carbajo.

Credit spreads are the difference between the performance of corporate-issued debt and the spot Treasury curve. Analysts look at these spreads to gain insight into the return investors get for owning riskier securities, as opposed to risk-free Treasury bonds. However, different segments of the corporate credit sector have different means and variance, which makes it difficult to compare the evolution of credit spreads over time and understand if financial conditions are tight or loose.

The FRED graph above allows us to compare spreads across different investment grade categories: The blue, red, green, purple, and teal lines are credit spreads from the whole US corporate, BBB, single-A, AA, and AAA indices, respectively.

The graph displays the z-scores of credit spreads from the end of 1996 to the present. These z-scores are the position of a raw variable in terms of its distance from the mean, measured in standard deviation units. Values below zero indicate a negative distance from a mean credit spread, indicating relatively small spreads. On the other hand, values above zero indicate relatively large spreads. Not surprisingly, there are spikes across all investment categories during recession periods (shaded in gray). These spikes indicate the relatively high risk of purchasing corporate debt at these points in time compared with the entire period.

Since the pandemic, credit spreads have narrowed, with indices below zero indicating levels beneath the historical average. Further, credit spreads are currently about half a standard deviation below the historical mean across all investment-grade categories. This suggests that financial conditions are loose.

How this graph was created: Search FRED for and select the following series IDs. For each new credit spread, click the “Edit Graph” then the “Add Line” option.

• BAMLC0A4CBBB
• BAMLC0A0CM
• BAMLC0A3CA
• BAMLC0A2CAA
• BAMLC0A1CAAA

Download the data and calculate the mean and standard deviation of each credit spread using your favorite tool. Go back to the graph in FRED and edit the formula for each line. Change each formula from a to (a-u/s) where u and s are the mean and standard deviations for a given credit spread, respectively.

Suggested by Anna Cole and Julian Kozlowski.

On December 4, 2024, the Bureau of Economic Analysis released their 2023 real GDP breakdown. Here are some highlights from the data set, some of which are shown in the FRED map above:

• In 2023, 91% of metropolitan statistical areas (MSAs) expanded and only 9% contracted.
• Nationally, real GDP increased by 2.9%.
• Five MSAs grew at a similar rate as the national average, including the large MSAs of Phoenix-Mesa-Scottsdale, AZ, and Pittsburgh, PA.
• The MSA with the most growth was Midland, TX, at 42.9%.
• The MSA with the least growth was Elkhart-Goshen, IN, at -9.3%.
• The largest MSA in the St. Louis Fed’s 8th District is St. Louis, which grew by 2.5%, which placed it at 178th in the nation or right at the 50th percentile among MSAs.

The FRED map above digs deeper, into the county level. The county with the most growth was Throckmorton, TX, at 125.8%, and the county with the least growth was Lincoln County, WA, at -39.6%. Since both of these counties are very small and not a part of an MSA, GDP can fluctuate greatly from one year to the next. Growth is also not uniform for the counties of an MSA. Using the St. Louis MSA as an example in the map above, 6 of the 15 counties in the St. Louis metro area experienced negative growth while 9 experienced positive growth.

There are many reasons why some counties grow while others contract. For example, the industrial composition can amplify the degree of expansion or retraction in relation to the national overall business cycle. Demographic makeup and migration patterns of a county also can be a factor. These reasons are  explored in more detail in this St. Louis Fed essay.

How these maps were created: First map: Search FRED for “Real GDP MSA” and click on the first choice. Click on the green “View Map” button and then the orange “Edit Map” button. Change units to “Percent Change from Year ago.” Then, switch the number of color groups to 2, the data grouped by to “User Defined Method” and then define the scales at 0 and 50. For values less than 0 choose red to show contraction and values less than 50 choose green to show expansion.
Second map: Repeat the exercise with “Real GDP county,” but define the scales at 0 and 5. All St. Louis area counties were under 5, which helps focus on them more closely.

Suggested by Jack Fuller and Charles Gascon.