Federal Reserve Economic Data

The FRED graph above plots two monthly U.S. data series from February 2019 through February 2022: monthly total new vehicle sales and the consumer price index for new vehicles. The numbers on the left vertical axis go from 0 to 180: The vehicle sales series stays in a broad neighborhood of 20 (which reflects 20 million units), whereas the price index stays in a range of 140 to 170. Because of the difference in values, this leaves a lot of white space in the middle of the graph. To better read the data, one can improve the graph by using different vertical axes for the two series.

The FRED graph below shows the same data with different axes for each series: right axis for the sales and left axis for the price index. Notice how much easier it is to understand the price and quantity dynamics for such a simple adjustment of the graph.

Before the pandemic, auto sales were about 17.3 units million per month. At the start of the pandemic in spring 2020, vehicles sales immediately declined as many workers were laid off and consumers likely delayed large expenditures in light of uncertain economic times. By the spring of 2021, auto sales had recovered and surpassed its pre-pandemic level.

The next major change began soon after: Vehicle sales experienced a dramatic drop, driven largely by two factors. First, supply constraints due to production slowdowns early in the pandemic and shortages of inputs, including computer microchips, restricted the number of vehicles that were made available to consumers. Second, the spike in the spring of 2021 left dealer inventories low.

Auto sales have yo-yoed over the past two years, but auto prices have not. The blue line plots the consumer price index for vehicles over the same period. Note that this is a price index; the numerical value of the index should not be interpreted as the average dollar price of a car. By construction, the index is set equal to 100 for 1982. Changes in this price index over an interval of time can be read as the amount of inflation in auto prices.

Over the first twelve months since the start of the pandemic, the new vehicle price index rose about 1 percent. More recently (i.e., between March 2021 and February 2022), this price index rose about 13 percent. This upward price pressure is manifested by strong demand and limiting supply factors described above.

How these graphs were created: Search for and select “Consumer Price Index for All Urban Consumers: New Vehicles in U.S. City Average.” (Searching for some suitable subset of these words also works.) From the “Edit Graph” panel, use the “Add Line” tab to search for and select the “total vehicle sales” series. To focus on recent years, use the slider at the bottom of the graph or the YYY-MM-DD date box in the upper right side to start the series in 2019 or so. Use this first graph to create the second: From the “Edit Graph” panel, use the “Format” tab to change one of the lines’ “Y-axis position” to the right.

Suggested by Bill Dupor.

There is a lot of talk about how wild the housing market has been, including houses sold on the day of listing without any inspection and for much more than the asking price. Can we see evidence of this in the data?

This housing fervor should be reflected in the number of days that houses are on the market, which is what our FRED graph shows. We sampled three housing markets generally considered to be hot—San Francisco, Denver, and Austin—but the story is similar elsewhere. While the median days on the market are lower than usual, they’re not dramatically lower and certainly don’t show that most houses are sold unusually quickly. In fact, the typical seasonal pattern seems to persist.

Before trying to understand what’s really going on, we need to understand what the data are actually measuring. There are two ways to compute median days on the market:

1. Take all transactions in a month, look at how long the houses were on the market, take the median.
2. Take a snapshot on a particular day of the month, look at how long the houses currently for sale have been on the market, take the median.*

The first method takes into account all the quick sales, which is the methodology for the data shown in the graph. Here’s how the source, Realtor.com, defines the data:

The median number of days property listings spend on the market within the specified geography during the specified month. Time spent on the market is defined as the time between the initial listing of a property and either its closing date or the date it is taken off the market.

If this method includes the quick sales, why are the reported medians still high? First, the stories of quick sales may be for particular submarkets and not reflective of the overall market. Indeed, their could be a mismatch between what is offered and what is demanded. Second, there may be a cognitive bias at work that is similar to the frequency illusion: If many people are interested in a few houses, even if few or no people are interested in other houses, the perception is still that all houses are hot. A similar bias occurs on highways: They’re congested during rush hour, when many people are on them, but empty otherwise. So people think they’re always congested, whereas they’re actually almost always empty. Third, there may simply be a minimum number of days needed to close a sale and, thus, the data cannot go below that number.

* This second data methodology misses most of the quick sales, but underreports slow sales, which haven’t yet been concluded. So it may actually report a lower number of days on the market.

How this graph was created: Search FRED for “house days on market San Francisco” and make sure to take the monthly series in days. From the “Edit Graph” panel, open the “Add Line” tab and search for the Denver and then Austin series.

Suggested by Christian Zimmermann.

The FRED Blog has discussed interest rates before, including those used as benchmarks of overnight borrowing costs in financial markets. Today, we revisit the topic of overnight financial transactions by comparing the interest rates of two types of loans: secured and unsecured.

The FRED graph above shows two different interest rates paid by financial institutions for borrowing cash at the end of the business day and paying it back at the start of the next business day:

• The blue line shows the overnight unsecured AMERIBOR benchmark interest rate. Reported by the American Financial Exchange, this is a volume-weighted average of interest rates applied to transactions where the borrower does not offer a security as collateral for repayment.
• The red line shows the secured overnight financing rate (SOFR). Reported by the Federal Reserve Bank of New York, this is a volume-weighted median of interest rates applied to transactions where the borrower offers Treasury securities as collateral for repayment.

If you look closely on any given date (we suggest zooming in by clicking and dragging on the graph itself), you will notice that the interest rates are very similar. Despite the fact that overnight loans are paid back very quickly, in less than 24 hours, secured transactions generally record lower interest rates than unsecured transactions: The collateral offered in secured borrowing reduces the amount of potential losses associated with lending, making it cheaper for both lender and borrower.

However, there are days—or even weeks—when unsecured borrowing is cheaper than secured borrowing. You can see here a FRED graph showing the difference between the AMERIBOR and SOFR interest rates. The largest spike of the SOFR series, on September 17, 2019, provides an example. On that date, as described by Sriya Anbil, Alyssa Anderson, and Zeynep Senyuz, a momentaneous shortage in liquidity resulted in a momentous increase in secured borrowing costs and a minimal increase in unsecured borrowing costs. The New York Fed quickly intervened to address this acute need for liquidity, and interest rates promptly returned to their pre-shortage levels.

How this graph was created: Search for and select “Overnight Unsecured AMERIBOR Benchmark Interest Rate.” From the “Edit Graph” panel, use the “Add Line” tab to search for and select “Secured Overnight Financing Rate.”

Suggested by Diego Mendez-Carbajo.