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Upheaval in the U.S. housing market

Tracking higher prices and lower supply state by state

In 2020, being confined at home—say, with children or new work requirements—may have changed people’s housing preferences. At least temporarily. New demand for space has led to a rush on single-family homes and, naturally, a stronger-than-usual increase in prices.

Our GeoFRED map above shows that price increases were unequal across the U.S. states from 2019:Q4 to 2020:Q4. California had among the smallest price increases, while the Mountain West region and to some extent the South had strong increases.

The supply of housing can’t easily accommodate increases in demand, especially when they’re sudden. It takes time to buy land, plan, and build. Also, construction costs have been higher because of pandemic restrictions, shortages in materials, and increased demand. This has all translated in a dramatic decrease in the number of houses up for sale.

The GeoFRED map below shows how much that housing inventory has decreased in each state from February 2020 to February 2021. It looks remarkably similar to the above map. Quite a few states have less than half the inventory for sale from a year prior.

How these maps were created: For both, go to GeoFRED  and click the green “Build new map” button: In the cog-wheel tool menu, choose a state-level map and find the desired statistics. Choose your colors in the “Colors” section.

Suggested by Christian Zimmermann.

Are we expecting too much inflation?

CPI vs. University of Michigan's survey of consumers' inflation expectations

This FRED graph compares expected inflation and actual inflation. In recent years, expectations (in red) have been consistently above realizations (in blue). Why?

How people form expectations is a fascinating topic, as expectations drive so many economic decisions. One important point here is that, individually, we notice relatively few prices in an inflation measure. That is, individuals buy fewer goods than are included in the basket that determines the CPI. Also, we tend to recall only a few of the prices we encounter, in particular those that changed or changed more than we might have expected. (Read more about individual perceptions and bias.)

The graph below shows there’s quite a bit of variance in price changes across categories of goods. As expectations of future inflation are largely determined by perceptions of past inflation, the end result is an upward bias in expectations.

How these graphs were created: For the first graph, click on the CPI link on the FRED home page: Use the “Edit Graph” panel to change units to “Percent change from year ago.” Use the “Add Line” tab to search for “inflation expectation” and use the Michigan index. Restrict the sample period to start in 2017. For the second graph, start from the CPI release table, check the desired components, and click “Add to Graph.” Then use the “Edit Graph” panel to change the frequency of each line to “Annual, end of period.” Finally, in the “Format” tab, change graph type to “Bar”, close the tabs, and select period 2017-01-01 to 2020-01-01.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: CPIAPPSL, CPIAUCSL, CPIEDUSL, CPIFABSL, CPIHOSSL, CPIMEDSL, CPIRECSL, CPITRNSL, CUSR0000SAG1, MICH

A downturn in travel of pandemic proportions

The latest data on miles traveled by air, rail, and road

It’s not news to say that people in modern economies travel. For work, school, or pleasure. For a few hours, days, weeks, months, or even longer. Going places has been an integral and obvious part of work and life for most American households and those of any wealthy economy.

It’s also not news to say that travel and work commutes came to a halt last year when governments and individuals began combating the COVID-19  pandemic. Many workers were directed to work from the confines of their own homes, and some were hit even harder and lost their jobs. Normally bustling cities, highways, airports, and train stations came to a standstill and starting looking like staged scenes in a post-apocalyptic movie.

To illustrate this dramatic context, we constructed this FRED graph to track the dramatic change in the number of miles traveled by planes, trains, and automobiles. FRED graphs allow you to scale the values in relation to a specific date, which in this graph is December 2019. We also give ourselves some room by starting back in 2018, two years before the pandemic, to view previous trends and seasonal patterns in the data.

Of course, the graph shows the expected collapse in travel. What seems surprising, however, is the extent of this collapse for planes and trains, the more “social” form of travel.

Air travel, measured in passenger miles, plunged in May 2020 to 3.5% of the level seen in December 2019. The dry period for this industry lasted from April to June 2020. The recovery has been sluggish and is far from complete. The volume of passenger mile traveled in December 2020 is still just a paltry 36.6% of the volume in December 2019.

Rail travel, also measured in passenger miles, had a more moderate collapse, reaching a low of 7.5%. The overall pattern here is very similar to that of air travel, but the recovery is even more sluggish. Rail passenger miles in December 2020 is even more paltry: 21.2% of what it was in December 2019.

Road travel, measured in vehicle miles, reveals some less-dramatic but still interesting patterns. As expected, we see a drastic fall for the months of March and April, but it is much less abrupt than it was for air and rail: It falls to only 61% of the level in December 2019. And some of the decline can be attributed to seasonality, as shown by the data of 2018 and 2019.

Of course, vehicles provide an important advantage in pandemic travel, which is that travelers can remain within their social bubbles. The recovery here is also much faster. By July, the volume of vehicle miles was already at 95.6% of the level in December 2019. However, the expected seasonal pattern would have July numbers normally 7% above December numbers, which indicates the real gap was around 13%. So, as of December 2020, miles were less than 90% of those in December 2019.

No doubt, 2021 and beyond will be different as we re-learn how to travel while keeping ourselves and others as safe as possible. Our methods of freight delivery may also change. For now, we know the ongoing COVID crisis has accelerated the trend of working from home. It may also affect other trends (e.g., millennials’ reluctance to buy cars) and potentially reallocate economic activity across geographic areas, which could have vast implications for us all.

How to make these graphs: Search FRED for “miles” and choose the series “Rail Passenger Miles” (series ID RAILPMD11). From the “Edit Graph” panel, use “Add Line” to search for miles again and select “Air Revenue Passenger Miles” (AIRRPMTSID11) and “Vehicle Miles Traveled” (TRFVOLUSM227NFWA). Under “Units” choose “Index…” and set 100 to December 2019. With the “Format” tab, increase the weight of each line and choose your colors. Finally, use the slider (below graph) or date picker (above graph) to begin the display of data in 2018.

Suggested by Alexander Monge-Naranjo.

View on FRED, series used in this post: AIRRPMTSID11, RAILPMD11, TRFVOLUSM227NFWA


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