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Risks in commercial real estate financing

New insights from the Research Division

The FRED Blog has discussed the stress in the rental industry during the COVID 19-induced recession and the recent tightening in lending standards. Today, we focus on a broad change in the commercial real estate market brought about by the pandemic and its downside risks for the financial sector.

The FRED graph above shows data from the Board of Governors of the Federal Reserve System about the percent change from a year ago in the value of commercial real estate loans made by all commercial banks. The monthly data are available since June 2005.

Cycles of expansion and contraction in lending are clearly visible, but the underlying fundamentals in the real estate market are not. For example, the pandemic boosted the ability to work away from the office and reduced the demand for office space. Weaker demand depresses prices and lowers the collateral value of commercial real estate loans. That, in turn, increases financial risks for lenders.

Recent research from Miguel Faria e Castro and Samuel Jordan-Wood at the St. Louis Fed explore the financial risks associated with recent trends in the commercial real estate market. They find the risks from a potential downturn in that market are concentrated in smaller banks and not in the large bank holding companies that are commonly perceived as “too big to fail.”

For more about this and other research, visit the website of the Research Division of the Federal Reserve Bank of St Louis, which offers an array of economic analysis and expertise provided by our staff.

How this graph wase created: Search FRED for and select “Real Estate Loans: Commercial Real Estate Loans, All Commercial Banks.” From the “Edit Graph” panel, use the “Edit Line 1” tab to change the units to “Percent Change from Year Ago.” Last, use the “Format” tab to change the graph type to “Bar.”

Suggested by Diego Mendez-Carbajo.

Watching CPI and PCE inflation in FRED

Measures of inflation are some of the most popular data series on FRED. Two of the most important ones are the consumer price index (CPI), constructed by the Bureau of Labor Statistics (BLS) in the Department of Labor, and the personal consumption expenditures price index (PCE), constructed by the Bureau of Economic Analysis (BEA) in the Department of Commerce.

The CPI is probably the most widely watched measure of inflation and is used for many purposes, such as indexing Social Security payments. The Federal Open Market Committee (FOMC) has been looking at the PCE price index since the 1990s, however, and made that index the measure for its official inflation target in January 2012, when they introduced an official target.

To understand how the CPI and PCE inflation rates differ, consider a stylized representation of a price index for the year 2023 as the weighted sum of the prices of three types of goods, which we will call goods 1, 2, and 3.

P2023=w1,2023 p1,2023+w2,2023 p2,2023+w3,2023 p3,2023

The weights, w, represent how much money is spent on each good in the consumption basket. For example, gasoline would get a higher weight in a consumer price index than shoelaces. As relative prices, technology, or people’s tastes change, the weights in the baskets change.

Both the CPI and PCE are constructed in this general way, as the weighted averages of prices of various goods, but they differ.

  1. The two price indices measure somewhat different baskets of goods: The CPI is designed to measure the cost of living for an urban consumer, while the PCE measures a broader cost of living. Because of this difference in emphasis, the weights in the baskets differ and the CPI famously places more emphasis on the cost of housing.
  2. The weights in the CPI basket aren’t revised as often as those in the PCE basket, meaning that the PCE probably better measures consumer responses to rapidly changing relative prices.

The FRED graph above shows that monthly CPI (blue line) and PCE (red line) inflation move closely together, but the CPI generally exceeds the PCE. Over the full sample since 1960, the arithmetic average of 12-month CPI inflation was 3.77% and the standard deviation—a measure of volatility— of that series was 2.83%. The analogous figures for the PCE price index inflation were 3.31% and 2.43%. That is, the CPI inflation rate was 0.46% higher on average and somewhat more volatile. The fact that the PCE weights are revised more often than the CPI weights helps explain the higher average CPI inflation because consumers tend to substitute away from products whose prices rise sharply and the PCE index more quickly reflects such behavior.

The purple line on the FRED graph shows the difference between the CPI and PCE inflation rates, with an average value of 0.46%. This difference is almost always positive but small, usually in the range of 0 to 1%, but it does increase with overall inflation rates.

One component that may also help depict the difference between these two price indexes is annual inflation in “imputed rental of owner-occupied housing.” Shown by the green line, this is basically what a homeowner would have to pay to live in the house if they were renting it. Since CPI has higher weights for housing, this imputed rent should contribute much more to the CPI than the PCE. And it does seem to be correlated with the overall difference between the CPI and PCE inflation rates (purple line), but saying any more would require more careful analysis.

How this graph was created: On FRED, search for and select “CPI.” From the “Edit Graph” panel, change “Units” from “Index” to “Percent Change from Year Ago.” With the “Add line” option, search for “PCE” and select “Personal Consumption Expenditures: Chain-type Price Index,” then click “Add data series.” Repeat for “imputed rent,” selecting “Imputed rental of owner-occupied housing.” Add a line again, with the CPI and PCE series and apply formula a-b. Toward the top of the editing box, select “Percent Change from Year Ago” in the “Units” box and select “Copy to all.” Adjust the sample period to start on 1960-01-01.

Suggested by Christopher Neely.

How are we transporting ourselves? Part 2

Public transportation before, during, and after the pandemic

In early 2020, the COVID-19 pandemic severely reduced the number of miles Americans drove their cars, as we described in a previous blog post. The pandemic also decreased public transit ridership in a similar way: Many people started working from home, and others took the safety precaution of avoiding travel that would put them in close proximity to others.

However, the use of public transit has not rebounded from the pandemic as quickly as the number of vehicle miles traveled has. In fact, public transit ridership appears to be leveling off at 30% below pre-pandemic levels.

The reasons for the quick rebound in total vehicle miles driven can explain some of the lag in public transit ridership: A study by the Cleveland Fed showed that large numbers of residents in large metro areas migrated to small and midsize metro areas. These residents must now drive farther to reach those large metro areas, which has boosted vehicle miles driven.

But large metro areas also include well-established public transit systems—think, New York City, Chicago, and Washington D.C. Fewer residents in these large metro areas means fewer riders that could use those public transit systems. (Many midsize and small metro areas are less densely populated and have less-extensive public transit systems, so their populations rely more on motor vehicles.)

Fewer people available to use public transit also means less money for it, since there are fewer paying customers. A decline in public transit revenue can reduce hours and available routes, which can further reduce the opportunities for ridership.

There also has been an increase in remote work, which further reduces demand for daily trips. This movement of people and where they can work helps explain why, as of December 2023, public transit ridership numbers have not recovered to levels seen before the pandemic as total vehicle miles has.

How this graph was created: In FRED, search for and select the seasonally adjusted “public transit ridership” series. Select the timeline of 2010 to the latest data for 2023 to focus on the decline of ridership below 2019 levels.

Suggested by John Fuller and Charles Gascon.

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