“Every spirit builds itself a house; and beyond its house, a world”

—Nature, Ralph Waldo Emerson (1803-1882)

Today, the FRED Blog considers a more down-to-earth version of Emerson’s lofty concept: How is a home price index built? The FRED graph above starts us off by showing three headline indicators of U.S. home prices:

• The all-transactions house price index for the United States (in green), produced by the U.S. Federal Housing Finance Agency, measures quarterly changes in single-family home values. It uses sample data from repeated sales of the same property. It is not adjusted for seasonal changes in home values. First released in 1996, this index extends back to the first quarter of 1975.
• The S&P/Case-Shiller U.S. national home price index (in red), produced by Standard and Poor’s Dow Jones Indices, measures monthly changes in single-family home values. It also uses sample data from repeated sales of the same property, although it considers only “arm’s length” transactions (i.e., those where the buyer and seller are separate parties). This index, like the previous one, is not seasonally adjusted. First released in 2006, this index extends back to December 1987.
• The Zillow home value index for all homes including single-family residences, condos, and co-ops in the United States (in blue), produced by Zillow, measures the typical dollar value of a composite of homes. It is produced monthly, using sample data from a proprietary estimate of a home’s market value called a “Zestimate.” This value is “smoothed seasonally adjusted” to account for changes in home values related to the calendar. First released in 2019, this index extends back to January 2000.

So. What does the graph tell us? All three indexes show a marked up-and-down cycle in the late 2000s and faster growth in home prices after 2020. In fact, despite their substantially different methodologies, the last two (monthly) indexes report very similar home price growth, almost 30%, between February 2020 and November 2021.

How this graph was created: Search for and select “Zillow Home Value Index (ZHVI) for All Homes Including Single-Family Residences, Condos, and CO-OPs in the United States of America.” From the “Edit Graph” panel, use the “Add Line” tab to search for and select “S&P/Case-Shiller U.S. National Home Price Index” and “All-Transactions House Price Index for the United States.”

Suggested by Diego Mendez-Carbajo.

Some commodities have seen some wild price fluctuations during the COVID-19 pandemic. One that was much discussed in the news is softwood lumber. While FRED does not have data on its retail price, it does have the price that the producer gets: the Producer Price Index, formerly called the Wholesale Price Index.

Our first FRED graph shows how unusual these price fluctuations have been. While the price stayed within a narrow band for years, it has suddenly spiked and plummeted in unprecedented ways since the middle of 2020. When data points rise or fall by multiples of their preceding values, it’s useful to transform the data to ensure an accurate understanding. In this case, taking logs of lumber prices provides us with that clarity—and a nice pun!

Our second FRED graphs shows exactly the same data, except that we took logs (i.e., the natural logarithm). The advantage of such a graph is that a 10% price increase looks the same whether the price is high or low. And the graph confirms that even when the price was high, the fluctuations were proportionally (or in percentages) very large.

How these graphs were created: Search for “PPI softwood lumber,” open the graph, and restrict the sample period to start in 2014 (due to preceding data interruptions). For the second graph, click on “Edit Graph” and set units to “Natural log” (last field).

Suggested by Christian Zimmermann.

After COVID-19 induced a recession, the FRED Blog discussed the consequent drop in spending on services caused by mandated social distancing. This decreased demand for services (i.e., work done on one’s behalf) was partially offset by an increased demand for goods. Today, we revisit the topic to gauge the recovery in consumption spending on services.

The FRED graph above shows data from the Personal Income and Outlay Survey from the U.S. Bureau of Economic Analysis in inflation-adjusted U.S. dollars. The values are presented as a custom index equal to 100 in February 2020, the start of the COVID-19-induced recession. This data transformation allows us to easily observe the evolution of the three main components of personal consumption: services (in red), durable goods (in purple), and nondurable goods (in green).

The initial value of all three categories of consumption spending is noted by the dashed black line, and they all decreased between February and April 2020. Remarkably, spending on both durable and nondurable goods grew past their pre-recession levels soon afterward. However, the recovery in services spending has been much slower. At the time of this writing, spending on services stands at 99.3% of pre-recession levels. Because household purchases of services represent the majority of personal consumption expenditures, that is good news for overall economic activity.

Nevertheless, the combination of sustained high spending on durable goods and the type of supply chain bottlenecks documented by Fernando Leibovici and Jason Dunn might be helping to fuel the ongoing surge in consumer price inflation.

How this graph was created: From FRED’s main page, browse data by “Release.” Search for “Personal Income and Outlays” and click on “Table 2.8.6. Real Personal Consumption Expenditures by Major Type of Product, Chained Dollars.” From the table, select the “Durable goods,” “Nondurable goods”, and “Services” series and click on “Add to Graph.” To change the units of the series, select “Units: Index (Scale value to 100 for chosen date)” and click on “Copy to all.” To add the custom horizontal line, use the “Add Line” tab and click on “Create user-defined line.” Enter “100” as both the start and end value. Use the “Format” tab to change the line colors, styles, and marks.

Suggested by Diego Mendez-Carbajo.