Federal Reserve Economic Data

The FRED® Blog

Changes in retail sales: Not by bread alone

The FRED Blog has previously discussed the impact of social distancing on retail sales for food and beverages. In today’s post, the pie graphs break down retail sales for items other than food and beverages.

The first graph shows consumer shopping habits in February 2020, before social distancing was mandated or encouraged across the U.S. At that time, the largest non-food category was motor vehicle and parts dealers.

The second pie graph shows the same categories of retail sales in April 2020, a full month after social distancing. Note how sales from non-store retailers, which include home delivery sales and electronic shopping, is now the largest category. You may also notice the category that has contracted the most is clothing and clothing accessory stores. Social distancing may have reduced consumer need for fashion, except for maybe “I *heart* social distancing” sweatpants.

How these graphs were created: From FRED’s main page, browse data by “Release.” Search for “Advance Monthly Sales for Retail and Food Services” and select “Advance Monthly Sales for Retail and Food Services by Kind of Business, Millions of Dollars, Seasonally Adjusted.” From the table, select each of the 11 non-food-related categories and click on “Add to Graph.” From the “Edit Graph” panel, use the “Edit Line” tab to change graph type to “Pie.” To show data from different months, edit the date above the graph canvas.

Suggested by Diego Mendez-Carbajo.

View on FRED, series used in this post: RSBMGESD, RSCCAS, RSEAS, RSFHFS, RSGASS, RSGMS, RSHPCS, RSMSR, RSMVPD, RSNSR, RSSGHBMS

The GDP of Washington, DC

The FRED Blog's 600th post

The FRED Blog’s 600th post concentrates on Washington, DC—partly because “DC” is 600 in Roman numerals, but also because DC’s GDP might surprise you.

The District of Columbia isn’t a state and is invisible on GeoFRED maps of the U.S., but in many respects it’s treated like a state. At the time of this writing, FRED has 2,001 data series about the District of Columbia, most from state-level sources.

DC has 0.2% of the U.S. population, which is larger than the population of both Vermont and Wyoming. DC has 0.7% of the nation’s GDP, which is larger than the GDP of 16 states and is equal to the combined GDPs of Vermont, Wyoming, and Montana. The FRED graph above shows DC and 11 of those states with smaller GDP. (We’d show all 16, but FRED graphs limit the number of series to 12.)

Is this sizeable GDP driven by government? Of course, DC is the nation’s capital and much of the economic activity in DC is from government. But, as the graph below shows, there’s much more than that. And, while real GDP from government has grown a bit, real GDP from non-government sources has grown faster.

How these graphs were created: For the first graph: Search for “GDP District of Columbia,” click on the series name, and shrink the sample period to the minimum. From the “Edit Graph” panel, use “Add Line” to add the states one by one. From the “Format” tab, choose graph type “Bar.” For the second graph, search again for the GDP of DC, but this time take the real series, as we want to show several periods. Add the other series by searching for “District of Columbia Government GDP,” again being careful to take the real series.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: ARNGSP, DCGOVRGSP, DCNGSP, DCRGSP, DENGSP, MENGSP, MSNGSP, MTNGSP, NENGSP, NHNGSP, NMNGSP, SDNGSP, VTNGSP, WVNGSP

New data on the real estate market

Visualizing seasonality in house listings and prices

FRED keeps adding new data.* The latest batch is detailed data on the housing market from Realtor.com. The FRED graph above shows the well-known seasonal pattern in the number of properties actively on sale: The real estate market is much thinner in the winter, and sellers often wait for spring to put their properties up for sale.

But not everyone can wait. Financial circumstances, job-related moves, or new family situations may force an owner to put a house up for sale at a moment that’s not optimal. On the other side of the market, job or family circumstances may force some people to look for a house when it’s not the best time to do so.

It turns out the first story is more common: that is, too many sellers chasing too few buyers (at least in relative terms). This imbalance leads to lower prices in the winter, as we can see in the graph below.

*Today, FRED has over 763,000 U.S. and international time series from 94 sources.

How these graphs were created: Search FRED for “Housing inventory” and click on the series you want displayed. Both series shown here should be in the first page of results.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: ACTLISCOUUS, MEDLISPRIUS


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