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The house price tumble in pictures

FRED recently added county-level data on house prices. As with a lot of regional data, it’s best to look at it on our mapping tool, GeoFRED, which lets you visualize the distribution and the evolution of economic and socio-demographic statistics. One particularly interesting thing you can do with these new data is to replay the housing crisis of 2007-2008. The map shows the change in house prices that took place in 2006: The darker areas show the counties where growth was the highest, mostly the West and Florida. The maps below* show the change in 2007, when the West Coast and Florida were suddenly the areas with the lowest growth, and in 2008, when this downturn expanded to most of the West. If you go to GeoFRED, you can cycle through more years and see how crisis unfolded.

* Note that the colors in these maps denote lower values.

How these maps were created: Go to GeoFRED and select the county maps. Look for “All-Transactions House Price Index” in the dropdown menu. Click on the arrows in the legend box to change the displayed year.

Suggested by Christian Zimmermann.

Sectoral trends in activity Exploring FRED release tables for GDP by industry

The graph above shows the value added to the U.S. economy for select industries since 2005. The period covers the run-up to the last recession, the recession itself (gray area), and the prolonged recovery since then. While a dozen years is a short economic history in which to see major sectoral changes, we can still see some here. A striking detail is that the FIRE (finance, insurance, real estate) sector was hit hard by the recession, but has since continued its upward trend. Construction, however, is still struggling to get closer to pre-recession levels, while manufacturing is almost there. Finally, the federal government registered a slight bump after the recession, while state and local governments are on a slow but continuous rise. FRED has much more on activity by sector, so feel free to explore from the handy release tables.

How this graph was created: Start from the release table for GDP by industry, choose which measure you want, then check the sectors and click “Add to Graph.”

Suggested by Christian Zimmermann.

View on FRED, series used in this post: RVAC, RVAF, RVAFIRL, RVAMA, RVASL

On the importance of consumer durables Consumption over the business cycle

Consumption is the largest part in a household’s expenses and, for that matter, the nation’s expenses as well. Therefore it’s natural that the movements of consumption through the business cycle are of particular interest: first, because it accounts for over half of GDP; second, because it’s an essential part of the well-being of households. But not all components of consumption have the same characteristics. The consumption of government services is typically left unmeasured and may in fact be unmeasurable. As for private consumption, we like to divide it into three buckets: services (such as hair cuts, movie tickets, and medical visits), non-durable goods (such as food, newspapers, and medication), and durable goods (such as cars, televisions sets, and bathtubs). As the graph shows, these three components typically fluctuate in the same direction, but not at the sample amplitude. Indeed, if a recession hits, households usually can postpone the purchase of a new refrigerator but not daily essentials. When the economy booms again, they can afford to catch up on all the durables.

The graph above shows this as a percentage change for each component. The graph below shows the change in dollars, and the three types of consumption seem to look much more similar. The reason is that durable purchases are on average smaller than the other two, but proportionally more variable. In absolute terms, the variability of the three happens to be similar, at least for the United States. The difference here is that the average growth of services is higher than the growth for goods.

How these graphs were created: For the first, search for “Real Consumption Per Capita,” check the three series, and add them to the graph. From the “Edit Graph” tab, change units to “Percentage Change from Year Ago” and apply to all. For the second graph, change all units to “Change from Year Ago.”

Suggested by Christian Zimmermann.

View on FRED, series used in this post: A795RX0Q048SBEA, A796RX0Q048SBEA, A797RX0Q048SBEA

Diverging forecasts Different stories from hard and soft data

Leading up to the 2017:Q1 GDP release, the two GDP tracking indicators in FRED have told starkly different stories of expected growth. These indicators, also referred to as Nowcast indicators, combine higher-frequency (e.g., monthly) economic data released before the GDP data to estimate growth in the current quarter. As shown in the graph above, in the beginning of April the GDPNow indicator from the Atlanta Fed forecasted a significant slow-down in growth, predicting 0.638 percent annualized growth in the first quarter. In contrast, the St. Louis Fed Economic News Index forecasted annualized GDP growth to be higher, at 2.89 percent in Q1.

What is driving the difference? An analysis into the data underlying the GDP trackers identifies stark differences between “hard” data and “soft” data in the first months of 2017. The Nowcasts rely on both soft data such as consumer and business surveys and hard data such as retail sales and industrial production. The GDPNow indicator uses more hard data, taking an accounting approach to building a forecast; the St. Louis Fed’s News Index is based more on soft data, which is surveyed from news reports. (For more insight on this topic, see this recent Economic Synopses essay.)

The graph below illustrates the contrast between a soft data series and a few hard data series over the beginning of 2017. The blue line is the University of Michigan Consumer Confidence Index, and the red and green lines are industrial production and retail sales, respectively. Each series is indexed to 100 at October 2016 to show the progression over the end of 2016 and beginning of 2017. Since October 2016, consumer confidence has risen dramatically while retail sales and industrial production have risen steadily but slowly. Soft data in 2017 have so far told a much more positive story of economic growth than hard data. One reasons analysts identify as a possible cause for the divergence between survey and hard economic data is consumer and business optimism after the election of Donald Trump that is not yet reflected in the hard data.

How these graphs were created: Top graph: Search for “GDPNow” in the FRED search box and graph the first series that is returned. Click the “Edit Graph” button and select the middle “Add Line” menu. First, search for “St. Louis Fed Nowcast” and add the St. Louis Economic News Index as a new line. Repeat this process for real GDP, selecting the series with units in percent change from preceding period at an annualized rate. Adjust the date range to 2015:Q3 to 2017:Q1. Bottom graph: Search for “Consumer Sentiment” in the FRED search box and graph the first series that is returned. Repeat the process above to add industrial production and retail and food services as additional lines on the graph. Once all three lines are added, select “Edit Line 1” on the “Edit Graph” menu and change the units to “Index.” Set the date to October 2016 and click “Copy to all.”

Suggested by Maximiliano Dvorkin and Hannah Shell.

View on FRED, series used in this post: A191RL1Q225SBEA, GDPNOW, INDPRO, RSAFS, STLENI, UMCSENT

Not all recessions are created equal Differences in European unemployment rates

The previous recession was a worldwide phenomenon. It originated with a financial crisis in the United States that resonated in other countries, in particular Europe. The graph above shows the unemployment rate for the U.S. and a few European countries. It is taken from the OECD’s Main Economic Indicators, which goes through the trouble of trying to harmonize the definitions across countries, thus making them comparable. What is striking is how varied the experience has been. The gray area represents the period of the U.S. recession. It is remarkable that Germany’s unemployment rate actually was going down through much of this period. In contrast, unemployment shot up in Spain and, to a lesser degree, in Italy. And the U.K., arguably with the strongest financial ties to the U.S., experienced a relatively minor increase in unemployment. How can such varied experiences be explained? For one, the financial crisis was not the only economic event happening across those countries. Second, the labor market institutions and traditions differ a lot as well. Spain in particular is a poster child of rigid labor laws, and Germany was still in the transitional phase of labor market reforms.

How this graph was created: Search for “harmonized unemployment rate total,” then use the tags in the side bar to limit choices to frequency “monthly” and “seasonally adjusted.” Check the countries you want to display and click on “Add to Graph.” Finally, let the sample period start in 2002.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: LRHUTTTTDEM156S, LRHUTTTTESM156S, LRHUTTTTGBM156S, LRHUTTTTITM156S, LRHUTTTTUSM156S


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