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The FRED® Blog

From floor plans to roof tiles The stages of putting up new houses for sale

FRED has a lot of data on the real estate market at the regional, national, and international levels; and this graph offers a sample. Here we examine the stage of construction for new houses for sale in the U.S. Indeed, you can buy a house while it's still in the planning stage, while it's being constructed, or after it's completed. Generally, most new houses are put up for sale while construction is still going on. The business cycle can affect this timing, however, and there are a few perspectives to consider. During a downturn, when houses are more difficult to sell, the stock of completed houses for sale can build up. Also, if prospects for selling a house worsen, a builder may delay construction until a sale is made, which would increase the stock of unstarted houses for sale. On the other hand, a builder may simply abandon the idea of new construction if there are too many finished houses already on the market, decreasing the stock of unstarted houses for sale. This last story seems to apply well to the recent past. In fact, during the previous recession, the number of unstarted houses decreased. How this graph was created: Search for "new houses for sale by stage" and select the three series. Click on "Add to Graph." From the "Edit Graph" section, open the "Format" panel to change the graph type to "Bar" with regular stacking. Suggested by Christian Zimmermann.
View on FRED, series used in this post: NHFSEPCS, NHFSEPNTS, NHFSEPUCS

Of places and patents Tracking changes in the geography of U.S. innovation

[geofred id="8Ct" esize="medium" height="900" width="1600"] The first U.S. patents were granted over 225 years ago, with three granted in 1790 according to the U.S. Patent and Trademark Office. Since then, these exclusive rights for inventions have become much more common and have expanded to cover a greater variety of inventions. In 2015, nearly 300,000 U.S. inventions received patents. The growth in patents has gone hand in hand with growth in innovation, but its geographic distribution has varied over time and continues to change. The map above shows the number of patents assigned in U.S. counties for the month of October 2016. The counties with more patents (shown by darker colors) tend to be clustered around urban centers such as Los Angeles, Phoenix, Denver, Miami, and Chicago. This distribution could be due to the higher population density in these urban counties—that is, more patents may occur simply because there are more residents. Many of these cities are also home to universities that promote research and development in technology.
Regional patent approvals can rise and fall, and some regions can surpass others. The graph above shows monthly patent approval numbers for a specific geographical region that includes Los Angeles County and nearby Santa Clara County, colloquially known as Silicon Valley. Patents in Silicon Valley began to surpass those in Los Angeles as early as 2001. The graph below (of patents per capita) shows that population growth in Santa Clara County was not the cause of its spike in patent approval. The number of patents per thousand residents was nearly identical in both counties in the early 1980s, but now Silicon Valley’s is 15 times higher than Los Angeles's.
What explains the rise of one county’s innovation over its neighbor's? Silicon Valley has been a leader in technological development since the early 20th century thanks to its longstanding location for (i) U.S. Navy research, (ii) Stanford University and its graduates' jobs in computer production, and (iii) robust venture capital investment. Since about 1995, however, Internet-based firms in Silicon Valley (Google, Apple, Amazon...) have been propelled to the forefront of the tech economy. The types of inventions these companies produce, such as software processes, have been covered by patent law only fairly recently. As these companies gained prominence and profit, the number of patents they were granted increased as well and the trends seen in these graphs came to light. NOTE: We did notice the steep drop in Santa Clara County's patent approvals over the past six data points. It is likely due to the time it takes to grant a patent, currently averaging 24 months. Washington, DC, has a similar drop-off in new patent assignments. How these graphs were created: Go to GeoFRED and click "Build New Map" in the upper right. From the "Tools" menu in the upper left, select "County" for the "Region Type" and type "patent" in the "Data" section's search bar. Select “new patent assignments by county,” and then select the desired date in the "Date" menu. For the first graph, search FRED for “patents Los Angeles” and select the relevant series. From the “Edit Graph” section, under the “Add Line” tab, search for and select “patents Santa Clara.” Change the frequency of both series to “Quarterly.” To transform the first graph into the second: For the Los Angeles series, use the "Edit Line 1" option in the “Edit Graph” section to search for “population Los Angeles” in the “Customize Data” search bar. Select that series and click “Add.” In the formula bar, write a/b and click “Apply.” Repeat this process with Line 2, searching for “population Santa Clara.” Suggested by Maria Hyrc, Diego Mendez-Carbajo, and Katrina Stierholz.
View on FRED, series used in this post: CALOSA7POP, CASANT5POP, USPTOISSUED006037, USPTOISSUED006085

Taking the pulse of the economy Connecting the San Francisco Tech Pulse with other economic indicators

The San Francisco Tech Pulse is a measure of the overall health of the American tech sector; it's calculated using variables such as employment and consumption in the sector and investment in technology. The graph shows the Tech Pulse as well as total U.S. employment, CPI, and GDP indexed to January 2000. These other indicators are common benchmarks of general economic health: Rising GDP, slow changes in CPI, and high employment all indicate a strong economy. During the 2008 recession, the indicators behaved as we would expect them to during such an economic downturn: employment fell steadily, as did GDP, and CPI spiked and then fell in a spell of high inflation followed by deflation. The tech pulse also plummeted, which makes sense considering it's the sum of the above indicators in a specific area of the economy. Yet the Pulse began to rise earlier than the general indicators. This early recovery, beginning in April 2009, could indicate that the tech sector was one of the first parts of the economy to gain strength after the recession and assisted in the overall economic recovery. However, the overall impact of technology shouldn't be overestimated. During the earlier recession, in 2001, the other indicators remained fairly stable compared with the Tech Pulse, which decreased substantially. This drastic fall could demonstrate the opposite of the pattern we see in 2008: that the technology sector was a major loser in that recession and it was the rest of the economy that helped maintain relative stability. How this graph was created: Search for and select “San Francisco Tech Pulse.” From the “Edit Graph” panel and the “Add Line” tab, search for and select the other series shown here: "GDP," “CPI,” and “Employment.” In the “Units” section, select “Index (Scale value to 100 for chosen date),” set the date as January 1, 2000, and click “Apply to all.” Suggested by Maria Hyrc and Christian Zimmermann.
View on FRED, series used in this post: CPIAUCSL, GDPC1, PAYEMS, SFTPINDM114SFRBSF

The Great Recession’s economic sneezes, colds, and hiccups An international comparison of unemployment rates

The Great Recession (December 2007—June 2009 in the U.S.) impacted unemployment rates very differently across countries. The graph above shows four different countries with noticeable patterns. In each country, unemployment increased during the course of the recession, with the U.S. recession marked by a gray bar. In the U.S. and Japan, the increase was from a relatively low level (below 5%); in France and Germany, however, the unemployment rate at the start of the recession was higher (above 7.5%). In the U.S., the unemployment rate more than doubled, while in Japan the increase was relatively moderate. In the aftermath of the recession, both these countries experienced long transitions back to their pre-recession level of unemployment: Japan waited until 2013 and the U.S. until 2015. In France, the unemployment rate behaved very differently: It increased by more than 2 percentage points during the recession, but has not exhibited any sign of convergence back to its pre-recession level since then. In fact, it increased even more in 2012 and 2013. Germany presents yet another pattern: After increasing slightly during the recession, the unemployment rate continued on a downward trend that had started back in 2005. The German unemployment rate has now reached a level that's well below its pre-recession level and is comparable to that of Japan and the U.S. How this graph was created: In FRED, search for and select “Harmonized Unemployment Rate: Total: All Persons for United States.” From the “Edit Graph” section, select the “Add Line” tab, add “Harmonized Unemployment Rate: Total: All Persons for Japan.” Repeat for “Harmonized Unemployment Rate: Total: All Persons for France” and “Harmonized Unemployment Rate: Total: All Persons for Germany.” Suggested by Guillaume Vandenbroucke and Heting Zhu.
View on FRED, series used in this post: LRHUTTTTDEM156S, LRHUTTTTFRM156S, LRHUTTTTJPM156S, LRHUTTTTUSQ156S


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