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Posts tagged with: "CUUR0000SEHA"

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Is the rent too high?

Way more than 525,600 minutes of rent data

If you’re a renter and have been complaining that your rent keeps rising, the statistics seem to back you up. In the graph, the purple line shows the evolution of rents in the U.S. as a whole, while the light blue line shows the general price level (CPI). Clearly, rents are increasing faster than prices overall. Of course, location matters for anything related to housing, and there are large regional differences: Rents in the New York and San Francisco areas have clearly appreciated more than average. Rents in the Detroit area have increased but well below the average rate; still, they’re keeping up with general inflation.

Note, however, that the graph shows the evolution of rents, but not their level. It shouldn’t be too surprising that rents in 1984 (the beginning of this sample) were higher in New York and San Francisco than in Detroit. And that gap has increased even more over time.

How this graph was created: Search for “rent CPI CBSA” (which stands for core-based statistical area, a metropolitan area defined around a core) and select the area you want shown. We selected semi-annual data instead of monthly, as these data are not collected every month. Click on “Add to Graph.” Add the remaining two series the usual way: From the “Edit Graph” panel, open the “Add Line” tab, search for “rent CPI” and add it, then search for “CPI” and add it. The last step is to limit the sample period to start on 1984-01-01.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: CPIAUCSL, CUUR0000SEHA, CUUSA101SEHA, CUUSA208SEHA, CUUSA422SEHA

Is the housing price-rent ratio a leading indicator?

Economic forecasters are always on the lookout for variables that can help predict upcoming recessions. One such variable that has gotten some recent attention is the housing price-rent ratio. As this ratio becomes higher, the rental option becomes more attractive. If it rises high enough, some households might switch from owning their homes to renting them; then the demand for owner-occupied housing would fall. The result is a contraction in the housing market that can have adverse effects on the entire economy. This narrative seems to match well with the behavior of the housing price-rent ratio leading up to the Great Recession. So if the housing price-rent ratio is on the rise again, does that mean it’s cause for concern? Let’s try to evaluate whether the housing price-rent ratio is a reliable leading indicator by graphing it, with data going back to 1975.

To be considered a leading indicator, a variable must change in sign prior to the beginning of each recession. (Recessions, as defined by NBER, are shown by gray shading.) The Great Recession started in December 2007. As we can see, the housing price-rent ratio reached its peak in April 2006, approximately two quarters prior to the start of the recession. In other words, the housing price-rent ratio seems in this case to have been a leading indicator. But for a complete evaluation, all the recession episodes must be examined. In January 1980, the U.S. economy suffered from double-digit inflation. To solve that problem, Paul Volker essentially created a recession. This recession began in January 1980. The housing price-rent ratio peaked in the second quarter of 1979 and then declined. It could again be argued that the price-rent ratio predicted this recession. In July 1981, another recession started. For this recession, whether the housing price-rent ratio correctly indicated a coming recession is less clear. The housing price-rent ratio didn’t suggest an upcoming recession in March 2001, as the ratio steadily increased.

A second condition for a variable to be a leading indicator is that it doesn’t suffer from the false-positive problem. This problem would occur when the house price-rent ratio decreases but no recession occurs. There are a number of instances when the housing price-rent ratio does suffer from this problem.

So it’s not clear whether the housing price-rent ratio qualifies as a leading indicator: It fails to identify some recessions and gives false-positive readings at other times. But in the two major recessions since 1975 (the 1980 and 2007 recessions), the housing market played a leading role; so, these recessions were predicted correctly by the housing price-rent ratio.

How this graph was created: Search for and select the series called “All-Transactions House Price Index for the United States.” Then, in the customize data option of the “Edit Graph” menu, search for and select the series called “Consumer Price Index for All Urban Consumers: Rent of primary residence.” Finally, in the formula tab, enter a/b to divide the home price index by the rent price index.

Suggested by Ryan Mather and Don Schlagenhauf.

View on FRED, series used in this post: CUUR0000SEHA, USSTHPI

Is Inflation Running Hot or Cold?

One popular measure of the price level is the consumer price index (CPI), which measures the average change over time in the prices paid by urban consumers for a market basket of goods and services. This index can be broken down into smaller component indexes, each representing a different subset of goods and services. So changes in the aggregate price level can be traced back to changes in the price levels of the underlying components. As described in a recent Economic Synopses essay, we have developed a “heat map” that visually represents CPI data in FRED: specifically, the relative inflation levels of various CPI components over the past 10 years. The heat map shown here lists the components in order according to their weight in the overall index as of July 17, 2015.

2015 July 20 FRED Blog post heat map x800

How this heat map was created: We used the FRED Add-In for Microsoft Excel (view instructions for installing the Add-In here) to download the FRED data: year-over-year percent change in each CPI component index over the past 10 years. We normalized each value by subtracting the series mean and dividing by its standard deviation calculated over the past 10 years to take into account differences in long-term trends and volatility across series. Each colored box in the heat map corresponds to the normalized inflation value for a given CPI component for a particular month. Blue represents an inflation value below the long-term trend of the index, and red represents an inflation value above the long-term trend. The darker the color, the greater the difference between that particular inflation value and the long-run average for the component index in terms of standard deviations.

Because we’re comparing series against their long-run averages, it’s possible for a “blue” series to have a higher inflation rate than a “red” series. For example, for June 2015, owners’ equivalent rent is red, with an inflation value of 2.95 percent; water, sewer, and trash is blue, and yet has a higher inflation value of 4.65 percent. The reason is that the June 2015 owners’ equivalent rent inflation is above its 10-year average of 2.16 percent; and the June 2015 water, sewer, and trash inflation is below its 10-year average of 5.11 percent.

An Excel file containing a version of this heat map can be found here, and anyone who downloads the FRED Excel Add-In has the ability to easily update the heat map when new data are released. Simply select the tab containing the raw data and press the “Update Data” button from the FRED Excel Add-in. (More instructions and details are provided in the Excel file.)

Suggested by Joseph T. McGillicuddy and Lowell R. Ricketts.


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