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House hunting

State-by-state differences in house price appreciation

It’s no secret house prices differ across the U.S. There are also large differences in how these prices change over time. In the short-term, the data include a lot of noise and temporary regional peculiarities. Over the longer haul, though, clear trends can emerge. The map shows a price index for all house transactions: The index was set to a value of 100 in the first quarter of 1980, and the map shows the index values as of the third quarter of 2017. So, we can see how house prices have increased over the past 37 years. Keep in mind this is a nominal index and that price increases are expected everywhere, given general price inflation. For comparison, an index value of 316 in 2017:Q3 would reflect price increases that have exactly kept pace with the consumer price index. For example, house price increases in Missouri and New Mexico (index values of 317) are nearly even with inflation.

Unfortunately, the District of Columbia, which has the highest house price inflation, isn’t visible on this map. D.C. has an index value of 856, which reflects a 5.9% increase per year in nominal terms and a 2.8% increase per year in real, general-inflation-adjusted terms. The location with the lowest house price inflation is West Virginia, with a value of 233, which reflects a 2.3% increase in nominal terms and a –0.8% increase per year in real terms. In fact, house prices in 12 states have appreciated below the CPI, meaning that houses there have appreciated less than the average of all consumer goods.

Does this means real estate isn’t a good investment? While the numbers shown here can provide rough estimates, it’s important to understand their limitations. This index is computed by looking at transactions that involve single-family homes with conventional mortgages that satisfy the guidelines of Freddie Mac or Fannie Mae and is based on repeat sales of such properties. Thus, this map does not reveal the prices for all houses and the quality of the relevant housing stock may also change over time. For example, purchased houses may become larger over time, and the qualifications for inclusion in this index may also change.

How this map was created: The original post referenced an interactive map from our now discontinued GeoFRED site. The revised post provides a replacement map from FRED’s new mapping tool. To create FRED maps, go to the data series page in question and look for the green “VIEW MAP” button at the top right of the graph. See this post for instructions to edit a FRED map. Only series with a green map button can be mapped.

Suggested by Christian Zimmermann.

BRICS and blocs

Beware of categorizations

Understanding the global economy has become more important for policymakers, given the increased interdependence in trade and capital flows. For the same reason, though, tracking the different economies has also become more complex. So it’s not surprising analysts find it convenient to group countries in blocs according to a characteristic or commonality. Examples include the G-7 (Canada, France, Germany, Italy, Japan, U.K., and U.S.), meant to designate the largest economies in the world, and the BRICS (Brazil, Russia, India, China, and South Africa), meant to designate the most significant emerging economies for their size and fast growth. And then there are the so-called PIIGS (Portugal, Ireland, Italy, Greece, and Spain), meant to designate European countries that were struggling to service their external debt a few years ago.

These classifications can be useful at certain points in time, but analysts and policymakers should keep in mind at least two important limitations. Countries in these blocs can behave very differently. And the classifications can very quickly become outdated, even if they continue to remain in use. I’ll illustrate these two limitations with FRED data for the BRICS.

Let’s look at real total GDP for these five countries, normalized so they all equal 100 in 1990. Using an index allows us to abstract from the large size differences of these countries and also gives a more transparent picture of how quickly each of these countries has grown between 1990 and 2014. The graph shows some large differences, indeed. China has grown dramatically since 1990—by a factor of 6. India is a distant second, growing by a factor of 4. Both Brazil and South Africa, in the middle, have doubled the size of their economies. Russia, which is last, has grown by barely 18% during the sample period; in fact, if anything, Russia’s economy was below its initial 1990 size for much of the 1990s and early 2000s.

So, should the BRICS be grouped together as a bloc? Only Brazil and South Africa behave in a reasonably similar way. Otherwise, these countries have big differences in economic behavior and their resulting relative importance.

A similar exercise could be done for the G7, where the relative importance of countries has also changed considerably. You may have known this already, but the G-7 no longer represents the seven largest economies: Canada and Italy have been replaced by China and India. And the internal rankings have also changed for the European countries, with Germany in 4th place, the U.K. in 5th, and France in 6th.

How this graph was created: Search for “real GDP at constant national prices for [country]” where [country] can be replaced by the actual name of the country you want. Select the units so that all variables are scaled by an index that sets the value of 100 for 1990. Choose 3 for the width for all lines.

Suggested by Alexander Monge-Naranjo.

View on FRED, series used in this post: RGDPNABRA666NRUG, RGDPNACNA666NRUG, RGDPNAINA666NRUG, RGDPNARUA666NRUG, RGDPNAZAA666NRUG

How a year of NAFTA news affected exchange rates

Markets overshoot in the short term

Exchange rates are among the most volatile macro series. When allowed to float, exchange rates are much more responsive to news and shocks than interest rates or the prices of goods and services. They are volatile at lower frequencies (e.g., monthly and quarterly) but especially volatile at higher frequencies (e.g., hourly, daily, or weekly). Moreover, exchange rates tend to “overshoot,” with much stronger responses to news and shocks in the short term than in the medium and long terms. This behavior, which is the opposite of Paul Samuelson’s Le Chatelier’s principle, was first formalized by Rudiger Dornbusch more than forty years ago.

A relevant example is the recent exchange rate behavior for Mexico and Canada, two of the U.S.’s main trading partners. The graph shows the daily exchange rate of the Mexican peso (left axis) and the Canadian dollar (right axis) in terms of U.S. dollars. And the three vertical lines identify three recent U.S. events: election day (Nov. 8, 2016) in green, inauguration day (Jan. 20, 2017) in purple, and the day the president announced his intention to renegotiate NAFTA (April 27, 2017) in blue.

During the recent presidential campaign, the Republican candidate clearly and ardently advocated restraining some international trade in general and terminating NAFTA specifically. However, the dominant view (both within the U.S. and worldwide) was that the Democratic candidate would win the election. The 2016 election result was a surprise for most observers and, as argued here, for investors as well. Between Tuesday, Nov. 8, and Thursday, Nov. 10, there’s a clear and significant jump in the peso-to-dollar exchange rate: from 18.435 pesos per U.S. dollar to 20.493. That’s more than 10% in just a few hours. The Canadian dollar also depreciated, but only about 1.3%, from 1.333 to 1.347. The responses of these exchange rates seem to suggest that, on Nov. 8, investors took seriously the prospect that NAFTA would be terminated or renegotiated and that such a change could reduce the value of investments in Mexico.

After the election, both exchange rates stabilized and even moved downward. However, the Mexican peso began a bumpy ride: It depreciated significantly, peaking on Jan. 20, 2017, the date the new administration took office. The peso then appreciated continuously but only until April, when the president used Twitter to announce his inclinations to terminate NAFTA. This position was reinforced by the fact that the U.S. also imposed preliminary countervailing duties on Canadian imports of softwood. Both the peso and the Canadian dollar depreciated with respect to the U.S. dollar. On April 27, 2017, the president announced his intention to renegotiate NAFTA. Eventually, both currencies appreciated.

Since September, though, both currencies have depreciated: 5% for the Canadian dollar and 9% for the Mexican peso. Two factors may be at work: a potential increase in U.S. interest rates and the news that renegotiating NAFTA has proven to be very difficult for all three countries.

How to make this graph: Search for the daily series of the Mexican peso exchange rate: DEXMXUS. From the “Edit Graph” menu, choose “Add Series” and search for the similar Canadian dollar exchange rate: DEXCAUS. From the “Format” menu, select the right axis for the Canadian exchange rate. In both series, select 3 for the line width. To create the vertical lines, open the “Add Line” tab and select “user-defined line”: Enter the date you want for both the start and end dates, and use values to fill most of the vertical space. (By the way, the exchange rates aren’t tracked on holidays or weekends, so there are some blank segments in the series.)

Suggested by Alexander Monge-Naranjo.

View on FRED, series used in this post: DEXCAUS, DEXMXUS


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