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New to FRED: Manufactured home prices

Single and double wide data!

FRED has just added data from the U.S. Census Bureau for an additional type of real estate: manufactured homes. This market is separate from and smaller than the more popular and widely watched single-family homes market, but the price data for manufactured homes have several interesting characteristics.

First, manufactured homes are more uniform than other homes. For example, single-family homes come in a variety of sizes, they have tended to become larger over time, and the size composition of single-family home sales may vary from one period to another. Manufactured homes come in two standard sizes, single and double, and separate statistics are collected for each.

Second, the price of manufactured homes includes only the house—that is, the land is not part of it. This should make the price more informative. However, the market for manufactured homes is thinner, which makes measurements less precise and thus more volatile.

The graph above compares the prices of manufactured homes (single and double) with two popular single-family home price indexes. It’s striking that their trends are quite similar, despite the differences noted above. It’s a coincidence, though, that the levels of the single-family home price indexes line up with the manufactured home series. (In the graph, the value 100 could be any year.) It’s also clear, as noted above, that the price of manufactured homes is more volatile, as the market is likely too thin.

How this graph was created: Start from the release page for manufactured homes, click on the link to the release table with prices, check the two national series, and click “Add to Graph.” From the “Edit Graph” panel, use the “Add Line” tab to search for “house price” and select the S&P/Case-Shiller National series and then the All-Transaction House Price Index. From the “Format” tab, make sure the scale for these series is on the right. Finally, restrict the sample to start when all data are available.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: CSUSHPINSA, SPDNSAUS, SPSNSAUS, USSTHPI

The impact of social distancing on leisure and hospitality

State-level data from the BLS

The FRED Blog has discussed the impact of the COVID-19 pandemic on national retail sales and employment. And Leibovici, Santacreu, and Famiglietti index the contact-intensity of a range of occupations and estimate the economic impact of their reduced activity. Their work ranks several leisure and hospitality occupations in the high-contact category.

Today, we look at the impact that social distancing has had on employment specifically in the leisure and hospitality industry.

The GeoFRED map above shows the percent change in employment levels in the leisure and hospitality industry by U.S. state between May 2019 and May 2020. Note that the data are seasonally adjusted. That means they discount regularly occurring increases and decreases in activity due to seasonal demand, such as winter skiing in Colorado or summer vacationing in Florida.

The number of employees in the leisure and hospitality industry decreased in all 50 states during May compared with a year ago. That decrease ranged from 18% in Oklahoma to 62% in New York. The median value was 38%.

And to learn about how closing restaurants and hotels spills over to total employment, read the work of Garriga and Sanchez.

How these maps were created: In GeoFRED, click on “Tools” and select the “Choose Data” tab. First, select “Region Type: State.” Next, look for “All Employees: Leisure and Hospitality, Seasonally Adjusted, Monthly, Thousands of Persons.” Change the units to “Percent Change” and edit the legend by entering the following values: -50, -40, -30, -20, -17. Lastly, change the color to a divergent palette of your choice.

Suggested by Diego Mendez-Carbajo.

Constructing a bilateral real exchange rate

How to create new series on FRED

FRED lets you create commonly used data series that are not predefined. For example, you can normalize current account balances or government budget balances by GDP and you can deflate nominal data with a price index.

One popular variable that you can create is a bilateral real exchange rate index. While a nominal exchange rate is the relative price of 2 monies (e.g., the relative price of a euro in terms of U.S. dollars), a real exchange rate is the relative price of consumption baskets in two countries. A consumption basket is a set of goods and services that represent the purchases of a typical consumer in country in a given year. Thus the real exchange rate is the price of European goods in terms of U.S. goods. One converts a nominal exchange rate into a real rate by multiplying by the ratio of the national price levels:

U.S. goods per euro area goods basket = (USD per euro) * (euro price level) / (U.S. price level)

FRED has many kinds of broad, real effective exchange rates. Here is a list of FRED’s real U.S. exchange rates. These are effective or trade-weighted real exchange rates. They are weighted averages of bilateral real exchange rates. Currencies of the countries that the U.S. trade with the most receive the highest weights in the formula. Real effective exchange rates provide a look at changes in the overall value of foreign consumption baskets in terms of the U.S. consumption basket. When a real effective exchange rate rises (falls), the average foreign consumption basket becomes more (less) expensive in terms of U.S. consumption.

But what if you want to see the price of foreign consumption in terms of U.S. consumption for a particular country or area? For example, what if you want to see the real exchange rate for the dollar per euro, as we detailed at the start of the post? You can construct such a bilateral real exchange rate yourself in FRED using monthly price and exchange rate data from the U.S. and the euro area. The following instructions give you the graph at the top of this post.

1. Search for “euro” in the FRED search box and select “U.S. / Euro Foreign Exchange Rate.” The default graph will be a daily exchange rate (DEXUSEU).


2. Because consumer price series are monthly (or quarterly), use the orange “Edit Graph” button on the right hand side to change the frequency to monthly and the aggregation method to “average.” This series is series “a” in the graph. Keep the editing box open.

3. Add the U.S. and euro area CPI series using the “customize data” area.

a. To add the U.S. CPI data, type “cpi” directly under the text that says “You can begin by adding a series to combine with your existing series.” Click on the first series in the popup list called “Consumer price index for all urban consumers.” Click “Add” on the right-hand side of the box and the U.S. CPI series became series “b” in the graph. Note that the graph itself has not changed.

b. To add the euro area CPI data, type “euro cpi” directly under the text that says “You can begin by adding a series to combine with your existing series.” Click on the first series in the popup list called “Harmonized Index of Consumer Prices: All Items for the Euro Area.” Click “Add” on the right-hand side of the box and the euro CPI series became series “c” in the graph.

4. Now that we have defined our exchange rate and price series, we use them to construct a real exchange rate by typing the following formula in the “Formula” box near the bottom of the editing box: a*c/b. Then click “Apply” to the right of the box. The picture in the graph will finally change to the bilateral real exchange rate, i.e., baskets of U.S. goods per basket of euro area goods.

5. To change the real exchange rate to an index, select “Index (Scale value to 100 for chosen date)” from the “Units” box at the bottom of the editing box and then type “1999-01-01” in the date box. Close the edit box with the X in the upper right-hand corner.

6. To see a long span of the data series that you have created, select “Max” from the data range choices, i.e., “1Y | 5Y | 10Y | Max”, at the top of the graph.

Suggested by Chris Neely.

View on FRED, series used in this post: CP0000EZ19M086NEST, CPIAUCSL, DEXUSEU


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