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Posts tagged with: "CSUSHPINSA"

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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

Incomes determine house prices

An illustration for San Francisco

Ask someone in San Francisco what that area’s major problem is and they’ll likely complain about housing prices and how they keep getting worse. The first graph shows us this complaint is likely accurate. Indeed, house prices in the Bay Area have increased faster than the national average, with a significant run-up around the year 2000. Why has this been happening? Are people flocking there and has the increased demand for housing driven up the prices?

The second graph shows us that a large influx of residents is unlikely to be the reason behind high housing prices: The size of the working population in the area compared with the U.S. average or even the California average has in fact decreased. Thus, proportionally fewer people are living in the Bay Area, yet house prices have still gone up. What’s that all about?

The third graph traces the evolution of personal incomes in the Bay Area compared with the U.S. average. And here we see that the buying power in the Bay Area has increased significantly more than for the rest of the country. Assuming the housing stock has remained basically unchanged, there have been fewer people with much more money chasing the same houses. So house prices increase. Note how incomes increase pretty fast around the year 2000, precisely when houses got significantly more expensive. We can’t confirm this assumption because FRED doesn’t offer data for the inventory of houses in the Bay Area. Yet, the area is known for its aversion to new housing developments, so the assumption is at least likely to apply when comparing the area with the U.S. overall, which we’ve done throughout this post.

How these graphs were created: For the first graph, search for “San Francisco house price” and take the Case-Shiller series. Click on the “Edit Graph” button and add the U.S. national house price index. Apply formula a/b and choose as units the index scale, setting 100 at the end of the 1990-1991 recession. Proceed similarly for the other graphs.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: A792RC0A052NBEA, CANA, CSUSHPINSA, PAYEMS, SANF806NA, SANF806PCPI, SFXRSA

Investing in FRED

FRED doesn’t provide advice on how you should invest your savings. Different circumstances warrant different portfolios; and, as we often hear, past performance is no guarantee of future results. But FRED can show how various forms of investment have performed over the past 40 years or so. Here, we compare stocks, gold, and real estate.

  • Stocks. There are many different stock indexes, but the Wilshire 5000 index is the most comprehensive. It shows the value of a portfolio of stocks with the dividends reinvested in that portfolio.
  • Gold. It doesn’t really matter which price index you use because they’re all very similar in the long run and there’s no dividend to account for.
  • Real estate. This is tricky, so we use two series: The Case-Shiller house price index captures the value of the house itself but not the (implicit) rent from it that could be reinvested in the same way dividends can be reinvested for stocks. The Wilshire index dedicated to real estate funds (REIT) does account for reinvestment.

The graph shows that, in the long-run, stocks and real estate are quite similar. But gold clearly lags and is similar to owning a house but not living in it or renting it out. Of course, in the short run, things can look different from this long-run picture and individual stocks or houses could perform differently from the big indexes.

How this graph was created: Search for and select the first series, add it to the graph, and use the “Edit Graph” button to add the three other lines individually. Restrict the sample period to start on 1975-01-01. Then, to have the lines start at the same point, select units “Index (scale value to 100 for date chosen)” choosing 1975-01-01 as the date. That will not work for the fourth series because it starts later. Apply formula a*1.27 so that it starts on the same level as the home price index. For longer time series, it’s a good idea to take logarithms so the data in the early years are distinguishable. Do this by choosing units “Natural Log” at the bottom of each line panel. Finally, in the “Format” panel, change the color of the gold price series to gold.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: CSUSHPINSA, GOLDPMGBD228NLBM, WILL5000IND, WILLREITIND


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