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Posts tagged with: "SANF806PCPI"

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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

“Log in” to regional data

FRED has compiled regional U.S. data for many economic indicators. The vast amount of regional data can make searching through the categories a bit overwhelming. But there are simpler ways to find what you want: You can search the releases if you have a good idea what you’re looking for. You can also use tags to quickly narrow down your search results—for example, by selecting specific geographies and geography types. Here, we look at per capita income for a sample of metropolitan statistical areas (MSAs) across the U.S.

In the graph above, we took the natural logarithm for each series, for the following reason: If the economic aggregate you’re looking at is growing at a constant rate and you have a sufficiently long sample, the data series will look convex and may give the impression that growth has been explosive. But if you take the natural logarithm, then a constant growth rate will look like a straight line. The graph above uses the natural logarithm: All four metropolitan areas have a kink around 1980 with a subsequent slowdown. The graph below doesn’t use the natural logarithm: That kink is not visible. Also, below it looks like San Francisco is taking off and separating from the others. Above, the distance between the lines can be interpreted as percentage difference, and it is clear that in relative terms San Francisco stays within range.

How this graph was created: Go to the list of MSAs for which FRED has per capita income, select the series you want, and click the “Add to graph” button. That’s the bottom graph. For the top graph, go to the graph tab and, for each series, expand “Create your own transformation” and select “Natural Log.”

Suggested by Christian Zimmermann

View on FRED, series used in this post: BIRM801PCPI, LEBA142PCPI, SANF806PCPI, STLPCPI

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