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Posts tagged with: "A792RC0A052NBEA"

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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

Taxing couples

A history of tax exemptions for couples

FRED’s recent addition of data from the Internal Revenue Service is a gold mine of interesting factoids. The data cover different tax returns and drill down to particular line items. There are even time series on amounts for exemptions, deductions, and credits. The graph shows one such exemption, the personal exemption for married couples, in three versions: the nominal value as written in the tax code (blue), the real value after adjusting for inflation using the consumer price index (red), and the real value adjusting for the nominal increase in incomes using personal income per capita (green).

Move the glider to look at different time periods and you’ll notice the exemption was quite high (even in nominal terms) in the first years after the income tax was introduced, which is one factor explaining why only a minority of households were paying any tax in the first years. That eroded substantially after WWII, when the exemption was small. It has increased recently in nominal terms and keeps up with inflation but not with the increase in incomes. Indeed, it’s now trending, in real terms as deflated by income, to the lowest it has ever been. In terms of 1982-84 prices, it’s now at about $2300, compared with about $2000 at its lowest point.

How this graph was created: The exemption is among the most popular in the data release, so click on it and you have the blue line. From “Edit Graph,” use the add line feature to search for the same exemption and add to the line CPI (using the longer series) and apply formula a/b*100. Again add a line with the same exemption, add to it personal income per capita (make sure not to use the real series) and apply formula a/b*14000 (with 14000 being the factor needed to make the line roughly match the $2000 exemption in 1982-84, which is the base year for the CPI).

Suggested by Christian Zimmermann.

View on FRED, series used in this post: A792RC0A052NBEA, CPIAUCNS, IITPEMC

The convergence of income across U.S. states

Do poor areas tend to “catch up” to richer ones? After much analysis in the economics literature, the evidence is still mixed. But what do the data in FRED show us?

These two graphs trace the evolution of per capita personal income of several U.S. states over almost a century. Here, we choose the 24 most-populated states in 1930 and rank them by their initial level of per capita income. We divide the state’s per capita income by the U.S. average: A value above 1 means the state’s per capita income is above the national average, and a value below 1 means the state’s income is below average. The top graph shows the “poor” states, and the bottom graph shows the “rich” states in this sample. The graphs suggest that state incomes have gradually converged from 1929 to the early 1980s. However, this convergence seems to have stopped since then. In fact, in some cases, state incomes seem to be diverging again.

A possible explanation for this lack of convergence could be differences in the cost of living. It’s more expensive to live in, say, California and New York; so differences in real income could be more compressed than the differences in nominal income, which these graphs show.

How these graphs were created: From the FRED Release view, search through the “State Personal Income Per Capita” release for the states you want and click “Add to Graph.” Then modify each line as follows: From the “Edit Graph” section, under the “Edit Line” tab, type “Personal Income Per Capita” in the search box in the “Customize Data” section. Select the annual series and add that series to the line; then type a/b in the formula section. To remove the many (many) series titles above the graph, go to the “Format” tab and deselect “Title.”

Suggested by Maximiliano Dvorkin and Hannah Shell.

View on FRED, series used in this post: A792RC0A052NBEA, ALPCPI, AZPCPI, CAPCPI, COPCPI, FLPCPI, GAPCPI, ILPCPI, INPCPI, MAPCPI, MDPCPI, MIPCPI, MNPCPI, MOPCPI, NCPCPI, NJPCPI, NYPCPI, OHPCPI, PAPCPI, SCPCPI, TNPCPI, TXPCPI, VAPCPI, WAPCPI, WIPCPI


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