Federal Reserve Economic Data

The Federal Reserve began a tightening cycle in March 2022 by increasing the so-called federal funds rate. But much earlier, in November 2021, Fed officials announced the end of their extremely accommodative policy during the pandemic. With this announcement, financial markets immediately priced-in an increase in the cost of borrowing in anticipation of a future higher policy rate path to combat inflation.

The FRED graph above shows three different ways of capturing the impact that tighter credit conditions have on the financial burden of servicing mortgage debt. This burden is usually calculated in terms of a household’s interest payments for a mortgage as a fraction of that household’s disposable personal income. These data series tend to move with interest rate changes, following the path of inflation shown by the purple dashed line, which is the year-over-year percent change in the consumer price index for all urban consumers (CPI), excluding food and energy, often referred to as CPI-Core.

The green line in the FRED graph shows US mortgage debt service payments as a percent of disposable personal income, as reported by the Board of Governors of the Federal Reserve System. Notice that, since the pandemic, it has been relatively flat, around 4%—which is 300 basis points lower than it was in 2007. Despite the increase in interest rates, disposable incomes have grown at the same speed and the financial burden according to this measure has remained unchanged.

The red line calculates a similar ratio using the interest rate for a 30-year fixed-rate mortgage, outstanding one-to-four-family residential mortgage balances, and the same series for personal income as for the green line. The path of this red line, until 2021, is very similar to the green line’s. But one can see the impact of the policy tightening as the increase in interest rates and outstanding balances offsets the increase in disposable income. Around 2021, this ratio was about 2%, but recent policy tightening has increased mortgage rates to around 7%, causing the ratio to more than double since 2021.

What would the costs have been if rates had not risen? The blue line shows the calculation of a hypothetical counterfactual of the previous constructed variable shown by the red line: Here, the interest rate does not change over time, but is set to a constant value of 3%. (This is the level of mortgage rates in the summer and fall of 2021.) You can see that this series remains flat, below 2%, since the growth in personal disposable income is roughly the same as outstanding mortgage balances. Considering this rough “back of the envelope” calculation, one can argue that household costs of servicing new loans are double what they would have been without recent monetary policy tightening.

Despite these higher costs, why do we still see a very strong economy and high levels of consumption spending? The historical series of mortgage debt service payments as a percentage of disposable income indicates that current mortgage-servicing costs are very low compared with the levels in 2006-08—in fact, they’re nearly half the size.

How this graph was created: Search FRED for the “consumer price index for all urban consumers excluding food and energy” and change the units from “Index” to “Percent change from a year ago.” Add the rest of the series and perform the calculations as follows: Click “Edit Graph,” open the “Add Line” tab, and search for “Households and Nonprofit Organizations; One-to-Four-Family Residential Mortgages; Liability, Level (Billions)”; then click “Add.” Now search for and add “Personal Income (Billions)” and the 30-year fixed-rate series, which shows as percent. Each of the series is ordered alphabetically: a, b, c, and d. To calculate our new variable of mortgage burden, in the formula box type ((b*(d/100))/c)*100. The numerator and the denominator are in billions, and the interest rate is transformed from 5% to 0.05 by dividing by 100. The measure is shown in percent terms, which is why the formula multiplies the ratio by 100. The counterfactual—represented by the blue line, with a fixed mortgage rate—is an exercise in manipulating variables using a constant: The modified formula replaces this variable by a 3/100 constant value, which uses a modified formula of ((b*(3/100))/c)*100.

Suggested by Carlos Garriga.

More than half of the data in FRED can be displayed in choropleth maps, which is a type of data visualization where geographical areas are colored differently according to the range of their data values. The contours of the geographical areas in FRED maps represent political and statistical boundaries. Political boundaries shape the counties, states, and nations we’re familiar with. But what is a statistical boundary? Who draws them? How did those boundaries come to be?

Statistical boundaries are groupings of smaller geographies, such as counties, or states, that are drawn by data collection organizations like the US Census and the US Bureau of Economic Analysis (BEA) to facilitate the presentation and analysis of statistical and economic information.

The FRED map above shows data on the number of people residing in the US during 2022. The BEA organizes them into eight regions: New England, Mideast, Great Lakes, Plains, Southeast, Southwest, Rocky Mountain, and Far West.

Each region represents groupings of states with similar economic and social conditions. This classification was drawn after the 1950 Census to group states with similar economic and social indicators. You can learn more about the process of drawing BEA regions here.

Economic and social conditions change over time, so certain statistical boundaries are periodically revised. Research can be used to draw alternative boundaries for regional economic data geographies. You can read about an alternative definition of economic regions here.

Where can you go next using FRED maps? Read this short essay to learn more about data geographies in FRED.

How this map was created: In FRED, search for “Resident Population in the Plains BEA Region.” Click on “View Map.”

Suggested by Diego Mendez-Carbajo.

FRED currently has more than 822,000 data series, and you can leverage this wealth of information to create new data. For example, you can reasonably approximate the value of the exchange rate between the Swedish krona and the euro by using two different, yet related, data series.

The FRED graph above uses quarterly exchange rate data from the Organisation for Economic Co-operation and Development. We start with the exchange rate between the Swedish krona relative to the US dollar. Next, we customize the data by adding a second series to Line 1 in the graph: the exchange rate between the US dollar and the euro. Last, we combine both series by applying the formula a/b, which stands for the transformation krona per dollar / euro per dollar.

The resulting series is a very close approximation of the quarterly average exchange rate between the Swedish krona and the euro reported by the Central Bank of Sweden. Låt oss fundera med FRED®!

Coda: Coincidentally, you can create that very same data approximation by using data about Sweden’s GDP reported by Eurostat, the European Union’s statistical authority. The quarterly GDP figures available in FRED are reported both in the domestic currency (the krona) and in the currency of the Eurozone (the euro). We customized the data by applying this formula: GDP measured in krona / GDP measured in euros. You can see the result side by side with our earlier work here.

How this graph was created: Search FRED for and select “National Currency to US Dollar Exchange Rate: Average of Daily Rates for Sweden.” From the “Edit Graph” panel, use the “Edit Line 1” tab to customize the data by searching for and selecting “Currency Conversions: US$ Exchange Rate: Average of Daily Rates: National Currency:usd for the Euro Area (19 Countries).” Next, create a custom formula to combine the series by typing in a/b and clicking “Apply.”

Suggested by Diego Mendez-Carbajo.