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New details on mortgage rates

What impact does a FICO score have?

FRED now offers Optimal Blue Mortgage Market Indices, which provide a more-detailed look at mortgage rates. These indices are computed daily from actual mortgage closings and cover about 35% of the U.S. market.

The FRED graph above compares the weekly rates from Freddie Mac (red line) and from Optimal Blue (blue line). The latter also covers mortgages that aren’t managed by Freddie Mac, but with the restriction that they must be “conformable”—that is, the loan amount can’t exceed the limit for the property and its location.

In the second graph, we see that the loan amount influences the loan rate: The closer your loan is to the full value of the house, the more you have to pay. But the difference doesn’t look too large or unpredictable. Keep in mind, though, the composition of the loans for these two series may change for reasons that may correlate with the size of the loan: for example, the creditworthiness of the borrower.

So our last graph looks at different levels of creditworthiness—specifically, the FICO score of the borrower. The differences between the series don’t look dramatic, but borrowers definitely care about them. The difference between the rate for the highest score and the rate for the lowest score is about half a percentage point, which actually can add up significantly over 30 years.

How these graphs were created: For all graphs, start from the relevant release calendar. For the first, select the conforming series, and click “Add to Graph.” From the “Edit Graph” panel, use the “Add Line” tab to search for and select the average 30-year mortgage. For the second and third graphs, select the relevant series in the release table and click “Add to Graph.”

Suggested by Christian Zimmermann.

View on FRED, series used in this post: MORTGAGE30US, OBMMIC30YF, OBMMIC30YFLVGT80FGE740, OBMMIC30YFLVLE80FB680A699, OBMMIC30YFLVLE80FB700A719, OBMMIC30YFLVLE80FB720A739, OBMMIC30YFLVLE80FGE740, OBMMIC30YFLVLE80FLT680

Are jobs in education still recession-proof?

Studying employment data in the education sector

The U.S. economy is in recession, and the unemployment rate is above 10%. But the start of the school year is around the corner, and teachers are going back to work.

Historically, student enrollment in colleges and universities increases during recessions,* but what do the data on educational employment show us? Here, we look at two graphs—one for New York City (the most populous U.S. city) and one for California (the most populous U.S. state)—to see if employment in the education sector really is recession-proof.

Both FRED graphs above show there was no noticeable change in college, university, and professional school employment (the blue area) during the 1990-1991, 2001, and 2007-2009 recessions. The data, by the way, are seasonally adjusted to account for regular summer layoffs when schools are out. For reference, we also plot the employment in elementary and secondary schools (the green area). Student enrollment in those schools is mandatory, so one would expect a constant-size labor force, likely increasing due to population growth.

But before we call employment in the education sector “recession-proof,” we have to examine the latest data. In March and April, the mandated social distancing to combat the COVID-19 pandemic in the U.S. resulted in a decrease in employment in higher education both in New York City and California. By June, employment figures bounced back; but as the recession and pandemic continue, employment in the education sector may decrease again.

*For some summer reading, look at work from Harris Dellas and Plutarchos Sakellaris that shows college enrollment increases during recessions: When people are out of work, they choose to increase their education and accumulate “human capital.” (As economists would say, the opportunity cost of schooling decreases during recessions because there are fewer good alternatives.)

How these graphs were created: For both graphs: Search for a series in FRED, then click on “Edit Graph”; then open the “Add Line” tab and search for another series. Once done, open the “Format” tab and choose the graph type “Area” with stacking.

Suggested by Diego Mendez-Carbajo.

View on FRED, series used in this post: SMU06000006561110001SA, SMU06000006561120001SA, SMU06000006561130001SA, SMU06000006561160001SA, SMU36935616561110001SA, SMU36935616561130001SA

Location, location, location in house price data

Manufactured home prices help separate the house from the land

The FRED graph above shows the average price of single-family homes in the four Census regions. Homes in the Northeast are about twice as expensive as in the Midwest or the South, with the West in between. Why so? It could be that the houses have different characteristics (e.g., size and amenities), but it more likely has to do with the location.

The second graph shows prices for manufactured homes in the same four Census regions. These homes come in a fairly standard size and layout. But more importantly for our purposes here, they’re priced at the seller location: in the Northeast, Midwest, South, or West. And they’re priced without the land they’ll be on.

The graph shows there’s no systematic or notable difference in the level of prices in the different regions. Which leads us to conclude that the main suspect for the price differences across regions (in the first graph) is the price of land. Which, obviously, differs by location.

How these graphs were created: Use the release table for home sales, check the average price for each region, click on “add to graph” and start the sample period on 2014-01-01. For the second graph, go to the release table for average manufactured home prices, check the four regions, and click on “add to graph.”

Suggested by Christian Zimmermann.

View on FRED, series used in this post: ASPMW, ASPNE, ASPS, ASPW, SPSNSAMW, SPSNSANE, SPSNSASO, SPSNSAWE


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