Federal Reserve Economic Data

The FRED® Blog

Back and forth between buying and building houses

What's behind two different responses in the housing market?

Monetary policy affects interest rates, which affect mortgages, which affect decisions in the housing market. That may be easy to understand, but the housing data may not have such clear-cut patterns. Let’s see what FRED has to show us.

The red line in the graph is the average 30-year fixed-rate mortgage (right axis) from the early 1970s to 2019. The blue line in the graph is the ratio of housing starts built by contractors over housing starts built by owners (left axis) for the same period.

From 1985 to 2007, this ratio was generally flat, around 1.5, implying contractors built approximately 60% of housing starts. But during episodes of macroeconomic turbulence, the ratio has diverged from its historical average. But not in a consistent way… In the late 1970s and early 1980s, this ratio declined sharply, to below 1.0. This implies individual owners built more housing starts than developers during this period. But during the Great Recession, this ratio increased sharply, to over 2.0, peaking at 2.6 in 2016, which implies contractors built 72% of housing starts.

Why would the ratio plummet in the late 1970s and rise sharply in the late 2000s? In both cases, GDP declined and unemployment rose, but this housing measure behaved differently.

Maybe you’ve already seen it, but a clear difference between these two episodes is the level of mortgage rates: Rates were much higher in the 1970s and 80s and much lower leading up to and through the Great Recession. As mortgage rates go up, the ratio goes down and vice versa. A potential reason is that, as the price of mortgages increases, the cost of purchasing a new home from a contractor increases relative to the cost of building one’s own home. And, if the costs are basically the same, many would-be homeowners might choose to build their own home rather than purchase one that someone else built.

The late 1970s was a period of high inflation; in an effort to reduce inflation, the Federal Reserve imposed higher interest interest rates, which included mortgage rates. In contrast, during the Great Recession, the Federal Reserve slashed interest rates and, by extension, mortgage rates. It looks like homeowners respond to changes in these interest rates: building their own houses to try to economize on the financing during periods of high rates and purchasing new houses from contractors during periods of low rates.

How this graph was created: Search for “New Privately Owned Housing Starts” and select “Contractor-Built-One-Family Units, Thousands of Units, Seasonally Adjusted.” From the “Edit Graph” panel, under the box “Modify frequency,” select “Semiannual.” Use the “Customize Data” option to search for “New Privately Owned Housing Starts in the United States by Purpose of Construction, Owner-Built One-Family Units” and select “Thousands of Units, Seasonally Adjusted.” This latter series is now labeled as b. Under “Customize data,” type a/b into the “Formula” box and select “Apply” to get the ratio of the two series. Now select “Add Line” and search for “30-Year Fixed Rate Mortgage Average in the United States, Percent, Semiannual, Not Seasonally Adjusted.” Under the box “Modify frequency,” select “Semiannual.” Under “Format,” under the option for “LINE 2,” select “Y-Axis Positon” as “Right.”

Suggested by Matthew Famiglietti and Carlos Garriga.

View on FRED, series used in this post: HOUSTCB1FQ, HOUSTOB1FQ, MORTGAGE30US

Take note: FRED has updated some series names

The FRED Team has just automated the process of how it names many of its data series. Because FRED aggregates data from 89 different sources, choosing the right name for any of the 627,000 data series is no small matter. Yes, the Bard wrote “A rose by any other name would smell as sweet.” But in the world of data, a confounding name can be a thorny problem.

Let’s choose a common example. The data series for the unemployment rate in the U.S. is collected by the Bureau of Labor Statistics (BLS). But the media can choose to report the data with a variety of names: national unemployment rate, civilian unemployment rate, official unemployment rate, harmonized unemployment rate, or U3.

The FRED graph below shows two series: the unemployment rate (from the BLS) and the harmonized unemployment rate (from the OECD). Why do we see only one line? Because the series are one and the same. So, what is the correct name for the unemployment rate data series? The answer depends on the source of the data. So, FRED will now display the series name as reported by the source of the data from the most comprehensive machine-readable location.

In the case of the BLS, that location is series LNS14000000. The series is accessible through the LABSTAT public database, which contains current and historical surveys and press releases. For the BLS series LNS14000000, the name of the data series is “unemployment rate,” so FRED will call it simply that: unemployment rate.

Although the FRED data series identifiers have not changed, there are 2,782 data series names that have changed. For a complete list, see this CSV file. You’ll notice that many data series in FRED related to the consumer price index now have updated names.

Suggested by Diego Mendez-Carbajo and Maria Arias.

View on FRED, series used in this post: LRHUTTTTUSM156S, UNRATE

Where is the U.S. growing?

Population growth in metropolitan statistical areas

If you’ve looked at FRED data, you’ve probably seen the term MSA, which is “metropolitan statistical area,” which the Census defines as “a core area containing a substantial population nucleus, together with adjacent communities having a high degree of economic and social integration with that core.” It’s that high degree of economic integration that can make MSAs more comprehensive and relevant than just the specific governmental boundaries of cities and counties. In fact, MSAs often span several counties and sometimes straddle state borders. Think of it as a commuting basin… Or create your own metaphor!

The GeoFRED map here shows population growth for MSAs: Red is at the strong end of the growth spectrum and dark blue is at the weak end. What’s behind these changes in population? The main drivers are moves between MSAs, moves from rural areas into MSAs, and immigration from abroad. In some years, the boundaries of some MSAs are adjusted and can lead to substantial increases. If you follow the “View on GeoFRED” link below the map, you can select different years and see migration patterns over time, which are affected by local economic conditions. Some MSAs, like New York–Northern New Jersey–Long Island, change pretty drastically from year to year. Other MSAs are steadier: For example, growth in St. Louis is consistently slow and growth in Las Vegas–Paradise is consistently fast.

How this map was created: The original post referenced an interactive map from our now discontinued GeoFRED site. The revised post provides a replacement map from FRED’s new mapping tool. To create FRED maps, go to the data series page in question and look for the green “VIEW MAP” button at the top right of the graph. See this post for instructions to edit a FRED map. Only series with a green map button can be mapped.

Suggested by Christian Zimmermann.



Back to Top