Federal Reserve Economic Data

In some areas of the U.S., housing has become so expensive that people find it difficult or impossble to afford housing anywhere near where they work. The recent focus on the homeless population in Los Angeles highlights the most extreme form of this situation: Many of the homeless in that area are not only employed, but also are experiencing homelessness for the first time. Unaffordable housing and long commutes are particularly burdensome for low-income individuals, but these issues have consequences for all Americans. (Check out a previous FRED blog post on the distribution of commute times in the U.S. for more information.)

The maps in this post show U.S. county-level data from 2016 for two concepts: the homeownership rate and burdened households. Both depict a spatial representation of affordable housing in the U.S. Homeownership is clustered away from urban centers. Counties such as Los Angeles, Suffolk, and Cook (home to the cities of L.A., Boston, and Chicago) report homeownership rates of 48, 37, and 54 percent, respectively, significantly lower than the national average of 70 percent.

Burdened households lack access to affordable housing, which the U.S. Department of Housing and Urban Development defines as “housing for which the occupant(s) is/are paying no more than 30 percent of his or her income for gross housing costs, including utilities.” The maps show that the least affordable housing, represented by low homeownership rates and a high density of burdened households, lies in urban areas rich with economic opportunity.

Living close to work has a significant beneficial impact on employment and happiness. If they can choose to, individuals are likely to live closer to where they work; and workers with accessible jobs are more resistant to joblessness and long periods of job searching. Proximity matters the most for low-income residents, who are more constrained by housing and commuting costs. Hence, accessibility to employment increases the chances not only of working but also of escaping welfare.

More affordable housing has the potential to increase efficiency and optimization, key concepts in the study of economics: Low-income residents might gain greater economic mobility, and more high-skilled, talented individuals might move into urban areas to help maximize the economic potential of those areas. Also, the average American might simply be able to cut down on time spent in traffic getting to work.

How these maps were created: The original post referenced interactive maps from our now discontinued GeoFRED site. The revised post provides replacement maps 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 Elizabeth Tong and Christian Zimmermann.

If the map above looks familiar, either you’re experiencing déjà vu or you read our post last year about regional price parities (RPPs), which measure cost of living in metropolitan areas. Cost of living is generally persistent over time, which is why our updated map of the 2016 RPPs looks eerily similar to last year’s map. (The data are released on a two-year lag, by the way.) A reminder: The national average cost of living is set equal to 100. So, an RPP above 100 means an area is more expensive than the national average and an RPP below 100 means it’s less expensive than the national average. Of the 349 metro areas in the data, 94 fall within 5 percent of the national average.

As we showed last year, high cost of living remains concentrated in the Northeast and on the West Coast. As of 2016, San Jose, CA, takes the title of most-expensive metro area, with a cost of living 27 percent above the national average. The Midwest and South are still the least-costly places to live. In the cheapest metro area, Morristown, TN, the cost of living is more than 20 percent below the national average.

And why are some metro areas more expensive than others? Housing. The single largest consumer expenditure category is housing, and that drives most differences in cost of living (source). The map below shows the RPPs for rents, which range from nearly 50 percent below the national average to over 200 percent above. Because households spend about 20 percent of income on housing, high rent prices beget high cost of living overall. It’s no coincidence that San Jose also has the highest rent RPP.

In contrast, the goods RPPs on the next map show much less regional variation. Unlike housing, goods are more easily tradeable, so arbitrage tends to suppress regional price differences. For example, if a laptop in San Jose costs more than the same laptop in Morristown, a consumer in San Jose may just buy the cheap laptop online from Morristown and have it shipped. To compete with its rivals in Morristown, retailers in San Jose would have to cut prices. Consequently, goods prices are much more uniform nationwide: All metro areas fall within 15 percent of the national average.

The regional variation in goods prices that does exist likely results from goods that are more difficult to buy online, like fresh foods. For these items, businesses in areas with higher rent costs may charge higher prices to consumers to compensate, while businesses in areas with lower rent costs may charge lower prices. That said, some economists have found that most variation in food prices may be due to measurement error (source).

How these maps were created:The original post referenced interactive maps from our now discontinued GeoFRED site. The revised post provides replacement maps 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 Charles Gascon and Andrew Spewak.