Long Commutes in Puerto Rico and Finding the Next Detroit

Categories: Commentary, Uncategorized |

September 22, 2014

This week we explore newly released commuting data from the U.S. Census Bureau and demonstrate use of DIVER Analytics-Filter Module to prioritize credit surveillance work.

Commuting Data Yields Mix of Predictable, Surprising and Relevant Results

Last week the Census Bureau released an update to county-level data regarding commuting in the largest counties in the United States (population > 65,000).  Nationwide, the average commuting time of 24 minutes was unchanged between 2012 and 2013.  A look at the counties with the longest commutes yields some predictable results (NY), but also some surprises (PR), and some with relevance to toll road investors (MD, VA):

US Counties with Longest Average Commuting Time

(In minutes)


Source:  U.S. Census Bureau, DIVER Analytics, DIVER Data Solutions

The (non-Manhattan) counties of New York City all place in the top 15 counties by commuting time.  This is not a surprise.  Despite a well-developed mass transit system, the concentration of population and employment in New York City creates daunting commuting challenges.  Even within Manhattan, average reported commuting time is 31 minutes.

The Metropolitan Transportation Authority’s big-ticket capital projects are unlikely to provide much relief to the four outer boroughs. Two of the projects are within Manhattan (2nd Ave Subway, No.  7 extension) and the other may help some Queens commuters, but will mostly impact Long Islanders (East Side Access).

The municipalities of Toa Alta (46 min.) and Toa Baja (39 min.) in Puerto Rico have some of the longest commuting times to work of any of the counties tracked by the Census.  Toa Alta and Toa Baja are located west of San Juan.  Both have high rates of workers commuting “out of county” to work:  Toa Alta 88%, Toa Baja 76%.

Long commuting times may be one of the few challenges faced by Puerto Rico that has not gotten sufficient focus.  David Martin, author of Puerto Rico: The Economic Rescue Manual, believes that: “Traffic and the lack of efficient non-automotive transportation is one of the most pressing problems in Puerto Rico”.  This is a reminder that PREPA’s fuel mix is only one of the problems Puerto Rico needs to solve.

Puerto Rico Commuting Times by Municipality

(in minutes)

9-22 2 9-22 3

Source:  U.S. Census Bureau, DIVER Analytics-Map Module.

Maryland (#1) and Virginia (#8) rank high vs. other states in commuting times.  Commuting times are a function of both congestion levels and distance traveled.  An urban area with high levels of congestion lengthens commuting times.  Homes located a long distance from work is another factor.  A desire for affordable housing and open space has lead many commuters to live farther and farther from work.

In numerous Maryland and Virginia counties both these factors are present, resulting in four counties appearing in the list of the 15 worst U.S. county commutes.  While this is bad news for commuters, it is potentially good news for investors.   Several municipal bond obligors have borrowed to help alleviate (or capitalize on) this problem:  Dulles Toll Road, 95 Express Lanes, and the MD DOT borrowing for Intercounty Connector. If the bonds issued to finance the 495 Express Lanes are converted from variable rate to fixed rate (without the current bank LOC), it will join the list.

Maryland Commuting Times by County

(in minutes)

9-22 49-22 5

Source:  U.S. Census Bureau, DIVER Analytics-Map Module

Virginia Commuting Times by County

(in minutes)

9-22 69-22 7

Source:  U.S. Census Bureau, DIVER Analytics-Map Module

Finding the Next Detroit (or Scranton)

While Detroit’s problems have rightfully garnered national attention, its decline was long anticipated and well publicized.  For municipal investors, a challenge is to anticipate and avoid problems in smaller cities whose decline is less publicized.  The recent news that the difficulties in Scranton, PA are approaching a crisis prompted us think about ways to scrub portfolios for potential trouble spots.

Using the DIVER Analytics-Filter Module we screened our database of counties using economic and demographic criteria that can highlight geographies with heightened risk.

Screening the universe of over 3,100 counties to find situations of declining population, declining employment, declining income, and income levels below average yields a list of 487 counties – perhaps bonds of these counties deserve extra scrutiny.  Users of DIVER Analytics can compare this list to their portfolio holdings to focus and prioritize surveillance efforts.

Filtering for Economically/ Demographically Challenged Munis

9-22 8

Source:  DIVER Analytics-Filter Module

This week, the Lumesis team will be in New Jersey and New York City.

Have a great week,


Mike Craft
Managing Director, Credit, Lumesis, Inc.


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