Tuesday, January 19, 2016

An Interesting Housing Graph

Jason Schrock, the Chief Economist of Colorado Governor's Office of State Planning and Budgeting sent along this interesting graph.

At first, the relationship seems obvious, but I think it is a bit more subtle than it first appears.

I have previously tried to look at housing expense as a function of things like population change, housing permits, and net domestic migration as a proportion of population.  The problem with some of those measures is that some cities have low growth because they have dysfunctional housing policies and some cities have low growth because of some other source of stagnation.

This graph solves that problem by measuring housing affordability against (housing starts / population growth).  This is interesting, because after you think about it for a little bit, there isn't necessarily a reason why this would have to be the case.  The relationship must be somewhat subtle.  In cities that aren't building enough new housing units, there will be behavioral and structural changes about housing usage to utilize the available units more intensively.  So, I don't think this is a direct relationship as much as an indirect measure of how those secondary effects of constricted housing supply would show up in the same cities where households are bidding up the existing housing stock.  But, even though it is slightly subtle, I think this tells a strong story.

Also, notice that the relationship doesn't exactly look linear.  As with the other measures I have been looking at, I think there are three subgroups of cities here.  There are the Open Access cities, which do not have a dysfunctionally constrained housing market, and therefore do not tend to have desperate marginal behavior that leads to more intensive usage of housing units.  Among those cities, there is no relationship.  The regression line is horizontal, or very slightly downward sloping.  Then there are the few Closed Access cities that are simply in a class of their own dysfunctionality.  Then there are a few cities that are sort of at a crossroads, that might end up as a full-fledged Closed Access city within a couple of decades if they continue to attract workers without expanding their housing availability.

I suspect that if this graph ended at 2005, there would be a few more of those in-between cities, since at the time, some cities, like Phoenix and Las Vegas, appear to have temporarily had more housing demand, either from migration from the high cost cities, or from investor activity as homeowners captured capital gains from the high cost cities and reinvested, than their local housing permitting processes could accommodate.


  1. This chart confirms your views regarding closed access cities, Kevin. However, the solution, imo, rests with the employers. They don't have to have all their business in one city. That is not even smart regarding things like earthquakes. Why would google keep all its computers in one place, pretty much? And in a very earthquake prone area that is subject to liquification. It makes no sense.

  2. Gary, I would note that for a lot of businesses, locality is actually essential. Restaurants, hospitals, etc, so only a fraction (what fraction I have no idea) of employment actually can be moved.

    But even for the fraction of employment that could be relocated (such as Google in your example), there are, it is argued, significant advantages of geographical clustering, especially regarding the labor market. Having many employers in an industry and many laborers in an industry residing within the same city or region (e.g. Silicon Valley) makes for more effective competition between prospective employees for employment as well as between employers for prospective employees, and one could argue leads to a more optimal allocation of labor. If, say, each tech company were based in a different city, then each company's labor force (we'll say programmers) is largely determined by which programmers happen to live in a given company's city, rather than, say, what skills they have and what that company's particular focus is.

    If, however, all the programmers and all the tech companies are in the same city, then competition isn't as hindered by 'stickiness' because an employee wouldn't have to move from one to some random city to get another job, then move to yet another city to get a different job after that; and employers and employees are more likely to 'match' (e.g., a python programmer won't have to settle for working a ta company that requires him to code in Perl, a language he's not as good at, because that's the only company in his town, and a firm that does all it's programming in Perl won't have to settle for a Python programmer because he's there aren't many Perl programmers in the city they're based in). There's a great deal of literature on geographical clustering of industries. I'm sure some economists have argued that the valued are overrated, but there is certainly an argument to be made for 'having all one's eggs in one basket' so to speak.

    Of course, there is the separate question of why an industry would happen to cluster in an metro area with highly restricted housing policies and high housing costs. Maybe the industry's cluster location was already decided before housing prices skyrocketed? Maybe the clustering of a high-skilled industry there actually helped cause the skyrocketing of housing prices? Maybe there's some other factor which drew companies in the industry to cluster in that city that also happens to correlate with high housing costs? Many possible answers.