I actually have a copy of Matthew Yglesias’ The Rent Is Too Damn High, but haven’t worked my way through it (I couldn’t pass up such a potentially target-rich environment). From what I’ve read so far, it seems primarily aimed at upper-middle class urban concerns, but that will have to be a topic for another time (along with a whole set of problems).
What is missing so far (and, in fairness, maybe I just haven’t read far enough) is the role income inequality plays in high housing prices. Put another way, the rent is too high, but that’s because middle class and lower income wages are too low. Higher wages would make rents more affordable as well as lower incomes for the wealth, thereby stymieing housing prices in urban areas. And wages are too low:
But when you drill down into the state level data, the importance of wages relatively to housing prices emerges. If we, for example, look at Massachusetts (pdf), we see something very interesting. If we compare Lowell, Easton-Raynham, and Greater Boston, they all have high fair market rents ($1,107, $1,222, and $1,323 respectively). But Lowell is far and away the most affordable because of its high mean hourly wage (Lowell = $20.78, Easton-Raynham = $10.49, Greater Boston = $20.32). Lowell needs one mean wage to pay the rent, Boston, even with its high prices, needs 1.3 mean wages, and Easton-Raynham needs 2.1 mean wages.
Yes, Boston’s housing prices are higher, but the real issue is earnings. We need to raise wages. And by doing so, we would help equalize incomes, which would stop rents from increasing*†.
The rent is not too damn high, the wage is too damn low.
*As far as I can tell, Yglesias doesn’t mention the role of income inequality in driving up housing rents. It’s simple: rich people bid up rents. Even when there are vacancies (unless they’re massive), prices will remain high. Rents in Boston have leveled off with only slight increases, not due to vacancies, but because earnings are low. The other issue is that poorer people are ‘zoned out’ of suburban areas, and are forced to compete with each other at the lower end of the housing market, also driving up prices.
†Another issue that I don’t think Yglesias tackles is that rents never drop in nominal terms (though landlords might offer discounts), unless the economy craters Detroit-style. In many urban areas, large real-estate companies own a significant fraction of rental properties (and are also involved in the home purchase and construction markets). These companies use their rental properties as collateral for ongoing and future projects. While they might offer temporary discounts to effectively lower rents (e.g., something broke, so you get a discount for that month or paying a realtor’s finder fee, etc.), if they lower the ‘official’ rent, the value of their properties decreases, raising the interest on their ongoing loans. These companies drive the prices in the non-corporate rental market, making prices sticky. Simplistic economic models need not apply… (if you’re wondering how I know this, I have friends in Boston real estate–that, and the most important day in my building for the landlord is ‘Bank Walk-Through Day’).
Are you reviewing a book you haven’t finished reading yet? It’s only 75 pages or so. I agree that wages are a problem but I’m not sure that Yglesias misses that entirely. If anything, the title should be “the [city] housing is too damn scarce” if there’s less income inequality and still not enough housing, it still gets bid up till the market clears at the maximum price and the lower earners are banished from the desirable areas. Ask your friends in the Boston real estate market what would happen if height limits or parking requirements on new construction went away.
The only problem with the NLIHC’s graphic is that they group the data at the state level, so large high-rent metro areas skew the state average upward. If you look at the county level data from the NLIHC you see that for the vast majority of people it’s still impossible to afford rent on a two-bedroom apartment, but it’s not quite as bad as when you use the state level grouping. For instance, here’s the NLIHC’s data for Indiana: http://www.openheatmap.com/view.html?map=MoncureThebanTressful
I don’t think this analysis holds up, actually…