"Ed Glaeser makes the case for housing deregulation for Brookings:
Housing advocates often discuss affordability, which is defined by linking the cost of living to incomes. But the regulatory approach on housing should compare housing prices to the Minimum Profitable Construction Cost, or MPPC. An unfettered construction market won't magically reduce the price of purchasing lumber or plumbing. The best price outcome possible, without subsidies, is that prices hew more closely to the physical cost of building.In a recent paper with Joseph Gyourko, we characterize the distribution of prices relative to Minimum Profitable Construction Costs across the U.S... We base our estimates on an "economy" quality home, and assume that builders in an unregulated market should expect to earn 17 percent over this purely physical cost of construction, which would have to cover other soft costs of construction including land assembly.We then compare these construction costs with the distribution of self-assessed housing values in the American Housing Survey. The distribution of price to MPPC ratios shows a nation of extremes. Fully, 40 percent of the American Housing Survey homes are valued at 75 percent or less of their Minimum Profitable Production Cost... Another 33 percent of homes are valued at between 75 percent and 125 percent of construction costs.We should blame the government, especially local government:
But most productive parts of America are unaffordable. The National Association of Realtors data shows median sales prices over $1,000,000 in the San Jose metropolitan area and over $500,000 in Los Angeles. One tenth of American homes in 2013 were valued at more than double Minimum Profitable Production Costs, and assuredly the share is much higher today. In 2005, at the height of the boom, almost 30 percent of American homes were valued at more than twice production costs.
How do we know that high housing costs have anything to do with artificial restrictions on supply? Perhaps the most compelling argument uses the tools of Economics 101. If demand alone drove prices, then we should expect to see places that have high costs also have high levels of construction.The reverse is true. Places that are expensive don't build a lot and places that build a lot aren't expensive. San Francisco and urban Honolulu have the highest ratios of prices to construction costs in our data, and these areas permitted little housing between 2000 and 2013. In our sample, Las Vegas was the biggest builder and it emerged from the crisis with home values far below construction costs.The top alternate theory is wrong:
The primary alternative to the view that regulation is responsible for limiting supply and boosting prices is that some areas have a natural shortage of land.Albert Saiz's (2011) work on geography and housing supply shows that where geography, like water and hills, constrains building, prices are higher. He also finds that measures of housing regulation predict less building and higher prices.But lack of land can't be the whole story. Many expensive parts of America, like Middlesex County Massachusetts, have modest density levels and low levels of construction. Other areas, like Harris County, Texas, have higher density levels, higher construction rates and lower prices...If land scarcity was the whole story, then we should expect houses on large lots to be extremely expensive in America's high priced metropolitan areas. Yet typically, the willingness to pay for an extra acre of land is low, even in high cost areas. We should also expect apartments to cost roughly the cost of adding an extra story to a high-rise building, since growing up doesn't require more land. Typically, Manhattan apartments are sold for far more than the engineering cost of growing up, which implies the power of regulatory constraints (Glaeser, Gyourko and Saks, 2005).Which regulations are doing the damage? It's complicated:
Naturally, there are also a host of papers, including Glaeser and Ward (2009), showing the correlation between different types of rules and either reductions in new construction or increases in prices or both. The problem with empirical work any particular land use control is that there are so many ways to say no to new construction. Since the rules usually go together, it is almost impossible to identify the impact of any particular land use control. Moreover, eliminating one rule is unlikely to make much difference, since anti-growth communities would easily find ways to block construction in other ways.Functionalists are wrong, as usual:
Empirically, there is also little evidence that these land use controls correct for real externalities. For example, if people really value the lower density levels that land use controls create, then we should expect to see much higher prices in communities with lower density levels, holding distance to the city center fixed. We do not (Glaeser and War, 2010). Our attempt to assess the total externalities generated by building in Manhattan found that they were tiny relative to the implicit tax on building created by land use controls (Glaeser, Gyourko and Saks, 2005).What's to be done? State governments are our least-desperate hope:
The right strategy is to start in the middle. States do have the ability to rewrite local land use powers, and state leaders are more likely to perceive the downsides of over regulating new construction. Some state policies, like Masschusetts Chapter 40B, 40R and 40S, explicitly attempt to check local land use controls. In New Jersey, the state Supreme Court fought against restrictive local zoning rules in the Mount Laurel decision. If states do want to reform local land use controls, they might start with a serious cost benefit analysis and then require localities to refrain from any new regulations without first performing cost-benefit analyses of their own.It will be a great day when constructing new housing regulations is as big a bureaucratic nightmare as constructing new housing is now!"
Thursday, June 22, 2017
Ed Glaeser makes the case for housing deregulation
See Build, Baby, Build by Bryan Caplan of EconLog.