My last two posts about Demographia’s analysis of house prices prompted quite a bit of discussion. I thought that it may be worth expanding on my points and clarifying why they mean that we should take Demographia’s conclusions with a large grain of salt.
Economists (and statisticians) have a term for what Demographia has done: “omitted variable bias”. This can occur when there are multiple variables that have a causal impact on an outcome. If we attempt to understand that outcome without considering all explanatory variables, we run the risk of biasing our conclusions.
Economists are trained to identify and address issues arising from omitted variable bias. Here, for example, is a comment on the topic from a widely used undergraduate econometrics textbook:
Now suppose that, rather than including an irrelevant variable, we omit a variable that actually belongs in the true (or population) model. This is often called the problem of excluding a relevant variable or underspecifying the model. We claimed in Chapter 2 and earlier in this chapter that this problem generally causes the OLS [ordinary least squares regression] estimators to be biased.
Now, I realise that’s a bit obscure, so let me be more specific. Here’s a list of (some) factors that can influence house prices, with a view on their expected impact:
|Expectations for future population and economic growth||Future expectations tend to be capitalised into house prices – i.e. prices will tend to be higher in areas with better growth prospects|
|Consumer and natural amenities||People are willing to pay more to live in nicer places|
|Interest rates (and availability of finance)||Lower interest rates enable people to afford a larger mortgage on a given level of income|
|Construction sector productivity||Lower productivity will increase the cost of supplying new dwellings|
|Tax policies, including capital gains taxes and mortgage interest deductions||Taxation of capital gains will reduce willingness to invest in housing for capital gains|
Tax subsidies for mortgage-holders will tend to push prices up
|Other housing market policies, such as rent subsidies or public housing provision||Rent subsidies tend to be captured by landlords and thus will tend to push up prices|
Ongoing construction of state housing will add supply to the low end of the market and thus constrain price increases
|Urban planning policies||Policies that constrain the development of new housing in desirable areas, or make it more costly or uncertain to develop, will reduce supply and push up prices|
Demographia only addresses one of those seven variables. Because they fail to account for the other six potentially explanatory variables, their estimates of the welfare impact of urban planning policies are not likely to be reliable. Without controlling for other potential explanations for high housing prices, it’s impossible to say whether their conclusions about any individual city are correct or not.
Consequently, my recommendation to people seeking to understand the impact of urban planning policies on housing costs is simple: Ignore Demographia and go read the relevant economics literature instead. For those who are interested in doing so, here are a few papers that I have learned a lot from. They apply a range of modelling approaches, but what they have in common is that they undertake a detailed analysis of rules, rather than making sweeping and unsupported generalisations:
- Glaeser, Gyourko and Saks (2005) studied the impact of building height limits in Manhattan by looking at the gap between observed sale prices and the marginal cost to add another floor to high-rise buildings. They find that constraining the supply of high-rise apartments imposed a significant “regulatory tax” on residents.
- Grimes and Liang (2007) took at look at land prices around Auckland’s Metropolitan Urban Limit, finding evidence of a “boundary discontinuity”. They interpreted this as evidence that the the MUL is overly restrictive.
- Kulish et al (2011) developed a hypothetical model of the impact of several factors on housing and transport costs. They modelled density restrictions as well as increased transport costs and lower building productivity, finding that building height limits raise housing costs and increase sprawl. I have previously discussed their findings.
- MRCagney (2013) examined the impact of minimum parking requirements in Auckland, finding that they impose a loss on developers and businesses by forcing them to over-supply parking. They also cause worse congestion, meaning that not even drivers benefit. Luke C briefly discussed this study in a post on the Unitary Plan parking rules.
My favourite economics paper on planning regulations is Cheshire and Sheppard’s 2002 study on the impact of greenbelt rules in Reading, UK. The authors observed that greenbelts have both positive and negative effects. On one hand, they limit the supply of land for new housing, which drives up costs. On the other hand, they give residents access to public open spaces, which people like. Rather than ignoring this trade-off, they used house price data to model it.
Overall, Cheshire and Sheppard did find that allowing development in Reading’s greenbelt would make people better off. However, they also found that a failure to consider the amenities produced by planning rules would have resulted in too high an estimate of the gains in wellbeing. In other words: right direction, wrong magnitude.
In light of the evidence, my view is that failing to account for urban amenities and other explanatory variables in an analysis of the impact of supply restrictions can result in two errors:
- First, it can make us over-optimistic about the degree to which loosening rules will affect housing prices. That’s not to say that less restrictive planning regulations couldn’t make us better off – just that we should not expect house prices to fall by 60-75% as Demographia implies when it says that Auckland should have a median multiple of 3.
- Second, it can lead to perverse outcomes, by encouraging us to eliminate rules that are serving a useful purpose. Often (although not always) planning rules are managing the external social or environmental costs associated with some developments. A proper cost-benefit analysis – which Demographia has not done – will consider both the pluses and minuses of rules.
In short, housing markets are complex, and any analysis needs to consider that fact. To reiterate my point from last week: Demographia takes an inappropriate, overly simplistic view of house prices. This may be good for grabbing headlines, but it’s not good economics.