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At a recent conference, a panel of very senior researchers compared the research questions being asked and the techniques being used 50 years ago with now, and they looked forward.

Policies that were attractive then are hard to justify now (e.g. Regulation Q). Research then was based on the best available data, which differs little from what can be seen in a student report today: e.g. averages and bar charts showing differences between categories. Academics and careful researchers have learned how using such aggregated data can hide effects. The technology available now to manipulate new types of data is so powerful that one wonders why use anything else.

Migration in the U.S. has ebbed and flowed.  During the 1970s and 1980s, people moved out of cities in the northern US “Rust Belt” to live in the southern “Sun Belt”. During the 1990s and more recently, the resurgence of some (but not all) big northern US cities offers important lessons: human capital and innovation matters. This resurgence has encouraged many researchers to wonder about the rise of “city-states” as the primary actor in economic development. Glaeser’s book notes the many benefits of cities. He also noted that real estate markets face a challenge dealing with such long slow cycles: it is easy to build during a long growth period but it takes a long time for the housing stock to decay to the “right level” for a city in decline. Consequently, housing supply conditions affect how growth potential is expressed.

The last panelist, Susan Wachter, focused on the policy dimension and that probably has little interest to Canadian readers. She was emphatic about the need to focus on the data and to listen to what it was saying. Her thoughts on the contribution (or not) of public housing tracts show how this policy has evolved as people learn from history.

One factoid from recent history was particularly thought-provoking and connected a number of themes: during the worst of the Credit Crunch, 1/4 of US mortgages were below water. Mortgages expose banks to the risk that a person may not repay their mortgage and that scared banks and the US government. If you think about it carefully, the risk is shared poorly for two reasons. First, that there are two dominant sources of risk: repaying the mortgage and the uncertainty about the future value of the home. Second, the cost of risk to a bank is very low and, in some cases, the cost of risk to a person is high. So, it would be interesting to negotiate a more efficient way to share those costs than forcing the homeowner to bear most of the risk, perhaps using a “shared appreciation mortgage”.

There was general agreement that, even now, too few finance “experts” understand how to real estate finance.  Financial analysis is much easier if markets are informational efficient, as is thought to be true of stocks.  But, if the price of an asset (e.g. real estate) can deviate from its fundamental value for a long period of time (but not forever) then a valuation exercise requires more than an extrapolation based on recent prices.

Some stupid ideas remain uncorrected after 50 years. Robert Shiller noted that media report on the prices of homes measured in nominal terms, which creates a positive bias.  Many people think (inaccurately) that the prices of homes cannot fall and that there cannot be extended periods of time where inflation-adjusted returns are negative.

So, real estate research has not run out of good questions.

PA

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