Betting the House, not the Economy

Òscar Jordà, Moritz Schularick and Alan Taylor recently put out an interesting paper on the relationship between interest rates, the housing market and financial crises. It’s been reviewed very positively, for example here and here. They take advantage of the macroeconomic trilemma – countries with fixed exchange rates and open capital markets ‘import’ the monetary policy of other countries –  by treating these imported interest rates as exogenous, providing them with an independent random variable.

People seem to be interpreting it as saying that loose monetary policy causes financial instability through the transmission mechanism of a housing bubble.

To be fair, the opening line of the abstract does give that impression:

Is there a link between loose monetary conditions, credit growth, house price booms, and financial instability?

as does the first paragraph’s use of ‘thus’:

Do low interest rates cause households to lever up on mortgages and bid up house prices, thus increasing the risk of financial crisis?

So one could be forgiven for thinking their conclusion was basically

  • Loose Monetary Policy -> Mortgage & Housing Bubble -> Financial Instability

… Well, one could be forgiven for thinking that unless one was going to write about the article. If you were going to do that, you should actually read the article. Then you would realize they in fact show two separate things:

  • Loose Monetary Policy -> Lower Long-term interest rates + Mortgage & Housing Increases
    • (with reasonable p-values in general)



  • Mortgage & Housing Market Bubble -> Financial Instability
    • (with ok p-values, and some suspicion about how canonical their independent variable was)


Despite having a clever way of treating monetary policy as an independent variable, they never directly test

  • Loose Monetary Policy -> Financial Instability

even though this would be a major victory for the ‘low interest rates caused the bubble and crisis’ crowd.

Why not test this directly? The authors don’t say, but I suspect it’s because the test would fail to yield significant results. Absence of evidence of such a connection is evidence of its absence. And looking at the significance levels of the two results they did provide, I suspect that combining them would cease to be significant (unless their is another, parallel causal mechanism).

Which is a shame! Their independent variable looked really cool, as did their data set.

I think there’s an underlying theoretical reason to not expect it to work, however (quite apart from nothing ever working in macroeconomics. They rightly make much of their finding that exogenous low interest rates cause increases in housing prices. But this is not necessarily caused by increased demand ‘bidding up’ the value of housing in a bubble.

Rather, consider what sort of an asset housing is. Houses allow you to avoid paying rent in the future; their value is the capitalized value of avoided future rent. When interest rates are low, those future rent payments are discounted at a lower rate, so are more valuable: low interest rates increase the inherent value of housing. House prices rising when rates are low isn’t a bubble unless the interest rates themselves are a bubble; it’s rational cross-asset pricing. So we should expect exogenous falls in interest rates to increase house prices.

But wait there’s more!

Exogenous falls in interest rates probably mean rates are now too low (from a Taylor Rule perspective, or similar), or at least not-as-excessively-high as before. This will tend to increase inflation. And as home ownership represents a hedge against rent inflation, higher inflation yet again increases the value of home-ownership. So once again we have a non-bubble based reason to expect exogenous falls in interest rates to increase house prices.

So we have two reasons to think that low interest rates should cause non-bubble increases in house prices, and journal article that is mildly supportive of this thesis.

3 thoughts on “Betting the House, not the Economy

  1. Ben Kuhn says:

    > Absence of evidence of such a connection is evidence of its absence.

    But the strength of the evidence depends strongly on the power of the test! The authors may not have included it because their confidence interval on the effect size was like 0.1x to 10x. Don’t commit the standard social-science fallacy of ignoring effect sizes and just looking at the asterisks.


    • Thanks for your comment!

      I’m not quite sure I follow your logic. My impression of this sort of macroeconomics is that any evidence of this sort of effect had coefficient =/= 0 would be worth publishing. If they could say


      then I would expect them to do so.

      But I’m not really sure I understand your comment. Could you provide an example of an edge-case (or a pair, one on each side) so we can make the disagreement concrete?


      • Sorry for not seeing your reply earlier.

        A statistical test works by producing a confidence interval for the relevant parameter, and seeing if that confidence interval excludes zero. A way in which a non-significant result could fail to be strong evidence of no connection is as follows:

        Suppose that the confidence interval contains zero, but also many other values. If the data are very noisy, so that the confidence interval includes zero but also (1 unit of loose monetary policy corresponds to 10 units of financial instability) and (1 unit of loose monetary policy corresponds to -10 units of financial instability), then you would not expect the test to come out positive unless the effect of monetary policy were unbelievably strong. Even if the true effect were large, you would be quite surprised to see the test come out positive.

        This is known as a test having “low statistical power” (low probability of coming out positive even if the null hypothesis is false). A low-powered test failing to reject the null is only weak evidence of a small effect, because it would *also* fail to reject a null hypothesis of a large effect. Or in Eliezer-speak, absence of extremely surprising/rare evidence is only weak evidence of absence.


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