I have a new chapter out with José-Luis Peydró, Jagdish Tripathy, and Arzu Uluc in the Research Handbook of Macroprudential Policy (Edward Elgar, edited by David Aikman and Prasanna Gai; the working paper version is here). It surveys what we have learned from more than a decade of borrower-based macroprudential tools—limits that restrict how much households can borrow, typically against their homes. There is a lot to say about these tools, so I am going to write a four-part series for the blog. But first things first. What does “macroprudential” mean?
The term contains two ideas: macro and prudential. Prudential regulation tries to ensure that banks and other financial intermediaries operate with sound levels of solvency and liquidity. It is not always successful, as the case of Silicon Valley Bank illustrates; but its objective is to keep the financial system robust. Historically, prudential regulation had been micro: it has focused on the soundness of individual institutions. The intuition was that if every single individual institution is sound, the system would be. This intuition is however wrong. And here is where the “macro” part comes in: it recognises that there are risks that can build up at system level that would be difficult to notice by looking at individual institutions. In other words, macroprudential policy understands that risks are endogenous and tries to prevent their excessive build-up.
What are some of these risks? Imagine that I am looking to buy a house. I want the best house for my family and myself, so I get as much mortgage as the bank will lend to me and buy the most expensive one I can afford. Great. The microprudential regulator looks at the mortgage market, realises that defaults are very low, and sees no issue with the bank providing the credit.
Now imagine a recession arrives. My household income suffers. But I still need to repay the mortgage. I might need to seriously cut other expenses. No dining out or ordering delivery. Maybe I was looking to change my car, but now I will have to wait. New clothes? A luxury. This all makes sense from my individual perspective. But what happens when many thousands of households do the same at the same time? It becomes a negative macroeconomic shock. Spending goes down, hiring too, maybe companies start firing employees… And the recession becomes deeper.
And nobody did anything wrong! When I got the mortgage, I weighed my own risks. I did not take into account how my spending cuts would affect aggregate demand. And why should I have? I am only one household; my effect on aggregate demand is minuscule. But as the number of affected households increases, the effect becomes significant. This is what economists call an externality: the private costs of my leverage (credit) are lower than the social ones, so we borrow too much. The consequences are well documented in Jordà, Schularick and Taylor (2016): recessions preceded by household credit booms are deeper and longer.
This is the case for macroprudential policies. A well-known one is the countercyclical capital buffer, or CCyB in regulatory jargon. Banks are regularly subject to capital regulation, which requires them to fund part of their assets with equity rather than only deposits and other debt. CCyB increases these requirements when the cycle is good, that is, when the economy is doing well and there is plenty of credit. The idea is to increase the solvency of banks precisely in a moment when doing so is cheaper. This may lead to less credit in the expansion, but this might be the right call given the negative externalities mentioned above. Moreover, better solvency in good times means better readiness for when the downturn comes. This is not just theoretical: there is evidence that the CCyB was useful in sustaining credit during the Covid-19 pandemic.
The CCyB, however, works on the lender’s balance sheet: it makes banks better able to absorb losses, but it does nothing about how much debt I take on in the first place. A second family of tools goes after the leverage itself: the so-called “borrower-based” tools. These are limits on how much households can borrow relative to the value of the property (loan-to-value) or to their income (loan-to-income), limits on the share of income going to repayments (debt-service), and tests of whether borrowers could keep paying if rates rose (affordability). They typically target mortgage lending, although some countries cover other types of credit as well. And they have been popular: the figure below shows how often countries have tightened them in recent decades. Note how direct this is. In the example above, the bank was happy to lend and the microprudential supervisor saw no problem; a borrower-based limit is the regulator saying no anyway—not because I could not repay, but because of what thousands of households like mine do to the economy when we all cut back at once.
That directness comes with a catch, and it is the theme of the rest of this series. The CCyB binds on banks; borrower-based tools bind on households. And not on all households equally, because not everyone needs to borrow at high multiples to buy a home: the young, the lower-income, the first-time buyer do—households with the income to service a mortgage, but without the savings for a large deposit. These tools are distributional by design. And this is the topic for the next post.
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