Cross-Border Banking Flows as a Metric for the Global Financial Cycle

Perhaps Nomi Prins did not choose the title of her piece “The next financial crisis will be worse than the last.” But the idea that there is going to be another financial crisis in the center of the world economy in the near term even vaguely comparable in virulence to the GFC has, as we shall see, no basis in reality. The reason is straightforward. Financial crises are denouements of credit booms, not asset price booms—all credit booms are attended by asset price booms but not the other way around—and while there is certainly an asset price boom in global markets, there is no credit boom at the center of the world economy. The chances of a banking crisis are even more remote than credit indicators suggest because of the extraordinary surveillance of the balance sheets of global banks since the GFC. Even if all legal-regulatory innovations over the past decade—especially the tighter limits on capital ratios—are bull, the sheer fact of enhanced regulatory and independent balance sheet surveillance means that banks find it much more difficult to hide risks on and off their balance sheets.

We now have a good handle on the mechanics of financial booms and crises. Financial booms are banking expansions. Banks are special because, yes, they create money by lending. But do not let the Bank of England distract you. The issue is that the excess elasticity of bank balance sheets mechanically generates lending booms since bank assets are the liabilities of non-banks. As a rule, credit booms emerge from the mutually-reinforcing interaction of property prices and bank lending. As collateral values go up, more can be lent against the same property; in turn, greater lending pushes up property prices further. Credit booms show up in credit gap measures such as credit-to-GDP ratios. We also understand how credit booms end. The stock of outstanding debt lags behind credit gaps. Once the debt burden, which is a function of the stock of outstanding debt not credit growth, becomes intolerable, credit defaults puncture the boom and precipitate a financial crisis. That’s why the best predictors of financial crises are credit gaps and debt ratios.

Metropolitan banking is international. As the day progresses, the trading book of global banks passes from Hong Kong to London to New York. The transatlantic circuit is especially important. The mid-2000s financial boom was driven in large part by a transatlantic, European banking glut. In other words, there is good reason to believe that cross-border banking flows are a especially good barometer of the global financial cycle. I therefore decided to analyze the JEDH database on cross-border banking and debt flows.

I’ll probably have much more to report later. But here’s the basic picture. Figure 1 displays three variables; all standardized to have mean 0 and variance 1. “CoreFPC” is the first principal component of the cross-border flows of Japan, Germany, France, Italy, Netherlands, Norway, Sweden, Denmark, and Finland. Roughly speaking, it captures the common variation in the series. “G2” is the sum of the cross-border flows of the United Kingdom and the United States. “China” is the sum of the cross-border flows of China, Hong Kong, and Macau.


Figure 1. Cross-border banking flows.

A few observations are in order. The tight coupling of Anglo-Saxon finance (“G2”) and the rest of the core (“coreFPC”) is manifest; thus allowing us to interpret either as providing a fair metric for the global financial cycle. We will use the former because (a) it is a tighter, more parsimonious definition; (b) its applicable in more general settings in the sense that we can extend many macrofinancial variables back to the 19th century without changing our center countries. Hélène Rey’s notion of the global financial cycle—as the covariation of risk premia embedded in global asset prices—is less relevant to macrofinancial stability than our metric. Although banking expansions can be read off of asset prices, it is a noisier metric precisely because not all asset price booms are attended by real financial booms that end in tears. Cross-border banking flows provide a finer measure of banking gluts than the compression of risk premia because all lending booms are attended by cross-border banking flows and vice-versa.

This metric confirms what credit gaps and debt service ratios can tell us about the buildup of financial imbalances in the center of the world economy. Interestingly, by this metric the Chinese cycle seems to have turned. This was not clear when we looked the credit gap (Figure 2).


Figure 2. China’s macrofinancial vitals.

Finally, as a sanity check we look at the bond market. The slope of the yield curve is the best predictor of US recessions out there. When the yield curve inverts it heralds a recession in the near term. There is good reason for this. The inversion of the yield curve destroys banks’ net interest margins; forward-looking measures of banks’ net worth fall; banks respond by shedding assets; finally, the attendant fall in bank lending pushes the macroeconomy into recession—this is Adrian and Shin‘s risk-taking channel of monetary policy. The term spread has indeed compressed, but the yield curve is still upward-sloping.


Figure 3. The term spread.

Because the current asset price boom is unattended by a credit boom in the center of the world economy, the possibility of a financial crisis comparable to the GFC is remote. And despite the “age” of the expansion, a normal recession does not yet seem to be on the cards either.





The President and the Stock Market

Authers’ Note today sounded downright exasperated with the President’s tweets. Trump claimed credit for the fastest 1,000 point gain in the Dow’s history:


That’s too cute by half. Each 1000 percent gain gets mathematically smaller as the Dow goes up; eg, the move from 10,000 to 11,000 is a 10 percent move whereas that from 24,000 to 25,000 is just 4 percent. Properly compared, the performance of the Dow under Trump lies between equivalent periods in Obama’s two terms.

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But the Dow is a stupid aggregate of stocks for the simple reason that it is price-weighted. “Over the past year,” Auther notes, “two great industrial giants have been the alpha and omega of the Dow: Boeing has doubled, gaining $85bn in market cap, while GE has collapsed by more than 40 per cent, shedding a staggering $118bn. But due to the ridiculous way in which the Dow is calculated, Boeing accounts for a rise in the Dow of 1,064 points, while GE accounts for a fall of only 84 points.” That’s because Boeing has 600 million shares outstanding valued at $309 each for total market cap of $184 billion; while GE has 8.7 billion shares worth just $18.5 each for a total of $161 billion. The differential sensitivity of the index is an artifact of the practically irrelevant question of how many shares the firms have outstanding.

If we ignore the Dow and look, as all serious investors and analysts do, at the S&P500, even Obama’s 1st year in office comes out ahead of Trump’s.

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In a more longer term perspective, the Trump rally hardly stands out either.

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More generally, Presidents have little influence over the stock market; central banks much more so. But we should not even exaggerate the influence of the latter. Stock valuations are sky-high not just because of monetary accommodation but mostly because the near-term global macroeconomic outlook is especially positive for risk assets. Not only is global growth more robust than it has been in a decade; there is little sign of inflation, which implies that there is little incentive for central banks to take away the punch bowl any time soon. It is this benign macro environment that is compressing market-wide risk premia.

Investors are facing two main risks in the near term. The first is that growth may falter and cash flow expectations may need to be marked down, with attendant corrections in asset valuations. In the extreme, the economy may even plunge into a recession; although that scenario seems unlikely in the near term—the yield curve may be shallower than before but it is still sloping upwards. The second is that inflation might finally show up, forcing the Fed to hike much faster than anticipated, thereby precipitating a risk-off. In other words, markets have been in a sort of Goldilocks Zone. Either a significant deceleration in global growth or a significant acceleration that eliminates global slack can precipitate a sell-off.

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Source: US Treasury.


Does Consumer Confidence Predict Output Growth?

Tip of the hat to Ted Fertik for flagging Mian, Sufi and Khoshkhou (2017). The authors examine the role that partisan bias plays in consumer expectations. One of their findings is that consumer confidence is extraordinarily high among Trump supporters. But that this has not translated so far into higher spending.

I was skeptical of the notion that consumer confidence—as opposed to CEO confidence, investor confidence, and the risk appetite of intermediaries—had discernible and predictable effects on real economic activity. So I examined the data. Turns out that it does. And that it does so through a clear channel: Consumer confidence predicts discretionary consumer spending which in turn is a strong correlate of real GDP (RGDP) growth.

The evidence can be read off Figure 1. Read clockwise from top-left. (1) University of Michigan’s Index of Consumer Sentiment (MCSI) predicts RGDP growth even after controlling for lagged RGDP. (2) MCSI predicts growth in real discretionary consumer spending. (3) MCSI does not predict RGDP growth shocks orthogonal to discretionary consumer spending. That is, the residuals obtained by projecting change in log RGDP onto change in log consumer spending are not correlated with lagged consumer spending. (4) Discretionary consumer spending is contemporaneously correlated with growth in RGDP.


Figure 1. The empirical evidence for the confidence channel. The data is at the quarterly frequency and for the period 1960Q1-2017Q3. Source: Haver Analytics.


Markets Celebrate the GOP’s Trillion Dollar Giveaway to the Rich

So you thought fiscal hawks were serious about the US deficit? Haha. Despite last minute snags the Republicans are expected to push through a trillion dollar tax-cut for their paymasters. One is told by Very Serious People that markets, bond vigilantes in particular, punish fiscal profligacy by demanding higher costs of borrowing. That’s poppycock. Both stocks and bonds are booming for good reason. The tax cut delivers a large, positive wealth shock to investors and the market is repricing to reflect investors’ greater appetite for risk. The bond market refuses to believe that the tax cut—a major fiscal shock when the economy is at full employment—will have much of an effect on economic activity. For the compression of the term spread means that the market expects subdued inflation and a shallow path of policy rates. Were a major pickup in economic activity around the corner, the yield curve would become steeper not shallower. So the euphoria in the stock markets is due not to expectations that animal spirits unleashed by the tax cut and regulatory “reform” would deliver higher growth. Rather it is due to the straightforward transfer of resources from the public sector to corporations and investors. What the market is celebrating in other words, is not the expected growth of the pie but redistribution of the pie in its favor.


A Major Rethink is Underway at the Fed

United States monetary policymakers made their bones during the 1970s stagflation crisis. Figure 1 displays the macroeconomic vitals and the policy rate from the mid-1960s to the mid-1990s.


Figure 1. Unemployment, core inflation, output gap and the policy rate from the mid-1960s to the mid-1990s.

The stagflation crisis taught central bankers that inflation can be very costly to tame; that inflation expectations play a dominant role in the inflation process and are even harder to tame; that elected officials with an eye on the next election have an inflationary bias so that the Fed had to be sufficiently independent of politics to deliver the bitter medicine. Most of all, they learned that the Fed had to stay one step ahead of inflation. Specifically, they learned that they had to tighten policy in anticipation of an acceleration in inflation. And that they could rely on the Phillips curve to anticipate inflation. That is, when domestic measures of slack (such as the unemployment rate or the output gap) showed that the economy was overheating, they could reliably expect inflation to pick up.

Inflation expectations are said to be anchored if temporary shocks don’t change long-run expectations; that is, they are relatively insensitive to incoming data. Consistent with its price stability mandate, the Fed wants the public’s inflation expectations to be anchored near the target rate of inflation. Although the Fed did not officially adopt a numerical target until 2012 when it chose 2 percent, it was widely understood to be within that ballpark under Greenspan. Nevertheless, it literally took decades. By the end of the 20th century, they had finally managed to anchor inflation expectations close to the target.


Figure 2. Expected inflation in the United States.

But just as central bankers began to congratulate themselves for finally having anchored the public’s inflation expectations, the inflation process mutated. The Phillips curve, on which the Fed (and all macroeconomic models) relied on to forecast inflation, weakened in the 1990s and broke down after 2000. Figure 3 displays the breakdown.


Figure 3. The Phillips curve weakened in the 1990s and broke down in the 2000s.

As a result of the second unbundling and the further integration of global product markets, global slack replaced domestic slack as the strongest predictor of changes in inflation. In other words, the inflation process became globalized. But Fed policymakers continue to rely on measures of domestic slack to anticipate inflation even as they concede that the relationship has weakened.

Since domestic measures of slack have vanished, the Fed expects inflation to be around the corner. It has hiked four times (by 25 basis points each time) in the past two years (12’15, 12’16, 3’17, 6’17). Bond markets are assigning an 80 percent probability to another 25 basis point hike this December. Meanwhile, the Fed has blamed transitory factors (such as one-off mobile price plan changes) for the failure of inflation to pick up.


Figure 4. US unemployment, output gap, core inflation and the policy rate.

Things are now coming to a head. Core inflation slowed to just 1.3 per cent year-on-year in August despite further tightening in the labor market and the vanishing of the output gap. Minutes of the FOMC’s September meeting show policymakers troubled by the failure of inflation to appear as anticipated. From the minutes:

Many participants expressed concern that the low inflation readings this year might reflect not only transitory factors, but also the influence of developments that could prove more persistent, and it was noted that some patience in removing policy accommodation while assessing trends in inflation was warranted.

Meanwhile, Daniel Tarullo, who left the Fed’s Board of Governors this year, confessed that the Fed is driving blind. “The substantive point,” he said, “is that we do not, at present, have a theory of inflation dynamics that works sufficiently well to be of use for the business of real-time monetary policymaking.” The Fed should therefore not rely on the Phillips curve, but instead pay more attention to “observables”. That’s just a fancy way of saying that the Fed should wait to see the whites of inflation’s eyes before tightening further.

So the doves are gaining the upper hand at the FOMC. But even more significant developments are underway.

Former Fed Chair Ben Bernanke, the intellectual father of extraordinary monetary policy, has proposed a new monetary policy framework that makes the recent hikes look even more suboptimal.

A central bank could target the rate of inflation or the price level. When the monetary authority targets inflation, it responds to cyclical departures from the target rate by leaning against the departure in order to push inflation back to target. It does not bother “making up” for lost inflation. With price level targeting on the other hand, the monetary authority obeys the “makeup principle”. If inflation is too low, policy remains accommodative even after the target is hit; letting it overshoot to make up for lost inflation. Under price level targeting, average inflation is likely to be close to the target over the medium term (ie, over the cycle). But there are issues with price level targeting.

Price level targeting becomes problematic when there is a negative productivity shock that pushes up inflation. For then the monetary authority is committed (as it must be to maintain credibility) to punish the economy well after inflation is under control. In the extreme, periods of high inflation would call for prolonged lowflation or even outright deflation to get back to the target price level. That’s close to being unacceptable.

Bernanke’s proposal instead requires the monetary authority to practice inflation targeting under normal conditions but shift to price level targeting once the economy hits the zero lower-bound. In effect, the monetary authority would commit to “lower for longer” for much longer. It would run the economy too hot for as long as it takes for the actual price level to close the gap with the target price level. It thus solves the problem of policy asymmetry at the zero lower-bound by essentially borrowing policy room from the future.


Figure 5. Price level, inflation and the policy rate.

This is the most accommodative monetary policy framework that has ever been proposed. It goes well beyond waiting for the whites of inflation’s eye to begin tightening. Figure 5 shows the price level, inflation rate and the policy rate since we hit the zero lower-bound. Since inflation has run persistently below target for essentially the entire period, the actual price level has continued to diverge from the proposal target level (which mechanically increases at the rate of 2 percent per annum). Under his proposal, not only would the Fed not have hiked until today, it would commit to not hiking for many, many years to come. Given that system-wide overcapacity is likely to persist for a long time and assuming that global slack continues to drive US inflation, the nominal policy rate under his proposal would remain stuck at the zero lower-bound through to 2030!!

This amounts to a thinly-veiled but nonetheless extraordinarily powerful critique of Fed policy. Bernanke is in effect saying that the Fed should’ve never lifted off in anticipation of inflation. Instead, it should’ve promised to not lift-off even after observing above target inflation for a considerable amount of time.

Lael Brainard, perhaps the sharpest knife in the Fed drawer and not coincidently the Policy Tensor’s favorite central banker, favourably reviewed Bernanke’s proposal. Her remarks are worth reading in full. Both Bernanke and Brainard made their remarks at the Peterson Institute which has conveniently put the videos online. On that site, you can also find presentations by Summers and Blanchard of their joint paper on stabilization policy under secular stagnation—no doubt an important contribution.

So a major rethink is well underway among central bankers. And not a moment too soon. Reviewing these developments together with the markets makes it clear that the bond market is too confident of a December hike. That should get priced out soon.



Are Shocks to Housing Priced in the Cross-Section of Stock Returns?

In the previous post I argued that the risk premium on property is due to the fact that the marginal investor in housing is your average homeowner who finds it extraordinarily hard to diversify away the risk posed by her single-family home to her balance sheet. If I am right, this means that housing wealth is a systematic risk factor that ought to be priced in the cross-section of expected stock excess returns (ie, returns in excess of the risk-free rate).

Assume that the marginal investor in the stock market is your average homeowner. Since it is so hard for her to diversify away the risk posed by fluctuations in property values, she should value stocks that do well when property markets tank. Conversely, stocks whose returns covary with returns on property should be less valuable to her. Given our assumption that your average homeowner is the marginal investor in equities, expected returns on stocks whose returns covary strongly with property returns should be higher than expected returns on stocks whose returns covary weakly (or better yet, negatively) with property returns. This is what it means for shocks to housing to be priced in the cross-section of expected stock returns.

We want our risk factor to capture broad-based fluctuations in housing wealth. Ideally, we would use a quarterly time-series for total returns (including both rent and capital gains) on housing wealth owned directly by US households. I am unaware of the existence of such a dataset—if you know where I can find the data, please get in touch.

We can also instrument fluctuations in housing wealth by using a property price index. Here we use the US property price index reported by the Bank of International Settlements. For return data we use 250 test assets from Kenneth French’s website. (The same dataset I used in my paper, “The Risk Premium on Balance Sheet Capacity.”)

We’re now going to jump straight into the results. For our econometric strategy please see the appendix at the bottom.

Figure 1 displays a scatterplot of the cross-section of expected stock returns. Along the X axis we have the betas (the sensitivity of the portfolio’s return to property returns) for our 250 test assets, and along the Y axis we have the mean excess returns of the portfolios over the period 1975-2016.


Figure 1. Housing and the cross-section of expected stock excess returns.

Guys, this is not bad at all. Our single factor explains 20 percent of the cross-sectional variation in expected excess returns. By comparison, the celebrated Capital Asset Pricing Model, for instance, is a complete washout.


Figure 2. The CAPM fails catastrophically in explaining the cross-section of expected excess returns.

It is very hard for single factor models to exhibit such performance. Table 1 displays the results from the second pass. We see that the mean absolute pricing error is large because the zero-beta rate does not vanish. Indeed, at 1.8 percent per quarter it is simply not credible. But the risk premium on property returns is non-trivial and significant at the 5 percent level.

Table 1. Property returns and the cross-section

Estimate Std Error p-Value
Zero-beta rate 0.018 0.006 0.002
Property return 0.007 0.004 0.049
R^2 0.195
Adj-R^2 0.192
MAPE 0.022

I have a lot of professional stake in the failure of this model actually. I have argued that stock returns are explained by fluctuations in the risk-bearing capacity of the market-based financial intermediary sector. In other words, the central thrust of my work is to say that we ought to pay less attention to the small-fry and considerably greater attention to the risk appetite of the big fish, for that is what drives market-wide risk appetite. Fortunately for my thesis, property shocks do well, but not nearly as well as balance sheet capacity.

Figure 3 displays yet another scatterplot for the cross-section. On the X axis we have the factor betas (the sensitivities of the portfolios to balance sheet capacity) and on the Y axis we have, as usual, mean excess returns over 1975-2016.


Figure 3. Balance sheet capacity explains the cross-section of stock returns.

In Table 2 and Figure 3 we’re only looking at a single-factor model with balance sheet capacity as the sole systematic risk factor. That’s a parsimonious theory that says: exposure to fluctuations in the risk-bearing capacity of broker-dealers explains the cross-section of asset returns. The empirical evidence is pretty compelling that this is the right theory. We see that balance sheet capacity singlehandedly explains 44 percent of the cross-sectional variation in expected stock excess returns. What is also manifest is the vanishing of the zero-beta rate; and the attendant vanishing of the mean absolute pricing error. Other single factor models cannot even dream of competing with balance sheet capacity in terms of pricing error. Indeed, I have shown in my paper that even the pricing errors of standard multifactor benchmarks, Fama and French’s 3-factor model and Carhart’s 4-factor model, are significantly bigger than our single factor model’s 48 basis points. We can thus have good confidence that the evidence does not reject our parsimonious asset pricing model.

Table 2. The Primacy of Balance Sheet Capacity

Estimate Std Error p-Value
Zero-beta rate 0.002 0.010 0.440
Balance sheet capacity 0.095 0.038 0.007
R^2 0.442
Adj-R^2 0.440
MAPE 0.005

I know what you are thinking. If these things are priced in, there must be a way to make money off it. How do I get some of that juicy risk premium? Aren’t they non-traded factors? Yes, they are. But you can still harvest the risk premium on non-traded factors, eg by constructing factor mimicking portfolios. Briefly, you project your factor onto a bunch of traded portfolios and use the coefficients as weights to construct a portfolio that tracks your non-traded factor.

Figure 4 displays the risk-adjusted performance of portfolios that track benchmark risk factors and the two risk factors discussed in this essay. We report Sharpe ratios (the ratio of a portfolio’s mean excess return to the volatility of the portfolio return) rescaled by the volatility of the market portfolio for ease of interpretation.


Figure 4. Risk-adjusted performance of traded portfolios for size, market, value, momentum, property, and balance sheet capacity.

The results are consistent with our previous findings. The stock portfolio that tracks property outperforms standard benchmarks convincingly. In turn, the portfolio that tracks balance sheet capacity outperforms the portfolio that tracks property. But let’s be very clear about what Figure 4 does not say. There is no free lunch. More precisely, there is no risk-free arbitrage.

The existence of these two risk premiums imply instead that there is risk arbitrage. That is, you can obtain superior risk-adjusted returns than the market portfolio by systematically harvesting these risk premiums. The existence of the two risk premiums is due to structural features. Specifically, the property premium exists because non-rich homeowners must be compensated for their exposure to housing; while the risk premium on balance sheet capacity exists because of structural features of the market-based financial intermediary sector—features that I explain in detail in the introduction of my paper. Since we can expect these structural features to persist, we should therefore not expect these risk premiums to vanish (or perhaps even attenuate much) upon discovery.

Appendix. Cross-Sectional Asset Pricing

We can check whether any given risk factor is priced in the cross-section of excess returns using standard 2-pass regressions where you first project excess returns \left(R_{i,t}\right) onto the risk factor \left(f_t\right) in the time series to obtain factor betas \left(\beta_i\right) for assets i=1,\dots,N,

R_{i,t}=\alpha+\beta_i f_{t}+\varepsilon_{i,t}, \qquad t=1,\dots,T,

and then project mean excess returns \left(\bar R_i\right) onto the betas in the cross-section to obtain the price of risk \lambda,

\bar R_{i}=\gamma^{0}+\lambda\hat\beta_{i}+e_{i}, \qquad i=1,\dots,N.

The scalar \gamma^{0} is called the zero-beta rate. If there is no arbitrage, the zero-beta rate must vanish. If the zero-beta rate is statistically and economically different from zero, then that is a failure of the model. That’s why the mean absolute pricing error is a better metric for the failure of an asset pricing model than adjusted-R^2. It’s given by,

\text{MAPE}:=|\gamma^{0}|+\sum_{i=1}^{N}\omega_{i}|\hat e_{i}|,

where \omega_{i} are weights that we will discuss presently.

If you try this at home, you need to know that (1) ordinary least squares (OLS) is inefficient in the sense that the estimator no longer has the lowest variance among all linear unbiased estimators; (2) OLS standard errors are an order of magnitude too low (and the estimated coefficients are attenuated, though still consistent) because their computation assumes that the betas are known, whereas we are in fact estimating them with considerable noise in the first pass.

The solution to (1) is well-known. Simply use weighted least squares (WLS) where the weights are inversely proportional to the mean squared errors of the time-series regressions,

\omega_i \propto \left[\frac1{T}\sum_{t=1}^{T}\hat\varepsilon^2_{i,t}\right]^{-1},\qquad \sum_{i=1}^{N}\omega_{i}=1.

The solution to (2) is to use errors-in-variable (EIV) corrected standard errors. In our work, we always use WLS for the second pass and report EIV-corrected standard errors wherever appropriate.






Why Housing Has Outperformed Equities Over the Long Run

Jorda et al. are at it again. Over the past few years, they have constructed the most useful international macrofinancial dataset extending back to 1870 and covering 16 rich countries. The Policy Tensor has worked with the previous iteration of their dataset. I documented the reemergence of the financial cycle; the empirical law that all financial booms are, without exception, attended by real-estate booms; and that what explains medium-term fluctuations not just in real rates (a result originally obtained by Rey) but also in property returns, is the consumption to wealth ratio (equity returns on the other hand are explained by balance sheet capacity, not the consumption to wealth ratio).

There are two main findings in Jorda et al. (2017). First, they corroborate Piketty’s empirical law that the rate of return exceeds the growth rate. The gap is persistent and is only violated for any length of time during the world wars. Excluding these two ‘ultra-shortage of safe asset’-periods, the gap has averaged 4 percent per annum. That is definitely enough to relentlessly increase the ratio of wealth to income and drive stratification, as Piketty has shown.


Jorda et al. (2017)

The second finding is truly novel. Jorda et al. (2017) find that housing has dramatically outperformed equities over the long run. This is true not just in the aggregate but also at the country level.


Jorda et al. (2017)

Matt Klein over at Alphaville is truly puzzled by this failure of standard asset pricing theory. As he explains,

The ratio between the average yearly return above the short-term risk free rate and the annual standard deviation of those returns — the Sharpe Ratio— should be roughly equivalent across asset classes over long stretches time. There might be short periods when an asset class’s Sharpe ratio looks unusually high, especially in individual countries, but things tend to revert to their long-term average sooner or later.

More generally, the expectation of asset pricing theory is that Sharpe ratios should be roughly equal across not just asset classes but arbitrary portfolios as well. Deviations from equality imply the existence of extraordinary risk premia which ought to be eliminated through investors’ search for higher risk-adjusted returns.

This, of course, goes back to the hegemonic idea of Western thought. Competition serves as the organizing principle of evolutionary biology, economic theory, and international relations; as the cornerstone of America’s national ideology; and as the guiding star of modern governance and reform efforts. But there are some rather striking anomalies of this otherwise compelling broad-brush picture of the world—persistent sources of economic rents and the existence of substantial risk premia, eg on balance sheet capacity.

But I believe something much more elementary is going on with property. The next figure shows the global wealth portfolio. We see that housing constitutes the bulk of global wealth.


Jorda et al. (2017)

What explains the superior risk-adjusted performance of housing is the fact that housing assets are not, in fact, owned by the rich or market-based financial intermediaries like other asset classes, but quite broadly held by the small-fry. More precisely, the marginal investor in housing is your average homeowner who finds it extraordinarily hard to diversify away the risk posed by her single-family home to her balance sheet. Since it is so hard for her to diversify this risk away, she must be compensated for that risk.

Put another way, the risk premium on property is high because property returns are low when the marginal value of wealth to the marginal investor is high (ie, when times are bad for the average homeowner) and high precisely when the marginal value of wealth to the marginal investor is low (ie, when times are good for the average homeowner). This is as it should be given the relatively progressive vertical distribution of housing wealth.