The 2000s housing bubble was greatly exaggerated

"...In 1999, Mr Bernanke had chided the Bank of Japan for failing to rekindle Japanese growth after a bubble burst, despite reducing interest rates to zero. Yet in the 15 years after he christened the saving glut, finance ministries and central bankers around the world became familiar with the struggle to maintain steady growth in the context of zealous saving. Since 2000 alone, the value of global wealth held by households, firms and governments has roughly tripled, from $160trn to $510trn, or from about 460% of global gdp to 610%, according to McKinsey Global Institute, a think-tank…The contribution of growth in reserves to savings was most pronounced around the time Mr Bernanke sounded his warning. From 1998 to 2008, official foreign-exchange reserves jumped from 5.2% of global gdp to 11.5%, powered by a steady rise in oil prices and reserve accumulation by China. During this period, reserve growth probablydominated other sources of saving; research by Francis Warnock and Veronica Cacdac Warnock of the University of Virginia suggests that reserve-accumulation in the year to May 2005 alone reduced the yield on ten-year Treasury bonds by 0.8 percentage points.Reserve growth paused during the global financial crisis, then resumed in the years after, reaching a peak of 15.2% of global gdp in 2013(see chart 2).... Work by Lukasz Rachel, of the London School of Economics, and Larry Summers, of Harvard University, reckons that over the past half century, rising government debt across rich economies pushed up interest rates by about 1.5 percentage points.This effect was more than balanced out by other factors in the past, but might not be in the decades ahead....Increased inequality accounts for about 0.6 percentage points of the decline in rich-world interest rates since the 1970s, say Messrs Rachel and Summers...High-rolling households have not been alone in stockpiling savings. For decades, corporations have been hoarding money as well, retaining a large share of their hefty net profits. According to Peter Chen, of the Analysis Group, an economic consultancy, and Brent Neiman, of the University of Chicago, and Loukas Karabarbounis, of the University of Minnesota, annual global corporate saving rose from less than 10% of world gdp to nearly 15% between 1980 and 2015. The corporate sector has been acting as a net lender to the global economy, rather than as a net borrower from it...Corporate saving, in contrast, rose relatively slowly before 2000, then much faster thereafter, as firms salted away cash from increased profits. In America, for instance, corporate profits have hovered above 10% of gdp for most of the period since 2006, after never rising above 8% over the prior quarter century....In a recent paper examining the effects of demographic change on saving,Etienne Gagnon, Benjamin Johannsen and David López-Salido of the Federal Reserve Board suggest that ageing in America may account for about one percentage point of the drop in interest rates since the 1980s.(Other recent work finds still larger effects, of as much as three percentage points.) If past is prologue, rates seem sure to remain low. Barring a surge in procreation, or the embrace of a dystopian “Logan’s Run” approach to the aged, the world’s population will continue to get older. The share of global population over the age of 50 rose from 15% in the 1950s to 25% today, say Adrien Auclert and Frédéric Martenet, of Stanford University, Hannes Malmberg, of the University of Minnesota, and Matthew Rognlie, of Northwestern University. It is expected to rise to 40% by 2100(see chart 3). That may well turn out to be an underestimate, if recent fertility trends are anything to go by. In 2021, India’s birth rate declined to just 2.0 children per woman—below the rate at which births and deaths are in rough balance. Indeed, a growing number of emerging markets have flipped to the slow population growth common in rich countries. Recent research by Matthew Delventhal of Claremont McKenna College, Jesús Fernández-Villaverde of the University of Pennsylvania and Nezih Guner of the Universitat Autònoma de Barcelona concludes that such transitions—the switch from high mortality and fertility rates to low ones which accompanies economic development—are happening faster over time. The transition took a half century or more 100 years ago, but now tends to be compressed into just two or three decades. Some 80 countries have completed this transition, and in virtually all the rest it is under way....Recent work by Noëmie Lisack, of the Banque du France, Rana Sajedi, of the Bank of England, and Gregory Thwaites, of the University of Nottingham, estimates that this habit of leaving behind savings will by mid-century depress interest rates by nearly half a percentage point relative to current levels. With neither inequality nor the level of reserves showing signs of sustained fall, the ineluctable force of demography should continue to drive savings growth. The world, in other words, may come to look ever more like Japan. There, the median age is 48, more than a quarter of the population is over 65, and the yield on a 30-year government bond is a cool 0.8%, despite a government debt load of 259% of gdp. A generation ago, Mr Bernanke reckoned that Japan’s lacklustre growth and subterranean rates of inflation and interest were the consequence of “self-induced paralysis” by the central bank. Today, such realities seem more like the dull fate of a world with more savings than it quite knows what to do with...."

Economist Staff, "Why the world is saving too much money for its own good," The Economist, February 5, 2022, https://www.economist.com/briefing/2022/02/05/why-the-world-is-saving-too-much-money-for-its-own-good

Why the world is saving too much money for its own good

In 2005, ben bernanke, then a member of the Federal Reserve’s Board of Governors, wondered at a tide of money washing over American shores—and worried about its consequences. Grasping in a speech for a way to describe the phenomenon, he coined a phrase. “Over the past decade,” he noted, “a combination of diverse forces has created a significant increase in the global supply of saving—a global saving glut.” Savers of all sorts—from older Americans preparing for retirement to oil-exporting countries accumulating sovereign-wealth funds—were shoving more money into stocks and bonds than could be put to use by those looking to invest in plants and equipment.

Now the pandemic has shaken pillars of the global economy in ways which could fundamentally alter saving patterns. Tight labour markets are shifting money to workers who are eager to spend, contributing to the highest inflation in a generation. Central banks which had found themselves unable to push interest rates down enough to keep inflation from falling below their targets are beginning to push rates up to keep inflation from soaring. Yet while new enthusiasm for government borrowing or a retreat from globalisation could help to drain savings and establish a new normal, it seems more probable that Mr Bernanke’s glut will persist, thanks to old habits and old people, who are a growing share of the world’s population.

The rising reservoir of global savings, most of which is held in bank deposits, bonds, corporate equity and property, has been fed by three main tributaries: governments hungry for foreign-exchange reserves, penny-pinching households and firms, and workers nearing retirement age. It was the first flow, saving by governments, which preoccupied Mr Bernanke. Governments’ accumulation of foreign-exchange reserves adds to saving in two ways. Resource-exporting economies save part of the windfall earned from their exports and plough it into stocks and bonds. Some of these piles are held as official reserves; the Russian government has reserves, excluding gold, valued at $460bn, while Saudi Arabia’s are worth $440bn. Windfalls have also been shifted into sovereign-wealth funds; that of Abu Dhabi is worth almost $700bn, while Norway’s is valued at more than $1.3trn.

Other economies pile up foreign-exchange reserves as they intervene in markets to reduce the value of their currencies, to boost exports or to build up a hoard of safe assets which can be drawn upon in times of financial stress. In effect, these interventions squeeze consumption in their home economies, reducing spending relative to production and thus contributing to current-account surpluses which must be absorbed by the rest of the global economy. Reserves held by South Korea, Singapore, Taiwan and India have grown into the hundreds of billions. No country has engaged in such practices to more disruptive global effect than China, which holds some $3.2trn in foreign-exchange reserves.

Reserves plateaued thereafter (and indeed fell slightly as a share of gdp), and a decline in the years ahead cannot be ruled out. A protracted period of post-pandemic financial stress could force some emerging economies to deplete their reserves. If economic strains and geopolitical tensions force Russia and China to draw down their hoards, that might place upward pressure on interest rates.

Yet it is also possible that the pandemic will lead to a new surge in reserves. Yes, a shift to zero-carbon energy may eventually doom fossil-fuel windfalls, but the transition might well mean high prices for oil and gas, since new production is likely to stagnate. Meanwhile, the pandemic and its aftermath will probably reinvigorate the appeal of defensive foreign-exchange reserves. During the financial panic of March 2020 and again in recent months, as straitened global conditions squeezed emerging markets, the economies which weathered stresses best were those with ample foreign-exchange reserves. This lesson has already been put to use. By the autumn of 2021, reserves were roughly $1trn higher than they were before covid-19.

The effects of reserve accumulation could also be offset by increased government borrowing. Government debt loads, already high pre-pandemic, have exploded over the past two years; in 2020 alone, public debt as a share of gdp surged by nearly 20 percentage points across advanced economies, to 123%, and nearly ten points across emerging economies, to 63%. Work by Lukasz Rachel, of the London School of Economics, and Larry Summers, of Harvard University, reckons that over the past half century, rising government debt across rich economies pushed up interest rates by about 1.5 percentage points. This effect was more than balanced out by other factors in the past, but might not be in the decades ahead.

A first-class problem

A second stream of saving has flowed from the households and firms which have done best over the past few decades. Since the 1970s, inequality has risen across many economies. Wealthier households have a higher propensity to save, so this shift in the distribution of income contributed to the saving glut, according to work by Atif Mian, of Princeton University, Ludwig Straub, of Harvard University, and Amir Sufi, of the University of Chicago. From 1983 to 2019, the share of American income going to the top 10% of the income distribution rose by 15 percentage points, they reckon. Because of this “saving glut of the rich”, average annual saving by the top 1% of American earners alone has outstripped annual average net domestic investment since 2000. Increased inequality accounts for about 0.6 percentage points of the decline in rich-world interest rates since the 1970s, say Messrs Rachel and Summers.

High-rolling households have not been alone in stockpiling savings. For decades, corporations have been hoarding money as well, retaining a large share of their hefty net profits. According to Peter Chen, of the Analysis Group, an economic consultancy, and Brent Neiman, of the University of Chicago, and Loukas Karabarbounis, of the University of Minnesota, annual global corporate saving rose from less than 10% of world gdp to nearly 15% between 1980 and 2015. The corporate sector has been acting as a net lender to the global economy, rather than as a net borrower from it.

As with reserve accumulation, the relative importance of such factors has waxed and waned. Income inequality rose sharply from around 1980 to 2000. In the years since, it has levelled off in some economies, like Britain’s, and increased at a slower pace in others, like America’s. Corporate saving, in contrast, rose relatively slowly before 2000, then much faster thereafter, as firms salted away cash from increased profits. In America, for instance, corporate profits have hovered above 10% of gdp for most of the period since 2006, after never rising above 8% over the prior quarter century.

Income inequality and corporate profitability cannot be forecast with any certainty. Both reflect the interaction of myriad forces, from the balance of corporate and labour power, to the state of technological progress and productivity growth, to government tax and regulatory policy. It is possible that the trends of the past half century might be upended by the pandemic and its aftermath. Over the past 18 months, tight labour markets helped push wages upward and strengthened workers’ leverage in bargaining with their employers. Slower growth in the labour forces of ageing societies could help to preserve these gains, and perhaps enable a resurgence by organised labour. Firms—especially big and profitable technology ones—are in the cross-hairs of regulators looking to boost competition. Better times for workers should also squeeze profits, in addition to reducing inequality.

A retreat from globalisation could amplify these trends. It would increase the earning power of the working masses in rich countries, while hitting the profits of multinational firms and the higher incomes of their white-collar workers. On the other hand, substantial reversals in inequality are relatively rare in recent economic history. The great compression in incomes that occurred from the 1910s into the post-war decades occurred as fortunes were hammered by the Depression and liquidated to fund wars, as taxes on the rich soared well above current levels. For now, such upheavals seem unlikely.

What is more, as Mr Mian and co-authors write, the effects of inequality on saving can feed on themselves. As high saving by the rich pushes down interest rates, they argue, poorer households increase their borrowing to sustain their consumption. But as debt piles up, they find themselves forced to reduce spending to pay back loans. Their debt payments, furthermore, represent a transfer of more money to rich households whose purchases of assets (like mortgage-backed securities) effectively finances the borrowing of the non-rich. The trap which results—of perpetually high inequality, low interest rates, and high debt among poorer households—could prove difficult to escape, sustaining the savings of the rich as a potent macroeconomic force.

Far more certain is the third great river of savings, whose flow, which has grown in importance, might well swamp other post-pandemic changes in behaviour. The world is not getting any younger, and in coming decades the savings of the old stand to apply relentless pressure on the macroeconomy. Across time and countries, household saving follows a reliable pattern. When workers are young, they save little or even take on debt. Their savings rise through their 30s and 40s before peaking a decade or so before retirement. As populations have grown older over the past half century, in the rich world especially, the share of workers in their prime saving years has risen as well, leading to ever more money in nest-eggs and ever lower yields on the assets therein.

That may well turn out to be an underestimate, if recent fertility trends are anything to go by. In 2021, India’s birth rate declined to just 2.0 children per woman—below the rate at which births and deaths are in rough balance. Indeed, a growing number of emerging markets have flipped to the slow population growth common in rich countries. Recent research by Matthew Delventhal of Claremont McKenna College, Jesús Fernández-Villaverde of the University of Pennsylvania and Nezih Guner of the Universitat Autònoma de Barcelona concludes that such transitions—the switch from high mortality and fertility rates to low ones which accompanies economic development—are happening faster over time. The transition took a half century or more 100 years ago, but now tends to be compressed into just two or three decades. Some 80 countries have completed this transition, and in virtually all the rest it is under way.

What is more, the pandemic further depressed birth rates in many countries. China’s birth rate touched a record low in 2021, potentially bringing forward the era of declining Chinese population. America experienced a baby bust too, which in combination with falling immigration depressed the population growth rate to just 0.1% in 2021—the smallest annual increase on records going back to 1900. The end of the pandemic could bring a rebound in birth rates. But there is no mistaking the broader trend: the world is greying, fast.

Innumerable shades of grey

Will the effect of ageing on saving necessarily remain the same in future as it was over the past half century? In an influential book, Charles Goodhart, of the London School of Economics, and Manoj Pradhan, of Talking Heads Macroeconomics, a research firm, argue that the greying of the population will depress interest rates only up to a certain point, after which there will be a “great demographic reversal”. Their view rests in part on the observation that while workers on the verge of retirement save heavily, those already retired begin to spend down their stores of stocks and bonds. An increase in the share of the population above retirement age, then, could mean that the proportion of workers in their high-saving years will peak and then decline, dragging down saving and pushing up interest rates.

A great demographic reversal seems intuitive, particularly in places like America where an oversize cohort—the baby-boomers—is easing into retirement. But other economists say there are reasons to expect ageing to continue to depress interest rates. They note, for example, that it is the age profile of a population as a whole which matters. Even as more people retire, the age of the typical working person will continue to rise toward those prime saving years. There are boomers aplenty, but the median age in America is still just 38. Another reason is that, in the emerging world, a larger share of workers have their prime saving years still ahead of them. The median age in India is only 28, for instance. So long as financial markets remain reasonably integrated around the world, higher saving anywhere helps to depress interest rates everywhere.

Perhaps most important, people in retirement do not tend to spend everything. Rather, for a number of motives—to avoid outliving their savings, or to provide for heirs, among others—they tend to maintain large stocks of wealth well into retirement. In Britain, for instance, as of 2018, people 80 or older held more wealth than those aged 45 to 49. Recent work by Noëmie Lisack, of the Banque du France, Rana Sajedi, of the Bank of England, and Gregory Thwaites, of the University of Nottingham, estimates that this habit of leaving behind savings will by mid-century depress interest rates by nearly half a percentage point relative to current levels. With neither inequality nor the level of reserves showing signs of sustained fall, the ineluctable force of demography should continue to drive savings growth.

The world, in other words, may come to look ever more like Japan. There, the median age is 48, more than a quarter of the population is over 65, and the yield on a 30-year government bond is a cool 0.8%, despite a government debt load of 259% of gdp. A generation ago, Mr Bernanke reckoned that Japan’s lacklustre growth and subterranean rates of inflation and interest were the consequence of “self-induced paralysis” by the central bank. Today, such realities seem more like the dull fate of a world with more savings than it quite knows what to do with.

The FRBNY's Dean Parker and Moritz Schularick find that term spread is a useful indicator of financial crisis both domestically and overseas The effect is grounded in an uptick at the short end of the yield curve likely driven by higher risk taking by financial intermediaries in the run up to a crisis, "...It is clear that theterm spread is significantly lower in the years before a financial crisis, both in the international sample and in the U.S. case. When all countries are considered, the term spread is approximately one percentage point lower on average in the two years before the onset of a crisis. For the United States specifically, this effect is almost doubled in the year before an onset, reaching 2 percentage points lower than usual on average.For a concrete example, in the run-up to 2007-08 financial crisis in the United States, long-term rates declined despite increasingly tight monetary policy, giving rise to Alan Greenspan’s famous description of the development as a “conundrum.” As a result, the term spread fell by 3 percentage points at the time. Similarly, though not as widely discussed contemporarily, the term spread declined a total of 3.3 percentage points in the United States between 1924 and 1928 in the lead-up to the 1929 crash and Great Depression. In the run-up to normal recessions, however, we observe only limited movement.... There are various possible explanations for the predictive ability that depend on whether the decrease of the term spread is driven by the short-term rate rising or the long-term rate falling. As seen in the panel chart below, when taken in isolation, rising short-term rates seem to play the dominant role for the narrowing of the term spread. This is because the long-term rate does not seem to behave differently in the run-up to a crisis. Such an increase in short-term funding rates relative to the yield on long-term investments is indicative of declining interest margins and higher risk taking by financial intermediaries. Our findings hence echo the evidence for rising financial risk taking in financial markets before financial crises that is reflected both in quantities of credit growth and a depressed price of risk..."

Dean Parker and Moritz Schularick, "The Term Spread as a Predictor of Financial Instability," Liberty Street Economics, November 24, 2021,
https://libertystreeteconomics.newyorkfed.org/2021/11/the-term-spread-as-a-predictor-of-financial-instability/

The Term Spread as a Predictor of Financial Instability

The term spread is the difference between interest rates on short- and long-dated government securities. It is often referred to as a predictor of the business cycle. In particular, inversions of the yield curve—a negative term spread—are considered an early warning sign. Such inversions typically receive a lot of attention in policy debates when they occur. In this post, we point to another property of the term spread, namely its predictive ability for financial crisis events, both internationally and in historical U.S. data. We study the predictive power of the term spread for financial instability events in the United States and internationally over the past 150 years.

The Term Spread as a Crisis Predictor

The behavior and slope of the yield curve are longstanding tools for predicting the onset of economic recessions within the next year, or future employment growth. Consideration of the yield curve as a predictor for recessions rose to prominence with Estrella and Mishkin’s (1996) paper. Rudebusch and Williams (2008) also highlighted the power of the term spread in predicting aggregate economic downturns. While there are many academic studies and other articles on the efficacy of the term spread and its inversion for predicting the onset of normal recessions, little is known about its forecasting performance for financial crises. Our study aims to close this gap.

In normal times, a bond with a longer maturity fetches a higher rate on the market due to increased duration risk. However, in the lead up to an economic downturn, various factors can alter the shape of the yield curve. Changes in risk attitudes and search for yield, central bank policy actions, and international demand for safe assets can all influence the term spread and drive the yield for short-term debt closer to or even above the yield for long-term debt. Are such changes in the term spread predictive of a higher risk of financial instability?

The Behavior of the Term Spread around Financial Crises

For our analysis, we use the Macrohistory Database compiled by Jordà, Schularick, and Taylor (2017). The data cover eighteen advanced economies around the world from 1870 to 2017 and contains country specific short- and long-term rates. For the United States specifically, these are the three-month Treasury bill and ten-year Treasury bond rates, respectively. The main benefit of the Macrohistory Database is that it enables examining financial crises in a variety of economic and temporal contexts. Our international sample consists of eight-three crises out of 2,304 total observations, or approximately four percent of the sample. The United States has six years marked as crisis years over the past 128 years: 1873, 1893, 1907, 1930, 1984, and 2007. A financial crisis is defined by Jordà, Schularick, and Taylor as a period of banking distress characterized by major bank failures, exceptional losses in the banking sector, and/or significant government intervention. Briefly, the 1873 and 1893 crises were bank runs precipitated by railroad failures, 1907 was brought on by the collapse of Knickerbocker Trust, 1984 marks the beginning of the Savings & Loan crisis, and 1930 and 2007 saw the Great Depression and Global Financial Crisis, respectively. We also corroborate our results using a different definition of financial instability, namely large declines in the market value of bank equity (Baron, Verner, and Xiong 2020).

The chart below plots the term spreads in the five years before and after a financial crisis or recession. We test, using regression analysis, whether the level of the term spread is above or below normal levels in these windows. The blue dots and bars in the chart represent point estimates and confidence intervals, respectively. They can be read as the average levels of the term spread in the lead-up to and aftermath of an event compared to periods where no event occurred. Positive values imply a higher level of the term spread than usual and negative values imply a lower level than usual.

Using the Term Spread as a Forecasting Tool

With the knowledge that something abnormal occurs in the term spread in the years preceding a financial crisis, the natural next step is to examine its efficacy as a predictive tool and how it functions when used as part of a larger model including other known predictors of crises. Schularick and Taylor (2012) show that real bank-loan growth is a strong predictor of impending crisis and that model serves as the base for our later models. As for definitions of financial crises, we use both the Jordà-Schularick-Taylor narrative and the bank equity declines from Baron, Verner, and Xiong (2020).

We run logistic regressions to predict the two types of crises and compare models using real bank-loan growth and term spread as predictors. We also test the inclusion of an inverted yield curve (a negative term spread). The results are reported in the table below with each column reporting a separate regression.

Our main finding is that, for both crisis definitions, including the term spread alongside standard predictors yields a significant improvement in forecasting power. A smaller term spread increases the risk of a financial crisis. Using the inverted yield curve as a predictor results in a slightly worse predictive ability of the model.

There are various possible explanations for the predictive ability that depend on whether the decrease of the term spread is driven by the short-term rate rising or the long-term rate falling. As seen in the panel chart below, when taken in isolation, rising short-term rates seem to play the dominant role for the narrowing of the term spread. This is because the long-term rate does not seem to behave differently in the run-up to a crisis. Such an increase in short-term funding rates relative to the yield on long-term investments is indicative of declining interest margins and higher risk taking by financial intermediaries. Our findings hence echo the evidence for rising financial risk taking in financial markets before financial crises that is reflected both in quantities of credit growth and a depressed price of risk (Krishnamurty and Muir 2017; Baron and Xiong 2017).

The term spread is often used as an early warning indicator for recessions. In this post, we showed that it performs well as a predictor of financial crises, both internationally and in the United States alone. There is a significant benefit to including the term spread as a predictor for two separate crisis definitions. We identified that this effect is driven by the short end of the yield curve rising and offered a potential explanation based in higher risk taking by financial intermediaries in the time before a crisis.

Research from the Fed finds evidence of risk adverse savings flowing out of the PRC into the United States having a significant impact on real estate prices in impacted area“…We find evidence of an impact of foreign capital inflows on the evolution of house prices in major U.S. cities. The magnitude of the premium in price growth in China-exposed over non- exposed areas averages about 2 percent in the two years following each of the two episodes of China shocks in the last decade. Then, using local projections, we find that Chinese capital inflows to the United States explain the majority of the widening in price growth differentials after Chinese stress episodes. Overall, the evidence we present is consistent with U.S. residential real estate serving as a safe haven asset for foreign Chinese households….”

William Barcelona, Nathan Converse, and Anna Wong, "U.S. Housing as a Global Safe Asset: Evidence from China Shocks," Federal Reserve Board, November 2021, https://www.federalreserve.gov/econres/ifdp/us-housing-as-a-global-safe-asset-evidence-from-china-shocks.htm

Relationship Between Excess Price Gaps and Capital Infows from China

".... Figure 12 plots the relationship between deposit inflows from China and Hong Kong along with the evolution of the gap in house price growth between China-exposed ZIP codes and the matched controls.10 Recall that in Section 2 we presented evidence that funds brought to the U.S. via the banking system were being used for house purchases with a lag. The relationship between inflows and the estimated treatment effect also exhibits this behavior: the contemporaneous correlation is only 0.06, but rises to 0.36 with a three-quarter lag. This is why Figure 12 plots the treatment effect three quarters ahead. The degree of comovement between the two series is striking, and we see that peaks in capital infows from China and Hong Kong coincide with peaks in the treatment effect three quarters ahead. It is also notable that there was essentially no relationship between the two series prior to 2010, the year in which China liberalized some controls on capital outflows...”

The Effect of Chinese Demand on U.S. House Prices

"..The difference in the mean growth rates of the two distributions provides the average treatment effect on the treated (ATET) of exposure to foreign Chinese capital over the 6-year window. For treatment definition 1, the mean house prices over this period for the treatment grew 7 percent faster than for the control group, or 1.1 percent faster per year. For treatment definition 2, mean house price growth for the treatment group was 14 percent faster over the 6 year period, or 2.2 percent per year, compared to that of the control. Another way to visualize the cumulative impact is to look at the evolution of the house price index of the treatment and control group (Figure 9). Because of their differential growth rates, the house price level of the treatment group has diverged significantly from the control group in recent years. For the 20 cities included in the treatment definition 1, the divergence picked up after 2008, and for the 34 cities in the treatment group 2, the divergence picked up since 2010...."

Core of paper, "...Aggregate capital flows data from balance of payments (BOP) accounts offer additional clues to the size of foreign purchases of U.S. residential real estate. While the most recent IMF Balance of Payments Manual (BPM6) species that foreign purchases of residential real estate should be included in foreign direct investment, the U.S. and indeed many countries do not measure such flows. As a result, these flows are captured only in the residual statistical discrepancy line of the balance of payments, or as the difference between the U.S. measured current account deficit and the measured financial inflows from abroad that finance that deficit. When the measured current account deficit exceeds the recorded net financial inflows, a positive statistical discrepancy arises. In the U.S. case, the statistical discrepancy primarily reflect two missing assets: financial derivatives, and the object of interest for our study foreign purchases of U.S. residential real estate assets.Since 2008, the U.S. BOP statistical discrepancy experienced several episodes of turning positive, meaning that some U.S. capital inflows are not being captured in official statistics. In particular, since 2010 we note a striking increase in the comovement of private capital outfows from China and missing net capital in ows to the United States as captured by the statistical discrepancy line in the U.S. BOP. Figure 1 shows that the 12-quarter rolling correlation between the two variables went from being zero or negative to above 0.8 and remained elevated from 2012 to 2017. We next explore bilateral capital ows data between the U.S. and China at a more granular level...."

Specifically, “…house prices in major U.S. cities that are highly exposed to demand from China have on average grown 7 percentage points faster than similar neighborhoods with low exposure over the period 2010-2016….”

What this looks like in terms of an impact on American real estate, “.. , the estimates in Figure 13 imply that a one percentage point increase inflows generates an 0.008 percentage point widening in the gap in price growth between China-exposed and non-exposed areas in the U.S. This effect may seem small at first glance, but recall from Figure 12 above that inflows reached roughly $70 billion during periods of concern about a China hard landing in 2012 and 2015. Taking a concrete example, banking inflows from China were $10.1 billion in July of 2015 and $32.2 in August of that year. Our estimates imply this increase explains 90 percent of the widening gap in house price growth between China-exposed areas and those not exposed...."

Their bottom line, The results, shown in Table 1, further confirm that negative (positive) shocks from China are associated with an increase (decrease) in U.S. statistical discrepancy net inflows since 2010Q2, with a three quarter lag. The pre-2010 China shocks are not important in explaining the safe haven flows, as none of these Chinese variables are significant when interacted with the pre-2010Q2 dummy. The VIX, our measure of global financial conditions, is insignificant across all specifications,suggesting that unrecorded capital inflows to the U.S. are better explained by Chinese factors than by global financial conditions more generally. Strikingly, the regressions using Chinese capital outflows variables have substantial explanatory power: the R-square is 0.42 for the net foreign reserves sales regressions, and 0.28 for the regressions with Chinese money and deposit outflows. A back of the envelope calculation based on these results suggests that each $1 billion in net reserves sales by the Chinese authorities since 2010Q2 is associated with a $0.3 billion increase in unrecorded capital inflows to the United States; for each $1 billion in net money and deposit out flows from China, there is a $0.7 billion increase..."

“…The model also reproduces the empirical pattern shown in Table 4 that higher price dividend growth during the boom forecasts higher future dividend growth and not large relative price declines. For this exercise, we collapse the model time series by year and regress the log changes in dividends and price between model years corresponding to 2006 and 2019 on the log change in the price-dividend ratio during the boom. An additional log point of growth in the price-dividend ratio in the boom predicts additional 0.41 log point of dividend growth in the bust-rebound (compared to 0.20 in column (3) of Table 4) and additional 0.03 log point of price growth (compared to 0.03 in column (6) of Table 4)….echo the result from Table 4 that price rent growth in the boom not associated with long-run fundamentals strongly predicts subsequent price decline. However, they also suggest that the role of investors was mostly or wholly orthogonal to the role of fundamentals and less important to explaining the entirety of the boom or the full 1997-2019 period, which are the focus of our paper…”

New NBER argues that there was no housing bubble, national real house prices rose 80% between 1997 and 2006, lost two-thirds of their gain by 2012, but then rose again. The episode isn't best characterized as a boom-bust, but a boom-bust-rebound, "...We reevaluate the 2000s housing cycle from the perspective of 2020.1National real house prices grew steadily between 2012 and 2019, with the largest price growth in the same areas that had the largest booms between 1997 and 2006 and busts between 2006 and 2012. As a result, the areas that had the largest booms also had higher long-run price growth over the entire 1997-2019 period. With “2020 hindsight,” the 2000s housing cycle is not a boom-bust but rather a boom-bust-rebound...."

"...We argue that this pattern reflects a larger role for fundamentals than previously thought. In a first step, we use a standard spatial equilibrium framework to motivate several determinants of house prices. We find that these explain cross-city variation in long-run house price growth in reduced-form and structural supply regressions as well as the amplitude of the boom-bust-rebound across cities and severity of the foreclosure crisis. In a second step, we introduce a “neo-Kindlbergererian” model of a fundamentally rooted house price cycle in which belief over-reaction amplifies the boom and a foreclosure spiral exacerbates the bust, and discipline the model with our empirical moments. The estimated model generates the boom-bust-rebound from a single fundamental shock and quantitatively matches the cross-city patterns...."

Their housing cycle dynamic, "...House prices clear a Walrasian market, with demand emanating from potential entrants and supply coming from the the construction of new homes and foreclosures. An endogenous boom-bust-rebound cycle occurs in response to a single change in the city’s fundamental, an increase in the growth rate of the income and amenities or “dividend” from living in the city...."

Evidence: "... Figure 1 shows the national Case-Shiller house price index, deflated using the GDP price index. After a period of zero real growth, the series begins to rise in the late 1990s, peaks in 2006Q2, reaches a local trough in 2012Q1, and then grows again through the end of our sample in 2019. In what follows, we measure the boom as house price growth between 1997 and 2006, the bust as price growth between 2006 and 2012, and the rebound as price growth between 2012 and 2019.We start the boom in 1997 because very few cities have booms that start before that year..."

"... Here, we revisit the increase in price-rent ratios with the benefit of 2020 hindsight and show that the component associated with long-run fundamentals predicts subsequent rent growth and not future price decline, consistent with our interpretation of this component as fundamentally-based. We start by characterizing the behavior of rents. Panel (a) of Figure 5 plots the growth rate of real rents in each CBSA between the 2000 Census and the 2018 American Community Survey (ACS) against the long-run fundamental, again measured as the second stage fitted value from column (3) of Table 2. Areas with higher fundamentals experienced larger rent growth over the BBR, with the relationship especially strong for larger CBSAs. Panel (b) shows the timing of rent growth using BLS CPI rent data for the 22 CBSAs with annual data since 1987, grouped into population-weighted quartiles of the long-run fundamental. Rents rise fastest in areas with the highest fundamentals, and this growth appears to represent a break from the pre-boom period. Column (1) of Table 4 shows that CBSA-level price-rent increases in the boom correlate positively with the long-run fundamental. We measure the log growth in the price-rent ratio using 2000 Census and 2006 ACS mean rent and the same house price data as above. The bivariate relationship has an R2 of 0.29. Columns (2) and (5) report the correlation of price-rent growth in the boom with subsequent rent and price growth over the 2006-18 period. This user-cost decomposition (Poterba, 1984) closely resembles the Campbell and Shiller (1988b) exercise of decomposing variation in the price-dividend ratio of a stock into future dividend growth and returns with rents replacing dividends as the cash-flow measure.15 Larger price-rent growth during the boom forecasts both higher subsequent rent growth and future relative price declines. Columns (3) and (6) restrict the variation in the price-rent ratio in the boom to the part associated with the long-run fundamental. Specifically, these columns report regressions of subsequent rent and price growth on the fitted value of the growth of the price-rent ratio from column (1). Strikingly, the rise in price-rent ratios associated with long-run fundamental growth predicts even faster subsequent rent growth than in column (2) and no subsequent price decline, validating our labeling of this component as a fundamental. Columns (4) and (7) show that the part of price-rent growth not explained by long run fundamentals predicts no subsequent rent growth and large subsequent price declines. These columns make clear that our empirical evidence admits the possibility of aspects of the housing boom not associated with long-run fundamentals; in fact, the part of price rent increases not correlated with long-run fundamentals looks very bubble-like ex post…"

Greg Obenshain and Eldar Safarov report on a Andrei Shleifer paper (attached) we previously highlighted that argues financial crisis are relatively predictable, "...The authors define periods when three-year debt growth has been in the top 20% of historical increases and three-year asset price growth has been in the top third of historical increases as the “Red Zone,” shown in the upper right-hand corner of the figure below... The probability of experiencing a financial crisis within one year of entering the Red Zone is over 13%, compared to a 4% probability in the entire dataset. Remarkably, the probability of entering a financial crisis within three years of entering the Business Red Zone is 45% and an astounding 69% in the rare instances when both the household and business Red Zones are breached. The authors note that crises do not immediately follow high debt and asset price growth, suggesting they take time to develop.... And most interestingly, we have just entered a Red Zone as of the end of 2020.This is driven by the rise of debt-to-GDP and the equity market rally. Even if debt-to-GDP is calculated using the pre-COVID GDP level, this result holds. So, if crises are by-products of credit and asset price growth cycles, as the authors’ evidence strongly suggests, then the US may have just entered the danger zone. This does not mean that we need to worry about the sky falling just yet. The authors show clearly that crises take time to develop, and only materialize 36% of the time after a Red Zone has been entered...."

Are Financial Crises Predictable?

The consensus wisdom among academics and practitioners alike is that crises are inherently unpredictable. But Harvard’s famed behavioral economics group has recently produced a new paper suggesting the consensus wisdom on predicting crises might be wrong—at least on a time horizon of three years or longer. Instead, they argue that rapid expansions of credit can drive asset price booms, which are followed by credit busts.

Building off earlier academic research, the authors combine data from 42 countries between 1950 and 2016 to study financial crises in the context of business and household credit growth as well as equity and home price growth. They define crises as declines in bank equities by greater than 30% or banking panics, which capture events such as the Great Recession (2007) and the Savings and Loan Crisis (1990), but not the bust of the dot-com bubble. In line with the theories of Hyman Minsky and Charles Kindleberger, the authors find that financial crises tend to follow periods that feature both elevated debt and asset price growth. The authors define periods when three-year debt growth has been in the top 20% of historical increases and three-year asset price growth has been in the top third of historical increases as the “Red Zone,” shown in the upper right-hand corner of the figure below.

The table below shows the probability of entering a financial crisis once the Red Zone has been breached, broken down by the type of Red Zone event: business, household or both.

As a leading indicator, the Red Zone would appear to be effective. 64% of crises were preceded by either the business or household Red Zone warning within the prior three years. When the authors expand their three-year debt growth to include the top 40% of historical increases and the three-year price growth to be in the top two-thirds of historical increases, the Yellow Zone precedes 82% of crises within the prior three years. But like most leading indicators, their presence does not necessarily result in the event. Red Zones and Yellow Zones were followed by crises only 36% and 20% of the time. To give a sense of what this data looks like, we show the Red and Yellow Zone indicators for the US housing sector along with the real housing price index.

Ideally, we’d show the same chart for US businesses debt and asset price growth. However, there were no business Red Zones in the United States in the authors’ dataset. But while the authors needed to study 42 countries across 60 years so that they could get statistically significant results, we are not similarly constrained. We can use their insights to see what happens if we use US data alone. Specifically, rather than global cut-off values for Red and Yellow Zones, we can use only the US data to define the Yellow and Red Zones. The results are shown below.

Credit and asset booms are what predict busts. In part, the authors argue, because investors tend to extrapolate these booms into the future, taking more risks than they should. Investors feel an inevitable desire to “reach for yield” in low-yielding environments, spurring credit growth that drives asset growth until the inevitable reckoning when some portion of the assets and some portion of the debt go bad.