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(Evan, Nonresident Senior Fellow @ Brookings, “Around the halls: Built environment issues that could define 2022,” pg online @ //um-ef)

DEMOCRACY DEPENDS ON DELIVERING HOUSING AFFORDABILITY Evan Siddall, Nonresident Senior Fellow The next year’s most pressing issue in the built environment relates to the complicated interaction of the COVID-19 pandemic, climate change, low interest rates, uncertainty on housing prices, and the resulting increase in socioeconomic inequality. These factors are hurting housing affordability and creating a new landed gentry of homeowners who keep getting wealthier. Essential workers, nurses, teachers, and the people we take for granted to make our morning coffee—the backbone of our economy—can no longer afford to buy homes and are falling ever farther behind. The pandemic-related urban exodus has also driven suburban housing prices higher, as have supply chain distractions and a skilled labor shortage. Monetary policy is aggravating the problem, since housing markets are highly rate-sensitive. Legacy fiscal policies and government-sponsored enterprises (Fannie Mae and Freddie Mac in the U.S., and my former employer, Canada Mortgage & Housing Co. in Canada) continue to add inflationary stimulus in the form of mortgage securitization and mortgage insurance. If we are to preserve the integrity of our western liberal democratic system, we need to attend to housing affordability so that everyone can enjoy the safety and sense of belonging that a secure home represents.

The next stock market crash can easily kick-start a recession. That's because stocks are shares of ownership in a company. As a result, the stock market reflects investors' confidence in the future earnings of all these companies. Corporate earnings are dependent on the health of the U.S. economy. That means the stock market is also an indicator for the U.S. economy itself.

A crash signals a massive loss of confidence in the economy.

With stock market prices hitting new highs despite interest rate- and money supply headwinds, this is a timely opportunity to explore how a stock market sell-off may trigger a GDP recession ("recession").

A recession may come about in many ways. But since GDP (nominal and real) is largely a reflection of the level of debt-inflated consumer spending, changes in the supply of money and consumers' time preferences (i.e. the spending vs. saving decision) always play major roles. These two are also capable of bringing about a stock market correction since broad-based increases in stock prices over time are driven by monetary inflation and a reduced demand for money to hold (i.e. decreased demand for cash balances). Our focus here will however be on how a stock market sell-off may trigger a recession.

Rosnick (2013) has argued that a univariate model provides a better prediction of the unemployment rate than Farmer’s published model. I show here, that although the univariate model provides more accurate out-of-sample forecasts than the VECM, a bivariate model that includes information from the stock market outperforms both alternatives. My results establish that the stock market contains significant information that helps to predict the future unemployment rate. A big stock market crash, in the absence of central bank intervention, will be followed by a major recession one to four quarters later. Further, the connection between changes in the stock market and changes in the unemployment rate has remained structurally stable for seventy years.

Standard models of intertemporal choice suggest that unexpected and permanent drops in wealth reduce consumption, and that this reduction equals the annuity value of the drop in wealth. There is, however, much disagreement about the magnitude of the impact of wealth shocks on consumption. Most of the literature attempting to estimate this impact is based on two implicit assumptions: (i) wealth shocks (whether due to house price changes or movements in stock prices) are not predictable, and therefore not anticipated by consumers; (ii) current prices are the best predictors of future asset prices, and therefore changes in asset prices constitute a permanent wealth shock. According to the permanent income hypothesis, it follows from (i) and (ii) that wealth shocks should have a relatively large impact on consumption, equivalent to the annuity value of the wealth shock (in the order of 2 to 5 percent, depending on the assumed real interest rate). Several studies, relying on macroeconomic or regional data, regress the logarithm of consumption, consumption growth or saving on shocks to housing or financial wealth, but no consensus has yet emerged on the link between house prices and consumption.3 Studies using microeconomic data allow researchers to dig deeper into this link. While changes in stock prices imply unambiguous wealth effects on consumption, as discussed in Sinai and Souleles (2005), Campbell and Cocco (2007) and Attanasio et al. (2009), the consumption response to a house price decline is quite heterogeneous across the population. Most empirical analyses using micro-data refer to the U.S. and the U.K. Engelhardt (1996) estimated an MPC of 0.03 or higher for the U.S. in the 1980s, and Juster et al. (2001) found an even higher MPC out of stock price changes. On the other hand, Hoynes and McFadden (1997) found that households who had experienced housing capital gains increased their saving rather than their spending, and Hryshko et al. (2010) find that after a job loss homeowners can smooth consumption easier than renters in times of higher house prices. In the UK Disney et al. (2010) find a relatively low MPC out of housing wealth (of the order of 0.01), while Campbell and Cocco (2007) a relatively strong response for older households that own their home. Attanasio et al. (2009) conclude that the co-movements in consumption and house prices are not generated by a causal link running from the latter to the former, but by common factors, contradicting the findings in Campbell and Cocco (2007).

5. Conclusions and future research The long-term history for 30 countries indicates that stock-market crashes (cumulative real returns of –0.25 or worse) have substantial predictive power for depressions (cumulative macroeconomic declines by 10 percent or more). In a non-war environment, the realization of a stock-market return of –0.25 or worse implies that the probability of a minor depression (macroeconomic decline by 10 percent or more) is 22 percent, and the probability of a major depression (decline by 25 percent or more) is 3 percent. When the stock return is –0.40 or worse, the corresponding depression probabilities are 30 percent and 4 percent. The probabilities are 46 percent and 8 percent, respectively, when a stock-market crash of –0.25 or worse in a non-war period is accompanied by a currency or banking crisis and occurs during a time of global economic turmoil. Given the stock-market crashes for the United States and other countries in 2008 (characterized as non-war, featuring a currency or banking crisis, and occurring during a global event), these last probabilities applied at the beginning of 2009. The long-term data also show that the majority (67 percent) of minor, non-war depressions are accompanied by stock-market crashes, whereas most major, non-war depressions (83 percent) are accompanied by these crashes. Therefore, in the absence of a stock-market crash, the occurrence of a depression is highly unlikely. The full history of 3037 annual observations reveals 71 matched cases of stock-market crashes and depressions, of which 41 percent are associated with war. The years contained in the 71 cases constitute 12 percent of the overall sample (because the average duration of a crisis was 4.9 years). The co-movement between stock returns and macroeconomic changes for the 71 cases may be sufficient to explain the observed equity premium of 7 percent, assuming that the coefficient of relative risk aversion is between 3.5 and 4. The required coefficient falls to 3.5 or less if we bring in the additional 19 percent of the sample that features a stock-market crash or depression but not both. A crucial aspect of this analysis is that the computed covariances allow for a flexible timing pattern between stock returns and macroeconomic changes. In future research, we plan to assess this flexible covariance approach in more detail. Part of this research involves the roles of missing data and wartime price controls, which can distort the covariances calculated from a rigid timing structure.

This paper provides a theoretical study that links unemployment to the stock market bubbles and crashes. Our theory is based on three observations from the U.S. labor, credit, and stock markets. First, the U.S. stock market has experienced booms and busts and these large swings may not be explained entirely by fundamentals. Shiller (2005) documents extensive evidence on the U.S. stock market behavior and argues that many episodes of stock market booms are attributed to speculative bubbles. Second, the stock market booms and busts are often accompanied by the credit market booms and busts. A boom is often driven by a rapid expansion of credit to the private sector accompanied by rising asset prices. Following the boom phase, asset prices collapse and a credit crunch arises. This leads to a large fall in investment and consumption and an economic recession may follow.1 Third, the stock market and unemployment are highly correlated.2 Figure 1. plots the post-war U.S. monthly data of the price-earnings ratio (the real Standard and Poor’s Composite Stock Price Index divided by the ten-year moving average real earnings on the index) constructed by Robert Shiller and the unemployment rate downloaded from the Bureau of Labor Statistics (BLS).3 This figure shows that, during recessions, the stock price fell and the unemployment rate rose. In particular, during the recent Great Recession, the unemployment rate rose from 5.0 percent at the onset of the recession to a peak of 10.1 percent in October 2009, while the stock market fell by more than 50 percent from October 2007 to March 2009. [Insert Figure 1 Here.] Motivated by the preceding observations, we build a search model with credit constraints, based on Blanchard and Gali (2010). The Blanchard and Gali model is isomorphic to the Diamond-Mortensen-Pissarides (DMP) search and matching model of unemployment (Diamond (1982), Mortensen (1982), and Pissarides (1985)). Our key contribution is to introduce credit constraints in a way similar to Miao and Wang (2011a,b,c, 2012a,b).4 The presence of this type of credit constraints can generate a stock market bubble through a positive feedback loop mechanism. The intuition is the following: When investors have optimistic beliefs about the stock market value of a firm’s assets, the firm wants to borrow more using its assets as collateral. Lenders are willing to lend more in the hope that they can recover more if the firm defaults. Then the firm can finance more investment and hiring spending. This generates higher firm value and justifies investors’ initial optimistic beliefs. Thus, a high stock market value of the firm can be sustained in equilibrium. There is another equilibrium in which no one believes that firm assets have a high value. In this case, the firm cannot borrow more to finance investment and hiring spending. This makes firm value indeed low, justifying initial pessimistic beliefs. We refer to the first type of equilibrium as the bubbly equilibrium and to the second type as the bubbleless equilibrium. Both types can coexist due to self-fulfilling beliefs. In the bubbly equilibrium, firms can hire more workers and hence the market tightness is higher, compared to the bubbleless equilibrium. In addition, in the bubbly equilibrium, an unemployed worker can find a job more easily (i.e., the job-finding rate is higher) and hence the unemployment rate is lower. [Insert Figure 2 Here.] After analyzing these two types of equilibria, we follow Weil (1987), Kocherlakota (2009) and Miao and Wang (2011a,b,c, 2012a,b) and introduce a third type of equilibrium with stochastic bubbles. Agents believe that there is a small probability that the stock market bubble may burst. After the burst of the bubble, it cannot re-emerge by rational expectations. We show that this shift of beliefs can also be self-fulfilling. After the burst of the bubble, the economy enters a recession with a persistent high unemployment rate. The intuition is the following. After the burst of the bubble, the credit constraints tighten, causing firms to reduce investment and hiring. An unemployed worker is then harder to find a job, generating high unemployment. Our model can help explain the high unemployment during the Great Recession. Figures 2 and 3 plot the hires rate and the job-finding rate from the first month of 2001 to the last month of 2011 using the Job Openings and Labor Turnover Survey (JOLTS) data set.5 These figures reveal that both the job-finding rate and the hires rate fell sharply following the stock market crash during the Great Recession. In particular, the hires rate and the job-finding rate fell from 4.4 percent and 0.7, respectively, at the onset of the recession to about 3.1 percent and 0.25, respectively, in the end of the recession.

[June, London, , “Climate Change & the Financial Sector: An Agenda for Action”]

Climate change poses a major risk to the global economy: It affects the wealth of societies, the availability of resources, the price of energy and the value of companies. At the same time, the need to revolutionize the way we use energy opens up a new universe of options for economic development and social benefits.

[UNEP Finance Intiative, Sponsored by the Federal Ministry of Education and Research, , “Advancing adaptation through climate information services”, Accessed July 20]

Shifting to climate-change resilient economies will not be achieved only by governments building dams, improved water systems, and similar large-scale infrastructure. As with the move to a green economy, effective adaptation, in a systemic sense, will only occur if the millions of dispersed business decisions taken every day start to account for climate change factors and impacts. Within the group of private sector decision-makers, representatives of the financial services sector – banks, investors and insurers – stand out in the commercial landscape essentially because of their ability to effectively influence business practice and emerging trends in the real economy: Their daily engagement with clients and investees of all types and sizes and across virtually all sectors of the economy shapes the current and future reality of production processes and services. As such, the financial services sector can be a powerful conduit towards economic systems that are better prepared for the challenges of climate change.

(Robert J. Barro – PhD in Economics @ Harvard University, Paul M. Warburg Professor of Economics @ Harvard University, senior fellow of the Hoover Institution of Stanford University, and a research associate of the National Bureau of Economic Research. José F.Ursúa – PhD candidate in Economics @ Harvard University, “Stock-market crashes and depressions,” Research In Economics, Volume 71, Issue 3, September 2017, ScienceDirect)

5. Conclusions and future research The long-term history for 30 countries indicates that stock-market crashes (cumulative real returns of –0.25 or worse) have substantial predictive power for depressions (cumulative macroeconomic declines by 10 percent or more). In a non-war environment, the realization of a stock-market return of –0.25 or worse implies that the probability of a minor depression (macroeconomic decline by 10 percent or more) is 22 percent, and the probability of a major depression (decline by 25 percent or more) is 3 percent. When the stock return is –0.40 or worse, the corresponding depression probabilities are 30 percent and 4 percent. The probabilities are 46 percent and 8 percent, respectively, when a stock-market crash of –0.25 or worse in a non-war period is accompanied by a currency or banking crisis and occurs during a time of global economic turmoil. Given the stock-market crashes for the United States and other countries in 2008 (characterized as non-war, featuring a currency or banking crisis, and occurring during a global event), these last probabilities applied at the beginning of 2009. The long-term data also show that the majority (67 percent) of minor, non-war depressions are accompanied by stock-market crashes, whereas most major, non-war depressions (83 percent) are accompanied by these crashes. Therefore, in the absence of a stock-market crash, the occurrence of a depression is highly unlikely. The full history of 3037 annual observations reveals 71 matched cases of stock-market crashes and depressions, of which 41 percent are associated with war. The years contained in the 71 cases constitute 12 percent of the overall sample (because the average duration of a crisis was 4.9 years). The co-movement between stock returns and macroeconomic changes for the 71 cases may be sufficient to explain the observed equity premium of 7 percent, assuming that the coefficient of relative risk aversion is between 3.5 and 4. The required coefficient falls to 3.5 or less if we bring in the additional 19 percent of the sample that features a stock-market crash or depression but not both. A crucial aspect of this analysis is that the computed covariances allow for a flexible timing pattern between stock returns and macroeconomic changes. In future research, we plan to assess this flexible covariance approach in more detail. Part of this research involves the roles of missing data and wartime price controls, which can distort the covariances calculated from a rigid timing structure.

(Evan, Nonresident Senior Fellow @ Brookings, “Around the halls: Built environment issues that could define 2022,” pg online @ //um-ef)

DEMOCRACY DEPENDS ON DELIVERING HOUSING AFFORDABILITY Evan Siddall, Nonresident Senior Fellow The next year’s most pressing issue in the built environment relates to the complicated interaction of the COVID-19 pandemic, climate change, low interest rates, uncertainty on housing prices, and the resulting increase in socioeconomic inequality. These factors are hurting housing affordability and creating a new landed gentry of homeowners who keep getting wealthier. Essential workers, nurses, teachers, and the people we take for granted to make our morning coffee—the backbone of our economy—can no longer afford to buy homes and are falling ever farther behind. The pandemic-related urban exodus has also driven suburban housing prices higher, as have supply chain distractions and a skilled labor shortage. Monetary policy is aggravating the problem, since housing markets are highly rate-sensitive. Legacy fiscal policies and government-sponsored enterprises (Fannie Mae and Freddie Mac in the U.S., and my former employer, Canada Mortgage & Housing Co. in Canada) continue to add inflationary stimulus in the form of mortgage securitization and mortgage insurance. If we are to preserve the integrity of our western liberal democratic system, we need to attend to housing affordability so that everyone can enjoy the safety and sense of belonging that a secure home represents.

5. Conclusions and future research The long-term history for 30 countries indicates that stock-market crashes (cumulative real returns of –0.25 or worse) have substantial predictive power for depressions (cumulative macroeconomic declines by 10 percent or more). In a non-war environment, the realization of a stock-market return of –0.25 or worse implies that the probability of a minor depression (macroeconomic decline by 10 percent or more) is 22 percent, and the probability of a major depression (decline by 25 percent or more) is 3 percent. When the stock return is –0.40 or worse, the corresponding depression probabilities are 30 percent and 4 percent. The probabilities are 46 percent and 8 percent, respectively, when a stock-market crash of –0.25 or worse in a non-war period is accompanied by a currency or banking crisis and occurs during a time of global economic turmoil. Given the stock-market crashes for the United States and other countries in 2008 (characterized as non-war, featuring a currency or banking crisis, and occurring during a global event), these last probabilities applied at the beginning of 2009. The long-term data also show that the majority (67 percent) of minor, non-war depressions are accompanied by stock-market crashes, whereas most major, non-war depressions (83 percent) are accompanied by these crashes. Therefore, in the absence of a stock-market crash, the occurrence of a depression is highly unlikely. The full history of 3037 annual observations reveals 71 matched cases of stock-market crashes and depressions, of which 41 percent are associated with war. The years contained in the 71 cases constitute 12 percent of the overall sample (because the average duration of a crisis was 4.9 years). The co-movement between stock returns and macroeconomic changes for the 71 cases may be sufficient to explain the observed equity premium of 7 percent, assuming that the coefficient of relative risk aversion is between 3.5 and 4. The required coefficient falls to 3.5 or less if we bring in the additional 19 percent of the sample that features a stock-market crash or depression but not both. A crucial aspect of this analysis is that the computed covariances allow for a flexible timing pattern between stock returns and macroeconomic changes. In future research, we plan to assess this flexible covariance approach in more detail. Part of this research involves the roles of missing data and wartime price controls, which can distort the covariances calculated from a rigid timing structure.

WHAT are the effects of financial market disruptions on economic activity? The recent global financial crisis suggests that the effects are large and highly persistent: by 2017, ten years after the beginning of the crisis, the U.S. and U.K. GDPs remain far—10 percentage points (pp) or more—from their precrisis trends (figure 1). More systematic narrative studies of financial stress episodes point to similar conclusions. For instance, Romer and Romer (2017) study a panel of OECD countries and find that GDP is typically 9 pp lower five years after an extreme financial stress episode like the recent crisis.1 Surprisingly, however, these numbers stand in sharp contrast with the findings of another influential literature on the importance of financial markets for economic activity. Multivariate time series models (i.e., structural VARs) find relatively mild and short-lived effects of financial shocks— shocks to the effective “risk-bearing capacity” of the intermediary financial sector. For instance, the results of Gilchrist and Zakrajšek (2012) imply that output should be only 1.3 pp lower five years after an adverse financial shock like the one experienced in the recent crisis.2 To make sense of this conundrum, we first point to separate shortcomings of the two aforementioned approaches noted— narrative accounts and structural VARs. On the one hand, and unlike structural VARs, narrative accounts are not designed to identify the causal effect of financial strains on economic activity, only the existence of a correlation. On the other hand, structural VARs do not take into account that financial shocks are likely to have asymmetric effects on economic activity, as has been predicted theoretically (Mendoza, 2015; He & Krishnamurthy, 2013; Brunnermeier & Sannikov, 2014). In contrast, narrative accounts implicitly allow for asymmetric effects because they focus only on adverse financial developments: negative shocks. We then consider an empirical model, a vector movingaverage (VMA) model, designed to address these separate limitations identifying the causal effects of financial shocks and taking into account the possible asymmetric effects of financial shocks. Like VARs, they can incorporate structural identifying restrictions to tease out causal effects, but unlike VARs, they can easily be generalized to allow for asymmetric effects of shocks. Our baseline evidence is based on U.S. data, and we establish the causal effect of financial shocks by using an identification strategy that builds on but also expands (Gilchrist & Zakrajšek, 2012, GZ). We isolate innovations to the excess bond premium (EBP)—the component of credit spreads purged from the expected default risk of borrowers—that are contemporaneously orthogonal to macrovariables, and we separate the EBP innovations into monetary shocks and financial shocks using a proxy variable approach based on Romer and Romer’s (2004) narrative measure of exogenous monetary policy changes. We find that a favorable financial shock—an easing of financial conditions—has little effect on economic activity, but an adverse financial shock has large and persistent effects on economic activity. These results help reconcile the seemingly contradictory findings between narrative accounts and structural time series analyses: structural VARs have found mild and transitory effects of financial shocks on GDP because VARs are linear models, in which the large and persistent effects of adverse shocks are mixed with the (according to our results) small and transitory effects of favorable shocks, leading to mild average effects of financial shocks. In contrast, narrative studies focus solely on crisis episodes, adverse events, which have large and persistent effects on output. Our estimated effects of financial market disruptions are smaller than narrative studies like Romer and Romer (2017), however, consistent with the fact that some of the movements in financial distress identified by narrative studies are likely endogenous. We then use our model to revisit the effects of the financial crisis on output, in particular on the large gap that opened between output and its precrisis trend. To do so, we conduct a counterfactual model simulation based on parameters estimated with pre-2007 data, in which we turn off the financial shocks experienced over 2007–2008. We find that without the 2007–2008 financial shocks, the decline in output would have been a lot milder and only transitory. We conclude that a large fraction (over two-thirds) of the gap between current GDP and its precrisis trend was caused by the financial crisis. As additional evidence, we also consider the effects of financial shocks from U.K. data, and we obtain very similar conclusions. We follow the same identification strategy as in the United States, using data on the excess bond premium from Bleaney, Mizen, and Veleanu (2016) and the narrative measure of exogenous monetary policy changes from Cloyne and Hürtgen (2016). As with the United States, financial market disruptions have large and persistent effects on output, so that a large fraction of the gap between current U.K. output and its precrisis trend can be attributed to the financial crisis.

[Consultancy firm that aims to magnify the positive impact of the private sector on society April 14, , “Strong financial sector is condition for sustainability agenda”]

Without a strong financial sector, i.e. a sound number of diverse banks, private equity companies, insurers and a stock exchange, our economy will do much worse. The financial sector is a condition for a healthy corporate sector and development of the real economy. Likewise a strong financial sector is needed to facilitate the transition towards a more efficient, clean and green economy. It is simply foolish to believe that this transition will ever be realized without finance. Obviously banks and other financial institutions still have to proof their social usefulness to many. The reputation of bankers is terrible. However, anybody who promotes the transition towards a sustainable economy should quit bashing bankers and look for new ways of cooperation. Hopefully the fact that politicians are worrying about the future of the Amsterdam Stock Exchange is a signal that the mood around the financial sector is changing. The sector still has a lot to improve. But we should all realize that we are better off with a financial sector that can still do better, than with no financial sector at all.

This is bad news. After all, the housing market overheats every decade or two. If the system is stable enough, it can cool off without catastrophe. Now, however, the trillion-dollar overnight repo market in housing mortgages is so large that, when the housing market retreats, financial stability could be threatened. Existing reforms do not adequately mitigate this risk, largely because they depend on the authorities and the banks to complete a complex and untested repayment process within 48 hours of a bank’s collapse. This would be extremely difficult to achieve if multiple banks failed simultaneously. In the face of a housing crisis, it is plausible that lenders would again panic, deciding that they cannot depend on untested processes to stabilize the banks and withdrawing their overnight loans. Banks, stripped of cash, would then cut lending, as they did in 2008 and 2009, plunging into a recession yet again. The grim irony here is that, prior to such a shock, preparing for restructuring encourages lenders to provide more overnight loans. This expands the overnight market, makes mortgage lending easier, and increases the costs of collapse. This is not mere speculation. Most observers of the mortgage market believe that its growth accelerated in 2005, after Congress exempted mortgage bonds from most bankruptcy procedures – a move that would eliminate waiting time for repayment. That change convinced mortgage lenders that their activities were ultra-safe: they no longer even had to worry about the quality of the borrower. When crisis struck, that confidence quickly faded, and investors fled.

(Michelle, “Adjustable-rate mortgages are making a comeback. But are these loans right for you?,” pg online @ //um-ef)

Acopy edited adjustable-rate mortgages, known as ARMs, are back, despite having earned a bad reputation at the height of the housing crisis. Post-crisis borrowers saw them as risky because of their changing interest rates and blamed the glut of foreclosures on the inability of homeowners to handle higher payments when the loans reset. “ARMs became a four-letter word after the housing crisis,” says Ann Thompson, a retail sales executive for Bank of America in San Francisco. “They got a bad rap and were lumped in with ‘pick-a-payment’ loans, which allowed people to pay as little or as much as they wanted on their mortgage.” Lately there’s been a resurgence in ARMs. In January 2019, 8.6 percent of new mortgage loans had an adjustable rate, compared with 5.5 percent in January 2018, according to Ellie Mae, a software company that processes 35 percent of mortgages in the United States.

(Justin, “Key Indicator Hints America Is Headed For Its Worst Real Estate Crash In History,” pg online @ //um-ef)

Although it’s impossible to predict economic crashes with certainty, a key economic indicator suggests the U.S. housing market is on the verge of an unprecedented crash, one that could end up being the biggest in America’s history. Following the 2008 stock and real estate market crashes, the Federal Reserve, Democratic-led Congress, and the presidential administrations of George W. Bush and Barack Obama began an unprecedented effort to pump new dollars into the financial system — and, to a lesser extent, the economy at large. The strategy behind the flood of quantitative easing, government takeovers, stimulus checks, and government welfare programs that followed was that the Fed, working in conjunction with Congress and the White House, needed to prop up the economy to keep it from sliding completely off the cliff. One of the primary tools the Fed used to accomplish its goals was to keep interest rates at near-zero for years on end. From 1980 to 2000, the Fed’s federal funds rate — the primary driver of interest rates economywide — rarely dropped below 4 percent, and it was common for interest rates to be 5 percent or higher. However, from 2009 through 2016, interest rates were consistently much lower than 1 percent. Beginning in 2017, the first year of the Donald Trump presidency, the Fed began to more aggressively raise rates, but it only briefly topped 2 percent in 2018 and 2019 before the Fed once again slashed rates to near-zero as part of its plan to address the effects of the Covid-19 lockdowns. When interest rates are kept low, it’s easier for governments to spend more money than they take in, because debt is cheap. Additionally, banks and other financial institutions are more likely to lend out money for high-priced items. The real estate market is especially sensitive to rate changes, because a home is usually the biggest purchase a person will make in his or her lifetime, and the vast majority of purchasers rely on large mortgages to complete the purchase. When interest rates are kept extremely low, people can afford to take on more debt, because the monthly payments cost less. As a result, sellers increase their prices. This is one of the reasons the real estate market crashed so hard in 2008. Following the September 11, 2001, terrorist attacks, the Fed kept interest rates low, encouraging people to take on higher-than-usual levels of debt, especially in the real estate market. Rather than learn its lesson from the 2008 crash, the Fed doubled down on this failed strategy, and then tripled down during the Covid-19 response. Congress and the White House were all too willing to cheer the Fed on, since lower interest rates have helped them expand government programs without begging foreign governments to finance U.S. debt. As a result of these policies, a shockingly large price bubble appears to have formed in the real estate market. The average sales price of a home in the fourth quarter of 2021 was $477,900, compared to $403,900 in the fourth quarter of 2020 and $384,600 in the fourth quarter of 2019. That’s a $93,300 increase in just two years, by far the biggest increase ever recorded in just 24 months. Further, the 12-month home sales price increases for the second, third, and fourth quarters of 2021 were all above 17 percent, the highest hike recorded over a three-quarter period since at least 1963, the earliest date in the Fed’s data made available online. Put simply, Americans have literally never seen housing prices skyrocket like they are now for this long of a period. And every time they have approached the numbers we are seeing today in the past — in the 1970s, late-1980s, and early to mid-2000s — there was a massive real estate or stock market crash that soon followed (or both). There appear to be no exceptions, other than a few rare cases where housing prices increased quickly immediately after a crash had occurred. Determining the size of a market correction is extremely difficult, but if the 2008 crash is an indicator of what’s in store for us today, then if the current real estate bubble pops soon, as all bubbles inevitably do, it could end up being the largest real estate crash in history. The bubble that developed from 2002 to 2007 peaked at around a 47 percent price increase, before plummeting by 20 percent from 2007 to the first quarter of 2009. If we see a similar pattern emerge for the bubble that has been developing since roughly 2012, then we could see housing prices drop by 30 to 40 percent over a two-year period. Whatever the final numbers end up being, the evidence is clear: based on data reported over the past six decades, America appears to be on the verge of an epic real estate crash. As painful as such a correction would be, it is likely necessary. The price increases we’ve been seeing in recent years are primarily the result of inflation and reckless monetary policy, not real economic growth. However, there is a chance that housing prices will not drop, or only drop minimally. If the Fed decides to continue to keep interest rates low, despite the ongoing inflation crisis, it might prevent a real estate crash the size and scale of the one discussed above. It will come at a cost, though — more inflation, even bigger market distortions, and perhaps the collapse of the dollar.

Despite its length, the current expansion has been weak, ranking ninth among the 10 post-WWII business cycles.1 Only the previous cycle, ending in the second quarter of 2009, was weaker. That cycle was dominated by the housing boom and bust and culminated in the Great Recession. As Table 1 shows, the average rate of economic growth beginning in 2002 has been far below that in all of the other business cycles listed. Table 2 shows that the composition of economic growth also has changed in recent decades, generally shifting in favor of housing and consumer spending.2 Only during the brief 1958-61 cycle did residential investment—which includes both the construction of new housing units and the renovation of existing units—contribute proportionally more to the economy’s growth than it has during the current cycle. Perhaps surprisingly, homebuilding subtracted significantly from economic growth during the previous business cycle even though the period included the housing bubble. The crash in residential investment was so severe between the fourth quarter of 2005 and the second quarter of 2009 that it erased all of housing investment’s previous growth contributions.3 Personal consumption expenditures (i.e., consumer spending) also have been very important in recent cycles. Consumer spending has contributed 74.9 percent of overall economic growth during this cycle so far, a share exceeded in only two other cycles. Not surprisingly, strong residential investment and strong consumer spending tend to coincide when households are doing well.4 The combination of weak overall GDP growth and strong contributions by both residential investment and consumer spending mark the defining characteristic of the current business cycle: Household-related spending is driving the economy like never before. Fully five-sixths, or 83 percent, of total growth since the economy began to recover in 2009 has been fueled by household spending. Hence, the continuation of the current expansion may depend largely on the strength of U.S. households.

The more likely cause of a downturn this year is a sharp cutback by consumers. I cautioned in my earlier consumer spending forecast that growth of spending is significantly greater than growth of incomes. There are always grumpy people complaining that others are spending beyond their means, but the aggregate data don’t support that hypothesis. However, over the course of 2017 the savings rate has dropped as consumers’ lower income growth did not trigger slower spending growth. And note this discussion is about growth rates, not the actual level of spending. I think it’s likely consumers will cut back a little, with a risk that they cut back a lot. The usual trigger of spending reductions is loss of jobs—not happening lately, and not going to happen unless something else is the cause. Consumers might also cut back on spending if interest rates rise sharply, though that does not seem likely. Attitudes might turn negative, if there were some precipitating factor—but what could that be? Consumers have watched the country’s politics turn increasingly acrimonious, have watched a hugely divisive president, and have seen Oklahoma lose the Rose Bowl, and yet all the recent indicators of attitudes remain elevated.

The S&P 500 delivered roaring gains approaching 27% in 2021, but the benchmark index has stumbled into 2022, suffering losses over 7% year-to-date. Tech stocks have endured an especially brutal sell-off. The stock market is a leading economic indicator that reflects on overall economic activity, how investors feel about the economy and the profitability of corporations. A bull market can be misleading, but generally, the stock market enjoys sustained periods of growth when businesses are profitable, when unemployment is low, when GDP is up, when consumers are confident and spending money and when the economy is strong.