Real Estate Research provides analysis of topical research and current issues in the fields of housing and real estate economics. Authors for the blog include the Atlanta Fed's Kristopher Gerardi, Carl Hudson, and analysts, as well as the Boston Fed's Christopher Foote and Paul Willen.
November 03, 2015
Keeping an Eye on the Housing Market
In a recent speech, Federal Reserve Bank of San Francisco President John Williams suggested that signs of imbalances were starting to emerge in the form of high asset prices, particularly in real estate. He pointed out that the house price-to-rent ratio had returned to its 2003 level and that, while it may not be at a tipping point yet, it would be important to keep an eye on the situation and act before the imbalance grows too large. President Williams is not the only one monitoring this situation. Many across the industry are keeping a watchful eye on the rapid price appreciation (see here, here, and here), including my colleagues and me at the Atlanta Fed.
While it is too soon to definitively know if a bubble is forming, the house price-to-rent ratio seems like a relevant measure to track. Why? Basically, because households have the option to rent or own their home, equilibrium in the housing market is characterized by a strong link between prices and rents. When prices deviate substantially from rents (or vice versa), the cost-benefit calculus in the rent-versus-own equation changes, inducing some households to make a transition. In effect, these transitions stabilize the ratio.
In an effort to better understand house price trends, we chart the house price-to-rent ratio at an annual frequency on top of a stacked bar chart depicting year-over-year house price growth (see chart below). Each stacked bar reflects the share of ZIP codes in each range of house price change. Shades of green indicate house price appreciation from the year-earlier level, and shades of red indicate house price decline. The benefit of considering house price trends through the lens of this stacked bar chart is, of course, that it provides a better sense for the distribution of house price change that is often masked by the headline statistic.
Looking at these two measures in concert paints an interesting picture, one that doesn't appear to be a repeat of the early 2000s. While the house price-to-rent ratio indicates that house prices on a national basis have been increasing relative to rents, the distribution of house price change looks a bit different. In 2003, roughly 20 percent of ZIP codes across the nation were experiencing house price appreciation of 15 percent or more on a year-over-year basis. In 2014 and 2015, less than 5 percent of ZIP codes experienced this degree of appreciation.
To better understand the regional variation, we repeated this exercise at a metro level using the Case-Shiller 20 MSAs (see charts below). (House price-to-rent ratios for Las Vegas and Charlotte were not calculated because the Bureau of Labor Statistics does not provide an owners' equivalent rent for primary residence series for these markets.) This more detailed approach reveals that elevated price-rent ratio readings were only present in a few, perhaps supply-constrained, metropolitan areas (see top right corner of each chart for the Saiz supply elasticity measure). Moreover, current home price appreciation across ZIP codes does not have the breadth that was present during the early 2000s.
Notes: (1) All price-to-rent ratios are indexed to 1998, except Dallas and Phoenix, which are indexed to 2002. (2) SE = Saiz's Supply Elasticities. Pertains to city boundaries, not metropolitan areas. For more information, see Albert Saiz, "The Geographic Determinants of Housing Supply," The Quarterly Journal of Economics (August 2010) 125
As John Krainer, an economist at the San Francisco Fed, pointed out in a 2004 Economic Letter, "it is tempting to identify a bubble as a long-lasting deviation in the price-rent ratio from its average value. But knowing how large and long-lasting a deviation must be to resemble a bubble is not obvious." We will continue digging and report back when we think we know something more.
Jessica Dill, economic policy analysis specialist in the Atlanta Fed's research department
August 27, 2015
The Multifamily Market: Is a Hot Market Overheating?
Moody's/RCA National commercial property price index, which is based on repeat-sales transactions, has risen 36 percent over the past two years. Such increases in commercial real estate (CRE) prices have raised concerns that the market is overheating (see here). Multifamily is one CRE property type that for a couple of years has been attracting a great deal of lender interest and thus growing concern regarding potential overheating (see here).
Looking around Midtown Atlanta, it is easy to wonder if multifamily housing construction is getting ahead of itself. According to the Midtown Alliance, within just a 0.5-square-mile portion of Midtown Atlanta, 981 units have been recently delivered, 3,392 units are under construction and 4,732 are in various stages of planning. Dodge Pipeline reports that the entire Midtown/Five Points submarket has 4,865 units under way. For reference, peak activity in the Midtown/Five Points area from 2003 to 2007 was 4,636 units under construction with a total of 10,831 units completed. The question arises as to what extent are happenings in Midtown indicative of the broader market trend.
Yield spreads—the capitalization rate on recent apartment transactions (current rental income divided by sales price) minus the yield on Treasury bonds—serve as one indicator of optimism in a market. A narrow spread is consistent with reduced pricing for risk, which is associated with “frothiness.” According to Real Capital Analytics, apartment yield spreads in the second quarter of 2015 stood at 366 basis points (bps), which is around 250 bps higher than prerecession lows and in line with 2003–04 levels (see chart 1). So by this measure, apartment activity does not appear too frothy on a nationwide market basis.
Of course, yield spreads vary significantly by market area and by property type. Breaking the U.S. market into six major markets (Boston; New York; Washington, D.C.; Chicago; San Francisco; and Los Angeles) and all others reveals that the major markets have seen yield spreads fall relative to all other markets. (The major markets account for 36 percent of transaction dollars with New York and San Francisco alone accounting for 20 percent of the U.S. total.) Though shrinking during the last several quarters, the current 150 bps gap between the major and non-major markets is wider than at any time since 2002. One possible explanation is that the anticipated rent growth of the projects sold in the major markets is higher than in nonmajor markets.
So what to make of this? While multifamily markets have been active during the postrecession period, this activity is not necessarily unjustified. Given that the population of 20- to-34-year-olds will continue to grow, demographics point to greater demand for rental property (see chart 2). Supply has not yet shown signs of deteriorating fundamentals since vacancy rates have remained low as new product has been delivered, and rent growth has held steady (see chart 3).
How long will preferences for renting persist? How long can real rents continue to grow? How is this new activity being financed? If new projects are penciled out using unrealistic rent growth assumptions and demand falls, rent growth expectations won't be met and the projects may look overdone in retrospect. Regardless of whether current activity indicates overheating, it seems important to keep a close eye on demand.
By Carl Hudson, director for the Center for Real Estate Analytics in the Atlanta Fed's research department
May 20, 2015
Are Millennials Responsible for the Decline in First-Time Home Purchases?
First-time homebuyers play a critical role in the housing market because they allow existing homebuyers to sell their homes and trade up, triggering a cascade of home sales. While their share of all purchases has remained fairly flat over time (see our previous post on this topic), the number of first-time homebuyers has declined precipitously since the real estate crash. Many think of first-time homebuyers as younger households, and believe millennials are largely behind the decline in first-time homebuying. There are a variety of theories about why millennials have been slow to enter homeownership. One theory says that millennials would rather rent in dense urban areas where land is scarce than buy homes in the suburbs. Another theory blames steep increases in student debt for crowding out mortgage debt and reducing the homeownership opportunities of younger generations. Yet another theory argues that because the recession lowered incomes, younger people can't afford to buy. Finally, underwriting standards tightened after the recession, causing mortgage lenders to require larger down payments and higher credit scores in order to buy a home. Some worry that this more stringent lending environment has raised the bar too high for millennial homebuyers in particular.
We can't examine all these theories in a blog post, but we can examine the validity of the assumption that millennials are driving the decline in first-time homebuyers. We approached this from two angles. We first looked at whether the age distribution of first-time homebuyers has changed, and then we tried to discern patterns in first-time home buying across states. In general, we find that the age distribution of first-time homebuyers has become younger, not older, since the crisis. We also found that the dramatic fall in purchases varies much more strongly across states than by age. The preliminary figures suggest that housing market and local economic conditions may explain as much or more of the decline in first-time homebuyers than a generational divide.
Searching the data for first mortgages
Our analysis is based on the Federal Reserve Bank of New York Consumer Credit Panel/Equifax data. This data set provides longitudinal, individual data, using a 5 percent sample of all persons with a credit record and social security number in the United States.i We examined the age, location, and credit scores of people who bought homes for the first time and looked at how these characteristics changed after the crisis.ii
To identify first-time homebuyers, we flagged the first year of the oldest mortgage for each individual in the credit panel. This reveals the first instance of someone obtaining a mortgage, even if they subsequently buy another home or even transition back to renting. The trade-off is we were able to observe only those who use debt finance, and thereby excluded all cash purchases. While many homeowners do own their homes outright, we expect most first-time buyers and certainly most young buyers to have a mortgage.iii
Having isolated first-time homebuyers in this data set, we looked at their purchasing trends and demographic attributes from 2000 to 2014. In this data set, we found that roughly 1 percent to 2 percent of the population purchased a mortgage-financed home for the first time in a given year. Forty-nine percent to 53 percent had no mortgage (this category combines renters and those who own their homes outright), and 45 percent to 50 percent were paying down an existing mortgage.
Buyers aren't getting older
We found that the number of first-time home buyers fell precipitously after the crash, from 3.3 million a year to around 1.5 million to 1.8 million. However, the age distribution of these first-time homebuyers does not change dramatically, though the median age of actually went down slightly since the peak. If we were to believe that the decline in first-time buyers was driven primarily by younger workers requiring more time to amass a down payment or pay off student debts, then we would expect to see first-time buyers getting older.
We did not see a strong explanation for dramatic declines in first-time homebuyers when we compared younger and older adults. It doesn't appear that millennials are driving the decline. By comparison, when we reviewed the number of first-time home purchases by state, we found very dramatic differences that population alone cannot explain. Unsurprisingly, first-time homebuying fell further in places where the housing crisis hit the hardest.
The chart shows the number and percent change in first-time homebuyers from 2001 to 2011 by state. There is a wide variety in the percent change in first-time homebuyers, with declines as strong as 65 percent in some states and as low as 10 percent in others. North Dakota was the only state to have increases in first-time homebuyers, likely due to the oil industry growth there.
This analysis does have some weaknesses. For one, as we mentioned, it omits cash buyers, who are an increasingly important segment of the housing market, especially in hard-hit states like Georgia and Florida. Also, other research has shown that the transition from renter to owner and back can happen many times in a person's lifetime, and this data set does not control for homeownership "spells" older than one year (see Boehn and Schlottman 2008). Notwithstanding, this analysis suggests that the decline in first-time homebuying is driven not by swiftly changing preferences nor the economic constraints of the younger generation but by regional and local economic conditions. Stay tuned for more, as we plan to look further into how the real estate crisis altered the home purchase decisions of young first-time homebuyers relative to older generations.
By Elora Raymond, graduate research assistant, Center for Real Estate Analytics in the Atlanta Fed's research department, and doctoral student, School of City and Regional Planning at the Georgia Institute of Technology, and
Jessica Dill, economic policy analysis specialist in the Atlanta Fed's research department
i The data is a 2.5 percent sample of all individuals with a credit history in the United States. So, for example, this sample resulted in 636,638 records in 2014, which would correspond to an estimated 254,655,200 individuals with credit records and social security numbers in 2014.
ii We excluded anyone who had an older mortgage in a prior year. Doing so resulted in only a very small percentage of records being excluded.
iii Our approach and results are similar to those cited in Agarwal, Hu, and Huang (2014), who find that the homeownership rate between 1999 and 2012 varies between 44 percent and 47 percent for individuals aged 25—60 using a different time frame and age distribution of the same data set. Because our definition—and that of Agarwal, Hu, and Huang—is unique, it differs from the widely cited homeownership rate published by the U.S. Census Bureau. The rate published by the Census Bureau ranges between 65 and 68 percent for individuals over 25 years old and is calculated by dividing the number of owner-occupied households by the total number of occupied households. Homeownership rates have also been derived using other data. Gicheva and Thompson (2014) derive a homeownership rate using the Survey of Consumer Finance and find the mean homeownership rate to be 61 percent between 1995 and 2010. Gerardi, Rosen, and Willen (2007) used the Panel Survey of Income Dynamics (PSID), which tracks households over time and captures changes in tenure status, to identify home purchasers. They reported a range of 5.6 percent (in 1983) to 9.6 percent (in 1978) of households buying homes in the 1969—99 timeframe.
April 20, 2015
Income Growth, Credit Growth, and Lending Standards: Revisiting the Evidence
Almost a decade has passed since the peak of the housing boom, and a handful of economics papers have emerged as fundamental influences on the way that economists think about the boom—and the ensuing bust. One example is a paper by Atif Mian and Amir Sufi that appeared in the Quarterly Journal of Economics in 2009 (MS2009 hereafter). A key part of this paper is an analysis of income growth and mortgage-credit growth in individual U.S. ZIP codes. The authors find that from 2002 to 2005, ZIP codes with relatively low growth in incomes experienced high growth in mortgage credit; that is, income growth and credit growth were negatively correlated during this period.
Economists often cite this negative correlation as evidence of improper lending practices during the housing boom. The thinking is that prudent lenders would have generated a positive correlation between area-level growth in income and mortgage credit, because borrowers in ZIP codes with high income growth would be in the best position to repay their loans. A negative correlation suggests that lenders instead channeled credit to borrowers who couldn't repay.
Some of the MS2009 results are now being reexamined in a new paper by Manuel Adelino, Antoinette Schoar, and Felipe Severino (A2S hereafter). The A2S paper argues that the statistical evidence in MS2009 is not robust and that using borrower-level data, rather than data aggregated up to the ZIP-code level, is the best way to investigate lending patterns. The A2S paper has already received a lot of attention, which has centered primarily on the quality of the alternative individual-level data that A2S sometimes employ.1 To understand the relevant issues in this debate, it's helpful to go back to MS2009's original statistical work that uses data aggregated to the ZIP-code level to get a sense of what it does and doesn't show.
Chart 1 summarizes the central MS2009 result. We generated this chart from information we found in either MS2009 or its supplementary online appendix. The dark blue bars depict the coefficients from separate regressions of ZIP-code level growth in new purchase mortgages on growth in ZIP-code level incomes.2 (These regressions also include county fixed effects, which we discuss further below.) Each regression corresponds to a different sample period. The first regression projects ZIP-level changes in credit between 1991 and 1998 on ZIP-level changes in income between these two years. The second uses growth between 1998 and 2001, and so on.3 During the three earliest periods, ZIP-level income growth enters positively in the regressions, but in 2002–04 and 2004–05, the coefficients become negative. A key claim of MS2009 is that this flip signals an important and unwelcome change in the behavior of lenders. Moreover, the abstract points out that the negative coefficients are anomalous: "2002 to 2005 is the only period in the past eighteen years in which income and mortgage credit growth are negatively correlated."
There are, however, at least three reasons to doubt that the MS2009 coefficients tell us anything about lending standards. First of all, the coefficients for the 2005–06 and 2006–07 regressions are positive—for the latter period, strongly so. By MS2009's logic, these positive coefficients indicate that lending standards improved after 2005, but in fact loans made in 2006 and 2007 were among the worst-performing loans in modern U.S. history. Chart 2 depicts the share of active loans that are 90-plus days delinquent or in foreclosure as a share of currently active loans, using data from Black Knight Financial Services. To be sure, loans made in 2005 did not perform well during the housing crisis, but the performance of loans made in 2006 and 2007 was even worse.4 This poor performance is not consistent with the improvement in lending standards implied by MS2009's methodology.
A second reason that sign changes among the MS2009 coefficients may not be informative is that these coefficients are not really comparable. The 1991–98 regression is based on growth in income and credit across seven years, while later regressions are based on growth over shorter intervals. This difference in time horizon matters, because area-level income and credit no doubt fluctuate from year to year while they also trend over longer periods. A "high-frequency" correlation calculated from year-to-year growth rates may therefore turn out to be very different from a "low-frequency" correlation calculated by comparing growth rates across more-distant years. One thing we can't do is think of a low-frequency correlation as an "average" of high-frequency correlations. Note that MS2009 also run a regression with growth rates calculated over the entire 2002–05 period, obtaining a coefficient of -0.662. This estimate, not pictured in our graph, is much larger in absolute value than either of the coefficients generated in the subperiods 2002–04 and 2004–05, which are pictured.
A third and perhaps more fundamental problem with the MS2009 exercise is that the authors do not report correlations between income growth and credit growth but rather regression coefficients.5 And while a correlation coefficient of 0.5 indicates that income growth and credit growth move closely together, a regression coefficient of the same magnitude could be generated with much less comovement. MS2009 supply the data needed to convert their regression coefficients into correlation coefficients, and we depict those correlations as green bars in chart 1.6 Most of the correlations are near 0.1 in absolute value or smaller. To calculate how much comovement these correlations imply, recall that the R-squared of a regression of one variable on another is equal to the square of their correlation coefficient. A correlation coefficient of 0.1 therefore indicates that a regression of credit growth deviated from county-level means on similarly transformed income growth would have an R-squared in the neighborhood of 1 percent. The reported R-squareds from the MS2009 regressions are much larger, but that is because the authors ran their regressions without demeaning the data first, letting the county fixed effects do the demeaning automatically. While this is standard practice, this specification forces the reported R-squared to encompass the explanatory power of the fixed effects. The correlation coefficients that we have calculated indicate that the explanatory power of within-county income growth for within-county credit growth is extremely low.7 Consequently, changes in the sign of this correlation are not very informative.
How do these arguments relate to A2S's paper? Part of that paper provides further evidence that the negative coefficients in the MS2009 regressions do not tell us much about lending standards. For example, A2S extend a point acknowledged in MS2009: expanding the sample of ZIP codes used for the regressions weakens the evidence of a negative correlation. The baseline income-credit regressions in MS2009 use less than 10 percent of the ZIP codes in the United States (approximately 3,000 out of more than 40,000 total U.S. ZIP codes). Omitted from the main sample are ZIP codes that do not have price-index data or that lack credit-bureau data.8 MS2009 acknowledge that if one relaxes the restriction related to house-price data, the negative correlations weaken. Our chart 1 conveys this information with the correlation coefficients depicted in red, which are even closer to zero. A2S go farther to show that if the data set also includes ZIP codes that lack credit-bureau data, the negative correlation and regression coefficients become positive.
But perhaps a deeper contribution of A2S is to remind the researchers that outstanding questions about the housing boom should be attacked with individual-level data. No one doubts that credit expanded during the boom, especially to subprime borrowers. But how much of the aggregate increase in credit went to subprime borrowers, and how did factors like income, credit scores, and expected house-price appreciation affect both borrowing and lending decisions? Even under the best of circumstances, it is hard to study these questions with aggregate data, as MS2009 did. People who take out new-purchase mortgages typically move across ZIP-code boundaries. Their incomes and credit scores may be different than those of the people who lived in their new neighborhoods one, two, or seven years before. A2S therefore argue for the use of HMDA individual-level income data so that credit allocation can be studied at the individual level. This use has been criticized by Mian and Sufi, who believe that fraud undermines the quality of the individual-level income data that appear in HMDA records. We should take these criticisms seriously. But the debate over whether lending standards are best studied with aggregate or individual-level data should take place with the understanding that aggregate data on incomes and credit may not be as informative as previously believed.
2 Data on new-purchase mortgage originations come from records generated by the Home Mortgage Disclosure Act (HMDA). Average income at the ZIP-code level is tabulated in the selected years by the Internal Revenue Service.
3 Growth rates used in the regressions are annualized. The uneven lengths of the sample periods are necessitated by the sporadic availability of the IRS income data, especially early on. The 1991 data are no longer available because IRS officials have concerns about their quality.
4 Chart 2 includes data for both prime and subprime loans. The representativeness of the Black Knight/LPS data improves markedly in 2005, so LPS loans originated before that year may not be representative of the universe of mortgages made at the same time. For other evidence specific to the performance of subprime loans made in 2006 and 2007, see Figure 2 of Christopher Mayer, Karen Pence, and Shane M. Sherlund, "The Rise in Mortgage Defaults," Journal of Economic Perspectives (2009), and Figure 1 of Yuliya Demyanyk and Otto Van Hemert, "Understanding the Subprime Mortgage Crisis," Review of Financial Studies (2009). For data on the performance of GSE loans made in 2006 and 2007, see Figure 8 of W. Scott Frame, Kristopher Gerardi, and Paul S. Willen, "The Failure of Supervisory Stress Testing: Fannie Mae, Freddie Mac, and OFHEO," Atlanta Fed Working Paper (2015).
5 MS2009 often refer to their regression coefficients as "correlations" in the text as well as in the relevant tables and figures, but these statistics are indeed regression coefficients. Note that in the fourth table of the supplemental online appendix, one of the "correlations" exceeds 1, which is impossible for an actual correlation coefficient.
6 Because a regression coefficient from a univariate regression is Cov(X,Y)/Var(X), multiplying this coefficient times StdDev(X)/StdDev(Y) gives Cov(X,Y)/StdDev(X)*StdDev(Y), which is the correlation coefficient. Here, the Y variable is ZIP-code–level credit growth, demeaned from county-level averages, while X is similarly demeaned income growth. As measures of the standard deviations, we use the within-county standard deviations displayed in Table I of MS2009. Specifically, we use the within-county standard deviation of "mortgage origination for home purchase annual growth" calculated over the 1996–02 and 2002–05 periods (0.067 and 0.15, respectively) and the within-county standard deviation of "income annualized growth" over the 1991–98, 1998–2002, 2002–05, and 2005–06 periods (0.022, 0.017, 0.031, and 0.04, respectively). Unfortunately, the time periods over which the standard deviations were calculated do not line up exactly with the time periods over which the regression coefficients were calculated, so our conversion to correlation coefficients is an approximation.
7 It is true that the regression coefficients in the MS2009 coefficients often have large t-statistics, so one may argue that ZIP-level income growth has sometimes been a statistically significant determinant of ZIP-level credit growth. But the low correlation coefficients indicate that income growth has never been economically significant determinant of credit allocation within counties. It is therefore hard to know what is driving the income-credit correlation featured in MS2009, or what may be causing its sign to fluctuate.
8 Though house prices and credit bureau data are not required to calculate a correlation between income growth and mortgage-credit growth, the authors use house prices and credit bureau data in other parts of their paper.
January 22, 2014
Wall Street and the Housing Bubble
The conventional wisdom on the 2008 financial crisis is that finance industry insiders on Wall Street deceived naïve, uninformed mortgage borrowers into taking out unaffordable mortgages and mortgage-backed security (MBS) investors into purchasing securities backed by bad loans—mortgages and securities that had not been properly vetted and that would eventually default. This theory is on display front and center in the Academy Award-winning documentary Inside Job, and it has motivated new regulations aimed at realigning incentives among Wall Street insiders and their customers. (One such rule is the risk retention requirement in the Dodd-Frank Act, which we will discuss in some detail in a future post.)
We've written in support of an alternative hypothesis for the financial crisis—specifically, that overly optimistic views about house prices, not poorly designed incentives on Wall Street, are the better explanation for the crisis (for an example, see this 2012 paper). This alternative theory holds that investors lost money not because they were deceived by financial market insiders, but because they were instead misled by their own belief that housing-related investments could not lose money because house prices were sure to keep rising.
A new paper makes an important empirical contribution to this debate by inferring the beliefs of Wall Street insiders during the height of the bubble. The paper, titled "Wall Street and the Housing Bubble," performs a clever analysis of personal housing-related transactions (like home purchases) made by individuals who worked in the mortgage securitization business during the peak of the housing boom. The behavior of these mortgage insiders is compared with that of a control group of people who worked for similar institutions in the finance industry but did not have any obvious connection to the mortgage market. What the analysis finds should be an eye-opener for believers in the inside-job explanation of the crisis. There is no evidence that mortgage insiders believed there was a housing bubble in the 2004–06 period. In fact, mortgage insiders were actually more aggressive in increasing their personal exposure to housing at the peak of the boom. The increase in insider exposure contradicts the claim that insiders sold securities backed by loans that they knew would eventually go bad when the housing bubble burst.
The authors construct a random sample from the list of attendees of the 2006 American Securitization Forum, which is a large industry conference featuring employees of most of the major U.S. investment and commercial banks (as well as hedge funds and other boutique firms). The sample is mainly comprised of vice presidents, managing directors, and other nonexecutives in mid-managerial positions whose jobs focused on the structuring and trading of MBS. The authors refer to this group as "securitization agents." As a comparison group, they use a random sample of Wall Street equity analysts who covered firms that were in the S&P 500 in 2006 but did not have a strong connection to the housing market (in other words, the sample includes no homebuilders). These equity analysts worked for similar financial institutions, had similar skill sets, and likely experienced similar income shocks (in the form of bonuses during the boom) but did not have any experience in the securitization business and thus did not have access to any insider information. (As a second control group, the authors use a random sample of lawyers who did not specialize in real estate law.) The names of the securitization agents and the equity analysts are then matched to a database of publicly available information on property transactions. The final data set contains information on the number of housing transactions, the sale price of each transaction, some mortgage characteristics, and income at the time of origination for each individual in the sample spanning the period 2000–10.
Armed with this unique data set, the authors then implement a number of empirical tests to determine whether the securitization agents' beliefs about the likelihood of a housing crash differed from the beliefs of the control groups. The first test considers whether the securitization agents timed the housing market cycle better than the comparison groups by reducing their exposure to the market at the peak of the bubble (2004–06) by either selling their homes outright or downsizing. The second test is slightly weaker in that it simply tests whether the securitization agents were more cautious in their housing transactions by avoiding home purchases at the peak of the bubble to a greater extent than the control groups. The third test looks at whether the average return on housing transactions during the entire sample period was higher for the securitization agents. The final test considers a prediction of the permanent income hypothesis: if securitization agents were armed with superior knowledge of the impending collapse of the housing bubble, then through reductions in their expectations of permanent income, they should have decreased the size of their housing purchases relative to their current incomes by a greater amount than the comparison groups.
The results of these empirical tests show very little evidence to support the inside-job theory of the financial crisis. The authors conclude that there is "little systematic evidence that the average securitization exhibited awareness through their home transactions of problems in overall house markets and anticipated a broad-based crash earlier than others." If anything, the authors are being a little timid in their interpretation as the empirical results clearly show that securitization agents were significantly more aggressive in their housing transactions during the bubble period, which suggests that they held even more optimistic expectations of housing prices dynamics than did the control groups.
This is an important paper because it sheds light on one of the most striking aspects of the financial crisis, which the inside-job theory is unable to reconcile: the financial institutions involved in the creation of the subprime MBS and collateralized debt obligations (CDO)—the true "insiders," if you will—lost enormous amounts of money on those securities. The table clearly supports this observation. The firms that lost the most money from mortgage-related credit losses were the same investment and commercial banks that are being accused of profiting off of naïve investors by selling securities comprised of loans that they knew would eventually go bad. The table shows that these firms lost enormous sums of money, and the paper provides a simple answer to explain why: like the rest of the market, agents working at those firms believed that housing prices would continue to rise so that even the riskiest mortgages would continue to perform well.
Kris Gerardi, financial economist and associate policy adviser at the Federal Reserve Bank of Atlanta, with
Chris Foote, senior economist and policy adviser at the Federal Reserve Bank of Boston.
December 16, 2013
Many Banks, Many Builders: Concentration in the Construction Industry
The home construction industry has always been fragmented, with small construction firms holding a larger market share than big firms. This fragmentation was especially noticeable during the housing boom. In 2005, for example, the top 10 construction firms in the country had just 25 percent of the market share, and the top 200 firms had less than 50 percent. One explanation for the fragmentation is that construction just doesn't have many economies of scale.
Not surprisingly, these numbers started to shift during the recession, especially in areas hit harder by the housing bust—when bank lending to small builders all but disappeared. According to Bloomberg, the market share of the top 100 firms in the West and South grew by 10 percent during the crisis. In the Midwest, the market share of closings of the top 10 builders grew from 20 percent to 30 percent after 2007. Note that small banks—and therefore small bank failures—had also been concentrated in these three regions (see chart 1).
In places where there are many small banks that can supply small private construction firms, do large builders have as much of an edge, or does low concentration in the banking industry lead to low concentration in the construction industry? This question is difficult to answer definitively, but it's clear there is a relationship. The scatter plot (chart 2) shows that in 2006, in places with lots of banks (upper left)—places like Atlanta, Los Angeles, and Kansas City—large builders tended to have a small market share. In cities like Tucson, Las Vegas, and Albuquerque (bottom right), where there weren't many banks, the large builders dominated. It would seem that in places where construction and development (C&D) loans are tight, we can expect to see many more of the larger builders.
And in times when C&D loans are tight—say, during financial crises—we can also expect larger firms to dominate and smaller firms to fall by the wayside. A 2008 Real Estate Economics paper by Brent Ambrose and Joe Peek showed how private builders fell into decline in the 1990s because of differential access to capital during the banking crisis. (The raft of small banks failures here in Georgia is an illustration of how this interconnection runs both ways: after 2007, 55 Atlanta banks failed when the real estate market turned and small construction firms defaulted on their loans en masse.)
Since the banking crisis of 2007, bank construction lending has plummeted. In past posts (here and here), we've documented just how tight credit has been: total outstanding C&D loans plummeted from $454.6 billion in 2006 to $188.4 billion in 2013. Because of the tight credit, the large public builders have been much more resilient than large private builders. As Builder magazine reported, "private builders' access to capital continued to be all but blocked in 2012, [but] public builders tapped into cheap bond money and sold stock, creating sizable war chests many have begun to deploy to buy land and lots for what they hope will be a continuing industry recovery."
Many large and medium-sized private builders have been attracted to this source of funding, so much so that between January and August of 2013, WCI Communities, TRI Pointe Homes Inc., Taylor Morrison Home Corp., UCP Inc., William Lyon Homes, and LGI Homes all had initial public offerings. Public builders have always dominated the charts, but in 2013, the top 12 firms—with a combined $97 million in revenue and 316,802 closings—were all public. (See chart 3.)
And while the large private builders are going public, the small private firms are going out of business entirely. Bloomberg reported this past spring that membership in the National Association of Home Builders, an association of small private firms, has plunged 44 percent since 2007. By contrast, large and medium-sized firms have had strong increases in market share, as noted earlier.
How does the growing concentration in the construction industry affect the rest of us? Is this the kind of phenomenon that could have a wider impact, by altering home price dynamics, increasing sprawl or decreasing affordability?
We hope to take on some of these questions in a future post.
By Carl Hudson, Director, Center for Real Estate Analytics in the Atlanta Fed's research department, and
By Elora Raymond, graduate research assistant, Center for Real Estate Analytics in the Atlanta Fed's research department/PhD student, School of City and Regional Planning, Georgia Institute of Technology
July 10, 2013
Why Housing Rebound May Continue at a Slower Rate Than Hoped For
Perhaps it's because I've worked with bank examiners for many years, but I often question financial news that seems too optimistic. On July 1, 2013, the U.S. Census Bureau reported that overall construction spending increased in May. Private residential construction, which generally leads the economy, grew 24.4 percent from May 2012 to May 2013. Beyond being cautious with one data point, I think that there are several reasons why housing's rebound may be slower than hoped.
To be clear, residential real estate conditions have been improving, albeit from record low levels of activity. Sales of both new and existing houses have been trending up recently, but remain near historically low levels. Additionally, the quantity of new and existing homes readily available for sale is low. Homebuilders in the Sixth Federal Reserve District (which includes Alabama, Florida, and Georgia and parts of Louisiana, Mississippi, and Tennessee) recently reported that new home sales and construction have been ahead of year-earlier levels and that buyer traffic remains strong (see this SouthPoint post). Builders noted, however, that access to financing and a shortage of developed lots continued to constrain construction activity. In conjunction with the recent construction spending data release, it is this last point that I aim to dig into a bit deeper in this blog post.
Since the housing bust, construction and development (C&D) lending has been in sharp decline in terms of aggregate dollars and as a percent of total bank assets. Using year-end data, we find that C&D loans peaked in 2007 at $629.4 billion. As of 2012, they stood at $203.8 billion. As of March 2013, C&D loans accounted for 1.4 percent of bank assets, unchanged from December 2012 and the lowest level since at least 1991. The decline in C&D lending is broad based given that similar trends are seen for banks under and over $1 billion in total assets. With the recent reports of growing construction spending, will bank lending practices dampen construction growth going forward?
Banks represent a significant funding source for homebuilders, especially nonpublic homebuilders. Using data from 1991 to 2012, there appears to be a strong, positive relationship between bank construction lending and private residential construction put in place—see the chart. Of course, it's impossible to tell from this chart whether construction activity is responding to changes in credit supply or credit supply is responding to changes in construction demand. However, banks have been extremely tight with credit in the aftermath of the financial crisis, and there aren't many signs that banks plan to change course any time soon. So it may be reasonable to assume that a continued reduction in bank C&D lending is likely to limit future gains in construction activity.
A case for optimism
In conversations with banks of various sizes, two things are often repeated. First, bankers indicate there is little appetite for growth in C&D lending and second, banks of various sizes want to increase commercial and industrial lending (C&I). For many banks, a move from C&D lending to C&I lending is easier said than done—the skillsets needed for C&I lending differ from those associated with C&D. Acquiring C&I expertise is a challenge particularly for smaller banks. So what's a community bank going to do?
An old adage is to do what you know best. For many community banks that would be C&D lending. Given the reports of lot shortages and house inventory being low, it would seem that profitable opportunities for C&D lending exist. There is nothing wrong with C&D loans appropriately underwritten and subject to reasonable risk management. A key question is when banks start moving back to C&D lending, will they be able to resist the shortcuts of the last cycle? Let's hope that banks can successfully navigate a return to C&D lending so that the housing market can continue to recover.
By Carl Hudson, Director, Center for Real Estate Analytics in the Atlanta Fed's research department
June 12, 2013
Is Investing in Housing Really a Losing Proposition?
In a recent article in the New York Times (here), Robert Shiller notes that a home may not be such a great investment after all. After adjustments are made for inflation, Shiller says that real home prices are more or less flat over the long term and that investors can make better returns by investing elsewhere. Bill McBride and Tom Lawler from Calculated Risk have chimed in on this debate several times over the past few years (here, here and here) by pointing out that there are several methodological issues with the way Shiller calculated home prices before 1986, and that using an alternative series results in a clear upward slope.
While we acknowledge that the gains over time are sensitive to the index you choose to use, we think it's also important to note that returns on investments in housing have not consistently increased regardless of which index you use. Even if you exclude the most recent bubble, there have been notable ups and downs, although none as severe. Shiller and Lawler's work conclude that long-run returns have averaged somewhere between 0.2 percent and 1.2 percent, depending on which series you use, but neither touches on the distribution of returns. This got us wondering—with average returns so close to zero, just how often has the housing market produced losers? And how does investing in housing compare to investing in equities, as Shiller seems to prefer?
As a first step toward answering these questions, we computed the average annual return of home prices across all possible combinations of start and stop points using the Shiller house price series from 1926 to 2012. The distribution depicts returns concentrated around zero with some skewness to the right. Eighty percent of all start-stop point observations experience some degree of positive return (see chart 1).
We acknowledge that this exercise alone is imperfect because it fails to take into account the duration of ownership. Based on analysis published (here) earlier this year by Paul Emrath at the National Association of Home Builders, we assume that the average homeowner lives in his or her home for 13.3 years. We applied this duration to our analysis and found that the volatility in the data series is significant enough to change the distribution of returns. The average annual returns for an asset held for a period of 13 or more years is substantially less volatile than for an asset held for fewer than 13 years, and those investing for the longer term were much more likely to have positive returns. Perhaps more important than the shape of each curve is that both are concentrated at or just above zero. We compute that 40 percent of homes owned for less than 13 years have negative average annual returns, compared to 12 percent of homes owned for 13 years or more (see chart 2). Interestingly, while a much greater portion of those owning for 13 or more years obtain positive returns, the average annual return was actually slightly higher for those owning fewer than 13 years (0.95 percent versus 1.03 percent).
Since it is pretty clear that the volatility in returns varies by length of ownership, we apply weights for average length of ownership using Emrath's Survival Table. Using the weights, we recomputed average annual returns across all possible combinations of start and stop points for average length of ownership. The distribution continues to show that returns are concentrated around zero with skewness to the right; two-thirds of all investors in this distribution experience some degree of positive return (see chart3).
After getting a better feel for average annual returns on homes purchased using Shiller's real home price index, we thought it would be interesting to run through this same exercise with the S&P 500 Index (which we used as a proxy for the stock market) to provide an apples-to-apples comparison of the average annual returns that one could expect from an alternative investment in stocks. The results depict a wider distribution, with longer, fatter tails and some skewness to the right. In other words, there is more volatility in terms of return, but with that volatility comes an opportunity for larger gains over time (see chart 4). In fact, the weighted average annual return of the S&P 500 is 4.55 percent, compared to 0.97 percent for the Shiller real home price index.
As a final exercise, we added a time dimension and charted the average annual return on assets for housing and the S&P 500 Index assuming that each asset is held for 13 years from its purchase (see chart 5).
It's important to note that the distributions of returns for housing in all these computations are not the distribution of returns for every possible house purchase. Likewise, the returns shown for the S&P 500 are not the entire universe of returns from buying and selling individual stocks. Instead, these returns are based on a pool of housing and a pool of stocks. Therefore, the chart speaks not to the distributions of returns to individual assets, but the group as a whole. Further, the returns to housing in the chart ignore the fact that homeowners might have additional gains from owning if their mortgage replaces rent. Indeed, according to some calculations, homeowners who buy a home today and hold it for seven years can expect to pay 44 percent less than people who choose to rent.
Depicting average annual returns in this format helps to demonstrate two points. First, Shiller's point that "real home prices rose only 0.2 percent a year, on average" was not far off the mark, as returns on investments in housing using our approach do appear to hover around zero for most of the time series. Second, Shiller's comment that "it's hard for homes to compete with the stock market in real appreciation" seems to be fair. If a home is purchased only as an investment and not as a place to live, this comparison of average annual returns clearly shows that investing in equities offers favorable returns more often than investing in housing.
By Ellyn Terry, an economic policy specialist, and
Jessica Dill, senior economic research analyst, both in the Atlanta Fed's research department
June 20, 2011
The housing wealth effect revisited
A lot of economic researchers have struggled to answer what seems like a simple question: how much does consumption rise when housing prices go up? Many economists believe that, by increasing the wealth of homeowners, rising house prices during the housing boom had a substantial positive effect on consumption (see this speech by former Fed Chairman Alan Greenspan). Along the same lines, the dramatic decline in home values during the Great Recession is thought by many to be a drag on consumption today.
Conventional wisdom among market participants, and to some extent academics, is that the marginal propensity to consume (MPC) out of housing wealth is somewhere between 3 and 5 cents per dollar for households in the United States (see this paper by Bostic, Gabriel, and Painter for a nice survey of the literature on housing wealth effects). In other words, on average, every $1 increase in housing wealth results in a 3–5 cent increase in consumption. However, many researchers are highly skeptical of these numbers for a number of reasons (see, for example, this 2009 critique). First, a basic life-cycle model that incorporates Friedman's permanent income hypothesis (PIH) would predict that modest changes in housing wealth (both anticipated and unanticipated) should result in changes in consumption that are smaller than the 3–5 cent estimate.
Second, most research that finds big MPCs out of housing wealth estimate these with aggregate consumption and wealth data (see, for example, this 2005 study by Case, Quigley, and Shiller). Aggregate regressions like these are highly susceptible to an omitted variables critique; it may be that both consumption and housing prices are driven by some other variable not accounted for in the regressions. One possibility is that as expected future income in a particular area goes up, the anticipated increase would cause residents in this area to consume more—because their future incomes are higher—at the same time house prices are rising—because more people would want to live in that particular area and enjoy these higher incomes. Ideally, one would use micro-level consumption and housing data to control for these confounding variables. Unfortunately, high-quality panel data on both housing values and household consumption have been hard to find.
An innovative new paper by Jie Gan of the Hong Kong University of Science and Technology recently published in the Review of Financial Studies may have partly solved these data issues. The paper uses property-transaction and credit-card data from Hong Kong to estimate the causal effect of changes in housing wealth on individual-level consumption behavior. For her mortgage and housing data, Gan uses a data set similar in quality and scope to those now being used in the United States (including some papers that we have written). Specifically, Gan obtained detailed mortgage data and some demographic information from a large Hong Kong bank for the period 1988–2004. She also obtained government data on the universe of all housing and mortgage transactions in Hong Kong from the early 1990s to the mid-2000s, which allowed her to construct district-level house price indices. For her consumption data, Gan used monthly credit card statements from the largest credit card issuers in Hong Kong, available for the early 2000s. While credit card spending is certainly not a perfect measure of consumption, Gan argues that credit
cards account for more than 20 percent of consumer spending in Hong Kong and are used to purchase a diverse, representative set of products.
Merging these three data sets yields a panel of about 12,000 homeowners along with monthly information on credit expenditures and home values during the 2000–2 period. Gan then estimates a regression of quarterly consumption growth, measured with the individual-level credit-card data, on quarterly growth in lagged housing values, measured at the district level, and a set of individual fixed effects that control for time-invariant individual characteristics. The regression also includes a set of interaction variables between quarterly time dummies and occupational categories. These variables control for changes in income growth that may be correlated with both house price movements and consumption growth.
Gan finds that the elasticity of consumption growth to house price growth is about 0.17, which implies that a 10 percent increase in house price growth leads to a 1.7 percent increase in consumption growth. While this is a large effect, it translates into a slightly lower MPC out of housing wealth than the 3–5 cent effect common in previous studies. The reason is that the estimated sensitivity of consumption growth to house price growth that Gan estimates must be multiplied by the consumption-to-housing wealth ratio in Hong Kong in order to construct an estimated MPC out of housing wealth. The consumption-to-housing-wealth ratio is relatively small in Hong Kong (11.5 percent) because housing is extremely expensive. Consequently, the MPC out of housing wealth in Hong Kong is about 2 cents per dollar. While 2 cents seems low, especially given previous empirical evidence, Gan argues that this seemingly low MPC should not be construed as evidence that changes in housing wealth have only a small effect on economic activity. To the contrary, because housing is so expensive in Hong Kong, a small MPC implies substantial impacts of changes in house prices on the real economy.1
Given these large and statistically significant results, an obvious question is whether they truly correspond to causal effects, or whether omitted variables might still be causing problems. The answer is probably mixed. On one hand, Gan's data is a significant improvement over previous studies on multiple dimensions. The micro-level credit-card panel data allow her to use individual-level fixed effects, something not possible when using aggregate consumption data. As Gan points out, a fixed-effects specification controls for unobserved household heterogeneity that could lead to biased estimates. Other studies have used micro-level consumption data from the Panel Study of Income Dynamics (PSID), but that data set only measures expenditures on food and has been found to contain substantial measurement error (see Runkle 1991 for evidence of substantial measurement error in the PSID measure of food consumption). Furthermore, the district-level house price indexes used to calculate estimates of home values are more disaggregated than the state-level and even MSA-level house price indexes that previous studies have used. As a result, Gan's price indexes probably suffer less from measurement error than the price indexes in other work.
But even with these significant improvements in data quality, Gan may not have solved the endogeneity issue. As Gan writes in her paper, "[s]ince housing prices are available only at the aggregate level or at the level of the metropolitan statistical areas (MSAs), the observed consumption sensitivities may be driven by economic- or MSA-wide shocks that simultaneously affect housing prices and consumption." In other words, from an econometric standpoint, a regression of consumption growth at the individual level on house price growth at the district level is only identified from time-series changes in average consumption growth at the district level and house price growth at the district level.2 That means that other, unobserved district-level variables could potentially be driving the correlation between consumption growth and house price growth. As noted above, one potential omitted variable is expectation of higher future income at the district level.
Gan downplays this issue by noting that many Hong Kong residents tend to work and reside in different districts. As a result, district-level shocks are less likely to simultaneously influence both housing prices and consumption. But as an empirical matter, it is not clear from the paper how common it is to work and reside in separate districts. And from a theoretical perspective, it is not clear whether this argument effectively rules out the simultaneity issue. If a significant fraction of individuals who work in one district reside in the same outside district, then a shock to employment in a district could cause prices and consumption to co-move in the outside district. In addition, there are other types of district-level shocks that could create simultaneity regardless of the commuting patterns of its residents. For example, a public works project that improved existing infrastructure or that developed new park space (or created some other desirable public good) in a district would be expected to increase the attractiveness of the district—thus increasing housing demand, which would raise prices and possibly also average district-wide consumption, by changing the income/wealth composition of its population. To completely solve the simultaneity issue, one would need time-series variation in home values at the individual level. Unfortunately, that data is simply not available at this point—in Hong Kong or anywhere else.
Innovative tests of the housing wealth channel
Still, while Gan's data may not completely solve the simultaneity issue, some of her additional empirical tests go a long way toward alleviating these concerns. The first is a check on whether the consumption–housing wealth relationship is stronger for people who own multiple homes. A pure housing wealth effect would predict that the consumption behavior of individuals with higher levels of housing wealth would be more sensitive to changes in wealth. This is exactly what Gan finds in her data.
In a series of other tests, Gan tries to distinguish between the role of credit constraints and a precautionary savings motive in generating the positive housing-wealth effect. The credit constraint story is that increasing housing wealth relaxes borrowing constraints for individuals, which results in higher consumption. This effect is expected to be relevant only for households that are borrowing-constrained. The precautionary savings motive refers to the tendency for risk-averse individuals to accumulate wealth in order to self-insure against negative future income or wealth shocks. If individuals consider housing equity to be a component of precautionary savings, then an increase in housing wealth might increase consumption by reducing other components of precautionary savings. For example, if a household is saving a certain percentage of each paycheck for precautionary motives, then an increase in housing equity might be considered to be a viable substitute, and the household might be expected to decrease the percentage saved of each paycheck, and thus increase consumption.
Gan focuses on households that refinance as a first test in distinguishing between these two effects. She argues that borrowing-constrained households would need to refinance in order to access any equity increases, while precautionary savers would not need to refinance, since they could increase consumption by decreasing other forms of saving. Thus, if the relaxation of borrowing constraints is driving the positive elasticity of consumption growth to changes in housing wealth, then the elasticity estimate should be larger among households that refinance.3 Gan finds evidence of both effects: the estimated elasticity is significantly higher for households that refinance but is still positive and statistically significant for households that do not.
In a second test, she identifies households that are likely borrowing-constrained based on their use of credit card lines and separately estimates regressions for households that are close to their credit limit and those that are far from their limit. If the credit-constraint channel is present, the elasticity should be higher for the households that are close to their limit, while the precautionary savings channel, in contrast, predicts that the elasticity should be higher for less-constrained households and thus households that are far from their credit limit. The results of this exercise provide support for the precautionary savings motive, as less-leveraged households have a stronger consumption elasticity than more-leveraged households.
Gan performs a few more clever tests to try to distinguish between the credit-constraint and precautionary-savings channels. She finds strong evidence in favor of the precautionary savings channel and little evidence of an important role for credit constraints. It appears that in Hong Kong, households use housing wealth as an important component of an overall self-insurance strategy and view increases in housing wealth as a substitute for other types of savings and thus an opportunity to increase consumption. This is a very interesting and important finding on its own, but we also view it as strong evidence that Gan has truly identified a causal relationship between housing prices and consumption. The reason is that if simultaneity bias is truly responsible for the positive estimate of the consumption elasticity, then we wouldn't expect the estimate to be sensitive to different samples. The fact that it is, and in ways that are consistent with theory, suggests that Gan has really identified the impact of housing wealth on consumption behavior.
One final caveat is that Gan focuses solely on homeowners, leaving renters out of her analysis. We would expect renters to be hurt by increases in housing values. Thus, while Gan finds a significant positive effect of housing wealth on consumption for the population of homeowners in Hong Kong, we interpret her results as an upper bound of the effect of housing wealth on aggregate consumption in Hong Kong.
Research economist and assistant policy adviser at the Federal Reserve Bank of Atlanta
1 I believe that what Gan means here, although it's not completely clear, is that since the housing stock in Hong Kong is so highly valued, a 1 percent change in house prices translates into a lot of additional consumption, even with a marginal propensity to consume out of housing wealth of 2 cents.
2 The reason is that any time the variation in a right-hand side variable is at a more aggregated level than the dependent variable, the coefficient estimate associated with that right-hand side variable is identified from the variation in both variables at the more aggregated level.
3 Another potential way that a borrowing-constrained household could access increases in housing equity is through home equity lines of credit (HELOC). HELOCs do not require households to refinance their mortgage. This possibility is not discussed in the paper, perhaps because HELOCs are not quite as popular in Hong Kong as they are in the United States.
September 16, 2010
Answering the bloggers
In this post, we depart from our usual practice of addressing research by other authors and discuss a recent paper of our own: "Reasonable people did disagree: Optimism and pessimism about the U.S. housing market before the crash." We first review the main points of the paper, and then touch on some of the comments and criticisms that have been raised about it in various blogs.
Optimists, pessimists, and agnostics: Revisiting the three housing-boom camps
The paper basically asks a simple question: What did professional economists think about the housing boom while it was in full swing? Did most of them warn that the boom was really a bubble, sustained only by its own momentum and destined to crash someday? Or did most professional economists think that the run-up in housing values was entirely justified by fundamental factors, like low interest rates or higher rents? Our paper mainly discusses, rather than evaluates, the housing literature at this time. As a result, the paper is really more of a literature review rather than formal economic research. But we think that this type of review is helpful if people think that a disruptive gyration in asset prices could happen again.
By looking at the research papers and opinion pieces of professional economists, we concluded that these economists could be separated into three camps. All three camps agreed that house prices had risen dramatically—that was obvious from the data—but they differed on why. The "optimist" camp argued that there were good reasons why house prices had gone up, while the "pessimists" argued that there were no good reasons. A third group, whom we label "agnostics," refused to take a position one way or the other. Remarkably, most economists fell into this third camp. We argue that one reason for the popularity of the agnostic position is that economists generally believe that asset prices reflect fundamental factors unless there is a lot of strong evidence to the contrary. After all, asset prices are set in free markets, by people using their own money. Who are economists to say that the prices that people have agreed upon are wrong?
The strongest forms of this view comprise the various flavors of efficient-market theory, which argues that asset prices are always correct in some sense. While most economists would not go that far, they would still demand a lot of evidence before they would label some asset-price increase a bubble. This tendency to view asset prices as reflecting all available information makes economists generally reluctant to predict where asset prices are headed, or to say that some asset price is unsustainable. As we write in the paper:
In a sense, this reluctance to commit should not surprise anyone familiar with modern asset-pricing theory. The "Fundamental Theorem of Asset Pricing" implies that the evolution of asset prices is, to a first approximation, unpredictable. If housing was so obviously overvalued, as the pessimists suggested, then investors stood to make huge profits by betting against housing. By doing so, investors would have ensured that house prices would have fallen immediately. Regardless of whether the theory of the unpredictability of asset prices is correct, the theory is part of the basic training of almost every economist. Consequently, any economist who suggests to his or her peers that an asset is over- or undervalued faces a heavy burden of proof.
The optimists were reasonable people
Among bloggers, the paper has generated a mixed reaction. Naturally, we think the people that like the paper are absolutely correct in their assessments. But more seriously, we also think that our critics fail to appreciate the major point we were trying to make. In fact, some of their arguments actually provide further support for our position.
First off, many critics have said that housing-market optimists were right-wing ideologues who were deluded by efficient-market theory into thinking that asset bubbles were impossible. This story simply doesn't square with the facts. Two of the most prominent optimists were Chris Mayer and Todd Sinai. Both are professors at top business schools (Columbia and Wharton respectively), and both serve as co-heads of the Real Estate program at the National Bureau of Economic Research, a research body that includes economists of all ideological stripes. Even more importantly, one of Chris Mayer's seminal real-estate papers discusses "loss aversion" among potential house sellers. Loss aversion is a behavioral theory that blends psychology and economics, so it is not exactly high on the right-wing ideological research agenda, which is firmly rooted in the free market, rational decision-making tradition. By virtually any definition, Chris Mayer and Todd Sinai are reasonable people. Even so, based on their joint research, Mayer identified people who were predicting a collapse in house prices at the end of 2005 as "Chicken Littles."
The second issue has to do with economics as a science. Paul Krugman argues that "the evidence just screamed bubble." He points to the following picture, which shows real housing prices from the late 1970s through 2005.
This picture might convince the lay person that housing prices were headed for a fall. But the point of our paper is that convincing economists of this fact would have required much more work. Despite what some influential commentators might have us believe, large and persistent increases in real house prices have occurred in the U.S. without subsequent crashes. For example, Robert Shiller has found that U.S. housing prices increased by 60 percent between 1942 and 1947, adjusted for inflation. (This increase basically reversed a long-lasting period of low housing prices that occurred during the Depression.) After 1947, house prices were basically flat until the close of the 20th century. Nobody refers to the 1940s period as a house price bubble, because housing prices remained high for a very long time.
Another example of a long-lasting swing in housing prices is the boom–bust cycle along the U.S. coasts during the late 1980s and early 1990s. Krugman's post states that the late-'80s price rise in southern California depicted in this figure was a bubble, but that claim is debatable. Even after their recent crash, California housing prices are approximately 20 percent higher than they were during their late-1980s peak.
The responsibility of policymakers to create a robust financial system
What is most puzzling to us is the statement by some bloggers that we are essentially trying to exonerate the economics profession for failing to miss the housing bubble. Exactly the opposite is true. What we try to argue in the paper is that the economics profession simply doesn't have the tools to achieve real-time consensus on whether a bubble exists in this or that asset market. One response to this problem is for economists to relax their views on the correctness of asset prices. Undoubtedly, the recent housing cycle will nudge a lot of economists away from efficient-markets theory and the fundamental unpredictability of asset prices. For policymakers, we would argue the main lesson of our literature review is that they should not depend on economists to achieve consensus on whether some asset price reflects a potentially disruptive bubble. Rather, policymakers should construct a financial system that is robust enough to withstand steep drops in asset prices. If our review of the housing literature is any indication, there will be little warning of the next asset-market crash in the peer-reviewed economics literature.
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