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
September 17, 2015
Do Millennials Prefer to Live Closer to the City Center?
In past posts (Part 1 and Part 2), we examined whether millennials were driving the decline in first-time homebuyers. We concluded that, if anything, first-time homebuyers were becoming younger over time and location and economic conditions appeared to be a much stronger predictor of declines than a generational divide. In this post, we look into whether millennials prefer to live close to the city core or in the suburbs. Where millennials settle could determine whether our cities continue to grow, what our transportation infrastructure expenditures should be, and whether homebuilders should focus their efforts on multifamily housing in urban locations or traditional single-family homes in the suburbs.
This question has received a fair amount of attention—see here, here, here, and here. A number of observers have speculated whether the recent surge in millennials living in cities represents a change in preferences or whether it's simply an artifact of financial constraints—tighter underwriting standards, weak income growth, or larger student debt. Nielsen's survey of young adults finds that millennials prefer the lifestyle afforded by dense urban environments, but the National Association of Homebuilder's survey of young homebuyers finds that just 10 percent would prefer to live in the city while a whopping 66 percent want to live in the suburbs. Setting preferences aside, others debate whether millennials really are moving to the city. While recent data confirm that young people are moving to the cities at much higher rates than in the 1990s, it's also true that the raw majority of young people choose the suburbs over the city.
This research on young adults tends to combine renters and homeowners in one category. Renters tend to experience credit and financial barriers to location, and are limited in their location choice by the distribution of rental housing stock. That can make it difficult to distinguish whether young people who move to the city do so because they prefer urban life or because there is more rental housing stock in the city than in the suburbs. To shed light on the question of where millennials prefer to live, we segment out a group of young adults who experience relatively fewer restrictions on where to live: first-time homebuyers. Our data set allows us to identify first-time millennial homebuyers and the census tracts where they bought their first homes (a previous post describes the data).
Using this data, we ask if first-time millennial homebuyers are more likely to live near the city center than either existing homeowners or older first-time homebuyers. Finally, we look at how other factors like creditworthiness and student debt levels appear to influence this decision.
Below, we chart the median distance from the central business district (CBD) of first-time and existing homeowners by age bracket from the years 2001 to 2014. We find that existing homeowners tend to live, on average, 6.3 to 6.5 miles from the city center. First-time homebuyers tend to live closer in regardless of age, on average 5.8 to 5.9 miles away from the city center. Beginning in 2003, younger first-time homebuyers trended towards more central locations. During the 2007–09 recession, the spread between older and younger first-time homebuyers collapsed. After the recession, the spread widened again. It's difficult to say whether the shift in purchase patterns is the result of financial constraints or changing preferences, but the tendency appears to be for newer and younger homeowners to purchase homes closer to the city center.
What this chart cannot tell us is whether the trend that has younger people living closer to the city center reflects uniform preferences or whether this is an artifact of stronger economic growth in denser cities. In other words, is this trend the result of strong home buying in compact cities and weak sales in sprawling metropolises (that is, between cities), or is it the result of all buyers nationwide choosing to move closer to the city center (that is, within cities)?
To further investigate whether millennials prefer to live close to the city center, we perform several regressions to see how age relates to first-time homebuyer location decisions before and after the crisis. We control for a few key variables—namely, credit score, mortgage size, and student debt levels. The sample includes first-time homebuyers aged 18–60 who chose to purchase homes in the 50 largest metropolitan statistical areas (MSA) in the United States. We calculate distance by matching the census tract variable in the Federal Reserve Bank of New York Consumer Credit Panel with census tract data on distance from city center provided by CityObservatory.
Because some cities are more compact than others, we add MSA-level fixed effects. To control for the influence of nationwide effects such as the introduction of quantitative easing and the first-time homebuyer tax credit, we control for year-fixed effects as well in each regression. These controls should adjust for all region and time invariant factors that might affect both the age and location choice of home purchases.
Since creditworthiness typically increases with age and households with higher credit scores tend to be less constrained in their location choice, we also add a risk score variable to see whether age is simply a proxy for the ability to borrow. Similarly, we include mortgage balances. Finally, we add student debt balances to see whether the higher student debt burdens of young people can explain the discrepancy between the location choices of older and younger buyers.
The results are featured in the table below. On the right side of the table—from 2001–05 (that is, before the housing market crisis)—age appeared to have had a small impact on location and was not significant. Other factors such as size of mortgage and amount of student debt seemed to be larger determinants of location. Homebuyers with larger mortgages and with more student debt were more likely to live farther from the city center.
On the left side of the table—from 2006 to 2014 (that is, during and after the housing crisis)—age appeared to have had a small but significant relationship with location. Buyers who were one standard deviation younger located 0.03 standard deviations closer to the city center. With more controls included in the regression, this relationship declined to 0.02 standard deviations closer to the city center.
During and after the crisis, risk score became a stronger determinant of location as well. As risk scores increased by one standard deviation, buyers moved closer to the city center by 0.04 to 0.03 standard deviations, depending on the specification. This suggests that credit-constrained homebuyers are more likely to live father away from the city center and that, all else equal, younger homebuyers prefer to live closer to the city center.
While it appears that, on average, younger homebuyers prefer to live closer to the city center, can we say this reflects a preference for urban life? The average distance from city center—five-and-a-half miles—could very well describe areas with moderate density and single-family housing stock in moderate-sized cities. To focus on whether younger homebuyers are interested in living in the central city, we repeat these results using a logistic regression predicting the likelihood a first-time homebuyer will purchase within one mile of the city center. Controlling for all available factors, we find that younger buyers are significantly more likely to live in the heart of downtown. For each additional year, the odds that a buyer will decide to live within one mile of the city center drop by 6 percent.
By using this unique data set, we hope that we have shed some additional light on the age and location decisions of first-time homebuyers. Our interpretation of the data suggests that first-time homebuyers became more likely to buy closer to the city center during and after the housing market crisis and that young homeowners (first-time and existing) are more likely to live closer to the city center than older homeowners. Moreover, creditworthiness, total mortgage balance, and student debt loads appear to matter when the time comes to decide where to buy. In short, although age may not affect whether someone buys a house, our analysis suggests it may influence where they buy.
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
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 14, 2015
The Effectiveness of Restrictions of Mortgage Equity Withdrawal in Curtailing Default: The Case of Texas
As an economist who has studied the causes of the recent mortgage default and foreclosure crisis, I am often asked how to design policies that will minimize the likelihood of another crisis. My typical response to such a question is that one of the most effective ways of lowering mortgage defaults would be to limit borrower leverage by either increasing down payment requirements at the time of purchase or limiting home equity withdrawal subsequent to purchase.
The reason behind my belief is twofold. First, economic theory tells us that being in a situation of negative equity (where the remaining balance of the mortgage is greater than the market value of the property) is a necessary condition for default and foreclosure. Homeowners with positive equity will almost always have a financial incentive to sell their homes instead of suffering through the foreclosure process, while borrowers who are “under water” have a difficult time refinancing or selling (since they would need to have enough cash at closing to cover the difference between the outstanding balance of the mortgage and the sale price/appraisal of the house) and have less of a financial incentive to continue paying the mortgage. Second, numerous empirical studies in the literature have confirmed the theory by documenting a strong positive correlation between the extent of negative equity and the propensity to default on one’s mortgage.
New evidence on preventing defaults
An important new paper by Anil Kumar, an economist at the Federal Reserve Bank of Dallas, provides new evidence that shows just how effective restricting leverage can be in preventing mortgage defaults. His paper confirms many of the findings in previous studies that have shown a positive relationship between negative equity and default. However, it goes a step further by using plausibly random variation in home equity positions created by a government policy that placed explicit restrictions on home equity withdrawal.
Kumar's paper is a significant contribution to the literature because it seems to overcome a serious identification issue that has plagued most empirical studies on the topic. The major challenge is that a homeowner can partially control his or her equity position through decisions about initial down payments on purchase mortgages and decisions about cash-out refinancing and home equity loans or lines of credit subsequent to purchase. As a result, it's unclear whether homeowners with more negative equity are more likely to default because of their worse equity positions or because of other reasons (unobserved by the researcher) that happen to be correlated with the decision to put less money down at purchase or to extract more equity over time.
Both theory and empirical evidence tell us that more impatient individuals tend to borrow more and are more likely to default on their debts. Thus, it might simply be the case that more impatient borrowers who are less likely to repay any type of debt choose to put less money down and extract more equity over time, creating the observed correlation between negative equity and the propensity to default. To put it in the language of econometrics, there are both selection and treatment effects that could be driving the correlation that we see in the data, and the policy implications of restricting borrower leverage are likely very different depending on which cause is more important.
Do home equity restrictions cause lower default rate?
The paper focuses on a policy enacted in the state of Texas that placed severe restrictions on the extent of home equity withdrawal. The Texas constitution, enacted in 1876, actually prohibited home equity withdrawal. The prohibition was eventually lifted in 1997 and the restrictions were further relaxed in 2003, but even in the post-2003 period, Texas law placed serious limits on equity withdrawal, which remain in effect today.1 Subsequent to purchase, a borrower cannot take out more than 50 percent of the appraised value of the home, nor exceed 80 percent of total loan-to-value (LTV). For example, if a borrower owned a home worth $200,000 and had an outstanding mortgage balance of $140,000, the borrower would be allowed to take out only $20,000 in a cash-out refinance. It is important to note that this LTV restriction does not bind at the time of purchase, so a homebuyer in Texas could take out a zero-down-payment loan, and thus begin the homeownership tenure with an LTV ratio of 100 percent (we will come back to this issue later).
Here's a nice quote in the April 4, 2010, issue of the Washington Post crediting the cash-out restriction for Texas weathering the foreclosure crisis better than many areas of the country.
But there is a broader secret to Texas's success, and Washington reformers ought to be paying very close attention. If there's one thing that Congress can do to help protect borrowers from the worst lending excesses that fueled the mortgage and financial crises, it's to follow the Lone Star State's lead and put the brakes on "cash-out" refinancing and home-equity lending.
At first glance, the data suggest that such a sentiment may be correct. In the figure below, we display subprime mortgage serious delinquency rates (defined as loans that are at least 90 days delinquent) for Texas and its neighbors (Arkansas, Louisiana, New Mexico, and Oklahoma). We focus on the subprime segment of the market because these are the borrowers who are more likely to be credit-constrained and thus more likely to extract home equity at any given time. It is apparent from the figure that Texas had the lowest subprime mortgage delinquency rates over most of the sample period. While the paper uses a slightly different data set, a similar pattern holds (see Figure 1 in the paper). The figure is certainly compelling and suggests that the home equity withdrawal restrictions in Texas had an important effect on default behavior, but a simple comparison of aggregate default rates across states really doesn’t tell us whether the policy had a causal impact on behavior. There could be other differences between Texas and its neighboring states that are driving the differences in default rates. For example, house price volatility over the course of the boom and bust was significantly lower in Texas compared to the rest of the country, which could also explain the differences in default rates that we see in the figure.
The paper uses a relatively sophisticated econometric technique called "regression discontinuity" to try to isolate the causal impact of the Texas policy on mortgage default rates. We won't get into the gory details of the methodology in this post, so for anyone who wants more details, this paper provides a nice general overview of the technique. Essentially, the regression discontinuity approach implemented in the paper compares default rates over the 1999–2011 period in Texas counties and non-Texas counties close to the Texas borders with Louisiana, New Mexico, Arkansas, and Oklahoma while controlling for potential (nonlinear) trends in default rates that occur as a function of distance on each side of the Texas border. The paper also controls for other differences across counties that are likely correlated with mortgage default rates (such as average house price appreciation, average credit score, and more). The idea is to precisely identify a discontinuity in default rates at the Texas border caused by the restrictions on home equity withdrawal in Texas. This strikes us as a pretty convincing identification strategy, especially in light of the fact that information on actual home equity withdrawal is not available in the data set used in the paper.
The estimation results of the regression discontinuity specification show that the equity restriction policy in Texas lowered overall mortgage default rates over the 13-year period by 0.4 to 1.8 percentage points depending on assumptions about sample restrictions (including counties within 25, 50, 75, or 100 miles of the border) and functional form assumptions for the “control function” (that is, whether distance to the border is assumed to be a linear, quadratic, or cubic polynomial). At first glance, this may not seem like a large effect, but keep in mind that the average mortgage default rate over the entire sample period was only slightly above 3 percentage points in Texas and 4 percentage points in the neighboring states. The paper also restricts the sample to subprime mortgages only and finds significantly larger effects (2 to 4 percentage points), which makes sense. We expect subprime mortgage borrowers to be affected more by the equity restriction since they are more likely to withdraw home equity.2 The paper implements a battery of robustness checks to make sure that the results aren’t overly sensitive to functional form assumptions and adds controls for other types of state-level policy differences. Based on the results of those tests, the findings appear to be quite stable.
But is it a good policy?
So the paper appears to confirm what previous research on the relationship between equity and mortgage default has found, although it uses methods that aren’t quite as clean as the regression discontinuity approach employed in this analysis. However, it doesn’t mean that such a law change is necessarily good policy. While it seems to be effective in reducing defaults, it may also have some real costs. The most obvious one is the decrease in the volume of low-cost secured credit that many borrowers used to improve their circumstances during the housing boom. An unintended consequence of the policy might have been to push financially distressed households into higher-cost credit markets like credit cards or payday loans. A second drawback of the policy may have been that it increased homeowner leverage at the time of purchase. As there were no restrictions on LTV ratios at the time of purchase, many homebuyers may have decided to make lower down payments, knowing that their access to equity would be restricted in the future. It’s also possible that this may have resulted in a larger volume of subprime mortgage lending in Texas. Households with relatively high credit scores who could have obtained a prime mortgage with significant down payments (say, 20 percent), may have turned to the subprime segment of the market, where they could obtain loans with low down payments but with much more onerous contract terms.
While it’s not clear whether the actual Texas policy of restricting home equity extraction is welfare-improving, it might seem from the research that restricting borrower leverage is an effective way to reduce mortgage default rates. But limiting borrower leverage is very unpopular. In fact, it probably isn’t too much of an exaggeration to say that the vast majority of market participants are adamantly opposed to such policies. After all, it is perhaps the only policy upon which both the Center for Responsible Lending (CRL) and the Mortgage Bankers Association (MBA) share the same negative view.3 Thus, while such policies have been adopted in other countries, don’t expect to see them adopted in the United States any time soon.4 To the contrary, policy is more likely to go in the opposite direction as evidenced by the Federal Housing Finance Agency’s announcement to relax down payment requirements for Fannie Mae and Freddie Mac.
By Kris Gerardi, financial economist and associate policy adviser at the Federal Reserve Bank of Atlanta
1 Before 1998, both home equity lending (loans and lines of credit) and cash-out refinancing were explicitly prohibited in Texas. A 1997 constitutional amendment relaxed this ban by allowing for closed-end home equity loans and cash-out refinancing as long as the combined LTV ratio did not exceed 80 percent of the appraised value (among a few other limitations that are discussed in the paper). In 2003, another constitutional amendment passed that further allowed home equity lines of credit for up to 50 percent of the property’s appraised value, although still subject to a cap on the combined LTV ratio of 80 percent.
2 The effects are actually smaller for the subprime sample when compared to the average default rate over the entire sample period, since the average rate is significantly higher in the subprime segment of the market (10 percent subprime default rate compared to the 3 percent overall default rate in Texas).
September 18, 2014
The Economic Effects of Urban Renewal
Editor's note: An earlier version of this post inadvertently included a paragraph from last week's post. The corrected post is below, and we apologize for the oversight.
This year, the 50th anniversary of the "War on Poverty," has seen an effort in the news media and among policy commentators to review the success and failure of past efforts to address poverty (see, for example, this, this, this, and this). Some of these efforts have included place-based policies such as the Model Cities program, which attempted to improve housing stock and reduce urban blight at the neighborhood level. In part, this renewed interest is policy-relevant: many cities are struggling with blight in the wake of the foreclosure crisis, and place-based policy has returned to popularity. For these reasons and more, I was quite interested to read a recent article in the American Economic Journal: Applied Economics. "Slum Clearance and Urban Renewal in the United States" by William J. Collins and Katherine L. Shester revisits the topic of urban renewal programs in the latter part of the last century.
The set of policies loosely referred to as "urban renewal" has been controversial since implementation. In fact, the programs changed a lot from 1950 to 1974, largely in reaction to the outraged response and perceived failures of early efforts. Title I of the 1949 Housing Act, which focused on "slum clearance," was a precursor to the 1954 Housing Act, which shifted the emphasis away from demolition and towards rehabilitation and preservation. Later legislation added programs to smooth the relocation process for those who were displaced by Title I programs and to direct resources towards the elderly poor. Throughout the 1960s, policy shifted away from changing the quality of housing stock towards creating a suite of policies focused on healthy communities. In 1965, as a result of a major reorganization, the Housing and Home Finance Agency, which had administered Title I, became the Department of Housing and Urban Development, commonly known as HUD. Finally, in 1968, the Fair Housing Act passed, further affecting the dispersal of funds.
In the early sixties, Jane Jacobs was one of the more famous critics of the destruction of historic neighborhoods and reconstruction along rationalist, modernist lines. In her 1961 classic, The Death and Life of Great American Cities, she argued that cities embodied organized complexity and that so-called "disorderly" slums were better than the rationally planned spaces that displaced them, both economically and socially. Other research on urban renewal has focused on political, social, and legal implications. This line of inquiry has focused on the impact of eminent domain on property rights, aesthetic concerns about how to incorporate historic preservation into revitalization, and concerns of justice and equity, primarily the issue that urban renewal placed the burden of displacement and disruption onto poor and minority residents without due consultation or compensation (see Gans 1962, Gotham 2001, Jacobs 1961).
The 2013 Collins and Shester paper cites this literature, but is distinct from it in its quantitative, nationwide study of economic impacts. It evaluates the effect of a series of programs over a 30-year period across 458 cities, and calculates that effect on broad economic outcomes. The authors measure urban renewal by combining the dollars allocated under the various programs implemented between 1950 and 1974. They evaluate the combined effect of these programs using a regression model. This model estimates the impact of federal dollars spent on the change in economic health of each city between 1950 and 1980. Using census-region fixed effects, the authors evaluate the impact of expenditures on median income, median property values, the employment rate, and the percentage of people living in poverty.
The authors' first-stage findings show that federal dollars spent on urban renewal projects between 1950 and 1974 had a negative effect on various economic outcomes. However, Collins and Shester suspect there is endogeneity in the relationship they are trying to uncover. That is, they say we cannot be sure what causes what: did urban renewal cause economic growth or decline, or did blighted cities pursue more urban renewal? In the latter case, even if the program improved the economy, these cities might still be doing more poorly than cities that had no blight to begin with.
The authors deal with endogeneity using an instrumental variable approach. That is, they seek to use exogenous variation in the allocation of federal funds. The variable they use is the year in which a state passed enabling legislation that made these sorts of projects legal. At first glance, this isn't a great instrument. Instrumental variables have to meet what's called the "exclusion restriction" to be credible. That restriction is untestable; you have to evaluate this claim on its merits. So, for us to believe this instrument delivers credible result, we have to be convinced that a state's decision to pass enabling legislation affects economic outcomes only by the way it influences urban renewal expenditures. There can't be any other chain of effects of related issues that connect those two events—the instrument and the outcome.
Collins and Shester perform several tests to justify their instrument. First, they look just at the effect of the instrument in places where court cases affected the timing of the laws passing. Then they perform a test of known effects to see whether their model predicts the economic growth in rural areas where urban renewal was not pursued. Finally, they use an alternate specification of the instrument. The instrument holds up under these examinations.
The authors then use their metric to predict the urban renewal funds distributed, and then use that predicted value in the original model. In this specification, urban renewal dollars have a strong positive effect on income and property values. These findings are consistent across several specifications and robustness checks. Furthermore, they find no effect on employment or poverty rates, leading them to posit that the positive effects they observe were not generated by displacement of poorer residents from inner cities. As a whole, these results suggest that overall, urban renewal programs created positive growth in average wages and property values.
A concern is that these conclusions rest on the credibility of the instrumental variable, and I'm not sure that the instrumental variable meets the exclusion restriction. I also wonder whether the average effects might reflect underlying variation in the effect of individual programs in urban renewal as well as different contexts where the program was applied. A map of the instrument (below) shows a strong spatial component to the instrument. Of the 458 cities that the authors measured in 1950–80, 68 percent of the cities, or 311, were in states that passed enabling legislation immediately. Regions in the Northeast, Midwest, and West pursued urban renewal programs immediately. These states were the most industrialized parts of the country; they experienced sectoral change and decline of their manufacturing center. The more agricultural, conservative areas of the country pursued funds relatively later, and received funds under later programs.
Source: Collins and Shester 2014, author's calculations
This makes me wonder if there isn't sufficient variation in the manufacturing states, and that the instrumental variable instead down weights these cases, providing in essence a regional estimate. Looking at the first stage results within each census region, we find that the results vary by region. For heavily industrial regions—the Mid-Atlantic, East North Central, and East South Central—urban renewal funding had a negative on growth. The other regions show a positive relationship between urban renewal and growth and economic growth.
There is also inconsistency in the second-stage, or instrumented, results within each region. The two regions in the Midwest, stretching from Wisconsin to New York, drop out as there is no variation. The regions on the eastern half of the nation show a positive effect, while those in the West show a negative effect.
Collins and Shester want to evaluate the treatment effect of urban renewal dollars by creating as-if-random variation in the administration of urban renewal funds. But if we aren't convinced that the instrument meets the exclusion restriction, or that the policy is having a constant effect, then what can we make of the results generated by this instrumental variable? We might surmise that the instrument is telling us something about the impact of the program in the subset of cities where the instrumental variable generates variation. If we believe that the study design can actually capture the effects of urban renewal, we might think of these new estimates as telling us the average effect of later urban renewal projects in 158 cities in the South and rural West, and not so much the effect of the program in the 311 cities where urban renewal was most intensively pursued.
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 Georgia Institute of Technology
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
December 04, 2013
Part 2: What Caused Atlanta's House Prices to Drop Again in 2011?
Note: This post is a follow-up to the November 8 post, "What Caused Atlanta's House Prices to Drop Again in 2011?"
Case-Shiller recently released its September data. Once again, the data show that Atlanta's year-over-year performance outpaced Case-Shiller's 20 metro area index—18.7 percent versus 13.3 percent, and Atlanta's low tier experienced its fifth consecutive month of year-over-year returns in excess of 50 percent (53.2 percent).
In the November 8 post, which explored the Case-Shiller house price tiers, we noted that from July 2011 to March 2012, both Atlanta thresholds—the low–middle and the middle-high—had noticeable declines, which corresponded to the time when the low tier's year-over-year performance began to recover. Given the methodology, we said that either all prices declined or a greater proportion of transactions came from lower-valued houses. Data on mortgage closings provide evidence consistent with the idea that investor activity in Atlanta's low tier influenced the market.
We can distinguish investor activity from "typical" house purchases by looking at the form of financing. Most owner-occupiers use mortgage financing whereas investors usually purchase with cash. Thus, if there is substantial investor activity in one of the three tiers, then we would expect that tier to be underrepresented in the number of mortgage closings.
To dig further into the issue of the 2011 house price drop, we looked at the distribution of mortgage closings by tier in the Lender Processing Services (LPS) Applied Analytics database. Residential mortgage servicing data from that database contain records from the servicing portfolios of the largest residential mortgage servicers in the United States. It covers about two-thirds of installment-type loans in the residential mortgage servicing market.
The chart displays the fraction of mortgage closings by Case-Shiller by tier from third-quarter 2008 to third-quarter 2013. We created the data for the chart by using each mortgage's sales price and assigning it to a tier as defined by the Case-Shiller thresholds for the month the mortgage closed (we excluded refinances). Once we'd bucketed the data this way, we calculated each bucket's percentage of the month's closing. If cash purchases were evenly distributed and the set of servicers in the LPS database is representative of Atlanta's overall market, we would expect each bucket to be one-third of the total.
From September 2008 to October 2011, the closings appear to be evenly distributed among the three buckets, with the shares varying between 25 and 43 percent. The average share for the low, middle, and upper tiers were 36 percent, 30 percent, and 34 percent, respectively. After November 2011, the low tier's share fell to an average of 18 percent, with a low share of 12.8 percent in May 2012.
Although the chart does not conclusively prove that investors entered the market en masse to purchase houses in Atlanta's low tier, the timing of the noticeable decline in the low tier's share of mortgage closings does coincide with the fall in Atlanta's low–middle and middle–high thresholds and the bottoming of the low tier's year-over-year price declines. More recently, the low tier's share of mortgage closings has been at its highest since November 2011—perhaps a sign that investor interest has cooled and we are now looking at a more normal market.
But with year-over-year prices in the low tier rising rapidly, let's hope buyers aren't expecting 50 percent year-over-year gains to be normal.
Carl Hudson, Director, Center for Real Estate Analytics in the Atlanta Fed's research department
November 08, 2013
What Caused Atlanta's House Prices to Drop Again in 2011?
What happened in Atlanta real estate the second half of 2011 and the first half of 2012? I asked myself this question after looking at the recent release of the Case-Shiller Home Price Indices for August. Atlanta home prices have recently been increasing at a faster rate than the composite index. Last month, the United States saw a 12.8 percent year-over-year increase in house prices while the Atlanta index rose 18.4 percent.
It wasn't long ago that things were much worse in Atlanta. From the pre-bust peak, the city experienced a 37 percent decrease in its Case-Shiller index versus a 33.8 percent decrease for the Case-Shiller 20-metro composite. From July 2011 to March 2012, Atlanta home prices took a second nosedive, almost as large as the initial bust in 2009 (see Chart 1). So what happened?
The Case-Shiller index is a repeated sales index, which means it uses the price change between two arms-length sales of the same single-family home. One way to gauge the amount of activity in a market is to look at the number of "sales pairs" in a period. To get a sense for whether Atlanta is experiencing particularly high volumes, we can look at Atlanta's sales volume relative to the nation's. In the third quarter of 2011, Atlanta's sales began to grow substantially, and Atlanta's share of composite sales pairs peaked in March 2012 at 9.7 percent, which is a much greater percentage than the 5 percent to 6 percent range from 2000 to 2005.
Around this time, many of the Atlanta Fed's local contacts reported that some investors were buying up distressed home to convert into rental property. Case-Shiller breaks its index into three price tiers—low, middle, and high. Looking at the tiers in Atlanta for the most recent data, the high end was up 12.9 percent year over year; the middle tier, 27.7 percent; and the low tier was up 52.5 percent. Looking back, we see that the growth rate in Atlanta's low-tier index started to recover in July 2011 (see Chart 2). It was not until March 2012 when the year-over-year changes in the middle and high tiers started their recent upward trends.
The price thresholds for the three tiers are computed using all sales for each period and are set so that each tier has the same number of sales. From July 2011 to March 2012, both thresholds (low–middle and middle–high) had noticeable declines (see Chart 3). Given the methodology, either all prices declined or a greater proportion of transactions came from lower-valued houses. Note that after March 2012, the breakpoints started to increase, which was the same time as when the year-over-year growth in the middle and high tiers started to improve.
Further work is needed in order to determine whether there really was a ramp-up in activity in the low end of the market. If such activity did occur, it raises the question as to what was driving the activity—could it have been investors? If not, how was this activity financed? Was this a case of inventory being absorbed, prices adjusting, and momentum moving from investors to "normal" buyers?
The low tier warrants attention given the fact that it may have driven Atlanta's recent house price performance. Understanding the July 2011 to March 2012 period may shed light on the factors that could influence the market going forward.
Carl Hudson, Director, Center for Real Estate Analytics in the Atlanta Fed's research department
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- Teachers Teaching Teachers: The Role of Networks in Financial Decisions
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