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
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.
September 20, 2013
Does the Mortgage Interest Tax Deduction Really Support Upward Mobility?
The mortgage interest tax deduction (MITD), a portion of the tax code that permits homeowners to deduct mortgage interest from their taxes, has widespread popular support. It is the second most common tax deduction, and the largest federal tax expenditure. It is particularly popular among middle class homeowners and the politicians they vote for. But it is often criticized by academics and policy wonks for benefiting well-to-do families who need it least, for encouraging overconsumption of housing, and for subsidizing sprawl. The map below, from a Pew Institute study released last week, shows the spatial distribution of the tax deduction. MITD claimants are concentrated in middle-class suburbs around (but not in) urban areas, where the houses are big and the homeownership rate is high.
A recent white paper released by Chetty, Hendren, Kline, and Saez (hereafter CHKS) from the Harvard/Berkeley Equality of Opportunity Project comes down in support of the MITD. The authors theorize that "these deductions may impact economic opportunity by providing opportunities for credit-constrained middle and low income families to become homeowners." The study finds that the MITD, along with many other tax expenditures, is correlated with higher income mobility. That is, in places where the MITD is larger, children's success in life is not bounded by their parent's wealth or poverty. It seems they are free to succeed or fail on their own merits.
In this blog post, I examine the way the MITD was measured in the CHKS study and propose an opportunity to improve this measurement. Then I re-estimate their results using an alternative metric. Using the CHKS study data set and our alternate measurement, it appears that the regressive nature of MITD expenditures corresponds with reduced mobility overall and has a large, negative association with the mobility of low-income Americans.
CHKS demonstrated that average MITD expenditures have a positive association with intergenerational mobility, but they were unable to find an effect when evaluating the progressivity of the MITD. This is probably because the metric was noisy. The authors calculated the progressivity of the MITD by subtracting MITD over adjusted gross income (AGI; MITD/AGI) for the top and bottom income cohorts. By this measure, the MITD looks progressive: as a percentage of AGI, the wealthy take a deduction that is, on average, 8.6 percent lower than the deduction taken by the poor.
But is this what the data are really telling us? Below is a chart showing the sum of all mortgage interest tax deductions in blue and the tax deduction as a percentage of adjusted gross income in green. We can see that the bulk of the deduction goes to people making between $100,000 and $200,000 a year.
Looking at the tax deduction as a percentage of income (in green), we can see that by just comparing the tails, the deduction looks progressive. Those with an adjusted gross income of less than $10,000 a year deduct a much higher percentage (15 percent) of their AGI than those who make $200,000 or more a year (just 3 percent). After the tax expenditure, the income distribution should be flatter than before. However, if you look at the middle of the distribution, the opposite story is true. The tax deduction is regressive—income brackets with higher incomes claim higher deductions as a percent of income. After the policy, the income distribution is more unequal than before.
Why do the tails tell a different story than the middle of the distribution? And why do people in the lowest income category—who typically don't own homes and can't qualify for a mortgage—have the highest percentage of deductions? Well, as it turns out, according to statisticians at the Internal Revenue Service (IRS), the bottom bracket is inflated with a number of wealthy folk who declare high losses to reduce their adjusted gross income. This explains why, for example, in well-to-do communities like Coral Gables, Florida, or Nantucket, Massachusetts, the average person with an AGI of less than $10,000 a year deducts close to 100 percent of that in mortgage tax interest.
The top bracket, by contrast, seems understated. Per filer, this group claims a much larger deduction than any other group. But because this bucket includes the Bloombergs and Buffetts of the world, this high MITD registers as just a small percentage of even higher AGI.
Because the data in the bottom income bracket is noisy, and the top bracket is skewed by those with extremely high income, it makes more sense to calculate the progressivity of the MITD by comparing the second-highest and second-lowest income cohorts. Below is a map of the results.
What happens if we plug this new metric into the CHKS study data? The initial results show that where the MITD is more regressive, parents' income is a better predictor of children's income, and mobility is lower. The results also show that a more regressive MITD corresponds with steep declines in mobility for low-income Americans. This finding makes sense, given that the benefit bypasses low-income homes, either because they are not homeowners or because they do not make enough money to itemize deductions.
It's important to note that the results are correlational, not causal. But if they have any interpretation at all, they suggest that the overall regressive structure of the MITD may be reducing equality of opportunity and making it harder for low-income families who do not own homes to achieve the American Dream.
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
August 29, 2013
Examining the Reported Decline in the First-Time Homebuyer Share
We've heard a fair amount of discussion lately about the decline in the first-time home buyer share of market, both from our business contacts in the homebuilding and real estate industries and from various media outlets. One example is the July 22 Wall Street Journal (WSJ) article that stated:
First-time home buyers, long a key underpinning of the housing market, are increasingly getting left behind in the real-estate recovery. Such buyers, typically couples in their late 20s or early 30s, have accounted for about 30% of home sales over the past year. They represented 40% of sales, on average, over the past 30 years, and accounted for more than 50% in 2009, when recession-era tax credits fueled the first-time market, according to data from the National Association of Realtors. The depressed level of first-time buyers could prove to be a drag on the housing rebound and the broader economic recovery over the longer haul.
Other media examples include a second WSJ article that ran on August 27, a Bloomberg Businessweek article that ran on August 7, and a USA Today article that ran on June 29. All articles cite data publicly available from the National Association of Realtors® (NAR).
When we took a closer look at these articles, we found that they actually intermingle two different sources of data, both from the NAR. The "30%" share of first-time home buyers over the past year comes from the Realtors Confidence Index, a monthly survey of approximately 3,500 Realtors that runs from October 2008 through July 2013. The "40%" average comes from a second NAR source, the Profile of Home Buyers and Sellers, an annual survey of approximately 8,500 households that runs from 2001 through 2012. We’ve graphed both time series in chart 1 below.
Not surprisingly, the two series do not completely align because each series was constructed with a different methodology. Given the different methodologies, it is not appropriate to use data from the annual NAR survey as a reference point for data from the monthly NAR survey.
With that said, the central question still lingers: has there in fact been a decline in the share of first-time buyers? Instead of comparing levels across data series, we would suggest a comparison of trends. To provide a broader perspective on the trend in the first-time buyer share, we introduce two additional monthly measures: the Census Bureau's American Housing Survey Public Use Microdata and the Campbell/Inside Mortgage Finance HousingPulse Tracking Survey.
It's important for us to note the methodology of each data series before jumping into a comparison of the trends. As we point out in the table, while the data are all survey-based, they have varying frequencies, coverage periods, and types of respondents.
|Census Bureau||American Housing Survey–Public Use Microdata||Monthly (See the methodological endnote)||Oct 1983–Sept 2011||Survey of 60,000 housing units reduced to owner-occupied households that were recent movers|
|Campbell/Inside Mortgage Finance||HousingPulse Tracking Survey||Monthly||July 2009–July 2013||Survey of (+/-) 2,000 real estate agents|
|National Association of Realtors||Realtors Confidence Index||Monthly||Oct 2008–July 2013||Survey of (+/-) 3,500 Realtors|
|National Association of Realtors||Profile of Home Buyers and Sellers||Annual||2001–2012||Survey of (+/-) 8,500 households|
As chart 2 shows, due to the differences in methodology, none of the series align perfectly. However, each series tells a similar story.
To get a better sense of the trend, we plotted each series individually (not shown). First, we examined the monthly American Housing Survey time series that we constructed (see the methodological endnote). The long-term linear trend line from October 1983 through September 2011 was slightly upward-sloping. Many have argued, though, that the first-time homebuyer tax credit program pulled demand forward and that the tax credit period (July 2008–September 2010) distorts the overall long-term trend. Indeed, when we exclude this time period, we find that the slope becomes slightly downward-sloping. We observe a similar trend when we fit a trend line to the National Association of Realtors' Profile of Home Buyers and Sellers time series. From 2001 through 2012, the trend is slightly upward-sloping when we include the tax credit period and slightly downwardly-sloping when we exclude the tax credit period.
When we take a closer look at the series with shorter time horizons, we generally find trends similar to those of the series with longer time horizons. The Campbell/Inside Mortgage Finance series clearly has a downward-sloping trend when we include the tax credit period and only a slightly downward-sloping trend when we exclude it. When the National Association of Realtors' Realtors Confidence Index is fitted with a trend line, the trend is clearly downward-sloping regardless of whether we include or exclude the tax credit period. We do think that it is important to point out that once we remove the tax credit period from these shorter term series, we're left with slightly less than three years’ worth of data. Given the shorter length of these series, we feel they are more likely to reflect shorter-term fluctuations than any shift in the longer-term trend.
With that said, it was striking to us how close to zero the slopes were of the slightly downward-sloping trend lines that exclude the tax credit period. When we calculate the trend of first-time buyer participation using an OLS regression and incorporate the tax credit period as a 0,1 dummy variable (which had a value of 1 if the tax credit was in effect, and 0 otherwise), we find that the slightly downward-sloping trends are not statistically different from zero after accounting for the effect of the tax credit.
In other words, we agree that the tax credit period distorts the trend, we think it is best to exclude it when interpreting the trend in first-time buyer share, and we interpret the trend in the first-time buyer share as flat across each data series when the tax credit period is excluded.
So to wrap up, we agree with the WSJ's statement that first-time buyers are a key underpinning of the housing market. However, we do not share the concern about weakness in housing demand going forward because we are not convinced that the data indicates a material decline in first-time buyer participation. Claims of a decline in first-time buyer participation that appear to be based on a comparison of data across different surveys should be treated with caution. There are several sources of data available for tracking the first-time buyer share of market. In comparing the trends of each series separately, we don’t find there to be much in the way of a material decline in the share of first-time home buyers over the time periods and data series we examined.
By Jessica Dill, senior economic research analyst, and
Ellyn Terry, an economic policy specialist, both in the Atlanta Fed's research department
(Return to table) | Methodological endnote: In order to get a longer-term view of the data, we constructed a monthly time series of first-time homebuyer share using the American Housing Survey (AHS) back through 1983. Our first-time buyer data point is derived from several questions in the AHS. First, we determine the current status of the person occupying the unit (PERSON module—Chart A). We then drill down further and look at the recent mover module to flag all respondents that moved residences since the prior survey year (RMOV module—Chart A).
Of those who indicated that they currently own their residence and indicated that they recently moved, we created flags for their status in their previous residence. The portion of respondents that indicated that they rented their previous residence or that they lived rent-free at their previous residence were combined to create our estimate of the first-time buyer share. We took this exercise one step further by utilizing the move date to construct a monthly time series of first-time buyer share dating back to 1983 from the biennial AHS dataset. While we did not weight the data, we did compare our numbers to yearly tables on the AHS website containing weighted data by survey year (as opposed to move date) and found our numbers to be comparable.
June 26, 2013
Is the Desire to Become a Homeowner a Thing of the Past?
Now that the worst of the housing downturn appears to be in our rearview mirror, many conversations about housing have shifted their focus from how to stave off further deterioration to figuring out where things currently stand and what the future trajectory will look like. At their core, these conversations seek to determine whether dynamics in the housing market have fundamentally changed since the recent recession or whether they have been only temporarily stymied and will eventually return to their previous trend.
It is with this shift in focus in mind that we consider the recent trend in the homeownership rate. It's no secret that the homeownership rate fell 4.25 percent from its peak of 69.25 percent in the second quarter of 2004 to 64.99 percent in the first quarter of 2013 (see chart 1).
This decline invites a few questions, such as: Should we accept the long-run average from 1965 to 2013 of 65.3 percent as the new normal? Should we expect some type of bounce back to the long-run trend in the homeownership rate's growth (that is, 0.24 percent per year)? Or should we expect the homeownership rate to continue falling as credit conditions remain tight and preferences for homeownership versus renting potentially shifted in a permanent way?
Eric Belsky, managing director of Harvard University's Joint Center for Housing Studies, explores dynamics that influence the homeownership rate in a recent working paper. To learn from his insights, the Atlanta Fed recently invited Belsky to discuss his paper with staff and leaders from the business, civic, and not-for-profit communities. (You can see his presentation on the Atlanta Fed website.)
In his opening remarks, Belsky—who is up to date on the latest literature and survey evidence on homeownership as well as an active contributor to national housing policy discussions—said that the homeownership "dream" is still alive. The will to become a homeowner is clearly still present, he said, regardless of age or income. Evidence for this can be found in numerous surveys, including surveys conducted by Fannie Mae, Pew Charitable Trusts, the New York Times/CBS News Poll, the National Association of Home Builders, JP Morgan Chase, Gallup, and the American Enterprise Institute. To be fair, some groups, such as the MacArthur Foundation, do find survey evidence against this claim.
However, the way to becoming a homeowner, Belsky pointed out, has been impeded by credit and other market conditions. Trends in FICO scores offer one source of evidence that credit conditions continue to be restrictive. In his slides, Belsky included a couple of charts that depict the credit risk profile of loans owned by Fannie/Freddie and loans insured by the Federal Housing Administration (see slides 13 and 14). Since neither chart includes the most recent data, they show how this picture has evolved in recent years. To bring the picture up to date, we created a similar chart with data from Lender Processing Services through 2013 showing trends in FICO scores for conventional (that is, conforming) mortgage originations. Our chart, like those in Belsky's slide deck, shows that there has clearly been a shift over the last few years in originations to borrowers with higher credit scores and lower risk profiles (see chart 2).
The Federal Reserve Board Senior Loan Officer Opinion Survey (SLOOS) offers yet another source of evidence that credit conditions are still restrictive. In the April 2013 release of the survey, 89.1 percent of all respondents indicated that banks' credit standards for approving applications for residential mortgages have remained basically unchanged.
In a May speech to the Housing Policy Executive Council, Fed Governor Elizabeth Duke picked up on this point and expanded on it by highlighting responses to some of the special questions posed to bankers.
The April SLOOS offers some clues about why mortgage credit is so tight for borrowers with lower credit scores. Banks participating in the survey identified a familiar assortment of factors as damping their willingness to extend any type of loan to these borrowers.... Respondents appeared to put particular weight on GSE putbacks, the economic outlook, and the risk-adjusted opportunity cost.... Over time, some of these factors should exert less of a drag on mortgage credit availability.
Perhaps more importantly, Governor Duke later stated that:
Although I expect housing demand to expand along with the economic recovery, if credit is hard to get, much of that demand may be channeled into rental, rather than owner-occupied, housing.
While the idea of homeownership may continue to be appealing, the bounce back in the homeownership rate appears to be a ways off. Based on the ramping up of operations, both multi-family developers and single-family rental investors and operators seem to think this bodes well for them. Meanwhile, single-family builders have also ramped up production from historically low levels to help meet the demand that exists from home buyers. While all sources of housing production may fare well in the short term, longer-term implications for housing demand and the housing stock have yet to become clear.
We invite you to watch a video of the talk that Professor Belsky gave on May 21 and to contribute to the conversation by posting your comments below.
Jessica Dill, senior economic research analyst 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
November 17, 2011
Taking on the conventional wisdom about fixed rate mortgages
The long-term fixed rate mortgage (FRM) is a central part of the mortgage landscape in America. According to recent data, the FRM accounts for 81 percent of all outstanding mortgages and 85 percent of new originations.1 Why is it so common? The conventional wisdom is that the FRM is a great product created during the Great Depression to bring some stability to the housing market. Homeowners were defaulting in record numbers, the story goes, because their adjustable rate mortgages (ARMs) adjusted upward and caused payment shocks they could not absorb.
In a Senate Committee on Banking, Housing, and Urban Affairs hearing on October 20, some experts presented testimony that followed this conventional wisdom. As John Fenton, president and CEO, Affinity Federal Credit Union, who testified on behalf of the National Association of Federal Credit Unions, laid out in his written testimony:
Prior to the introduction of the 30-year FRM, U.S. homeowners were at the mercy of adjustable interest rates. After making payments on a loan at a fluctuating rate for a certain period, the borrower would be liable for the repayment of the remainder of the loan (balloon payment). Before the innovation of the 30-year FRM, borrowers could also be subject to the "call in" of the loan, meaning the lender could demand an immediate payment of the full remainder. The 30-year FRM was an innovative measure for the banking industry, with lasting significance that enabled mass home ownership through its predictability.
Of course, this picture of the 30-year FRM as bringing stability to the housing market has profound implications for recent history. Many critics attribute the problems in the mortgage market that started in 2007 to the proliferation of ARMs. According to the narrative, lenders, after 70 years of stability and success with FRMs, started experimenting with ARMs again in the 2000s, exposing borrowers to payment shocks that inevitably led to defaults and the housing crisis. Indeed, one of the other panelists at the hearing, Janis Bowdler, senior policy analyst for the National Council of La Raza, argued in her written testimony that "when the toxic mortgages began to reset and brokers and lenders could no longer maintain their refinance schemes, a recession ushered in record-high foreclosure rates."
I argue, on the other hand—both in my testimony at the hearing and in this post—that the narrative of the fixed rate mortgage as an inherently safe product invented during the Depression that would have mitigated the subprime crisis because it
eliminated payment shocks does not fit the facts.
Parsing the myths around the fixed rate mortgage
First, the FRM has been around far longer than most people realize. Most people attribute the FRM's introduction to the Federal Housing Administration (FHA) in the 1930s.2 But it was the building and loan societies (B&Ls), later known as savings and loans, that created them, and they created them a full hundred years earlier. Starting with the very first B&L—the Oxford Provident Building Society in Frankfort, Pennsylvania, in 1831—the FRM accounted for almost every mortgage B&Ls originated. By the time of the Depression, B&Ls were not a niche player in the U.S. housing market. They were, rather, the largest single source of funding for residential mortgages, and the FRM was central to their business model.
As Table 2 of my testimony shows, B&Ls made about 40 percent of new residential mortgage originations in 1929 and 95 percent of those loans were long-term, fixed-rate, fully amortized mortgages. Importantly, B&Ls suffered mightily during the Depression, so the facts simply do not support the idea that the widespread use of FRMs would have prevented the housing crisis of the 1930s.
Source: Grebler, Blank and Winnick (1956)
Note: Market percentage is dollar-weighted. Building and loan societies were the main source of funds for residential mortgages and almost exclusively used long-term, fixed-rate, fully amortizing instruments.
To be sure, at 15–20 years, the terms on the FRMs the FHA insured were somewhat longer than those of pre-Depression FRMs, which typically had 10–15 year maturities.3 The 30-year FRM did not emerge into widespread use until later. It must be stressed that none of the arguments that Fenton made hinge on the length of the contract. Furthermore, the argument that Bowdler made in her testimony—that by delaying amortization, a 30-year maturity lowers the monthly payment as compared to a loan with shorter maturity—applies as much to ARMs as it does to FRMs.
But even though the ARMs may not have caused the Depression, FRM supporters might ask, didn't the payment shocks from the exotic ARMs cause the most recent crisis? Again, the data say no. Table 1 of my Senate testimony shows that payment shocks actually played little role in the crisis.
Of the large sample of borrowers who lost their homes, only 12 percent had a payment amount at the time they defaulted that exceeded the amount of the first scheduled monthly payment on the loan. The reason there were so few is that almost 60 percent of the borrowers who lost their homes had, in fact, FRMs. But even the defaulters who did have ARMs typically had either the same or a lower payment amount due to policy-related cuts in short-term interest rates.
To be absolutely clear here, my discussion so far focuses entirely on the question of whether the design of the FRM is inherently safe and eliminates a major cause of foreclosures. The data say it does not, but that does not necessarily mean that the FRM does not have benefits. As I discussed in my testimony, all else being equal, ARMs do default more than FRMs, but since defaults occur even when the payments stay the same or fall, the higher rate is most likely connected to the type of borrower who chooses an ARM, not to the design of the mortgage itself.
The difficulty of measuring the systemic value of fixed rate mortgages
One common response to my claim that the payment shocks from ARMs did not cause the crisis is that ARMs caused the bubble and thus indirectly caused the foreclosure crisis. However, it is important to understand that this argument, which suggests that the FRM has some systemic benefit, is fundamentally different from the argument that the FRM is inherently safe. This difference is as significant as that between arguing that airbags reduce fatalities by preventing traumatic injuries and arguing that they somehow prevent car accidents.
Measuring the systemic contribution of the FRM is exceedingly difficult because the use of different mortgage products is endogenous. Theory predicts that home buyers in places where house price appreciation is high would try to get the biggest mortgage possible, conditional on their income, something that an ARM typically facilitates. When the yield-curve has a positive slope (in most cases) and short-term interest rates are lower than long-term interest rates, ARMs loans offer lower initial payments compared to FRMs. Thus, it is very difficult to disentangle the causal effect of the housing boom on mortgage choice from the effect of mortgage choice on the housing boom.
In addition, there is evidence from overseas that suggests that the FRM is not essential for price stability. As Anthony B. Sanders, professor of finance at the George Mason School of Management, points out in his written testimony, FRMs are rare outside the United States. A theory of the stabilizing properties of FRMs would have to explain why Canadian borrowers emerged more or less unscathed from the global property bubble of the 2000s, despite almost exclusively using ARMs.
By Paul Willen, senior economist and policy adviser at the Boston Fed (with Boston Fed economist Christopher Foote and Atlanta Fed economist Kristopher Gerardi)
1 First liens in LPS data for May 2011.
May 21, 2010
Are there really social benefits of homeownership?
Over the past half century or so, the U.S. government has supported homeownership with numerous policies. For example, it created the government-sponsored enterprises (GSEs) to develop a stable secondary mortgage market so that households could obtain the necessary financing, with particular focus on less advantaged households. The federal government has also chosen to subsidize homeownership through the tax code, implementing tax deductions for mortgage interest and property taxes. The result of such policies has been the upward trend in the homeownership rate from about 62 percent in 1960 to a peak of 69 percent in 2004. (The foreclosure crisis has resulted in a decrease in the homeownership rate to about 67 percent as of the first quarter of 2010.)1
One of the main rationales for the government's pro-homeownership stance is the social benefit that homeownership is believed to produce. Basically, many perceive that homeownership gives individuals a stronger incentive to improve their neighborhood and community. A fairly extensive literature on this subject has purported to confirm such beliefs. Evidence supports the notion that homeowners participate more in the political system than renters (DiPasquale and Glaeser 1999) and are more likely to become involved in community activism in general (Rohe and Stegman 1994). Alba, Logan, and Bellair (1994) and Glaeser and Sacerdote (1999) have found a negative correlation between homeownership and the incidence of crime. Other researchers have found some evidence that homeowners take better care of their homes than renters do (Mayer 1981).
Other factors may drive both homeownership and community activism
But there is a very difficult econometric problem that many of these studies either do not address at all or do not address completely: the possible existence of unobserved characteristics that are correlated with both homeownership and the tendency to participate in community activism. That is, the types of people who are likely to become homeowners may also be the same people who are more likely to participate in their community. If this is the case, then these studies are mistakenly identifying homeownership as a causal factor of these social outcomes and falsely concluding that homeownership yields positive social benefits. To avoid this econometric issue and truly identify the causal effect of homeownership on participation in these various activities, we need to find some way to create random variation in homeownership decisions that is not correlated with any unobserved characteristics of individuals.
Matching savings program facilitates study of homeownership's social benefits
A new study by Gary V. Engelhardt, Michael D. Erikson, William G. Gale, and Gregory B. Mills in the Journal of Urban Economics attempts to accomplish such a task. The authors performed a field experiment with low-income renters in Tulsa, Oklahoma, from 1998 to 2003 that subsidized saving for a home purchase through what is called an Individual Development Account (IDA). They started with a pool of individual renters interested in such a program and then randomly picked a sample of them to participate. (Participation after selection was optional.) The program matched participants' saving specifically for a future home purchase at a 2:1 rate for annual deposits of up to $750 for three consecutive years. Thus, counting both deposits and matched funds, at the end of the three years, a participant could accumulate up to $6,750. This may not sound like that much, but it is a non-trivial fraction (about 11 percent) of the median house value in Tulsa for a similar low-income population of homeowners during the same period. Indeed, the IDAs appear to have encouraged homeownership: the authors find that after four years, the individuals who were given the option to participate in the program (the treatment group) had a homeownership rate of 7–11 percentage points higher than the individuals not given the option (the control group).
The authors use participation in the IDA—more specifically, the ability to participate, which was randomly assigned—as an instrument for homeownership. They collected information for their sample of renters and homeowners on these attributes: the extent of interior and exterior home maintenance expenditures; political involvement (propensity to vote, amount of support in time and money given to political candidates, tendency to write to or call public representatives); neighborhood involvement (volunteering and fundraising for a church, school, or other neighborhood organization; time spent working on neighborhood projects; and time spent participating in community associations); and time spent giving to other community members (providing childcare or care for another adult, watching someone else's home or pet, and making calls or writing/reading letters for someone else). Thus, their empirical strategy is a two-stage regression in which the first stage uses IDA participation to instrument for the probability of becoming a homeowner, and the second stage regresses the various measures of social involvement on the component of the variation in homeownership decisions that is due to the IDA experiment.
Their first finding is that when they don't instrument for homeownership decisions, they find very large social benefits, which is consistent with the previous literature. For example, becoming a homeowner increases the probability of having called or written a public representative by more than 17 percentage points and of voting in an election by almost 24 percentage points! They also find that becoming a homeowner seems to significantly increase the amount of exterior home maintenance by 13 percentage points.
Controlling for ownership finds negative relationship, underscores need for more research
But the more interesting finding is that when they do instrument for homeownership, all these positive effects disappear. In fact, in some cases the estimated effects become negative. For example, becoming a homeowner makes one less likely to volunteer or help raise money for a church, school, or neighborhood organization, and makes one less likely to become involved in local politics. The evidence regarding maintenance isn't quite as definitive. The estimated effect of homeownership on the likelihood of performing exterior maintenance is not precisely measured (the point estimate is positive but is not statistically significantly different from zero). The estimated effect of homeownership on the likelihood of performing interior maintenance is positive and statistically significant, but interior maintenance is really an internal benefit of homeownership rather than a social benefit (what you do inside of your home does not really affect the value of your neighbors' homes).
In our opinion, this is a really nice piece of work, on a very topical subject. It emphasizes the need to revisit some of the findings of the early literature on the social benefits of homeownership, as many of the positive effects found in that literature appear to be the result of spurious correlation—unobserved characteristics that influence the likelihood of an individual both to become a homeowner and to participate in his or her community to a greater extent. The study has some issues, which the authors themselves point out, with the design and implementation of the IDA experiment that might not make it completely representative of the entire U.S. population. The experiment was performed on low-income, employed individuals in Tulsa, where housing prices are relatively low. In addition, by simply signing up for the program, the individuals were likely signaling that they were more motivated to save (and thus more patient) than others. The authors also point out another potential problem, which is the possible conflation of homeownership and wealth effects resulting from the IDA experiment design. The matching funds increased individuals' wealth in addition to making them more likely to become homeowners. If increased wealth has an effect on the various social outcomes studied in the paper, then IDA participation would be capturing both homeownership effects and wealth effects. In any event, at the very least, the paper is a nice starting point for future research on this important topic!
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