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 Jessica Dill, Kristopher Gerardi, Carl Hudson, and analysts, as well as the Boston Fed's Christopher Foote and Paul Willen.
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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.
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