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It’s a straightforward question asked by nearly all sellers of homes: Is it worth paying a 6 percent commission to a Realtor to sell this house? In other words, will a Realtor be able to command a price greater than 106 percent of the price an owner could get selling the house himself or herself?
If you live in Madison, Wisconsin and quite possibly other towns, the answer to that question is no. In Madison, which has an active for-sale-by-owner website called FSBOMadison.com, sellers with a little patience sold their homes for higher prices than those who sold via a Realtor.
That’s the key finding of a paper by Igal Hendel, a professor of economics at Northwestern University, Aviv Nevo, a professor of marketing at the Kellogg School of Management and a professor of economics at Northwestern, and François Ortalo-Magné, a professor of real estate at the University of Wisconsin School of Business. Their research spans the years 1998 to 2004, and its conclusions up-end the received wisdom passed on by real estate agents themselves, whose trade groups have in the past argued that agents’ commissions are justified by their ability to achieve a higher sale price.
“Our key finding is that Realtors do not offset the cost of their commission; they do not get you a higher price,” says Nevo. “Your cost for the Realtor is your full commission.”
The study compared sales via the Multiple Listing Service (MLS), used by Realtors, to those via FSBOMadison.com, used by home owners, where a listing costs $150. With full access to data from both platforms, Nevo and his colleagues found that their raw data confirmed that owner sellers achieved higher prices for their homes. The average premium was 11 percent, or $14,800. That’s on top of the funds that sellers who used Realtors lost in the form of a commission. After accounting for the increase in home prices and market share for FSBO over the period encompassed by their data, the researchers found that the premium decreased to 4 percent, or $3,000, a value that remains statistically significant.
Nevo chalks up this premium to the self-selecting nature of homeowners who were willing to sell their homes themselves. “Those who sell their own homes are probably better at bargaining,” says Nevo. “They would get a higher price even if they used a Realtor. In fact, that’s part of what we find.”
In a variety of ways, Nevo and his colleagues controlled for the self-selecting nature of those who were bold enough to sell their homes themselves. One of the ways they controlled for the difference between seller types exploited the fact that some home sellers show up in the data twice—once when selling their home via a Realtor, and another time when selling a home themselves. Sellers who used FSBOMadison.com to sell a second home after they sold their first home via a Realtor were notably different from homeowners who only sold their homes through a Realtor. Specifically, they tended to get a higher than expected price when they sold their first home via a Realtor. This suggests that they were special in some way—more confident or more patient, perhaps—and that these traits would allow them to get a higher price for their home no matter what means they were using to sell it. It further suggests that sellers who used FSBOMadison.com were more likely to be able to command a higher price for their homes. Once the fact that FSBOMadison.com users were a special breed is taken into account, the premium accorded by FSBOMadison.com is not statistically significant.
“One idea is that people who use this for-sale-by-owner platform are the ones who are more patient,” says Nevo.
Patience is a requirement for owner-sellers because the one advantage Realtors offer over FSBOMadison.com is that they sell homes in less time. This and the convenience offered by a Realtor are, according to this study, the only advantages for which homeowners are paying with their 6 percent commission.
Those who sell their own homes are probably better at bargaining. They would get a higher price even if they used a Realtor.”
Whether or not this finding generalizes to the rest of the country is unknown. The researchers involved do have anecdotal evidence that, says Nevo, “the Madison market is not as unique as you might think,” but nothing rigorous that could be published.
Also unknown is whether or not these results apply to the current housing market, which has been deflating for some time. The time period studied, which begins in 1998, includes an era before the housing market took off, suggesting that the effect is not dependent on rapidly rising house prices.
While Nevo and his colleagues studied home prices from the perspective of sellers, their results are also relevant to buyers. Their work suggests that buyers who want to save money on a home might actually be better off going to a Realtor because those selling through a Realtor are likely to be less patient and / or less confident in their ability to negotiate the price of a home.
“I think there was a perception by the general public, including us before we wrote this article, that if you buy from an owner, you get better deal,” says Nevo. “It’s possible, in that case, that buyers had their guard down a bit. Which is also another explanation for why owners were getting higher prices.” In addition, there is selection on the buyer’s side of the transaction: Those with more time to look around were buying on MLS, not from owners.
The researchers also attempted to tease out the effects of a for-sale-by-owner listing service on the overall efficiency of the real estate market in Madison. They found that FSBOMadison.com catered to a different set of buyers and sellers than Realtors and the MLS listings.
“Different people want different things; therefore, FSBOMadison is a good thing because it gives you more variety in the market,” says Nevo.
In the long run, he doesn’t see for-sale-by-owner sites replacing the MLS listings; rather, the two will co-exist, each serving a slightly different set of needs.
*This article appeared in the November, 2010 issue of Kellogg Insight.
What killed the economy?
It’s a question that lingers nearly a decade since the global financial crisis that began in 2007. Craig Furfine, clinical professor of finance at the Kellogg School, proposes a different answer to that query than many analysts have offered: complexity.
“I saw commercial mortgage securitization deals becoming increasingly complex in the years leading up to the financial crisis, and I wanted to figure out why that was the case,” he says. “The question I wanted to answer was: Did underwriters [investment banks] use deal complexity as a way to make it easier to sell lower-quality loans?”
It is a topic of natural interest for Furfine. His experience as an economist for the Federal Reserve and the Bank for International Settlements, combined with his ongoing research in real estate finance, gave him an “inherent interest” in analyzing the complexity of commercial mortgage securitization deals in the run-up to the financial crisis. That led to a specific research question and study.
“The ‘simplest’ deals in my study are still pretty complicated,” he says, “but is the variation in that complexity informative of something?” According to the empirical results Furfine found, the answer is unambiguously yes: the more complex the securitization deal, the more likely the loans within it would go bad.
“Did underwriters use deal complexity as a way to make it easier to sell lower-quality loans?”
Furfine emphasizes that his finding is an unusual correlation, not necessarily the solution to an economic whodunit. “A more benign way to state my research question would be: Were loans packaged into more complex deals of lower quality?” he says. “I’ve presented evidence consistent with that being true. But if that’s true, then how did those loans get there?”
His research provided important clues to answer these questions.
Furfine focused on the complexity of commercial mortgage-backed security (CMBS) deals. “It seemed like everyone thought those kind of securities were just too complicated [in retrospect],” Furfine says. “But I found there was in fact a lot of information that investors could have had about the risks they were taking. So I thought there must be something more to the complexity.”
One of Furfine’s main focuses became the number of AAA-rated
tranches contained in each deal’s capital structure. CMBS deals pool many mortgage loans on commercial real estate such as office buildings. Access to the aggregate cash flow generated by the loan payments is divided into segments called “tranches,” which are then rated by ratings agencies based on their perceived risk and sold as bonds to investors. Tranches rated “AAA” represent the highest-quality, lowest-risk segments of the cash flows aggregated in the deal. “AAA means that you have priority over the cash flow [generated by the loans] relative to other bond holders in the deal,” Furfine says. In his analysis, a higher number of AAA-rated tranches was one indication that the CMBS deal was more complex.
But how would that complexity correlate with loan performance?
To correlate CMBS complexity with the performance of loans contained in each deal, Furfine accessed prospectus supplements for 337 such deals offered between 2001 and 2007. As expected, the average number of AAA tranches in a CMBS deal increased significantly during that period, from five to ten per deal. To assess loan performance, Furfine cross-referenced the loans against monthly servicer reports from 2010.
“Every month, the borrowers are making payments and there’s a report generated for every deal that tells me: Did the borrower pay this month? Are they past due? Have I started a foreclosure against the borrower?” he says. “I linked the initial information about each loan when it was made to how it was performing in 2010, and I showed that after controlling for the observable characteristics of the loan, the likelihood of a loan becoming nonperforming is positively correlated with the number of AAA-rated tranches in the deal.”
These findings, Furfine says, point to a puzzle at the heart of the financial crisis: “There’s no reason why the number of tranches in the deal could cause the loan to go bad. That’s not the source. That means that there’s some characteristic of the loans, unobservable to investors, that is causing them to default more often. What I found most puzzling is that the characteristics of the deal predict behavior of the loan above and beyond the key underwriting characteristics of the loan, like loan-to-value and debt service coverage ratios.”
Furfine notes another element of mystery in the findings, as well. Since the complexity of the deal (e.g., a high number of AAA tranches) is, in fact, observable—even without an intensive empirical study like Furfine’s—the market should reflect this complexity in the pricing of the tranches. “If investors can see a bit of information, they’re going to use it when they decide how much to pay,” Furfine says. “What I’m saying is, here’s a piece of info that’s observable – deal complexity – but I show that it’s not being used to determine price. This means that investors don’t think that deal complexity is relevant. Yet at the same time, complexity correlates with underlying loan performance, which would clearly be useful to know. So that’s a mystery.”
Furfine has some ideas about potentially solving that mystery.
“Imagine that investors have a certain, finite amount of time in which to make their decisions,” Furfine says. “If the deal structure were very simple, all of the investors’ time would be spent analyzing the quality of the underlying loans. But if I increase the complexity of the deal, then investors have to divert some of their time to analyzing the deal structure—which necessarily means taking attention away from the loans. All else equal, this gives the underwriter an incentive to place loans of lower quality into the pool.”
As further evidence for this, another empirical result of Furfine’s paper shows that the correlation between AAA tranches and likelihood of loan default exists only in a subset of CMBS deals: deals that contain loans originated by the same underwriter who structured the deal. “In those deals, the more complex the deal was, the more likely it is the loan is going to end up in default,” Furfine says. “For securitizations that are being put together by third parties, this result doesn’t exist. It’s the closest thing to a smoking gun I have in the paper.”
One complication, however, is the quality of the loans originated by the underwriter tended to be of higher quality than other loans in the same pool. “The evidence seems to suggest,” Furfine says, “that the underwriters combined their own high-quality loans—where quality is not readily observable—with lower-quality loans originated by others in more complex securitization deals.” But he points out that the banks did not necessarily structure deals in a certain way to hide information: “The interpretation of my results focused on unobservable measures of loan quality, so something that maybe the underwriters have a sense of, but not something that would be easily transmittable to investors in some sort of table form.”
More specifically, he notes that underwriters, or investment banks, could disguise “soft” information about poor loan quality—so called because it is difficult to transmit in observable financial data like prospectuses—by embedding it in highly tranched deal structures. An example of soft information would be knowledge of whether the major tenants of a commercial office building might intend to vacate the building in the near future.
Moreover, Furfine proposes that the correlation between deal complexity and poor loan performance could be an emergent, unintended consequence of financial innovation. “One possible reason why deal complexity increased in the run-up to the financial crisis was because underwriters were catering to the demand of the investors,” Furfine says. For example, an investor might be more interested in buying a AAA bond that pays back its principal in five years versus nine years, which incentivizes the underwriter to find a way to create that financial product.
But that process could also sweep poorer-quality loans into the deal structure. “There’s a downside,” Furfine notes. “Once I have served my investor community by creating these more complicated securities, it also creates this negative incentive: I am more able to hide things from my investors.”
Furfine observed that CMBS deals had as many as fifty tranches in the years leading up to the financial crisis. In the immediate aftermath, the first CMBS deal had only four. “This implies that investors had a potentially greater appreciation of this negative aspect of deal complexity,” Furfine says. But that appreciation has been short-lived: “A recent CMBS deal had nineteen tranches,” he says, and “deal complexity is already trending back up. It’s a case of ‘once bitten, then forget that I was bit.’”
According to Furfine, the risk-retention policies implemented in the wake of the financial crisis—those that force underwriters to retain some of the risk of their loans/bonds rather than passing most or all of it to investors—mitigates, but does not eliminate, the incentive for underwriters to tranche deals in increasingly complex ways. “It’s a little bit more costly to tranche, but if I can sell the bulk of my securities for more if I do so, then I’m going to keep doing it,” he explains.
Then again, knowledge of the fact that a negative correlation exists between complexity and loan performance could naturally undermine those incentives. “When an investment bank presents a really complicated deal, investors might push back and say, ‘I like that you’ve carved out these special securities, but on the other hand I appreciate the fact that when you did that, you probably made the loan quality worse in ways that I can’t easily measure,’” he says.
Either way, if investors do not want to get bitten twice when it comes to complex CMBS deals, they need to take the motive of the underwriter into account. “I think moving forward, investors are going to appreciate the fact that deal complexity is a choice being made by the underwriter,” Furfine says, “and as a choice, it might signal something about the underlying loans that are being securitized.”
What accounts for the tendency of housing prices to steadily increase for several years—and then steadily decline?
That deceptively simple question may have a new answer, based on the research of Charles Nathanson, an assistant professor of finance at the Kellogg School.
Few economic stories get more consistent press coverage than the housing cycle. The media regularly report on “housing starts,” or the number of new home constructions, along with the average cost of new homes and swings in supply and demand. Nearly everyone agrees that the health of the housing market is critical to the economy’s prospects.
But for all that, there are few certainties about precisely what drives the housing cycle, especially the kind of extreme boom and bust that happened in the early 2000s. Substantial research into the question has not yielded a clear picture of the housing crash. Researchers are also struggling to come up with ways of identifying—or preventing—bubbles.
The challenge is that so many variables—like fluctuating interest rates and construction costs—affect the workings of supply and demand. One thing is certain though: sellers eventually overshoot, buyers will not pay the asking prices, and the boom becomes a bust.
“How do buyers decide what they are willing to pay for what will likely become their most valuable asset?”
Nathanson and his coauthor, Edward Glaeser of Harvard University, suggest that an error buyers make in assessing the future value of a house helps to explain the ups and downs of the cycle. The error, which involves forecasting future home prices based on past and current ones, is small but widespread and contributes to momentum toward higher housing prices. When prices reach an unsustainable level, the momentum swings in the other direction, and prices return to the previous level.
For most people, buying a house is unlike any other purchase, and one with which they have minimal experience. “We usually think of people as better at things they do repeatedly,” Nathanson said. Buying a home is not one of those things for most people. Moreover, a home purchase typically requires the buyer to take on debt that she expects to pay back over the span of decades. Many buyers expect to sell their house at a profit before the loan is paid off. A house is an investment.
But how do buyers decide what they are willing to pay for what will likely become their most valuable asset?
“When you buy a house, you’re probably going to sell it at some point,” Nathanson said. “How much you pay for the house depends on how much you think you can sell it for in 7 to 10 years. That’s a difficult problem. To figure that out, you’re going to look at past house prices and say, ‘Are house prices in this area going up?’”
To be fair, predicting future prices based on past prices is “a very reasonable thing to do,” Nathanson said. But the problem is that it is hard to do this correctly. In fact, buyers fall into two distinct groups when it comes to forecasting house prices: those who consider all the relevant information, who behave in a way that maximizes profit, or whom economists label “rational” actors; and those who make assumptions based on limited information, or “naïve” buyers.
How do these groups behave when it comes to gauging home prices?
Rational buyers know that prices are not a precise reflection of supply and demand. They recognize that beliefs and expectations are baked into the numbers, and they filter out the psychological factors that inflate home prices, focusing instead on items tied more closely to demand. According to Nathanson, they look at things like “how strong the local economy is, how many people want to live in that neighborhood, and how good the local schools might be in the future.”
Naïve buyers, on the other hand, believe their information is better than it actually is. Consciously or not, they use a rule of thumb that past and current prices accurately reflect demand. “They see prices went up five percent and assume demand to live in that area went up five percent,” Nathanson said. This kind of buyer may look at a house listed for $200,000, see that other homes in the area have similar prices, and assume the prices accurately reflect demand. That makes them willing to pay the list price or even a little bit more if competing with others. “Then you get these things that look like housing bubbles,” Nathanson said.
The error naïve buyers make in forecasting future prices based on the past may seem small, but it can snowball. When the results of a model simulating rational behavior are set against a model simulating naïve behavior over several years, the effects are striking.
Nathanson and Glaeser found that over time the error leads naïve buyers to overestimate the actual level of housing demand—and thus overvalue homes. Future buyers, in turn, make the same error. This becomes a sequential and self-reinforcing process, and the gap between prices and demand grows ever-wider.
Interestingly, though naïve buyers expect prices to rise, they actually underestimate the near-term rise in the value of homes during a boom. This is true for the same reason that they err when looking to past prices for guidance. They do not consider the fact that future buyers will update their beliefs.
“Naïve buyers don’t realize that other buyers are learning from prices, too,” Nathanson said. “So if I’m thinking about buyers in the future, I know prices are going up today, but I think that buyers in the future aren’t going to learn anything from that. But in reality, they will look at the rising prices, and that will increase their optimism.” Thus, when current buyers fail to take the perceptions of future buyers into account, they underestimate the value of the house in question.
This error is key to understanding the housing market’s momentum—or the tendency for prices to continue on their current path within a given swing of a boom and bust cycle. The gap between naïve buyers’ expectations (what they think the house is worth) and reality (that it is worth more than they think it is) surprises them. It provides a psychological push, realized as momentum, that keeps prices ascending over the short run.
Past the five-year time horizon, however, buyers’ expectations outstrip the actual growth in prices. The same process, working in reverse, then pulls prices back down. Naïve buyers now overestimate the true level of demand and are surprised when homes are worth less than they think they are, creating momentum fueled by sequential undervaluing. And that contributes to a bust.
As Glaeser and Nathanson note, this is “only one possible model of semi-rationality in housing markets. Many other forms of irrationality may exist.”
But even if they do not provide the final word on the subject, the authors do offer an explanation for something that is typically assumed but not proven: the psychology, or “irrational exuberance,” that shapes the housing cycle.
“One of the stories [about the boom in the housing market in the early 2000s] is that people just got overly optimistic somehow,” Nathanson said. “And that the bust was predictable,” once buyers started to believe that houses were overvalued.
“We actually have a theory [presented above] for why people would consistently be too optimistic during booms and too pessimistic during busts,” Nathanson said. “In several other papers about housing cycles, including my own work, this optimism and pessimism is just assumed.”
Ultimately, the aim of understanding the ups and downs of the housing cycle is to help legislators and institutions create laws and policies that prevent the kind of dramatic swing in prices that happened from 2000 to 2006. The bust that followed inflicted devastation across the economy and played a critical role in the broader financial crisis that began in 2008.
As Nathanson and Glaeser write, “the economics of real estate bubbles is still in its infancy,” and it is too early to make policy prescriptions with any confidence.
But if their thesis holds up, “the lesson is that people are learning from prices the wrong way,” Nathanson said. “So the question is: Is there a better way for people to forecast future prices from past prices—one that people can use, and that’s not terribly complicated?” That is something Nathanson is exploring in current research.
As he pointed out, the practical implications of such follow-on research could be valuable: “There might be some very simple rules that people can use, that won’t lead them astray. If we can actually discover some simple rule people could use to avoid booms and busts, we could educate people about what that is.”
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