CHAPTER 30

Real Estate Investment Decision-Making in Behavioral Finance

Eli Beracha

Assistant Professor of Real Estate and Finance, University of Wyoming

Hilla Skiba

Assistant Professor of Finance, University of Wyoming

INTRODUCTION

The real estate market shares several psychological biases with traditional financial markets. Combining these behavioral biases with severe limits to arbitrage that include the illiquid aspects of the market, high transaction costs, and short sale restrictions often magnify the effect of cognitive and emotional issues on real estate valuations. As a result, real estate prices in the short and medium run often diverge from their fundamental values and market price adjustments are gradual. This is particularly noticeable in the residential real estate market where most participants are inexperienced, unsophisticated investors and driven by financial, emotional, and personal motives.

Irrational behavior of real estate market participants was partly a driving force behind the residential real estate boom and bust cycle that occurred during the first decade of this millennium. Because this cycle has had severe negative effects on both the United States and the global economy, it serves as a reminder of the widespread economic consequences that aggregated irrationality by real estate market participants can cause.

This chapter focuses on established psychological attributes in residential and commercial real estate and in real estate investment trusts (REITs) and has the following organization. The first section discusses the importance of the real estate market and the links between this market and the overall economy and financial markets. The next section discusses specific limitations to arbitrage (e.g., illiquidity, uniqueness of the properties, and short sale constraints) and then reviews common micro-level biases that influence individual decision-making including overconfidence, slow reaction to information, mental accounting, familiarity, anchoring, loss aversion, and ownership bias. This section also discusses how these behavioral tendencies manifest themselves specifically in real estate markets. The third section reviews why macro-level biases largely drive the documented phenomena in the real estate markets. These three market-related phenomena are momentum in real estate returns, real estate bubbles, and the real estate price overreactions. Specifically, this section discusses the recent bubble and correction that took place between 2000 and 2012. The final section offers a summary and conclusions.

THE REAL ESTATE MARKET AND THE GENERAL ECONOMY

The value of the primary residence represents the largest component of the net worth for most U.S. homeowners and those in most of the developed countries around the world. Regardless of whether an increase in housing wealth increases utility, home values affects many households' financial decisions. For example, homeowners who witness a rapid price appreciation in the value of their home are likely to feel better about their financial situation and spend their disposable income more freely than they otherwise would (Campbell and Cocco 2005). Because consumer spending is a major driver of gross domestic product (GDP) growth, in aggregation, the performance of the housing market has a material effect on the growth of GDP around the world. For example, Case, Quigley, and Shiller (2005) show that the effect of housing wealth on consumer spending is greater than the effect of wealth created by the stock market. They partially attribute this to the public perception that housing wealth is more permanent relative to stock market wealth.

The effect of the real estate market on the U.S. economy became even more pronounced as recent innovations in the financial markets introduced products, such as the home equity line of credit (HELOC) and low cost refinancing that allow homeowners to access their home equity more quickly and cheaply. With these products (combined with careless actions by some financial institutions), homeowners can easily convert increases in the value of their residence into spendable cash by borrowing money against the value of their home. As a result, during the housing boom of the early 2000s, many homeowners in states such as California, Florida, and Nevada could borrow and spend large amounts of money. Moreover, because the general consensus during that period was that housing prices could not decrease, homeowners believed that they could continuously borrow money that they would never have to pay back as long as their houses appreciated faster than the rate of interest. These unsustainable housing market conditions propelled a surge in consumer spending that supported a seemingly healthy U.S. GDP growth.

Just as rapid appreciation of the value of homes infused spending in the United States, the sharp decline in home prices that started in 2007 triggered a substantial decrease in spending that was a main driver of the “Great Recession.” The decrease in spending resulted from the wealth destruction caused by lower home valuations combined with the reintroduction of borrowing constraints to many American households as home equity disappeared in many homes across the United States. Although states experiencing a more rapid decline in home values were affected to a larger extent, the impact of declining home values was widespread.

REAL ESTATE MARKET AND FINANCIAL MARKET

Many individuals have a direct financial exposure to the real estate market (mainly through their home) and to the traditional financial markets. This implies that the expected performance and volatility of one market may influence individuals' investment behavior and their decisions with respect to asset allocation. Cocco (2005) finds a generally negative relationship between households' investments in housing and their investments in financial markets. This is particularly true for younger and poorer households with limited total financial wealth. Cocco also maintains that homeowners seek a degree of total level of risk exposure for their financial nest egg. As a result, higher volatility in home prices causes homeowners to invest a smaller portion of their wealth in the stock market, which some view as riskier than the housing market.

The initial down payment that homeowners make when they purchase their home also affects their attitude toward the risk associated with the real estate portion of their portfolio. Homeowners who purchase their home with little or no money down do not have “skin in the game” because in most cases they are not liable for any negative equity that may result from a reduction in the value of their home (Ben-David 2012). In these cases, homeowners face an asymmetric outcome where they would benefit from an appreciation in their home value, but will not suffer when their home depreciates. Rational participants who face a “heads I win, tails you lose” scenario are likely to make larger and riskier bets than they otherwise would. During the early 2000s, lenders only required little or no down payment from potential homeowners and this arguably helped to facilitate a greater appetite for real estate risk exposure.

Prices of both financial and real assets should reflect general strong economic growth. Evidence also shows a feedback loop between the performance of financial assets and the housing market at the local level. Anderson and Beracha (2012) illustrate that the performance of companies' stocks headquartered in a particular city have a material effect on home values located in the same city. The effect is most noticeable for homes located in the more expensive areas of the city, which are likely to be the areas where executives (those that may have the most exposure to financial markets) choose to reside.

INEFFICIENCIES AND THE REAL ESTATE MARKETS

According to Ritter (2003) the two building blocks of behavioral finance are (1) limits to arbitrage and (2) cognitive psychology. Limits to arbitrage refer to the difficulty of buying or selling a financial asset and this may make arbitraging misvaluations difficult. Evidence indicates that due to both behavioral biases and limits to arbitrage, traditional financial markets, such as the stock market, are not fully efficient. Examples of large misvaluations include historic events such as the Japanese stock price bubble in the 1980s, the October 1987 market crash, and the technology bubble of 1999–2000. Because the real estate market is especially prone to both limits to arbitrage and behavioral biases, it displays a greater level of price inefficiencies compared to the stock market. Several authors find that the real estate market historically has not been efficient and that investors can earn abnormal returns in the long run. For example, Coyne, Goulet, and Picconi (1980) document consistent outperformance of real estate over stocks and bonds on a risk-adjusted basis. Several authors, including Case and Shiller (1989, 1990) also report predictability in the home prices.

The following sections discuss how the real estate market displays the two building blocks of behavioral finance—limits to arbitrage and psychological biases—and review the related real state literature.

Limits to Arbitrage

The real estate market exhibits severe limits to arbitrage including illiquidity of the asset, high transaction costs, uniqueness and indivisibility, and severe constraints to short sales. This section reviews the specific characteristics of the real estate market that limit arbitrage.

Illiquidity and High Transaction Costs

Under normal market conditions, residential real estate is on the market for weeks or even months before a seller accepts an offer. Commercial real estate properties, particularly those with unique characteristics, are available for even longer before they sell. Because the seller often hires a professional agent to help market the property, the cost of real estate transactions that the seller incurs is substantial. For example, commissions of 5 to 6 percent of the sell price for residential homes and 2 to 3 percent on larger commercial properties are the general rule rather than the exception. Other monetary expenses such as packing and relocation costs as well as nonmonetary burdens such as hassle and effort can also be substantial.

Due to this long and expensive process, even if a particular real estate owner is aware of the fact that her property is overvalued, she will be reluctant to sell it and move to the sidelines until market prices return to equilibrium. As a result, real estate prices that deviate from equilibrium will take a long time to correct. The illiquidity of the real estate market also affects sellers' decisions about whether to accept an offer that is somewhat below their perceived value of the property. Sellers cannot directly observe the tradable value of their property and do not know when the next offer will come. Therefore, waiting for a higher offer may be a long and costly process that causes some sellers to accept a seemingly low offer.

Uniqueness and Indivisibility

Unlike the stock or the bond market where homogeneity exists, no two real estate properties are identical. This implies that in order for the real estate market to be efficient, millions of unique properties need to be fairly priced, accounting for each property's specific features, characteristics, and condition. Because the seller initially prices each property (the asking price) rather than the collective market forces, this introduces each seller's personal biases about the property. Such biases could be based on personal attachment to the property or the seller's preferences for real estate characteristics. These factors also contribute to price clustering (e.g., sellers price more properties at $200,000 than at their fair value of $198,357), which introduces price inefficiencies. Finally, because buyers typically cannot acquire a portion of a property, those who are looking for a property in a particular price range will not bid on a more expensive property even if they strongly believe that it represents a good market value. The same concept also applies for buyers with a high price range who by luck or skill identify undervalued properties priced well below their price range.

Short Sale Constraints

When real estate prices deviate from their fundamental values, market participants have difficulty forcing prices back to equilibrium. This is because short selling in the real estate market is not feasible. Similarly, simultaneous buying and selling of similar properties in different markets in order to benefit from price imprecision is expensive and nearly impossible to execute. Due to these short sale constraints, even if some real estate market participants realize that prices deviate from fundaments in a large way, their ability to benefit from the situation is limited. This increases the likelihood that prices will stay in disequilibrium for extended periods as pessimistic investors choose not to participate in the market. While real estate futures on a few housing markets are currently available for trading on the Chicago Board Options Exchange (CBOE), these futures are still subject to a very thin market and therefore cannot effectively address the real estate short sale constraint.

Psychological Biases

This section reviews common micro-level biases that influence individual decision-making specifically in real estate transactions. These biases include overconfidence, slow reaction to information, mental accounting, familiarity, anchoring, loss aversion, and ownership bias. The section also gives examples of behavioral biases in real estate markets.

Inexperienced Participants and Overconfidence

The majority of the participants in the residential real estate market and even a large portion of commercial real estate investors are novice or unsophisticated investors. This implies that most real estate participants have limited experience and lack knowledge about buying and selling real estate. Worzala, Sirmans, and Zietz (2000) survey pension fund and large insurance companies' fund managers on their risk/return assumptions about different investment alternatives including REITs. The survey results show that both types of investors assume a low risk/high expected return for familiar asset classes. Both investor classes are also less familiar with real estate investment and assign high risk/low expected return.

Behavioral finance documents that experience and overconfidence are negatively related so that the more experience investors gain, the less subject to overconfidence bias they become (Gervais and Odean 2001). More experienced investors can understand their own abilities better, whereas inexperienced participants often suffer from overconfidence and overly weight their own (limited) past experience rather than a larger and more inclusive sample. As Gervais and Odean show, inexperienced participants are also more inclined to make unwise investment decisions and to attribute their success to skill and their failure to bad luck. The authors also show that individual investors are more overconfident in their abilities compared with institutional investors. In an experimental study, Bloomfield, Libby, and Nelson (1999) show that less informed investors are more overconfident compared with their more informed counterparts.

In the context of real estate, an inexperienced individual who, for example, has recently sold her property for a large nominal profit compared with the purchase price, is likely to display a high level of confidence when searching for her next real estate property. As a result, she is likely to overpay for that property or reject an attractive potential investment opportunity. As previously discussed, experienced institutional investors in the real estate market, especially for residential real estate, are largely absent. Consequently, the pool of buyers and sellers is more likely to suffer from overconfidence than the pool of investors in the stock market.

Wang et al. (2000) study overconfidence in real estate markets from the perspective of builders. They find that overconfidence is related to overbuilding during bull markets and general volatility in some Asian real estate markets. The unique characteristic of many Asian real estate markets is that builders can sell units in developments before completion. The authors use this pre-sale market as a private signal to the developer that may fuel overconfidence and overbuilding.

Conservatism Bias and Slow Reaction to Information

Because real estate sellers and buyers typically do not adjust their reservation price based on the constant arrival of new economic data, they are subject to conservatism bias. Conservatism bias leads investors to underweight new information and to be slow to change their beliefs about an asset. For example, when the Federal Reserve announces an unexpected change of the discount rate or when the number of jobs created in the market during a particular period is different than anticipated, real estate participants do not modify their asking or reservation prices instantaneously. While new economic data eventually will have an effect on real estate transactions, the effect is often slow and gradual, unlike the immediate effect that similar information has on stock and bond prices. This phenomenon allows real estate participants who are aware of the eventual effect of current economic data to take advantage of the current prices that are yet to reflect this new information.

Mental Accounting

Behavioral studies such as Thaler (1985, 1999) document that investors tend to consider their assets separately rather than jointly and fail to see interactions between different asset classes. Financial economists often consider real estate and stock market investment and labor income as separate investment decisions. Mental accounting can lead to heavily locally biased portfolios, where labor income, stock market wealth, and real estate are all heavily dependent on local factors. Similarly, mental accounting may alter investors' decisions about whether to sell or buy a particular asset included in their portfolio, regardless of the expected performance of any or all other assets. Mental accounting is especially prevalent with regard to real estate because real estate often represents a large share of the wealth and many also attach a subjective and sentimental value to this portion of their portfolio.

Information and Familiarity

Many investors in the traditional financial markets have access to all publicly available information on a particular stock or bond. Due to recent technological advances, such information is easily and timely accessible via the Internet regardless of the investor's physical location. Consequently, all stock and bond investors can simultaneously evaluate the same information and act accordingly. Yet, even with such information transparency, investors still tend to invest more in securities based in their own country or region (local or home asset bias) and in securities that they are more familiar with (familiarity bias) (French and Poterba 1991; Coval and Moskowitz 1999; Huberman 2001).

In the real estate market, some relevant information about a particular property is publicly unavailable and much of this information is not readily accessible via the Internet. For example, a local real estate agent or a curious neighbor may know more about a particular property that is placed on the market before this information becomes public. Even when the details on a particular property are available via the web, other critical information about the property may not be. How desirable is the neighborhood in which the property is located? What is the quality of the schools surrounding the property? What are the recent dynamics and trends of the housing market in the area? How well has the property been maintained? These are only a few questions that are difficult to answer from a distance and therefore may only be available to local real estate participants. The difficulty of accessing real estate information from a distance only magnifies the already substantial local and familiarity biases that real estate participants display because most of them are reluctant to purchase a property in a distant and unfamiliar location regardless of the opportunities presented by that location. Different local tax and ownership rules as well as the inherently large expenditure that is associated with each single real estate transaction also contribute to investors' real estate local bias.

Garmaise and Moskowitz (2004) study the role of asymmetric information in the real estate markets and document that information barriers are major determinants of buyers' behavior. Market participants solve information barriers by purchasing nearby properties. Uninformed buyers also tend to focus on properties that are easier to value and have longer histories.

False Reference Points, Anchoring, and Money Illusion

Similar to other financial assets, when real estate owners consider selling their property, some irrelevant false reference points may influence their reservation price. For example, these false reference points may include the price they paid for the property, the price their neighboring property previously sold for, or the outstanding balance on their mortgage. Not only are these reference points often irrelevant, but sellers also tend to think about these values in nominal terms and not adjust them for inflation (Stephens and Tyran 2012). This kind of behavior, which the literature refers to as the “money illusion,” further reduces the relevance of the typical reference points, especially during periods of high inflation.

Northcraft and Neale (1987) study the anchoring heuristic in a group of students and real estate agents. These researchers ask subjects to make pricing decisions about real estate properties. Consistent with Slovic and Lichtenstein (1971) and Tversky and Kahneman's (1974) seminal work on the anchoring heuristic, Northcraft and Neale predict that an arbitrarily chosen anchor would influence value estimates of real estate prices and that estimates would be insufficiently adjusted away from the reference point. According to the authors, anchoring is relevant to a bargaining setting such as the market for residential real estate, where the fair market value of the property is not objectively determinable and buyers and sellers use a bidding process to arrive at the actual selling price. In their experiment, they find that the initial asking price of a house, which was manipulated among the subjects to be either near or far to the true value of the property, served as an anchor in the buying process, when all the other information about the property and neighborhood was held constant. Also, manipulated listing prices significantly influence the informed real estate professionals in the study.

Lambson, McQueen, and Slade (2004) also document that anchoring bias plays a role in buyers' willingness to pay a premium for a property. First, the authors document that out-of-state buyers (less informed investors) pay a premium for real estate. The higher prices are partially due to higher search costs, but can also be partially explained by anchoring effects. They study investors in the Phoenix real estate market and find that the buyers are influenced by prices of real estate in their home markets that serve as anchors in the decision-making process. Investors from home states that have overall higher price levels in real estate may shorten their search in the out-of-state market and are willing to pay higher prices on the same property compared to the informed local buyers.

Finally, Black and Diaz (1996) and Diaz (2009) document that potential homebuyers often use the property's asking price to estimate its value and that previous estimates by other experts influence the real estate appraisal process. In these studies, the researchers provide participants with different asking prices or appraisal values. These references points greatly influence the opinion of the participants about the property value regardless of their accuracy or relevancy.

Disposition Effect and Loss Aversion

Researchers have applied the concept of loss aversion to real estate markets to explain several observed phenomena, including low transaction volumes during trough periods and seller behavior in general. The disposition effect and more specifically loss aversion refer to the unwillingness of investors to realize losses, and what at least partially causes investors to hold on to their losing assets for too long. Loss aversion partly explains the documented strong positive correlation between prices of homes and sales volume. According to Genesove and Mayer (2001), sales volume can fall to one half from real estate peak to trough. Also, Stein (1995) documents positive correlation between volume and price level in the United States.

Genesove and Mayer (2001) apply a loss aversion story to the Boston condominium market from 1990 to 1997, during a period of an upswing and a downswing in the prices of the condominiums. According to prospect theory, a seller who is facing a loss is expected to set a higher reservation price than a seller facing an equal size gain on his home. Sellers, whose units' expected selling price has fallen below the original purchase price, set the asking price of their unit higher than others in a magnitude of 25 to 35 percent above the fair market value. The authors also find that owner-occupants suffer from loss aversion more than investors. Consequently, brokers in real estate markets avoid taking on clients who are facing large losses on their homes because of these sellers' unrealistic expectations about the target selling prices.

Seiler et al. (2008) study regret aversion and false reference points in the context of real estate markets. The authors use a survey method to study subjects who evaluate their experience in a real estate transaction on a home that appreciated in price over the study period. They give some subjects hindsight information that the home price peaked at a higher price than the current price. The subjects rate their experience significantly lower when they know that they could have made more money compared to when they did not know the high value of the property, even though the return is exactly the same. The authors also document that demographic differences are significant in regret aversion. Women tend to suffer more regret aversion and U.S. investors are more sensitive to regret aversion than their Asian counterparts. In a related study, Seiler and Seiler (2010) document that in severe market downturns, investors cope with losses in the form of false reference points, which in the long run will cause greater realized regret.

Ownership Bias

Theoretically, the decision about whether to buy or rent a home should be purely based on a financial cash flow analysis. According to that theory, an individual looking for a house would compare the present value of future rent payment on a particular property to the present value of all costs associated with purchasing a similar property. The individual should choose the option with the lower present value or be indifferent between buying and renting if the present value of each of these options is similar.

However, in reality the financial comparison between buying and renting is very complex and includes many subjective projections on future events that can substantially affect the decision. Most individuals are unable to fairly compare the financial expected benefits from buying and renting. Behavioral and nonmonetary factors can also influence them when making that decision. Homeownership in the United States is often referred to as the American dream. Purchasing a house can be an emotional event determining where a family will reside for the foreseeable future. Many view homeownership as wealth enhancing, a source of civic pride, and a way to improve self-esteem, prevent crime, and create child development and positive educational outcomes, among other benefits. As a result, about two thirds of U.S. households who own their primary residence are willing to pay a premium for owning rather than renting their residence.

Beracha and Johnson (2012) conduct a comprehensive comparison between buying and renting. Their results reveal that, on average, renting a residence is associated with a monetary benefit over buying one. This monetary benefit appears during the majority of the 32 years included in the analysis and across different locations. While the monetary benefit from renting is counterintuitive to the general population that views homeownership as wealth enhancing, it is consistent with the notion that homeownership is associated with a financial price premium. On average, from a purely monetary perspective, residential real estate appreciated about 2 percent too slow each year in order to justify buying over renting. This implies that, if rational and aware of the cost of ownership, homeowners are willing to pay 2 percent of their home value each year for the nonmonetary benefits associated with homeownership mentioned earlier. Moreover, a cross-sectional examination of the homeownership premium suggests that homeowners are paying the highest premium over renters in areas that experienced the highest price appreciation in the past. This behavior indicates that homeowners are projecting higher future price appreciation for the already most expensive and least affordable areas rather than more rational expectations of a general reversion to the mean.

OBSERVED INEFFICIENCIES IN REAL ESTATE MARKETS

This section reviews three macro-level phenomena in real estate markets that are largely driven by the micro-level behavioral factors already discussed in the previous section. These three phenomena are momentum in returns to real estate, real estate market bubbles, and overreaction of real estate prices. Some maintain that the global financial crisis and the Great Recession that began in 2007 was largely a result of spillover from the real estate market collapse (Shiller 2008). Therefore, understanding the behavioral biases that affect the broad real estate market becomes ever more important.

Momentum in Returns to Real Estate

The financial literature widely documents momentum in asset prices. Momentum can be defined as the positive autocorrelation in prices from the previous period to the next, so that last period's winners tend to be the winners of the present period. Researchers find that momentum in real estate relates to many different factors. Some of these factors include illiquidity (Jegadeesh and Titman 2001), high information asymmetry and turnover (Zhang 2006), and short sale constraints (Ali and Trombley 2006). In comparison to stocks, all the factors that relate to momentum are present in the real estate markets and most of them impose stronger constraints on efficiency.

In addition to limits to arbitrage, researchers also document behavioral biases relating to momentum in asset prices. These behavioral factors include investor overconfidence and self-attribution biases (i.e., investors attribute success to their own skill and failures to bad luck), documented in stocks by Daniel, Hirschleifer, and Subrahmanyam (1998) and Chui, Titman, and Wei (2010) among others. As discussed in the previous section on inefficiencies in real estate markets, a unique characteristic of this market is the level of inexperience displayed by the majority of the buyers and sellers.

The market for residential real estate is far from efficient. Rather, it displays high levels of momentum and price predictability from one quarter to the next. Case and Shiller (1989) are the first to point out high levels of autocorrelation in housing prices. They examine housing prices in four major U.S. cities and show that prices tend to follow a similar trend for at least one full year. Beracha and Skiba (2011) extend the analysis to cover all U.S. metropolitan areas. They document that housing momentum is not only statistically significant but also economically meaningful. The authors show that using a simple momentum strategy, based on previous real estate winners and losers, yields nearly 9 percent annually over a 26-year period. The momentum in housing prices is particularly high in the more volatile U.S. real estate markets, mainly in the Northeast and West regions, which are often characterized by an inelastic supply of land. The statistical and economic significance of the autocorrelation in housing price changes is present for seven calendar quarters, on average, which is much longer than the positive autocorrelation in stocks, which remains significant for less than 12 months (Jegadeesh and Titman 1993).

Researchers also document a momentum effect in REITs. Chui, Titman, and Wei (2003) and Brounen (2008) find high levels of positive autocorrelation in U.S. REITs. However, because REITs are in many ways similar to and traded like stocks, the price momentum that they exhibit is less material compared with the momentum in actual residential and commercial real estate markets.

Real Estate Bubbles

Behavioral biases are often present in asset pricing bubbles. This section discusses the real estate market bubble in the United States during the first decade on the new millennium and how behavioral biases relate to the severe overvaluation of houses.

Exhibit 30.1 displays the housing price index in real terms from 1890 to 2012. Despite a few periods of major price decreases (e.g., during the Great Depression) and increases (after World War II), the real cumulative home price appreciation during the 110 years spanning 1890 to 1999 was practically zero. Then, during the 7-year period beginning in the year 2000, home prices rose sharply and roughly doubled in real terms.

Shiller (2007) discusses the boom in housing prices in the 2000s and the consequences of these high prices to the future housing prices and the economy. As early as in 2005, Shiller points out that fundamentals such as rents or construction costs could not explain the price appreciation. Two commonly used metrics to valuate home prices are home prices relative to income or rent. Exhibit 30.2 illustrates that the ratio of median house price to per-capita income reached nearly an all-time high in 2005. Similarly, Exhibit 30.3 shows that the rent-to-price ratio also dipped to its lowest level at that time. These two ratios relative to historic norms suggest that the housing market was overpriced at that time.

Shiller (2007) attributes the rapid increase in the value of residential real estate during that period to behavioral and psychological factors. He contends that a feedback mechanism or a “social epidemic” that viewed housing as an investment opportunity because of extravagant expectations about the future fueled the unsustainable changes in home prices. The smooth and continuous increase in home prices during the bubble period is consistent with the momentum phenomenon and with the notion that real estate price deviation from fundamentals can last for years.

Piazzesi and Schneider (2009) offer more support for psychological drivers of the real estate bubble. They use survey-based analysis to study households' beliefs during the boom period. The authors first document a large heterogeneity in households' views about housing and the economy. Their cluster analysis reveals that a small “momentum cluster” was largely responsible for the increase in the prices far above their fundamental levels. Although the momentum cluster was small, its size was unprecedented in the real estate market and kept growing until the peak of the boom. Thus, the small number of extremely optimistic investors was largely responsible for the high prices without owning a large share of the housing stock.

In their two part analysis, Piazzesi and Schneider (2009) document that the first part of the boom (2002 and 2003) was largely due to the view that the time to buy real estate was good because of favorable credit conditions. During the second phase of the boom (2004 to 2006), the overall enthusiasm about housing was declining. However, the number of extremely optimistic investors increased from 10 percent to 20 percent.

Piazzesi and Schneider (2009) also point out two important distinctions between the stock market and the housing market. Stock market prices may rise above their fundamentals when pessimistic investors are not allowed to short (Miller 1977) and optimists own all the available supply of the security. Unlike the stock market, optimists in the housing market cannot buy all the housing supply and drive prices high because they own 100 percent of the asset. However, in the housing market, the recorded prices only reflect transactions that have actually taken place, which represent a small fraction of the market. Thus, unlike in more active markets, the optimists can have a large effect on prices without owning a large share of the overall market.

Barberis (2011) discusses psychological biases that may have led to the housing bubble and the consequent financial crisis of 2007–2008. He suggests that the real estate bubble may have formed because of several behavioral biases. Barberis contends that investors have a tendency to extrapolate past outcomes too far into the future, where the extrapolation itself is motivated by representativeness. Representativeness heuristic refers to individuals' tendency to categorize a situation or its likelihood based on past experiences. Daniel et al. (1998) propose that the real estate bubble may have formed because of investors' overconfidence and that people overestimated the precision of their forecasts. When investors gather information about an asset, they give too much weight to positive information about the asset because of overconfidence.

Barberis (2011) also notes that the effect of playing with “house money” could have helped fuel the housing overpricing. After gains, people become less risk averse and become less concerned about future losses, and the buying behavior will drive prices even higher. According to Barberis, representativeness bias may have had most to do with the housing bubble, so that people over-extrapolate the past outcomes when estimating the future. Given that the broad U.S. housing market had never experienced a sharp and lasting nominal price decline since the Great Depression, past outcomes certainly did not help to hinder the growth.

In addition to the households, rating agencies and suppliers of loans to the subprime borrowers also fueled the real estate bubble. The rating agencies were overconfident in giving high ratings to the subprime securities and underestimated probabilities of defaults. Similarly to households, representativeness bias may have caused lenders and rating agencies to extrapolate past outcomes, fueled by the notion that housing prices never fall.

The banks' balance sheets of subprime securities also contributed to the crisis. Barberis (2011) uses behavioral biases to explain why banks were caught holding these assets and why rating agencies were giving AAA ratings to risky securities. Although some may argue that banks' risk taking and bad incentives were to blame, Barberis offers an alternative explanation. He asserts that a reasonable explanation of banks' behavior may have included cognitive dissonance (i.e., the mental conflict that occurs when new information conflicts with previously held beliefs), so that bankers may have manipulated their own beliefs to hold on to a positive self-image and the idea that the job they were doing was valuable to the society, instead of risky and hazardous.

Overreaction in Real Estate Prices

Seminal work by De Bondt and Thaler (1985, 1987) in behavioral finance documents that investors overreact to dramatic news events and as a result past losers tend to outperform past winners and past winners tend to underperform past losers in the long run (in case of stocks in 36-month windows). Overreaction to negative news by real estate market participants during the market downturn in 2007 to 2009 also drove home prices below their fundamental values.

As Exhibit 30.1 shows, the housing market experienced an unprecedented price decline beginning in 2006 and only began showing some signs of stabilization in 2012. This continuous five-year decline was sharp enough to fully erase the real housing price gains that took place during the bubble period. The smooth and consistent nature of the price decline is also consistent with the housing price momentum phenomenon. This period was a mirror image of the early 2000s, when the momentum in real estate prices drove prices far above their fundamentals during the bull market. During that period a feeling of extreme optimism dominated the market and positive feedback traders were willing to pay higher and higher premiums on their homes. During the bear market the situation reversed. Many buyers suffered great losses and exited the market completely. Pessimism replaced the feeling of optimism and a fear-fueled negative momentum drove prices below their fundamental values. Potential buyers were afraid of a continuous downward trend in home prices and sat on the sidelines. During that time, many homeowners used the price for which they purchased their house or the value of their house at the peak of the housing market as a false reference point. Thus, homeowners who did not have to sell their houses refused to so at fair market prices.

Barberis (2011), among other scholars, points out that loss aversion may have driven the large declines in prices after the bubble burst. Evidence shows that real estate market participants suffer from loss aversion, and that loss aversion especially determines seller behavior in the housing market (Lane, Seiler, and Seiler, 2011). Loss aversion also explains why houses tend to remain unsold on the market for long periods during a bust period. This can partially explain both the overreaction in home prices and the slow rebound period from extreme drops in such prices.

Lane et al. (2011) suggest other behavioral explanations to the slow rebound in home prices during the bust periods. They document that behavioral biases have a large impact on homeowners' unwillingness to list their properties for sale, when they are “underwater” (i.e., the loan amount on the home is greater than the fair market value of the home). The most important behavioral bias is familiarity bias, which in this case represents the unrealistic thought that owners' current property presents the best return potential. The status quo bias or inertia, which sellers also exhibit, causes them to stay in their current homes simply because they already own them and may explain some unwillingness to sell.

Finally, mental accounting can be linked to the periods of overreaction in the housing markets. Seiler, Seiler, and Lane (2012) document that mental accounting affects loss aversion. When subjects of their study move away from holding real estate in isolation toward holding real estate as a part of their portfolio, they tend to be more willing to sell their real estate for a loss. On the other hand, when investors consider real estate in isolation, they are less likely to recognize their losses.

Exhibits 30.2 and 30.3 show that the historical ratio of median house price to per-capita income and price-to-rent ratios reached their all-time highs in 2005. It also shows that after the 2007–2012 market correction, both the price to per-capita income ratio and the price-to-rent ratios are at their all-time lows in 2012. The all-time low levels of these two important ratios indicate that the market sharply overreacted after the peak of the bull market.

Beracha, Skiba, and Hirschey (2012) study the overreaction in the U.S. housing market. They document that the overreaction from 2007 to 2011, similar to the market bubble, is regional in nature. The authors also find areas that experienced the most severe overvaluation during the bubble often exhibit the most obvious undervaluation at the end of the bust.

SUMMARY

Research shows that the real estate market, especially the market for residential real estate, is inefficient. Many market participants are inexperienced, novice investors who are driven by psychological biases. When the unsophisticated investor base is combined with severe limits to arbitrage, misvaluations often occur and price corrections take a long time to occur. Real estate prices tend to be predictable for many quarters and the real estate market is prone to overvaluations that are followed by sharp corrections. Given that the real estate market is a substantial part of the economy and an important determinant of consumer confidence and spending, understanding the underlying motives of buyers and sellers in the markets becomes especially important.

Many common cognitive biases and emotional factors documented in this market drive prices above their fundamental values during bull markets and below fundamentals during bear markets. During bull markets, investor overconfidence, self-attribution bias, and ownership bias drive prices higher. During bear markets, loss aversion, false reference points and anchoring, status quo bias, and familiarity bias drive prices below their true values.

Irrational behavior by real estate market participants partly drove the residential real estate boom and bust cycle that took place during the first decade of this millennium. This boom and bust cycle has had severely negative effects on the United States as well as the global economy and shows the widespread economic consequences of irrationality by real estate market participants.

DISCUSSION QUESTIONS

1. Discuss why limits to arbitrage are severe in the real estate market.

2. Explain why real estate market participants are likely to be overconfident and how overconfidence can lead to overvaluations.

3. What other psychological biases besides overconfidence influence real estate market participants' decision-making?

4. Which behavioral biases likely drove the overreaction in real estate prices that took place from 2007 to 2012?

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ABOUT THE AUTHORS

Eli Beracha is an Assistant Professor at the College of Business at the University of Wyoming. He teaches real estate finance, investments, and corporate finance at the undergraduate and MBA levels. Professor Beracha conducts empirical research in the areas of real estate and finance and he is the editor of the Journal of Real Estate Practice and Education. He has published in such journals as Real Estate Economics, Journal of Real Estate Finance and Economics, Journal of Real Estate Research, Financial Analysts Journal, and Journal of Financial Research. Professor Beracha is also the recipient of a several prestigious awards such as the 2012 ARES “Best Housing Research Paper Award” and the 2009 “Red Pen Award” from the Journal of Housing Research. In addition to his scholarly work, he has more than 12 years of practical experience in the area of real estate investments. Professor Beracha received his PhD in finance from the University of Kansas.

Hilla Skiba is an Assistant Professor at the College of Business at the University of Wyoming. She teaches behavioral finance, international finance, investments, and corporate finance at the undergraduate and master levels. Her research interests are mainly in the areas of international finance, institutional investor performance, and real estate finance. Specifically, her work deals with cultural influences on financial decision-making, underdiversification and performance, and the behavior of real estate market participants. Her work has been published in journals such as Journal of Real Estate Finance and Economics, Journal of Banking and Finance, Journal of Corporate Finance, and Journal of Housing Research. Her research has earned several awards including the best paper award at the Midwestern Finance Association and a finalist for the best paper award at the Financial Management Association. Professor Skiba received her PhD in finance from the University of Kansas.