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Electronic copy available at: http://ssrn.com/abstract=2266068
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SPECULATIVE TRADING IN REITS Benjamin M. Blaua and Ryan J. Whitbyb Abstract: The role of speculative trading in markets is often debated. The recent extremes in the real estate economic cycle has created an ideal setting to investigate the role of speculative trading in the marketplace. Specifically, we focus on speculative trading in REITs during the recent boom and bust in real estate. While we find a strong relationship between speculative trading in REITs and the economic cycle, we do not find evidence that speculative trading is related to future returns. Increased speculative trading is apparent in REITs during the boom years, but the level of speculative trading in REITs is unrelated to the negative returns in REITs exhibited after the bust. aBlau is an Assistant Professor in the Jon M. Huntsman School of Business at Utah State University, Logan, Utah 84322. Phone: 435-797-2340. Fax: 435-797-2301. Email: ben.blau@usu.edu. bWhitby is an Assistant Professor in the Jon M. Huntsman School of Business at Utah State University, Logan, Utah 84322. Phone: 435-797-9495. Fax: 435-797-2301. Email: ryan.whitby@usu.edu.
Electronic copy available at: http://ssrn.com/abstract=2266068
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1. INTRODUCTION
In both the popular press and academic research, much has been said about speculative
trading. With regards to the energy markets, the Wall Street Journal reported that Commodity
Futures Trading Commission Chairman Gary Gensler indicated that “every option must be on
the table” to curb “excessive speculation.”1 On the other end of the spectrum, Buyuksahin and
Harris (2010) state that “speculators provide immediacy and facilitate the needs of hedgers by
mitigating price risk, while adding to overall trading volume, which contributes to more liquid
and well-functioning markets.” A similar conclusion is reached in Friedman (1953). Other
research describes both the positive and negative externalities associated with speculation in
markets (Stein, 1987; Wang, 2010). Regardless of which side of the debate has more merit, a
better understanding of speculative trading is critical to the design and improvement of financial
markets. In this paper, we focus on the role of speculative trading in REITs during the latest real
estate economic cycle.
The recent boom and bust in real estate has created an ideal setting to investigate the role of
speculative trading in the marketplace. The U.S. Commodity Futures Trading Commission
defines a speculator as "a trader who does not hedge, but who trades with the objective of
achieving profits through the successful anticipation of price movements."2 With respect to real
estate, we can think of speculation in several ways. One avenue for speculation in real estate is
to buy and sell actual properties to try and profit from changing prices. In recent years, extreme
examples were reported of speculators overbuilding and property “flipping” in cities across the
United States. Speculators were betting that rapidly rising prices would translate into large
profits. Another natural avenue to speculate on real estate is through the equity markets and the
1 “The Politics of Speculation” The Wall Street Journal, July 29, 2009 2 "CFTC Glossary: A guide to the language of the futures industry". cftc.gov. Commodity Futures Trading Commission. 2012.
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purchase of REITs. REITs are an attractive asset to speculators for several reasons. First, REITs
trade in liquid markets, which allows positions to be opened and closed daily if desired. Second,
the transaction costs associated with purchasing REITs are orders of magnitude smaller when
compared to purchasing real property. Third, while exposure to real estate through REITs is
more diversified, the numerous REITs available for purchase allow speculators to focus on
specific property types or regions without facing the varying levels of information asymmetry
that would be encountered with more direct investments. While we focus our analysis on
speculative trading in REITs, we acknowledge that the role of speculators in direct real estate
markets could be quite different. Although speculative trading in the assets held by REITs
would eventually show up in prices and could be related to the speculative trading of REIT
securities, the illiquidity, uniqueness, and high transaction costs associated with the underlying
assets allows for potentially large deviations between REIT prices and the underlying Net Asset
Values (NAVs).
We focus our analysis on two primary questions. First, what is the relationship between
speculative trading and REITs during the recent real estate economic cycle? Both the anecdotal
evidence of speculation in real property in recent years and the ease and convenience of trading
equity REITs suggest an increase in the speculative trading of REITs. We find a strong
relationship between our proxy for speculative trading and the economic cycle in real estate.
While speculative trading in REITs is indistinguishable from speculative trading in non-REITs
over our entire sample (1993-2011), mean difference of -0.0073 with a t-statistic of -0.55, the
mean difference between REITs and Non-REITs during the real estate boom years3 (2003-2007)
3 We focus on 2003 to 2007 because those are the years with the highest median home values and the highest prices of the Ziman REIT index. In unreported tests, we also examine various time windows (i.e. 2002 to 2005, 2002 to 2006, 2002 to 2007, etc.) for robustness and find qualitatively similar results.
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is 0.0194 with a t-statistic of 2.68. Furthermore, our multivariate tests confirm the strong
relationship between speculative trading and REITs during the boom years is robust.
The second question that we examine is whether or not speculative trading in REITs is
related to future returns. Stein (1987) develops a model that shows that although increased
speculation can benefit markets through greater risk sharing, it is also possible for increased
speculation to change the information content of prices enough to have a destabilizing influence
that outweighs the benefits. If speculative trading adversely affects market prices by driving
them too high or too low, then speculative trading should have some predictive power with
respect to future returns. In other words, if more speculation causes prices to deviate from
fundamentals, then REITs with more speculative trading should have larger reversals.
Conversely, if speculative trading is unrelated to future returns, then the relation between
speculative trading and prices in REITs is less clear. Results in this study do not show that
REITs with more speculative trading have larger losses during the bust period than REITs with
less speculative trading. Differences between the top and bottom quartiles of firms ranked by
speculative trading during the boom period indicate that firms with the most speculative trading
actually had higher average returns than firms with the least speculative trading after the market
crash. While average returns for the top and bottom quartiles are not significantly different, they
are consistently positive, which does not support the notion that speculators attenuated the
dramatic price reversal in REITs after 2007.
While our results confirm that speculative trading in REITs increased during the recent real
estate boom and decreased after the bust, we do not find evidence that supports the idea that the
corresponding increase in speculative trading during the boom period harmed the well
functioning REIT market or adversely affected market participants, with respect to return. Our
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evidence is not consistent with the idea that increases in speculative trading harms markets, at
least not with respect to the market for REITs.
2. RELATED LITERATURE
Speculative trading has been examined from many different perspectives. Stein (1987)
develops a model that considers the role of increased speculation in financial markets. He notes
that speculation can benefit the marketplace by lowering the aggregate risk aversion or by
changing the information content of prices. In some cases, speculators can negatively impact the
market by making prices “noisier,” or changing the information content in a way that inflicts a
negative externality on those already in the market. Wang (2010) considers the case where
speculative traders add noise to the market and finds that speculative noise trading increases
liquidity, but also results in less efficient prices.
While many papers have addressed speculative trading with theoretical models, few papers
have examined the question empirically. Llorente, Michaely, Saar, and Wang (2002) examine
the relation between return and volume for individual stocks and develop a model where returns
generated by speculating tend to continue and returns generated by hedging tend to reverse. We
utilize their measure as our proxy for speculative trading. They describe the rationale behind
their measure as follows:
“[W]hen a subset of investors sell a stock for hedging reasons, the stock’s price must decrease to attract other investors to buy. Since the expectation of future stock payoff remains the same, the decrease in the price causes a low return in the current period and a high expected return for the next period. However, when a subset of investors sells a stock for speculative reasons, its price decreases, reflecting the negative private information about its future payoff. Since this information is usually only partially impounded into the price, the low return in the current period will be followed by a low return in the next period, when the negative private information is further reflected in the price. This example shows that hedging trades generate negatively autocorrelated returns and speculative trades generate positively autocorrelated returns (pg. 1005).”
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A detailed description of how speculative trading is calculated can be found in the Data
section of the paper. Grishchenko, Litov, Mei (2006) utilize the measure of Llorente et al (2002)
in the examination of stocks from emerging markets. Han and Kumar (2012) examine the role of
speculative trading by retail investors and find that stocks that are dominated by speculative
retail trading tend to be overpriced with significantly negative alphas. Buyuksahin and Harris
(2010) examine the role of speculative trading in the crude oil futures market and find little
evidence that speculators Granger-cause price changes.
The recent volatility in the real estate markets and the associated financial crisis have also
been the focus of many studies. Devos, Ong, Spieler, and Tsang (2012) examine the role of
institutional investors in REITs during the financial crisis and find that institutional ownership
increased prior to the crisis, but declined significantly during the crisis. Huang (2011)
investigates the role expectations played in the recent housing boom and bust through a volatility
feedback model. She finds a strong positive relationship between housing market volatility and
expected housing returns. Anderson, Brooks, and Tsolacos (2011) test for periodic, partially
collapsing speculative bubbles in US REITs. Driessen and Van Hemert (2012) study the pricing
of commercial real estate derivatives during the financial crisis and find little systematic
mispricing relative to REITs.
While there are several papers that look at speculative trading in real estate, we are the first
to closely examine the role of speculators in REITs during the recent boom and bust. Tegene
and Kuchler (1993) examine speculative trading in farmland and find little evidence that
speculative trading affects prices. Bjorklund and Soderberg (1999) look at speculative trading of
real estate in Sweden and find that speculation partly explains the real estate bubble during the
1980s. Malpezzi and Wachter (2005) develop a model that examines land speculation. They
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find that land speculation only impacts prices when supply is inelastic. Case, Cotter, and Gabriel
(2011) develop a multifactor asset-pricing model for housing and conclude that speculative
forces are an important determinant for U.S. housing returns. Focusing more on real estate after
the financial crisis, Zhou and Anderson (2011) examine the role of herding behavior in the US
REIT market and find that investors are more likely to herd in REITs when market conditions are
turbulent. They also find that circumstances that lead to herding have evolved since the recent
financial crisis.
This paper contributes to the literature in several ways. First, we find clear evidence that
speculators utilized REITs during the housing boom. Understanding the role of speculators in
different markets and time periods allows for both better market design and regulation. Second,
examining speculative trading during both the boom and bust periods allow us to examine
whether speculative trading exacerbated the pain felt during the bust. While we find significant
increases in speculative trading, we do not find that more speculative trading resulted in bigger
losses or more volatility. Third, while many theoretical models have examined speculative
trading, fewer papers have documented the role of speculation empirically. While our analysis is
focused on the REIT market, our results have broader implications and support the model
developed by Llorente et al. (2002).
3. DATA DESCRIPTION
The data used in the analysis is obtained from a variety of sources. From the Center for
Research on Security Prices (CRSP), we obtain daily returns, volume, prices, shares outstanding,
etc. From Ziman, we obtain the REIT type (i.e. equity, mortgage, or hybrid) and property type
(i.e. diversified, residential, retail, etc.). Our sample time period extends from 1993 to 2011 and
we obtain the universe of REITs that are available on CRSP/Ziman Real Estate. We begin our
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analysis in 1993 to coincide with the modern REIT era. The total number of unique REITs is
5004. The total number of REIT-year observations is 3,814. Similarly, from CRSP we gather
data on the universe of non-REITs. In the subsample of non-REITs, we have 17,795 unique
securities and 139,031 non-REIT-year observations. From CRSP/Ziman, we obtain the
following property types for our sample of REITs: Diversified, Retail, Residential,
Industrial/Office, Self Storage, Hotel/Lodging, and Healthcare. Of the 500 (3,814) REITs
(REIT-year observations), CRSP/Ziman does not provide a property focus for 9 REITs (15
REIT-year observations). Further, CRSP/Ziman classifies the property focus of 4 REITs (10
REIT-year observations) as “unknown” and 30 REITS (207 REIT-year observations) as
“unclassified”. In the case that CRSP/Ziman does not provide a property type, or provides a
property type of “unknown” or “unclassified”, we classify these REITs with a property type of
“Other”.
To provide an estimate of speculative trading, we follow Llorente, Michaely, Saar, and Wang
(2002), who examine the dynamic relation between returns and volume of individual securities.
They argue that after controlling for volume, hedging trades will generate negatively
autocorrelated returns while speculative trades will generate positively autocorrelated returns.
We closely follow the empirical methods of Llorente et al. (2002) when estimating speculative
trading. For instance, we estimate daily turnover, which is equal to the ratio of daily volume to
shares outstanding. Lo and Wang (2000) indicate that the daily time series of turnover is
nonstationary, so Llorente et al. (2002) detrend the time series and take the log of turnover. On
days when volume is zero, they add a small constant (0.00000255), which has been shown to
maximize the likelihood of normally distributed trading volume at the daily level (Richardson,
4 We also analyze only equity REITs identified by Feng, Price, and Sirmans (2011) and find even stronger results. Results for the full sample are reported so that differences between REIT type can be analyzed in our multivariate analysis.
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Sefcik, and Thompson, 1986; Ajinkya and Jain, 1989; Cready and Ramanan, 1991). We
calculate our measures of turnover in the following way.
logturnoveri,t = log(turnoveri,t + 0.00000255) (1)
Vi,t = ∑!
=
!
1
200,, log
200
1log
s
titi turnoverturnover (2)
Vi,t is our measure of trading activity that is used to estimate speculative trading and is
obtained by taking the difference between the log of turnover and mean of the log of turnover
from day t-1 to t-200, where day t is the current trading day. Using CRSP daily returns and Vi,t
we then estimate the following time series equation for the universe of securities in our sample.
Ri,t+1 = β0 + β1Ri,t + β2Ri,t!Vi,t+ εi,t+1 (3)
Equation (3) shows a simple autoregressive formula where daily returns for each stock on
day t+1 are regressed on daily returns for each stock on day t. Llorente et al. (2002) include the
interaction between Ri,t and Vi,t in equation (3) to obtain the estimate for speculative trading. The
idea is that the larger (and more positive) the estimate for β2, the more likely that trading activity
increases the return autocorrelation. Therefore, the estimate for β2 is our estimate for speculative
trading according the model in Llorente et al. (2002). While the argument by Llorente et al.
(2002) that positively autocorrelated returns proxy for speculative trading is fairly
straightforward, the idea of hedging trades for an individual security is somewhat awkward.
They also describe hedging trades as allocational shocks, which we think is more representative.
In other words, trades are being made for allocation or liquidity reasons, not because of any
information or expectation related to future prices. Regardless of how a negative estimate of β2
is described, we think that Llorente et al. have developed a useful measure that empirically
identifies speculative trading for individual securities. We recognize, however, that the relation
between speculative trading and hedging might not be as simple as the linear model proposed by
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Llorente et al. (2002). And that using the estimate of β2 as a proxy for the level of speculative
trading assumes a linear relation that, if misspecified, could result in incorrect inferences.
Therefore, in unreported tests, we conduct a variety of robustness tests in which we censor the
estimate of β2 to only be positive. That is, we approximate speculative trading using the estimate
of β2 when β2 is positive – zero otherwise. We also use a censored estimate of β2 to equal this
estimate when the estimate is in the 75th percentile (in a particular year), zero otherwise. We also
use a simple binary specification in which speculative trading is equal to 1 if β2 is in the 75th
percentile, zero otherwise. We replicate the analysis below using these censored estimates in
various Tobit regressions and find results consistent with the reported findings. We choose to
focus our attention on results that use the continuous estimate of β2 because it is easier to
interpret and directly comparable to other papers that use Llorente et al. (2002).
Table 1 reports statistics that summarize our sample. Panel A reports the summary statistics
for our sample of REITs while Panel B shows the statistics for our sample of non-REITs. The
variables we analyze in the table include Spec, which is our measure of speculative trading or our
estimate of β2 from equation (3) for each stock in each year during our sample time period. We
also include the market capitalization (Size), the price of each security (Price), and the daily
turnover (Turn). We estimate a daily Capital Asset Pricing Model during each year for each
stock and obtain an estimate for systematic risk (Beta). Using the residual returns from the daily
CAPM model, we calculate the standard deviation of these residuals in order to calculate
idiosyncratic volatility (IdioVolt).
Panel A shows that the average REIT has a Spec of 0.0010, an average market capitalization
of $1.13 billion, and an average price is $21.30. The average REIT has a turnover of 1.1508,
suggesting that approximately 115% of shares outstanding are traded during a particular year.
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The average REIT has a Beta of 0.5569 and an average idiosyncratic volatility of 2.23%. Panel
B shows that the average non-REIT has Spec of 0.0083, Size of $1.93 billion, a Price of $30.04,
a Turn of 1.9723, a Beta of 1.0851, and an IdioVolt of 0.0379.
Panel C reports the difference between the means reported in Panels A and B. Comparing
Spec in Panels A and B, we see that Spec is more than eight times larger for our sample of non-
REITs. However, while the difference is -0.0073, the difference is not statistically different from
zero (t-statistic = -0.55). In column [2], we find that the difference in Size between Panels A and
B is -$805,204,614 and is statistically different from zero (t-statistic = -4.56) indicating that
REITs are generally smaller, in terms of market capitalization, than non-REITs. Column [3]
shows that the difference in Price is statistically close to zero (difference = -8.74, t-statistic = -
0.56). In column [4], the difference in Turn is -0.8215 and is statistically significant (t-statistic =
-3.66) indicating that non-REITs generally have more trading activity than REITs. Finally,
columns [5] and [6] show that the differences in systematic risk (Beta) and idiosyncratic risk
(IdioVolt) between Panels A and B are both statistically significant (differences = -0.5282, -
0.0156; with respective t-statistics = -4.72, -28.29), indicating that non-REITs have more
systematic and idiosyncratic risk than REITs. The findings from Table 1 indicate that while
various characteristics differ between our samples of REITs and non-REITs, the measure of
speculative trading is similar between samples during our time period of 1993 to 2011.
4. EMPIRICAL TESTS
Next, we begin to address our research question examining speculative trading in REITs
during the period when real estate prices were the highest. We begin by plotting median real
estate prices according to S&P/Case Shiller. The top panel of Figure 1 shows median real estate
prices as well as the normalized price of the Ziman REIT index from 1993 to 2011. As seen in
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the figure, there is generally an upward trend in median home prices from 1993 to 2001.
However, in 2003, both median home prices and prices of the Ziman index were higher thant
they had been in the previous 10 years. Prices seem to peak in 2005 and 2006 and then
subsequently decline. In the second panel of Figure 1, we focus on the percentage change in
median real estate prices and 3-year cumulative returns of the Ziman REIT index. We find that
in the second panel, boom period in REITs and in median home prices appears to occur between
2004 to 2006. Beginning in 2002, and continuing until 2005, the percentage change in median
home prices was above 10%. In 2004 and 2005, the percentage change in prices was above
14.5%. The 3-year cumulative return of the Ziman REIT index is highest in 2005 at nearly 70%.
The bottom panel of the figure shows speculative trading for our sample of REITs and non-
REITs. While the results in Table 1 show that the mean estimates of speculative trading between
samples are statistically similar when examining the entire time period, we find some variation in
speculative trading across time. In particular, we find that speculative trading in REITs becomes
observationally higher than speculative trading in non-REITs from 2002 to 2008 (with the
exception in 2005). We recognize that other security-specific factors might be influencing this
variation so we are cautious when inferring anything from the figure.
3.1 Speculative Trading in REITs and Non-REITs During Periods With Increasing Real Estate
Prices – Univariate Tests
Next, we test for statistical differences in speculative trading between samples across time.
Table 2 reports our estimates for speculative trading for various time windows. Panel A reports
the results for relatively equal time periods. The panel shows that Spec for REITs is 0.0211
during the five year period of 1993 to 1997 while Spec for non-REITs is 0.0258. The difference
is -0.0048 but statistically close to zero (t-statistic = -1.05). During the five-year period from
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1998 to 2002, the mean estimate for speculative trading in REITs is -0.0180 while the mean
estimate for speculative trading in non-REITs is 0.0079. The difference in this second time
window is also statistically close to zero (difference = -0.0026, t-statistic = -1.20). During the
period 2003 to 2006, we find that Spec for REITs is 0.0278 while Spec for non-REITs is 0.0118.
The difference is 0.0160 and is statistically different from zero (t-statistic = 2.92). In economic
terms, Spec for REITs is nearly 2.4 times larger than Spec for non-REITs during the period. We
also note that this period corresponds with a period when real estate prices were highest (see
Figure 1). Finally, in the last five-year period from 2007 to 2011, the mean estimate for
speculative trading for REITs is -0.0270 while the mean estimate for speculative trading for non-
REITs is -0.0140. The difference in means is again statistically close to zero (difference = -
0.0130, t-statistic = -1.57).
Panel B reports time windows that directly correspond to Figure 1. First, the time period
2002 to 2005 is the period when the percentage change in median real estate prices was the
highest and the 3-year cumulative return of the Ziman REIT index. The average percentage
change during this four-year period was more than 12.6%. Second, the time period from 2003 to
2007 represents a period when real estate prices and the prices of Ziman REIT index were
highest. Third, the period from 2008 to 2011 represented a period of sharp decline in real estate
prices. Table 2 Panel B shows that the mean estimate for Spec for REITs was 0.0382 during the
first time window. The mean estimate for Spec for non-REITs was 0.0160. The difference is
both economically and statistically different from zero (difference = 0.0222, t-statistic = 3.88). In
particular, Spec for REITs during this period was approximately 2.4 times larger than Spec for
non-REITs. In our second time period (2003 to 2007), we also find that the mean estimate for
Spec was significantly larger than the mean estimate for Spec for our sample of non-REITs
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(difference = 0.0194, t-statistic = 2.68). In economic terms, the Spec for REITs was nearly 3.5
times larger than Spec for our sample of non-REITs. Finally, we find that, during our last time
period (2008 to 2011), the mean estimate for speculative trading for our REIT sample was -
0.0410 while the mean estimate of speculative trading for our non-REIT sample was -0.0160.
The difference is statistically different from zero and suggests that, according to the model in
Llorente et al. (2002), more hedge trading occurred in REITs than in non-REITs during this last
time period.
The results from Table 2 indicate that while our estimates for speculative trading are similar
across samples when examining the entire time period, sample differences in speculative trading
occur during the most recent time period. Further, the univariate tests in Table 2 seem to
indicate that speculative trading in REITs was more prevalent during periods when real estate
prices were highest and were growing at the fastest rate.
We continue our tests by estimating univariate correlations between our estimate for
speculative trading and other security-specific characteristics that we use in our multivariate
analysis below. Table 3 reports Spearman correlations between Spec, and several variables that
are defined in Table 1 (i.e. Size, Price, Turn, Beta, and IdioVolt). After pooling our sample of
REITs and non-REITs together, we also include an indicator variable REIT, which equals one if
a particular security is a REIT – zero otherwise. Panel A reports the correlations using all years
in our sample time period. Focusing on the first row of Panel A, we observe that speculative
trading is negatively related to Size, Price, Turn, and Beta, and positively to IdioVolt. We also
find that Spec is unrelated to the dummy variable REIT (correlation = -0.0030, p-value = 0.264).
These results tend to support our findings in Table 1 that show that speculative trading is similar
between our two samples when examining the entire time period.
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We also note that there exists a strong cross-correlation between Size, Price, Turn, Beta, and
IdioVolt. In column [7] we see that the REIT indicator variable is directly related to Size and
Price. However, we find that the variable REIT is inversely related to Turn, Beta, and IdioVolt.
For brevity, we report the results for the time period 2003 to 2007 in panel B also similar
results are found when we examine the time period 2002 to 2005. In the first row of Panel B, we
find that speculative trading is negatively related to Size, Price, Turn, Beta, and IdioVolt.5
However, we find that Spec is also positively related to the indicator variable REIT supporting
our findings in Table 2.
3.2 Speculative Trading in REITs and Non-REITs During Periods With Increasing Real Estate
Prices – Multivariate Tests
Thus far, Tables 2 and 3 have shown that our estimate of speculative trading for REITs is
similar in magnitude to the estimate for speculative trading for non-REITs when examining the
period prior to 2002. However, beginning in 2003 and continuing to 2007, the level of
speculative trading in REITs is markedly higher than the level of speculative trading in non-
REITs. This observed increase in speculative trading in our REIT sample corresponds with a
period when real estate prices and Ziman REIT index prices were highest. We recognize,
however, the need to control for other factors that might influence the level of speculative
trading. Therefore, we estimate the following equation using pooled data that includes both the
REIT-year observations and the non-REIT-year observations.
Speci,t = β0 + β1ln(Sizei,t ) + β2ln(Pricei,t) + β3Turni,t + β4Betai,t + β5IdioVolti,t + β6REITi + εi,t (4)
The dependent variable is Spec, which is the Llorente et al. (2002) measure of speculative
trading. We include as independent variables the natural log of market capitalization (ln(Sizei,t )),
5 We note that the univariate correlation between size and speculative trading is only marginally significant in Panel B (p-value = 0.115).
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the natural log of price (ln(Pricei,t)), the share turnover (Turni,t), the level of systematic risk
(Betai,t), and the idiosyncratic volatility (IdioVolti,t). The variable of interest is the indicator
variable REIT, which equals one if the security is a REIT – zero otherwise. We report t-statistics
that control for two dimensional clustering although similar results are found when we use White
(1980) standard errors that control for conditional heteroskedasticity. Furthermore, we include
Year Fixed Effects in some of the econometric specifications.6 Given the level of cross
correlation that exists between the independent variables (see Table 2), we estimate variance
inflation factors to determine whether our results suffer from multicollinearity bias. Variance
inflation factors are relatively small and are each under three indicating that our findings do not
suffer from bias casued by multicollinearity. However, in Table 4, we report various
specifications of equation (4) by including different combinations of independent variables to
show that our results hold regardless of the control variables we include.
Table 4 Panel A reports the results for the entire sample time period. Columns [1] through
[3] show the results without controls for Year Fixed Effects. Column [1] shows that Spec is
negatively related to both the natural logs of Size and Price. However, the indicator variable
REIT produces an estimate that is statistically close to zero (estimate = -0.0031, t-statistic = -
0.47). Column [2] provides some evidence that idiosyncratic volatility is directly related to
speculative trading activity. However, Turn and Beta do not provide estimates that are
statistically different from zero. The variable of interest, REIT, again produces an estimate that
is both economically and statistically insignificant (estimate = -0.0022, t-statistic = -0.32). In the
full model (without Year Fixed Effects), column [3] shows that Spec is negatively related to the
natural logs of Size and Price and is unrelated to Turn, Beta, and IdioVolt. Further, the estimate
6 Because we include the variable REIT, we cannot include security fixed effects because the variable REIT does not vary across the time series and therefore, cross-sectional fixed-effects estimates will be inconsistent.
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for REIT is -0.0024 and is statistically close to zero (t-statistic = -0.34). We are able to draw
similar conclusions when examining the results in columns [4] through [6] that control for Year
Fixed Effects. A few results are noteworthy. First, the natural log of Size produces an
insignificant estimate in columns [4] and [6]. Second, the estimate for the indicator variable
REIT is statistically close to zero in all three columns. These findings support the results in
columns [1] through [3] and further provide evidence that, when examining the entire sample
time period, the level of speculative trading in REITs is similar to the level of speculative trading
in non-REITs.
Panel B presents the results when examining the period when real estate prices and Ziman
index prices were highest (years 2003 to 2007). We only discuss the findings in column [6] for
brevity. During this period, we find that the natural log of Size produces a positive and
significant estimate (estimate = 0.0034, t-statistic = 2.14). Turn, on the other hand, produces an
estimate that is negative and marginally significant (estimate = -0.0001, t-statistic = -1.69).
Again, the indicator variable REIT produces an estimate that is both positive and significant
(estimate = 0.0176, t-statistic = 2.29). Relative to the mean estimate for speculative trading for
REITs during the entire time period, the estimate is more than 17 times greater than the mean.
As before, the estimate for REIT is positive and significant in each of the columns in Panel B of
Table 4 and suggests that the level of speculative trading in REITs was greater than the level of
speculative trading in non-REITs during the period when real estate prices were highest. Similar
results are found when we use various time windows to capture the real estate boom years. We
also note that the regression results are robust when we redefine the dependent variable as the
estimate of β2 in equation (3) if the estimate is in the 75th percentile in a particular year – zero
otherwise. The Tobit regression results are qualitatively similar to those reported in Table 4.
18
3.3 Speculative Trading in REITs During the Boom and Bust Periods – Univariate Tests
Next, we compare the level of speculative trading in REITs during the periods when real
estate prices were highest and the growth rate in real estate prices was the highest. Table 5
reports the level of speculative trading in REITs for the period when real estate prices and Ziman
index prices were highest and the rest of the sample time period. For exposition, we denote the
period 2003 to 2007 as the boom period and the years 1993 to 2001, and years 2006 to 2011 as
the non-boom period. In the first row of Panel A, we find that the mean level of speculative
trading is -0.0090 during the non-boom period. However, we find that the mean level of Spec is
0.0272 during the boom period. The difference is -0.0362 (t-statistic = -3.82). In economic
terms, the level of speculative trading in REITs is nearly four times greater during the boom
period than during the non-boom period. In rows 2 through 4, we partition the REITs into equity
REITs, mortgage REITs, and hybrid REITs to determine which type of REIT drives the observed
increase in the level of speculative trading. We find that the level of speculative trading in equity
REITs during the boom period is significantly greater than the level of speculative trading in
equity REITs during the non-boom period (difference = -0.0457, t-statistic = -4.09). Further, we
do not find that speculative trading during the boom period is statistically different than
speculative trading during the non-boom period for either mortgage REITs or hybrid REITs.
These results indicate that equity REITs drive the observed increase in speculative trading during
the boom period.
Next, we examine whether the property focus of REITs play a role in the level of speculative
trading during the boom period. Using the property-type focuses from CRSP/Ziman, we
partition the level of speculative trading during the boom period and non-boom period for each
19
of the eight property focuses.7 Focusing primarily on column [3], we find that REITs with a
property focus of “Diversified”, “Residential”, “Industrial/Office”, and “Hotel/Lodging” have
higher levels of speculative trading during the boom period than during the non-boom period.
3.4 Speculative Trading in REITs During the Boom and Bust Periods – Multivariate Tests
We recognize the need to control for other factors that influence the level of speculative
trading in REITs in a multivariate framework. Therefore, we estimate the following equation
using pooled REIT-year observations.
Speci,t = β0 + β1ln(Sizei,t ) + β2ln(Pricei,t) + β3Turni,t + β4Betai,t + β5IdioVolti,t
+ β6DYEARt +εi,t (5)
Once again, the dependent variable is Spec, which is the Llorente et al. (2002) measure of
speculative trading. As independent variables, we include the natural log of market
capitalization (ln(Sizei,t )), the natural log of price (ln(Pricei,t)), the share turnover (Turni,t), the
level of systematic risk (Betai,t), and the idiosyncratic volatility (IdioVolti,t). The variable of
interest is the indicator variables DYEAR. DYEAR is defined as an indicator variable (D03-07)
that equals one during the years 2003 through 2007 – zero otherwise. We report t-statistics that
control for two dimensional clustering although similar results are found when we use White
(1980) standard errors that control for conditional heteroskedasticity. In order to test for the
presence of multicollinearity, we again estimate variance inflation factors. We find that inflation
factors are each below 3.30 indicating that our results are not subject to multicollinearity bias.
When including DYEAR, we do not include Year Fixed Effects in order to meet the full rank
condition required for consistent estimates. Like previous tables, we estimate different versions
of equation (5) to show that the main inferences that we can draw from our results are robust to
7 We note that CRSP/Ziman also includes the property focus “mortgage”, but nearly all of these REITs are considered mortgage REITS, so we did not include mortgage as a property type as the results are almost identical to the REIT-type “mortgage”.
20
the inclusion of different combinations of control variables. As before, our results are
qualitatively similar across columns so, for brevity, we only discuss the results of the full model
(column [3]).
Column [3] shows that Turn produces a negative estimate (estimate = -0.0028, t-statistic = -
2.47). All of the other control variables produce estimates that are statistically close to zero. We
do find, however, that the variable D03-07 produces a reliably positive estimate (estimate =
0.0411, t-statistic = 5.36). In terms of magnitude, the estimate for D03-07 is more than 40 times
greater than the mean estimate for Spec for REITs during the entire time period (Table 1). These
results are similar in sign and magnitude to those in columns [1] and [2] and indicate that the
level of speculative trading in REITs was substantially higher during the period when real estate
prices and REIT prices were the greatest.
Next, we extend the tests from Table 6 to include the REIT type. In Table 5, we found that
the higher levels of speculative trading in REITs during the period when real estate prices were
highest were driven by equity REITs. To test for the effect of REIT type in a multivariate
framework, we extend equation (5) in the following way.
Speci,t = β0 + β1ln(Sizei,t ) + β2ln(Pricei,t) + β3Turni,t + β4Betai,t + β5IdioVolti,t + β6E-REITi +
β7DYEARt + β8E-REITi!DYEARt + εi,t (6)
As before, the dependent variable is Spec. As independent variables, we include the natural
log of market capitalization (ln(Sizei,t )), the natural log of price (ln(Pricei,t)), the share turnover
(Turni,t), the level of systematic risk (Betai,t), and the idiosyncratic volatility (IdioVolti,t). We
also include the indicator variable DYEAR, which is equal to the indicator variable D03-07.
D03-07 equals one when the years are between 2003 and 2007. We include an indicator variable
capturing whether the REIT is an equity REIT (E-REIT). Finally, we also include an interaction
between these indicator variables. As before, we report t-statistics that control for two
21
dimensional clustering although similar results are found when we use White (1980) standard
errors that control for conditional heteroskedasticity.8
Table 7 reports the results from estimating equation (6). Column [1] is similar to column [3]
(in the previous table. The difference however, is that we include the indicator variable E-REIT.
Column [1] shows that the estimate for E-REIT is statistically close to zero (estimate = 0.0038, t-
statistic = 0.31). However, we still find that the variable D03-07 produces a reliably positive
estimate (estimate = 0.0414, t-statistic = 6.01). Column [2] shows the results when we include
the interaction variable. Consistent with our univariate findings in Table 5, we see that the
interaction estimate is positive and significant (estimate = 0.0465, t-statistic = 3.08) indicating
that the increase in speculative trading during years 2002 to 2005 is driven by equity REITs as
opposed to mortgage REITs or hybrid REITs. Further, we find that the estimate for D03-07 is
positive but statistically close to zero (estimate = 0.0049, t-statistic = 0.45). This statistically
insignificant estimate indicates that the level of speculative trading in mortgage REITs and
hybrid REITs does not increase during the period 2003 to 2007.
We continue our multivariate tests by estimating the following equation using pooled REIT-
year data.
Speci,t = β0 + β1ln(Sizei,t ) + β2ln(Pricei,t) + β3Turni,t + β4Betai,t + β5IdioVolti,t + β6DYEARt +
β7PropTypei + β7DYEARt!PropTypei +εi,t (7)
The dependent variable and the independent variables have been defined in the previous
equation with one exception. Instead of including the indicator variable E-REIT, we include a
different indicator variable PropType. We define PropType seven different ways. First,
PropType is defined as Diverse, which equals one if the property focus of the REIT is classified
8 We should note that we do not include Year Fixed Effects because doing so would violate the full rank requirement for consistent estimates. In particular, the indicator variable DYEAR and Year Fixed Effects are linear functions of each other.
22
as “Diversified” according the CRSP/Ziman – zero otherwise. Second, we define PropType as
Retail, which is equal to one if the property focus is “Retail” – zero otherwise. Third, we define
PropType as Resident, which is equal to one if the property focus is “Residential” – zero
otherwise. Fourth, we define PropType as Office, which is equal to one if the property focus is
“Industrial/Office” – zero otherwise. Fifth, we define PropType as Storage, which is equal to one
if the property focus is “Self Storage” – zero otherwise. Sixth, we define PropType as Lodging,
which is equal to one if the property focus is “Hotel/Lodging” – zero otherwise. Finally, we
define PropType as Health, which is equal to one if the property focus is “Healthcare” – zero
otherwise. The omitted category is “Other” which accounts for REITs that are unclassified or
unknown according to the CRSP/Ziman data. As before, we include an indicator variable
DYEAR, which is defined as D03-07. The variable of interest is the interaction between DYEAR
and PropType. We again report t-statistics that account for two dimensional clustering and do
not control for Year Fixed Effects in order to obtain full rank.
In Table 5, we provide evidence that the increase in speculative trading in REITs is driven
primarily by REITs with “Diversified”, “Residential”, “Industrial/Office”, and “Hotel/Lodging”
property focuses. The estimation of equation (7) and the interaction estimates in particular will
allow us to make inferences about which property type attracts the most speculative trading after
including some control variables. Table 8 reports the results. In each of the columns, we find
that the estimate for D03-07 is positive and significant indicating that regardless of which
interaction variable is included, REITs generally had higher levels of speculative trading during
the period when real estate prices grew the most. Column [1] shows that the interaction between
D03-07 and Diverse is statistically close to zero (estimate = -0.0062, t-statistic = -0.32). In fact,
in each column, the interaction variables produce estimates that are statistically insignificant.
23
Some other noteworthy results are that the indicator variables Diverse, Resident, and Storage
produce reliably positive estimates indicating that, in general, REITs with these types of the
property focuses have higher levels of speculative trading. The insignificant interaction
estimates, however, suggest that after controlling for other factors that might influence the level
of the speculative trading, no particular property type drives the observed increase in speculative
trading during the period when real estate prices were growing the most.
3.5 Speculative Trading and Future REIT Returns – Univariate Tests
In our last set of tests, we examine whether the level of speculative trading contributed to the
substantial crash in REITs during the year 2008. In unreported results, our sample of REITs
underperformed during 2008. The average CRSP raw return for our REIT sample during 2008
was -36.7%. Table 9 provides some univariate tests. In particular, the table reports the REIT
returns in 2008 across four portfolios of REITs that are based on the level of speculative trading
during the period 2003 to 2007. Quartile I (Q I) contains the REITs with the least speculative
trading during the time period while Quartile IV (Q IV) contains the REITs with the most
speculative trading during the time period. Column [5] reports the difference in 2008 REIT
returns between extreme quartiles along with a corresponding t-statistic testing for significance
of the difference. We report four different measures of returns. First, we include CRSP raw
returns. Second, we calculate adjust returns (Adj. Returns) as the difference between a particular
REIT raw return and the return of the CRSP Ziman (value-weighted) index. Third, we estimate a
daily Fama-French Three-Factor model and obtain the residual returns. Therefore, FF3F Returns
are the cumulative 2008 returns from daily FF3F residual returns. Similarly, Fama-French Four-
Factor Returns (FF4F Returns) are the cumulative 2008 returns from daily FF4F residual returns,
where the fourth factor is the momentum factor.
24
In the first row of Table 9, we find that 2008 CRSP raw returns are neither increasing nor
decreasing across speculative trading quartiles. Column [5] shows that the difference between
extreme quartiles is statistically close to zero (difference = 0.1736, t-statistic = 1.13). Similar
results are found when we examine Adj. Returns, FF3F Returns, and FF4F Returns. In each
case, the difference between extreme quartiles is effectively zero indicating that the level of
speculative trading during the period when real estate prices grew the most is orthogonal to the
large price decline in REITs during 2008.
3.6 Speculative Trading and Future REIT Returns – Multivariate Tests
We recognize the need to control for other factors that possibly influenced the level of 2008
REIT returns. Table 10 reports the results from estimating the following equation using cross-
sectional data.
2008Returnsi = β0 + β1ln(Sizei,) + β2ln(Pricei) + β3Turni + β4Betai + β5IdioVolti,+ β6Speci +εi (8)
The dependent variables include our four measures of returns during 2008 (Raw Returns,
Adj. Returns, FF3F Returns, and FF4F Returns). As independent variables, we include the
natural log of market capitalization (ln(Sizei)), the natural log of price (ln(Pricei)), the share
turnover (Turni), the level of systematic risk (Betai), and the idiosyncratic volatility (IdioVolti).
The variables of interest are the variable Spec, which is Llorente et al. (2002) measure of
speculative trading. All independent variables are measured from years 2003 to 2007. We
report t-statistics that control conditional heteroskedasticity using White (1980) standard errors.
The results are qualitatively similar across columns so for brevity, we will only discuss our
findings in column [1]. We find that Size, Price, Turn, Beta, and IdioVolt produce estimates that
are statistically close to zero. These results indicate that none of the control variables, which are
measured from 2002 to 2005 in this column, explain the variation in 2008 REIT returns.
25
Furthermore, we find that the estimate for the variable of interest Spec is also statistically close
to zero (estimate = 1.1983, t-statistic = 1.34). The insignificant estimate for Spec indicates that,
while REIT speculative trading levels were unusually high during 2003 to 2007, the high levels
of speculative trading did not contribute the substantial price decline of REITs in 2008. In each
column, the estimate for Spec is statistically close to zero. These results support our univariate
tests in Table 9 and suggest that the unusual levels of speculative trading in REITs during the
period when real estate prices were increasing did not affect the REIT returns in 2008.
5. CONCLUSION
To examine the role of speculative trading in REITs during the most recent boom and bust
period in real estate, we utilize a measure of speculative trading developed by Llorente et al.
(2002). They argue that hedging trades will generate negatively autocorrelated returns while
speculative trades will generate positively autocorrelated returns. While we find no differences
between the speculative trading in REITs and non-REITs from 1993 to 2011, we do find
differences in speculative trading for specific subsamples. Corresponding with the period when
median home prices and prices of the Ziman REIT index were highest (2003-2007), we find
significant differences in speculative trading between REITs and non-REITs. In unreported
results, we find similar results for other various time windows around this period. Furthermore,
the differences in speculative trading appear to be driven by equity REITs, but not by any
particular property type, although we do find that some property types generally have more
speculative trading.
To better understand the impact of speculative trading on market participants, we also
examine the relation between speculative trading during the boom years and returns after the
bust. While many have recently criticized the role of speculators in financial markets, we do not
26
find evidence that speculative trading during the real estate boom period attenuated the drastic
decline in REIT prices after 2007. That is, REITs with more speculative trading during the boom
years did not experience larger losses after the bust. While speculators make an easy target for
those worried about increased market volatility, at least in REIT markets, we do not find
evidence that increased speculation harmed market participants.
27
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29
Table 1 Summary Statistics The table reports statistics that describe our samples. Panel A reports the results for our sample of equity REITs while Panel B shows the results for our sample of non-REITs. In Panel C, we report the difference in means between Panels A and B. Spec is the Llorente et al. (2002) measure of speculative trading. Size is market capitalization obtained from CRSP. Price is the CRSP closing price. Turn is share turnover and is calculated by dividing volume by shares outstanding. Beta is obtained from estimating a standard CAPM model using daily returns during each year. IdioVolt is the standard deviation of daily residual returns (or the residuals from the daily CAPM model). We include the universe of REITs and non-REITs with data available on CRSP from 1990 to 2010. The summary statistics that are reported are means and medians across the entire time period. The differences in means in Panel C are accompanied with a t-statistic testing whether the differences are significantly different that zero according to a standard two-tailed t-test. *,**.*** denotes statistical significance at the 0.10, 0.05, and 0.01 levels, respectively. Panel A. REIT Sample Spec Size Price Turn Beta IdioVolt
[1] [2] [3] [4] [5] [6] Mean Median
0.0010 0.0132
1,127,715,681 384,689,375
21.30 17.36
1.1508 0.7312
0.5569 0.4980
0.0223 0.0155
Panel B. Non-REIT Sample Mean Median
0.0083 0.0150
1,932,920,295 157,746,938
30.04 13.16
1.9723 0.8445
1.0851 0.8637
0.0379 0.0287
Panel C. Difference between Panels A and B Difference t-statistic
-0.0073 (-0.55)
-805,204,614***
(-4.56)
-8.74
(-0.56)
-0.8215***
(-3.66)
-0.5282***
(-4.72)
-0.0156***
(-28.29)
30
Table 2 Speculative Trading in Sub Periods The table reports the Llorente et al. (2002) measure of speculative trading for various sub time periods. In Panel A, we report the coefficient for speculative trading in relatively equal time periods. In Panel B, however, we report sub time periods that related to the real estate boom period. For instance, from year 2003 to 2007, median home prices and prices of the Ziman REIT index were highest. Column [1] reports speculative trading from our sample of REITs while column [2] shows the results for our sample of non-REITs. Column [3] shows the difference between the two samples with a corresponding t-statistic tests for statistical significance. *,**.*** denotes statistical significance at the 0.10, 0.05, and 0.01 levels, respectively.
Panel A. Relatively equal Sub time periods
Spec for REITs
Spec for Non-REITs Difference between
Columns [1] and [2]
[1] [2] [3] 1993-1997 1998-2002 2003-2006 2007-2011
0.0211
-0.0180
0.0278
-0.0270
0.0258
0.0079
0.0118
-0.0140
-0.0048 (-1.05) -0.0026 (-1.20)
0.0160*** (2.92)
-0.0130 (-1.57)
Panel B. Distinct time periods relating to the growth in real estate prices 2002-2005 2003-2007 2008-2011
0.0382
0.0272
-0.0410
0.0160
0.0078
-0.0160
0.0222***
(3.88) 0.0194***
(2.68) -0.0250***
(3.68)
31
Table 3 Spearman Correlation Matrix The table reports Spearman Correlation coefficients for all years in the sample time period (Panel A) and years 2003 to 2007 (Panel B). Spec is the Llorente et al. (2002) measure of speculative trading. Size is market capitalization obtained from CRSP. Price is the CRSP closing price. Turn is share turnover and is calculated by dividing volume by shares outstanding. Beta is obtained from estimating a standard CAPM model using daily returns during each year. IdioVolt is the standard deviation of daily residual returns (or the residuals from the daily CAPM model). REIT is an indicator variable equal to unity if the security is a REIT – zero otherwise. We report corresponding p-values in brackets below each correlation coefficient. *,**.*** denotes statistical significance at the 0.10, 0.05, and 0.01 levels, respectively. Panel A. All Years – 1993 to 2011 Spec Size Price Turn Beta IdioVolt REIT
[1] [2] [3] [4] [5] [6] [7] Spec Size Price Turn Beta IdioVolt REIT
1.0000 -0.0980*** [0.000] 1.0000
-0.1103*** [0.000]
0.7301*** [0.000] 1.0000
-0.0546*** [0.000]
0.3168*** [0.000]
0.1990*** [0.000] 1.0000
-0.0228*** [0.000]
0.0873*** [0.000]
-0.0431*** [0.000]
0.3135*** [0.000] 1.0000
0.0423*** [0.000]
-0.5192*** [0.000]
-0.6594*** [0.000]
0.0935*** [0.000]
0.2118*** [0.000] 1.0000
-0.0030 [0.264]
0.0539*** [0.000]
0.0419*** [0.000]
-0.0227*** [0.000]
-0.0473*** [0.000]
-0.1191*** [0.000] 1.0000
Panel B. Years 2003 to 2007 Spec Size Price Turn Beta IdioVolt REIT
1.0000 -0.0085 [0.115] 1.0000
-0.0187*** [0.001]
0.6755*** [0.000] 1.0000
-0.0683*** [0.000]
0.3736*** [0.000] 0.2310 [0.000] 1.0000
-0.0336*** [0.000]
0.0823*** [0.000]
-0.0522** [0.000]
0.3133*** [0.000] 1.0000
-0.0323*** [0.000]
-0.4145*** [0.000]
-0.5895*** [0.000]
0.1588*** [0.000]
0.2899*** [0.000] 1.0000
0.0280*** [0.000]
0.0774*** [0.000]
0.0668*** [0.000]
-0.0140*** [0.009]
-0.0184*** [0.001]
-0.1228*** [0.000] 1.0000
32
Table 4 Regression Results The table reports the results from estimating the following equation for all years in the sample time period (Panel A) and years 2003 to 2007 (Panel B). Speci,t = β0 + β1ln(Sizei,t ) + β2ln(Pricei,t) + β3Turni,t + β4Betai,t + β5IdioVolti,t + β6REITi + εi,t
The dependent variable is Spec, which is the Llorente et al. (2002) measure of speculative trading. As independent variables, we include the natural log of market capitalization (ln(Sizei,t )), the natural log of price (ln(Pricei,t)), the share turnover (Turni,t), the level of systematic risk (Betai,t), and the idiosyncratic volatility (IdioVolti,t). The variable of interest is the indicator variable REIT, which equals one if the security is a REIT – zero otherwise. We report t-statistics that control for two dimensional clustering although similar results are found when we use White (1980) standard errors that control for conditional heteroskedasticity. In Columns [4] through [6], we also include Year Fixed Effects. *,**.*** denotes statistical significance at the 0.10, 0.05, and 0.01 levels, respectively. Panel A. All Years [1] [2] [3] [4] [5] [6] Intercept Ln(sizei,t) Ln(pricei,t) Turni,t
Betai,t
IdioVolti,t
REITi Year Fixed Effects
0.0688*** (3.42)
-0.0021*** (-2.76)
-0.0088*** (-2.80)
-0.0031 (-0.47)
No
-0.0044 (-0.64)
-5.78E-5 (-0.27) -0.0001 (-0.25)
0.3388* (1.83)
-0.0022 (-0.32)
No
0.0624*** (3.62)
-0.0021*** (-2.95)
-0.0076*** (-3.85) 2.5E-6 (0.01)
-4.1E-6 (-0.03) 0.0725 (0.41)
-0.0024 (-0.34)
No
0.0671*** (3.68)
-0.0005 (-0.72)
-0.0104*** (-3.22)
-0.0040 (-0.62)
Yes
0.0203** (2.24)
-1.89E-6 (-0.09)
-3.38E-6 (-0.25)
0.3303* (1.74)
-0.0029 (-0.42)
Yes
0.0612*** (3.57)
-0.0005 (-0.69)
-0.0095*** (-4.69) 8.6E-5 (0.38)
-1.3E-6 (-0.10) 0.0641 (0.36)
-0.0033 (-0.48)
Yes
Panel B. Years 2003 to 2007 Intercept Ln(sizei,t) Ln(pricei,t) Turni,t
Betai,t
IdioVolti,t
REITi Year Fixed Effects
-0.0494 (-1.50) 0.0032 (0.021) -0.0020 (-1.14)
0.0179*** (2.85)
No
0.0083 (0.75)
-0.0001* (-1.67) -0.0002 (-0.23) 0.0004 (0.01)
0.0192** (2.21)
No
-0.0559 (-0.94)
0.0032** (2.11)
-0.0008 (-0.13)
-0.0001** (-2.11) -0.0003 (-0.34) 0.1111 (0.24)
0.0183** (2.25)
No
-0.0407 (-1.39)
0.0034** (2.33)
-0.0018 (-1.01)
0.0174*** (2.93)
Yes
0.0221*** (3.20)
-0.0001 (-1.05) -0.0002 (-0.22) -0.0451 (-0.25)
0.0185** (2.26)
Yes
-0.0442 (-0.78)
0.0034** (2.14)
-0.0012 (-0.19)
-0.0001* (-1.69) -0.0002 (-0.36) 0.0579 (0.13)
0.0176** (2.29)
Yes
33
Table 5 Time Series Properties of Speculative Trading in REITs The table reports the Llorente et al. (2002) measure of speculative trading for REITs during the real estate boom period, which we define as years 2003 to 2007. The non-boom period in Panel A consists of years 1990 to 2002 and years 2008 to 2011. Column [3] reports the difference, along with corresponding t-statistics testing for statistical significance, between the boom period and non-boom periods, respectively. We also partition our sample of REITs into various property focuses. *,**.*** denotes statistical significance at the 0.10, 0.05, and 0.01 levels, respectively.
Non-Boom Period
Boom Period Difference between
Columns [1] and [2]
[1] [2] [3] All REITs Equity REITs Mortgage REITs Hybrid REITs Diversified Retail Residential Industrial/Office Storage Hotel/Lodging Healthcare Other
-0.0090
-0.0110
-0.0020
0.0181
0.0112
-0.0390
0.0027
-0.0070
0.0218
0.0004
-0.0060
-0.0100
0.0272
0.0347
0.0006
0.0068
0.0415
0.0208
0.0381
0.0492
0.0085
0.0377
0.0207
0.0188
-0.0362*** (-3.82)
-0.0457*** (-4.09) -0.0026 (-0.18) 0.0113 (0.52)
-0.0303* (-1.79) -0.0598 (-1.57)
-0.0354** (-2.40)
-0.0562*** (-4.62) 0.0133 (0.40)
-0.0373** (-2.57) -0.0260 (-1.49) -0.0288 (-1.59)
34
Table 6 Regression Results – The table reports the results from estimating the following equation for all years in the sample time period using only our sample of REITs. Speci,t = β0 + β1ln(Sizei,t ) + β2ln(Pricei,t) + β3Turni,t + β4Betai,t + β5IdioVolti,t + β6D02-05t + β7D03-07t +εi,t
The dependent variable is Spec, which is the Llorente et al. (2002) measure of speculative trading. As independent variables, we include the natural log of market capitalization (ln(Sizei,t )), the natural log of price (ln(Pricei,t)), the share turnover (Turni,t), the level of systematic risk (Betai,t), and the idiosyncratic volatility (IdioVolti,t). The variable of interest is the indicator variables DYEAR. DYEAR is defined as an indicator variable D03-07, which equals unity when the years are between 2003 and 2007 – zero otherwise. We report t-statistics that control for two dimensional clustering although similar results are found when we use White (1980) standard errors that control for conditional heteroskedasticity. *,**.*** denotes statistical significance at the 0.10, 0.05, and 0.01 levels, respectively. [1] [2] [3] Intercept Ln(sizei,t) Ln(pricei,t) Turni,t
Betai,t
IdioVolti,t
D03-07t Year Fixed Effects
0.0955 (1.13)
-0.0056 (-1.25) 0.0011 (0.27)
0.0408*** (5.59)
No
-0.0094 (-0.87)
-0.0043* (-1.90) -3.3E-5 (-0.28)
0.2251* (1.84)
0.0386*** (4.16)
No
0.0772 (0.87)
-0.0044 (-1.08) 0.0003 (0.06)
-0.0028** (-2.47) -4.1E-6 (-0.04) 0.0415 (0.16)
0.0411*** (5.36)
No
35
Table 7 Regression Results by REIT Type The table reports the results from estimating the following equation for all years in the sample time period using only our sample of REITs. Speci,t = β0 + β1ln(Sizei,t ) + β2ln(Pricei,t) + β3Turni,t + β4Betai,t + β5IdioVolti,t + β6REIT-Typei + β7DYEARt + β8REIT-Typei!DYEARt + εi,t
The dependent variable is Spec, which is the Llorente et al. (2002) measure of speculative trading. As independent variables, we include the natural log of market capitalization (ln(Sizei,t )), the natural log of price (ln(Pricei,t)), the share turnover (Turni,t), the level of systematic risk (Betai,t), and the idiosyncratic volatility (IdioVolti,t). The variable of interest is the indicator variable DYEAR, which is equal to D03-07. D03-07 equals one when the years are between 2003 and 2007 – zero otherwise. We also include a dummy variable capturing whether the REIT is an equity REITs (E-REIT). Finally, we also include an interaction between these dummy variables. We report t-statistics that control for two dimensional clustering although similar results are found when we use White (1980) standard errors that control for conditional heteroskedasticity. *,**.*** denotes statistical significance at the 0.10, 0.05, and 0.01 levels, respectively. [1] [2] Intercept Ln(sizei,t) Ln(pricei,t) Turni,t
Betai,t
IdioVolti,t
E-REITi D03-07t E-REITi !D03-07t Year Fixed Effects
0.0766 (0.88)
-0.0045 (-1.05) -0.0002 (-0.05)
-0.0027** (-2.52) -8.2E-6 (-0.07) 0.0389 (0.15) 0.0038 (0.31)
0.0414*** (6.01)
No
0.0923 (1.09)
-0.0047 (-1.12) -4.1E-5 (-0.01)
-0.0024** (-2.18) 1.8E-5 (0.18) 0.0124 (0.05) -0.010 (-0.67) 0.0049 (0.45)
0.0465*** (3.08)
No
36
Table 8 Regression Results The table reports the results from estimating the following equation for all years in the sample time period using only our sample of REITs. Speci,t = β0 + β1ln(Sizei,t ) + β2ln(Pricei,t) + β3Turni,t + β4Betai,t + β5IdioVolti,t + β6DYEARt + β7PropTypei + β7DYEARt!PropTypei +εi,t
The dependent variable and the independent variables have been defined previously. However, we include an indicator variable DYEAR, which is defined as D03-07, which equals one when the years are between 2003 and 2007 – zero otherwise.. PropType is a dummy variable capturing the property type of the REIT. We omit the property type dummy variable is Other. We also include an interaction between the DYEAR and PropType. t-statistics control for two dimensional clustering *,**.*** denotes statistical significance at the 0.10, 0.05, and 0.01 levels, respectively. [1] [2] [3] [4] [5] [6] [7] Intercept Ln(sizei,t) Ln(pricei,t) Turni,t
Betai,t
IdioVolti,t
D02-05t Diversei Retaili Residenti Officei Storagei Lodgingi Healthi
D03-07t!Diversei D03-07t!Retaili D03-07t!Residenti D03-07t!Officei D03-07t!Storagei D03-07t!Lodgingi D03-07t!Healthi Year FE
0.0648 (0.67)
-0.0039 (-0.82) -0.0011 (-0.24)
-0.0027** (-2.45) -1.7E-5 (-0.15) 0.0209 (0.09)
0.0420*** (6.08)
0.0229** (2.43)
-0.0165 (-0.51)
0.0205** (2.29)
0.0159* (1.73)
0.0292** (2.49)
0.0160* (1.74) 0.0099 (0.86)
-0.0062 (-0.32)
No
0.0675 (0.73)
-0.0039 (-0.81) -0.0014 (-0.33)
-0.0027** (-2.40) -1.2E-5 (-0.12) 0.0006 (0.01)
0.0356*** (5.12)
0.0210** (2.51)
-0.0240 (-0.58)
0.0201** (2.18)
0.0157* (1.67)
0.0281** (2.39)
0.0160* (1.74) 0.0096 (0.82)
0.0297 (0.78)
No
0.0650 (0.68)
-0.0039 (-0.82) -0.0010 (-0.23)
-0.0027** (-2.45) -1.6E-5 (-0.15) 0.0223 (0.09)
0.0418*** (5.78)
0.0215** (2.57)
-0.0165 (-0.51)
0.0211** (2.35)
0.0159* (1.73)
0.0292** (2.48)
0.0160* (1.74) 0.0099 (0.87)
-00024 (-0.14)
No
0.0681 (0.70)
-0.0040 (-0.84) -0.0010 (-0.23)
-0.0027** (-2.41) -1.3E-5 (-0.12) 0.0245 (0.10)
0.0370*** (4.80)
0.0211** (2.53)
-0.0168 (-0.51)
0.0203** (2.27) 0.0088 (0.92)
0.0284** (2.42)
0.0162* (1.75) 0.0098 (0.86)
0.0261 (1.56)
No
0.0611 (0.63)
-0.0037 (-0.77) -0.0012 (-0.27)
-0.0028** (-2.46) -1.8E-5 (-0.17) 0.0279 (0.12)
0.0425*** (6.44)
0.0215** (2.58)
-0.0165 (-0.51)
0.0205** (2.28)
0.0159* (1.72)
0.0352*** (2.77)
0.0159** (1.72) 0.0098 (0.85)
-0.0439 (-1.20)
No
0.0651 (0.68)
-0.0039 (-0.82) -0.0010 (-0.24)
-0.0027** (-2.45) -1.6E-5 (-0.15) 0.0203 (0.09)
0.0415*** (6.01)
0.0214** (2.57)
-0.0165 (-0.51)
0.0204** (2.28)
0.0159* (1.72)
0.0292** (2.48) 0.0161 (1.55) 0.0099 (0.86)
-0.0001 (-0.01)
No
0.0654 (0.68)
-0.0040 (-0.83) -0.0010 (-0.23)
-0.0027** (-2.45) -1.6E-5 (-0.15) 0.0216 (0.09)
0.0424*** (6.30)
0.0215** (2.58)
-0.0164 (-0.50)
0.0205** (2.29)
0.0160* (1.73)
0.0294** (2.50)
0.0161* (1.75) 0.0136 (1.08)
-0.0135 (-0.69)
No
37
Table 9 The Relation between 2008 REIT Returns and Speculative Trading during the Real Estate Boom Period The table reports different measures of returns during 2008 when REITs decreased by nearly 36%, on average. We report returns across portfolios based on the level of speculative trading in REITs during the period when real estate prices were highest. The table reports the results for portfolios sorted on REIT speculative trading during years 2003 to 2007. We report CRSP raw returns and adjusted returns (Adj. Returns), which are defined as the difference between raw returns and Ziman REIT Value-Weighted Index return during 2008. We also report residual returns from a Fama-French 3 Factor model (FF3F Returns) and the residual returns from a Fama-French 4 Factor model (FF4F Returns). In column [5], we report the differences between extreme portfolios along with corresponding t-statistics. *,**.*** denotes statistical significance at the 0.10, 0.05, and 0.01 levels, respectively. Q I Q II Q III Q IV Q IV – Q I
[1] [2] [3] [4] [5] Raw Returns Adj. Returns FF3F Returns FF4F Returns
-0.4244
-0.7929
-0.1110
-0.1127
-0.4677
-0.8362
-0.1444
-0.1481
-0.4903
-0.8588
-0.1614
-0.1681
-0.2508
-0.6193
0.0533
0.0497
0.1736 (1.13) 0.1736 (1.13) 0.2313 (1.25) 0.1624 (1.25)
38
Table 10 Cross-Sectional Regression Results The table reports the results from estimating the following equation for the years 2003 to 2007 using the cross-sectional sample of REITs. 2008Returnsi = β0 + β1ln(Sizei,) + β2ln(Pricei) + β3Turni + β4Betai + β5IdioVolti,+ β6Speci +εi
The dependent variables include our four measures of returns during 2008. As independent variables, we include the natural log of market capitalization (ln(Sizei)), the natural log of price (ln(Pricei)), the share turnover (Turni), the level of systematic risk (Betai), and the idiosyncratic volatility (IdioVolti). The variables of interest are the variables Spec, which is Llorente et al. (2002) measure of speculative trading during the years 2003 to 2007. We report t-statistics that control conditional heteroskedasticity using White (1980) standard errors *,**.*** denotes statistical significance at the 0.10, 0.05, and 0.01 levels, respectively. Independent Variables Measured from 2003 to 2007
Raw Returns Adj. Returns FF3F Returns FF4F Returns
[1] [2] [3] [4] Intercept Ln(sizei) Ln(pricei) Turni
Betai
IdioVolti
SPECi
Adjusted R2
0.4350 (0.41)
-0.0456 (-0.79) 0.0956 (0.86)
-0.0406 (-0.37) 0.0575 (0.50)
-17.0888 (-1.33) 1.1983 (1.34)
0.0566
0.0665 (0.06)
-0.0456 (-0.79) 0.0946 (0.86)
-0.0406 (-0.37) 0.0575 (0.50)
-17.0888 (-1.33) 1.1983 (1.34)
0.0566
-0.2602 (-0.30) 0.0090 (0.19) 0.0539 (0.59)
-0.0312 (-0.35) 0.0303 (0.32)
-13.3003 (-1.25) 0.6394 (0.86)
0.0646
-0.2785 (-0.32) 0.0105 (0.22) 0.0509 (0.56)
-0.0283 (-0.31) 0.0195 (0.20)
-13.1968 (-1.25) 0.5903 (0.80)
0.0625
39
Figure 1. The figure shows median real estate prices and the prices of the Ziman REIT index (normalized to $1) in the top panel; the change in median real estate prices and the change in the Ziman REIT index prices in the second panel. These data are obtained from S&P/Case Shiller. The bottom panel shows the time series properties speculative tradin in REITs obtained from estimating the speculative trading model in Llorente et al. (2002).
0.00
1.00
2.00
3.00
4.00
5.00
6.00
7.00
$0
$50,000
$100,000
$150,000
$200,000
$250,000
$300,000Median Home Prices and Ziman REIT Index Prices
Median Home Prices Ziman REIT Index Prices (Normalized to 1)
-0.25-0.20-0.15-0.10-0.050.000.050.100.150.20
-0.40
-0.20
0.00
0.20
0.40
0.60
0.80
Changes in Median Home Prices and the Ziman REIT Index
Ziman Index 3-Yr Cumulative Return %Change in Median Home Prices
-0.15
-0.10
-0.05
0.00
0.05
0.10
0.15
Speculative Trading in REITs and Non-REITs
Spec-REITs Spec Non-REITs
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