asymmetric information and the predictability of real estate returns

Journal of Real Estate Finance and Economics, 20:2, 225±244 (2000)

# 2000 Kluwer Academic Publishers, Boston. Manufactured in The Netherlands.

Asymmetric Information and the Predictability of RealEstate Returns

MICHAEL COOPER

Krannert School of Management, Purdue University, West Lafayette, IN 47907-1310

DAVID H. DOWNS

Terry College of Business, University of Georgia, Athens, GA 30602-6255E-mail: [email protected]

GARY A. PATTERSON

School of Management, State University of New York, New Paltz, NY 12561

Abstract

This article examines the relation between systematic price changes and the heterogeneity of investors'

information sets in real estate asset markets. The empirical implications rely on a theoretical economy in which

information asymmetry alters the dynamic relation between returns and trading volume. We employ a ®lter-rule

methodology to determine predictability in returns and augment the return-based conditioning set with trading

volume. The additional conditioning information is necessary since the model is underspeci®ed when

predictability is based on returns alone. Our results provide new insight into the coexistence of informational and

noninformational exchange in the speculative markets for real estate assets. Speci®cally, we ®nd that the

predictability of real estate returns is generally more indicative of portfolio rebalancing effects than an adverse-

selection problem. These results are unique in addressing the time-variation in information asymmetry.

Key Words: information, predictability, real estate

1. Introduction

The predictability of asset returns has been the focus of a large body of academic research,

with studies attributing this apparent phenomenon to informational inef®ciency, investor

irrationality, time variation in risk premia, and other market-speci®c effects. Recently, Mei

and Gao (1995) examine whether the short-term predictability of real estate assets is

exploitable in an economically meaningful sense.1 They ®nd that the real estate security

market is ef®cient with respect to trading pro®ts and thus that the real estate security

markets are not accessible to competent arbitrageurs. Their study portrays a general

condition of ef®ciency in the real estate markets without considering speci®c market

conditions that may alter the behavior of asset prices. In contrast, other studies suggest that

market conditions may in¯uence informational ef®ciency and, consequently, asset prices.

Damodaran and Liu (1993) conduct a study that focuses on events producing periods of

asymmetric information that affect price movements in real estate assets. They examine a

sample of REITs that choose to reappraise themselves, an action that endows the REIT

insiders with private information. By identifying an event that heightens information

asymmetry, their study demonstrates that the trading activity of informed investors could

in¯uence the price-formation process. However, the extent to which informed trading

might in¯uence the predictability of real estate assets is largely an empirical question.

In this article we study the predictability of real estate returns for evidence of

information-based trading in a speculative market for real estate assets. We conduct our

research of speculative markets as presented by Wang (1994) and assume the existence of

heterogeneous traders and asymmetric information. In this economy, investors trade for

informational and noninformational purposes. The informed investors possess hetero-

geneous endowments of (1) private information about the future cash ¯ows of the

underlying asset and (2) private investment opportunities. In this market, the uninformed

investor rationally extracts information from prices and other public signals to estimate

expected returns. Consequently, the two classes of investors trade competitively based on

(1) the noninformational motives of the uninformed, (2) the informational motives of the

informed (that is, trading based on private information), and (3) the noninformational

motives of the informed (that is, portfolio rebalancing to accommodate private investment

opportunities). Thus, our article extends the prior research of Mei and Gao (1995) and

Damodaran and Liu (1993) by examining predictability in short-term returns in a market

containing information-based trading in real estate securities and where investors are

heterogeneous in their information and private investment opportunities (Wang, 1994).2

The heterogeneities in Wang's model serve to characterize the popular idea of REIT

insiders as private-market participants capitalizing on ®nancing opportunities in the

publicly traded markets. Wang shows that heterogeneity among investors gives rise to

different dynamic relations between trading volume and returns.3 In essence, a high return

accompanied by high volume implies low future returns ( price reversals) if informed

investors are trading for changes in their private investment opportunities and not because

of private information. Yet when informed investors condition their trades upon private

information, then high future returns ( price continuations) are expected when high returns

are accompanied by high trading volume. The model demonstrates that the underlying

motivation behind investor behavior produces different volume-return interactions that

affect the pattern of return behavior. Wang's model allows us to characterize the nature of

investor heterogeneity by examining the pattern of expected returns that emerges from the

interaction between returns and trading volume.

A testable implication of the dynamic relation between returns and trading volume is

that the return reversals documented by Mei and Gao (1995) will be correlated with

volume. To test this possibility we form contrarian portfolios with the aid of a ®lter-rule

methodology (Cooper, 1999). This approach avoids the criticism levied against previous

short-horizon contrarian papers that base their portfolio weights on the cross-sectional

distribution of lagged returns.4 More important, the ®lter method offers ¯exibility for the

detection of nonlinearities in the predictability of price changes. We test the effect of

volume on the autocorrelation of weekly returns with the realized portfolio returns acting

as proxies for the expected returns in the Wang model.

We ®nd strong evidence of nonlinearities in the predictability of real estate returns when

we introduce volume into the trading rule. Speci®cally, the price-volume dynamic differs

226 COOPER, DOWNS AND PATTERSON

between high- and low-volume periods, where the high-volume periods re¯ect the

exchange of real estate assets motivated by private information. We also observe

predictable patterns of return reversals when we form portfolios using a ®lter rule based

only on lagged prices, which is consistent with earlier papers. The study highlights the

adverse-selection problem faced by investors who trade in public real estate markets

where representative insiders may have both private information and private investment

opportunities.

The remainder of this article is organized as follows. Section 2 describes the application

of a ®lter rule to determine return predictability. In Section 3 we describe the data and then

present our empirical results in Section 4; the analysis focuses on the price-volume

dynamic for speculative trading of real estate assets. We conclude in Section 5.

2. Empirical methods

2.1. Improving signal quality in short-term predictability

To address the testable implication concerning predictability (that return reversals are

correlated with volume), we employ two signi®cant modi®cations to the overreaction

portfolio formation methodology used by Mei and Gao (1995). These modi®cations are

designed to boost the signal-to-noise ratio of the security-selection process used to form

contrarian portfolios. Speci®cally, our modi®cations to the signal extraction process

include (1) the use of ®lters and (2) the use of a conditioning variable other than price

changesÐnamely, volume.

The ®lter-rule method allows us to screen on the magnitude of lagged returns and

percentage changes in volume when forming loser and winner portfolios. In contrast, prior

short-term contrarian papers' portfolio formation methodology (Lehmann, 1990; Mei and

Gao, 1995) typically emphasizes forming portfolios by investing in all securities in their

sample, giving greater weight to securities with larger relative lagged cross-sectional

returns. Including stocks regardless of lagged return magnitudes results in inclusion of

securities into the overreaction portfolios that may not be subject to investor overreaction.5

In contrast, the ®lter portfolio formation method includes an asset in a loser (winner)

portfolio only if its lagged weekly return moved down (up) by a threshold amount. Hence,

our method will provide a more sensitive measure of predictability for analysis of the

price-volume dynamic. Other papers that use variations of the ®lter-rule method to boost

the sensitivity of their analysis include Alexander (1961), Fama and Blume (1966),

Sweeney (1986, 1988), Brown and Harlow (1988), Lakonishok and Vermaelen (1990),

Bremer and Sweeney (1991), Corrado and Lee (1992), Cox and Peterson (1994), and

Fabozzi, Ma, Chittenden, and Pace (1995).

Our second method to improve the signal-to-noise ratio in a weekly overreaction

portfolio strategy is to utilize variables not directly derived from a security's price.

Because of the scarcity of macroeconomic and microeconomic time-series variables at

shorter time intervals, a natural choice would be to examine the time-series properties of

volume as it relates to subsequent weeks' return behavior. Theoretical papers that have

ASYMMETRIC INFORMATION AND THE PREDICTABILITY OF REAL ESTATE RETURNS 227

taken this approach include Blume, Easley, and O'Hara (1994), Campbell, Grossman, and

Wang (1993), and Wang (1994), who present models suggesting there is valuable

information in the time series of lagged volume for predicting a security's price

movement. Conrad, Hameed, and Niden (1994) examine the interaction between lagged

percentage changes in transactions, lagged returns, and subsequent weekly returns to

individual NASDAQ securities. They employ an overreaction portfolio weighting scheme

that produces returns from negative autocorrelation. Motivated by these results, we

incorporate a lagged, individual security volume measure into the overreaction portfolio

formation rules. Additionally, the joint use of volume and return ®lters allows this article

to examine the heterogeneity of investor behavior.

2.2. Filter-rule methodology

The methodology we use is a ®rst-order ®lter rule where lagged information from one

week (that is, either returns or returns and volume) is used to predict future returns. In all,

six strategies are examined. The ®rst two strategies are price-only strategies. For example,

if last week's return is negative, it falls into the strategy of loser-price ®lter. Hence, the two

price-only strategies are loser-price and winner-price, and they form portfolios that

provide a baseline for interpreting the price-volume results. The remaining four strategies

incorporate both price and volume information. For example, if last week's return and

percentage change in volume for a security are each negative, the security is assigned to a

loser-price±low-volume ®lter strategy. Likewise, the four price and volume strategies are

loser-price±low-volume, loser-price±high-volume, winner-price±low-volume, and

winner-price±high-volume.

Past week's returns are classi®ed as winners or losers using the following criteria:

Return states �For k � 0; 1; . . . ; 4:

loserk ? A if ÿ k ? A4Ri;t71 � ÿ�k � 1� ? A

winnerk ? A if k ? A � Ri;t715�k � 1� ? A

(

For k � 5 :loserk ? A if Ri;t715ÿ k ? A

winnerk ? A if Ri;t71 � k ? A;

�8>>>>><>>>>>:

�1�

where Ri;t is the nonmarket adjusted return for security i in week t, k is the ®lter counter

that ranges from 0,1, . . . ,5, and A is a parameter equal to 2 percent.


The low and high states for percentage change in individual security weekly volume

(termed ``volume returns'') are de®ned to be

Volume return states �For k � 0; 1; . . . ; 4 :

lowk ? B if ÿ k ? B4VRi;t71 � ÿ�k � 1� ? B

highk ? C if k ? C � VRi;t715�k � 1� ? C

(

For k � 5 :lowk ? B if VRi;t715ÿ k ? B

highk ? C if VRi;t71 � k ? C; �2��

8>>>>><>>>>>:where VRi;t is the volume return for security i in week t, k is the ®lter counter that ranges

from 0,1, . . . ,5, B is a parameter equal to 15 percent, and C is a parameter equal to 50

percent.

The percentage change in individual security weekly volume, termed volume returns,

adjusted for the number of outstanding shares of a security, are de®ned as

VRi;t �Vi;t

Si;t

ÿ Vi;t71

Si;t71

" #=

Vi;t71

Si;t71

" #; �3�

where Si;t is the number of outstanding shares for security i in week t, and Vi;t is the weekly

volume for security i in week t. Thus, k � A, k � B, and k � C are the grid increments for the

price ®lters, low volume ®lters, and high volume ®lters, respectively. For each of the

strategies, the applicable price and volume ®lters are varied over their respective domains,

resulting in 36 sets of price and volume ®lter combinations for the price and volume

strategies.

The speci®c ®lter breakpoints are determined by examining the overall sample

distributions of the weekly price return and volume return from our sample of REITS and

then choosing appropriate ®lter values to span the distributions.6 Speci®cally, the return

®lters start at zero percent and increment in steps of 2 percent, to a maximum (minimum)

of positive (negative) 10 percent for winner (loser) ®lters. The low-volume return ®lters

begin at zero percent and increment in steps of 15 percent to a minimum of negative 75

percent. The high-volume return ®lters start out at zero percent and rise to a maximum of

250 percent in increments of 50 percent. The skewness in the volume return distribution

produces the asymmetry in the ®lters for volume.

For each combination of ®lter values, the securities whose lagged weekly returns (or

returns and volume) meet the ®lter constraints are formed into equally weighted portfolios

during week t. All portfolios are held for a period of one week and then liquidated. The

resulting portfolio's mean return is calculated for weeks in which nonzero positions are

held. If mean returns of the portfolios are signi®cantly different from zero, this is taken as

evidence in favor of return predictability. Thus, the null hypothesis of no predictability is

that the mean return of a portfolio equals zero.7

We use moment conditions to calculate test statistics for the mean returns since there

may be some dependence in the time series of portfolio returns, both contemporaneously


and across periods. Speci®cally, moment conditions are estimated with a generalized

method of moments estimator (Hansen, 1982), and Newey and West (1987) weights are

employed on the variance/covariance matrix to compute the mean and standard errors of

the time series of trades for each portfolio and to perform comparisons between the means

of different strategies. Comparing the mean returns in a GMM framework has the

advantage of controlling for contemporaneous and times series correlations in the portfolio

returns.

3. Data

To examine the interactions between lagged returns and volume, we construct a data set of

Wednesday-close to Wednesday-close weekly returns and weekly volume for 301 Real

Estate Investment Trusts (REITs) in the CRSP ®le between 1973 and 1995. Securities are

included in the sample for week t if they have daily volume in each of the previous 10

trading days. Since the weights placed on individual securities to form portfolios are based

on nonmarket adjusted returns, the portfolio returns associated with our ®lter-based

strategy should not emanate from index autocorrelation.8

Table 1 reports sample statistics for the data set. The mean market equity over the entire

sample period is $119 million, and the average share price is approximately $15.4. REITs

with a share price less than $5 are screened out of the sample as a precaution against bid-

ask bounce effects. The cross-sectional average of individual security weekly

autocorrelation coef®cients is ÿ 7.07 percent at the ®rst lag and ÿ 2.77 percent at the

Table 1. Summary statistics for the REIT sample �N � 301�, 1973 through 1995.

Mean Median Standard Deviation Minimum Maximum r1 (s.d.)

Five-day return (%) 0.267 0.0 3.914 ÿ 46.078 112.00 ÿ 7.073

(13.647)

Four-day return (%) 0.205 0.0 3.532 ÿ 40.298 96.296 ÿ 3.236

(11.402)

VRi;t�%� 67.363 ÿ 2.152 643.521 ÿ 100.00 1049.00 ÿ 17.810

(12.226)

Capitalization ($, millions) 119 57.2 168 0.018 1760

Price ($ per share) 15.430 13.75 8.947 5.00 132

Notes. Five-day return is a Wednesday-to-Wednesday close weekly holding period return. Four-day return is a

``skip-day'' Wednesday-to-Tuesday close four-day holding-period return. REITs with prices less than $5 per

share are excluded from the sample. The mean, median, and standard deviation of capitalization and price are

calculated across time and across securities. The statistic r1 is the average ®rst-order autocorrelation coef®cient

of weekly returns of individual stocks. The population standard deviation is given in parentheses. Since the

autocorrelation coef®cients are not cross-sectionally independent, the reported standard deviations cannot be used

to draw the usual inferences; they are presented as a measure of cross-sectional variation in the autocorrelation

coef®cients.


second lag. The negative autocorrelation is consistent with overreaction for individual

stocks, or it may indicate the existence of a bid-ask spread effect. For this reason we also

report the four-day return's (for example, the skip-day returns) ®rst-order autocorrelation

of ÿ 3.24 percent. The magnitude of this statistic strongly suggests that negative

autocorrelation induced by the bid-ask spread is not driving the negative autocorrelations

exhibited in the full weekly returns.

In addition, Table 1 presents descriptive statistics for the volume return ( percentage

change in volume) measure used with the ®lter strategies. The measure of volume we use,

VRit as de®ned in equation (3), is the average percentage change in weekly volume, and

over the 1,294-week sample period, VRi;t averages 67.4 percent.

4. Strategies that condition on price and volume

The empirical analysis in this article relies on the use of information from trading volume

to augment a simple, lagged price ®lter rule. In turn, we attempt to determine whether

predictability in real estate returns is related to volume and interpret our ®ndings in the

context of the return-volume dynamics of Wang (1994). Theoretical and empirical works

suggest there is valuable information in the time series of lagged volume for predicting a

security's price movements (Blume, Easley, and O'Hara, 1994; Campbell, Grossman, and

Wang, 1993; Conrad, Hameed, and Niden, 1994). The inclusion of volume, which

represents the trading activity of investors, as well as an analysis of the behavior of

portfolio returns, may also provide important information about the level of heterogeneity

of investors in the real estate market. By concurrently examining return behavior and the

investors' information sets, Wang (1994) argues that one may more accurately identify

those periods where the predictability of returns is attributable to the information

heterogeneity in asset markets.

Figure 1 illustrates the general pattern in weekly portfolio returns when portfolio

construction is conditioned on different values of lagged returns and lagged volume. We

®nd that conditioning on negative percentage changes in volume results in increased

negative return-autocorrelations for the more extreme winner and loser ®lters. In contrast,

conditioning on positive changes in volume results in decreased negative return-

autocorrelations for the more extreme winners and losers. Thus, we observe an inverse

relation between volume and the autocorrelation of returns, which supports critical tenets

of the Wang modelÐnamely, that prices alone are not suf®cient to resolve the

identi®cation problem of investor heterogeneity.

Table 2 presents detailed results for the graphical relations shown in Figure 1 and

allows us to focus on a major element of our results. Speci®cally, we ®nd the behavior

of portfolio returns in week t reveals two distinct interactions between price and

volume based on the level of lagged volume. Low-volume portfolios (that is, stocks

with a negative percentage change in volume from week tÿ 2 to week tÿ 1)

experience considerably greater reversals (that is, stocks with positive (negative) returns

in week tÿ 1 experience larger magnitude negative (positive) returns in week t) than

the high-volume portfolios (that is, stocks with a positive percentage change in volume


from week tÿ 2 to week tÿ 1). Additionally, portfolios at the extreme-price±low-

volume ®lters consistently have greater reversals than do the portfolios with

comparable price-only ®lters. Panel A of Table 2 shows that portfolios from the

loser-price±low-volume strategy yield larger weekly portfolio returns than those

produced by the price-only strategies shown in the ``No volume ®lter'' row. For

example, the average weekly returns approach 3 to 4 percent when we jointly condition

on extreme losers and low volume. When interpreted in the context of Wang's model,

an environment with a greater proportion of trades motivated by private investment

opportunities will produce the observed reversals in portfolio returns.

The returns from winner portfolios also experience greater reversals when volume is

Figure 1. Weekly portfolio returns conditioning on percentage changes in price and volume.

Note. This ®gure presents the average weekly portfolio returns based on a contrarian trading strategy using

price-volume ®lters. The greatest return reversals occur with the loser-price±low-volume portfolios in the

upper right quadrant. The upper left quadrant presents the performance of the loser-price±high-volume

portfolios. The lower left and right quadrants, respectively, re¯ect the average weekly returns of the winner-

price±high-volume and winner-price±low-volume portfolios.


Table 2. Weekly portfolio returns to price and volume strategies.

Panel A: Loser-Price±Low-Volume

Lagged Weekly Return Filter ( percent)

Volume Filter (%) 50 and� ÿ2

5ÿ 2 and� ÿ4

5 ÿ 4 and� ÿ 6

5ÿ 6 and� ÿ8

5ÿ 8 and� ÿ10

5ÿ 10

No volume

®lter

Mean (%) 0.202 0.561 0.835 1.212 1.354 2.200

Standard

deviation

1.944 2.519 3.342 4.236 5.499 7.008

N 1279 1260 1097 786 496 477

t-statistic 2.677 6.624 7.843 7.579 5.271 6.338

50 and� ÿ 15

Mean (%) 0.157 0.650 0.911 1.251 0.889 3.476

Standard

deviation

2.645 4.068 4.369 5.426 5.394 5.764

N 833 602 285 111 50 41

t-statistic 1.567 4.008 3.892 2.727 0.890 3.953

5ÿ 15 and� ÿ 30

Mean (%) 0.201 0.503 0.866 1.426 2.188 1.240

Standard

deviation

2.958 3.660 3.874 4.684 7.734 7.727

N 878 641 273 121 49 44

t-statistic 1.874 3.455 3.065 3.234 1.696 1.840

5ÿ 30 and� ÿ 45

Mean (%) 0.225 0.415 0.449 0.955 2.271 4.655

Standard

deviation

3.003 3.852 4.892 4.726 7.166 10.640

N 879 632 332 109 48 38

t-statistic 2.004 2.757 1.796 2.402 2.494 2.570

5ÿ 45 and� ÿ 60

Mean (%) 0.265 0.361 0.860 1.342 ÿ 0.738 3.039

Standard

deviation

2.900 3.400 4.304 5.275 7.668 9.187

N 834 579 279 121 42 42

t-statistic 2.687 2.586 3.425 3.453 ÿ 0.531 2.433

5ÿ 60 and� ÿ 75

Mean (%) 0.158 0.415 0.858 1.106 1.200 3.292

Standard

deviation

2.608 4.181 5.393 5.303 4.582 7.596

N 713 440 203 86 33 19

t-statistic 1.690 2.274 2.188 2.011 1.206 1.558

5ÿ 75 Mean (%) 0.014 0.190 1.156 2.118 1.451 3.784

Standard

deviation

2.790 3.533 4.514 6.590 6.923 10.699

N 682 370 164 73 29 18

t-statistic 0.127 1.080 2.937 2.800 1.411 3.085


Table 2. (continued)

Panel B: Loser-Price±High-Volume


Volume Filter (%) 50 and� ÿ2

5ÿ 2 and� ÿ4

5ÿ 4 and� ÿ6

5ÿ 6 and� ÿ8

5ÿ 8 and� ÿ10

5ÿ 10

No volume

®lter

Mean (%) 0.202 0.561 0.835 1.212 1.354 2.200

Standard

deviation

1.944 2.519 3.342 4.236 5.499 7.008

N 1279 1260 1097 786 496 477

t-statistic 2.677 6.624 7.843 7.579 5.271 6.338

� 0 and550

Mean (%) 0.118 0.664 0.823 1.100 0.706 1.892

Standard

deviation

2.724 3.741 4.709 4.724 5.499 7.887

N 1095 909 523 243 113 96

t-statistic 1.262 5.394 3.672 3.182 1.309 2.871

� 50 and5100

Mean (%) 0.398 0.458 0.615 1.413 2.353 1.659

Standard

deviation

2.911 4.174 3.926 4.515 8.555 6.493

N 834 613 329 152 87 89

t-statistic 3.827 2.448 2.651 3.835 2.238 2.582

� 100 and5150

Mean (%) 0.382 0.376 0.943 1.744 0.420 1.345

Standard

deviation

3.012 3.430 5.413 4.155 5.198 7.841

N 604 410 206 106 56 54

t-statistic 2.684 2.187 2.564 4.668 1.063 1.355

� 150 and5200

Mean (%) 0.436 0.366 1.024 1.499 0.933 0.289

Standard

deviation

2.701 3.666 6.504 6.423 7.696 6.726

N 401 243 142 65 45 50

t-statistic 3.777 1.173 1.692 2.496 0.804 0.057

� 200 and5250

Mean (%) 0.169 0.507 1.359 1.225 2.102 1.505

Standard

deviation

3.218 3.512 4.446 4.881 6.567 5.611

N 316 179 94 44 23 32

t-statistic 1.012 2.005 3.363 0.910 1.555 2.144

� 250 Mean (%) 0.461 0.351 0.811 1.182 1.475 1.470

Standard

deviation

3.309 3.733 4.108 4.993 5.452 7.941

N 621 460 274 169 99 148

t-statistic 3.355 1.976 3.548 2.746 2.902 1.869



Panel C: Winner-Price±Low-Volume


Volume Filter (%) � 0 and52

� 2 and54

� 4 and56

� 6 and58

� 8 and510

� 10

No volume

®lter

Mean (%) 0.192 0.121 0.063 ÿ 0.086 ÿ 0.021 ÿ 0.376

Standard

deviation

1.499 2.165 3.177 4.003 4.612 5.687

N 1278 1256 1151 925 662 748

t-statistic 3.597 1.665 0.643 ÿ 0.643 ÿ 0.064 ÿ 1.676

50 and� ÿ15

Mean (%) 0.221 0.216 0.207 ÿ 0.301 0.841 0.025

Standard

deviation

2.537 3.626 3.826 4.635 5.548 5.796

N 865 606 336 154 71 78

t-statistic 2.659 1.274 0.994 ÿ 0.941 1.470 ÿ 0.130

5ÿ 15 and� ÿ30

Mean (%) 0.334 0.302 0.149 ÿ 0.841 ÿ 1.238 0.529

Standard

deviation

2.960 3.688 4.077 4.371 4.647 7.124

N 894 649 313 171 59 89

t-statistic 3.123 2.057 0.679 ÿ 2.648 ÿ 2.536 0.542

5ÿ 30 and� ÿ45

Mean (%) 0.266 0.087 ÿ 0.086 ÿ 0.029 ÿ 0.169 ÿ 0.524

Standard

deviation

2.616 3.203 3.813 4.107 4.840 6.245

N 879 606 338 165 69 72

t-statistic 2.922 0.660 ÿ 0.335 0.024 ÿ 0.294 ÿ 0.764

5ÿ 45 and� ÿ60

Mean (%) 0.179 0.305 ÿ 0.465 0.726 ÿ 0.675 0.457

Standard

deviation

2.706 3.483 3.760 6.019 4.357 8.986

N 876 555 261 119 73 67

t-statistic 1.964 1.981 ÿ 1.682 1.209 ÿ 1.176 0.374

5ÿ 60 and� ÿ75

Mean (%) 0.114 0.037 ÿ 0.417 ÿ 0.706 ÿ 1.876 ÿ 0.359

Standard

deviation

2.575 3.756 3.883 4.312 5.956 6.257

N 777 442 208 95 34 40

t-statistic 1.057 0.068 ÿ 1.383 ÿ 1.420 ÿ 1.084 ÿ 0.890

5ÿ 75 Mean (%) 0.205 ÿ 0.026 ÿ 0.532 ÿ 1.035 ÿ 0.978 ÿ 3.330

Standard

deviation

2.954 3.588 3.879 4.907 5.326 6.681

N 780 385 149 101 44 34

t-statistic 1.887 ÿ 0.235 ÿ 1.611 ÿ 1.807 ÿ 1.318 ÿ 3.208



Panel D: Winner-Price±High-Volume


Volume Filter (%) � 0 and52

� 2 and54

� 4 and56

� 6 and58

� 8 and510

� 10

No volume

®lter

Mean (%) 0.192 0.121 0.063 ÿ 0.086 ÿ 0.021 ÿ 0.376

Standard

deviation

1.499 2.165 3.177 4.003 4.612 5.687

N 1278 1256 1151 925 662 748

t-statistic 3.597 1.665 0.643 ÿ 0.643 ÿ 0.064 ÿ 1.676

0 � and550

Mean (%) 0.183 0.040 0.154 ÿ 0.177 0.386 ÿ 0.438

Standard

deviation

2.529 2.774 6.095 4.426 6.198 7.158

N 1098 919 648 359 180 196

t-statistic 2.265 0.410 0.635 ÿ 0.807 1.215 ÿ 1.154

50 � and5100

Mean (%) 0.406 0.129 0.213 0.273 ÿ 0.337 ÿ 0.752

Standard

deviation

2.636 3.170 4.388 5.045 4.961 5.313

N 845 644 403 238 131 145

t-statistic 4.150 1.084 0.792 0.820 ÿ 0.732 ÿ 1.889

100 � and5150

Mean (%) 0.273 0.104 0.311 ÿ 0.041 0.075 ÿ 0.786

Standard

deviation

2.991 3.426 4.366 4.675 4.264 6.872

N 629 445 259 143 87 119

t-statistic 2.136 0.607 1.183 0.139 0.406 ÿ 0.966

150 � and5200

Mean (%) 0.345 0.020 ÿ 0.496 ÿ 0.439 0.303 ÿ 0.575

Standard

deviation

3.113 3.422 3.453 3.795 6.059 6.256

N 421 260 164 80 47 77

t-statistic 2.240 0.110 ÿ 2.012 ÿ 0.706 0.562 ÿ 0.728

200 � and5250

Mean (%) 0.250 0.353 ÿ 0.431 0.294 ÿ 0.005 0.375

Standard

deviation

3.623 4.582 4.027 4.439 6.408 5.879

N 321 174 98 53 37 52

t-statistic 1.324 0.907 ÿ 0.789 0.553 ÿ 0.025 0.105

� 250 Mean (%) 0.331 0.376 ÿ 0.156 ÿ 0.148 ÿ 0.096 ÿ 0.639

Standard

deviation

3.130 3.540 3.933 4.445 4.912 5.669

N 655 471 321 182 97 229

t-statistic 2.778 1.999 ÿ 0.653 ÿ 0.377 ÿ 0.267 ÿ 1.666


added to the portfolio formation process. Panel C shows the results of the winner-price-

low-volume strategy where a ÿ 0.376 percent �tÿ statistic � ÿ1:676� weekly return

from the ``greater than 10 percent'' ®lter for the price-only strategy decreases to ÿ 3.330

percent �tÿ statistic � ÿ3:21� when low-volume (< ÿ 75 percent) REITs are considered.

The return pattern associated with low volume is particularly evident at higher absolute

magnitude price ®lters in both winner (> 10 percent) and loser (< ÿ 10 percent) portfolios.

Overall, we observe large increases in the level of reversals from incorporating volume

information into the more extreme price-®lter rules. One interpretation of this result is that

transitory shifts in noninformational demand are more pronounced and persistent when

volume is low and returns are either very high or very low. This ®nding is consistent with

the argument that less active stocks are problematic not because there are too many

informed traders but because there are too few uninformed ones (Easley, Kiefer, O'Hara,

and Paperman, 1996). Downs and GuÈner (1999) document a similar phenomenon among

publicly traded real estate ®rms. Their paper shows that the higher levels of information

asymmetry contribute to a less-liquid, less-active REIT market, perhaps due to the

adverse-selection problem faced by uninformed investors.

In contrast to the preceding discussion, conditioning on high volume lowers the

magnitude of return reversals, though the autocorrelation pattern remains strongly

negative. The loser-price±high-volume results in Panel B of Table 2 reveal a trend of

decreasing return reversals across the price ®lters for increasing levels of volume. For

example, loser portfolios formed by the ``ÿ 10 percent or less'' price-only ®lter generated

weekly returns of 2.20 percent �tÿ statistic � 6:34�. At the same price ®lter, but also

conditioning on a weekly volume ®lter of greater than 250 percent, weekly returns

diminish to 1.47 percent �tÿ statistic � 1:87�. The same pattern of decreased reversals in

subsequent weekly portfolio returns is seen in Panel D of Table 2 when a winner-price±

high-volume strategy is used to form portfolios. For both losers and winners, the increased

return reversals found in portfolios that condition on low volume are more evident at

higher price ®lters.9 These results suggest that the information content during periods of

high volume re¯ects a market that is responding to private information trades as well as

trades motivated by changing investment opportunities.

The empirical results show that the magnitude of return predictability varies

considerably between high- and low-volume periods. These results, with different price-

volume dynamics, are consistent with an economic model in which the relative proportion

of trades motivated by private information and by heterogeneous investment opportunities

Note. Panels A, B, C, and D give the corresponding portfolio's means, standard deviations, and t-statistics for a

mean equal to zero null hypothesis for the four joint price and volume strategies for weeks in which equity

positions are held. Securities are included in a given portfolio if the lagged weekly return and lagged volume

return (percentage changes in volume) meet the ®lter conditions for both lagged return and lagged volume. Four

price-volume strategies are examined: loser-price±low-volume, loser-price±high-volume, winner-price±low-

volume, and winner-price±high-volume in panels A, B, C, and D, respectively. A ``No volume ®lter'' corresponds

to a price-only strategy and is included for comparison purposes with the volume strategies. The sample consists

of REITs for the period from January 1973 to December 31, 1995. N is the number of portfolio weeks the strategy

traded at the respective price and volume ®lter levels out of a possible 1294 weeks. The t-statistics are robust to

heteroskedasticity and autocorrelation.


will affect the behavior of expected returns. By interpreting these test results in the context

of Wang's model, the periods with high volume contain a greater proportion of private

information, which leads to less predictable reversals in portfolio returns; the reverse is

observed in periods of low trading volume.

4.1. Robustness

As a summary measure of the price-volume dynamic relation, we report the correlation of

volume and subsequent returns. This statistic allows us to formally test the relation

between volume and future returns and, as proposed by Wang (1994), to identify a

dominant trading behavior in the speculative exchange of real estate assets. Though the

correlation analysis cannot separate the data into high- and low-volume periods, it

measures the general price-volume relation that may help identify whether private

information or investment opportunities is, on average, the primary motivator of trading

activity. For the entire sample period, the correlation between the absolute value of weekly

portfolio returns and the lagged volume ®lters is negative and signi®cant (ÿ 0.17 with a p-

value of 0.038). This ®nding, interpreted in the context of Wang's model, supports the

dominance of noninformationally motivated trading over informationally motivated

trading, which our earlier tests more clearly identify to be strongest during periods of low

volume.

We also consider the robustness of our results by drawing attention to the volume

measure, VRit, used in the ®lter-based method. Our ®ndings show that the incorporation

of volume improves the predictability of returns, but in a manner not entirely consistent

with a model in which investors trade because of their differences. In other words,

Wang (1994) emphasizes price changes accompanied by high volume when identifying

the type of information that produces price reversals or continuations. We ®nd that

large absolute magnitude price changes (in week tÿ 1) accompanied by high volume

(such as the percentage change in volume from week tÿ 2 to week tÿ 1 is positive)

will reverse (in week t) but this pattern is stronger during low-volume periods. To

ensure that our volume measure is not biasing the test results, we construct volume

measures using other time horizons. Speci®cally, we examine returns to strategies that

condition on longer horizon volume measures, so we construct two volume measures

that employ an average of the last 4 and 20 weeks of volume to form trading shocks

relative to longer-term volume expectations. The substitute volume measures are

de®ned as

VRit;m �Vit ÿ �1=m�Pm

j� 1

Vi;tÿ j

�1=m�Pmj� 1

Vi;tÿ j

; �4�


where m is equal to 4 or 20, the number of weeks used to form the volume average for

security i in week t.Similar to the calculations for one-week volume returns, weekly portfolio returns are

calculated using the four price-volume strategies (such as loser-price±low-volume).

Subsequently, we construct the summary measure of the price-volume dynamic relation as

above. Recall this is the correlation of the absolute value of the weekly portfolio returns

and these alternate volume measures.

The correlation coef®cients between weekly portfolio returns and the lagged four-week

and 20-week volume measures are both negative and signi®cant at 7 0:16 �p � 0:05� and

ÿ 0:20 �p � 0:02�, respectively. These results, with their negative relationships between

volume and expected returns, support our earlier test results, which suggest that

speculative trading in real estate assets is dominated by portfolio rebalancing and not

private information. However, this strict test precludes the dynamic nature of information

asymmetry and the existence of a weak dominance in informational trading over other

motives. For this reason, we turn to our ®nal set of results.

4.2. Additional analysis conditioning on price and volume

To examine the association between expected returns and the return-volume dynamic, we

run a cross-sectional regression of the average of all week t portfolio returns (RET) on

week tÿ 1 returns (RET_LAG):

RET � 0:48�ÿ 0:11 ? RET LAG Adj. R2 � 0:56; N � 144: �5��8:79� �ÿ13:64� t-statistics in parentheses

To the extent that RET measures the expectation of future returns conditioning on

current returns for each price-volume ®lter, the signi®cant negative parameter estimate is

consistent with earlier studies that ®nd return reversals for real estate securities (Mei and

Gao, 1995). Additionally, we use information within trading volume to resolve the

identi®cation problem associated with investor heterogeneity. We conduct an alternative

test where we regress the average of all week t portfolio returns (RET) on week tÿ 1

returns with an emphasis on high-volume periods by the use of an interactive dummy

variable. The dummy variable HI_VOL has a value of 1 for all ®lter levels where volume

in week tÿ 1 is greater than or equal to 50 percent over the prior week's volume. HI_VOL

assumes a value of 0 otherwise:

RET �0:48�ÿ0:12 ? RET LAG� 0:04 ? RET LAG ? HI VOL

�8:79� �ÿ12:31� �2:61�Adj. R2 � 0:58; N � 144:

t-statistics in parentheses �6�


The coef®cients on the RET_LAG and the interactive term are both signi®cant at the 99

percent level, but the point estimates have opposite signs.10 The test shows that an

environment exists where low (high) returns typically imply high (low) expected returns

for real estate securities. Yet the positive coef®cient on the interactive term suggests that

information in high-trading-volume periods dampens the reversal effect that is found in

periods with lower volume. This mitigating effect on return reversals is consistent with the

trading behavior of heterogeneously informed investors.

Finally, we examine the robustness of the price-volume relationship across high- and

low-vacancy periods.11 This analysis allows us to assess the in¯uence of the economic

condition associated with the underlying property market on the price-volume dynamic.

As such, we construct an aggregate vacancy rate measure for income-producing properties

in the United States since 1980. The data are obtained from the 1997 United States Bureau

of the Census, Abstracts of the United States. A simple method to gauge the effect of

occupancy rates on the lagged return, lagged volume, subsequent reversal relationship is to

form equally weighted portfolios of stocks in the bottom or top half of the price ®lters (that

is, week tÿ 1 returns less than ÿ 6 percent (loser-price) or greater than 6 percent (winner-

price)) and the bottom half of the volume-return ®lters (that is, week tÿ 1 volume returns

less than 0 percent (low volume)). We name these portfolios loser-low and winner-low,

respectively, as they are formed by averaging the returns in the three most right columns of

Table 2, Panel A (loser-low) and the three most right columns of Table 2, Panel C (winner-

low).

The pattern that emerges is one of greater reversals in high-vacancy-rate years relative

to low-vacancy years for both the loser-low and winner-low portfolios. For example, the

loser-low's average weekly return is 3.85 percent (1.83 percent) in high (low) vacancy

years �paired t-statistic � 2:42�. The winner-low portfolio has average weekly returns of

ÿ 1.44 percent in high-vacancy periods versus returns of ÿ 0.818 percent in the low-

vacancy periods �paired t-statistic � 0:95�. Thus, the dampening of return reversals

during periods of low vacancy suggests that informed investors are trading on their private

information to extract what pro®ts are available from public-market real estate. Just as our

previous tests found a relative increase in asymmetric information during high-volume

periods, the greater activity occurring during strong real estate markets may provide

opportunities for insiders to exploit their private information more easily.

The greater return reversals observed during high-vacancy years suggest there is a

relative decrease in asymmetric information during weak real estate markets. In the

context of Wang's model, the heightened return reversals evident in high-vacancy periods

suggest that the trading of insiders in the real estate securities market is driven by their

private investment opportunities. In other words, the information advantage of REIT

insiders is less prominent in their trades than the need to rebalance portfolios in pursuit of

the private-investment (such as vulture) opportunities often associated with a depressed

real estate market.


5. Conclusion

Our study concludes that noninformational trading activity strictly dominates trading that

is motivated by the private information of informed investors. We arrive at this assessment

by examining the predictability of real estate returns in the context of the model in Wang

(1994), where investor heterogeneity, in terms of investment opportunities and

information, leads to alternative speci®cations of a price-volume dynamic. Our

observance of strict dominance is based on a price-volume relation that exists across all

periods and, in this sense, is consistent with the Mei and Gao (1995) approach to

documenting real estate market overreaction. However, Damodaran and Liu (1993)

provide compelling evidence that information asymmetry, a principal determinant of the

price-volume dynamic, changes across periods. Consequently, our study attempts to

reconcile some of the apparent contradictions in the real estate literature.

Our analysis of the predictability of real estate returns, conditioning on volume,

demonstrates that reversals are less pronounced during periods of high volume. This result

is consistent with a weak-form dominance of informationally motivated trading, which

may explain why Mei and Gao do not ®nd economically signi®cant price reversals. Most

important, our ®ndings contribute to the understanding of the time-variation in the

adverse-selection problem of real estate investors.

Intuition suggests that a sophisticated investor with private information about publicly-

traded real estate assets might also have competing private-investment opportunities. Our

research suggests that the predictability in real estate returns is more a function of the

rebalancing effects associated with the latter endowment opportunities than the market

corrections generated by the former asymmetric information (that is, private information)

opportunities. Our research also suggests that the risk of trading with a more-informed

investor is higher during periods of active trading as well as during periods when

occupancy rates are high.

Acknowledgments

This article is an extended version of the second half of an earlier working paper with the

same title. The ®rst half of the earlier working paper focuses speci®cally on trading

strategies (Cooper, Downs, and Patterson, 1999). We wish to thank Crocker Liu,

participants of the AREUEA and FMA meetings, and two anonymous reviewers for their

helpful suggestions. We are especially grateful to Chinmoy Ghosh.

Notes

1. In contrast to Mei and Gao (1995), Cooper, Downs, and Patterson (1999) use a ®lter-based trading strategy

and ®nd relatively strong evidence of short-term predictability for real estate securities. See also Liu and Mei

(1992), Mei and Liu (1994), and Ling, Naranjo and Ryngaert (1998) for evidence on the predictability of real


estate returns based on macroeconomic forecasting factors such as yield spreads, dividend yields, and

capitalization rates on equity REITS.

2. Whether a particular case of return predictability is attributable to market inef®ciencies or time-varying risk

premia is often a contentious point, especially in longer horizon predictability. Lehmann (1990) and others

suggest that this disagreement may be resolved by examining the predictability of short-term (weekly) stock

returns based on the assumption that expected returns are not likely to change over a week. Speci®cally,

Lehmann cites Sims (1984), who hypothesizes that as time intervals shorten, prices should follow a random

walk because there should be few systematic changes in valuation over daily and weekly periods if

information arrival is unpredictable. Thus, we examine weekly return horizons. Furthermore, our study

differs from previous research as we (1) employ a ®lter-based portfolio construction methodology, (2)

include volume as an additional forecast variable, and (3) interpret our ®nding based on a theoretical

framework in which the heterogeneity across investors gives rise to different price-volume dynamics.

3. Wang (1994) derives a relation between current period returns �Rt�, volume �Vt�, and expected returns

�Rt� 1�, E�Rt� 1jRt;Vt�%�f0 ÿ f1V2t �Rt. Here, f0 and f1 are constants, and the sign of f1 depends on the

information asymmetry between the two types of investors. Speci®cally, if f150, then trading is dominated

by informational motives and f140 indicates that trading is dominated by noninformational motives. An

inherent feature of the Wang model is the emphasis on trading motives of the informed (that is, competitive

trading to bene®t from private information or competitive trading to rebalance portfolios due to a shift in

private investment technology). For this reason, the implications of the model tend to highlight high volume.

One might expect the dynamic between current and expected returns to be similar for low-volume scenarios,

although the magnitude of the reversal or continuation may differ from the case where volume is high. See

Wang (1994) for a complete discussion of the implications.

4. Lehmann (1990) was the ®rst to examine short-horizon reversals using a relative cross-sectional weighting

method. Mei and Gao apply a similar method to examine reversals in the real estate securities market. Several

papers subsequent to Lehmann provide alternative explanations for the pro®ts found by employing cross-

sectional weighting methods. Lo and MacKinlay (1990), for example, show that up to 50 percent of

Lehmann's contrarian pro®ts are due to lagged forecastability across large and small securities. Other

important citations include Ball, Kothari, and Wasley (1995) and Conrad, Gultekin, and Kaul (1997).

5. The ®lter method may more closely correspond with the academic evidence on the psychology of

overreaction. Related studies (see DeBondt, 1989, for a review) show that individuals tend to overreact to a

greater degree when confronted with a large information shock relative to their prior base-rate expectations.

This realization leads DeBondt and Thaler (1985) to postulate an overreaction hypothesis that states, ``(1)

Extreme movements in stock prices will be followed by extreme movements in the opposite direction; (2)

The more extreme the initial movement, the greater will be the subsequent adjustment.'' This hypothesized

predictable behavior, manifested in extreme price movements, forms the basis of the ®lter rules. In these

rules, a security is included in a portfolio only if its lagged return is within the ®lter level. Thus, by employing

®lters on lagged returns, we are able to screen stocks for ``large'' past price movements, which may likely be

investor overreaction and subsequently eliminate securities that experienced smaller lagged returns (or those

that may be noise to a contrarian strategy). Cooper (1999) examines large capitalized NYSE and AMEX

stocks and ®nds that weekly contrarian strategies based on ®lter rules generally earn greater weekly pro®ts

than do portfolios formed from relative cross-sectional weighting rules.

6. The price and volume ®lter breakpoints are determined by using each variable's overall sample distribution

percentiles of approximately 1, 2.5, 5, 10, 25, 50, 75, 90, 95, 97.5, and 99 percent. As with all ®lter-based

methods, the primary goal in setting the breakpoints is to generate maximum dispersion in the return and

volume distributions. As such, the ®lter breakpoints are chosen to span the distribution of return and volume

conditioning variables and therefore are independent of the results.

7. We follow the practice of other short-horizon contrarian papers and report mean equal to zero t-statistics. We

also calculate t-statistics (not reported in the article) by subtracting the unconditional weekly mean return of

the sample from the return of each ®lter portfolio and ®nd that this measure of excess returns produces little

variation in the reported t-statistics.

8. Our method employs weights conditioning on raw returns. As such, the pro®ts from the ®lter strategies will

be based on individual security autocovariances and individual security unconditional mean weekly returns.


This is an important point as Conrad, Gultekin, and Kaul (1997) and Lo and MacKinlay (1990), using a pro®t

decomposition originally derived in Lehmann (1990), show that contrarian strategies that base their weights

on a security's deviation from an equally weighted index of those securities result in a large percentage of

pro®ts attributable to positive autocovariances of the returns of an equally weighted portfolio of the

component assets.

As we will report, the average weekly unconditional return of the REIT sample is 0.267 percent. This mean

return is relatively small compared to the magnitude of the pro®ts from many of this article's ®lters strategies,

suggesting that the primary source of predictability is individual security autocovariance.

9. Determining whether there are ``arbitrage'' opportunities in the publicly traded real estate markets is an

interesting topic that takes us away from our main objective of studying the characteristics of predictability in

the context of informational and noninformational motives for trading real estate. However, a casual

observation of the more extreme ®lters suggests the possibility for pro®table trades. Keim and Madhavan

(1997) report round-trip total execution costs of 0.96 percent ( price impact, bid-ask spreads, and commission

costs) calculated from actual trades placed by 21 institutional investors on the smallest quintile of NYSE

securities over the 1991 to 1993 period for medium-sized trades. The issue of trading costs is more fully

explored in Mei and Gao (1995) and Cooper, Downs, and Patterson (1999).

10. We investigate alternative speci®cations of the relation between expected returns and returns, which include

omitting the 36 extreme portfolios (that is, price ®lters 5ÿ 10%;4 � 10% percent, and volume ®lters

5ÿ 75%;4 � 250% percent). The results of this regression test do not change.

11. Liu and Mei (1992) show that REITs are a hybrid security in that returns are in¯uenced not only by stock-

market conditions but by conditions in the property markets, as well. Wang, Chan, and Gau (1992) and Ling

and Ryngaert (1997) ®nd evidence supporting the in¯uence of the changing real estate market environment

on real estate returns. We are indebted to Crocker Liu for suggesting this test.

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asymmetric information and the predictability of real estate returns

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