REITs: A Study of Macroeconomic Effects on Liquidity

Ran Lu1 2

University of Cincinnati

John Glascock2

University of Cincinnati

Abstract

In this research, we investigate the macroeconomic effects on REIT liquidity. We

focus on the pricing of liquidity. We apply cross-section tests to obtain monthly prices of

liquidity of REITs and examine the effects of macroeconomic factors on REIT liquidity. Our

results show that the change in crude oil prices has overall significant contemporaneous

effects on the price of liquidity for REITs. Also, the default premium and term premium

show significant contemporaneous powers on REIT liquidity when the economy is in

recession. Changes in inflation rates present the strongest results among all the

macroeconomic factors, giving us statistically significant predictive power on the pricing of

REIT liquidity when the economy is in a recessionary state. Additionally, our results

suggest evidence that the pricing of REIT liquidity is time-varying and can be partially

explained by business cycles.

This Version: March 25, 2011

                                                            1 Corresponding author: College of Business, Department of Finance, Cincinnati, OH 45221 Email: lur@mail.uc.edu 

2 We would like to thank Hui Guo, Mike Ferguson, Brian Hatch and Steve Slezak for their comments and suggestions. All errors are ours. 

1

I. Introduction

Recent research suggests that liquidity varies with economic conditions

[Brunnermeier and Pedersen (2009), Jensen and Moorman (2010) and Naes et al (2010)].

This implies that there is a time-varying component in the pricing of liquidity. Previous

literature has provided possible explanations in the time-variation in liquidity premium. Our

research contributes to this branch of literature by examining whether or not the time-

varying price of liquidity3 (more specifically, the time-varying price of REIT liquidity) is

affected by changes in macroeconomic variables.

We focus on REIT stocks only because previous literature suggests that REIT stocks

behave differently than the general market for stocks. Wang, Erickson and Chan (1995)

report that REIT stocks tend to have a smaller turnover ratio, a lower level of institutional

investor participation, and fewer security analyst followers. The depth and breadth of the

REIT market are both less than the market for general stocks. REIT stock market lacks of

attention from public. This makes REITs overall less sensitive to information shocks. The

uniqueness of the REIT market attracts the question whether or not the pricing of REIT

liquidity is affected by macroeconomic factors as common stocks are.

Glascock (1991) suggests that the real estate portfolio beta behave procyclically,

with a lower beta during recessionary states, and an increased beta during non-recessionary

states. The recessionary states are defined by the National Bureau of Economic Research.

We follow the path of the previous research and examine whether the liquidity premium of

REIT stocks changes with economic conditions. Our research complements previous studies

                                                            3 The price of liquidity is defined as the estimated coefficient on stock liquidity from Fama-MacBeth cross-section tests. This measure captures the aggregated weight of liquidity that should be priced in stock returns. 

2

by concentrating on Real Estate Investment Trusts (REITs), since previous research has

focused on common stocks’ liquidity. Recent experience in 2007 to 2008 tells us that

housing bubbles can lead to real economic crashes. Economic fluctuations certainly in turn

affect the housing market. If we consider REITs are portfolios of equities that are written on

real estate. It is worth of examining whether or not and how the price of REIT liquidity

changes across the phases of business cycles.

Because macroeconomic conditions are typically reported in a low frequency manner,

and we intend to cover a sample period as long as possible, TAQ data period is simply too

short for this study. We use Amihud’s (2002) illiquidity (ILLIQ) measure which is easily

calculated from daily REIT stock data.

ILLIQ is defined as the ratio between the absolute value of stock’s daily return and

its daily dollar volume and is designed to capture price changes relative to volume changes.

Thus lower units of this measure indicate higher levels of liquidity. ILLIQ can be easily

obtained from the CRSP-Ziman REIT daily data and CRSP stock daily data. To match with

the monthly frequency of macroeconomic variables, for each REIT stock, we aggregate the

daily ILLIQ into a monthly ILLIQ measure (which is the simple average among the daily

ILLIQ’s).

To control for fluctuations in liquidity levels within the market, we follow Amihud

(2002) and use the mean-adjusted ILLIQ measure (ILLIQMA) calculated as the ratio of the

individual REIT stock’s ILLIQ divided by the aggregated market level of illiquidity

(AILLIQ) for each month throughout the sample period from July 1983 through December,

2009.

3

Our main result is that the change in crude oil prices exhibits contemporaneous

effects on the price of REIT liquidity. Additionally, default premium and term premium

have a significant positive contemporaneous effect on the price of liquidity when the

economy is in recessionary states. The change in inflation rate also stands out as a

significant predictive variable to the pricing of REIT liquidity when we divide our sample

period into recessions and non-recessions according to the National Bureau of Economic

Research (NBER) Business Cycle Dates (see the appendix).

Our findings are consistent with Chordia, Sarkar and Subrahmanyam (2005) [CSS

henceforth]. They show that stock liquidity co-moves with bond liquidity. Also they posit

that macroeconomic factors contemporaneously influence liquidity and suggest that during

recessionary periods macroeconomic factors are predictive of the levels of liquidity. Our

study focuses on a niche sample of securities (REITs), and obtains results similar to the prior

research on common stocks (excluding REITs). Our results help us to better understand the

source of the commonality of liquidity and support the expectations and empirical work of

CSS [as well as the prior work of Chordia, Roll and Subrahmanyam (2000), Hasbrouck and

Seppi (2001), and Coughenour and Saad (2004)].

The remainder of the research is organized as follows. Section II provides some

related literature. Section III introduces the data and the liquidity measurement that is used

in this paper. Section IV part i examines how the liquidity is priced in cross-sectional level

for individual REIT stocks and obtain the price of REIT liquidity. Section IV part ii

investigates the macroeconomic effects on the price of REIT liquidity. Section V concludes.

4

II. Related Literature

Brunnermeier and Pedersen (2009) build a model which links market liquidity (the

ease with which an asset is traded) with funding liquidity (the ease with which traders can

obtain funding). They show that there is a mutually reinforcing relation between these two

aspects of liquidity. Their model explains the empirically documented outcomes that market

liquidity can suddenly become scarce when the economy is in a downturn. Thus,

Brunnermeier and Pedersen (2009) predict that an exogenous shock to speculators’ capital

should lead to a reduction in market liquidity.

The following papers empirically test the implications of the model in Brunnermeier

and Pedersen (2009). Hameed, Kang and Viswanthan (2009) show that the level of stock

market liquidity can suddenly decrease during a stock market decline. They also find that

when this occurs it takes on average two weeks for the market to recover. Naes, Skjeltorp

and Odegaard (2010) find a strong relation between stock market liquidity and the business

cycle. Jensen and Moorman (2010) find evidence of a systematic link between monetary

conditions and inter-temporal variation in the price of liquidity. Specifically, following an

expansive monetary policy shift, funding and market liquidity conditions improve. This

improvement is more beneficial for illiquid stocks.

Changes in interest rates, inflation rates, bond yields, Federal Reserve requirements

and monetary policies can act as an exogenous shock to the funding constraints and

influence funding liquidity. We observe that a substantial reduction in production activity

results in a market decline in economic activity. We also observe a high inflation rate and a

high unemployment rate during recessions. Therefore, it is straightforward to use

5

macroeconomic factors to describe different economic conditions and study macroeconomic

effects on the pricing of stock liquidity.

Chen, Roll and Ross (1986) [CRR henceforth] study the predictability of stock

returns based on macroeconomic factors. From the perspective of the market efficiency

hypotheses and rational expectations intertemporal asset pricing theory, asset returns should

depend on their exposures to the state variables that describe the economy. In their APT-

like pricing model, CRR find that the rate of growth in industrial production primarily

explains stock returns for their sample period, with unexpected inflation and a term structure

variable adding additional explanatory power. Watanabe (2004) using a vector

autocorrelation model shows that changes in the macroeconomic variables are

systematically related to the levels of market liquidity.

III. Data and liquidity measure

i. Stock data

REIT stock data are obtained from CRSP-Ziman, CRSP and Compustat for the

period January 1st, 1980 to December 31st, 2009. We identify REIT stocks using CRSP-

Ziman. Then we go to CRSP daily stock data using the REIT identifiers to obtain other

REIT stock information, including daily returns, price, trading volume, share outstanding,

which is used to calculate the Amihud measure of illiquidity. We need Compustat to include

the book values of REITs. After we combine all the available data information, the final

REIT sample includes 519 REIT stocks for the period August 1st, 1983 through December

31st, 2009.

6

ILLIQ is defined as the ratio between the absolute value of stock’s daily return and

its daily dollar volume and designed to capture the price impact. ILLIQ is thus calculated

from the CRSP REIT daily stock data. Higher measures of ILLIQ are associated with

greater illiquidity.

In order to match our monthly macroeconomic data, we follow a procedure similar to

Amihud (2002) by aggregating the daily ILLIQ into a monthly ILLIQ measure (which is the

simple average among the daily ILLIQ’s). Additionally, in order to control for the

fluctuation in liquidity level within the market, we use ILLIQMA, the mean-adjusted ILLIQ

measure calculated as the ratio of the individual REIT stock’s ILLIQ divided by the

aggregated market level AILLIQ for every month through the sample period from August,

1983 through December, 2009.

For individual stock in month t,

,

, ,

,1, ,

1 i tDi t d

i tti t i t d

RILLIQ

D Vol

,

(1)

where is the number of days for which data are available for stock i in month t. ,i tD , ,i t dR is

the return on stock i on day d of month t and is the respective daily volume in million

dollars. This ratio shows the price change per dollar of daily trading volume, or the daily

price impact of the order flow.

, ,i t dVol

For market-level ILLIQ in month t,

1

1 tN

ttt

AILLIQ ILLIQN

t (2)

7

where Nt is the number of stocks in month t.

Because liquidity level fluctuates rigorously sometime in the market, we use the

mean-adjusted illiquidity to control for the fluctuation in the liquidity level. Thus, for

individual stock i in month t, the mean-adjusted illiquidity measure

,,

i ti t

t

ILLIQILLIQMA

AILLIQ (3)

ILLIQMA is the liquidity measure that is used in future test.

ii. Macroeconomic variables

Following Chen, Roll and Ross (1986), Ferson and Harvey (1999) and Wanatabe

(2004), we choose the following macroeconomic state variables to be proxies for market

conditions:

IPG: the rate of growth in Industrial Production;

CPI: the change in realized inflation rate in percentage;

UEMP: the change in unemployment rate;

BAA: the default premium;

TRM: the term premium;

OIL: the change in crude oil price;

M1: the change in M1 supply;

M2: the change in M2 supply.

All the monthly macroeconomic data are obtained from Federal Reserve at St. Louis

Database (FRED) from August, 1983 to December, 2009.

8

We handle these macroeconomic state variables in the same manner as Liu and

Zhang (2006). The rate of growth in Industrial Production (IPG), which proxies for the

macroeconomic news and is our measure to capture the condition of the economy, is defined

as . We use the changes in Consumer Product Index (CPI) which is to proxy for the

inflation rate. We also use the change in Unemployment rate (UEMP) as a characteristic of

economic conditions.

Following the work of Wanatabe (2004), the term structure variable (TRM) is

defined as the premium between the ten-year government bond yield and the one-year

government bond yield. Default premium (BAA) is defined as the premium between

Moody’s BAA bond yield and Moody’s AAA bond yield. Crude oil price change (OIL)

variable is defined as (OILt-OILt-1)/OILt-1 in percentage.

According to Jensen and Moorman (2010), stock liquidity premium is time-varying

with monetary conditions. In this research, we use the changes (in percentage) in M1 and

M2 supply as proxies for monetary conditions.

Following Ferson and Harvey (1999), all the macroeconomic variables will be

demeaned when they are used in regressions later in this paper. The descriptive statistics of

our macroeconomic factors and the correlation matrix among macroeconomic variables are

presented in Table I.

[Insert Table I here]

From Panel B in Table I, we find that the rate of growth in industrial production (IPG)

has the highest correlation with the change in unemployment rate (UEMP) followed by the

default premium (BAA). The negative correlation between IPG and UEMP is consistent

with our expectation that when the real economic production is high the unemployment rate

9

is low. The change in inflation rate (CPI) is positively correlated with the change in oil price

(OIL). M1 and M2 are highly correlated as anticipated since M2 includes M1 by

construction.

IV. Test Methodology and Empirical Results

i. Cross-sectional tests

Our effort focuses on the macroeconomic effects on the price of REIT liquidity

instead of simple liquidity levels. Therefore, we are interested in obtaining the price of

REIT liquidity for every month. We apply the Fama-MacBeth (1973) cross-sectional tests

to obtain the price of REIT liquidity.

The first step of the test is to establish that ILLIQ is priced cross-sectionally in REIT

stock returns. Our sample is made up of REIT stocks for the period August, 1983

throughout December, 2009 (317 months). Monthly returns are obtained from CRSP. Book

equity values are obtained from Compustat. Table II shows the summary descriptive

statistics of stocks’ characteristics which are used in the cross-section regressions.

[Insert Table II here]

The average monthly excess return, which is defined as the returns net of the risk-

free rate, is 0.772 percent in the whole sample. The monthly risk-free rate is obtained from

the data library of Kenneth French. The average mean-adjusted illiquidity ILLIQMA is

0.9882 in our sample.

Following Fama and French (1992), we construct beta in several steps. First, 60-

month stock data are used for the estimation of individual stock’s beta in the simple CAPM

model below:

10

Rett = αt + βt*Rmt +εt (1)

Second, stocks are divided into 10x10 size-beta deciles for each month. Thus, for

each month, each stock is assigned into a portfolio.

Third, we calculate the value-weighted average portfolio return and estimate

portfolio betas by running a full-span time-series regression of portfolio returns on the

current and one-lagged value-weighted market returns.

Finally, portfolio beta is defined as the summation of the two coefficients on the

current and lagged value-weighted market return and assigned to every stock in that

portfolio. This methodology of estimating betas is to adjust for non-synchronous trading as

suggested by Scholes and Williams (1977), Dimson (1979), Fama and French (1992) and

Amihud (2002). In our sample, beta has a mean of 0.9364. The average beta is consistent

with the findings that REITs are less sensitive

Fama and French (1992) show that both size and book-to-market ratio have

significant effects on stock returns. Hence, in our cross-sectional analysis, we also control

for size and book-to-market effects on REIT returns. Size is defined as the market value of

equity of each REIT stock (ME) in June of every year. Following Fama and French (1992)

and Amihud (2002), we use the logarithm of ME to proxy for the size characteristic of

individual stocks. In our sample, Ln(ME) has a mean of 5.87. Book-to-market ratio

(BE/ME) is defined as the fiscal year-end book value of equity divided by the calendar

yearend market value of equity. Also, we use the logarithm form of BE/ME in our analysis.

From Table II, we can see that the average Ln(BE/ME) is -0.373.

11

The correlation matrix among all the stock characteristics variables is shown in

Table III. The asterisk represents 1 percent level of significance.

[Insert Table III here]

The correlations are as expected. The correlation between excess returns and

illiquidity is significantly positive (2.685 percent), which is consistent with previous

findings that illiquid stocks have higher expected returns in cross section tests (see Amihud

and Mendelson [1986], Pastor and Stambaugh [2003] and Acharya and Pedersen [2005]).

As expected, returns are negatively related with size, and positively related to book-to-

market variable. This correlation matrix confirms that liquidity performs as a common risk

factor for REIT returns.

Table IV presents the main results from our cross-sectional regressions following the

method in Fama and MacBeth (1973). The results are for the sample period August, 1983 to

December, 2009 (317 months).

[Insert Table IV here]

We perform cross-sectional tests for several models. Model 1 and Model 5 provide

similar results as in Fama and French (1992), and Model 3 and Model 6 present comparable

results to Amihud (2002). Model 7 shows that ILLIQ is a predictive variable in all

equations after we control for size and book-to-market effects.

In addition to Models 3 and 6 that control for beta and beta/size (similar to Amihud

[2002]), we also control for BE/ME with beta in Model 4 to see how ILLIQ performs.

Model 7 is our full model that controls for all three well-known common risk factors. Not

12

surprisingly, ILLIQ has significantly positive effects on stock returns across all the models

regardless of which variables are controlled for. The signs on the coefficients on beta,

Ln(ME), and Ln(BE/ME) are consistent with expectations, but not significant. Model 7

indicated that a 1 percent increase in the illiquidity will increase expected stock returns by

0.102 percent, which is significantly positive and economically sensible.

ii. Macroeconomic effects on the price of liquidity

As is generally accepted, macroeconomic conditions affect funding liquidity (the

ease of getting funds). Brunnermeier and Pedersen (2009) suggest that there is a reinforce

relationship between funding liquidity and market liquidity. When the economy is weak,

funding liquidity deteriorates and the stock market liquidity decreases. Then, lower levels of

market liquidity will in turn further reduce funding liquidity.

Based on this story, we expect that variations in economic conditions reflected by

macroeconomic variables will have effects on any stock market liquidity premium. When

the economy is in a weaker state, fewer goods are produced and inflation rates may be

expected to increase. This leads to a higher price of liquidity in the stock market. Therefore,

we would expect a general relationship between lagged market conditions and the price of

liquidity.

Our first step is to examine the correlations between the price of liquidity (LIQ) and

macroeconomic factors. The results reported for Model 7 in Table IV are coefficients of the

regression for the 317 months. Table V shows the descriptive statistics of LIQ in Panel A

and the contemporaneous correlations with the macroeconomic variables in Panel B.

[Insert Table V here]

13

Because LIQ is obtained from the results of Model 7 as shown in Table IV, the mean

of LIQ is exactly the same coefficient as the result reported for ILLIQMA in Model 7 in

Table IV. Panel B shows the correlations with the eight macroeconomic variables. All the

macroeconomic variables are defined in Table I and demeaned following the work of Ferson

and Harvey (1999). The prefix “D” indicates a demeaned variable.

We see that at the contemporaneous level, the price of REIT liquidity is negatively

correlated with the growth rate in industrial production (DIPG) and the change in inflation

rate (DCPI). LIQ is positively correlated with the change in unemployment rate.

Before we perform a time-series regression to test the explanatory power of

macroeconomic variables to the price of REIT liquidity (LIQ), we provide results of Unit

roots test of all the variables involved. The p-values of the tests are presented with five lags

as shown in Table VI. From Table VI, we see that all variables are stationary at the 5

percent level of significance.

[Insert Table VI here]

Next, we run time-series regressions to examine how the price of REIT liquidity

varies with contemporaneous and lagged macroeconomic conditions. Table VII presents the

main results of the contemporaneous time-series regressions.

[Insert Table VII here]

Table VII presents the results of contemporaneous time-series regressions for the

whole sample period. As shown in Table VII, we find contemporaneous macroeconomic

effects on the price of liquidity. The key explanatory variable is the change in crude oil

14

price (OIL) and monetary supply M2. Contemporaneously, when M2 increases, the price of

REIT liquidity decreases.

Furthermore, we run a time-series regression focusing on the lagged macroeconomic

effects on REIT liquidity. Table VIII shows the main results.

[Insert Table VIII here]

From Table VIII, disappointingly, none of the macroeconomic variables shows a

significant lagged effect on the price of REIT liquidity.

Because our main hypothesis is that the price of liquidity varies with economic

conditions and macroeconomic variables can explain the time variation in the price of

liquidity. Accordingly, our next step is to create a dummy variable which captures different

states of economy. To further examine economic conditions and the price of REIT liquidity

when the economy is in a recessionary period, we create a dummy variable (REC) which is

equal 1 if the market is in recession and 0 otherwise.

We use the National Bureau of Economic Research (NBER) Business Cycle data to

classify recessions during our sample period from August, 1983 to December, 20094.

Recessionary cycles as defined by the NBER are shown in the appendix. There are 34

recessionary months and 283 non-recessionary months throughout our sample period.

We repeat the time-series regressions in Table VII and Table VIII on the sample with

recession dummy variable. We create the interaction terms between macroeconomic

variables and the recession dummy variable. Therefore, the estimated coefficients on the

                                                            4 NBER business cycle dates end in a trough in June 2009. Therefore, we treat months after June 2009 as a non-recessionary state. 

15

interaction terms show the difference in the explanatory powers between expansionary state

and recessionary state.

Table IX shows the results of the time-series regressions of contemporaneous

macroeconomic effects when we partition economic conditions into recession and non-

recession periods.

[Insert Table IX here]

The coefficients on the macroeconomic variables represent the effects of the

variables on LIQ during expansions (REC=0). The coefficients on the interaction terms

represent the difference between the effects during recessions and those during expansions.

Hence, the effects from macroeconomic factors onto the price of REIT liquidity are the sum

of the according two coefficients.

Table IX tells us that the default premium (BAA) and term premium (TRM) have

strong explanatory powers over the price of REIT liquidity when the economy is in

recessionary state. When the economy is in downturn, the default premium increases and

the term premium shrinks. These indicate an increase in the price of REIT liquidity. The

change in oil price (OIL) shows a significantly positive influence on the price of REIT

liquidity when the economy is in non-recessionary state. One possible explanation is that,

when the economy is expanding, the liquidity premium is lower. Meanwhile, the demand for

crude oil is increasing. This drives up the oil price. Hence, we observe a significantly

negative contemporaneous correlation between REIT liquidity premium and change in oil

price.

[Insert Table X here]

16

Table X presents the results of time-series regressions of lagged macroeconomic

effects on the price of liquidity when we divide our sample into non-recessions and

recessions. Only the change in inflation rates (CPI) stands out as a significant predictive

power over the price of REIT liquidity when the economy is in a recessionary state. This

result is consistent with our conjecture that real estate market is more affected by the

inflation rate than industrial production.

In sum, we find that an increase in industrial production during recessionary periods

will decrease the price of stock liquidity and the price of liquidity falls with the increased

money supply (proxied by M2 in this research). A decrease in the price of stock liquidity

will improve the overall economy conditions. Indeed, we find that encouraging

macroeconomic policies, such as boost in real production and relaxation in monetary

policies, will help reduce the price of liquidity and lead to an economic recovery when the

economy is in recessionary state.

V. Concluding Remarks

Following the implications in Brunnermeier and Pedersen (2009), the work of Jensen

and Moorman (2010) and Naes et al (2010), we study macroeconomic effects on the price of

REIT liquidity.

Jensen and Moorman (2010) and Naes et al (2010) investigate whether or not

illiquidity premium is state dependent. Glascock (1991) suggests that the real estate

portfolio beta behave procyclically. Bredin et al (2007) examine the unexpected monetary

shock to REIT returns. We add to this literature by examining whether or not the price of

REIT liquidity is time-varying and state dependent on macroeconomic conditions.

17

Overall, we find that there exists an indirect channel through which macroeconomic

factors affect RETI returns by affecting the price of REIT liquidity. Generally, we find

strong contemporaneous effects from the change in crude oil prices. Both the default

premium and the term premium show significant contemporaneous powers over REIT

liquidity when the economy is in recession. Additionally, the change in inflation rate

presents significant predictive power in the price of stock liquidity in recessionary state.

Our findings agree with the results of Chen and Tzang (1988). They find that over

their 1973 to 1985 study period, both mortgage and equity REITs are sensitive to interest-

rate movements. The source of interest-rate sensitivity for equity REITs was changes in

expected inflation, not the changes in real interest rates. Our research differs from Chen and

Tzang (1988) in that we focus on the sensitivity of REIT liquidity rather than the sensitivity

of REIT returns.

Furthermore, our research shares the similar spirit of the work of Liang et al (1995)

and Allen et al (2000). The former paper finds that the interest-rate beta of REIT portfolio is

time varying. The latter one investigates what characteristics affect REIT return sensitivity

to interest rate changes. They find that REIT returns have significantly negative relation

with interest rate changes. Our research studies what macroeconomic factors affect REIT

liquidity premium. We find that REIT liquidity premium is negatively affected by the

changes in inflation rate.

Our results suggest evidence that the pricing of REIT liquidity is time-varying and

can be partially explained by business cycles. The results expand our understanding of the

relation between REIT liquidity and economic conditions.

18

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22

Appendix

National Bureau of Economic Research Business Cycle Date5

BUSINESS CYCLE REFERENCE DATES

DURATION IN MONTHS

PEAK TROUGH Recession*

Peak to Trough

Expansion

Previous trough to this peak

July 1953 May 1954 10 45

August 1957 April 1958 8 39

April 1960 February 1961 10 24

December 1969 November 1970 11 106

November 1973 March 1975 16 36

January 1980 July 1980 6 58

July 1981 November 1982 16 12

July 1990 March 1991 8 92

March 2001 November 2001 8 120

December 2007 June 2009 18 73

*Recessions start at the peak of a business cycle and end at the trough.

                                                            5 Sources from http://www.nber.org/cycles/cyclesmain.html 

23

Table I

Summary Statistics and Correlations among Macroeconomic Factors

Macroeconomic monthly data are obtained from Federal Reserve. The sample period is from August, 1983 throughout December, 2009. The rate of growth in industrial production (IPG), which proxies for the macroeconomic news and captures the conditions of the whole market, is defined as . We use the changes in Consumer Product Index (CPI) which is to proxy for the inflation rate. Unemployment rate (UEMP) is also considered to be a characteristic of market conditions. Term structure (TRM) variable is defined as the premium between the ten-year government bond yield and the one-year government bond yield. Default premium (BAA) is defined as the premium between Moody’s BAA bond yield and Moody’s AAA bond yield. Crude oil price change (OIL) variable is defined as (OILt-OILt-1)/OILt-1 in percentage. M1_change (M1) and M2_change (M2) measure the monetary policy. The asterisk “*” stands for 10% level significance. “**” stands for 5% level significance, and “^” stands for 1% level of significance.

Panel A: Summary Statistics IPG BAA CPI UEMP TRM OIL M1 M2 Mean 0.001851 0.010159 0.002454 1.89E-05 0.013611 0.00642 0.003842 0.004492Std 0.006339 0.004186 0.002618 0.001601 0.010206 0.086399 0.007788 0.003398N 317 317 317 317 317 317 317 317

Panel B: Correlation Matrix IPG 1 -0.35356 0.108255 -0.39917 -0.00538 0.102469 -0.20466 -0.22216BAA 1 -0.22394 0.33568 0.318592 -0.10643 0.345509 0.268031CPI 1 -0.12673 -0.08952 0.436011 -0.15147 -0.15698UEMP 1 0.081566 -0.04825 0.054052 0.090971TRM 1 -0.01515 0.359522 -0.14335OIL 1 -0.128 -0.14539M1 1 0.436369M2 1

24

Table II

Summary Statistics of REIT Characteristics in the Sample

This table shows the summary statistics of stocks from August, 1983 through December, 2009. EXRET is the excess return, which is the RETURN net of the monthly risk-free rate (30-day Treasury Bill rate). Risk-free rate is obtained from Ken French data library. ME, and BE/ME are all constructed following Fama and French (1992). BETA is the portfolio beta estimated by using 10x10 size-beta ranking portfolios. ME is the market value of equity (the absolute value of monthly closing price times the number of shares outstanding in June). BE/ME is the book-to-market ratio defined as the fiscal yearend book value of common equity divided by the calendar yearend market value of equity. ILLIQ is calculated as the mean of the absolute value of daily return divided by the daily dollar volume in every month, following Amihud (2002). ILLIQMA is the illiquidity mean-adjusted measure calculated as the ratio of the stock’s ILLIQ divided by the market illiquidity level of that month.

Variable N Mean Std Dev Median

EXRET beta LNME LNBM LIQMA

3163631636316363163631636

0.77247530.93643205.8716333

-0.37346750.9882214

8.96286810.38026831.57660440.72448223.8443780

0.6498483 0.8007580 5.9947504

-0.3300689 0.0661752

25

Table III

Correlation Matrix of Stock Characteristics Variables

The table presents the time-series means of the cross-sectional Pearson correlation relating to the sample of REIT stocks during August, 1983 to December, 2009. EXRET is the excess return, which is the RETURN net of the monthly risk-free rate (30-day Treasury Bill rate). Risk-free rate is obtained from Ken French data library. ME, and BE/ME are all constructed following Fama and French (1992). BETA is the portfolio beta estimated by using 10x10 size-beta ranking portfolios. ME is the market value of equity (the absolute value of monthly closing price times the number of shares outstanding in June). BE/ME is the book-to-market ratio defined as the fiscal yearend book value of common equity divided by the calendar yearend market value of equity. ILLIQ is calculated as the mean of the absolute value of daily return divided by the daily dollar volume in every month, following Amihud (2002). ILLIQMA is the illiquidity mean-adjusted measure calculated as the ratio of the stock’s ILLIQ divided by the market illiquidity level of that month. The correlation coefficients followed by * are significant at 1% level based on their time-series standard error.

EXRET Beta Ln(ME) Ln(BE/ME) ILLIQMA

EXRET 1 -0.01157 -0.02567* 0.03818* 0.02685*

Beta 1 0.05099* 0.01134 -0.02799*

Ln(ME) 1 0.34445* -0.36663*

Ln(BE/ME) 1 0.08264*

ILLIQMA 1

26

Table IV

Fama-MacBeth cross-section test of REIT stock returns

This table presents the main results in the Fama-MacBeth (1973) cross-sectional regressions. The reported results contain of the time-series average of the slopes and the average t-statistics are shown in the parentheses. T-value for 5% level significance is 1.96. EXRET is the excess return, which is the RETURN net of the monthly risk-free rate (30-day Treasury Bill rate). Risk-free rate is obtained from Ken French data library. ME, and BE/ME are all constructed following Fama and French (1992). BETA is the portfolio beta estimated by using 10x10 size-beta ranking portfolios. ME is the market value of equity (the absolute value of monthly closing price times the number of shares outstanding in June). BE/ME is the book-to-market ratio defined as the fiscal yearend book value of common equity divided by the calendar yearend market value of equity. ILLIQ is calculated as the mean of the absolute value of daily return divided by the daily dollar volume in every month, following Amihud (2002). ILLIQMA is the illiquidity mean-adjusted measure calculated as the ratio of the stock’s ILLIQ divided by the market illiquidity level of that month. The dependent variable is the monthly excess returns of stocks. Beta, ME and BE/ME are estimated in the manner of Fama and French (1992). ILLIQ is estimated as in Amihud (2002). The last column reports the average adjusted R-squares of the cross-sectional regressions.

Dependent Variable: Excess Return for individual REIT stocks

MODEL Intercept β ILLIQ Ln(ME) Ln(BE/ME) Adj. R2

1.064 -0.356 0.011 MODEL1

(4.55) (-1.16)

0.647 0.072 0.013 MODEL2

(2.34) (1.71)

0.996 -0.373 0.089 0.024 MODEL3

(4.11) (-1.22) (2.61)

1.015 -0.379 0.090 0.130 0.033 MODEL4

(4.25) (-1.24) (2.69) (1.43)

1.798 -0.249 -0.145 0.069 0.040 MODEL5

(5.08) (-0.84) (-2.33) (0.74)

1.384 -0.336 0.104 -0.065 0.041 MODEL6

(3.39) (-1.12) (2.83) (-0.97)

1.294 -0.308 0.102 -0.057 0.081 0.048 MODEL7

(3.24) (-1.03) (2.74) (-0.83) (0.87)

27

28

Table V

Summary Statistics of the price of REIT liquidity

and the Correlation Matrix with Macroeconomic Factors

Panel A presents the descriptive statistics of the price of liquidity that is subtracted from the results of the cross-sectional test in Table IV. Panel B shows the correlations between LIQ and the macroeconomic variables. The rate of growth in industrial production (IPG), which proxies for the macroeconomic news and captures the conditions of the whole market, is defined as . We use the changes in Consumer Product Index (CPI) which is to proxy for the inflation rate. Unemployment rate (UEMP) is also considered to be a characteristic of market conditions. Term structure (TRM) variable is defined as the premium between the ten-year government bond yield and the one-year government bond yield. Default premium (BAA) is defined as the premium between Moody’s BAA bond yield and Moody’s AAA bond yield. Crude oil price change (OIL) variable is defined as (OILt-OILt-1)/OILt-1 in percentage. M1_change (M1) and M2_change (M2) measure the monetary policy. Following the spirit of Ferson and Harvey (1999), all the macroeconomic factors are demeaned in the tests.

Panel A: Descriptive Statistics of LIQ

Mean 0.101905

Std 0.661359

N 317

Panel B: Correlation with Macro Factors

IPG -0.10111

BAA 0.037241

CPI -0.05101

UEMP 0.047685

TRM -0.02528

OIL -0.14081

M1 0.054996

M2 -0.05423

Table VI

Unit Root Test Results of Stationarity of Macroeconomic Variables

Unit Root tests are performed to check the stationarity of LIQ and macroeconomic variables. LIQ is obtained from the estimated coefficients on ILLIQMA of Model 7 in Table IV. The rate of growth in industrial production (IPG), which proxies for the macroeconomic news and captures the conditions of the whole market, is defined as . We use the changes in Consumer Product Index (CPI) which is to proxy for the inflation rate. Unemployment rate (UEMP) is also considered to be a characteristic of market conditions. Term structure (TRM) variable is defined as the premium between the ten-year government bond yield and the one-year government bond yield. Default premium (BAA) is defined as the premium between Moody’s BAA bond yield and Moody’s AAA bond yield. Crude oil price change (OIL) variable is defined as (OILt-OILt-1)/OILt-1 in percentage. M1_change (M1) and M2_change (M2) measure the monetary policy. All the variables are stationary at level with the cut-off p-value of 5%.

UNIT ROOTS TESTS (p-values)

LIQ DIPG DCPI DEMP DBAA DTRM DOIL DM1 DM2

LAG=0 0.0001 0.0001 0.0001 0.0001 0.0202 0.0322 0.0001 0.0001 0.0001

LAG=1 0.0001 0.0001 0.0001 0.0001 0.0005 0.0006 0.0001 0.0001 0.0001

LAG=2 0.0001 0.0001 0.0001 0.0001 0.0040 0.0090 0.0001 0.0001 0.0001

LAG=3 0.0001 0.0001 0.0001 0.0001 0.0036 0.0038 0.0001 0.0001 0.0001

LAG=4 0.0001 0.0001 0.0001 0.0001 0.0016 0.0150 0.0001 0.0001 0.0001

LAG=5 0.0001 0.0001 0.0001 0.0001 0.0003 0.0080 0.0001 0.0001 0.0001

29

30

Table VII

Time-series Regression Results of Contemporaneous Macroeconomic Effects

This table presents the results of time-series regressions which intend to investigate the contemporaneous macroeconomic effects on the price of liquidity (LIQ). LIQ is subtracted from the results in Table IV and serves as the dependent variable here. The rate of growth in industrial production (IPG), which proxies for the macroeconomic news and captures the conditions of the whole market, is defined as . We use the changes in Consumer Product Index (CPI) which is to proxy for the inflation rate. Unemployment rate (UEMP) is also considered to be a characteristic of market conditions. Term structure (TRM) variable is defined as the premium between the ten-year government bond yield and the one-year government bond yield. Default premium (BAA) is defined as the premium between Moody’s BAA bond yield and Moody’s AAA bond yield. Crude oil price change (OIL) variable is defined as (OILt-OILt-1)/OILt-1 in percentage. M1_change (M1) and M2_change (M2) measure the monetary policy. Based on the unit root test results in Table VI, appropriate levels of variables are applied in the regression analysis. This table shows the results for the whole sample period from August 1983 through December 2009. T-statistics are shown in parenthesis under the estimated parameters. Estimated parameters are bolded if they are significant from zero at 5 percent level.

Dependent Variable: Price of Liquidity (LIQ)

intercept IPG CPI UEMP BAA TRM OIL M1 M2 Adj. R2

MODEL1

0.101

(2.75)

-10.54

(-1.80) 0.0071

MODEL2

0.102

(2.75)

-9.899

(-1.55)

-10.14

(-0.71)

1.952

(0.08) 0.0024

MODEL3

0.102

(2.74)

-9.71

(-1.45)

-10.68

(-0.73)

2.84

(0.11)

0.376

(0.04)

-2.00

(-0.52) -0.0031

MODEL4

0.101

(2.75)

8.25

(1.56)

-18.80

(-1.55) 0.0101

MODEL5

0.109

(2.78)

-8.85

(-1.32)

2.88

(0.18)

7.10

(0.27)

3.93

(0.37)

-6.77

(-1.55)

-1.10

(-2.32)

10.42

(1.72)

-32.90

(-2.47)0.0239

31

Table VIII

Time-series Regression Results of Lagged Macroeconomic Effects

This table presents the results of time-series regressions which intend to investigate the lagged macroeconomic effects on the price of liquidity (LIQ). LIQ is subtracted from the results in Table IV and serves as the dependent variable here. The rate of growth in industrial production (IPG), which proxies for the macroeconomic news and captures the conditions of the whole market, is defined as . We use the changes in Consumer Product Index (CPI) which is to proxy for the inflation rate. Unemployment rate (UEMP) is also considered to be a characteristic of market conditions. Term structure (TRM) variable is defined as the premium between the ten-year government bond yield and the one-year government bond yield. Default premium (BAA) is defined as the premium between Moody’s BAA bond yield and Moody’s AAA bond yield. Crude oil price change (OIL) variable is defined as (OILt-OILt-1)/OILt-1 in percentage. M1_change (M1) and M2_change (M2) measure the monetary policy. Based on the unit root test results in Table VI, appropriate levels of variables are applied in the regression analysis. This table shows the results for the whole sample period from August, 1983 through December, 2009. T-statistics are shown in parenthesis under the estimated parameters. Estimated parameters are bolded if they are significant from zero at 5 percent level.

Dependent Variable: Liquidity Premium

intercept IPGt-1 CPIt-1 UEMPt-1 BAAt-1 TRMt-1 OILt-1 M1t-1 M2t-1 Adj. R2

MODEL1

0.106

(2.88)

-6.33

(-1.08) 0.0006

MODEL2

0.106

(2.87)

-7.02

(-1.10)

-1.41

(-0.10)

-7.50

(-0.30) -0.0055

MODEL3

0.106

(2.86)

-6.44

(-0.97)

-1.05

(-0.07)

-8.41

(-0.32)

3.05

(0.29)

-1.48

(-0.38)-0.0115

MODEL4

0.106

(2.87)

-1.10

(-0.21)

2.32

(0.19) -0.0062

MODEL5

0.106

(2.85)

-7.12

(-1.05)

-6.08

(-0.37)

-9.97

(-0.38)

3.97

(0.36)

-1.31

(-0.30)

0.31

(0.65)

-1.49

(-0.24)

-1.22

(-0.09) -0.0194

32

Table IX

Time-series Regression Results of Contemporaneous Macroeconomic Effects (with Recession dummy)

This table presents the results of time-series regressions which intend to investigate the contemporaneous macroeconomic effects on the price of liquidity (LIQ) when we partition the economic conditions into expansions and recessions. REC is a recession dummy variable created according to NBER business cycle dates. REC=1 if the economy is in recessionary state and REC=0 otherwise. LIQ is subtracted from the results in Table IV and serves as the dependent variable here. The rate of growth in industrial production (IPG), which proxies for the macroeconomic news and captures the conditions of the whole market, is defined as . We use the changes in Consumer Product Index (CPI) which is to proxy for the inflation rate. Unemployment rate (UEMP) is also considered to be a characteristic of market conditions. Term structure (TRM) variable is defined as the premium between the ten-year government bond yield and the one-year government bond yield. Default premium (BAA) is defined as the premium between Moody’s BAA bond yield and Moody’s AAA bond yield. Crude oil price change (OIL) variable is defined as (OILt-OILt-1)/OILt-1 in percentage. M1_change (M1) and M2_change (M2) measure the monetary policy. Based on the unit root test results in Table VI, appropriate levels of variables are applied in the regression analysis. Interaction terms between macroeconomic variables and REC dummy variables are constructed. This table shows the results for the sample period from August, 1983through December, 2009. T-statistics are shown in parenthesis under the estimated parameters. Estimated parameters are bolded if they are significant from zero at 5 percent level.

Dependent Variable: Liquidity Premium

REC IPG

Rec*

IPG CPI

Rec*

CPI UEMP

Rec*

UEMP BAA

Rec*

BAA

TRM

Rec*

TRM

OIL

Rec*

OIL

M1

Rec*

M1

M2

Rec*

M2 Adj. R2

1

0.074

(0.56)

-12.06

(-1.52)

7.07

(0.53) 0.0021

2

0.100

(0.60)

-12.15

(-1.47)

9.29

(0.66)

1.83

(0.10)

-30.94

(-1.04)

-1.37

(-0.05)

-17.02

(-0.24) -0.0054

3

0.305

(1.45)

-13.04

(-1.57)

23.93

(1.54)

3.12

(0.16)

-28.11

(-0.90)

-3.27

(-0.11)

-65.03

(-0.84)

-21.30

(-1.38)

56.48

(2.24)

-0.074

(-0.02)

-29.77

(-1.57) 0.0021

4

0.092

(0.81)

-0.08

(-0.01)

21.39

(1.90)

-9.89

(-0.69)

-40.71

(-1.48)0.0103

5

0.313

(1.51)

-12.54

(-1.52)

24.86

(1.57)

18.71

(0.94)

-52.22

(-1.39)

-4.41

(-0.15)

-16.72

(-0.21)

-20.42

(-1.23)

49.30

(1.90)

-1.71

(-0.35)

-39.63

(-2.06)

-1.51

(-2.74)

1.52

(1.37)

3.61

(0.43)

18.96

(1.51)

-12.61

(-0.78)

-53.46

(-1.80)0.0350

33

Table X

Time-series Regression Results of Lagged Macroeconomic Effects (with Recession dummy)

This table presents the results of time-series regressions which intend to investigate the lagged macroeconomic effects on the price of liquidity (LIQ) when we partition the economic conditions into expansions and recessions. REC is a recession dummy variable created according to NBER business cycle dates. REC=1 if the economy is in recessionary state and REC=0 otherwise. LIQ is subtracted from the results in Table IV and serves as the dependent variable here. The rate of growth in industrial production (IPG), which proxies for the macroeconomic news and captures the conditions of the whole market, is defined as . We use the changes in Consumer Product Index (CPI) which is to proxy for the inflation rate. Unemployment rate (UEMP) is also considered to be a characteristic of market conditions. Term structure (TRM) variable is defined as the premium between the ten-year government bond yield and the one-year government bond yield. Default premium (BAA) is defined as the premium between Moody’s BAA bond yield and Moody’s AAA bond yield. Crude oil price change (OIL) variable is defined as (OILt-OILt-1)/OILt-1 in percentage. M1_change (M1) and M2_change (M2) measure the monetary policy. Based on the unit root test results in Table VI, appropriate levels of variables are applied in the regression analysis. Interaction terms between macroeconomic variables and REC dummy variables are constructed. This table shows the results for the sample period from July, 1983through December, 2009. T-statistics are shown in parenthesis under the estimated parameters. Estimated parameters are bolded if they are significant from zero at 5 percent level.

Dependent Variable: Liquidity Premium

REC IPGt-1

Rec*

IPGt-1 CPIt-1

Rec*

CPIt-1 UEMPt-1

Rec*

UEMPt-1 BAAt-1

Rec*

BAAt-1

TRMt-1

Rec*

TRMt-1

OILt-1

Rec*

OILt-1

M1t-1

Rec*

M1t-1

M2t-1

Rec*

M2t-1 Adj. R2

1

0.098

(0.74)

-6.44

(-0.82)

5.04

(0.38) -0.0041

2

0.071

(0.42)

-8.73

(-1.06)

10.59

(0.76)

16.22

(0.85)

-38.97

(-1.32)

-28.61

(-0.94)

50.33

(0.72) -0.0071

3

0.223

(1.16)

-9.62

(-1.16)

21.35

(1.40)

17.36

(0.91)

-38.89

(-1.25)

-30.45

(-1.00)

18.17

(0.24)

-18.19

(-1.18)

43.67

(1.67)

0.58

(0.14)

-29.02

(-1.64) -0.0058

4

0.129

(1.13)

3.87

(0.55)

-15.29

(-1.36)

-3.85

(-0.27)

24.10

(0.88)-0.0062

5

0.265

(1.37)

-9.08

(-1.09)

15.19

(0.97)

18.19

(0.90)

-76.71

(-2.02)

-29.45

(-0.97)

-19.84

(-0.25)

-22.29

(-1.32)

48.33

(1.78)

-1.47

(-0.30)

-26.70

(-1.44)

-0.013

(-0.02)

1.68

(1.49)

8.50

(0.99)

-20.12

(-1.58)

-2.33

(-0.14)

14.90

(0.50)-0.0073