Do Public Real Estate Returns Really Lead Private Returns?

R
M
A
T
Do Public Real Estate
Returns Really Lead
Private Returns?
A
TH
U
C
E
D
O
ELIAS OIKARINEN
TO
R
EP
R
is an Academy Research
Fellow at the School of
Economics at the University of Turku in Turku,
Finland.
[email protected]
CAMILO SERRANO
IT
IS
IL
LE
G
A
L
is head of analytics at IAZI
AG–CIFI SA in Zurich,
Switzerland.
[email protected]
aforementioned data complications and suggest that securitized returns lead their direct
counterparts even after controlling for
these data problems. However, there is one
remaining data issue that has not been considered previously: the “escrow lag” in the
recording of direct market transaction prices.
In indexes that track direct real estate market
performance, the recorded price might represent the agreed-upon price based on the
meeting of minds that occurred a few weeks
prior to recording. This delay, which could
potentially explain the lead–lag relationship observed in the literature, is commonly
referred to as the escrow period, during
which the due diligence process takes place.
We contribute to the literature by
providing a clearer picture of the dynamic
relationships between public and private real
estate returns by accounting for all the aforementioned data complications, including the
escrow lag issue. We also make a further contribution to the literature. While previous
research finds that REIT (real estate investment trust) returns react faster than private
market returns to fundamentals (Ling and
Naranjo [2015]), thus creating a lead–lag
relationship between the markets, this is the
first analysis to derive impulse responses of
real estate returns to shocks in the fundamentals to specifically examine the factors
behind the lead–lag relationships between the
securitized and direct markets.
R
TI
he securitized or public real estate
market is generally assumed to be
more informationally eff icient
than the direct or private market.
This is due to the greater liquidity, larger
number of market participants, lower transaction costs, and existence of a public market
place for real estate securities. Therefore,
securitized real estate prices are expected
to react faster to shocks in the fundamentals
than direct real estate prices. In line with
this hypothesis, empirical evidence suggests that public market returns lead direct
market returns (Barkham and Geltner [1995];
Oikarinen et al. [2011]; Yunus et al. [2012];
Ling and Naranjo [2015]) and, hence, that
there is a price discovery mechanism between
public and private real estate markets that can
have important predictability and portfolio
allocation implications.
However, the reported lead–lag relationship could in some cases be biased due
to data complications. These complications
include the use of appraisal-based data to
measure direct market capital values, the
different property-type mixes of securitized
and direct real estate indexes, and the fact
that public real estate returns are leveraged
whereas direct returns are not. A number of
studies (Geltner and Kluger [1998]; Li et al.
[2009]; Yavas and Yildirim [2011]; Hoesli
and Oikarinen [2012]; Ling and Naranjo
[2015]) have addressed some or all of the
IS
is a professor at the University of Geneva’s Finance
Research Institute and
Swiss Finance Institute in
Geneva, Switzerland, and
a member of the faculties
at the Business School at
the University of Aberdeen
in Scotland, U.K. and the
Kedge Business School in
Talence cedex, France.
[email protected]
C
T
M ARTIN HOESLI
LE
IN
A
N
Y
FO
MARTIN HOESLI, ELIAS OIKARINEN, AND CAMILO SERRANO
SPECIAL R EAL ESTATE ISSUE 2015
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We use sector level REIT and transaction-based
direct real estate total return data for the United States
to study the reaction patterns of public and private real
estate returns to economic shocks and to examine the
lead–lag relationships between the public and private
real estate markets. Unlevered data are used to avoid the
potential inf luence of leverage in the REIT data on our
conclusions. We add economic fundamentals in the analysis to allow for the inf luence of fundamentals on real
estate market dynamics. Moreover, we “lag” the direct
market data to take the escrow lag into account; market
price levels will thus more accurately ref lect those that
prevailed at the time of the meetings of minds.
We find tight long-term relationships in terms of
cointegration in all four sectors. Based on the impulse
responses derived from the estimated vector error-correction models, the results show that REIT returns lead
private real estate returns in the office and retail sectors
even after accounting for leverage, the inf luence of fundamentals, and a 90-day escrow lag. The lead–lag relationships are due to the slow reaction of private market
returns to shocks in REIT returns, the risk premium,
and consumer sentiment. In the industrial sector, in
contrast, the lead–lag relationship goes from the direct
market to REITs if a 90-day escrow lag is assumed. For
the apartment market, we do not find clear-cut evidence
of a lead–lag relationship going either way. Overall, the
findings suggest that a universal lead–lag relationship
going from the public to the private real estate market
is not as evident as has generally been thought.
The findings have several practical implications.
REIT returns can be used to predict direct real estate
returns in all four sectors, because even in the industrial
and apartment markets the direct real estate prices react to
deviation from the long-term relationships—despite not
observing a leading role of REITs in those two sectors.
More unexpectedly, REIT returns, too, have predictable components. Furthermore, over the long horizon,
the co-movement between securitized and direct real
estate markets is substantially stronger than suggested by
the quarterly correlation coefficients. Importantly, as the
observed direct returns include a recording lag due to the
escrow period, the perceived contemporaneous correlation
between REIT and direct index returns can be notably
lower than the actual co-movement even in the short
horizon. Therefore, the benefits of including securitized
real estate in a portfolio containing direct real estate (or
vice versa) are more limited than what is often thought.
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The findings further suggest that the implications
in the literature—that during crisis periods, one should
sell direct real estate and buy REITs, rather than the
other way round (Hoesli and Oikarinen [2012])—hold
even when accounting for the escrow lag and for the
time-varying liquidity in the private market. Our results
also show that there are notable differences across real
estate sectors with respect to the return dynamics; it is
thus desirable to use sector level data when evaluating
the return and price dynamics of REITs and direct real
estate and when making forecasts concerning future
price developments on those markets.
DATA DESCRIPTION
Although REIT returns include the impact of
leverage, available direct real estate market benchmarks
ref lect the performance of unleveraged properties. The
magnitude of leverage naturally affects the mean and
volatility of returns. Moreover, time variation in the
leverage may hinder the cointegration tests and distort
the estimated long-run parameters. In addition, there
can be notable differences in the dynamics across real
estate sectors so that the aggregate indexes can mask
valuable sector-specific information and could lead to
erroneous conclusions being drawn (Yavas and Yildirim
[2011]; Hoesli and Oikarinen [2012]). Therefore, we use
the sector-level unlevered U.S. equity REIT data presented in Ling and Naranjo [2015]). The Ling–Naranjo
REIT data, in which REIT returns are unlevered at
the firm level, are based on the CRSP-Ziman and
CRSP/Compustat databases and include separate series
for the office, apartment, retail, and industrial sectors.
For direct real estate, we use the sector-level transaction-based NCREIF indexes (TBI). These indexes are
available for the same four sectors. The sample period,
1994Q1–2010Q4, is limited by the availability of the
hedonic TBI indexes.1 All indexes are total return
indexes.
The TBI and REIT indexes may also exhibit differences with respect to the geographic distribution of
the properties. We performed Granger causality tests
across the four TBI regions (East, West, South, and Midwest) and found no evidence of lead–lag relationships.
Thus, differences in the geographical distribution of the
properties, at least at the four-region level, are unlikely
to have a notable inf luence on our findings. Locational
differences may, of course, be more subtle than portfolio
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composition dissimilarities across broadly defined geographical areas. For instance, there may be differences
between REIT and institutional investors with respect
to the quality and size of the cities in which they invest.
Empirical evidence by Malpezzi and Shilling [2000]
suggests that private investor holdings are more heavily
tilted toward high-quality metropolitan areas and toward
areas with greater density, higher average human capital
accumulation, and less stringently regulated real estate
markets than those of public investors. Given that such
metro areas should react relatively fast to changes in
fundamentals (Capozza et al. [2004]), such effects may
make it easier to reject the existence of lead–lag relationships between public and private real estate returns.
In addition to the real estate data, the analysis
includes several variables that may affect significantly
the real estate returns according to theory and previous
empirical evidence (Karolyi and Sanders [1998]; Ewing
and Payne [2005]; Hoesli and Oikarinen [2012]; Ling
et al. [2014]). These variables concern economic growth,
general price levels, short-term interest rates, the term
structure of interest rates, the default risk premium, and
consumer sentiment. We measure economic growth
with the change in U.S. GDP (GDP). Changes in the
consumer price index are used to track movements in the
general price level (INF), while the three month T-bill
rate and the spread between the 10-year government
Treasury bond yield and the three month T-bill rate
measure the short-term interest rates (IR) and the term
structure of interest rates (S), respectively. The spread
between low-grade corporate bond (Baa, Moody’s) and
the 10-year government Treasury bond yields is used as
the measure for the default risk premium (D).
Finally, we capture the sentiment (SE) using the
University of Michigan Consumer Sentiment Index
(UMCI) regarding the five-year economic outlook.
The UMCI is a survey gauging how consumers foresee
changes in the economic environment and is an official
component of the U.S. Index of Leading Economic Indicators (i.e., an indicator providing information regarding
future economic growth). A number of studies (e.g.,
Carroll et al. [1994]; Lemmon and Portniaguina [2006])
showed that the consumer confidence measured by the
UMCI predicts future economic activity.2 The sentiment component of the UMCI index can be regarded as
the component that is unrelated to prevailing economic
fundamentals. Hence, following Lemmon and Portniaguina [2006] and Ling et al. [2014], we regress UMCI
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on the economic fundamentals included in our study
and use the residual series of this OLS regression as the
sentiment measure.
SE, GDP, and INF stand in for the expectations
concerning real cash f low growth, whereas the other
fundamentals in the model (and the inf lation rate, to
some extent) represent the current and expected future
movements in the discount factor. The data are sourced
from Thomson Datastream.
In the econometric analysis, we use real indexes
for NAREIT, TBI, and GDP. The series are def lated
using the CPI and used in the natural log form. The
short-term interest rate is also def lated using the CPI.
Exhibit 1 shows that the REIT and TBI indexes generally
track each other closely. An exception is the apartment
sector, where the TBI index outperformed the unlevered
REIT index, especially during the early sample period.
Also, the global financial crisis of 2008 induced divergences between the public and private markets.
A major complication when studying the dynamics
between public and private real estate markets is that
the recorded prices for direct real estate might represent
the agreed-upon prices based on the meeting of minds
that occurred a few weeks prior to recording. This
delay is commonly referred to as the “escrow period”
or “escrow lag,” during which the due diligence process takes place. The duration of the escrow period can
vary across property types as highlighted by Crosby and
McAllister [2004] in their analysis of U.K. commercial real estate. They reported median escrow lags of
approximately 80 days for the industrial sector, 50 days
for offices, and 90 days for retail property. Unfortunately, there is no study reporting corresponding values
for the United States. According to several U.S. expert
opinions, however, the escrow period can be as long as
90 days for apartments and up to 180 days for the other
property types. The average escrow periods are shorter
than these maximum values, however, and in some
cases, the price is renegotiated during the due diligence
process. Given the expert opinions, our best estimate
for the escrow lag in TBI indexes is 90 days, and we use
this escrow lag assumption in our baseline estimations
in the econometric analysis.3
As the REIT index values are beginning-ofquarter values, they ref lect the 45-day escrow lag case.
This is because the TBI indexes show average quarterly
values by construction (i.e., they are based on all transactions that took place during a given quarter). For the
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EXHIBIT 1
Sector Level (unlevered) NAREIT and TBI Total Return Indexes, 1994Q1–2010Q4
purposes of the baseline 90-day escrow lag analysis, we
first compute a REIT index that better corresponds to
the nature of the TBI: For a given quarter, the index
value is calculated as the average of beginning and end
of quarter values in the REIT data. If the escrow lag
is 90 days, the TBI index return for quarter t actually
ref lects the direct market price change that took place
during quarter t – 1. Therefore, we lag the nominal
TBI indexes by one quarter, def late these indexes by the
CPI, and study these TBI index returns together with
the modified REIT index returns to study the baseline
90-day escrow case.
The escrow lag is of course potentially time varying.
In particular, the lag might have become shorter over
the sample period due to advances in information technology. However, our diagnostic checks show that the
estimated model parameters are stable over the sample
period, suggesting that the potential time variation in
the escrow lag should not have notable effects on our
findings. The escrow period can also vary with location
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and size of the property. Thus, we explore the robustness
of our findings to the escrow lag length assumption by
considering 45-day and 135-day escrow periods.
Exhibit 2 presents descriptive statistics for the
unlevered off ice (Off_TBI, Off_REIT ), apartment
(Apt_TBI, Apt_REIT), retail (Ret_TBI, Ret_REIT), and
industrial (Ind_TBI, Ind_REIT) sector total returns and
the fundamental variables. The means (ranging from
6.2% to 7.6%) and volatilities (from 7.1% to 11.0%) are
quite similar across unlevered public and private market
returns and across sectors. The industrial sector is the
most volatile one.
Exhibit 2 also reports the contemporaneous quarterly correlations of REIT returns with TBI returns.
The reported correlations are based both on the original
series and on the series adjusted for the 90-day escrow
lag assumption (TBIe). For all sectors except industrials,
the correlation is greater when the escrow period is
accounted for than when it is not. The within-sector
“escrow lag adjusted” correlations are between 0.24
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EXHIBIT 2
Descriptive Statistics of Differenced Variables, Unit Root Test Results, and Correlations of Real Estate Returns,
1994Q1–2010Q4
Notes: Within-sector correlations are bolded. The number of lags included in the DF-GLS tests is decided based on the Akaike information criterion
(AIC). A constant term (c) is included in the DF-GLS test if the series clearly seem to be trending or if the test results without the constant term suggests
that the series are exploding. s indicates the inclusion of seasonal dummies in the test. For the sentiment factor, the statistics are in index points, not in
percentages.
*and **denote statistical significance at the 5% and 1% levels, respectively, in the DF-GLS unit root test and in the correlation analysis.
(apartments) and 0.47 (offices) and statistically significant for all sectors. Based on the DF-GLS unit root
test, all return indexes are non-stationary in levels but
stationary in differences, as expected, and the fundamental variables, too, are I(1). An exception to this is
the sentiment variable, which is stationary.
ECONOMETRIC METHODOLOGY
There are sound a priori theoretical reasons to
expect that securitized and direct real estate total return
indexes should be cointegrated (Oikarinen et al. [2011]).
Because a cointegrating relationship can be an important
force driving the short-term return dynamics and hence
can substantially affect the lead–lag relationships, we
test for cointegrating relations and estimate such longterm relationships, as a preliminary step, employing the
Johansen [1996] maximum likelihood technique and
the small-sample corrected trace test ( Johansen [2002]).
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The trace test provides evidence of cointegration for
all the sectors.4 Hence, the deviations of private return
indexes from these long-term relationships are included
in the vector error-correction models (VECMs) that are
estimated to study the dynamics of real estate returns:
ΔXt = μ + Γ1ΔXt-1 + … + Γk-1ΔXt-l
+ α(TBIt-1 – TBI*t-1) + εt
where ΔXt is Xt – Xt-1, Xt is an eight-dimensional vector
including both the return index values and the fundamentals in period t, μ is an eight-dimensional vector of
drift terms, Γi is an 8 × 8 matrix of coefficients for the
lagged differences of the stochastic variables at lag i,5 and
ε is an eight-dimensional vector of white noise error
terms. α(TBIt-1 – TBI *t-1) forms the error-correction
mechanism, where α is a vector of the speed of adjustment parameters and TBIt-1 – TBI *t-1 is the deviation of
the TBI value from its cointegrating relation (TBI*)
with the REIT index in period t – 1. Because only the
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real estate indexes can be assumed to adjust toward a
cointegrating relation between those indexes, the alpha
is restricted to equal zero in the equations for the fundamentals. Real estate returns are allowed to inf luence
the economic fundamentals through the short-term
dynamics, however. In addition to the multiple variable models including the fundamentals, we estimate a
pairwise model for each sector. The lag length in the
models is decided by the Schwarz information criterion
(SIC).
The direction of the possible Granger causality is
tested using a standard F-test (equivalent to a t-test if the
lag length is 1) to examine the existence of a lead–lag
relationship between the assets. The multiple variable
models are also used to derive the impulse responses of
real estate returns to unanticipated changes in the fundamentals and in the real estate returns themselves. We use
the “generalized” impulse response functions (GIRFs)
developed by Pesaran and Shin [1998]. The GIRFs do
not require orthogonalization of shocks and are invariant
to the ordering of the variables in the VECM.
EMPIRICAL RESULTS
In our baseline estimations, we assume that the
escrow lag is 90 days. The Granger causality (GC) test
results are provided in Exhibit 3. The test statistics from
the pairwise models show whether there are lead–lag
relationships between the two series, while the statistics
from the multiple variable models indicate whether the
lead–lag relationships are due to differences in the reaction speeds to changes in the fundamentals and/or in
the returns in the other real estate market. In particular,
if GC disappears when fundamentals are added to the
model, then the perceived lead–lag relationship observed
in the pairwise model is likely due to differences in reaction speed to shocks in the fundamentals.
For the office sector, REIT returns lead direct
real estate returns, with the lead–lag relationship taking
place through both the long-run relationship and the
short-run dynamics. The GC analysis does not provide
clear-cut evidence of lead–lag relationships for the other
three sectors, even though REIT returns Granger cause
private market returns directly and through the deviation from the long-term relationship. The mixed evidence for those three sectors is because REITs also react
to deviations from the long-run equilibrium.
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The inclusion of the fundamentals alters the statistical significances to some extent but does not change
the main conclusions. The GC tests indicate that the
returns on REITs, too, contain predictable elements.
In addition to the predictability through the deviation
from the long-term relationship, Exhibit 3 shows that
REIT returns are predictable by their past values and by
the economic fundamentals.6
The GC tests examine the predictability of and
the lead–lag relationship between securitized and direct
real estate returns. However, they do not provide details
about the reaction patterns and adjustment speeds of
real estate prices to various shocks. For instance, the
GC statistics only show the statistical significance of
the adjustment speeds toward the long-run relation
(alphas), although the size of the alphas also inf luences
the dynamics and possible lead–lag relationships: Other
things being equal, the greater the alpha, the greater the
lagged response of private (public) returns to a shock
in the public (private) market returns. The adjustment
toward the long-run relations is relatively slow, and with
the exception of the industrial sector, alpha is greater for
direct real estate than for REITs. The private market
alpha in the multiple variable models is 0.19 for offices
(versus 0.04 and not statistically significant for REITs),
0.23 for apartments (0.13 for REITs), 0.19 for retail (0.08
and not statistically significant for REITs), and 0.21 for
industrial (0.28 for REITs).
To shed more light on the dynamics, we derive
impulse responses of the returns to shocks in the fundamentals and in the returns themselves. These impulse
responses are based on the same multiple variable models
as the GC tests.7 The GIRFs up to 10 quarters from
the shocks are shown in Exhibits 4–7. These impulse
responses correspond to shocks that are one standard
error in magnitude. Therefore, the estimated responses
show the economic significance of the shocks. In the
graphs, the horizontal axis shows the time from the
shock, while the vertical axis indicates the inf luence
of the shock on the returns in each period—from the
contemporaneous inf luence up to that taking place 10
quarters after the shock.
The estimated reactions are sensible and have the
expected signs, with the exception of the reaction to
an inf lation rate shock. While the empirical literature
presents mixed results regarding the inf lation hedging
qualities of direct real estate, empirical evidence has
generally suggested that securitized real estate does
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EXHIBIT 3
F-test statistics in the Granger Causality Tests, 1994Q1–2010Q4
Notes: The null hypothesis in the tests is that of no Granger causality. The models include one lag except for the office market pairwise model, which
includes two lags. To fulfill the assumption of normally distributed residuals, most of the models also contain one or more point dummy variables that take
the value of 1 in one period and of zero otherwise. The inclusion of these dummies does not notably affect the test statistics. The homoskedasticity of the
residuals can be accepted in all the models based on the ARCH(4) test.
*and **denote statistical significance at the 10% and 5% levels, respectively.
not provide a hedge against inf lation (see Hoesli et al.
[2008] for a review of the inf lation hedging literature).
The impulse responses graphed in Exhibits 4–7 suggest
that inf lation rate shocks induce money f lows into both
private and public real estate markets, thereby having a
positive (typically lagged) impact on the returns. A possible explanation is that real estate generally is viewed
as providing relatively good inf lation hedging effectiveness; increased f lows into the asset class will occur at
times of higher expected inf lation or greater inf lation
uncertainty.
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As expected, the impacts of shocks in economic
growth and sentiment are positive. Both of these shocks
imply larger cash f lows for real estate investments. The
positive impact on real estate returns of improved sentiment indicates that real estate prices embody forwardlooking components concerning economic growth.
An increase of the risk premium increases the discount factor for risky investments and thereby decreases
asset prices, as would be expected. Although the results
by Ewing and Payne [2005] suggest f light to safety (from
risky bonds to REITs; it is, of course, highly questionable whether REITs are safe), our results suggest that an
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EXHIBIT 4
Reactions of Public (REIT) and Private (TBI) Real Estate Returns to Shocks in the Fundamentals: Office Sector
EXHIBIT 5
Reactions of Public (REIT) and Private (TBI) Real Estate Returns to Shocks in the Fundamentals: Retail Sector
increase in the risk perceived by investors lowers REIT
prices via an increase in the discount factor. Expectedly,
we observe a negative impact of interest rate and term
spread shocks on real estate returns.
Of particular interest are whether the reactions
to shocks notably differ between the private and public
markets and whether and how quickly the REIT and
direct markets react to shocks occurring in the market
for the other type of real estate. The GIRFs provide
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evidence on these questions. Importantly, the upper-left
panels of Exhibits 4–7 show that the TBI reaction to
REIT shocks is lagged—that is, the direct returns react
slowly to innovations in REIT returns. In the retail
and office sectors, the TBI response peaks one quarter
after the shock, and the shock notably inf luences the
TBI returns even with longer lags. The lagged response
of the private market is even more pronounced in the
apartment sector, where the TBI reaction peaks three
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EXHIBIT 6
Reactions of Public (REIT) and Private (TBI) Real Estate Returns to Shocks in the Fundamentals: Apartment
Sector
EXHIBIT 7
Reactions of Public (REIT) and Private (TBI) Real Estate Returns to Shocks in the Fundamentals: Industrial
Sector
quarters after the shock. In the industrial sector, TBI
reacts notably quicker than in the other sectors.
Interestingly, the REIT market shows similar
lagged reaction to direct market shocks in the apartment and industrial sectors, as indicated by the lowerleft panels of Exhibits 6 and 7. The lag is particularly
prominent in the apartment sector, where the REIT
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reaction peaks two quarters from the shock. In the office
and retail sectors, the impact of a TBI shock on REITs
is much milder.
The impulse responses also suggest that it generally takes a while before real estate prices fully absorb
new information regarding the fundamentals, because
the response curves converge to zero only slowly. This
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may generate further predictability in real estate returns
in both public and private markets. The exhibits clearly
indicate that direct market prices react more sluggishly
than REIT prices to risk premium and sentiment shocks
(an exception to this is the industrial sector’s responses
to a shock in D). While the instantaneous reaction of
REITs to the shocks is notable and the impact on REIT
returns diminishes rapidly, the initial TBI reaction is
small or even has the “wrong” sign and the inf luence of
the shock on TBI lasts for several quarters. On the other
hand, the GIRFs show evidence of REITs adjusting more
sluggishly than TBI to shocks in the term spread. In sum,
the impulse responses confirm that REIT returns lead
TBI returns in the office sector and provide stronger
evidence of REIT returns leading TBI returns in the
retail sector than provided by the GC test results.
The differences across property sectors could be
due to varying levels of uncertainty in pricing across
sectors, with public markets adjusting more quickly to
the uncertainty than private markets. Cannon and Cole
[2011] showed that the average absolute percentage difference between appraised value and transaction price
is lower for multifamily properties than for offices.
Plazzi et al. [2010] reported that transaction-based cap
rates for offices are not able to capture the time variation in expected returns, providing further evidence of
the uncertainty in pricing office properties. Wheaton
[1999], in turn, documented the complexity of the office
and retail sectors in that there is a lag between employment growth and construction activity that does not
exist for the multifamily and industrial sectors.
We also conducted robustness checks for the real
estate market dynamics. First, we tested the robustness
of our findings to two alternative escrow lag lengths
(45 and 135 days).8 Then, we used the TBI “demand”
or “constant-liquidity” indexes developed by Fisher et
al. [2003] and Fisher et al. [2007] to test whether the
lead–lag relationships found in the baseline models could
be explained by the time-varying liquidity of direct real
estate.9 The results support the hypothesis that REIT
returns lead direct real estate returns in the office and
retail sectors. For the industrial sector, direct market
returns no longer lead REIT returns if the escrow lag
is 45 days, but there is no evidence of a lead–lag relationship going in the opposite direction either. In the
apartment sector, there is no clear evidence of a lead–lag
relationship at any of the assumed escrow lags. The
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results that are based on the demand indexes generally
are in line with the baseline findings.
SUMMARY AND PRACTICAL IMPLICATIONS
This article is the first that explicitly considers the
impacts of escrow lags on the reported lead–lag relationships between the securitized and direct real estate
markets. Our findings show that REIT returns lead
private real estate returns in the office and retail sectors
even after factoring for leverage and a 90-day escrow
lag. For the industrial sector, in contrast, the lead–lag
relationship goes from the direct market to REITs after
adjusting the data for a 90-day escrow period. For the
apartment market, we do not find clear-cut evidence
of a lead–lag relationship going either way. Our findings thus suggest that a universal lead–lag relationship
going from the public to the private real estate market is
not as evident as has generally been thought. Although
direct market prices respond sluggishly to the information that is revealed by REIT price movements—even
in the industrial sector—the REIT market reaction, too,
is clearly lagged with respect to direct market shocks in
the apartment and industrial sectors, taking account of
the escrow lag. We also find that, in the office and retail
sectors, REIT returns lead even the constant-liquidity
private market returns.
The results entail several implications for investors, analysts, and fund managers. First, the results
indicate that REIT return data can be used to predict
direct real estate returns in all the four sectors even
when factoring in the escrow lag. A more unexpected
finding is that the REIT market can be slow to respond
to information coming from the direct market: Private real estate data include predictive power with
respect to REIT performance in all the sectors except
for offices—if timely data on private market returns
are available without a notable lag. That is, the results
overall indicate that REIT and direct real estate markets absorb only sluggishly the information that is
revealed by unexpected price movements in the other
market. Regarding the REIT market reaction, this
may be a consequence of the data on direct real estate
performance not being available or appearing with a
notable lag only. Nevertheless, a well-informed investor
or fund manager may be able to take advantage of such
predictability in REIT prices, at least in the apartment
and industrial sectors.
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Second, our results provide one more piece of evidence showing that the co-movement between securitized and direct real estate markets over the long horizon
is substantially stronger than suggested by the contemporaneous quarterly correlation coefficients. Therefore,
from a long-term investor’s viewpoint, public and private
real estate are better substitutes for each other than
implied by the contemporaneous correlations, and the
benefits of including public real estate in a portfolio containing private real estate (or vice versa) are more limited
than what is suggested by quarterly correlations.
Third, the results are of importance concerning the
recommended shifts in allocations between private and
public real estate during crisis periods. Recent empirical
literature suggests that one should sell direct real estate
and buy REITs, rather than the other way around, during
crisis periods (Hoesli and Oikarinen [2012]). Based on
the GIRFs, this implication still holds when considering
the escrow lag issue and using the constant-liquidity
indexes. In particular, even when accounting for the
escrow lag and time-varying liquidity of direct real
estate, the direct market prices react notably slower than
REIT prices to sentiment and risk premium shocks—
shocks in variables that generally react the quickest and
strongest to a financial crisis. That is, whenever REIT
prices have notably dropped due to a large jump in the
sentiment or the risk premium, or both, it is not advisable to shift the allocation from REITs to direct real
estate, but rather it is recommended to proceed the other
way around. For instance, if a notable sentiment shock
has occurred some 45 days ago causing 30% lower REIT
prices today, our estimations suggest that direct market
prices are expected to decrease relative to REIT prices
by approximately 10% in the office sector, 35% in the
apartment and industrial sectors, and 50% in the retail
sector within the next one-and-a-half years (90-day
escrow lag assumed). Corresponding values for a risk
premium shock that has induced a 30% REIT price
drop are 15% (industrial), 20% (office), 25% (retail),
and 35% (apartments).
Finally, our results show that there are significant
differences across real estate sectors with respect to the
return dynamics. These differences can considerably
inf luence the extent of the aforementioned practical
implications. In particular, the predictability implications, including the proposed portfolio allocation shifts
during crisis periods, vary across sectors. Therefore, it
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is desirable to use sector-level data when evaluating the
return and price dynamics for REIT and direct real
estate markets, when making forecasts concerning future
price developments on those markets, and when considering the asset allocation between the markets.
Many observers believe that the private market
has become more efficient over time for various reasons. This would mean that the lag between private
and public real estate returns has diminished. Although
we do not focus on investigating the potential temporal
changes in the dynamics, we do not detect instability
in the model parameter estimates—suggesting that
no significant change has occurred during the sample
period.
ENDNOTES
We are grateful to Greg MacKinnon and Jim Clayton
for many constructive comments and to David Ling and Andy
Naranjo for providing us with their REIT data used in this
article. Elias Oikarinen is grateful for financial support received
from the OP-Pohjola Group Research Foundation.
1
Using these indexes yields more reliable conclusions
regarding the lead–lag relationships than using the new “simplified” TBI indexes that are likely to suffer from substantially greater short-term measurement error than the ones
based on the hedonic methodology. For instance, the new
TBI indexes generally show negative autocorrelation at the
one-quarter lag and positive autocorrelation only from twoquarters onwards. This is most likely due to measurement
error in the indexes and would bias the analysis on lead–lag
relationships.
2
The UMCI covers a range of various forecast horizons.
The five-year outlook is selected here based on the Akaike
information criterion.
3
For apartments, the lag is likely somewhat shorter.
4
Based on a recursive test, the stability of the long-run
relationship is not rejected for any of the sectors.
5
An exception is the sentiment, which is included in the
dynamics in levels as it is a stationary variable.
6
For further evidence on the predictability of REIT
returns by using their past values, see, for example, Serrano
and Hoesli [2010].
7
The alpha for office REITs is restricted to equal zero,
as in this sector the REIT returns do not react significantly
to deviations from the long-run relationship. In the retail
sector, we allow REIT returns to react to deviations from
the long-run relationship, although the alpha in the multiplevariable model is a borderline case regarding statistical significance. Based on the Hansen stability test, the stability of
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the estimated parameters over the sample period is accepted
for all models.
8
The 45-day escrow lag case corresponds to the analysis
that is commonly conducted in the extant literature, where
the REIT returns are based on beginning and end of quarter
values.
9
In order to have comparable series to the TBI total
return indexes, we added a rental income return component
to the constant-liquidity price changes based on the TBI
rental income component.
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To order reprints of this article, please contact Dewey Palmieri
at dpalmieri@ iijournals.com or 212-224-3675.
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