This document analyzes approaches to assessing the volatility of private market or direct real estate as an asset class. It finds that estimates of annualized volatility for direct real estate portfolios fall in a range of 6.5-9.0% when accounting for the autocorrelation and lack of statistical independence between quarterly returns. Publicly traded REITs have a historical volatility of around 13% due to leverage, but blending REIT and bond returns estimates the underlying real estate asset volatility at around 9%. The analyses provide useful guidance for quantifying direct real estate risk in portfolio allocation.

1. INVES TMENT INS IGHT
Assessing real estate volatility

3. Executive summary_______________________________ 3
Gauging the private real estate market_______________ 5
Autocorrelation and volatility_______________________ 5
Evidence from annual NCREIFreturns ________________ 6
Evidence from the REITmarket______________________ 7
Impact of real estate volatility assumptions
on asset allocation _______________________________ 8
Conclusion ______________________________________ 9
Real Estate Investment Group
The Real Estate Investment Group of JPMorgan
Fleming Asset Management, with 30 years of experience
in the private and public real estate markets, is well posi-
tioned to meet the increasing demands of institutional
real estate investors amidst a rapidly changing market
environment. The group comprises over 90 professionals,
organized within the key functional areas of portfolio
management: research, acquisitions, asset management,
finance, legal, valuation, client services, product develop-
ment, real estate securities, structured capital and fiduci-
ary services. We also have broad-based experience across
all four major property sectors (retail, offices, multifami-
ly and industrial) throughout the United States.
T
he J
PMorgan F
leming Asset Management Investment
Insight series conveys analysis and perspectives devel-
oped by J
PMorgan F
leming’s portfolio management and
research professionals. T
he series focuses on investment
topics and issues aimed at offering unique and useful
insights to ourclients.
Author:
Michael Giliberto, Ph.D., managing director, is Director
of Portfolio Strategy in the Real Estate Investment
Group. A firm employee since 1996, he focuses on port-
folio strategy and management, product design and risk
management. Previously
, he held senior research posi-
tions at Lehman Brothers and Salomon Brothers. Michael
holds a Ph.D. in finance from the University of
Washington, an M.A. in business economics from the
University of Hartford and an undergraduate degree
from Harvard University
.
Telephone: (212) 837-1693
michael.giliberto@jpmorganfleming.com
TABLE OF CONTENTS
As s e s s i n g r e a l e s t a t e vo la t i l i t y

5. • The following research illustrates several
approaches to assessing the volatility of private
market or direct real estate as an asset class.
These approaches yield reasonably consistent esti-
mates, falling in a range of 6.5%–9.0% annualized
standard deviation of total return for a diversified
portfolio of unleveraged, core properties.
• Our estimates take into account the lack of statis-
tical independence from quarter to quarter that is
characteristic of private market real estate
returns. Failure to adjust for this phenomenon can
lead to understating risk.
• We believe our estimates provide useful guid-
ance for quantifying the risk of direct real estate
for purposes of evaluating real estate’s potential
role in a multi-asset class portfolio. In addition,
our empirical estimates have intuitive appeal:
they lie between investment-grade bonds and
large-cap stocks.
Executive summary
INVES TMENT INS IGHT
As s e s s i n g r e a l e s t a t e vo la t i l i t y
3

7. Gauging the private real estate market
Diversification is a central principle of investing. Tools
such as mean-variance analysis provide insight into how
asset classes can be blended to produce portfolio alloca-
tions that maximize a portfolio’s expected return for a
specified level of risk; alternatively
, the allocation process
seeks to minimize risk for a target rate of return. Clearly
,
assessing risk is essential if one wishes to use such quan-
titative methods to help design portfolios.
In the private real estate market, as, say
, compared with
large-capitalization stocks, properties trade infrequently
,
transactions do not take place in continuously open, auc-
tion markets and transaction costs are high.
Consequently
, it seems unreasonable to expect data on
returns from private market investments in real estate to
have similar characteristics to liquid financial assets.
Some argue that private market real estate data exhibit
artificially lowvolatility and do not respond contempora-
neously to changes in market conditions. There is often
an implication that data are, therefore, worthless. We do
not think these concerns are unique to real estate. For
example, these concerns could potentially affect other
“alternative” investments, such as private equity
, some
categories of hedge funds and less-liquid securities.
Autocorrelation and volatility
As of December 31, 2002, the NCREIF Property Index
— which is used to provide the historical investment
risk/
return performance of private real estate — provided
100 quarters of performance data. The standard deviation
of quarterly total return is 1.7%. What is the annualized
standard deviation or volatility? The conventional answer
is 3.4%, which one gets by multiplying quarterly stan-
dard deviation by two to obtain an annualized value.
Of course, many look askance at 3.4%, saying it does not
make sense because it is too low
. The Lehman Aggregate
Bond Index, for example, has a 7.5% historical volatility
over the same 100 quarters. Howcan equity real estate
exhibit less risk?
The answer stems from the formula used to extend from
quarterly to annual measurement. The standard formula
applies when returns are statistically independent from
period to period.1
A consequence of independence is that
time series of returns will showno serial dependence or
correlation. That is, return in one period is uncorrelated
with return in another period. Many time series of finan-
cial market returns have little serial correlation, at least
when observations are made at intervals of a month or
longer. For example, monthly total returns on the
Wilshire 5000 stock index have a 0.03 correlation with
returns lagged one month. In contrast, NCREIF quarter-
ly returns have a 0.68 serial correlation. Wilshire’s 0.03
correlation is small enough to ignore when deriving
annualized standard deviation; NCREIF’s is not.
Over time, positive autocorrelation, such as that exhibit-
ed by NCREIF, causes the dispersion of return outcomes
over multi-period horizons to be greater than would be
the case with serially uncorrelated returns. This finding
has implications for risk, as it suggests higher volatility
for private market real estate returns than the reported
historical volatility
.
To illustrate these concepts, we conducted a simulation.
In the simulation, we randomly generated 20,000 obser-
vations drawn from a normal distribution with a zero
mean and standard deviation of one, which is abbreviat-
ed as N(0,1). These observations were transformed into
500 “time series” of returns, with each time series con-
taining 40 data points. Think of these as 500 samples of
“quarterly returns” over 10-year periods.
For the first batch of 500 time series, we transformed the
N(0,1) data into quarterly returns with a mean of 2.3%
and a quarterly standard deviation of 1.7%, correspon-
ding approximately to historical NCREIF data. We
assumed these quarterly returns had no serial correlation.
We then usedthe same N(0,1) datato create another
group of quarterlyreturns with serial correlation equal to
5
INVES TMENT INS IGHT
As s e s s i n g r e a l e s t a t e vo la t i l i t y

8. 6
0.68 to match NCREIF. We were careful to keep the mean
andstandarddeviation of returns within each time period
approximatelythe same for the uncorrelatedandautocor-
relatedoutcomes (see Charts 1 and2). Formal tests con-
firmedthat no differences were statisticallysignificant.
We then calculated the compound average annual return
(geometric mean) for each scenario. The distributions of
these returns clearly showthat outcomes are more dis-
persed when autocorrelation is present (see Chart 3), even
though the average return is about 9%. Simply put,
while quarterly standard deviations of returns are the
same, “risk” over a multi-period horizon is not.
This point is emphasized by transforming the distribu-
tions in Chart 3 to shortfall probabilities (see Chart 4).
For example, the probability that the cumulative return
over the investment horizon will average less than 7%
per annum, or about 2% belowthe expected value, is
about 2% for uncorrelated returns and more than 18%
for autocorrelated returns.
This section has demonstrated that the widely held view
that 3%–4% volatility for real estate is too low“no mat-
ter what the data say” is correct. This is not, however,
necessarily a shortcoming of NCREIF data or any other
return series that are based, at least in part, on infre-
quently observed private market valuations. It is a conse-
quence of the high autocorrelation that likely exists in
such series. This can be dealt with empirically
.
Evidence from annual NCREIFreturns
One technique toreduce autocorrelation’s influence on
volatilityestimates is touse annual returns (i.e., quarterly
returns compoundedover four quarters). As of December
31, 2002, NCREIF dataextendedfor 100 quarters. From
these datawe constructedfour sets of observations, one set
each for one-year periods ending in March, June,
September andDecember. Each set consists of non-overlap-
ping observations. Of course, the sets are not independent
because nearlythe same underlying dataare includedin
everyset. Table 1 presents astatistical summaryof these
annual returns.
1 4 7 10 13 16 19 22 25 28 31 34 37 40
Uncorrelated Autocorrelated
Quarter
0.025
0.024
0.023
0.022
0.021
0.020
0.018
0.019
Chart 1
Simulation model: Mean return
Source: J
PMorgan F
leming Asset Management
1 4 7 10 13 16 19 22 25 28 31 34 37 40
Uncorrelated Autocorrelated
Quarter
0.0190
0.0185
0.0180
0.0175
0.0170
0.0165
0.0160
0.0150
0.0155
Chart 2
Simulation model: Cross-sectional standard deviation
Source: J
PMorgan F
leming Asset Management
%
of
Cases
0
2% 3% 4% 5% 6% 7% 8% 10%
9% 11%12% 14%
13% 15%16%17%
5
10
15
20
25
30
35
40
Annualized return
Uncorrelated
Autocorrelated
Chart 3
Dispersion of autocorrelated returns
Source: J
PMorgan F
leming Asset Management
Return target
Probability
of
return
less
than
target
(shortfall)
120
140%
100
80
60
40
20
0
2% 3% 4% 5% 6% 7% 8% 10%
9% 11%12% 14%
13% 15%16% 17%
Uncorrelated
Autocorrelated
Chart 4
Shortfall probabilities
Source: J
PMorgan F
leming Asset Management

9. 7
The volatility of annual returns evidenced above is
clearly higher than the 3.4% volatility obtained by
annualizing quarterly standard deviations of return. As
demonstrated in the last section, this result is driven by
the significant autocorrelation that exists in NCREIF
quarterly returns.
However, using annual returns may not eliminate auto-
correlation’s effect. The annual total returns exhibited
first-order autocorrelation ranging from 0.77–0.82. The
approximate standard error for the autocorrelation in
these observations is 0.2, so the remaining autocorrela-
tion is statistically significant. We conclude that the
volatility estimated from annual NCREIF returns may
retain a downward bias relative to the “true” volatility
.
(One could, in theory
, use periods longer than one year to
further lower the effect of autocorrelation, but the num-
ber of available non-overlapping observations declines.)
Evidence from the REITmarket
Publicly traded equity real estate investment trusts
(REITs) provide an intriguing source of data for assessing
real estate risk. Giliberto and Mengden (1996) showthat
the aggregate income from private market real estate
(NCREIF data) is highly correlated with implied REIT
income.2
This result was updated and confirmed in
Giliberto (1999).3
A reasonable interpretation of these
findings is that properties held by REITs and those in
the NCREIF database are, by and large, exposed to the
same fundamental factors of supply and demand, which
drive market rents, vacancy rates and thereby cash flows.
Giliberto and Mengden attributed some of the apparent
“disconnect” between REIT returns and NCREIF per-
formance to differing valuation regimes.
The NCREIF database is constructed to be unleveraged,
whereas, REITs, in contrast, typically use leverage. As
leveraging an equity investment generally leads to
higher volatility, it, therefore, comes as no surprise that
the volatility of REIT total returns exceeds that of
NCREIF returns.
The historical volatility of equity REIT total returns is
13.5%, using monthly National Association of Real
Estate Investment Trusts (NAREIT) data from January
1972 through December 2002. The volatility is slightly
lower (12.8%) for the January 1978 through December
2002 time period, which corresponds to the NCREIF
database’s coverage. As previously mentioned, the
volatility of REIT returns reflects, in part, REITs’ use of
leverage. It is probable that the percentage of leverage
has varied over time, therefore, measured volatility picks
up “average” leverage.
To use REIT data to gain insight into the (unleveraged)
volatility of real estate assets held by REITs, we took a
page from the classic Miller-Modigliani (MM) analysis of
a firm’s capital structure. MM opined that shareholders
could undo corporate leverage structure by simply buying
a firm’s debt. (Or they could leverage up by borrowing to
buy equity shares.) A simple, macro approach to delever-
aging REITs is to blend REIT equity performance with
the performance of publicly traded corporate bonds. For
example, if a REIT’s (highly simplified) balance sheet is:
then a blend of 50% REIT (equity) share performance
and 50% debt performance should, in principle, approxi-
mate the performance of underlying real estate assets.4
We used Lehman Brothers’ data on bond performance,
and since many REITs currently have Baa unsecured debt
ratings, we used that component. REITs’ typical debt
issues are intermediate term (10 years and under).
Lehman provides data back to 1973 on the performance
of Baa intermediate corporate bonds. Starting in 1997,
returns are available for REITs as a sector within the
credit universe.
Since we did not have access to data on the changing
amounts of leverage on REIT balance sheets, we decided
T
able I. Mean and volatility of annual NCREIFtotal returns
Numberof Average
Y
earended observations return V
olatility
March 24 9.6% 6.4%
June 24 9.7% 6.4%
September 24 9.6% 6.3%
December 25 9.7% 6.2%
Sources: NCR
E
IF
, J
PMorgan F
leming Asset Management
Data constructed from quarterly total returns overthe period
1Q1978–4Q2002.
Assets Liabilities
Real estate 100
Debt 50
Shareholder equity 50

10. to examine several debt-equity blends. We believe this
provides a plausible range of volatility estimates. We cre-
ated two series of debt results. The first uses the overall
Lehman Baa intermediate corporate index, of which
REITs are a small component, from January 1973
through December 2002. The second debt series uses the
overall corporate bond data from 1973 through 1996 and
uses the REIT-specific bond index from 1997 onward.
Results were virtually identical for both bond series.
Chart 5 presents the results for the January 1978–
December 2002 period using the spliced bond data.
To understand these findings, let’s examine the 35%
leverage case. Suppose one bought $100 of real estate,
using $35 of borrowed money and $65 of equity
. The
volatilities of and correlation between total returns on
publicly traded REIT equity and debt are fixed by the
historical data. Specifically
, REIT volatility was 12.8%,
Baa bond volatility was 5.5% and correlation was 0.29.
Using these data, we calculatedthat the same real estate, if
financed 100% with equity
, would have had a volatility
of 9.1%. Put another way
, taking real estate that has 9.1%
volatilityandusing 35% debt financing causes the volatil-
ity of the now-leveraged equity position to rise to 12.8%.
Our belief is that over time REIT leverage probably has
been within the 35%–65% range. Reviewing the results
in Chart 5, REIT data suggest that underlying real estate
volatility lies within the 6.5%–9.1% range.
Interestingly
, when we used annual NCREIF returns to
adjust for serial correlation, volatility was approximately
6.4%. As we argued above, this estimate may still have
some downward bias because the serial correlation is quite
high and its effect may not be washed out within one
year. Additionally
, we might argue that the REIT estimates
could be biased upward since REITs’ assets include some
amount of riskier, value-added real estate. In addition,
REITs might perhaps be subject to “excess volatility” due
to being traded in the public equity market.5
Impact of real estate volatility assumptions on
asset allocation
Not surprisingly
, different numerical assumptions about
real estate volatility alter the allocation given to real
estate within a multi-asset class portfolio. To illustrate
this, we ran three portfolio optimizations. We picked a
different level of real estate volatility for each optimiza-
tion. All other factors, including expected returns,
volatilities for asset classes other than real estate, and
correlations, were held constant.6
As representative volatilities for real estate, we used (1)
3.4%, which is the annualized NCREIF volatility uncor-
rected for autocorrelation; (2) 7.6%, which is within the
range that we think is indicated by both adjusted
NCREIF and REIT data; and (3) 15%, which is more
akin to the volatility of equities. (We point out that
companies that are publicly traded often use debt financ-
ing. As a result, equity volatility assumptions reflect
financial leverage. While leverage frequently is used with
real estate, our volatility estimates and asset allocation
parameters reflect unleveraged core real estate.)
Optimizations generate ranges of portfolios along an
“efficient frontier.” We selected representative portfolios
fromeach efficient frontier using asimple rule. We picked,
in each of the three optimizations, the portfolio on the
efficient frontier that hadthe highest Sharpe ratio.7
This is
not necessarilyan appropriate guide tochoosing aportfolio
along the efficient frontier. The advantage for our exer-
cise was that this selection criterion could be applied
mechanically
. Chart 6 illustrates the outcomes. Given the
mechanical nature of the exercise, none of the allocations
in Chart 6 should be viewed as recommendations.
The results are as expected: the higher the assumed
volatility of real estate, the lower its allocation within a
portfolio. Importantly
, the influence of different assump-
tions can be significant. For example, we posit that 7.6%
8
Implied
real
estate
volatility
0
2
4
6
8
10
12%
Debt Equity
75%
9.1%
7.7%
6.5%
5.9%
25%
65%
35%
50%
50%
35%
65%
25%
75%
10.1%
Chart 5
Effects of varying debt-equity blends on implied volatility
Source: J
PMorgan F
leming Asset Management

11. is a reasonable estimate of volatility
. Doubling the
volatility to 15% causes the allocation to shrink by
almost 60% in this case. The variability of the output
(allocation) to the assumed input points out the need to
use careful sensitivity analysis in conjunction with alloca-
tion models.
Conclusion
In conclusion, our research suggests that the volatility of
a diversified portfolio of unleveraged direct, core real
estate is probably within the 6.5%–9% range. This
range estimate comes from (1) the private market, mak-
ing allowances for serial correlation and (2) the public
market, adjusting for leverage. When a narrower range is
called for, we recommend 7%–8%.
This does appear somewhat lowcompared with the 7.5%
historical volatility for the Lehman Aggregate Bond
Index. But historical bond returns include a period of
high inflation that boosted volatility
. Going forward,
bond volatility is expected to be moderate. In fact, over
the 10-year period ending December 31, 2002, the
Lehman Aggregate posted 4.2% volatility
. As a result,
bond volatility projections of 4%–5% are commonly
used today
. In that context, our recommended real estate
volatility is nearly double the bond projection, and it
will increase if the real estate is leveraged. However, we
believe that it is generally preferable to use unleveraged
real estate when examining strategic asset allocation.
Why? Because it provides the clearest perspective on
howreal estate interacts with other asset classes. And
that should lead, in turn, to more-informed portfolio
construction, which was, after all, the initial motivation
for undertaking this research.
Notes
1. The formula is annualized standard deviation equals
standard deviation of periodic return times the
square root of the number of periods in a year. For
quarterly data, this works out to multiplying by
two; for monthly data, one would multiply the
monthly standard deviation by the square root of 12.
2. Michael Giliberto and Anne Mengden, “REITs and
Real Estate: Two Markets Re-examined,” Real
Estate Finance, Volume 13, Number I.
(Spring 1996).
3. Michael Giliberto, “Public Portfolios of Private
Properties,”presentation to Association for
Investment Management andResearch conference
“ANewErafor Real Estate Investing,”
(November 1999).
4. A similar approach was used by Geltner, O’Connor
and Rodriguez. See “The Similar Genetics of Public
and Private Real Estate and the Optimal Long-
Horizon Portfolio Mix,” Real Estate Finance,
Volume 12, Number 3. (Fall 1995).
5. For an introduction to this concept in a real estate
context, see page 280 in Geltner and Miller,
Commercial Real Estate Analysis and Investments,
NewJersey: Prentice Hall, 2001.
6. We used assumptions developed by JPMorgan
Fleming’s Strategic Investment Advisory Group
(SIAG) for U.S. aggregate bonds, U.S. high yield,
U.S. large cap, U.S. small cap and international
(unhedged). The expected return and correlation
assumptions for U.S. real estate also were those used
by SIAG. This information is available upon
request.
7. The Sharpe ratio is the expected portfolio return
minus the risk-free interest rate divided by portfolio
volatility
.
9
%
of
portfolio
in
real
estate
Assumed real estate volatility
0% 2% 4% 6% 8% 10% 12% 14% 16%
0
10
20
30
40
50
60
70
80%
Chart 6
The impact of real estate volatility assumptions on asset allocation
Source: J
PMorgan F
leming Asset Management

12. Opinions and estimates offered constitute our judgment and are subject to change without notice, as are statements of financial market
trends, which are based on current market conditions. We believe the information provided here is reliable, but do not warrant its accura-
cy or completeness. This material is not intended as an offer or solicitation for the purchase or sale of any financial instrument.
References to specific securities, asset classes, and financial markets are for illustrative purposes only and are not intended to be, and
should not be interpreted as, recommendations.
Some of the data contained in the publication may have been obtained from Standard &Poor’s (“S&P”) ©2003. The McGraw-Hill
Companies, Inc., S&P is a division of the McGraw-Hill Companies, Inc. All rights reserved. Indices presented are representative of various
broad base asset classes. They are unmanaged and shown for illustrative purposes only. An individual can not invest directly in an index.
Past performance is no guarantee of future results.
The views and strategies described may not be suitable for all investors. This material has been prepared for informational purposes
only, and is not intended to provide, and should not be relied on for, accounting, legal or tax advice. Y
ou should consult your tax or legal
advisor regarding such matters.
JPMorgan Fleming Asset Management is the marketing name for the asset management business of J.P. Morgan Chase &Co.
We welcome all comments and questions. Please contact your relationship manager for more information.
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13. ■
■ Assessing real estate volatility
■
■ Managing corporate portfolios over the credit cycle
■
■ Reviewand outlook 2003:
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■
■ Asset allocation and sustainable payouts for endow-
ments and foundations
■
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■
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■
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Overlay
■
■ An explanation of the discount/
premium puzzle in
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■
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■
■ Characteristics of manager excess return
■
■ The portfolio impact of asymmetric correlations,
mean reversion and transaction costs
■
■ Determining tactical ranges
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