Cornell using economic value added (eva) as a barometer of hotel investment performance jan14

1. Center for Hospitality Research
Cornell Hospitality Report
Using Economic Value Added (EVA) as a Barometer of
Hotel Investment Performance
Matthew J. Clayton, Ph.D., and Crocker H. Liu, Ph.D.
Published in Association with the Cornell Center for Real Estate and Finance
Vol. 14, No. 2
January 2014
All CHR reports are available for free
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2. Cornell Hospitality Report
Vol. 14, No. 2 (January 2014)
© 2014 Cornell University. This report may
not be reproduced or distributed without the
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benefit of the hospitality industry by The Center
for Hospitality Research at Cornell University.
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4. Using Economic Value
Added (EVA) as a
Barometer of Hotel
Investment Performance
by Matthew J. Clayton and Crocker H. Liu
Executive Summary
In this report, we show how the popular and well known economic value added (EVA) technique can be used
as a barometer of investment performance for hotels and other property types. Economic value added
represents the return on a project in excess of its financing cost. The EVA metric complements traditional
measures which analyze the spread between the cap rate and either the ten-year U.S. Treasury bond rate
(riskless debt) or the BAA bond yield (risky debt). Although both RevPAR and EVA account for revenues, the
primary advantage of applying EVA analysis (versus cap rate spread) is that EVA also considers the cost of equity
financing (in addition to risky debt financing), which cap rate spread and RevPAR do not capture. Unlike cap rates,
EVA incorporates investors’ risk premium. To demonstrate this metric, we show that the EVA spread on hotels
reached its peak in the first quarter of 2004, at the start of the boom for hotels, while it hovered near zero during
2007, when hotel values reached their apex. The EVA spread then turned negative in the second quarter of 2008 as
hotel values had already started to decline, and it continues to remain negative in general, coinciding with
continued economic and property sector weakness. A negative EVA spread suggests that investors are buying
properties with the expectation of upside potential arising from higher cash flow growth rates typically achieved
through repositioning or renovating the hotel. We find evidence to suggest that once the EVA spread is below 1
percent, which is equivalent to a year-over-year change in RevPAR of approximately 1 percent, real estate
practitioners should start to check whether the canary in the coal mine is still chirping, or whether their deal has
expired.
4 The Center for Hospitality Research • Cornell University

5. About the Authors
This CHR Report is produced in conjunction with
Crocker H. Liu, Ph.D., is a professor of real estate at the School of Hotel Administration at Cornell where he
holds the Robert A. Beck Professor of Hospitality Financial Management. He previously taught at New York
University’s Stern School of Business (1988-2006) and at Arizona State University’s W.P. Carey School of Business
(2006-2009) where he held the McCord Chair. His research interests are focused on issues in real estate finance,
particularly topics related to agency, corporate governance, organizational forms, market efficiency and valuation.
Liu’s research has been published in the Review of Financial Studies, Journal of Financial Economics, Journal
of Business, Journal of Financial and Quantitative Analysis, Journal of Law and Economics, Journal of Financial
Markets, Review of Finance, Real Estate Economics and the Journal of Real Estate Finance and Economics. He is
currently the co-editor of Real Estate Economics, the leading real estate academic journal and is on the editorial
board of the Journal of Property Research. He also previously served on the editorial boards of the Journal of Real
Estate Finance and Economics and the Journal of Real Estate Finance.
Matthew J. Clayton, Ph.D., is associate professor of finance and Stone Family Faculty Fellow at the Cornell School of Hotel Administration,
where he teaches courses in corporate finance and asset valuation. His extensive publication record includes such journals as Journal of
Corporate Finance, Journal of Banking and Finance, Journal of Financial Markets, and Review of Financial Studies, and he has also been a
referee for these publications. In his previous appointment at the Kelley School of Business at Indiana University, he
was an Eli Lilly Faculty Fellowship, a Dean’s Council Faculty fellow, and a finalist for the Trustee Teaching Award.
He was also a university scholar at Northwestern University, where he was an Alan and Mildred Peterson Doctoral
Fellow. He is a member of the American Finance Association, Western Finance Association, and the Financial
Management Association.
The authors are grateful to STR and in particular, Duane Vinson, for providing historical STR Pipeline reports. We
also wish to thank our Cornell colleagues Walter Boudry, John Corgel, and Andrey Ukhov for helpful discussions.
Cornell Hospitality Report • January 2014 • www.chr.cornell.edu 5

6. COrnell Hospitality REport
Using Economic Value Added
(EVA) as a Barometer of Hotel
Investment Performance
In evaluating the performance of hotels or other property types, real estate analysts have
by Matthew J. Clayton and Crocker H. Liu
traditionally looked at the spread between the capitalization rate and debt financing, generally
using the yield on the ten-year U.S. Treasury bond (riskless debt) or sometimes the yield on a
BAA bond portfolio (risky debt). 1 This cap rate spread is normally positive and reflects the
additional compensation for liquidity, location, leasing, and tenant credit risk associated with real
estate. Changes in the spread indicate that investors demand greater compensation during recessions
but less during good times. For example, Jack Corgel notes that the cap rate spread using either debt
financing metric widens during recessions and narrows during economic expansions relative to its
long term average. So, when investors perceive higher risk, they seek a higher return over and above
the cost of debt financing.2 Real estate value tends to rise as the spread narrows. Overall, the spread
exhibits a reversion to the mean, so that a widening spread is expected to decline back to its long term
historical average, just as a narrowing spread should eventually expand back to the average.
1 The cap rate on the property can be thought of as the reciprocal of the EBITDA multiple (i.e., EBITDA ÷ enterprise value), where the enterprise value
is equal to the market value of stock + market value of debt of the firm. Alternatively, the cap rate is equal to the discount rate (r) – growth rate (g) with
a higher income growth rate resulting in a lower or compressed cap rate and hence a higher value for real estate. Formally, cap rate = k – g = rF + RP – g,
where rF is the risk free rate, RP is the risk premium and g is the growth rate. Rearranging the cap rate equation yields (cap rate – rF) = RP – g, so the
cap rate spread over the treasury rate is equal to the risk premium minus the growth rate. The cap rate spread is positively related to the risk premium
and inversely related to the property income growth rate expectations. The cap rate spread widens due either to increased or reduced risk, which
concommitantly increases or lowers the risk premium, or else due to a lower or higher income growth, and conversely the cap rate spread narrows as
income growth accelerates or risk diminishes, lowering the risk premium.
2 Jack Corgel, “How to Determine the Future Direction of Hotel Capitalization Rates,” Real Estate Issues, Vol. 28 (2003), pp. 44-48.
6 The Center for Hospitality Research • Cornell University

7. This type of analysis, while useful, does not take into
account the cost of equity financing. As real estate market
conditions worsen, lenders typically reduce the loan-to-val-ue
ratio, which means that investors have to put more equity
into the deal. The evaluation of cap rate relative to bonds’
return ignores the fact that equity investors demand a higher
rate of return than debt investors to compensate for the
larger risk associated with equity. Additionally, in periods
where interest rates are low, such as when the Fed is manag-ing
interest rates, measuring the spread between the cap rate
and interest rates merely reflects changes in the real estate
market rather than the interplay between the real estate mar-ket
and the capital market. Thus, the cap rate comparison
does not capture the return in excess of what is demanded
by all the investors providing capital (both debt and equity).
For these reasons, we propose the use of the economic value
added (EVA) metric, which recognizes borrowing costs from
both equity and debt financing, as an additional barometer
of hotel investment performance. 3 While the hotel literature
has recognized the suitability of the EVA technique in the
valuation of hotels,4 its use as a barometer for hotel perfor-mance
or real estate performance in general for any property
type represents a new analyst application to the best of our
knowledge.
3 Although Stern Stewart coined the term economic value added, Alfred
Marshall introduced the concept in 1890, calling it economic profit, which
he defined as total net gains less the interest on invested capital at the cur-rent
rate. For further discussion, see Nikhil Shil, “Performance Measures:
An Application of Economic Value Added,” International Journal of
Business and Management, Vol. 4 (2009), pp. 169-177; also, for discus-sions
of how to implement EVA as a decision-making or a valuation tool,
see: T. Copeland, T. Koller, and J. Murrin, Valuation: Managing and Mea-suring
the Value of Companies (New York: John Wiley and Sons, 1996);
G.B. Stewart, The Quest for Value: The EVA Management Guide (Harper
Business: New York, 1990); or G.B. Stewart, The Quest for Value: A
Guide for Senior Managers (Harper Business: New York, 1991).
4 See, for example: Jan deRoos and Stephen Rushmore, “Hotel Valuation
Techniques,” in Hotel Investments, 2nd edition, ed. Lori E. Raleigh
and Rachel J. Roginsky (East Lansing, MI: Educational Institute of the
American Hotel & Motel Association, 1999).
The main advantage of using the EVA relative to the
cap rate spread as a barometer of real estate investment
performance is that cap rate can change for many reasons.
Since cap rate does not explicitly estimate the risk premium,
a decrease in the cap rate spread could be caused by a
decreasing risk premium, by an increase in expected
growth rate, or a decrease in the risk-free interest rate. For
example, quantitative easing by the U.S. Federal Reserve
since 2008 has kept the risk-free interest rate very low. This
has contributed to a larger cap rate spread over this period.5
Because of this complexity, once one observes changes in
the cap rate, additional analysis is needed to determine the
reason for such changes before the implications on the real
estate market can be determined. EVA explicitly estimates
the required return for investors and is thus a direct
indicator of the profitability of real estate transactions. An
EVA decrease can be directly interpreted as a decrease in
the profitability of current real estate transactions. (See the
appendix for the EVA calculation and explanation.)
Data and Methodology
To illustrate the use of EVA as a barometer of investment
performance, we obtained quarterly data on the cap rate,
interest rate, mortgage constant, and the loan-to-value ratio
for various property types from the American Council of
Life Insurers (ACLI) publication Commercial Mortgage Com-mitments:
Historical Database. Returns for various property
types are taken from the National Association of Real Estate
Investment Trusts (NAREIT) website.6 We use the CRSP7
value weighted returns inclusive of dividends consisting
of all NYSE, AMEX, and NASDAQ stocks from Wharton
Research Data Services8 (WRDS) as our proxy for returns
5 For component analysis of how cap rate changes with the risk-free rate,
risk premium and growth rate, see: Corgel, op.cit.
6 http://www.reit.com/DataAndResearch/IndexData/FNUS-Historical-
Data/Monthly-Property-Index-Data.aspx
7 Center for Research in Security Prices, Booth School of Business, Uni-versity
of Chicago.
8 http://wrds-web.wharton.upenn.edu/wrds/
Cornell Hospitality Report • January 2014 • www.chr.cornell.edu 7

8. CROCI is analogous to a property’s cap rate, which is
defined as net operating income divided by property value.
We thus use the cap rate on new investment in each period
as our CROCI return measure. To see that EBITDA is
analogous to net operating income we provide the follow-ing
comparison:
Income Statement Comparison
Regular C-Corporation Property
Revenues Rental Income (100% Occupancy)
- Cost of Goods Sold + Other Income
Gross Profit Total Revenues
- Selling, General, and
Administrative Expenses - Vacancy ($)
EBITDA Effective Gross Income
- Depreciation and Amortization - Operating Expenses
EBIT (Operating Income) Net Operating Income (NOI)
Both EBITDA and NOI represent cash flow to the
enterprise (property here is regarded as a cash-flow en-terprise)
before accounting for non-cash expenses (that is,
depreciation and amortization). As with EBITDA, NOI al-lows
us to analyze a property on a capital structure-neutral
basis. The denominator of the cap rate, property value, rep-resents
the value of debt plus the value of equity and is thus
equal to the capital invested in the property. To calculate
the before-tax weighted average cost of capital (WACC) for
a given property type, we use information from the ACLI
and the preceding cost of equity as follows:
WACCt = wdebt,t*kdebt,t + wequity,t*kequity,t (3)
where wdebt,t is the weight of debt and is equal to the loan
to value ratio (LTVRt) at time t for a given property type;
kdebt,t is the cost of debt, which we set equal to the mortgage
constant13 at time t for a given property type; wequity,t is the
weight for equity14 and is equal to one minus the loan to
value ratio (1-LTVRt) at time t for a given property type;
and kequity,t is the cost of equity at time t for a given property
type. The WACC applies to both money borrowed from
debtholders (i.e., bond holders and bank loans) and to
capital acquired from equity holders. Thus, the WACC is
the required rate of return that various sources of capital
demand on their investment.
non-cash working capital from EBITDA. We do not use this measure
here since ACLI data do not include capex.
13 The mortgage constant is the loan payment or debt service on a $1
loan. If the loan is an interest only loan, then the mortgage constant is
equal to the interest rate. If the loan is either a partially amortizing or
fully amortizing mortgage then the loan payment consists of both inter-est
and principal so the mortgage constant is greater than the interest
rate.
14 The sum of the weight for debt and the weight for equity must equal
1.
on the market and the constant maturity ten-year Treasury
rate from the St. Louis Fed9 as our proxy for the risk free rate.
Although the various data series start at different periods of
time, we use the fourth quarter of 1998 as our starting date,
because this is the first time we can estimate beta from NA-REIT
returns, as discussed below.
The traditional formula for economic value added is
EVAt = (ROICt – WACCt)*Capitalt (1)
where ROICt is the return on invested capital at time t,
WACCt is the weighted average cost of capital at time t and
Capitalt is the economic capital employed at time t. The time
subscript t recognizes that both the return and financing
costs can change each period. For purposes of this study, we
use cash return on capital invested (CROCI) in lieu of the
standard measure, return on invested capital (ROIC). 10 The
CROCI measure is based on cash flow while ROIC is based on
earnings. We believe that cash flow is more important for real
estate investors as evidenced by the funds from operations
(FFO) metric that REIT investors and analysts focus on in
lieu of using earnings per share (EPS). The effect of non-cash
expenses such as depreciation and amortization is removed
under CROCI, which is calculated by dividing earnings be-fore
interest, taxes, depreciation, and amortization (EBITDA)
by the total capital invested, as follows:
(2)
Cash Return on Capital Invested (CROCI) = EBITDA
Capital Invested
Capital invested includes all sources of financing em-ployed
including both equity capital and long term loans.
Consequently, CROCI is a measure of investment returns
before taking into account the cost associated with financing
an investment under its particular capital structure. CROCI
is calculated on a cash basis and is a useful measure of a firm’s
ability to generate cash returns on its investments. However,
as Damodaran notes, there are drawbacks in adding back
depreciation to operating income.11 The main concern is that
firms with substantial depreciation requirements often have
to reinvest this money (in the form of capital expenditures) to
maintain the income stream over the long term.12 When deal-ing
with commercial real estate, it is easy to show that a firm’s
9 http://research.stlouisfed.org/fred2/
graph/?s%5B1%5D%5Bid%5D=DGS10
10 Based on an economic profit model, CROCI was developed by the
Deutsche Bank Group.
11 Aswath Damodaran, “Return on Capital (ROC), Return on Invested
Capital (ROIC), and Return on Equity (ROE): Measurement and Implica-tions,”
Stern School of Business, 2007; people.stern.nyu.edu/adamodar/
pdfiles/papers/returnmeasures.pdf.
12 A cash flow measure that would take this into account is free cash flow to
the firm (FCFF) which basically subtracts capital expenditures (capex) and
8 The Center for Hospitality Research • Cornell University

9. Exhibit 1
Comparison of EVA spread to cap rate (CROCI) in excess of risk-free rate
Exhibit 1: Comparison of EVA Spread to Cap Rate (CROCI) in Excess of the Risk Free Rate
0.14
0.12
0.12
0.10
0.1
0.08
0.08
0.06
0.06
0.04
0.04
0.02
0.02
0
0
1998
1999
-0.02
-0.02
-0.04
-0.04
-0.06
-0.06
1999
2000
2000
2001
2002
2002
2003
2004
CROCI-CROCI - 10YrTBond
10-year bond
EVA spread (CROCI - WACC)
Spread (CROCI-WACC)
2004
2005
2005
2006
2006
2007
2007
2008
2008
2009
2009
2010
2010
2011
2011
2012
S p r Spread
e a d
Sources: ACLI, Center for Real Estate and Finance at Cornell, Federal Reserve, NAREIT, STR
Source: ACLI, Center for Real Estate and Finance at Cornell, Federal Reserve, NAREIT, Smith Travel Research (STR)
2012
relatively higher risk of investing in a risky asset as opposed
to investing in the risk-free asset. The following example
illustrates these concepts.
Example
For the fourth quarter of 1998 (1998Q04), the ACLI report-ed
that cap rate on hotels (CROCI) was 8.9 percent, the loan
to value ratio (LTVR) was 69.2 percent, and the mortgage
constant (MC) was 8.6 percent. The yield on the ten-year
constant maturity Treasury bond was 4.65 percent, while the
estimated beta for hotel REITs was .481 for December 1998.
The market premium (RM – rF) of 4.5 percent is assumed.
15
Cost of equity98.04 = rF98.04 + β 98.04*(RM – rF) = .0465 + .481*.045 = .068145 or
6.81%
WACC98.04 = LTVR98.04*MC98.04 + (1- LTVR98.04)*kEquity,98.04
= .692*.086 + (1-.692)*.068145 = .080501
EVA Spread98.04 = (CROCI98.04 – WACC98.04) = .089 - .080501 = .008499 or .85%
Since the EVA spread is positive, hotel investors added
value during this period, as the return on hotel properties
purchased exceed borrowing costs by .85 percent.
Results
We first compare the EVA spread to the traditional measure
used in real estate, the risk premium (cap rate minus yield
on the constant maturity ten-year Treasury Bond), to see
To calculate the cost of equity for a given property type,
we first use the equity REIT returns for that property type
in conjunction with the market model to calculate the beta.15
The market model is as follows:
Rit = α + biRmt + εit t= 1,2,…., T (4)
where Rit is the REIT return on property type in period t and
Rmt is the return on the market portfolio in period t. Beta
(bi) is estimated using sixty months of returns. Given the
estimated beta, we next calculate the cost of equity using the
capital asset pricing model (CAPM) as follows:
kequity,t = rF + bit*(RM – rF) (5)
where kequity,t is the cost of borrowing money from equity
investors at time t; rF is the yield on the constant maturity
ten-year Treasury bond: bit is the beta estimated using the
market model; and (RM-rF) is the risk premium, which we set
equal to 4.5 percent. bit*(RM – rF) is the equity risk premium,
which is the excess return that an asset provides above the
risk-free rate to compensate investors for taking on the
15 For hotels, we also looked at the beta for the entire hospitality industry.
The problem with using a hospitality beta in lieu of the beta for hospital-ity
REITs is that the two betas are not similar in most time periods. The
hospitality beta is typically higher than that for the REITs. We felt that
since we are trying to measure of cost of equity financing for hotels it was
more appropriate to analyze hotel equity REITs.
Cornell Hospitality Report • January 2014 • www.chr.cornell.edu 9

10. Exhibit 2
Availability of debt and interest rate relative to real estate metrics
Exhibit 2: Availability of Debt and Interest Rate relative to Real Estate Metrics
0.12
0.12
0.10
0.1
0.08
0.08
0.06
0.06
0.04
0.04
0.02
0.02
2004.03
2005.01
2005.03
2004.01
2002.01
2002.04
2003.03
2001.02
2000.02
2000.04
0
1999.02
1999.04
S p r e a d a n d I n t e r e s t R a t e s
1998.04
--0.02
0.02
-0.04
-0.04
-0.06
-0.06
-0.08
-0.08
2006.03
2007.01
2006.01
2007.03
2008.03
2009.01
2009.03
2008.01
Spread & Interest Rate
CROCI - 10-year bond
EVA spread (CROCI - WACC)
Availability of debt
10-year T-bond (constant maturity)
CROCI-10YrTBond
CROCI-WACC
Availability of Debt
10YrTBond (Constant Maturity)
Sources: ACLI, Center for Real Estate and Finance at Cornell, Federal Reserve, NAREIT
Source: ACLI, Center for Real Estate and Finance at Cornell, Federal Reserve, NAREIT,
whether both provide similar signals regarding the health of
the real estate market. Exhibit 1 shows that the two measures
moved in tandem from the fourth quarter of 1998 through
the first quarter of 2008, when they diverged at the onset of
the financial crisis for commercial real estate. Statistically
speaking, the two series had a .73 (positive) correlation prior
to the financial crisis and a -.20 (negative) correlation after-wards.
Exhibit 2 depicts this divergence.
During the financial crisis, interest rates continued to
remain low with little volatility due to the Fed’s management
of the treasury rates, which suggests that most of the varia-tion
in risk premium arose from variations in the cap rate.
The availability of debt, however, also declined over the crisis
period. 16 As a result, a greater portion of the capital stack
(structure) comprised equity financing, which was costlier
than debt financing. Thus, borrowing costs exceeded returns.
This demonstrates that the availability of debt tends to move
positively with the EVA spread.
One driver of the EVA spread is the year-over-year
change in RevPAR, as shown in Exhibit 3, since CROCI for
16 The availability of debt financing is calculated using the total net bor-rowing
and lending from the Federal Reserve’s flow of funds database to
that quarter’s nominal GDP level where the variables are annualized.
2010.04
2011.02
2011.04
2010.02
2012.04
2012.02
0.40
0.40
0.35
0.35
0.30
0.30
0.25
0.25
0.20
0.20
0.15
0.15
0.10
0.10
0.05
0.05
0
0.00
-0.05
-0.05
-0.10
-0.10
Availability of Debt
A v a i l a b i l i t y o f D e b t
hotels is partly based on RevPAR. 17 Let’s look at why real
estate owners and analysts would want to monitor move-ments
in the EVA spread, given that RevPAR (or real estate
16
revenues in general) is a driver of the EVA spread and the
industry already monitors RevPAR. First, the EVA spread is
intuitively appealing since it measures economic profit, that
is, whether returns are greater than borrowing costs. A posi-tive
EVA suggests that returns are greater than borrowing
costs, while a negative EVA suggests that most of the excess
return over borrowing costs is coming from expected price
appreciation rather than current income. If EVA is approxi-mately
zero then one is agnostic regarding whether to invest
in the property. There is no need to use some long term
average or benchmark as an EVA comparison, as is the case
if one uses either RevPAR or the cap rate minus ten-year
Treasury as indicators of performance, since it is not obvious
what is “high” or “low.”
Moreover, since the EVA is linked to transaction vol-ume
and construction activity, it acts as a canary in the coal
mine for downturns in the commercial real estate market.
Exhibit 4 reveals that the volume of hotel transactions tends
17 We use the RevPAR for midscale chains, but the results are robust
regardless of chain scale.
10 The Center for Hospitality Research • Cornell University

11. Exhibit 3
Year-over-year change in RevPAR (midscale chains) as a driver of EVA spread
Exhibit 3: Year over Year Change in RevPar (MidScale Chains) as a Driver of the EVA Spread
0.06
0.06
0.04
0.04
0.02
0.02
0
0.00
1998.04
1999.02
1999.04
-0.02
-0.02
-0.04
-0.04
-0.06
-0.06
-0.08
-0.08
2000.02
2000.04
2001.02
2002.04
2003.03
2002.01
2004.03
2005.01
2004.01
2006.01
2006.03
2005.03
2007.03
2008.01
2007.01
2009.01
2009.03
2008.03
2010.04
2011.02
2010.02
EVA spread (CROCI - WACC)
EVA Spread (CROCI - WACC)
EVA EVA Spread spread (CROCI-(CROCI WACC)
- WACC)
STR Y-YOY O-Y RevPAR Change RevPar change (Midscale (midscale Chains)
chains)
Sources: ACLI, Center for Real Estate and Finance at Cornell, Federal Reserve, NAREIT, STR
Source: ACLI, Center for Real Estate and Finance at Cornell, NAREIT, Federal Reserve, STR
Exhibit 4: The EVA Spread and the Volume of Hotel Transactions
Exhibit 4: The EVA Spread and the Volume of Hotel Transactions
17
2012.04
2012.02
2011.04
0.30
0.30
0.25
0.25
0.20
0.15
0.10
0.05
0
0.00
-0.05
-0.05
-0.10
-0.10
-0.15
-0.15
-0.20
YOY RevPAR change (midscale chains)
Y-O-Y Change in RevPar (Midscale Chains)
Exhibit 4
450
450
EVA spread and volume of hotel transactions
400
400
400
350
350
350
300
300
300
250
250
250
200
200
200
150
150
150
100
100
100
2011.04
2012.01
2012.02
2012.03
2012.04
2012.04
2012.03
2011.03
2012.02
2011.02
2012.01
2011.01
2011.04
2009.04
2010.02
2010.03
2010.04
2011.01
2011.03
2010.04
2011.02
2009.02
2009.03
2010.03
2010.02
2009.01
2009.04
2008.03
2008.04
2009.03
2007.01
2007.02
2007.03
2007.04
2008.01
2008.02
2008.04
2008.01
2009.02
2008.03
2009.01
2008.02
2007.04
2006.04
2007.03
2006.02
2006.03
2007.02
2005.02
2005.03
2005.04
2006.01
2006.03
2007.01
2006.02
2006.04
2006.01
2004.04
2005.01
2005.04
2005.03
2004.03
2005.02
Num of Hotels Sold
EVA Spread (CROCI-WACC)
2004.01
2004.02
2005.01
Number of hotels sold
EVA spread (CROCI - WACC)
2003.03
2003.04
2004.02
2004.04
Spread (CROCI-WACC)
2002.04
2003.01
2004.01
2004.03
2003.04
2002.01
2002.03
2003.03
Num of Hotels Sold
2003.01
2001.03
2002.04
2001.02
2002.03
2000.02
2000.03
2000.04
2001.01
2001.02
2002.01
2001.01
2001.03
1999.04
2000.01
2000.04
2000.03
1999.03
2000.02
1999.02
2000.01
1999.01
1999.04
50
50
0
0
1998.04
1999.01
1999.03
1998.04
1999.02
N u m b e r o f H o t e l s S o l d
Number of Hotels Sold
Number of Hotels Sold
Source: ACLI, Center for Real Estate and Finance at Cornell, NAREIT
Sources: ACLI, Center for Real Estate and Finance at Cornell, NAREIT
Source: ACLI, Center for Real Estate and Finance at Cornell, NAREIT
18
18
-0.20
0.06
0.06
0.04
0.04
0.02
0.2
0.02
0
0.00
0.00
-0.02
-0.02
-0.02
-0.04
-0.04
-0.04
-0.06
-0.06
-0.06
-0.08
-0.08
-0.08
C R O C I – W A C C
CROCI-WACC
CROCI-WACC
Cornell Hospitality Report • January 2014 • www.chr.cornell.edu 11

12. Exhibit 5
Two Exhibit measures 5: Two of Measures real estate of Real performance Estate Performance and the and number the Number of rooms of Rooms in the in the pre-Pre-planning Planning and and Planning planning Stages
stages
0.12
0.12
0.1
0.1
0.08
0.06
0.04
0.02
0
2003
2004
2004
2005
2005
2006
2006
2007
2007
2008
2008
2009
2009
2010
2010
2011
2011
2012
2012
S p r e a d
Spread
1.00
0.80
0.60
0.40
0.20
0
0.00
-0.20
-0.20
-0.40
-0.40
-0.60
-0.60
CROCI CROCI-- 10YrTBond
Ten-year T bond)
EVA CROCI-spread WACC
(CROCI - WACC)
YOY Y-O-Y change Change in in number Pre-Planning of pre-(#planned Rooms)
rooms
YOY Y-O-Y change Change in in number Planning of (#planned Rooms)
rooms
Sources: ACLI, Center for Real Estate and Finance at Cornell, Federal Reserve, NAREIT, STR Pipeline
-0.02
-0.04
-0.06
-0.08
Source: ACLI, Center for Real Estate and Finance at Cornell, Federal Reserve, NAREIT, Smith Travel Research (STR) Pipline
to rise and fall as the EVA spread widens and narrows, with
volume falling off as the EVA spread approaches zero. As
shown in Exhibit 5, the EVA spread and the cap rate minus
ten-year Treasury are linked to the year-over-year (YOY)
change in the number of rooms in the pre-planning stage
and also the planning stage, which are highly correlated
(.92) with one another. Moreover, the YOY change in the
number of rooms in various stages of planning falls as
economic profit approaches zero. This statistic reflects the
decline in the availability of debt; so cheap funding for
projects becomes an issue in this scenario. The EVA spread
also exerts a positive influence on the year-over-year hotel
construction put in place, as evidenced in Exhibit 6. 18 As
18 The value of lodging construction put in place (total units: millions of
current dollars, not seasonally adjusted) is obtained from the U.S. Census
Bureau. The value of construction put in place is a measure of the value
of construction installed or erected at a site during a given time period.
It includes the cost of materials installed, labor cost, and a proportionate
share of the cost of construction equipment rental, contractor’s profit,
cost of architectural and engineering work, miscellaneous overhead, and
office costs chargeable to the project on the owner’s books, as well as
interest and taxes paid during construction. The total value-in-place for a
YOY Change in Hotel Construction Put in Place
Y-O-Y Change in Number of Rooms (Planning or Pre-Planning)
the EVA spread widens, the YOY change in hotel construc-tion
put in place increases on a one-year lag from the EVA’s
19
widening date. To calculate this, we plot the EVA spread in
time t against the year-over-year change in hotel construc-tion
put in place in time t+12 months. For example, at the
beginning of the sample, the EVA spread for 1998Q4 is
matched with the YOY change in hotel construction put in
place for 1999Q4, while at the end of the sample, the spread
is 2012Q1 with 2013Q1. Since construction put in place re-flects
construction costs, Exhibit 7 depicts how construction
costs vary with both the EVA spread and the risk premium.
As we discuss further in the implications section, it appears
that as economic profitability increases, that is, as the EVA
spread is positive and increasing due partly to the greater
availability of debt, construction costs also start to rise as
demand for new construction increases (see Exhibit 8 for a
correlation table of the various co-movements).
given period is the sum of the value of work done on all projects underway
during this period, regardless of when work on each individual project was
started or when payment was made to the contractors.
12 The Center for Hospitality Research • Cornell University

13. Exhibit 6: EVA spread versus Year over Year (Y-O-Y) change in Hotel Construction Put in Place 1 year subsequent
Exhibit 6: EVA spread versus Year over Year (Y-O-Y) change in Hotel Construction Put in Place 1 year subsequent
We plot the EVA spread in time t against the year over year change in hotel construction put in place in time t+12 months. For
example, the EVA spread for 1998Q4 (2012Q1) is matched with the Y-O-Y change in Hotel Construction Put in Place for 1999Q4
(2013Q1) at the start (end).
Exhibit 6
EVA spread versus year-over-year change in hotel construction with one-year lag
We plot the EVA spread in time t against the year over year change in hotel construction put in place in time t+12 months. For
example, the EVA spread for 1998Q4 (2012Q1) is matched with the Y-O-Y change in Hotel Construction Put in Place for 1999Q4
(2013Q1) 0.12
at the start (end).
0.12
0.10
0.10
0.08
0.08
0.06
0.06
0.04
0.04
0.02
0.02
0.00
1998.04
0.00
1999.02
-0.02
-0.02
-0.04
-0.04
-0.06
-0.06
-0.08
Spread
2002.01
2004.01
2004.03
2006.01
2006.01
CROCI - Ten-year T bond)
EVA spread (CROCI - WACC)
YOY change in value of hotel construction put in place
CROCI-10YrTBond
EVA Spread (CROCI-WACC)
YOY Change in Value of Construction Put in Place (Lodging)
2000.02
2000.02
2008.03
2009.03
2010.02
2009.03
2010.02
2011.02
2011.02
Source: ACLI, Bureau of the Census, Center for Real Estate and Finance at Cornell, NAREIT, Federal Reserve
Source: ACLI, Bureau of the Census, Center for Real Estate and Finance at Cornell, NAREIT, Federal Reserve
Exhibit 7
Real estate performance and construction costs
Exhibit 7: Real Estate Performance and Construction Costs
20
0.80
0.80
0.60
0.60
0.40
0.40
0.20
0.20
0.00
0.00
-0.20
-0.20
-0.40
-0.40
-0.60
-0.60
-0.80
YOY Change in Construction Put in Place (Lodging)
0.10
0.00
1998
1999
1999
2000
2000
2000
2006
2006
2006
2010
2011
2011
0.12
0.10
0.08
0.06
0.04
0.02
0.00
2003
2002
2003
2002
2003
2005
2005
Source: ACLI, Engineering Record (ENR), Center for Real Estate and Finance at Cornell, NAREIT, Federal Reserve
Cornell Hospitality Report • January 2014 • www.chr.cornell.edu 13
21
-0.08
1999
1999
2000
2001
2001
2001
2002
2004
2004
2004
2004
2005
2005
2006
2007
2007
2007
2007
2008
2008
2008
2008
2009
2009
2009
2009
2010
2010
2011
2011
2012
2012
Spread
-0.02
Year-over-Year Change in ENR Building Costs
CROCI-10YrTBond
CROCI-WACC
Y-O-Y Change in ENR Building Cost Index
S p r e a d
0.80
0.60
0.40
0.20
0
-0.20
-0.40
-0.60
-0.80
YOY Change in Hotel Construction Put in Place
0.12
0.1
0.08
0.06
0.04
0.02
0
-0.02
-0.04
-0.06
-0.08
CROCI - Ten-year T bond)
EVA spread (CROCI - WACC)
YOY change in Engineering Record
building cost index
1998.04
1999.02
1999.04
2000.04
2001.02
2002.04
2003.03
2005.01
2005.03
2006.03
2007.01
2007.03
2008.01
2009.01
2010.04
2011.04
2012.02
2012.04
S p r e a d
Year-over-year Change in ENR Building Costs
0.12
0.1
0.08
0.06
0.04
0.02
0
-0.02
-0.04
-0.06
-0.08
0.12
0.1
0.08
0.06
0.04
0.02
0
-0.02
Sources: ACLI, Engineering Record (ENR), Center for Real Estate and Finance at Cornell, Federal Reserve, NAREIT
2012
2012
20
-0.08
1999.04
2000.04
2001.02
2002.01
2002.04
2003.03
2004.01
2004.03
2005.01
2005.03
2006.03
2007.01
2007.03
2008.01
2008.03
2009.01
2010.04
2011.04
2012.02
2012.04
Spread
-0.80
YOY Change in Construction Put in Place (Lodging)
CROCI-10YrTBond
EVA Spread (CROCI-WACC)
YOY Change in Value of Construction Put in Place (Lodging)
Note: This graph shows the EVA spread in time t against the year-over-year change in hotel construction at time t+12 months.
Thus, for example, at the start of the series, the EVA spread for 1998Q4 is matched with the year-over-year change in hotel
construction put in place for 1999Q4, and at the end, the comparison is 2012Q1 with 2013Q1. Sources: ACLI, Center for Real
Estate and Finance at Cornell, Federal Reserve, NAREIT

14. Exhibit 8
Correlation matrix of real estate market variables
EVA
Spread
(CROCI
– WACC)
CROCI
– 10-yr
T bond
No. of
Hotels
Sold
No. of
Rooms in
Pre-planning
YOY Δ in
No. of
Rooms in
Final
Planning
YOY Δ in
No. of
Rooms
in
Planning
YOY Δ in
No. of
Rooms
in Pre-planning
YOY Δ in
Value of
Hotel
Con-struction
Put in
Place
Availability
of Debt
YOY Δ in
ENR
Building
Cost
Index*
EVA Spread
(CROCI – WACC)
1.00
CROCI – 10-yr T bond -0.15 1.00
No. of Hotels Sold 0.49 -0.54 1.00
No. of Rooms in Pre-planning -0.10 0.28 -0.43 1.00
YOY Δ in No. of Rooms in Final
Planning
0.03 -0.51 0.40 -0.28 1.00
YOY Δ in No. of Rooms in
Planning
0.27 -0.52 0.67 -0.32 0.68 1.00
YOY Δ in No. of Rooms in Pre-planning
0.31 -0.55 0.71 -0.40 0.85 0.92 1.00
YOY Δ in Value of Hotel
Construction Put in Place
0.53 -0.34 0.73 -0.10 0.49 0.50 0.69 1.00
Availability of Debt 0.64 -0.53 0.86 -0.27 0.36 0.64 0.67 0.76 1.00
YOY Δ in ENR Building Cost
Index*
0.45 0.01 0.16 0.02 -0.13 0.03 0.05 0.28 0.41 1.00
*Note: ENR = Engineering Record.
Exhibit 9
EVA spread for apartments
Exhibit 9: EVA Spread for Apartments18
1998.04
1999.03
2001.04
2002.03
2004.04
2005.03
2007.04
2008.03
Note: If any data are missing for a particular quarter, that quarter is omitted, so that no data are missing for any quarter shown.
Sources: ACLI, Center for Real Estate and Finance at Cornell, NAREIT
0.04
0.03
0.02
0.01
0.00
0
Apartment EVA Spread (CROCI - WACC)
-0.01
Source: ACLI, Center for Real Estate and Finance at Cornell, NAREIT
2012.02
2013.01
14 The Center for Hospitality Research • Cornell University
18If data is missing for a quarter, that quarter is omitted. No data is missing for any quarter.
23
-0.02
2000.02
2001.01
2003.02
2004.01
2006.02
2007.01
2009.02
2010.01
2010.04
2011.03
Apartments EVA Spread (CROCI-WACC)

15. Exhibit 10: EVA Spread for Office Buildings19
Exhibit 10
EVA spread for office buildings
0.05
0.05
0.04
0.04
0.03
0.03
0.02
0.02
0.01
0.01
0.00
0
1998.04
1999.03
-0.01
-0.01
-0.02
-0.02
-0.03
-0.03
-0.04
2004.01
2002.03
2003.02
2001.01
2001.04
2000.02
2012.02
2013.01
2010.04
2011.03
2009.02
2010.01
2007.04
2008.03
2006.02
2007.01
2004.04
2005.03
Office Building EVA Spread (CROCI - WACC)
Office EVA Spread (CROCI-WACC)
Note: If any data are missing for a particular quarter, that quarter is omitted, so that no data are missing for any quarter shown.
Sources: ACLI, Center for Real Estate and Finance at Cornell, NAREIT
-0.04
Source: ACLI, Center for Real Estate and Finance at Cornell, NAREIT
19If data is missing for a quarter, that quarter is omitted. No data is missing for any quarter.
We next examine whether the EVA spread on other
types of real estate exhibits behavior similar to that of hotels.
The EVA spread for apartments, office buildings, retail, and
industrial properties are graphed in Exhibits 8 through 11,
while Exhibit 12 contains the quarterly data used to plot the
EVA spread charts for various property types. 19 A summary
of these exhibits inclusive of Exhibit 4 is shown in Exhibit
13. A comparison of Exhibit 4 with Exhibits 9 through 12
reveals that the EVA spread is positive for all property types
over the first period before turning negative in the latter
portion of our study period. The notable exception occurred
during the period between the third quarter of 1999 and
the first quarter of 2000, when apartments, office, retail, and
industrial properties but not hotels experienced a negative
EVA spread in one or two quarters. This period is just prior
to the eight-month-long recession which started in March
2001, triggered by the dot-com bust. Since overbuilding did
not precede the 2001 recession, the commercial real estate
market remained relatively stable. A comparison of the Ex-hibits
9 through 12 with Exhibit 4 reveals that the magnitude
19 Unlike other property types, hotel data are not available for 2001Q4,
2002Q2, 2003Q2, and 2010Q1.
of the EVA spread differs, as does the start date of when the
spread turns negative. The high for most property types oc-curred
in the 2003 to 2004 period, about three to four years
prior to the crash in commercial real estate prices. The EVA
spread peaked at different times for the various property
types. For example, office buildings peaked first, in 2001Q1,
while apartments peaked last, in 2004Q3. Hotels had the
largest EVA spread at their peak (.047) in the first quarter
of 2004, while retail properties had the lowest EVA spread
(.025) at their peak in the first quarter of 2003.
The EVA spread for various property types also turned
negative at different times. The EVA spread turned negative
for apartments in the first quarter of 2006, for instance, fol-lowed
by retail in the fourth quarter of the same year. In the
next year, 2007, the EVA spread for office buildings turned
negative in the second quarter, and the EVA for industrial
properties did so in the third quarter, while that date for
hotels was the second quarter of 2008. The start date when
each property type experienced three successive quarters of
negative EVA spread (last column in Exhibit 12) is roughly
consistent with when the crash occurred in the commercial
real estate market.
24
Cornell Hospitality Report • January 2014 • www.chr.cornell.edu 15

16. Exhibit 11
EVA spread for retail properties
Exhibit 11: EVA Spread for Retail Properties20
0.02
0.01
0
1998.04
1999.03
-0.01
--0.02
--0.03
-0.03
0.00
2001.01
2001.04
2000.02
2006.02
Retail Retail EVA EVA Spread (Spread (CROCI - CROCI-WACC)
WACC)
2002.03
2003.02
2004.01
2004.04
2005.03
Note: If any data are missing for a particular quarter, that quarter is omitted, so that no data are
missing for any quarter shown.
Sources: ACLI, Center for Real Estate and Finance at Cornell, NAREIT
Source: ACLI, Center for Real Estate and Finance at Cornell, NAREIT
2007.01
2007.04
Exhibit 12
EVA spread for industrial properties
Exhibit 12: EVA Spread for Industrial Properties21
20If data is missing for a quarter, that quarter is omitted. No data is missing for any quarter.
25
2010.01
2010.04
2008.03
2009.02
2013.01
2011.03
2012.02
0.04
0.03
0.02
0.01
0.00
1998.04
1999.03
-0.01
-0.02
2001.04
2003.02
2004.01
2006.02
2007.01
Source: ACLI, Center for Real Estate and Finance at Cornell, NAREIT
2009.02
2010.01
2012.02
16 The Center for Hospitality Research • Cornell University
21If data is missing for a quarter, that quarter is omitted. No data is missing for any quarter.
26
-0.03
2000.02
2001.01
2002.03
2004.04
2005.03
2007.04
2008.03
2010.04
2011.03
2013.01
Industrial EVA Spread (CROCI-WACC)
Note: If any data are missing for a particular quarter, that quarter is omitted, so that no data are
missing for any quarter shown.
Sources: ACLI, Center for Real Estate and Finance at Cornell, NAREIT
0.04
0.03
0.02
0.01
0
-0.01
-0.02
-0.03
Industrial EVA Spread (CROCI - WACC)

17. Exhibit 13
Quarterly EVA spreads for hotels, apartments, office, retail, and industrial properties
Exhibit 13: Quarterly EVA Spreads for Various Property Types
Cornell Hospitality Report • January 2014 • www.chr.cornell.e2du7 17

18. Exhibit 14
Co-movement of EVA spreads for hotel, apartment, office, retain, and industrial properties
Exhibit 14: Co-movement of EVA Spreads for Hotel, Apartment, Office, Retail, and Industrial Properties
0.06
0.06
0.04
0.04
0.02
0.02
0.00
0
E V A S p r e a d
-0.02
-0.02
-0.04
-0.04
-0.06
-0.06
-0.08
HHootteelsls Hotels AAppaartrmtmenetns Apartments ts OOffifcfeice Office RReettaailil Retail Industrial
IInndduusstrtiraial l
1999.04
2000.02
-0.08
1999.02
1998.04
2002.01
2002.04
2001.02
2000.04
2004.03
2005.01
2004.01
2003.03
2006.03
2007.01
2006.01
2005.03
EVA Spread
Sources: ACLI, Center for Real Estate and Finance at Cornell, NAREIT
Source: ACLI, Center for Real Estate and Finance at Cornell, NAREIT
Summary of EVA Spreads for Various Property Types
28
2008.03
2009.01
2008.01
2007.03
2010.04
2011.02
2010.02
2009.03
2012.04
2012.02
2011.04
Property Type Max
Max
EVA
Spread
1st Turn
Negative
EVA
Spread
Begin
Date
Negative
(3
Quarters)
Hotels 2004Q1 .047 2008Q2 -.019 2008Q2
Apartments 2004Q3 .035 2006Q1 -.001 2006Q1
Office 2001Q1 .041 2007Q2 -.002 2007Q2
Retail 2003Q1 .025 2006Q4 -.004 2007Q2
Industrial 2003Q2 .028 2007Q3 -.004 2007Q3
Correlation of EVA Spreads
Hotels Apartments Office Retail Industrial
Hotels 1.000
Apartments .713 1.000
Office .775 .858 1.000
Retail .805 .836 .850 1.000
Industrial .757 .743 .825 .909 1.000
To get a better perspective on the extent to which the EVA spreads on various property types move together, we plot the EVA
spreads in Exhibit 14 and report the correlation among the EVA spread for various property types in the table above. The
EVA spreads all tend to move in the same direction.
18 The Center for Hospitality Research • Cornell University

19. Exhibit 15: Regression Analysis
Exhibit 15
Regression analysis summary
Applications for Practitioners
The reader can calculate EVA using the spreadsheet tool that
accompanies this report, which also shows the shareholder
value added for any given property type. You can use your
own data or those available from the sources listed at right.
The decision making criterion is that if EVA is greater
than zero for a property, then the property adds value to an
investor’s property portfolio since the return on the property
exceeds its borrowing (financing) cost.
One nice application of the EVA spread is that a posi-tive
spread indicates that investors are immediately adding
value by purchasing the hotel in question, while a negative
spread signals that investors should anticipate having to wait
until the hotel is sold or cash flows increase to realize any
investment gains above financing costs. Additionally, hotels
bought with a negative spread likely will need to be reposi-tioned
with additional capital expenditures (also known as
property improvement plans).
Since we have thus far used graphs as our primary tool
to show the link between EVA, the year-over-year change
in RevPAR, the transaction volume (number of hotels sold),
the YOY change in the number of hotel rooms in either the
pre-planning stage or in the planning stage, and the YOY
change in the value of construction put in place, we first
want to provide a more rigorous view of these linkages, as
Data Sources for EVA Calculation
Cap rates on various property types: https://www.rcanalytics.
com/ (Real Capital Analytics), https://www.acli.com/Tools/_
layouts/ACLI/PublicationOrders/InvestmentBulletin/
InvestmentBulletinCheckout.aspx (ACLI). An example of
information that the ACLI provides can be found here: www.redi-net.
com/adn_db/library/acli_off/2ndQtr2012.pdf. (Note: Both RCA
and ACLI require a subscription.)
REIT data for the cost of equity: http://www.reit.com/
DataAndResearch/IndexData/FNUS-Historical-Data/Monthly-
Property-Index-Data.aspx
Capital market data on property financing: http://www2.
cushwake.com/sonngold/ (Cushman and Wakefield), or https://
www.acli.com/Tools/_layouts/ACLI/PublicationOrders/
InvestmentBulletin/InvestmentBulletinCheckout.aspx (ACLI).
An example of information that the ACLI provides can be found here:
www.redi-net.com/adn_db/library/acli_off/2ndQtr2012.pdf
Risk-free rate: http://research.stlouisfed.org/fred2/
graph/?s%5B1%5D%5Bid%5D=DGS10
Returns on the market portfolio: http://www.factset.com/
(Factset), Bloomberg, http://wrds-web.wharton.upenn.edu/wrds/
(WRDS), http://www.russell.com/indexes/data/US_Equity/
Russell_US_index_values.asp (Russell Indices; these use the Russell
3000 as the market proxy)
29
Cornell Hospitality Report • January 2014 • www.chr.cornell.edu 19

20. Exhibit 16: Predictions from Regression Analysis
20 The Center for Hospitality Research • Cornell University
30
Exhibit 16
Predictions from regression analysis

21. shown in the linear regressions reported in Exhibit 15. The
first regression examines the relationship between EVA
spread and year-over-year change in RevPAR. The relation-ship
is statistically significant, with RevPAR accounting for
approximately 26 percent of the variation in the EVA spread.
The next set of regressions investigates whether we can
link the EVA spread to the number of hotels sold, the YOY
change in the number of hotel rooms in the pre-planning or
planning stage, and the YOY change in the value of lodging
construction put in place. In each case, the EVA spread is a
statistically significant driver of each of these variables, al-though
it exerts a stronger influence on transaction volume
and construction put in place.
To show how these variables are linked using the regres-sion
output in Exhibit 15, Exhibit 16 provides a sensitivity
analysis table. Reading from left to right, suppose that the
year-over-year change in RevPAR for midscale chain hotels
is 1.3 percent (.013). It then follows that the predicted EVA
spread is close to zero at .0075, which in turn suggests that
the number of hotels sold will be approximately 204 for that
quarter. Moreover, the model predicts that there will be no
year-over-year change in the number of rooms in the plan-ning
stage, a 5.3-percent change in the number of rooms in
the pre-planning stage, and a 14-percent increase in the YOY
change in the value of construction put in place. If the YOY
change in RevPAR declines to -2.1 percent, then the EVA
spread is .5 percent, which in turn reduces the transaction
volume to 199 hotels sold. While both the YOY change in
the number of hotel rooms in the pre-planning stage (4.4%)
and construction put in place (12%) remain positive, there
is now a decrease in the YOY change in the number of hotel
rooms in the planning stage. The point is that once the YOY
change in RevPAR declines to about 1 percent (.013) or
the EVA spread is below 1 percent, real estate practitioners
should start to check whether the canary in the coal mine is
still chirping.
Summary
In this report, we introduce the use of economic value added
as a technique to assess the investment performance of ho-tels,
as well as other property types. In contrast to traditional
measures of real estate analysis wherein the spread in the
cap rate relative to debt financing widens but continues to
remain positive, our EVA metric becomes negative during a
recession or economic crisis. The rationale for this differ-ence
is that our EVA measure also includes the cost of equity
financing. As capital market conditions worsen, lenders
lower the loan-to-value ratio, requiring investors to put
more equity into the deal. Since the cost of equity financ-ing
is higher than that of debt financing, it follows that the
weighted average cost of both debt and equity will increase.
An analysis by Suzanne Mellen of HVS noted that during the
financial crisis in late 2008, hotel earnings collapsed, coupled
with a freeze in the capital markets.20 She shows that cap
rates derived from selected lodging REIT data declined from
year end 2008 through year end 2010. Whatever the reason
for a decline in the cap rate, hotel investors should start to
become wary when the spread between the cap rate and
project WACC is below 1 percent. n
20 Suzanne Mellen, “Dramatic Decline in Hotel Capitalization Rates
Reflects Shift in Market Sentiment,” HVS Research, 2011 (www.hvs.
com/Jump/?aid=5046&rt=2).
Appendix: A Primer on Economic Value Added
To understand the concept underlying the EVA metric, assume that you
are an investor in a one-period world where the investment is made at
the beginning of the period and the return is realized at the end of the
period. If you have an unlimited budget, then you would choose every
project whose return is greater than its average financing borrowing cost,
using both debt and equity sources of capital. Assume for the time being
that all projects have similar risks and project risk is identical to firm risk,
so that the cost of capital for the project is identical to the cost of capital
for the firm. The graph at right depicts the capital budgeting decision,
with projects ranked in descending order of project return.
Project
Return
Accept Project
WACC (Borrowing Cost)
Reject Project
Project Number
Cornell Hospitality Report • January 2014 • www.chr.cornell.edu 21

22. Thus, the decision criteria are
If ⇒ Then
Project Return – Project Financing > 0 ⇒ Accept the Project
Project Return – Project Financing < 0 ⇒ Reject the Project
In a multi-period setting, the project return is equal to the internal rate
of return (IRR), while the weighted average cost of capital (WACC)
represents the discount rate and captures the required return demanded
by both debt and equity investors. The decision criteria are now:
If ⇒ Then
IRR – WACC > 0 ⇒ NPV > 0; Accept the Project
IRR – WACC < 0 ⇒ NPV < 0; Reject the Project
The IRR = WACC when the NPV = 0. The IRR recognizes all cash flows
over the entire period that a project is held.
If an investor wishes to calculate the return based on cash flow in the
current period, then the resulting return is known as the return on
invested capital (ROIC). When cash flows grow at the same rate as
inflation, IRR = ROIC. We focus on ROIC in lieu of IRR, as this measure is
calculated with current cash flow, whereas IRR needs a forecast of all
future cash flows associated with the project. ROIC also allows us to
determine whether a real estate investment immediately adds value in
the current period, whereas IRR is the present value of the long-term
perspective. The spread between the project return and project financing
is known as the economic profit margin, or economic value added
spread. When this spread is multiplied by the capital invested in the
project, the result is known as the economic value added or EVA.
EVA = (ROIC – WACC)*Invested Capital (1)
A word of caution is in order. If the risk for the project differs from the
risk for the firm (or the risk of an investor’s portfolio of properties) and
the investor mistakenly uses the same WACC for all projects, then the
investor will tend to reject profitable projects with less risk than the
overall firm (or his portfolio of properties) and accept unprofitable
projects with more risk than the overall firm.
WACC vs. RADR
Hurdle Rates
WACC
Project Risk
Risk Adjusted Discount Rate (RADR)
B
D


C 
A 

In the diagram above, the project risk is denoted as the risk adjusted
discount rate (RADR), which represents the discount rate that rises with
an increase in incremental project risk. RADR can be thought of as the
project WACC that accounts for the risk associated with a particular
project. The hurdle rate is the firm’s WACC, which remains constant
since it represents the firm’s minimum required return for all projects.
The investor’s decision will differ depending on whether one applies the
firm’s WACC (hurdle rate) or the project WACC (RADR). Suppose the
investor is considering four property deals, as shown in the above
diagram. If the investor uses the firm’s WACC to evaluate each property,
then property B and property C are accepted since their project return is
greater than the firm’s WACC (depicted as both projects being above the
horizontal WACC line). Project A and D would be rejected since their
return is lower than the firm’s WACC. On the other hand, if the project
WACC or RADR is used as the decision criterion, then project A and
project B are acceptable since they are both above the RADR line, while
project C and project D are unacceptable since their returns are both
below the cost of project financing. Therefore the use of a companywide
WACC is only warranted if both of the following conditions are met: (1)
the risk of the project is equal to the overall risk of the firm or the
average risk of the firm’s other projects, and (2) a project’s optimal long-term
capital structure equals the firm’s current capital structure. For this
reason, when we refer to WACC in our analysis, this means the project
WACC or the RADR.
One question which arises is, under what conditions should an investor
choose a project whose NPV < 0, that is, a project with a return that is
lower than its project financing? One plausible answer is whether there
is a real option associated with the project. This would occur, for
example, when an investor buys a NPV< 0 project as a turnaround play
and injects additional capital expenditures into the project over and
above his or her original equity with the expectation that the project will
succeed in the future such that all of the investor’s initial investment is
recaptured and a profit is realized.
22 The Center for Hospitality Research • Cornell University

23. Cornell Center for Hospitality Research
Publication Index
chr.cornell.edu
2014 Reports
Vol. 14, No. 1 Assessing the Benefits of
Reward Programs: A Recommended
Approach and Case Study from the
Lodging Industry, by Clay M. Voorhees,
PhD., Michael McCall, Ph.D., and Bill
Carroll, Ph.D.
2013 Reports
Vol. 13, No. 11 Can You Hear Me
Now?: Earnings Surprises and Investor
Distraction in the Hospitality Industry, by
Pamela C. Moulton, Ph.D.
Vol. 13, No. 10 Hotel Sustainability:
Financial Analysis Shines a Cautious
Green Light, by Howard G. Chong, Ph.D.,
and Rohit Verma, Ph.D.
Vol. 13 No. 9 Hotel Daily Deals: Insights
from Asian Consumers, by Sheryl E.
Kimes, Ph.D., and Chekitan S. Dev, Ph.D.
Vol. 13 No. 8 Tips Predict Restaurant
Sales, by Michael Lynn, Ph.D., and Andrey
Ukhov, Ph.D.
Vol. 13 No. 7 Social Media Use in
the Restaurant Industry: A Work in
Progress, by Abigail Needles and Gary M.
Thompson, Ph.D.
Vol. 13 No. 6 Common Global and Local
Drivers of RevPAR in Asian Cities, by
Crocker H. Liu, Ph.D., Pamela C. Moulton,
Ph.D., and Daniel C. Quan, Ph.D.
Vol. 13. No. 5 Network Exploitation
Capability: Model Validation, by Gabriele
Piccoli, Ph.D., William J. Carroll, Ph.D.,
and Paolo Torchio
Vol. 13, No. 4 Attitudes of Chinese
Outbound Travelers: The Hotel Industry
Welcomes a Growing Market, by Peng
Liu, Ph.D., Qingqing Lin, Lingqiang Zhou,
Ph.D., and Raj Chandnani
Vol. 13, No. 3 The Target Market
Misapprehension: Lessons from
Restaurant Duplication of Purchase Data,
Michael Lynn, Ph.D.
Vol. 13 No. 2 Compendium 2013
Vol. 13 No. 1 2012 Annual Report
2013 Hospitality Tools
Vol. 4 No. 2 Does Your Website Meet
Potential Customers’ Needs? How to
Conduct Usability Tests to Discover the
Answer, by Daphne A. Jameson, Ph.D.
Vol. 4 No. 1 The Options Matrix Tool
(OMT): A Strategic Decision-making
Tool to Evaluate Decision Alternatives,
by Cathy A. Enz, Ph.D., and Gary M.
Thompson, Ph.D.
2013 Industry Perspectives
Vol. 3 No. 2 Lost in Translation: Cross-country
Differences in Hotel Guest
Satisfaction, by Gina Pingitore, Ph.D.,
Weihua Huang, Ph.D., and Stuart Greif,
M.B.A.
Vol. 3 No. 1 Using Research to Determine
the ROI of Product Enhancements: A
Best Western Case Study, by Rick Garlick,
Ph.D., and Joyce Schlentner
2013 Proceedings
Vol. 5 No. 6 Challenges in Contemporary
Hospitality Branding, by Chekitan S. Dev
Vol. 5 No. 5 Emerging Trends in
Restaurant Ownership and Management,
by Benjamin Lawrence, Ph.D.
Vol. 5 No. 4 2012 Cornell Hospitality
Research Summit: Toward Sustainable
Hotel and Restaurant Operations, by
Glenn Withiam
Vol. 5 No. 3 2012 Cornell Hospitality
Research Summit: Hotel and Restaurant
Strategy, Key Elements for Success, by
Glenn Withiam
Vol. 5 No. 2 2012 Cornell Hospitality
Research Summit: Building Service
Excellence for Customer Satisfaction, by
Glenn Withiam
Vol. 5, No. 1 2012 Cornell Hospitality
Research Summit: Critical Issues for
Industry and Educators, by Glenn
Withiam
2012 Reports
Vol. 12 No. 16 Restaurant Daily Deals:
The Operator Experience, by Joyce
Wu, Sheryl E. Kimes, Ph.D., and Utpal
Dholakia, Ph.D.
Vol. 12 No. 15 The Impact of Social
Media on Lodging Performance, by Chris
K. Anderson, Ph.D.
Vol. 12 No. 14 HR Branding How
Human Resources Can Learn from
Product and Service Branding to Improve
Attraction, Selection, and Retention, by
Derrick Kim and Michael Sturman, Ph.D.
Vol. 12 No. 13 Service Scripting and
Authenticity: Insights for the Hospitality
Industry, by Liana Victorino, Ph.D.,
Alexander Bolinger, Ph.D., and Rohit
Verma, Ph.D.
Cornell Hospitality Report • January 2014 • www.chr.cornell.edu 23

24. Cornell University
School of Hotel Administration
The Center for Hospitality Research
537 Statler Hall
Ithaca, NY 14853
607.255.9780
shachr@cornell.edu
www.chr.cornell.edu