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

Center for Hospitality Research

Cornell Hospitality Report

Vol. 14, No. 2January 2014All CHR reports are available for free download, but may not be reposted, reproduced, or distributed without the express permission of the publisher

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

Cornell Hospitality ReportVol. 14, No. 2 (January 2014)© 2014 Cornell University. This report may not be reproduced or distributed without the express permission of the publisher.

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4 The Center for Hospitality Research • Cornell University

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.


by Matthew J. Clayton and Crocker H. Liu

Cornell Hospitality Report • January 2014 • www.chr.cornell.edu 5

ABoUT THE AUTHoRS

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.

This CHR Report is produced in conjunction with


In evaluating the performance of hotels or other property types, real estate analysts have 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.


by Matthew J. Clayton and Crocker H. Liu

CoRnELL HoSPITALITy REPoRT


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 MethodologyTo 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/

http://www.reit.com/DataAndResearch/IndexData/FNUS-Historical-Data/Monthly-Property-Index-Data.aspx


http://wrds-web.wharton.upenn.edu/wrds/


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:

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)

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

Cash Return on Capital Invested (CRoCI) = EBITDA

Capital Invested

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 ExpensesEBIT (Operating Income) net operating Income (noI)

http://research.stlouisfed.org/fred2/graph/?s%5B1%5D%5Bid%5D=DGS10



relatively higher risk of investing in a risky asset as opposed to investing in the risk-free asset. The following example illustrates these concepts.

ExampleFor 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.

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.

ResultsWe 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.

15

Exhibit 1: Comparison of EVA Spread to Cap Rate (CROCI) in Excess of the Risk Free Rate

Source: ACLI, Center for Real Estate and Finance at Cornell, Federal Reserve, NAREIT, Smith Travel Research (STR)

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CROCI-10YrTBond

EVA Spread (CROCI-WACC)

Exhibit 1

Comparison of EVA spread to cap rate (CRoCI) in excess of risk-free rate

CRoCI - 10-year bond

EVA spread (CRoCI - WACC)

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Sources: ACLI, Center for Real Estate and Finance at Cornell, Federal Reserve, NAREIT, STR


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.

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 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.

16

Exhibit 2: Availability of Debt and Interest Rate relative to Real Estate Metrics

Source: ACLI, Center for Real Estate and Finance at Cornell, Federal Reserve, NAREIT,

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CROCI-10YrTBondCROCI-WACCAvailability of Debt10YrTBond (Constant Maturity)

Exhibit 2

Availability of debt and interest rate relative to real estate metrics

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CRoCI - 10-year bondEVA spread (CRoCI - WACC)Availability of debt10-year T-bond (constant maturity)

Sources: ACLI, Center for Real Estate and Finance at Cornell, Federal Reserve, NAREIT

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Exhibit 2: Availability of Debt and Interest Rate relative to Real Estate Metrics

Source: ACLI, Center for Real Estate and Finance at Cornell, Federal Reserve, NAREIT,

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CROCI-10YrTBondCROCI-WACCAvailability of Debt10YrTBond (Constant Maturity)


17

Exhibit 3: Year over Year Change in RevPar (MidScale Chains) as a Driver of the EVA Spread

Source: ACLI, Center for Real Estate and Finance at Cornell, NAREIT, Federal Reserve, STR

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.03

2004

.01

2004

.03

2005

.01

2005

.03

2006

.01

2006

.03

2007

.01

2007

.03

2008

.01

2008

.03

2009

.01

2009

.03

2010

.02

2010

.04

2011

.02

2011

.04

2012

.02

2012

.04

EVA

Spr

ead

(CR

OC

I - W

AC

C)

-0.20

-0.15

-0.10

-0.05

0.00

0.05

0.10

0.15

0.20

0.25

0.30

Y-O

-Y C

hang

e in

Rev

Par (

Mid

scal

e C

hain

s)


STR Y-O-Y Change RevPar (Midscale Chains)

18

Exhibit 4: The EVA Spread and the Volume of Hotel Transactions

Source: ACLI, Center for Real Estate and Finance at Cornell, NAREIT

0

50

100

150

200

250

300

350

400

450

1998

.04

1999

.01

1999

.02

1999

.03

1999

.04

2000

.01

2000

.02

2000

.03

2000

.04

2001

.01

2001

.02

2001

.03

2002

.01

2002

.03

2002

.04

2003

.01

2003

.03

2003

.04

2004

.01

2004

.02

2004

.03

2004

.04

2005

.01

2005

.02

2005

.03

2005

.04

2006

.01

2006

.02

2006

.03

2006

.04

2007

.01

2007

.02

2007

.03

2007

.04

2008

.01

2008

.02

2008

.03

2008

.04

2009

.01

2009

.02

2009

.03

2009

.04

2010

.02

2010

.03

2010

.04

2011

.01

2011

.02

2011

.03

2011

.04

2012

.01

2012

.02

2012

.03

2012

.04

Num

ber o

f Hot

els

Sold

-0.08

-0.06

-0.04

-0.02

0.00

0.02

0.04

0.06

CR

OC

I-WA

CCNum of Hotels Sold


Exhibit 3

year-over-year change in RevPAR (midscale chains) as a driver of EVA spread

Exhibit 4

EVA spread and volume of hotel transactions

Sources: ACLI, Center for Real Estate and Finance at Cornell, Federal Reserve, NAREIT, STR


STR yoy RevPAR change (midscale chains)

EVA

spr

ead

(CRo

CI -

WA

CC)

0.30

0.25

0.20

0.15

0.10

0.05

0

-0.05

-0.10

-0.15

-0.20

yoy

RevP

AR

chan

ge (m

idsc

ale

chai

ns)

0.06

0.04

0.02

0

-0.02

-0.04

-0.06

-0.08

nu

mb

er

of

Ho

te

ls S

old

0.04

0.2

0

-0.02

-0.04

-0.06

-0.08

CR

oC

I–

WA

CC

400

350

300

250

200

150

100

50

0

Sources: ACLI, Center for Real Estate and Finance at Cornell, NAREIT

number of hotels sold


18

Exhibit 4: The EVA Spread and the Volume of Hotel Transactions


0

50

100

150

200

250

300

350

400

450

1998

.04

1999

.01

1999

.02

1999

.03

1999

.04

2000

.01

2000

.02

2000

.03

2000

.04

2001

.01

2001

.02

2001

.03

2002

.01

2002

.03

2002

.04

2003

.01

2003

.03

2003

.04

2004

.01

2004

.02

2004

.03

2004

.04

2005

.01

2005

.02

2005

.03

2005

.04

2006

.01

2006

.02

2006

.03

2006

.04

2007

.01

2007

.02

2007

.03

2007

.04

2008

.01

2008

.02

2008

.03

2008

.04

2009

.01

2009

.02

2009

.03

2009

.04

2010

.02

2010

.03

2010

.04

2011

.01

2011

.02

2011

.03

2011

.04

2012

.01

2012

.02

2012

.03

2012

.04

Num

ber o

f Hot

els

Sold

-0.08

-0.06

-0.04

-0.02

0.00

0.02

0.04

0.06

CR

OC

I-WA

CCNum of Hotels Sold



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

the EVA spread widens, the YOY change in hotel construc-tion put in place increases on a one-year lag from the EVA’s 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.

19

Exhibit 5: Two Measures of Real Estate Performance and the Number of Rooms in the Pre-Planning and Planning Stages

Source: ACLI, Center for Real Estate and Finance at Cornell, Federal Reserve, NAREIT, Smith Travel Research (STR) Pipline

-0.08

-0.06

-0.04

-0.02

0

0.02

0.04

0.06

0.08

0.1

0.12

2003

2004

2004

2005

2005

2006

2006

2007

2007

2008

2008

2009

2009

2010

2010

2011

2011

2012

2012

Spre

ad

-0.60

-0.40

-0.20

0.00

0.20

0.40

0.60

0.80

1.00

Y-O

-Y C

hang

e in

Num

ber o

f Roo

ms

(Pla

nnin

g or

Pre

-Pla

nnin

g)

CROCI-10YrTBondCROCI-WACCY-O-Y Change in Pre-Planning (#Rooms)Y-O-Y Change in Planning (#Rooms)

Exhibit 5

Two measures of real estate performance and the number of rooms in the pre-planning and planning stages

Sources: ACLI, Center for Real Estate and Finance at Cornell, Federal Reserve, NAREIT, STR Pipeline

CRoCI - Ten-year T bond)EVA spread (CRoCI - WACC)yoy change in number of pre-planned roomsyoy change in number of planned rooms

Sp

re

ad

1.00

0.80

0.60

0.40

0.20

0

-0.20

-0.40

-0.60

yoy

Chan

ge in

Hot

el C

onst

ruct

ion

Put i

n Pl

ace

0.12

0.1

0.08

0.06

0.04

0.02

0

-0.02

-0.04

-0.06

-0.08


20

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).

Source: ACLI, Bureau of the Census, Center for Real Estate and Finance at Cornell, NAREIT, Federal Reserve

-0.08

-0.06

-0.04

-0.02

0.00

0.02

0.04

0.06

0.08

0.10

0.12

1998

.04

1999

.02

1999

.04

2000

.02

2000

.04

2001

.02

2002

.01

2002

.04

2003

.03

2004

.01

2004

.03

2005

.01

2005

.03

2006

.01

2006

.03

2007

.01

2007

.03

2008

.01

2008

.03

2009

.01

2009

.03

2010

.02

2010

.04

2011

.02

2011

.04

2012

.02

2012

.04

Spre

ad

-0.80

-0.60

-0.40

-0.20

0.00

0.20

0.40

0.60

0.80

YOY

Cha

nge

in C

onst

ruct

ion

Put i

n Pl

ace

(Lod

ging

)

CROCI-10YrTBondEVA Spread (CROCI-WACC)YOY Change in Value of Construction Put in Place (Lodging)

21

Exhibit 7: Real Estate Performance and Construction Costs

Source: ACLI, Engineering Record (ENR), Center for Real Estate and Finance at Cornell, NAREIT, Federal Reserve

-0.08

-0.06

-0.04

-0.02

0.00

0.02

0.04

0.06

0.08

0.10

0.12

199819

9919

9919

9919

9920

0020

0020

0020

0020

0120

0120

0120

0220

0220

0220

0320

0320

0320

0420

0420

0420

0420

0520

0520

0520

0520

0620

0620

0620

0620

0720

0720

0720

0720

0820

0820

0820

0820

0920

0920

0920

0920

1020

1020

1020

1120

1120

1120

1120

1220

1220

1220

12

Spre

ad

-0.02

0.00

0.02

0.04

0.06

0.08

0.10

0.12

Year

-ove

r-Ye

ar C

hang

e in

EN

R B

uild

ing

Cos

ts

CROCI-10YrTBondCROCI-WACCY-O-Y Change in ENR Building Cost Index

Exhibit 6

EVA spread versus year-over-year change in hotel construction with one-year lag

Exhibit 7

Real estate performance and construction costs

Sp

re

ad

0.80

0.60

0.40

0.20

0

-0.20

-0.40

-0.60

-0.80

yoy

Chan

ge in

Hot

el C

onst

ruct

ion

Put i

n Pl

ace

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

20




-0.08

-0.06

-0.04

-0.02

0.00

0.02

0.04

0.06

0.08

0.10

0.12

1998

.04

1999

.02

1999

.04

2000

.02

2000

.04

2001

.02

2002

.01

2002

.04

2003

.03

2004

.01

2004

.03

2005

.01

2005

.03

2006

.01

2006

.03

2007

.01

2007

.03

2008

.01

2008

.03

2009

.01

2009

.03

2010

.02

2010

.04

2011

.02

2011

.04

2012

.02

2012

.04

Spre

ad

-0.80

-0.60

-0.40

-0.20

0.00

0.20

0.40

0.60

0.80

YOY

Cha

nge

in C

onst

ruct

ion

Put i

n Pl

ace

(Lod

ging

)


Sp

re

ad

year

-ove

r-yea

r Cha

nge

in E

nR

Build

ing

Cost

s

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

21

Exhibit 7: Real Estate Performance and Construction Costs

Source: ACLI, Engineering Record (ENR), Center for Real Estate and Finance at Cornell, NAREIT, Federal Reserve

-0.08

-0.06

-0.04

-0.02

0.00

0.02

0.04

0.06

0.08

0.10

0.12

199819

9919

9919

9919

9920

0020

0020

0020

0020

0120

0120

0120

0220

0220

0220

0320

0320

0320

0420

0420

0420

0420

0520

0520

0520

0520

0620

0620

0620

0620

0720

0720

0720

0720

0820

0820

0820

0820

0920

0920

0920

0920

1020

1020

1020

1120

1120

1120

1120

1220

1220

1220

12

Spre

ad

-0.02

0.00

0.02

0.04

0.06

0.08

0.10

0.12

Year

-ove

r-Ye

ar C

hang

e in

EN

R B

uild

ing

Cos

ts

CROCI-10YrTBondCROCI-WACCY-O-Y Change in ENR Building Cost Index

20




-0.08

-0.06

-0.04

-0.02

0.00

0.02

0.04

0.06

0.08

0.10

0.12

1998

.04

1999

.02

1999

.04

2000

.02

2000

.04

2001

.02

2002

.01

2002

.04

2003

.03

2004

.01

2004

.03

2005

.01

2005

.03

2006

.01

2006

.03

2007

.01

2007

.03

2008

.01

2008

.03

2009

.01

2009

.03

2010

.02

2010

.04

2011

.02

2011

.04

2012

.02

2012

.04

Spre

ad

-0.80

-0.60

-0.40

-0.20

0.00

0.20

0.40

0.60

0.80

YOY

Cha

nge

in C

onst

ruct

ion

Put i

n Pl

ace

(Lod

ging

)


CRoCI - Ten-year T bond)EVA spread (CRoCI - WACC)yoy change in value of hotel construction put in place

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


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 ∆ in no. of

Rooms in Final

Planning

yoy ∆ in no. of Rooms

in Planning

yoy ∆ in no. of Rooms in Pre-

planning

yoy ∆ in Value of

Hotel Con-

struction Put in Place

Availability of Debt

yoy ∆ in EnR

Building Cost

Index*

EVA Spread (CRoCI – WACC)

1.00

CRoCI – 10-yr T bond -0.15 1.00no. of Hotels Sold 0.49 -0.54 1.00no. of Rooms in Pre-planning -0.10 0.28 -0.43 1.00yoy ∆ in no. of Rooms in Final Planning

0.03 -0.51 0.40 -0.28 1.00

yoy ∆ in no. of Rooms in Planning

0.27 -0.52 0.67 -0.32 0.68 1.00

yoy ∆ in no. of Rooms in Pre-planning

0.31 -0.55 0.71 -0.40 0.85 0.92 1.00

yoy ∆ 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 1.00yoy ∆ 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

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

Apa

rtm

ent E

VA S

prea

d (C

RoCI

- W

ACC

)

23

Exhibit 9: EVA Spread for Apartments18


18If data is missing for a quarter, that quarter is omitted. No data is missing for any quarter.

-0.02

-0.01

0.00

0.01

0.02

0.03

0.04

1998

.04

1999

.03

2000

.02

2001

.01

2001

.04

2002

.03

2003

.02

2004

.01

2004

.04

2005

.03

2006

.02

2007

.01

2007

.04

2008

.03

2009

.02

2010

.01

2010

.04

2011

.03

2012

.02

2013

.01

Apa

rtm

ents

EVA

Spr

ead

(CR

OC

I-WA

CC

)

23

Exhibit 9: EVA Spread for Apartments18



-0.02

-0.01

0.00

0.01

0.02

0.03

0.04

1998

.04

1999

.03

2000

.02

2001

.01

2001

.04

2002

.03

2003

.02

2004

.01

2004

.04

2005

.03

2006

.02

2007

.01

2007

.04

2008

.03

2009

.02

2010

.01

2010

.04

2011

.03

2012

.02

2013

.01

Apa

rtm

ents

EVA

Spr

ead

(CR

OC

I-WA

CC

)


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

Exhibit 10: EVA Spread for Office Buildings19



-0.04

-0.03

-0.02

-0.01

0.00

0.01

0.02

0.03

0.04

0.05

1998

.04

1999

.03

2000

.02

2001

.01

2001

.04

2002

.03

2003

.02

2004

.01

2004

.04

2005

.03

2006

.02

2007

.01

2007

.04

2008

.03

2009

.02

2010

.01

2010

.04

2011

.03

2012

.02

2013

.01

Offi

ce E

VA S

prea

d (C

RO

CI-W

AC

C)

Exhibit 10

EVA spread for office buildings


offi

ce B

uild

ing

EVA

Spr

ead

(CRo

CI -

WA

CC)

24

Exhibit 10: EVA Spread for Office Buildings19



-0.04

-0.03

-0.02

-0.01

0.00

0.01

0.02

0.03

0.04

0.05

1998

.04

1999

.03

2000

.02

2001

.01

2001

.04

2002

.03

2003

.02

2004

.01

2004

.04

2005

.03

2006

.02

2007

.01

2007

.04

2008

.03

2009

.02

2010

.01

2010

.04

2011

.03

2012

.02

2013

.01

Offi

ce E

VA S

prea

d (C

RO

CI-W

AC

C)

0.05

0.04

0.03

0.02

0.01

0

-0.01

-0.02

-0.03

-0.04


25

Exhibit 11: EVA Spread for Retail Properties20



-0.03

-0.02

-0.01

0.00

0.01

0.02

0.03

1998

.04

1999

.03

2000

.02

2001

.01

2001

.04

2002

.03

2003

.02

2004

.01

2004

.04

2005

.03

2006

.02

2007

.01

2007

.04

2008

.03

2009

.02

2010

.01

2010

.04

2011

.03

2012

.02

2013

.01

Ret

ail E

VA S

prea

d (C

RO

CI-W

AC

C)


0.03

0.02

0.01

0

-0.01

-0.02

-0.03

Reta

il EV

A S

prea

d (C

RoCI

- W

ACC

)Exhibit 11

EVA spread for retail properties

25

Exhibit 11: EVA Spread for Retail Properties20



-0.03

-0.02

-0.01

0.00

0.01

0.02

0.03

1998

.04

1999

.03

2000

.02

2001

.01

2001

.04

2002

.03

2003

.02

2004

.01

2004

.04

2005

.03

2006

.02

2007

.01

2007

.04

2008

.03

2009

.02

2010

.01

2010

.04

2011

.03

2012

.02

2013

.01

Ret

ail E

VA S

prea

d (C

RO

CI-W

AC

C)

26

Exhibit 12: EVA Spread for Industrial Properties21



-0.03

-0.02

-0.01

0.00

0.01

0.02

0.03

0.04

1998

.04

1999

.03

2000

.02

2001

.01

2001

.04

2002

.03

2003

.02

2004

.01

2004

.04

2005

.03

2006

.02

2007

.01

2007

.04

2008

.03

2009

.02

2010

.01

2010

.04

2011

.03

2012

.02

2013

.01

Indu

stria

l EVA

Spr

ead

(CR

OC

I-WA

CC

)


0.04

0.03

0.02

0.01

0

-0.01

-0.02

-0.03

Indu

stria

l EVA

Spr

ead

(CRo

CI -

WA

CC)

Exhibit 12

EVA spread for industrial properties


Exhibit 13: Quarterly EVA Spreads for Various Property Types

Exhibit 13

Quarterly EVA spreads for hotels, apartments, office, retail, and industrial properties


28

Exhibit 14: Co-movement of EVA Spreads for Hotel, Apartment, Office, Retail, and Industrial Properties


-0.08

-0.06

-0.04

-0.02

0.00

0.02

0.04

0.06

1998

.04

1999

.02

1999

.04

2000

.02

2000

.04

2001

.02

2002

.01

2002

.04

2003

.03

2004

.01

2004

.03

2005

.01

2005

.03

2006

.01

2006

.03

2007

.01

2007

.03

2008

.01

2008

.03

2009

.01

2009

.03

2010

.02

2010

.04

2011

.02

2011

.04

2012

.02

2012

.04

EV

A S

prea

d

Hotels Apartments Office Retail Industrial

Sources: ACLI, Center for Real Estate and Finance at Cornell, NAREIT

0.06

0.04

0.02

0

-0.02

-0.04

-0.06

-0.08

EV

A

Sp

re

ad

Exhibit 14

Co-movement of EVA spreads for hotel, apartment, office, retain, and industrial properties

28

Exhibit 14: Co-movement of EVA Spreads for Hotel, Apartment, Office, Retail, and Industrial Properties


-0.08

-0.06

-0.04

-0.02

0.00

0.02

0.04

0.06

1998

.04

1999

.02

1999

.04

2000

.02

2000

.04

2001

.02

2002

.01

2002

.04

2003

.03

2004

.01

2004

.03

2005

.01

2005

.03

2006

.01

2006

.03

2007

.01

2007

.03

2008

.01

2008

.03

2009

.01

2009

.03

2010

.02

2010

.04

2011

.02

2011

.04

2012

.02

2012

.04

EV

A S

prea

d

Hotels Apartments Office Retail IndustrialHotels Apartments office Retail Industrial

Summary of EVA Spreads for Various Property Types

Property Type Max

Max EVA

Spread1st Turn

negativeEVA

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

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.

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


Applications for PractitionersThe 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

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)

Data Sources for EVA Calculation

29

Exhibit 15: Regression Analysis Exhibit 15

Regression analysis summary

https://www.rcanalytics.com/

https://www.rcanalytics.com/

https://www.acli.com/Tools/_layouts/ACLI/PublicationOrders/InvestmentBulletin/InvestmentBulletinCheckout.aspx



http://www.redi-net.com/adn_db/library/acli_off/2ndQtr2012.pdf





http://www2.cushwake.com/sonngold/

http://www2.cushwake.com/sonngold/







http://www.factset.com/

http://wrds-web.wharton.upenn.edu/wrds/

http://www.russell.com/indexes/data/US_Equity/Russell_US_index_values.asp

http://www.russell.com/indexes/data/US_Equity/Russell_US_index_values.asp

20 The Center for Hospitality Research • Cornell University30

Exhibit 16: Predictions from Regression Analysis Exhibit 16

Predictions from regression analysis


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.

SummaryIn 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

Reject Project

Project number

WACC (Borrowing Cost)


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.

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.

WACC vs. RADR

Hur

dle

Rate

s

WACC

Project Risk

Risk Adjusted Discount Rate (RADR)

B

D

C

A


Cornell Center for Hospitality Research

Publication Indexchr.cornell.edu

2014 ReportsVol. 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 ReportsVol. 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 ToolsVol. 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 PerspectivesVol. 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 ProceedingsVol. 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 ReportsVol. 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.

http://www.hotelschool.cornell.edu/research/chr/pubs/reports/abstract-17183.html





http://www.hotelschool.cornell.edu/research/chr/pubs/roundtableproceedings/roundtable-17203.html







Cornell UniversitySchool of Hotel Administration

The Center for Hospitality Research537 Statler Hall

Ithaca, NY 14853

[email protected]

www.chr.cornell.edu

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