Originally published in 1994, this is an analysis of more than
1,300 hotel transactions recorded between 1985 and 1992. The study
revealed a pattern of consistent overpayment by some types of buyers and
underselling by certain classes of sellers. Further comparisons of
specific hotel-property types with various combinations of buyers and
sellers showed that sale prices are frequently affected by certain
combinations of property characteristics with the attributes of buyers
and sellers. Individual buyers, for example, regularly overpaid for the
properties they bought and were particularly influenced by average daily
rate. On the other hand, the Resolution Trust Corporation created
considerable market distortion by regularly discounting hotels it sold,
because it undervalued its properties' proximity to city centers
and their ADRs.
**********
Each year ownership of hundreds of U.S. lodging properties is
transferred in the real-estate market. In theory, the mutually agreed
upon sale price for a property is based on the buyer's and the
seller's financial goals, their investment outlook, and their
knowledge of the property's characteristics. That price is strongly
influenced by the physical characteristics of the property (e.g., number
of rooms, restaurants, pools, etc.), its location relative to other land
uses, and the economic conditions of the market in which it is located.
Physical, locational, and economic factors cumulatively generate income
or loss and cause value changes over time.
Property prices relate directly to property fundamentals. The
analysis of those fundamentals underlies both traditional and currently
applied approaches to property valuation in the real-estate-appraisal
profession. (1) Because the physical, locational, and economic aspects
of a mid-market hotel, for example, are observable and the cash flows
from operations and future sales may be estimated by the buyer and
seller using standard financial-analysis techniques, the buyer should be
able to avoid overpaying for a hotel and the seller should be able to
avoid selling a hotel too cheaply. Extending this theoretical argument
to the general case of all lodging-property sales, neither a
buyer's nor a seller's wealth should be abnormally enhanced or
reduced as the result of any transaction.
But not all buyers and sellers are equal. Some buyers are better
informed than others about the local economic conditions that affect
property prices. Some sellers are not as adept at negotiation as others.
Some parties in transactions are more motivated than others to complete
transactions in a timely manner and may pay more or accept less for
expediency. Japanese hotel buyers during the late 1980s, for example,
are thought to have overpaid for properties, perhaps because of their
strong motivation to place money in the U.S. real-estate market when it
was booming, perhaps because they wished to obtain trophy properties, or
perhaps because they made purchase decisions without good information
about key property and market fundamentals. (2) The Resolution Trust
Corporation (RTC) is another example. Some believe that political
pressure from Congress pushed the RTC into selling property from its
portfolio quickly and cheaply instead of waiting until the real-estate
market recovered.
To show that a particular type of buyer overpaid or that a seller
undersold in the lodging-property market, however, is far too general a
finding to be useful to market participants. If some buyers pay premiums
and some sellers offer discounts, do these outcomes persist in all
transactions in which a specific type of participant is involved?
Suppose buyer premiums and seller discounts are the result of mistakes.
The root causes of such "errors" should be of interest to
market participants who want to fill information gaps and exploit
inefficiencies. Some foreign buyers, for example, are known for their
careful examination of the physical characteristics of properties, but
because of their foreign residency, they may not astutely evaluate how
local market economics affect real-estate prices or they may unduly
weight such factors as residual property value much more heavily than do
sellers based in the United States. Armed with the knowledge of how
mistakes are made, brokers and consultants may be of better service to
these buyers by providing detailed local marker information.
In this article we explore the idea that the transaction price may
be different for a given lodging property in the case of one buyer and
seller pair relative to another. The findings reported here are from a
statistical exploration that is made possible by a large database of
lodging-property transactions that occurred throughout the United States
during the late 1980s and early 1990s. We begin with a discussion of
previous research on the influence of buyers and sellers on property
prices, then we present the findings from our study and their
implications.
What We Already Know
Property-rights theory suggests that private contracts do not
influence real-estate prices in competitive markets unless the contracts
affect the underlying property rights. Private contracts including
leases, management agreements, franchise agreements, and contracts for
sale are outside the realm of rent and price formation unless they
restrict the use of the property. For example, a contract for sale
accompanied by a deed that restricts owners' rights to use a
property only as a hotel would diminish value because of the options it
destroys.
Only recently has serious testing begun on the effect of private
contracts on value. Sirmans and Sirmans present some evidence that
professional management has a positive effect on monthly apartment rent.
(3) Shilling, Sirmans, Turnbull, and Benjamin provide somewhat stronger
evidence that contingency clauses in contracts for sale lead to
significant increases in the prices of houses (such clauses often
involve the ability to obtain financing and the sale of the currently
owned property). (4) However, Hanson was unable to find differences
between the ratios of operating income to replacement costs for hotels
affiliating with a chain and engaging a management company versus
independent hotels. (5) Corgel also could not establish that the
franchise affiliations of hotels had a statistically significant effect
on hotel-sale prices. (6)
Each contract for sale represents the agreement on price and terms
reached by a specific buyer and seller combination. The idea that the
price of a lodging property may be different in the case of one buyer
and seller combination compared to another is rooted in the belief that
buyer and seller characteristics influence price formation even though
property rights have not been disturbed. In theory, a given buyer
behaves differently from other buyers and a given seller behaves
differently from other sellers for three reasons. (7) First, each buyer
and seller is capable of pricing errors because neither buyer nor seller
has all the information about every property in the market that is
necessary to set a perfect price for any single property. Second, buyers
and sellers are not equally patient. Some sellers, for example, are
overly eager to sell and thus sell at low prices while other sellers are
willing to wait for their price. Finally, there are strategic reasons
why market participants may be willing to transact for the same property
at different prices. A hotel company, for instance, may value a property
higher than an individual because of the competitive edge the property
provides to the brand.
The corporate-finance literature is rich with evidence that the
values of securities are affected by the presence of investors driven by
tax and leverage clienteles. Maris and Elayan review this literature and
find in their study a tax-induced clientele that is willing to pay more
for equity REITs. (8) We know of only one study that addresses these
issues in the market for real estate that does not involve securities.
During the 1980s some real-estate-market observers believed that
limited-partnership syndications overpaid for properties to gain maximum
tax subsidies for limited partners. Holding other factors constant,
Beaton and Sirmans accept the null hypothesis that the prices paid for
apartments by different types of buyer organizations are equal. (9) In
other words, their data indicate that the form of the buyer's
organization is unrelated to the price paid.
Lodging-property-transaction data
Our statistical study of the effects of buyers and sellers on
lodging-property sale prices relies on a large database of hotel and
motel transactions. For this purpose, a property is defined as a hotel
if it includes at least 150 rooms, meeting and banquet space, and
restaurant facilities. The data are national in scope and include a
large proportion of the lodging-property transactions that occurred
during the period beginning in the first quarter of 1985 and ending in
the last quarter of 1992. The data are detailed with respect to property
characteristics, location, and local economic information. Buyers and
sellers in the transactions are identified so that they may be
classified (see Exhibit 1).
The primary source of transaction information is the database of
the Hospitality Market Data Exchange (HMDE) maintained by Hospitality
Valuation Services (HVS). The HMDE contains the sale price, number of
rooms, date of sale, and general-location information for several
thousand properties. Some information about the characteristics of the
properties, such as average room rate, age, amenities, and the
conditions of the sales (e.g., financing terms and the organization
forms of buyers and sellers) were obtained during visits to the HVS
office. Other data were gathered from the following sources:
* Hotel & Travel Index, the AH&MA Hotel and Motel Redbook,
and Mobil Travel Guides;
* Members of the Hotel and Motel Brokers Association;
* Telephone interviews with hotel and motel managers;
* Bureau of Labor Statistics and U.S. Bureau of the Census; and
* Sales and Marketing Management magazine.
The database comprises more than 1,300 transactions. Although the
sample was not randomly chosen, efforts were made to avoid
concentrations of property sales by quarter, geographic region, chain
affiliation, and other property characteristics.
About 40 percent of the sales in the database are omitted from
consideration for parts of this study because average daily rate and
occupancy statistics are unavailable or the property has extraordinary
characteristics, such as casino gambling.
Perspective
A broad perspective on behavioral differences among participants in
lodging-property markets is gained from the descriptive statistics
presented in Exhibit 2. Although the exhibit presents only averages for
selected characteristics of transactions across buyer classifications,
it reveals quite clearly that different buyers favor different types of
properties. Not surprisingly, individuals generally purchase lower-price
properties, primarily older motels that are at a greater distance from
commercial centers and airports than the average. At the other end of
the buyer spectrum are institutions and foreigners who favor high-price
hotels. Institutions seem to be especially interested in newer hotels.
Partnerships, hotel corporations, real-estate corporations, and other
corporations represent a large and rather homogeneous middle class of
buyers. During the period 1990 through 1992, however, there was greater
diversity in the buying behavior of those entities.
Insight on the behavior of lodging-property-market participants is
gained from an examination of the prices they paid and received. Holding
all other factors constant, we investigated the effects of unique buyer
and seller factors on aggregate prices. More specifically, we determined
which effects on pricing of particular property characteristics, such as
room rate or age of property, caused the different classes of buyers to
overvalue or undervalue a property. By effects we mean overpayment by
buyers and underselling by sellers (i.e., selling too cheaply).
We use multivariate-regression procedures to determine the effects
of buyers and sellers on prices while holding other factors constant.
The model has the following general form:
[S.sub.i]= s ([P.sub.i], [L.sub.i], [E.sub.i], [Q.sub.i],
[T.sub.i], [X.sub.i], [Y.sub.i]; [beta], e),
where:
* [S.sub.i] is the cash equivalent sale price of the ith property;
* [P.sub.i] is a vector of property characteristics of the ith
property at the time of sale;
* [L.sub.i] is a vector of locational characteristics of the ith
property at the time of sale;
* [E.sub.i] is a vector of economic characteristics of the local
area in which the ith property is located at time of sale;
* [Q.sub.i] is an unobserved quality measure of the ith property at
the time of sale; (10)
* [T.sub.i] is the year of sale of the ith property;
* X is a vector of buyer classifications, one buyer class for the
ith property;
* Y is a vector of seller classifications, one seller class for the
ith property; and
* [beta], e are estimated parameters and the error terms of the
model, respectively.
Exhibit 3 shows which buyer classes paid premiums or gained
discounts and which sellers received premiums or gave discounts in the
lodging-property market during the study period. The determination as to
whether premiums or discounts occurred comes from the regression
coefficients for the X and Y variables. Although the procedure is
somewhat complicated, essentially it involves identifying buyer and
seller classes that either pay or receive 10 percent more and
identifying buyer and seller classes that either pay or receive 10
percent less than the theoretically correct prices predicted by the
regression model.
As indicated in Exhibit 3, some buyers consistently bought lodging
properties at premiums, particularly individuals and foreign buyers, and
some sellers consistently sold at discounts, particularly financial
institutions and the RTC. These are not shocking results. Theory tells
us that the less-informed buyers will err by overpaying and the
less-patient sellers will let properties go at discounts. (11)
Fortunately the data allow for a more penetrating analysis than
that just described. The results presented in Exhibit 4 are interesting
because they yield information about the characteristics that encouraged
buyers to pay premiums and sellers to accept discounts. Partnerships,
for instance, paid more per dollar of room rate than other buyers.
Likewise, individuals paid more per room, foreign buyers paid more for
proximity to an airport, and banks paid a premium for proximity to
commercial centers. A proper interpretation of the results is that some
buyers were willing to pay significantly more on average than others for
additional units of the particular characteristics they sought.
Where did financial institutions and the RTC go wrong as sellers in
the lodging-property markets? The analysis of property characteristics
suggests that these sellers accepted less than the market was willing to
pay for proximity to commercial centers and airports and for property
located in areas with greater effective buying income. Discounts,
however, were partially offset by premiums received for room rate and
local-area employment strength.
The Right Price
Prices of lodging properties are influenced by the behavior of
parties on both sides of transactions. Price discounts and premiums seem
to result from buyers' and sellers' information-gathering
capabilities, bargaining skills, and patience. As we expected,
individuals consistently paid premiums for properties. These premiums
are positively related to the number of rooms in a given property. Also
as we expected, some foreign buyers paid premiums based on the weight
they gave to the effective buying income of the local area and a
property's proximity to an airport. Finally, financial institutions
and the RTC discounted the properties they sold as compared to the price
they might have commanded in consideration of the location and local
economic conditions. The implications of our findings are as follows:
(1) When appraisers apply the sales-comparison approach to value,
they are justified in adjusting comparable sales to account for buyer
and seller influences.
(2) Brokers are better able to demonstrate the value of their
services, particularly in pricing properties for buyers and finding
high-paying buyers for sellers. Agents or brokers with access to
up-to-date data can recommend certain courses of action based on the
most recent transactions available for comparable deals.
(3) Lenders should be more careful when issuing loans to certain
classes of buyers for particular types of properties. For example, if an
individual (or group of individuals) seeks to purchase a large hotel,
the lender may wish to offer a loan at a slightly lower loan-to-value
ratio than the lender would offer to other borrowers.
This study does not answer two important questions. First, do all
buyers and sellers in a class behave in the same way? Aggregation of
market participants into classes is a limitation of the study. Foreign
buyers, for example, are not a homogeneous group and the results in
Exhibits 3 and 4 tend to confirm that foreign buyers behave differently
from one another (e.g., foreign buyers did not overpay during 1985-86).
Disaggregation of this and other classes of buyers and sellers was not
possible due to sample-size problems.
Second, do the premiums and discounts associated with classes of
buyers and sellers persist through time? Another limitation of the study
is that the results tend to be time specific. Some market participants
are only in the market for a brief period (e.g., RTC) and other market
participants will learn from their previous behavior.
The data are constantly improving and future studies using
more-complete data sets should not be burdened with the same limitations
as this study.
EXHIBIT 1
Classification of buyers and sellers
CLASSIFICATION CODE *
I. Individual or husband and wife Individual
II. Partnerships
a. Limited partnership--public Ltd. Partner
b. Limited partnership--private Ltd. Partner
c. General and other partnerships Gen. Partner
III. Domestic hotel corporations Hotel Co.
IV. Domestic real-estate corporations Real Estate Co.
V. Domestic non-real-estate corporations Corporation
VI. Domestic institutions
a. Life-insurance companies Life Co.
b. Banks Bank
c. Savings and loan companies S&L
d. Resolution Trust Corporation RTC
e. Other institutions (pension, government) Pension
VII. Foreign individuals, partnerships, banks, Foreign
hotel companies, and real-estate companies
* These codes are used in subsequent exhibits to identify specific
buyer and seller classes. Note that some subclassifications were
combined.
EXHIBIT 2
Mean values of selected characteristics from lodging-property
transactions 1985-1992, by buyer classification
BUYER CLASSIFICATIONS
SELECTED PROPERTY
CHARACTERISTICS All Buyers Individual Partnership
All periods (1985-1992)
Number of transactions 1,314 273 406
Cash equivalent price $11,010,126 5,646,353 9,893,522
Percent hotel (versus motel) 44.5% 17.5 48
Age of property 15.8 Yr. 19.3 14.2
Distance to airport 15.7 Mi. 19.8 14.7
Distance to commercial center 4.9 Mi. 5.3 4.9
Early period (1985-1986)
Number of transactions 350 59 142
Cash equivalent price $11,742,123 6,667,962 12,058,662
Percent hotel (versus motel) 50% 27 52
Age of property 14.6 Yr. 16.3 14.6
Distance to airport 12.9 Mi. 16.7 10.9
Distance to commercial center 4.9 Mi. 5.5 4.9
Middle period (1987-1989)
Number of transactions 534 109 156
Cash equivalent price $12,495,720 8,657,491 11,329,130
Percent hotel (versus motel) 44.5% 13 52
Age of property 16.3 Yr. 21.4 13.3
Distance to airport 15.9 Mi. 18.5 16.2
Distaru:e to commercial center 5.6 Mi. 6.4 5.5
Late period (1990-1992)
Number of transactions 430 105 108
Cash equivalent price $8,569,414 1,946,459 4,973,109
Percent hotel (versus motel) 39.9% 16 35.2
Age of property 16 Yr. 18.7 14.8
Distance to airport 17.6 Mi. 22.9 17.7
Distance to commercial center 4.9 Mi. 3.9 4.2
BUYER CLASSIFICATIONS
SELECTED PROPERTY Hotel Real-Estate Other
CHARACTERISTICS Corporation Corporation Corporation
All periods (1985-1992)
Number of transactions 221 211 78
Cash equivalent price 10,014,587 8,603,001 7,976,053
Percent hotel (versus motel) 55.6 46.9 44.8
Age of property 16.7 14.5 14.3
Distance to airport 13.9 14.9 14.6
Distance to commercial center 4.6 4.9 5.7
Early period (1985-1986)
Number of transactions 50 65 16
Cash equivalent price 8,372,469 12,401,650 12,023,753
Percent hotel (versus motel) 58 61 43
Age of property 14.5 12.6 16.4
Distance to airport 16.8 11.2 12.4
Distance to commercial center 4.9 5.4 2.6
Middle period (1987-1989)
Number of transactions 103 70 51
Cash equivalent price 11,277,690 7,365,196 7,414,263
Percent hotel (versus motel) 54 38 47
Age of property 17.1 16 13.8
Distance to airport 13.2 15.6 14.3
Distaru:e to commercial center 5 5.2 5.2
Late period (1990-1992)
Number of transactions 68 76 11
Cash equivalent price 9,308,796 6,494,239 4,693,150
Percent hotel (versus motel) 55.9 42.1 36.3
Age of property 17.9 14.7 13.5
Distance to airport 13 17.7 18.7
Distance to commercial center 3.9 4.3 12.1
BUYER CLASSIFICATIONS
SELECTED PROPERTY
CHARACTERISTICS Institution Foreign
All periods (1985-1992)
Number of transactions 51 74
Cash equivalent price 20,060,977 43,721,381
Percent hotel (versus motel) 60.7 74.3
Age of property 12.8 15.9
Distance to airport 14.9 14.1
Distance to commercial center 3.7 4
Early period (1985-1986)
Number of transactions 14 4
Cash equivalent price 23,353,036 64,987,500
Percent hotel (versus motel) 50 50
Age of property 11.8 24.5
Distance to airport 14.5 8.6
Distance to commercial center 4.3 1
Middle period (1987-1989)
Number of transactions 18 27
Cash equivalent price 31,737,604 49,449,387
Percent hotel (versus motel) 77 74
Age of property 13.8 17.7
Distance to airport 15.9 17.3
Distaru:e to commercial center 4.7 6.9
Late period (1990-1992)
Number of transactions 19 43
Cash equivalent price 6,573,182 38,146,482
Percent hotel (versus motel) 52.6 76.7
Age of property 12.7 14.1
Distance to airport 14.2 12.6
Distance to commercial center 2.2 2.5
EXHIBIT 3
Buyer and seller effects on sale prices of
lodging properties
Buyers Sellers
Premium Discount
(OVERPAID) Discount Premium (UNDERSOLD)
All periods Individual Gen. Partner Hotel Co. RTC
1985-1992 Ltd. Partner Real Estate Co. Corporation Life Co.
(n = 781) Foreign Bank Foreign S&L
Life Co.
Early period Individual Gen. Partner Bank Ltd. Partner
1985-1986 Corporation Hotel Co. Pension Gen. Partner
(n = 206) Life Co. Foreign Foreign S&L
S&L
Middle Individual Gen. Partner Individuals Life Co.
period Ltd. Partner Bank Life Co. Ltd. Partner RTC
1987-1989 Hotel Co. S&L Hotel Co.
(n = 371) Foreign Life Co. Corporation
Late period Individual Ltd. Partner Ltd. Partner Bank
1990-1992 Foreign Gen. Partner Corporation S&L
(n = 204) Hotel Co. Pension Life Co.
Bank RTC
EXHIBIT 4
Price (implicit) premiums and discounts of buyers
and sellers for selected property characteristics
Buyers Sellers
Premium Discount
(OVERPAID) Discount Premium (UNDERSOLD)
Room rate Partnership None Partnership None
Bank
RTC
Number Individual Foreign None Partnership
of rooms Hotel Co.
Real Estate
Co.
Age None Real Estate Co. Corporation None
of property Bank
Chain None None Corporation None
affiliation
Distance Foreign None None Bank
to airport
Distance to Bank None None RTC
commercial
center
Employment Corporation None RTC Bank
of local area
Effective Foreign None Hotel Co. None
buying income
of local area
(1.) For a summary of appraisal methodology; see: Stephen Rushmore,
Hotels and Motels: A Guide to Market Analysis, Investment Analysis, and
Valuations (Chicago: Appraisal Institute, 1992) chapter 5.
(2) "Why Japanese Buyers Pay a Premium for Hotels," Wall
Street Journal, July 9, 1991, p. B1. See also: M. Chase Burritt,
"Japanese Investment in U.S. Hotels and Resorts," Cornell
Hotel and Restaurant Administration Quarterly, Vol. 32, No. 3 (October
1991), pp. 60-66; and Tadayuki Hara and James J. Eyster, "Japanese
Hotel Investment: A Matter of Tradition and Realty," Cornell Hotel
and Restaurant Administration Quarterly, Vol. 31, No. 3 (November 1990),
pp. 98-104.
(3.) G. Stacy Sirmans and C.F. Sirmans, "Property Management
Designation and Apartment Rent," Journal of Real Estate Research,
Winter 1991, pp. 91-98.
(4.) James D. Shilling, C.F. Sirmans, Geoffrey K. Turnbull, and
John D. Benjamin, "Hedonic Prices and Contractual
Contingencies," Journal of Urban Economics, July 1992, pp. 108-118.
(5) Bjorn Hanson, "An Exploratory Study of Operating Income
Relative to Replacement Cost for Alternative Combination of Affiliation
and Management for Mid-Size and Full-Service Hotels," Ph.D. diss.,
New York University, 1991.
(6) John B. Corgel, "Brand Name Affiliation and Real Estate
Prices," working paper, School of Hotel Administration, Cornell
University, 1992.
(7) a formal presentation of the reasons for the differences in
buyer and seller behavior is found in Daniel C. Quan and John M.
Quigley, "Price Formation and the Appraisal Function in Real Estate
Markers," Journal of Real Estate Finance and Economics, June 1991,
pp. 127-146.
(8) Brian A. Maris and Fayez A. Elayan, "A Test for
Tax-Induced Investor Clienteles in Real Estate Investment Trusts,"
Journal of Real Estate Research, Summer 1991, pp. 169-189.
(9) William Beaton and C.F. Sirmans, "Do Syndications Pay More
for Real Estate?," Journal of the Real Estate and Urban Economics
Association, Summer 1986, pp. 206-215.
(10) This measure is the output from a room-rate regression.
Readers who are interested in knowing how this measure is produced
should consult John B. Corgel and Jan A. deRoos, "The ADR
Rule-of-Thumb as Predictor of Lodging Property Values,"
International Journal of Hospitality Management, No. 4, 1994, pp.
353-365.
(11.) is assumed that domestic partnerships, corporations,
real-estate corporations, hotel corporations, and financial institutions
are more informed buyers than individuals and foreign buyers.
John B. Corgel, Ph.D., is Baker Professor of Finance and Real
Estate at the Cornell University School of Hotel Administration (jc81).
Jan A. deRoos, Ph.D., is an associate professor of properties management
at the Cornell University School of Hotel Administration
(jad10@cornell.edu).
Buying High and Selling Low Revisited:
The "Quiet Industry
It is quieter now that the "noise traders" are absent
from the hotel real-estate market!
BY JOHN B. CORGEL AND JAN A. DEROOS
Hotel buyers and sellers in the past were subject to
"noise" due to the lack of evenhanded information about hotel
prices, often combined with some form of duress for buying or selling.
That has changed, as additional sources of information have grown up in
the past ten years and would-be hotel buyers and sellers have gained
sophistication in their transaction motivations. Thus, while the recent
hotel-industry recession seemed at first to be an echo of the downturn
ten years earlier--with the attendant expectations of distressed
property--buyers who expected a fire sale were frustrated. Sellers did
not panic, and institutional buyers stepped in with a lung-term approach
that allowed for reasonable valuations.
In the December 1994 issue of Cornell Hotel and Restaurant
Administration Quarterly, we published an article with the ironic title,
"Buying High and Selling Low in the Lodging-property Market."
The purpose of that article was to test the so-called "law of one
price" in the special case of the hotel real-estate market. This
well-known economics principle states that goods or services that are
either of equal quality or are perfect substitutes (for each other) will
sell at the same price. This principle is founded oil the assumptions
that the market is perfectly competitive, and that the buyers and
sellers who are involved in transactions, being equally informed and
totally free to deal, have no effect on settlement prices. Stated
differently in the context of hotel transactions, a hotel located in
city X at the corner of Y Street and Z Street will command the same
price per room regardless of whether the seller is either a large REIT
or a small limited-liability corporation, and whether the buyer is a
hotel-operating company or an individual investor. All that really
matters with respect to property pricing are the fundamentals of the
property, such as the number of rooms and location, that underlie the
hotel's ability to generate stable net operating income.
In addition to the two assumptions above, the law of one price
comes with a set of restrictive assumptions about the character of the
market. The most important of those restrictions are that information
costs are not prohibitively high so as to provide unfair advantages for
large-scale participants and that the parties in transactions are not
under duress.
Because of the location monopoly of a real-estate parcel, it is
commonly held that only imperfect substitutes exist in real-estate
markets and that information costs are naturally high. In addition,
real-estate market participants frequently find themselves under
pressure to sell too cheaply or to buy too dearly. Given these endemic
conditions in the hotel real-estate market, we fully expected going into
the 1994 study that the law of one price would be rejected in the case
of hotel real-estate markets. We expected to find that some investors
were buying high and some owners were selling low. Indeed, that is
exactly what we found! Beyond testing the law of one price, our specific
interests were to identify the "renegade" traders and estimate
the magnitude of their errors (clearly not for our gain--we're
researchers!).
Hypothesis testing in our study involved the development of a
hedonic (i.e., regression) pricing model incorporating important
fundamental variables. With the fundamentals held constant, we
introduced a series of indicator variables representing various types of
hotel buyers and sellers. The significance and size of the coefficients
on these buyer and seller variables allowed us to determine who overpaid
and who undersold their property, and by how much. A large data base of
hotel transactions completed during the period 1985 through 1992 that we
maintained then at Cornell was used to derive parameter estimates. The
following two points summarize our key findings (remembering that this
is a picture of the industry in the late 1980s and early 1990s).
* Foreign buyers (institutions and individuals), especially
Japanese buyers, substantially overpaid for hotels. These buyers were
new to the U.S. real-estate markets and really did not understand asset
pricing and the market distortions that occurred during the 1980s.
* Federally regulated institutions, especially the RTC,
substantially undersold hotel property because of pressure by Congress
to quickly rid their portfolios of commercial real-estate exposure.
The Hotel Real-estate Market Since '92
The hotel real-estate market has not undergone structural changes
during the past ten years. From 1992 through 1997 hotel property prices
rose steadily as a number of influences played out. These are:
(1) The mid-1990s was a period characterized by rising personal
'incomes and employment and, consequently, increasing RevPARs
throughout the United States. Property prices followed the resulting
upward movement of hotel income.
(2) As the ugliness of the early 1990s' real-estate crisis
became an increasingly distant memory, capital availability became less
of an issue for hotel investors and developers.
(3) The demand for REIT shares experienced an unprecedented rise as
institutional investors sought liquidity. Along with growth, hotel REITs
became increasingly aggressive bidders, creating an arbitrage
opportunity for private market sellers. So, by 1995, both price-per-room
and transaction volume were accelerating.
The hotel property market began cooling down well before the
recession of 2001, as all of the positive influences mentioned above
exhibited signs of fatigue. Investors in existing assets, capital
suppliers, and developers simultaneously and instinctively began slowing
down the pace of the market in 1999. A graph from a recent Merrill Lynch
industry report shows the slowdown in supply growth starting at the end
of 1999 (see Exhibit 1). The situation in that time period is different
from that of the early 1990s, when it took almost two years for supply
to adjust to the downturn in demand. These reactions provide the first
real-world evidence of the market discipline that analysts and academics
had been expecting for some time.
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Disciplined Markets Begin with Sophisticated Participants
The cliche that "experience is the best teacher" became a
reality in real-estate markets during the late 1990s. Everyone learned a
great deal about how these markets operate from the experiences dating
back to the late 1980s. Substantial learning occurred because this
period represents the first time in the history of real-estate trading
that reliable data were available to study market behavior through a
complete cycle. With availability came accessibility to even the
smallest hotel investors. For example, Hotel Brokers International (then
Hotel Brokers Association of America) in the mid-1990s began producing
the Transactions report that provides details of all hotel transactions
closed through its membership. These reports continue to be available to
the public for a modest charge. Other innovations in the hotel industry
that promoted market discipline since the early 1990s include:
(1) Occupancy, ADR, demand, and supply data. Smith Travel Research
continues to increase market coverage and reporting frequency.
(2) Construction activity. Lodging Econometrics, and recently Smith
Travel Research with the F.W. Dodge Company and Property and Portfolio
Research, began to offer detailed reports on hotel-construction pipeline
activity by brand and local market.
(3) Econometric forecasts. PricewaterhouseCoopers and (recently)
the Hospitality Research Group with Torto-Wheaton Research, produce
forecasts of hotel financial performance using sophisticated statistical
models.
(4) Operating-expense data. The Hospitality Research Group of PKF
Consulting extended its efforts to supply timely and accurate
benchmarking data, and Smith Travel Research initiated a service to
benchmark hotel financial performance to the NOI line.
What We Think Now
In light of the ongoing changes to the investment climate, the
transparency that comes from enhanced data availability, and the
continuing evolution of the capital markets' understanding of
lodging, we believe that the lodging real-estate market will continue to
become more efficient--and thus, based less on "noise." The
following are among the most important changes.
* Availability of an ever-broader range of capital sources will
continue to ensure that hospitality real estate does not face a
liquidity crisis and ensure that hotels are fairly priced. As an
example, over the past two years, the principals of many opportunistic
funds thought they could repeat their success of the early 1990s, when
they took advantage of the combined effects of an industry downturn and
a liquidity crisis by buying low and selling high. The majority of these
funds have sat on the sidelines this time around as investors with lower
required rates of return, such as the REITs, both public and private,
and the large institutional investors purchased properties that met
their investment criteria.
* Debt capital will continue to provide market discipline. The
current environment is dominated by first-mortgage providers who will
underwrite a loan with a 60-percent loan-to-value ratio, meaning that
equity providers (i.e., owners) must have a substantial stake in their
investments. Those investors who use the so-called mezzanine debt market
might be able to achieve debt leverage of 80 percent, but only by
pledging the majority of excess cash flow to the mezzanine lender; this
creates incentives for investors to consider only deals that are
economically viable.
* Financial innovations are changing the nature of lodging income.
Globally, the use of leases with hotel operators or the willingness of
operators to guarantee a minimum return on investment (or at least to
subordinate their fees to achieve a minimum return on investment) have
the effect of transforming what was a risky flow of net operating income
to a much less risky flow. This risk reduction allows for lower required
rates of return and higher pricing for hotels.
Just earn it. In summary, while there may be some potential for
extraordinary profits from investing in hotels, we believe that the
ability to simply buy low and sell high as a result of lodging market
cycles or capital market cycles will be greatly diminished in the
future. Opportunities to take advantage of noise traders (that is, those
whose motivations are based on factors other than the economics of the
deal) are gone, and there is no evidence of distressed selling in the
current environment, even though the challenges to the industry have
been great. Disciplined equity and debt capital, smart underwriting, and
broad capital markets will continue to weaken the ability for noise
trading to exist in the market for hotels as investment property.
Paraphrasing John Houseman's words in the old Smith Barney
advertisement, in today's lodging market, you have to make money
the old fashioned way, you have to earn it.
John B. (Jack) Corgel, Ph.D., is a professor of real estate at the
Cornell University School of Hotel Administration (jc81@cornell.edu),
where
Jan A. deRoos, Ph.D., is the HVS International Professor of Finance
and Real Estate (jad10@cornell.edu).
COPYRIGHT 2003 Cornell
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