Hotel Automated Valuation Model (AVM) Article

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10.1177/0010880404265322ARTICLE

An Automated

Valuation Model for

Hotelsby JOHN W. ONEILL

A stepwise regression analysis to create an auto-

mated valuationmodel (AVM)for hotels foundfoursig-

nificant factors that together provide a reasonable

estimate of a propertys value. The four factors are thetwelve-month lagging averages of net operating

income, average daily rate, occupancy, and number of

rooms. Number of rooms appears to stand in for the

extent of the hotels facilities. The regression analysis

tested the following factors but found that they were

not significant: region, location in a metropolitan area,

age of property (or date of construction), and date of

sale. Even though the regression indicates that the

operating ratios capture much of a hotels value, the

AVM is not intended to supplant more-sophisticated

methods of hotel valuation (such as discounted cash

flow).Moreover, thequestion remains of howto sepa-

rate the two aspects ofa hotel propertys value, thatofthe real estate and that of the business operation.

Keywords: hotel; value; purchase; sale

This article summarizes recently completedresearch intended to assisthotel managers,own-ers, potential purchasers, potential lenders, and

analysts with an interest in a hotel with the ability toquickly, cheaply, and objectively estimate the hotelsmarket value. Using data that are readily available tohotel stakeholders, a quick estimate of the marketvalue of that real property can be made using an auto-mated valuation model (AVM).

1However, despite the

fact that real estate appraisal has gradually evolvedfrom a largely manual process to an automated one(particularly for residential real estate), a usable AVMfor hotels has not yet been developed. The purpose ofthis article is to present a recently developed hotelAVM that has been refined to serve to accelerate the

advancement of AVMs for hotels and, perhaps moreimportant, to provide to those with an existing orpotentialinterest in a hotel a model to apply to estimate value.

This research results in a hotel AVM using the sta-tistical application of stepwise multiple linear regres-sion analyses. The goals for this AVM are to be practi-

260 Cornell Hotel and Restaurant Administration Quarterly AUGUST 2004

2004 CORNELL UNIVERSITY

DOI: 10.1177/0010880404265322

Volume 45, Issue 3 260-268


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cal (i.e., to provide analysts with a usableformula), to supplement traditionalappraisal and valuation training (althoughthe use of AVMs in residential real estate

hasalready gone beyond thesupplementalstage), and to be relatively simple to apply(i.e., users of the AVM should be able touse basic math and not need to use thestepwise linear regression analysis). Themethodology presented in this articleessentially employs the sales comparisonapproach, a commonly used technique toestimate hotel market value. Anothercommonly used technique for estimatinghotel value, the income capitalization ap-proach, capitalizes a hotels net operatingincome(NOI) to arrive at a valueestimate.While the model presented in this articledoes not specifically use the income ap-proach, it does make use of a hotels NOIas a predictorofvalue(moreon that later).

Under the traditional application of thesales comparison approach to estimatemarket value (i.e., likely sale price), a realestateappraiseranalyzes theeconomicsofrecent sales of comparable properties andmakes subjective adjustments to the infor-

mation available from those sale transac-tions to arrive at an estimate of marketvalue of the subject property or, in manycases, to arriveat a range (high and low)oflikely value of the subject. The AVM pre-sentedhere uses a similar but more mecha-nized, quicker, cheaper, and objectiveapproach, and it makes use of actual hotelsale transactions that have occurred in themarketplace. That means it does not re-quire analysts who themselves are using itto gather comparable hotel sale trans-

action data.The methodology employed in this

study is not intended to replace such vitalknowledge and skills as real estate ap-praisal training and professional hotelmanagement experience and education,but it is intended to assist analysts with

understanding thedynamics ofhotel prop-ert ies . Analysts conduct ing hotelappraisalassignments need a betterunder-standing of hotel properties, andmuch has

already been written on the topic to buildsuch understanding, including texts byStephen Rushmore.

2Nevertheless, as I

said, a usable hotel AVM has not yet beendeveloped despite thehigh level of interestin, attention to, and importance of hotelvaluation. Although computerized valu-ation methodologies are increasinglyavailable for commercial real estateproperties,

3the development of such

methodologies for lodging propertieshas progressed much more slowly.

4

A simple-to-use model based on operating ratios

gives a reasonably solid estimate of a hotels value.

For AVM formulation, this study usesa proprietary database compiled at theSchool of Hotel, Restaurant, and Recre-ation Management at The Pennsylvania

State University to develop and present amodel for use by those with an interest in ahotel, such as managers, owners, develop-ers, potential purchasers, potential lend-ers, and analysts. In other words, users ofthe formulas presented here do not needaccess to the proprietary database, butthey do need to have an interest in a hotelor at least to have access to fundamentaloperating figures of the hotel. The data-base used in this study consists of verifiedsales of hotels in the United States that

include operating information for thetwelve months preceding the sale. Theseproperties represent all hotel types (econ-omy, midscale, full service, all-suitewith-out food and beverage, and all-suite withfood and beverage) and all regions ofthe United States (New England, Mid-

AUGUST 2004 Cornell Hotel and Restaurant Administration Quarterly 261

AUTOMATED VALUATION MODEL FINANCE


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dle Atlantic, Southeast, Upper Midwest,

Lower Midwest, Southwest, and West).The database that was used to constructthe AVM comprises a sample of 327 hotelsale transactions for which I was able toobtain complete hotel operating and de-scriptive informationinformation thatwould normally be readily available to aparty with an interest in a hotel. For eachtransaction, the database includes (for thetrailing twelve months prior to the saletransaction) average daily rate, occu-pancy percentage, NOI,capitalizationrate

(cap rate), and room revenue multiplier(RRM), as well as number of guest rooms,sale price, age, sale date, and hotel type,that is, economy (75 properties), midscale(94 properties), full service (111 proper-ties), all-suite without food and beverage(32 properties), and all-suite with foodand beverage (15 properties). All saletransaction data were verified with aparty involved in the transaction (typi-cally, the seller or purchaser). The data-base includes information regarding

hotel sale transactions from 1990 through2002, so it subsumes a full economic cy-cle. Descriptive statistics are presented inExhibit 1.

Models using regression analysis topredict hotel sale price have been previ-ously developed based on a variety of the

factors considered in this study.5

The cur-

rent database size of 327 propertiesprovides excellent statistical powerAVMs that have been developed by otherresearchers forother types of real property(and have had statistical significanceusing regression analysis) have typicallyhad sample sizes ranging from 143 to 219properties.

6Exhibit 2 presents a summary

description of the variables used in thisstudy. It is important to note that variableslike cap rate, RRM, and price per roomwere not considered in the model pre-

sented here, because although these datawere available for each of the 327 hotelsale transactions, they are in fact directindicators of, and calculated as a functionof, actual sale price, and the intent of thisstudy was todevelop a model thatcould beused to predict hotel market value (likelysale price) using other (not directly re-lated) factors.

Using stepwise multiple linear regres-sion analysis, I found there to be four stepsresulting in significant regression coeffi-

cients.7 The R2 (regression coefficient)increased from .791 in the first step, to.892 in the second step, to .897 in the thirdstep, and to .900 in the fourth step, indicat-ing improved predictive ability in eachstep and showing that in the fourth step,the overall model with four variables


FINANCE AUTOMATED VALUATION MODEL

Exhibit 1:

Descriptive Statistics of Database Use to Construct the Automated Valuation Model

Average Net Room

Daily Capitalization Operating Revenue Price/ AgeStatistic Rooms Occupancy Rate Rate Income Multiplier Room (years)

Median 173 70.0 $78.25 10.6 $980,400 3.08 $59,338 11.0Mean 219 68.7 $83.15 10.7 $1,721,277 3.21 $74,020 16.1Standard

deviation 163 12.2 $37.28 2.2 $2,213,162 1.14 $58,330 15.2Minimum 35 18.5 $31.50 1.1 $67,840 0.70 $6,931 1.0Maximum 1,348 96.3 $250.50 20.4 $18,676,000 9.25 $479,167 98.0


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explains approximately 90 percent of thevariance in the hotel sale price per room.This R

2statistic exceeds the range of R

2

results of AVMs previously developed for

other types of real property, where R2

results haverangedbetween .772 and.888.8

Specifically, after a constant (y-intercept)of $42,873, the NOI-per-room variablewas automatically entered in the first step,and in subsequent steps,average daily rate(ADR), rooms, and occupancypercentagewere automatically added. Thus, themodel found NOI, on a per-guest-roombasis, to be the best predictor of hotel saleprice per room, as might be expected.After NOI per room was entered into the

model, the hotels ADR was found to bethe next most significant predictor of saleprice perroom. Thus, thetwo most signifi-cant predictors of a hotels sale priceappear to be its bottom line (i.e., NOI)and its top line indicator of ADR. AfterNOI and ADR, the number of guest rooms

was the next most significant predictor ofsale price, probably because the numberof guest rooms serves as a proxy for theextent and level of services and amenities

offered bya hotel (e.g., the numberof foodand beverage outlets, business amenities,and recreational amenities) because allother thingsbeing equal, largerhotelsusu-ally (though, granted, not always) containmore amenities. Finally, another top-lineindicator, the hotels occupancy level, is asignificant predictorof hotel sale price perroom, most likely because along withADR, occupancy is a determinant of hotelroom revenues per available room(RevPAR). These results indicate that

hotel purchasers may give credit to theupside potential of a propertythat is, theanticipated ability to improve on a hotelsbottom line is reflected in sale price ifoccupancy rate or ADR is relativelystrong, even when NOI is not. A summaryof the results of the stepwise multiple lin-



Exhibit 2:

Description of Variables Considered in Developing the Automated ValuationModel

Variable Description

Occupancy Occupancy percentage rate for trailing twelve months prior to saleADR Average daily rate for trailing twelve months prior to saleRooms Number of guest roomsNOI/room Net operating income divided by number of guest roomsRegcode Code indicating region of the United States in which hotel is

locateda

Metro Code indicating whether hotel is located in a major` metropolitan area

b

Type Code indicating type of hotelc

Open year Calendar year hotel originally openedSale year Calendar year hotel sale transaction occurred

a. Regcode consistsof a seriesof six dummy variables: 1 = New England; 2 = Mid Atlantic; 3 = Southeast;4 = Upper Midwest; 5 = Lower Midwest/Southwest; and 6 = West.b.Metro codes are asfollows:1 = hotelis locatedina metropolitan area with more thanonemillion permanentresidents; 0 = hotel is not located in a metropolitan area with more than one million permanent residents.c.Typeconsists ofa seriesof fivedummy variables: 1 = economy;2 = moderateservice (midscale);3 = fullservice; 4 = all-suite without food and beverage operations; and 5 = all-suite with food and beverageoperations.


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ear regression analysis is presented inExhibit 3.

Thus, stepwise multiple linear regres-sion analysis found that region code,metro, type, open year, and sale year werenot overall significant predictors of saleprice per guest room after the other fourvariables had been automatically in-cluded. While differences were found inthe summary statistics for sale price byregion (e.g., properties in New Englandtended to sell for higher prices per roomthan properties in the Southeast), appar-ently when hotel NOI, occupancy, ADR,and numberofguest rooms are included in

the model, region is not a significant pre-dictor of sale price, because variations ineconomics by region are captured in themodel by the variations in those monetarymeasures. Similar results were foundregarding the variable for large metropoli-tan areas (more than one million perma-nent residents). While properties in largermetro areas may sell for higher prices, themodel captures these differences throughvariations in its monetary measures. Simi-larly, while different types of hotels aver-

age different prices on a per-guest-roombasis, themodelcaptures thesedifferenceswith the four variables included in thestepwise regression.

The open-year variable was not signifi-cant, even when this variable was con-verted to age by subtracting the opening

year from the sale year. In other words,there appear to be many relatively oldhotels that do not significantly lose valueover time. Furthermore, the extent towhich age affects a hotels economicsappears to be captured by the modelsother four variables.

The sale-year variablealso was not sig-nificant. The database that was used toconstruct the AVM includes data regard-ing hotel sale transactions dating as farback as 1990, and clearly, there was atrend of increasing hotel sale prices be-tween 1990 and 2002, a period that in-cludes both the trough of an economic

recession and the peak of an economicexpansion. However, those increasingsalepricescorrelated with thechanges in occu-pancy, ADR, and NOI, statistics that cap-ture the increase in sale prices stemmingfrom improved operating performance.Therefore, sale year was ultimately ex-cluded as a variable in the model. This re-sult is interesting andpractical foranalystsbecause it indicates that the model of bestfit (with the four variables included) mayactually be applied to the coefficients pre-

sented here,relatively independent of timeperiod.

How to Apply the ModelIn short, a hotels value per guest room

may be estimated using the followingAVM formula:



Exhibit 3:

Summary of Overall Stepwise Multiple Linear Regression Analysis

Step Variable Added Beta Coefficient t Significance

1 NOI/room 5.615 16.492 p< .0012 ADR 615.039 12.310 p< .0013 Rooms 33.693 3.751 p< .0014 Occ 234.891 2.343 p< .05

Note: See Exhibit 2 for descriptions of variables.


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$42,873 (the constant from the regression)

+ NOI per room x 5.615+ ADR x 615.039

+ rooms x 33.693

+ occ9 x 234.891

= estimated value per room

In summary, in the overall model, thepredictor variables (NOI per room, occu-

pancy, ADR, and numberof rooms)corre-lated with theresponse variable(salepriceper room) with a regression coefficient, or

R2, of .900 (i.e., 90 percent). Considering

all of the hotel sale transactions, the aver-age actual sale price per room varied fromthe predicted sale price per room by amean of 9.8 percent (the median was 7.5



A Sample Calculation

An actual fifty-seven-room Hampton Inn in Ohio that operated with an annual NOI of

approximately $450,000, ADR of $76.81, and annual occupancy rate of 72.8 percent wouldbe valued as follows:

Coefficient $42,873+ $450,000/57 x 5.615 = +$44,329+ $76.81 x 615.039 = +$47,241+ 57 x 33.693 = +$1,921+ 72.8% x 234.891 = +$17,100

= $67,718/room

Thus, in the previous example, the subject fifty-seven-room hotel would have an esti-mated value of approximately $3,860,000 ($67,718 x 57). In fact, the hotel actually sold in2003 for $64,912 per room, or within 5 percent of the likely sale price predicted by the

AVM presented here. Thus, there are reasons why the actual value of this Hampton Innmay not be exactly $67,718 per room. An analyst could construct confidence intervals, forexample, the use of 95 percent confidence intervals would allow analysts to be essentially95 percent sure that the hotel value is actually within a calculated range.

To determine confidence intervals for the results of a multiple regression analysis, ana-lysts must calculate both the low and the high boundaries of the likely value range using

unique coefficients developed through matrix algebra, which in this case would involveconstructing a four-by-four matrix with sixteen terms, which is unwieldy.

a

Most analysts would prefer using statistical software such as SPSS to determine confi-dence intervals. In the SPSS Data Editor (SPSS 11.0 for Windows), one would select Ana-lyze, Regression, Linear, and then select Case Labels Variable(if a case labels variable isnot selected, the confidence interval values will not be added to the spreadsheet). Next,one would click on Statisticsand select which statistics one would want to have appear inthe output (e.g., covariance matrix, descriptives, collinearity diagnostics) and then click on

Save. Next, one would select Predicted Values Unstandardizedand then Predicted Inter-vals Meanand Individual. The default confidence interval is 95 percent. The unstandard-ized predicted values represent the regression line. The mean interval represents the con-fidence bands around the regression line. The individual interval represents theconfidence interval for any new predictions based on the regression solution. These selec-tions will result in five new variables being added to the spreadsheet: pre_1 is the pre-dicted value, lmci_1 is the lower bound of the 95 percent confidence interval for the mean(i.e., regression line), umci_1 is the upper bound, lici_1 is the lower bound of the 95 per-

cent prediction confidence interval (i.e., for new values), and uici_1 is the upper bound.J.W.O.

a. See, for example: Neter, Kutner, Nachtsheim, and Wasserman, Applied Linear Statistical Models, 4thed. (Chicago: Irwin, 1996).


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percent). Previous research has found thatactual real estateappraisals of commercial

properties differ from sale pricesby a vari-ance ofonly about 5 percent.

10Thus, while

the model presented here results in a ho-tel value estimate that comes close to thelevel of accuracy achieved by actual hotelappraisals, it is not quite so accurate onaverage.

Since the model I propose here natu-rally results in some differences betweenactual and predicted sale prices, it is help-ful to analysts to know what factors aremost likely to cause the model to be most

accurate.Therefore, I conducted an analy-sisof theresiduals(the difference betweenpredicted and actual hotel sale price foreach sale transaction in the database usedto construct the AVM). This analysisrevealed that the number of hotel guestrooms, occupancy, ADR, and NOI appearto predict residuals. This analysis, whichapplied multiple regression analysis withthe residual serving as the response vari-able, revealed that residuals are smallest(i.e., predictions are most accurate) forhotels with relatively more guest rooms,higher occupancy, higher ADR, andhigher NOI. Exhibit 4 summarizes keystatistics from the analysis of residuals. Inshort, 50 percent of the actual hotel saleprices used to construct the AVM were

within $7,710 per room of the value esti-mated by the AVM (i.e., the mediandifference between the actual and pre-dicted sale price was $7,710 per room), 75

percent were within $11,978, 90 percentwere within $16,798, and 95 percent werewithin $28,526 per room.

The AVM formula presented here gen-erally should be used in addition to thethree traditional real estate valuationapproachesof income capitalization, salescomparison, and cost. Despite thefact thatmore-sophisticated real estate valuationtechniques exist than AVMs, executives,investors, and real estate appraisers fre-quently use simpler methods,

11like

AVMs. Licensed real estate appraiserswho useAVMs should, however, be awareof Uniform Standards of ProfessionalAppraisal Practice Advisory Opinion 18that states,

The output of an AVM is not, by itself,

an appraisal. An AVMs output may

become a basis for appraisal, appraisal

review, or consulting opinions and con-

clusions if theappraiser believes theout-

put tobe credibleand reliablefor use ina

specific assignment.12

In short, because AVMs do not in them-selves constitute appraisals, they techni-cally should be considered limited or sup-plemental analyses.

A Reliable EstimateThe AVM formula presented in this

article should be useful to hotel analystswho desire a low-cost and objective esti-mate of hotel value. Also, this formulashould be beneficial for hotel stakeholderswhowish to acquire a quick valueestimateprior to obtaining a complete appraisal,which would normally consume severalweeks of time, cost several thousand dol-lars, and may not be entirely objective.



Exhibit 4:

Analysis of Residuals

Statistic Amount

N 327Minimum $5Maximum $58,815Median (50th percentile) $7,71075th percentile $11,97890th percentile $16,79895th percentile $28,526


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The research summarized in this articlefound that the AVM formula presentedhere hasa high levelof validity, thoughtheaverage level of accuracy is not quite as

good as an actualhotel appraisal. Analystsshould use such models as a reality checkfor more-sophisticated valuation analysesand perhaps use them in meetings and inthe field when computer technology is notreadily available. In addition, through ananalysis of residuals, this research foundthat theAVM presentedhere possesses thegreatest degree of accuracy when used toestimate the value of hotels that have re-latively more guest rooms, higher occu-pancy, higher ADR, and higher NOI.

One of the limitations of this researchis that even though the analysis subsumesboth the realty and the nonrealty aspectsof hotels, it does not consider each ofthose two components separately, whichis often necessary for actual hotel saletransactions. This limitation haslong beena challengewith thevaluation ofoperatingbusinesses such as hotels. Previous re-searchattemptedto separate thereal estateand operating components of a given

hotel through evaluating the occupancyand ADR premiums that a single hotelachieves in comparison to its competitorsand then concluded that such premiumsareattributable to thevalueof thepropertyname, reputation, and affiliation (i.e.,nonrealty).

13This conclusion is not sup-

ported in practice, however, because suchoccupancy and ADR premiums are in factoften attributable to a hotels physical lo-cation in its market (in other words,realty). Future research should empiri-

cally consider the effects that such ele-ments as hotel brand names have on over-all hotel market value (i.e., sale price) on anational basis. Evaluating the nonrealty-valuecontribution of hotel brand names insuch a manner may hold the greatest levelof promise for providing empirical and

valid support for separating the realty andnonrealty components of hotels.

In conclusion, the research presentedhere found statistical support for theuseof

a hotels trailing twelve-month occupancypercentage, ADR, NOI, and number ofguest rooms as predictors for estimating ahotels current market value. Such AVMswill probably always be useful for quickanalysis andforassessing theappropriate-ness ofmore-detailed analysis.Thosewhoare in an industry or who are analyzing anindustry should not ignore simple meth-odologies or rules of thumb that are be-coming commonplace in the industry, likeAVMsare in commercial real estateanaly-sis.

14However, in the analysis of commer-

cial real estate, the identification of themacroeconomic and local economic fac-tors that affect it should always be consid-ered as well.

15AVMs are probably most

beneficial for analyzing portfolios ofhotels rather than individual properties.Previous research has found that mostidiosyncratic risk in individual propertiesis diversified away at the portfolio level.

16

In short, commercial real estate valuation

remains art as well as science. Theaccuracyof analysiswill thereforealways,to some extent, be based on the artistry ofthe analyst.

Endnotes

1. Joseph K. Eckert, Patrick M. OConnor, and

Charlotte Chamberlain, Computer-assisted

Real Estate Appraisal: A California Savings

and Loan Case Study, The Appraisal Journal,

vol. 61 (October 1993), pp. 52432; John H.

Detweiler and Ronald E. Radigan, Computer-assisted Real Estate Appraisal: A Tool for the

Practicing Appraiser, The Appraisal Journal,

vol. 64 (January 1996),pp. 91101; andJohnH.

Detweiler and Ronald E. Radigan, Computer-

assisted Real Estate Appraisal: A Tool for the

Practicing Appraiser, The Appraisal Journal,

vol. 67 (July 1999), pp. 28086.




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2. Stephen Rushmore, Hotels & Motels: Valua-

tions and Market Studies (Chicago: Ap-

praisal Institute, 2001); Stephen Rushmore,

Hotels and Motels: A Guide to Market Analy-

sis, Investment Analysis, and Valuations(Chi-cago: Appraisal Institute, 1992); and Stephen

Rushmore, The Computerized Income Ap-

proach to HotelMotel Market Studiesand Val-

uations (Chicago: Appraisal Institute, 1990).

3. S. Kleege, WillComputersTake Over the Ap-

praisal Game? American Banker, June 13,

1997, p. 10.

4. John B. Corgel and Jan A. deRoos, Pure Price

Changes of Lodging Properties, Cornell Hotel

and Restaurant Administration Quarterly, vol.

33, no. 2 (April 1992), pp. 7077.

5. John W. ONeill and Anne R. Lloyd-Jones,

Hotel Values in the Aftermath of September

11, 2001, Cornell Hotel and Restaurant Ad-ministration Quarterly, vol. 42, no. 6 (Decem-

ber 2001), pp. 1021; and John W. ONeill and

Anne R. Lloyd-Jones, September 11th: Hotel

Values and Strategic Implications, Cornell

Hotel and Restaurant Administration Quar-

terly, vol. 43,no. 5 (October2002),pp. 5364.

6. Eckert , OConnor , and Chamberla in ,

Computer-assisted Real Estate Appraisal;

Detweiler and Radigan (1996), Computer-

assisted Real Estate Appraisal; and Detweiler

and Radigan (1999), Computer-assisted Real

Estate Appraisal.

7. R2= .900, F= 555.735.

8. Detweiler and Radigan (1996), Computer-

assisted Real Estate Appraisal; Detweiler and

Radigan (1999), Computer-assisted Real

Estate Appraisal; and Bennie D. Waller,

The Impact of AVMs on the Appraisal Indus-

try, The Appraisal Journal, vol. 67 (July

1999), pp. 28792.

9. Technically, the figure that should be used is

occupancy rate multiplied by 100. For a hotel

operatingwith an annualoccupancy levelof 70

percent, 70 should be used (not .70).

10. Brian R. Webb, On the Reliability of Com-mercial Appraisals: An Analysis of Properties

Sold from the Russell-NCREIF Index, Real

Estate Finance, vol. 11, no. 1 (Spring 1994),

pp. 6265.

11. Lawrence B. Smith, Rental Apartment Valua-

tion: The Applicability of Rules of Thumb,

The Appraisal Journal, vol. 53 (October 1985),

pp. 54152.

12. Appraisal Standards Board, Uniform Stan-

dards of Professional Appraisal Practice

(Washington, DC: The Appraisal Foundation,

2000), p. 144.

13. William Kinnard, Elaine M. Worzala, and Dan

Swango, Intangible Assets in an OperatingFirst-class Downtown Hotel: A Comparison of

Sources of Information in a Profit Center Ap-

proach to Valuation, The Appraisal Journal,

vol. 69 (January 2001), pp. 6883.

14. Shannon P. Pratt, RobertF.Reilly, andRobertP.

Schweis, Valuing a Business: The Analysis and

Appraisal of Closely Held Companies (New

York: McGraw-Hill, 2000), p. 274.

15. Jeffrey D. Fisher and Brian R. Webb, Central

Issues in the Analysis of Commercial Real Es-

tate, Journal of the American Real Estate and

Urban Economics Association, vol. 20 (Sum-

mer 1992), pp. 21128.

16. Brian R. Webb, Mike Miles, and David

Guilkey, Transactions-driven Commercial

Real Estate Returns: The Panacea to Asset Al-

location Models? Journal of the American

Real Estate and Urban Economics Associa-

tion, vol. 20 (Summer 1992), pp. 32558.

John W. ONeill, MAI, CHE, Ph.D., is anassistant

professor at the School of Hotel, Restaurant and

Recreation Management at The Pennsylvania

State University ([email protected]).



Hotel Automated Valuation Model (AVM) Article

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