An automated valuation model for hotels.(Finance)

An analysis of well more than 300 hotel purchase transactions found that a reasonably firm estimate of a hotel's market value can be made using operating ratios. Foremost among those ratios are net operating income per guest room, average daily rate, and occupancy, all having a strong effect on a hotel's sale price. Another strong influence on a hotel's value is the number of guest rooms, perhaps because it is an indicator of the extent of the hotel's physical plant. The regression analysis also examined such other factors as the region in which the hotel is located, whether the property is in a metropolitan area, its age, and the year of the property's sale. Even though hotels in some regions sell for higher prices than those in other regions, for instance, the operating ratios and number of rooms explain sale price better than does location. Figures derived from this automated valuation model (AVM) must be treated as estimates, and the model is not a substitute for such well-established, sophisticated valuation methods as discounted cash flow. Given the model's limitations, the AVM presented here might best be applied to a portfolio of hotels rather than to just one property.

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A stepwise regression analysis to create an automated valuation model (AVM) for hotels found four significant factors that together provide a reasonable estimate of a property's value. The four factors are the twelve-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 hotel's 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 hotel's value, the AVM is not intended to supplant more-sophisticated methods of hotel valuation (such as discounted cash flow). Moreover, the question remains of how to separate the two aspects of a hotel property's value, that of the real estate and that of the business operation.

Keywords: hotel; value; purchase; sale

This article summarizes recently completed research intended to assist hotel managers, owners, potential purchasers, potential lenders, and analysts with an interest in a hotel with the ability to quickly, cheaply, and objectively estimate the hotel's market value. Using data that are readily available to hotel stakeholders, a quick estimate of the market value of that real property can be made using an automated valuation model (AVM). (1) However, despite the fact that real estate appraisal has gradually evolved from a largely manual process to an automated one (particularly for residential real estate), a usable AVM for hotels has not yet been developed. The purpose of this article is to present a recently developed hotel AVM that has been refined to serve to accelerate the advancement of AVMs for hotels and, perhaps more important, to provide to those with an existing or potential interest in a hotel a model to apply to estimate value.

This research results in a hotel AVM using the statistical application of stepwise multiple linear regression analyses. The goals for this AVM are to be practical (i.e., to provide analysts with a usable formula), to supplement traditional appraisal and valuation training (although the use of AVMs in residential real estate has already gone beyond the supplemental stage), and to be relatively simple to apply (i.e., users of the AVM should be able to use basic math and not need to use the stepwise linear regression analysis). The methodology presented in this article essentially employs the sales comparison approach, a commonly used technique to estimate hotel market value. Another commonly used technique for estimating hotel value, the income capitalization approach, capitalizes a hotel's net operating income (NOI) to arrive at a value estimate. While the model presented in this article does not specifically use the income approach, it does make use of a hotel's NOI as a predictor of value (more on that later).

Under the traditional application of the sales comparison approach to estimate market value (i.e., likely sale price), a real estate appraiser analyzes the economics of recent sales of comparable properties and makes subjective adjustments to the information available from those sale transactions to arrive at an estimate of market value of the subject property or, in many cases, to arrive at a range (high and low) of likely value of the subject. The AVM presented here uses a similar but more mechanized, quicker, cheaper, and objective approach, and it makes use of actual hotel sale transactions that have occurred in the marketplace. That means it does not require analysts who themselves are using it to gather comparable hotel sale transaction data.

The methodology employed in this study is not intended to replace such vital knowledge and skills as real estate appraisal training and professional hotel management experience and education, but it is intended to assist analysts with understanding the dynamics of hotel properties. Analysts conducting hotel appraisal assignments need a better understanding of hotel properties, and much has already been written on the topic to build such understanding, including texts by Stephen Rushmore. (2) Nevertheless, as I said, a usable hotel AVM has not yet been developed despite the high level of interest in, attention to, and importance of hotel valuation. Although computerized valuation methodologies are increasingly available for commercial real estate properties, (3) the development of such methodologies for lodging properties has progressed much more slowly. (4)

For AVM formulation, this study uses a proprietary database compiled at the School of Hotel, Restaurant, and Recreation Management at The Pennsylvania State University to develop and present a model for use by those with an interest in a hotel, such as managers, owners, developers, potential purchasers, potential lenders, and analysts. In other words, users of the formulas presented here do not need access to the proprietary database, but they do need to have an interest in a hotel or at least to have access to fundamental operating figures of the hotel. The database used in this study consists of verified sales of hotels in the United States that include operating information for the twelve months preceding the sale. These properties represent all hotel types (economy, midscale, full service, all-suite without food and beverage, and all-suite with food and beverage) and all regions of the United States (New England, Middle Atlantic, Southeast, Upper Midwest, Lower Midwest, Southwest, and West).

The database that was used to construct the AVM comprises a sample of 327 hotel sale transactions for which I was able to obtain complete hotel operating and descriptive information--information that would normally be readily available to a party with an interest in a hotel. For each transaction, the database includes (for the trailing twelve months prior to the sale transaction) average daily rate, occupancy percentage, NOI, capitalization rate (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 properties), all-suite without food and beverage (32 properties), and all-suite with food and beverage (15 properties). All sale transaction data were verified with a party involved in the transaction (typically, the seller or purchaser). The database includes information regarding hotel sale transactions from 1990 through 2002, so it subsumes a full economic cycle. Descriptive statistics are presented in Exhibit 1.

Models using regression analysis to predict hotel sale price have been previously developed based on a variety of the factors considered in this study. (5) The current database size of 327 properties provides excellent statistical power--AVMs that have been developed by other researchers for other types of real property (and have had statistical significance using regression analysis) have typically had sample sizes ranging from 143 to 219 properties. (6) Exhibit 2 presents a summary description of the variables used in this study. It is important to note that variables like cap rate, RRM, and price per room were not considered in the model presented here, because although these data were available for each of the 327 hotel sale transactions, they are in fact direct indicators of, and calculated as a function of, actual sale price, and the intent of this study was to develop a model that could be used to predict hotel market value (likely sale price) using other (not directly related) factors.

Using stepwise multiple linear regression analysis, I found there to be four steps resulting in significant regression coefficients. (7) The [R.sup.2] (regression coefficient) increased from .791 in the first step, to .892 in the second step, to .897 in the third step, and to .900 in the fourth step, indicating improved predictive ability in each step and showing that in the fourth step, the overall model with four variables explains approximately 90 percent of the variance in the hotel sale price per room. This [R.sup.2] statistic exceeds the range of [R.sup.2] results of AVMs previously developed for other types of real property, where [R.sup.2] results have ranged between .772 and .888. (8)

Specifically, after a constant (y-intercept) of -$42,873, the NOI-per-room variable was automatically entered in the first step, and in subsequent steps, average daily rate (ADR), rooms, and occupancy percentage were automatically added. Thus, the model found NOI, on a per-guest-room basis, to be the best predictor of hotel sale price per room, as might be expected. After NOI per room was entered into the model, the hotel's ADR was found to be the next most significant predictor of sale price per room. Thus, the two most significant predictors of a hotel's sale price appear to be its "bottom line" (i.e., NOI) and its "top line" indicator of ADR. After NOI and ADR, the number of guest rooms was the next most significant predictor of sale price, probably because the number of guest rooms serves as a proxy for the extent and level of services and amenities offered by a hotel (e.g., the number of food and beverage outlets, business amenities, and recreational amenities) because all other things being equal, larger hotels usually (though, granted, not always) contain more amenities. Finally, another top-line indicator, the hotel's occupancy level, is a significant predictor of hotel sale price per room, most likely because along with ADR, occupancy is a determinant of hotel room revenues per available room (RevPAR). These results indicate that hotel purchasers may give "credit" to the upside potential of a property--that is, the anticipated ability to improve on a hotel's bottom line is reflected in sale price if occupancy rate or ADR is relatively strong, even when NOI is not. A summary of the results of the stepwise multiple linear regression analysis is presented in Exhibit 3.