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Hotel Investors often seem to have peculiar preconceptions about the main objective of hotel investment analysis.  Many believe the financing decision is the major part of the analysis. Others treat the forecasting of cash flows and hotel values as the primary focus.  Still others regard tax planning as the chief element in the process.  While all of these activities have major roles, none get at the actual crux of the hotel investment process. The real basis of investment analysis is the identification, measurement, and assessment of risk and the ways the investor can deal with risk. The concept of risk is therefore fundamental to any form of investment analysis in the hotel industry.

Yet, the basic tool to evaluate risk and return, the “hotel feasibility study” hasn’t changed in almost 50 years. Despite recommendations by scholars, industry professionals, and lenders for making hotel feasibility studies more effective, the essential form of the studies has remained unchanged since the early 1970’s.

Pellat1 noted in 1972 that “the contemporary models of real estate investment analysis all are grossly inadequate and are incapable of generating realistic estimates of the overall rate of return on a real estate investment and the risk of that investment.” He could easily have said the same thing today! The use of probabilistic simulation models using Monte Carlo techniques, (which we have used successfully for almost thirty years) can overcome many of the shortfalls of the outdated hotel feasibility report.

Hotel Investment Risk

Risk is a concept that investors, developers, and lenders associate strongly with the hotel industry, especially in emerging tourism destinations such as Indonesia. Many people consider investing in hotels a high-risk use of time and capital. Investors not only acquire an interest in a volatile form of real estate, but also participate in the highly specialised task of operating a service-oriented business. However, both activities affect the risks and returns associated with hotels.

Although both risk and return are acknowledged, our thinking and training emphasize the rate of return. But, if the variables affecting the risk-return relationship are more explicitly identified, quantified, and evaluated, more accurate projections can be made of the risk of a hotel investment. The stakeholders in hotels therefore need to spend as much time undertaking quantitative risk analysis as they do calculating rate of return.

Risk is defined as the probability that the expected cash flow or return will not be received. Risk exists in hotels because investors can not make perfect forecasts.  If they could, they would never make an investment that would yield less than the required rate of return.

Hotel investment analysis normally provides point estimates – that is, single-parameter estimates such as IRR, debt-coverage ratio, loan-to-value ratio, and market value. Point estimates are  the most probable numbers, not the only possible numbers.  Recognizing and dealing with other possibilities is a major function of risk analysis. The debt-coverage  and loan-to-value ratios provide some information about the risk profile of a hotel.   They do not, however, provide information about the probable deviations from the most likely values used in the analysis. A complete risk analysis provides information on the magnitude of possible deviations in cash flow that can occur under varying market and economic conditions and the probability associated with each of these projections.

Exhibit 1 illustrates the possible values for a debt-coverage ratio and displays the uncertainty inherent in the results. It clearly illustrates the small probability that the debt-coverage ratio will be less than 1. Although the mean debt-coverage ratio is 1.3 there is a 6.7 per cent probability that the debt-coverage ratio will be less than 1.

None of the  risk ratios currently being used by lenders and investors to assess hotel investments provide this type of  information. Most market and financial models used in hotel investment analysis are deterministic, a specific value for each input variable being used. Risk analysis models are said to be  probabilistic when  the values of many of the input variables are uncertain and  are defined as ranges with associated probability distributions, rather than as single-point estimates.

“What if?” or sensitivity analysis is the most commonly used “risk analysis” technique in hotel investment analysis. It reveals the relative sensitivity of returns to different variables by changing one or more of the values for the uncertain variables.  For example, how would a lower occupancy rate in the first two years affect the projected IRR? Assumptions that are typically examined in a sensitivity analysis include growth in RevPAR, stabilized occupancy and ADR, labor expenses, capital expenditures and the terminal capitalization rate. At best, sensitivity analysis is a crude analysis of risk because it fails to take into account the probabilities associated with all possibilities.

For this reason, Hotel Investment Strategies has pioneered the use of Monte Carlo simulation techniques for hotel investment analysis.

Monte Carlo Simulation

Monte Carlo simulation was developed in the early 1960’s; one of its first proponents was David Hertz, whose classic articlein the Harvard Business Review did much to bring the technique to a wider audience.

The Monte Carlo technique attempts to imitate the various ways in which all the variables influencing the investor’s rate of return could combine as the complex future unfolds.

Probability distributions, such as those illustrated in Exhibits 2 and 3, are used instead of point estimates for the uncertain variables. Ranges of probability can be determined with at least four methods including historical observations, controlled experiments and observation, theoretical distributions and subjective judgement.

For example, rooms payroll per occupied room is illustrated in Exhibit 2 by a lognormal distribution with a mean of $24 and a standard deviation of $2. An analysis of historical data has provided the basis for the type of distribution used for rooms payroll. Exhibit 3 illustrates an example combining four different expert opinions on the likely terminal cap rate where one expert (EVP Acquisitions) might be given twice the weight of the others due to his greater experience.

Probability distributions are derived for all of the uncertain variables including interest rates, market segment growth rates, department expenses, refurbishment costs, and future competition. The possible combinations of the values for each factor are then simulated to determine the range of possible outcomes and the probability associated with each.

In this way, Monte Carlo techniques estimate the probability as well as the magnitude of potential risks, thus providing a complete risk analysis. The technique overcomes the limitations of both sensitivity analysis and best case/worse case analysis. For this reason, Monte Carlo simulation should be the preferred risk analysis technique for hotel investment decisions, since it provides the best possible information on the risk-return profile of a lodging investment.

Exhibit 4 shows the distribution of possible IRR’s for a hotel investment. The height of each bar represents the probability that the outcome will occur in the range of the bar. Exhibit 5 provides the statistical output for the analysis. As illustrated, the  probable unleveraged IRR’s range from a minimum of 10.5% to a maximum of 20.8% with a mean of 15.5%. The percentile value indicates the percentage of the generated results that are less than or equal to the associated value.

For example, the 35th percentile value for the IRR, is 14.7%, means that there is a 35% probability that the investor will receive a return of less than or equal to 14.7%. Conversely, there is a 65%  probability the investor will receive  a return of 14.7% or better.  The analysis clearly illustrates the risk-return profile of the hotel and enables the investor to evaluate the expected returns and the risks of the investment.

Exhibit 5: Unleveraged IRR

The same type of analysis can be undertaken for any output the stakeholders are interested in. The analysis generates a range of possible returns rather than a single value and also computes the probability of receiving different rates of return, depending on how the future unfolds.

Ranking analysis identifies and ranks the most important sources of risk as shown in the tornado graph in Exhibit 7, with the longer bars representing the most significant input variables. The regression coefficients provide a measure of how much the output (in this case unleveraged IRR) would change if the input were changed by one standard deviation.

This type of analysis enables the investor to focus on the more significant risks, saving the time and money that might be expended on an analysis of less important factors.  The objective is to identify significant risks that must be managed and to screen those minor risks that can be accepted and so excluded from further consideration.

Scenario analysis identifies combinations of input variables that are the most important in causing a given output to achieve a user-specified target. For example, which variables contribute to a debt coverage ratio greater than 1.4 ? Or, which variables contribute to profits below $1 million?

In summary, Monte Carlo simulations provide considerable information about the hotel investment being analysed, including:

1) The likely range of outcomes the investor can realistically expect.

2) The probability of exceeding a target result.

3) The relative magnitude of various sources of uncertainty.

4) The sensitivity of the model’s results to uncertainty in each input, thus highlighting the major risk factors.

The use of risk simulation models by the stakeholders in hotels can have a substantial effect on their investment decisions.  The model outlined can help decision-makers to accommodate and weigh information about the risk characteristics of hotel investments and perhaps avoid unwise investments or make investments where the risk-return trade-offs are known and acceptable.  If you would like more information on Monte Carlo simulation and its application in hotel investment analysis, please contact us and request a complimentary copy of “Quantitative Risk Analysis for Hotel Investors”.

1) Pellat, P.G.K.:  “The Analysis of Real Estate Investments Under Uncertainty”, Journal of Finance, Vol 27, No.2, p.459.

2) David B. Hertz. “Risk Analysis in Capital Investment,” Harvard Business Review, Vol. 42, No.1 (1964): 95-106