HOTVal Toolkit

**************************************************************************** Scroll down to "Additional Files" to access the HOTVal Toolkit. **************************************************************************** HOTVal is a hotel valuation spreadsheet based on a regression model discussed in the Center for Real Estate and Finance at Cornell called Cornell Hotel Indices: Second Quarter 2012: The Trend is Our Friend by Crocker H. Liu, Adam D. Nowak, and Robert M. White, Jr. The model which will be continually updated, provides a rough estimation of the value of a hotel property once the user inputs information on whether the hotel is a large or small hotel, the year and quarter of the valuation, the state where the property is located, the number of rooms, the number of floors, the land area of the hotel property, the actual age of the hotel and whether the hotel is located in a Gateway city. For the first three inputs as well as the last input, if the user clicks on a cell highlighted in yellow, a pull down menu will appear to expedite inputting. The model is provided as a free public service by The Center for Real Estate and Finance at the School of Hotel Administration at Cornell University to academics and practitioners on an as-is, best-effort basis with no warranties or claims regarding its usefulness or implications. The estimates should be considered preliminary and subject to revision. *This October 2016 version updates the previous Hotel Valuation model, published in 2012 , provides valuation estimates up to and including the third quarter of 2016.

A Flow-Through Analysis of the US Lodging Industry During the Great Recession

Purpose – The objective of this exploratory study is to investigate the “flow-through” or relationship between top-line measures of hotel operating performance (occupancy, average daily rate and revenue per available room) and bottom-line measures of profitability (gross operating profit and net operating income), before and during the recent great recession. Design/methodology/approach – This study uses data provided by PKF Hospitality Research for the period from 2007-2009. A total of 714 hotels were analyzed and various top-line and bottom-line profitability changes were computed using both absolute levels and percentages. Multiple regression analysis was used to examine the relationship between top and bottom line measures, and to derive flow-through ratios. Findings – The results show that average daily rate (ADR) and occupancy are significantly and positively related to gross operating profit per available room (GOPPAR) and net operating income per available room (NOIPAR). The evidence indicates that ADR, rather than occupancy, appears to be the stronger predictor and better measure of RevPAR growth and bottom-line profitability. The correlations and explained variances are also higher than those reported in prior research. Flow-through ratios range between 1.83 and 1.91 for NOIPAR, and between 1.55 and 1.65 for GOPPAR, across all chain-scales. Research limitations/implications – Limitations of this study include the limited number of years in the study period, limited number of hotels in a competitive set, and self-selection of hotels by the researchers. Practical implications – While ADR and occupancy work in combination to drive profitability, the authors' study shows that ADR is the stronger predictor of profitability. Hotel managers can use flow-through ratios to make financial forecasts, or use them as inputs in valuation models, to forecast future profitability. Originality/value – This paper extends prior research on the relationship between top-line measures and bottom-line profitability and serves to inform lodging owners, operators and asset managers about flow-through ratios, and how these ratios impact hotel profitability.

Second Quarter 2016: Slowdown for Large Hotels Continues; Small Hotels Have Now Slowed as Well

Our Standardized Unexpected Price (SUP) metric continues to show a decline in the price of large hotels, and now also the price of small hotels has eased—even though hotel transaction volume has increased. Although debt and equity financing for hotels remain relatively inexpensive, we are concerned that the total volatility of hotel returns is greater relative to the return volatility for other commercial real estate. If this trend continues, lenders will eventually start to tighten hotel lending standards. Our early warning indicators all continue to suggest that the downward trend in hotel prices should continue into the next quarter. This is report number 19 of the index series.

Third Quarter 2016: Hotels Exhibit Positive Momentum

Our Standardized Unexpected Price (SUP) metric showed an uptick in the price of large hotels during the third quarter of 2016, with a continued decline in the price of small hotels. Although debt and equity financing for hotels were still relatively inexpensive during this quarter, we remain concerned that the increasing relative riskiness of hotels compared to other commercial real estate suggests that lenders will eventually start to tighten hotel lending standards if this trend continues. Our early warning indicators continue to suggest an eventual downward trend in large hotel prices. This is report number 20 of the index series.

Managing Rentals with Usage-Based Loss

Motivated by new and innovative rental business models, this paper develops a novel discrete-time model of a rental operation with random loss of inventory due to customer use. The inventory level is chosen before the start of a finite rental season, and customers not immediately served are lost. Our analysis framework uses stochastic comparisons of sample paths to derive structural results that hold under good generality for demands, rental durations, and rental unit lifetimes. Considering different \recirculation" rules | i.e., which rental unit to choose to meet each demand | we prove the concavity of the expected profit function and identify the optimal recirculation rule. A numerical study clarifies when considering rental unit loss and recirculation rules matters most for the inventory decision: Accounting for rental unit loss can increase the expected profit by 7% for a single season and becomes even more important as the time horizon lengthens. We also observe that the optimal inventory level in response to increasing loss probability is non-monotonic. Finally, we show that choosing the optimal recirculation rule over another simple policy allows more rental units to be profitably added, and the profit-maximizing service level increases by up to 6 percentage points.