CORNELL CENTER FOR HOSPITALITY RESEARCH CORNELL CENTER FOR REAL ESTATE AND FINANCE Cornell Hotel Indices: Fourth Quarter 2021 Converging Towards Normalcy by Crocker H. Liu, Adam D. Nowak, and Robert M. White, Jr. Executive Summary Hotel prices continue to converge toward pre-pandemic levels. Gains posted were smaller relative to the previous quarter but higher year over year. Hotels in both gateway and non-gateway cities continue to exhibit positive performance, with hotels in non-gateway cities posting greater gains. Transaction volume continued strong for large and small hotels quarter over quarter and year over year, although the increase in volume was smaller in this instance than was the increase in the prior period. Our moving average trendlines indicate that large hotels are priced to buy, while small hotels are priced at market (priced fairly). Large hotels declined from their statistical high set last quarter, based on our stan- dardized unexpected price (SUP) performance metric. In terms of financing hotels, mortgage financing volume continued to rise, as the cost of financing hotels slightly diminished this quarter. Among factors that have contributed to this situation are the relative risk premium, which has remained stationary this quarter, and a continued decline in the hotel delinquency rate. Hotel deals continue to look profitable, based on our economic value added (EVA) and shareholder value added (SVA) metrics. Looking toward the next quarter, our leading indicators of hotel price performance indicate that we should expect slower or declining price momentum for larger hotels but positive price gains for smaller hotels. CREF Hotel Indices • CHR Report • January 2022 • www.cref.cornell.edu • Vol. 22 No. 1 1 ABOUT THE AUTHORS Crocker H. Liu, Ph.D., is a professor of real estate at the School of Hotel Administration at Cornell, where he holds the Robert A. Beck Professor of Hospitality Financial Management. He previously taught at New York University’s Stern School of Business (1988-2006) and at Arizona State University’s W.P. Carey School of Business (2006-2009) where he held the McCord Chair. His research interests are focused on issues in real estate finance, particularly topics related to agency, corporate governance, organizational forms, mar- ket efficiency and valuation. Liu’s research has been published in the Review of Financial Studies, Journal of Financial Economics, Journal of Business, Journal of Financial and Quantitative Analysis, Journal of Law and Economics, Journal of Financial Markets, Journal of Corporate Finance, Review of Finance, Real Estate Economics, Journal of Urban Economics, Regional Science and Urban Economics, Journal of Real Estate Research, and Journal of Real Estate Finance and Economics. He is the former co-editor of Real Estate Economics, the leading real estate academic journal. He continues to be on the editorial board of Real Estate Economics. He is also an associate editor of Financial Review. He previously served on the editorial boards of Journal of Real Estate Finance and Economics, Journal of Property Research, and Journal of Real Estate Finance. He is a past president of AREUEA (2019), the leading real estate academic organization. Professor Liu earned his BBA in real estate and finance from the University of Hawaii, an M.S. in real estate from Wisconsin under Dr. James A. Graaskamp, and a Ph.D. in finance and real estate from the University of Texas under Dr. Vijay S. Bawa. Adam D. Nowak, Ph.D., is an associate professor of economics at West Virginia University. He earned degrees in mathematics and economics at Indiana University–Bloomington in 2006 and a degree in near-east languag- es and cultures that same year. He received a Ph.D. from Arizona State University. He was the research analyst in charge of constructing residential and commercial real estate indices for the Center for Real Estate Theory and Practice at Arizona State University. Nowak’s research has been published in Review of Financial Stud- ies, American Economic Review: Insights, Economic Inquiry, Journal of Urban Economics, Regional Science and Urban Economics, Journal of Applied Econometrics, Real Estate Economics, and Journal of Real Estate Research. Robert M. White, Jr., CRE, is the founder and president of Real Capital Analytics Inc., an international research firm that publishes the Capital Trends Monthly. Real Capital Analytics provides real time data con- cerning the capital markets for commercial real estate and the values of commercial properties. Mr. White is a noted authority on the real estate capital markets with credits in the Wall Street Journal, Barron’s, The Econ- omist, Forbes, New York Times, and Financial Times, among others. He is the 2014 recipient of the James D. Landauer/John R. White Award given by The Counselors of Real Estate. In addition, he was named one of National Real Estate Investor Magazine’s “Ten to Watch” in 2005, Institutional Investor’s “20 Rising Stars of Real Estate” in 2006, and Real Estate Forum’s “10 CEOs to Watch” in 2007. Previously, Mr. White spent 14 years in the real estate investment banking and brokerage industry and has orchestrated billions of commercial sales, acquisitions and recapitalizations. He was formerly a managing director and principal of Granite Partners LLC and spent nine years with Eastdil Realty in New York and London. Mr. White is a Counselor of Real Estate, a Fellow of the Royal Institution of Chartered Surveyors, and a Fellow of the Homer Hoyt Institute. He serves on the board of directors for the Pension Real Estate Association and the advisory board for the Real Estate Research Institution. He is also a member of numerous industry organizations and a supporter of academic studies. Mr. White is a graduate of the McIntire School of Commerce at the University of Virginia, and his research has been published in Journal of Real Estate Finance and Economics. Acknowledgments We wish to thank Glenn Withiam for copy editing this paper. Disclaimer The Cornell hotel indices produced by The Center for Real Estate and Finance at the School of Hotel Administration at Cornell Univer- sity are provided as a free service to academics and practitioners on an as-is, best-effort basis with no warranties or claims regarding its usefulness or implications. The indices are not audited, and they are not necessarily free of errors or omissions although every effort has been made to minimize these. The reported indices for any quarter of any year should be considered preliminary and subject to revision. 2 The Center for Real Estate and Finance • Cornell University CORNELL CENTER FOR REAL ESTATE AND FINANCE Cornell Hotel Indices: Fourth Quarter 2021 Analysis of Indices through Q4, 2021 Hotels in all regions continue their positive price momentum. For the most recent quarter (2021Q4), Exhibits 1a through 1d show that all regions continued to regain ground lost prior to the initial onset of the pandemic. This is especially prominent in Exhibit 1d, which shows that all regions are either converging to their respective standardized mean of zero or have surpassed it. Quarter over quarter, Exhibit 1b shows that only the South Atlantic region continued to experience a double-digit price gain (10.7%). All other regions posted single-digit gains, except for the Mid-Atlantic region which posted a quarterly loss of 1.7 percent. However, the quarterly positive gains for all regions except the West South Central region were lower than the gains recorded in the prior quarter-over-quarter period. Year over year, the price paid for hotels increased in all regions except for the Mountain region (-3.7%). All regions except for the Midwest region experienced double-digit price gains. Compared to the prior year-over-year period, all regions fared better this period, consistent with our positive-price-momentum story. exhibit 1a Time series hotel performance for seven regions Sources: Cornell Center for Real Estate and Finance, CoStar, Real Capital Analytics CREF Hotel Indices • CHR Report • January 2022 • www.cref.cornell.edu • Vol. 22 No. 1 3 exhibit 1b Cross-section hotel performance for seven regions 8.9% QoQ 0.1% QoQ 48.2% YoY 4.3% QoQ 5.4% YoY 33.2% YoY -1.7% QoQ 23.8% YoY 10.7% QoQ 37.7% YoY 2.1% QoQ -3.6% YoY 2.7% QoQ Sources: Cornell Center for Real Estate and Finance, CoStar, Real Capital Analytics 12.5% YoY exhibit 1c Changes in regional price indices, year over year and quarter over quarter Note: Regions are as follows: Middle Atlantic region: New Jersey, New York, and Pennsylvania; New England region: Connecti- cut, Maine, Massachusetts, New Hampshire, Rhode Island, and Vermont; South Atlantic region: Delaware, District of Columbia, Florida, Georgia, Maryland, North Carolina, South Carolina, Virginia, and West Virginia; East South Central region: Alabama, Kentucky, Mississippi, Tennessee; East North Central region: Illinois, Indiana, Michigan, Ohio, and Wisconsin; Mountain region: Arizona, Colorado, Idaho, Montana, New Mexico, Nevada, Utah, and Wyoming; West North Central region: Iowa, Kansas, Minne- sota, Missouri, Nebraska, North Dakota, and South Dakota; Pacific: Alaska, California, Hawaii, Oregon, and Washington. 4 The Center for Real Estate and Finance • Cornell University exhibit 1d Regional comparison of standardized unexpected prices (SUP), with confidence boundaries Midwest Middle Atlantic Mountain New England Pacific South Atlantic West South Central CREF Hotel Indices • CHR Report • January 2022 • www.cref.cornell.edu • Vol. 22 No. 1 5 exhibit 2 Hotel performance for gateway cities versus non-gateway cities Cities that we define as gateway cities are Boston, Chicago, Honolulu, Los Angeles, Miami, New York, San Francisco, and Washington, DC. Sources: Cornell Center for Real Estate and Finance, CoStar, Real Capital Analytics Hotels in both gateway and non-gateway cities (that is, large hotels and small hotels combined) rose 10 exhibit positive performance, with hotels in non-gateway percent this quarter, compared to 29 percent in the prior cities posting higher gains. Exhibit 2 shows that there quarter, as shown in Exhibits 3a, 3b, and 3c.1 Year over was little price movement for hotels in gateway cities this year, while transaction volume continued strong, increas- quarter (.5%) compared to last quarter (7.4%). That finding ing 100 percent, this increase was smaller than the volume stands in contrast to the experience of non-gateway city growth in the prior period, which was 159 percent. Separat- hotels, where prices rose 6.5 percent this quarter (albeit this ing transactions by hotel size, transaction volume rose 182 increase was lower than the gain of 10.1 percent in the prior percent year over year and 10 percent quarter over quarter quarter). Year over year, the price of hotels in both gateway for large hotels, while the transaction volume for smaller and non-gateway cities increased over 20 percent, with hotels increased 82 percent year over year and 10 percent gateway cities experiencing a price appreciation of 20.1 per- quarter over quarter. These increases were smaller than the cent compared to a 21.5-percent gain in non-gateway cities. year-over-year and quarter-over-quarter gains posted in These increases exceeded the change in hotel prices in the the prior period. Exhibit 4 and Exhibit 5 show the year- prior period for both gateway cities (6.5%) and non- over-year trends in the number of transactions for large gateway cities (10.6%). hotels and small hotels. Transaction volume continues to be strong for both large and small hotels year over year and quarter over 1 Note that the number of transactions is limited to the sales that are includ- quarter. The transaction volume on all hotel transactions ed in the hedonic index. As such, it should not be construed necessarily as being representative of the total market activity. 6 The Center for Real Estate and Finance • Cornell University exhibit 3a Transaction volume (observed) and median sale price (1995–2003) CREF Hotel Indices • CHR Report • January 2022 • www.cref.cornell.edu • Vol. 22 No. 1 7 exhibit 3b Transaction volume (observed) and median sale price (continued, 2004–2012) 8 The Center for Real Estate and Finance • Cornell University exhibit 3c Transaction volume (observed) and median sale price (concluded, 2013–2021) Source: Cornell Center for Real Estate and Finance CREF Hotel Indices • CHR Report • January 2022 • www.cref.cornell.edu • Vol. 22 No. 1 9 exhibit 4 Median sale price and number of sales (hotels with sale prices of $10 million or more) Sources: CoStar, Real Capital Analytics exhibit 5 Median sale price and number of sales (hotels with sale prices less than $10 million) Sources: CoStar, Real Capital Analytics 10 The Center for Real Estate and Finance • Cornell University exhibit 6 Hotel indices through 2021, quarter 4 Source: Cornell Center for Real Estate and Finance CREF Hotel Indices • CHR Report • January 2022 • www.cref.cornell.edu • Vol. 22 No. 1 11 exhibit 7 Hedonic hotel indices for large and small hotel transactions Sources: Cornell Center for Real Estate and Finance, CoStar, Real Capital Analytics Our moving average trendlines indicate that large price of large hotels fell 1 percent, while small hotel prices hotels are priced to buy, while small hotels are fairly increased 3 percent this quarter. Year over year, Exhibit priced. Large hotels declined from their statistical high 8 shows that large hotels increased 10 percent compared set in the prior quarter, based on our standardized unex- to 13 percent in the prior year-over-year period. Exhibit 9 pected price (SUP) performance metric. Exhibit 7, which shows that small hotels rose 8 percent year over year com- graphs the prices reported in Exhibit 6, shows that the pared to 2 percent in the prior period. 12 The Center for Real Estate and Finance • Cornell University exhibit 8 Year-over-year change in large-hotel index with a moving average trendline Sources: Cornell Center for Real Estate and Finance, CoStar, Real Capital Analytics exhibit 9 Year-over-year change in small-hotel index with a moving average trendline Sources: Cornell Center for Real Estate and Finance, CoStar, Real Capital Analytics CREF Hotel Indices • CHR Report • January 2022 • www.cref.cornell.edu • Vol. 22 No. 1 13 exhibit 10 Moving average trendline for large hotel index Sources: Cornell Center for Real Estate and Finance, CoStar, Real Capital Analytics exhibit 11 Moving average trend line for small hotel index Sources: Cornell Center for Real Estate and Finance, CoStar, Real Capital Analytics 14 The Center for Real Estate and Finance • Cornell University exhibit 12 Standardized unexpected price (SUP) for large hotel index Sources: Cornell Center for Real Estate and Finance, CoStar, Real Capital Analytics Our moving average trend lines for large hotels Our standardized unexpected price (SUP) metric (Ex- (Exhibit 10) show that the price for large hotels remains hibit 12) shows that the standardized price for large hotels above both its short-term and long-term moving averages, declined from its new statistical high recorded in the prior indicating that large hotels are still a buy. In contrast, small period. In contrast, the standardized price for small hotels hotels (Exhibit 11) now appear to be fairly priced since the has now risen slightly above its standardized average small-hotel price is equal to both its short-term and long- (zero), as shown in Exhibit 13. term moving averages. CREF Hotel Indices • CHR Report • January 2022 • www.cref.cornell.edu • Vol. 22 No. 1 15 exhibit 13 Standardized unexpected price (SUP) for small hotel index Sources: Cornell Center for Real Estate and Finance, CoStar, Real Capital Analytics exhibit 14 Moving average trend line for repeat-sale index Sources: Cornell Center for Real Estate and Finance; CoStar, Real Capital Analytics 16 The Center for Real Estate and Finance • Cornell University exhibit 15 Standardized unexpected price (SUP) for hotel repeat-sale index (full sample) Sources: Cornell Center for Real Estate and Finance, CoStar, Real Capital Analytics Repeat-sales metrics: Prices remain above their that good hotels continue to remain a buy opportunity. moving average; hotel prices continue to reach new highs. Our SUP performance metric (Exhibit 15) indicates that the Exhibit 14 shows that our repeat sale indicator continues to standardized price, based both its 3-year moving average remain above both its short-term and long-term moving av- and its 5-year moving average, continues to remain above erages, similar to that of large hotels.2 This is another signal its statistical upper boundary. Exhibit 16 shows that the re- peat sale price index is 10 percent year over year, compared 2 3We report two repeat-sale indices. The repeat sale full sample index uses all to a rise of 5.7 percent in the prior period. Quarter over repeat-sale pairs, whereas the repeat-sale index with a base of 100 at 2000Q1 uses quarter, the index increased 5.1 percent, bettering a only those sales that occurred on or after the first quarter of 2000. In other words, the latter repeat-sale index thus doesn’t use information on sales prior to the 4.2-percent rise in the prior quarter-over-quarter period. first quarter of 2000. As such, if a hotel sold in 1995 and then sold again in 2012, it would be included in the first repeat sale index (i.e., repeat sale full sample 3 This is the latest information reported by the Mortgage Bankers Associa- index), but it would not be included in the latter repeat-sale index. tion as of the writing of this report. CREF Hotel Indices • CHR Report • January 2022 • www.cref.cornell.edu • Vol. 22 No. 1 17 exhibit 16 Year-over-year change in repeat-sale hotel index, with a moving average trendline Sources: Cornell Center for Real Estate and Finance, CoStar, Real Capital Analytics Mortgage-financing volume continues to rise both Sonnenblick Goldman, declined this quarter for both Class year over year and quarter over quarter. Exhibit 17 shows A and Class B and C hotels.4 As of this writing (in Decem- that the mortgage origination volume for hotels, as re- ber 2021), the interest rate on Class A hotels stood at 5.4 ported for the third quarter of 2021, rose 850 percent year percent, compared to 5.44 percent in September 2021 and over year, exceeding the 231-percent increase in the prior 5.5 percent in December 2020. The interest rates on Class period. Quarter over quarter, the mortgage originations B and C hotels are 4 basis points lower, at 5.6 percent, rose 60 percent, which is a smaller gain than the 231-per- compared to 5.64 percent last quarter and 5.7 percent in cent increase posted in the previous quarter. The maximum December 2020 (Exhibit 18). Thus, we see that rates have loan-to-value (LTV) ratio for hotels remained at 65 percent. moved imperceptibly lower year over year and quarter The cost of hotel debt financing fell slightly this over quarter. quarter as well as year over year. The cost of obtaining 4 hotel debt financing, as reported by Cushman Wakefield The interest rate reported by Cushman Wakefield Sonnenblick Goldman (CWSG) is based on deals that CWSG has brokered as well as their survey of rates on hotel deals. 18 The Center for Real Estate and Finance • Cornell University exhibit 17 Mortgage origination volume versus the loan-to-value ratio for hotels Sources: Mortgage Bankers Association, Cornell Center for Real Estate and Finance, Cushman Wakefield Sonnenblick Goldman exhibit 18 Interest rates on Class A versus Class B & C hotels Sources: Cornell Center for Real Estate and Finance, Cushman Wakefield Sonnenblick Goldman CREF Hotel Indices • CHR Report • January 2022 • www.cref.cornell.edu • Vol. 22 No. 1 19 exhibit 19 Interest rate spreads of hotels versus non-hotel commercial real estate Sources: Cornell Center for Real Estate and Finance, Cushman Wakefield Sonnenblick Goldman exhibit 20 Risk differential between hotel REITs and equity REITs Sources: NAREIT, Cornell Center for Real Estate and Finance 20 The Center for Real Estate and Finance • Cornell University exhibit 21 30-plus-day delinquency rate for hotels Source: Trepp The relative risk premium that lenders require for REITs relative to the total risk of an equally weighted port- hotels over other commercial real estate has remained folio of commercial real estate equity REITs (that is, office, stable this quarter relative to the prior quarter. Exhibit industrial, retail, and multifamily). The risk differential, 19 shows the spreads between the interest rates on Class which should reflect the risk that is unique to hotel prop- A and of Class B and C full-service hotels compared to erties, is currently at 4.7 percent, compared to 11 percent in the (equally weighted) interest rate on other (non-hotel) the prior quarter.6 This indicates that the perceived default commercial real estate. The positive spread (i.e., the hotel risk for hotels has declined dramatically relative to other real estate premium) indicates that lenders demand more major types of commercial real estate. Thus, financing for compensation to make hotel loans compared to loans on hotels should be lower than it has been, relative to other other major commercial property types, because hotels are major property types. perceived to represent a greater risk.5 The monthly hotel The delinquency rate on hotel loans continues to real estate premiums for both higher quality (Class A) and decline toward its pre-pandemic average. The CMBS lower quality (Class B and C) hotels have declined imper- delinquency rate (30-plus days) for lodging properties ceptibly, from 2.15 percent (Class A) and 2.25 (Class B and continued to decline in December from its high of 24.3 C) last quarter to 2.09 percent and 2.19 percent this quarter. percent in June of last year. As of this writing, it is at 7.8 Thus, although the relative premium for hotel properties percent compared to 11.45 percent in September. The fell slightly since last quarter, that premium has remained 7.8-percent delinquency rate for hotels is comparable to in the same range since January 2021. the retail delinquency rate (7.87%). The December 2021 Another way to view hotel default risk is to look at delinquency rate for other property types, as reported by the equity market. Exhibit 20 shows the total risk of hotel Trepp is as follows: 0.39 percent for industrial, 5 The interest rate on hotel properties is generally higher than that for apart- ment, industrial, office, and retail properties in part because hotels’ cash flow is 6 The risk differential calculation is as follows: (σHotel - σCRE = 8.76% - commonly more volatile than that of other commercial properties. 4.02%). CREF Hotel Indices • CHR Report • January 2022 • www.cref.cornell.edu • Vol. 22 No. 1 21 exhibit 22 Standardized 30-plus-day delinquency rate for hotels Source: Trepp 0.28 percent for multifamily, and 2.36 percent for office. for hotels should continue to narrow, which in turn should Exhibit 21 displays the historical 30-plus-day delinquency lead to a lower cost of debt financing for hotels. rate for hotels. Additionally, Exhibit 22, which shows the Hotel investment based on operating performance is standardized version of the 30-plus-day delinquency rate currently financially feasible. Our EVA indicator (Exhibit for hotels, reveals that the delinquency rate for hotels 23) is at about 1 percent, while our equity holder value whose loans are securitized as part of CMBS deals is added (SVA) metric is 2.5 percent (as of October 2021). now hovering around its long-term average.7 Using the These values signal that deals on existing hotels are above standardized metric, the fact that the delinquency rate has breakeven. The return that an investor receives from reverted to zero suggests that the relative risk premium operations is therefore above the total weighted average borrowing cost (WACC) of 7.6 percent (cost of equity is 7 The advantage of standardizing an indicator is that the mean is set equal 9.1%). Most of the juice is coming from anticipated future to zero and the standard deviation is set equal to 1. If the indicator is above or below 1.645 (Z-score), then this indicates that the indicator has hit a statistically price gains. significant new high or low. 22 The Center for Real Estate and Finance • Cornell University exhibit 23 Economic value added (EVA) and equity (shareholder) value added (SVA) for hotels Sources: Cornell Center for Real Estate and Finance, Cushman Wakefield, NAREIT, Real Capital Analytics, St Louis Fed exhibit 24 Standardized unexpected RevPAR (36-month moving average) vs. NAREIT lodging-price index Sources: Cornell Center for Real Estate and Finance, CoStar (STR), NAREIT CREF Hotel Indices • CHR Report • January 2022 • www.cref.cornell.edu • Vol. 22 No. 1 23 exhibit 25 Standardized unexpected NAREIT lodging/resort price index Sources: Cornell Center for Real Estate and Finance, NAREIT Our reading of the tea leaves suggests we should see moving average of the ABI index, we should expect posi- slower to negative price momentum for large hotels, but tive price momentum in the next period. positive price gains for small hotels near term. Our stan- The National Association of Purchasing Managers dardized unexpected RevPAR (Exhibit 24) has converged (NAPM) index (shown in Exhibit 27), which is an indica- to its standardized mean of zero. We anticipate continued tor of anticipated business confidence, fell 4 percent this positive price momentum in the near term. quarter, compared to a 0.8-percent rise last quarter.9 It also Exhibit 25 displays the standardized unexpected price declined 3.3 percent year over year, down from its year- of the NAREIT Lodging Index. Since the standardized un- over-year gain of 10.3 percent in the prior period. Thus, we expected lodging price index continues to rise this quarter, expect the prices of high-price hotels to decline near term. we expect our hotel price based on repeat sales to continue to rise near term. 9 The ISM: Purchasing Managers’ Index, (Diffusion index, SA) also known The architecture billings index (ABI) for commercial as the National Association of Purchasing Managers (NAPM) index is based on and industrial property, shown in Exhibit 26, fell 12 percent a survey of over 250 companies within twenty-one industries covering all 50 states. It not only measures the health of the manufacturing sector but is a proxy this quarter, but rose 7 percent year over year, compared for the overall economy. It is calculated by surveying purchasing managers for to a 6-percent decline in the previous quarter and a rise of data about new orders, production, employment, deliveries, and inventory, in descending order of importance. A reading over 50% indicates that manufactur- 33 percent in the prior year-over-year period.8 Based on the ing is growing, while a reading below 50% means it is shrinking. 8 www.aia.org/practicing/economics/aias076265 24 The Center for Real Estate and Finance • Cornell University exhibit 26 Repeat sales index versus the architecture billings index Sources: American Institute of Architects, Cornell Center for Real Estate and Finance Center for Real Estate and Finance exhibit 27 Business confidence and high-price hotels index Sources: Cornell Center for Real Estate and Finance, Institute for Supply Management (ISM) CREF Hotel Indices • CHR Report • January 2022 • www.cref.cornell.edu • Vol. 22 No. 1 25 exhibit 28 Consumer confidence and low-price hotels Sources: Conference Board, Cornell Center for Real Estate and Finance In contrast, the Conference Board’s Consumer Con- fidence Index (graphed in Exhibit 28), which we use as a Hotel Valuation Model (HOTVAL) proxy for anticipated consumer demand for leisure travel Has Been Updated and a leading indicator of the hedonic index for low-price hotels, rose 6 percent this quarter and increased almost 31 e have updated our hotel valuation percent year over year. Thus, we expect low-price hotels to regression model to include the transac- outperform high-price hotels near term. Wtion data used to generate this report. We We also look at the expected growth rate in Wall Street analysts’ earnings (revenue) estimates for hotel REITs both provide this user-friendly hotel valuation model in in terms of next quarter earnings per share (EPS) and also an Excel spreadsheet entitled HOTVAL Toolkit as annual EPS.10 As shown in Exhibit 29, analysts are ex- a complement to this report which is available for pecting average quarterly EPS to rise between 66 percent download from our CREF website. (median) and 272 percent (mean) and average annual EPS to rise between 8 percent (median) and 15 percent (mean). Since analysts’ estimates reflect the earnings guidance from management, this suggests that we should expect prices to rise, reflecting continued positive guidance regarding the EPS. n 10 We obtain the growth rate in earnings and revenue estimates from https:// www.earningswhispers.com. 26 The Center for Real Estate and Finance • Cornell University exhibit 29 Analysts’ forecasts of hotel REIT earnings CREF Hotel Indices • CHR Report • January 2022 • www.cref.cornell.edu • Vol. 22 No. 1 27 Appendix SUP: The Standardized Unexpected Price Metric The standardized unexpected price metric (SUP) is similar to the standardized unexpected earnings (SUE) indicator used to determine whether earnings surprises are statistically significant. An earnings surprise occurs when the firm’s reported earnings per share deviates from the street estimate or the analysts’ consensus forecast. To determine whether an earnings surprise is statistically significant, analysts use the following formula: SUE SUP data and σ calculation for high-price hotels Q = (AQ – mQ)/sQ (12 quarters/3 years) Price where SUEQ = quarter Q standardized unexpected earnings, surprise High-price Moving indicator A = quarter Q actual earnings per share reported by the firm, Quarter hotels m average σ (SUP) Q mQ = quarter Q consensus earnings per share forecasted by analysts in quarter Q-1, and sQ = quarter Q standard deviation of earnings estimates. From statistics, the SUEQ is normally distributed with a mean of zero and a standard deviation of one (~N(0,1)). This calculation shows an earnings surprise when earnings are statistically significant, when SUEQ exceeds either ±1.645 (90% significant) or ±1.96 (95% significant). The earnings surprise is positive when SUEQ > 1.645, which is statistically significant at the 90% level assuming a two-tailed distribution. Similarly, if SUEQ < -1.645 then earnings are negative, which is statistically significant at the 90% level. Intuitively, SUE measures the earnings surprise in terms of the number of standard deviations above or below the consensus earnings estimate. From our perspective, using this measure complements our visual analysis of the movement of hotel prices relative to their three-year and five- year moving average (µ). What is missing in the visual analysis is whether prices diverge significantly from the moving average in statistical terms. In other words, we wish to determine whether the current price diverges at least one standard deviation from µ, the historical average price. The question we wish to answer is whether price is reverting to (or diverging from) the historical mean. More specifically, the question is whether this is price mean reverting. To implement this model in our current context, we use the three- or five-year moving average as our measure of µ and the rolling three- or five- year standard deviation as our measure of σ. Following is an example of how to calculate the SUP metric using high price hotels with regard to their three-year moving average. To calculate the three-year moving average from quarterly data we sum 12 quarters of data then divide by 12: Average (µ) = (70.6+63.11+58.11+90.54+95.24+99.70 +108.38+99.66+101.62+105.34+109.53+115.78) = 93.13 12 Standard Deviation (σ) = 18.99 Standardized Unexp Price (SUP) = (115.78-93.13) = 1.19 18.99 28 The Center for Real Estate and Finance • Cornell University About the Cornell Hotel Indices In our inaugural issue of the Cornell Hotel Index series, we introduced three new quarterly metrics to monitor real estate activity in the hotel market. These are a large hotel index (hotel transactions of $10 million or more), a small hotel index (hotels under $10 million), and a repeat sales index (RSI) that tracks actual hotel transactions. These indices are construct- ed using the CoStar and RCA commercial real estate databases. The large and small hotel indices are similar in nature and construction to the consumer price index (CPI), while the repeat sale hotel index is analogous to the retail concept of same store sales. Using a similar logic process for hotels, we compare the sales and resales of the same hotel over time for that index. All three measures provide a more accurate representation of the current hotel real estate market conditions than does reporting the average transaction prices, because the average-price index doesn’t account for differences in the quality of the hotels, which also is averaged. A more detailed description of these indices is found in the first edition of this series, “Cornell Real Estate Market Indices,” which is available at no charge from the Cornell Center for Real Estate and Finance. Starting with our 2018Q1 issue, we introduced the Gateway Cities Index as a new metric in our hotel analytics arsenal.1 In our 2019Q2 issue, we introduced our new Regional Indices to add further granularity to hotel performance. More recently, we have included information on hotel delinquencies as well as short-term and long-term hotel earning expectations to aid hotel decisionmakers. We also present updates and revisions to our hotel indices along with commentary and supporting evidence from the real estate market. Starting in 2021Q2, we included standardized unexpected price for our regional price indices as well as standardized unexpected RevPar for the U.S. as a whole. We also introduced Shareholder Value Added as a complementary metric to EVA so that readers can now compare the profitability (EVA) of hotel real estate to investors’ equity return (SVA). 1 Cities that we define as gateway cities are Boston, Chicago, Honolulu, Los Angeles, Miami, New York, San Francisco, and Washington, DC. For a general discus- sion on what constitutes a gateway city, please see Corgel, J.B. (2012), What is a Gateway City?: A Hotel Market Perspective, Center for Real Estate and Finance Reports, Cornell University School of Hotel Administration (https://scholarship.sha.cornell.edu/cgi/viewcontent.cgi?article=1007&context=crefpubs). The study of Corgel, J. B., Liu, C., & White, R. M. (2015). Determinants of hotel property prices. Journal of Real Estate Finance and Economics, 51, 415-439 finds that a significant driver of hotel property prices is whether a hotel is located in a gateway city. The presumption is that hotels (and other real estate) in gateway cities exceed other cities as IRR generators in part due to a generally stronger economic climate as a result of higher barriers to entry, tighter supply, and/or relatively stronger performance in terms of revenue per available room than other top cities that are not gateways. CREF Hotel Indices • CHR Report • January 2022 • www.cref.cornell.edu • Vol. 22 No. 1 29 Cornell Center for Real Estate and Finance Steven Carvell, Arthur Adler ’78 and Karen Newman Adler ’78 Director Elizabeth Cunningham, Program Manager Ray Potter CALS ’87, MBA ’92 CREF Advisory Board Founder and Managing Partner R3 Funding Arthur Adler ’78 David Hirschberg Michael Profenius, P’15, P’17 Chairman, CREF Managing Director Chief Operating Officer President H.I.G. Realty Partners Northwood Investors Adler Advisors LLC Jeffrey Horwitz Rachel Roginsky ’79 Jun Ahn ’00 (MPSRE) Partner, Mergers and Acquisitions Private Principal CEO, Core Value, Managing Director of Equity Real Estate (Head) Lodging and Pinnacle Advisory Group Real Estate Division Gaming (Head) Private Equity Corporate David Rosenberg P ’11, P’13, P’19 YIDO Governance International Practice Group Chief Executive Officer Bob Alter ’73 Proskauer Rose LLP Sawyer Realty Holdings President Ruby Huang ’97 Chuck Rosenzweig ’85, JD ’88 Seaview Investors LLC Senior Vice President, Asset Management Founder and Managing Partner Richard Baker ’88 Starwood Capital Group Criterion Real Estate Capital Governor and Chief Executive Officer David Israel ’09 Ben Rowe ’96 Hudson’s Bay Company (HBC) Senior Vice President, CHA Founder and Managing Partner Michael Barnello ’87 hotelAVE KHP Capital Partners Former President & CEO Dana Jacobsohn ’92 Richard Russo ’02 LaSalle Hotel Properties Senior Vice President, Global Mixed-use Principal Robert Buccini A&S ’90 Development Highgate Co-president Marriott International, Inc. John Ryan The Buccini/Pollin Group David Jubitz ’04 Founder and CEO Marty Burger P’17, P’20 Co-chief Investment Officer Metro Development Group Chief Executive Officer Clearview Hotel Capital C. Patrick Scholes ’94 Silverstein Properties Alan Kanders ’87 Managing Director, Lodging and Leisure Equity Adam Burinescu CALS ’03 Principal Research Managing Director Three Wall Capital Truist Securities Centerbridge Partners Rob Kline ’84 Nirav Shah MMH ’05 Rodney Clough ’94 CEO & Co-founder Regional Development Managing Partner The Chartres Lodging Group Hyatt HVS Jason Lee ’95 Matthew Shore ’00 Howard Cohen ’89 Managing Director, Chief Investment Officer– Cheif Investment Officer Chief Executive Officer Asia and Senior Portfolio Manager DRA Advisor Atlantic | Pacific Companies AEW Seth Singerman ’99 Kevin Davis Michael Lipson Managing Partner Senior Managing Director—Hotels and Chairman of the Board and CEO Singerman Real Estate (SRE) Hospitality Group Access Point Financial LLC Jackie Soffer P’20 JLL Terence Loh ’97 Chairman & CEO Navin Dimond P’14, P’19 Senior Vice President Turnberry CEO and Chairman CDIB Capital Robert Springer ’99 Stonebridge Companies Neil Luthra EVP and Cheif Investment Officer Adam Docks Founding Partner Sunstone Hotel Investors Partner and Firmwide Co-chair, Newbond Holdings Andrew Taffet ’05 Hotels and Leisure Industry Group Jay Mantz P ’21 Chief Investment Officer and Head of Asset Perkins Coie LLP President, New York Management Joel Eisemann, MPS RE ’80 Rialto The Carrington Companies, LLC Chief Development Officer, The Americas Alfonso Munk ’96 Alan Tantleff ’87, P ’18 InterContinental Hotels Group (IHG) Chief Investment Officer–Americas Senior Managing Director–Corporate Finance, Habib Enayetullah Hines Practice Leader, Hospitality Gaming and SVP for Real Estate and Asset Management Chip Ohlsson Leisure Hilton Worldwide Executive Vice President and Chief FTI Consulting Russell Galbut ’74 Development Officer, North America Dan Unger ’97 Managing Principal Wyndham Hotel Group Chief Development Officer Crescent Heights Mark Owens ’00 Tishman Nolan Hecht ’97 Executive Vice President and Head of Eva Wasserman, Senior Managing Director and Head of Real Estate Hospitality Capital Markets Managing Director Certares Management LLC CBRE Hotels GEM Realty Capital Kate Henrikson ’96 Daniel Peek ’92 Robert White SVP, Investment and Portfolio Analysis President, Hotel Group President RLJ Lodging Trust HWE Real Capital Analytics Kenneth Himmel ’70 David Pollin ’90 Shai Zelering ’01 President and CEO Co-founder and President Managing Partner Related Urban The Buccini/Pollin Group Brookfield Real Estate Group 30 The Center for Real Estate and Finance • Cornell University Center for Hospitality Research Reports Cornell Hotel Indices: Converging Toward Normalcy Center for Hospitality Research Reports Linda Canina, Dr. Michael Dang Director Vol. 22 No. 1 (January 2022) Nicole McQuiddy-Davis, Program Manager Glenn Withiam, Contributing Editor Kate Walsh, Dean, E.M. Statler Professor Cornell SC Johnson College of Business © 2022 Cornell University. This report may not be reproduced Nolan School of Hotel Administration or distributed without the express permission of the publisher. Statler Hall Ithaca, NY 14853 CHR Reports and the CREF Report series are produced for 607-254-3383 • www.chr.cornell.edu the benefit of the hospitality real estate and finance industries by The Center for Real Estate and Finance at Cornell University. CHR Advisory Board Pablo Alonso Mark Lomanno Michele Sarkisian Chief Executive Officer CHR Advisory Board Chair Partner HotStats Partner & Senior Advisor Avenger Capital Scott Barghaan Kalibri Labs Stacy Silver VP and General Manager, Travel, Robert Mandelbaum ’81 President Transportation and Hospitality Vertical Director of Research Information Silver Hospitality Group Salesforce Services Scott Berman ’84 CBRE Hotels Research Liesl Smith Senior Vice President, Marketing & Principal & US Hospitality Industry Kelly McGuire MMH ’01, PhD ’07 Sales Enablement Leader Manging Principal, Hospitality FreedomPay PwC ZS Randell Smith Vivek Bhogaraju MMH ’06 Jacqueline Nunley Founder GM, Lodging Revenue Performance Senior Industry Advisor— Travel and STR Solutions Hospitality Expedia Group Salesforce Scott Taber ’85 Senior Vice President, Global Carolyn Corda MPS ’89 David Oppenheim Hospitality Chief Marketing Officer and Chief Senior Vice President of Global Insights, Four Seasons Hotels and Resorts Commercial Officer Analytics, & Data ADARA IHG SriHari Thotapalli Worldwide Technology Leader for Ian-Michael Farkas Andrada Paraschiv Hospitality Vice President, Strategic Accounts Vice President of Hospitality AWS Local Measure Beekeeper Paolo Torchio, MMH ’04 Chuck Floyd, P ’15 and ’18 Michael Partridge ’92 Chief Strategy Officer Global President of Operations Vice President of Sales & Revenue Cendyn Hyatt Analysis Emily Weiss Eliot Hamlisch Marriott International Managing Director, Global Travel Executive Vice President, Loyalty & Stephanie Perrone Goldstein ’01 Industry Revenue Optimization Data, Analytics, and AI Leader, Travel Accenture Wyndham Hotels & Resorts and Hospitality Industry Rick Werber ’82 Steve Hood Deloitte Senior Vice President, Engineering & Senior Vice President of Research Jess Petitt ’05 Sustainability STR Senior VP, Commercial Strategy, Host Hotels & Resorts Ashli Johnson Insights, and Analytics Vice President of Education Hilton Michelle Woodley ’89 President AAHOA Geoffrey Ryskamp Preferred Hotels & Resorts Klaus Kohlmayr VP, Sector Head—Retail and Hospitality Ahmed (Joe) Youssef Chief Evangelist and Head of Strategy Medallia Chief Executive Vice President, Business IDeaS Guido Salvatori, MMH ’02 Intelligence and Data Solutions Jamie Lane Senior Director, Integrations Amadeus Vice President of Research Duetto AirDNA CREF Hotel Indices • CHR Report • January 2022 • www.cref.cornell.edu • Vol. 22 No. 1 31