Fourth Quarter 2018: David Hotels Continue to Dominate the Goliaths

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1 Cornell University School of Hotel Administration The Scholarly Commons Cornell Real Estate Market Indices Center for Real Estate and Finance Fourth Quarter 218: David Hotels Continue to Dominate the Goliaths Crocker H. Liu Cornell University School of Hotel Administration, Adam D. Nowak West Virginia University Robert M. White Jr Real Capital Analytics, Inc. Follow this and additional works at: Part of the Real Estate Commons Recommended Citation Liu, C. H., Nowak, A. D., & White, R. M. (219). Fourth quarter 218: "David" hotels continue to dominate the "Goliaths. Center for Real Estate and Finance Reports Hotel Indices, 8(1), This Article is brought to you for free and open access by the Center for Real Estate and Finance at The Scholarly Commons. It has been accepted for inclusion in Cornell Real Estate Market Indices by an authorized administrator of The Scholarly Commons. For more information, please contact

2 Fourth Quarter 218: David Hotels Continue to Dominate the Goliaths Abstract Hotels in gateway cities continue to shine, with prices rising 25 percent year over year, compared to an 8-percent rise for hotels in non-gateway cities. Hotel financial performance is still breakeven, with operating profit similar to a hotel property s borrowing cost, based on economic value analysis. Year over year, mortgage financing volume for hotels fell, as interest rates on Class A and Class B and C hotel deals have risen. A reading of our tea leaves suggests prices are expected to decline for the Goliaths, that is, large hotels, while prices moderate (at best) or decline slightly (at worst) for smaller David hotels. This is report number 29 of the index series. Supplemental File: Hotel Valuation Model (HOTVAL) We provide this user friendly hotel valuation model in an excel spreadsheet entitled HOTVAL Toolkit as a complement to this report which is available for download from 1/ Keywords Cornell Hotel Indices, economic value analysis (EVA), hotel prices, hedonic hotel index, gateway cities Disciplines Real Estate Comments Required Publisher Statement Cornell University. This report may not be reproduced or distributed without the express permission of the publisher. This article is available at The Scholarly Commons:

3 COrnell Center for Real Estate and finance Report Cornell hotel indices Cornell Hotel Indices: Fourth Quarter 218: David Hotels Continue to Dominate the Goliaths Crocker H. Liu, Adam D. Nowak, and Robert M. White, Jr. Executive Summary Hotels in gateway cities continue to shine, with prices rising 25 percent year over year, compared to an 8-percent rise for hotels in non-gateway cities. Hotel financial performance is still breakeven, with operating profit similar to a hotel property s borrowing cost, based on economic value analysis. Year over year, mortgage financing volume for hotels fell, as interest rates on Class A and Class B and C hotel deals have risen. A reading of our tea leaves suggests prices are expected to decline for the Goliaths, that is, large hotels, while prices moderate (at best) or decline slightly (at worst) for smaller David hotels. This is report number 29 of the index series. CREF Hotel Indices January Vol. 8 No. 1 1

4 About the Authors Crocker H. Liu 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 ( ) and at Arizona State University s W.P. Carey School of Business (26-29) 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, market 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 the Journal of Real Estate Finance and Economics. He is the former co-editor of Real Estate Economics, the leading real estate academic journal, where he continues to serve on the editorial board. He recently joined the editorial board of Financial Review. He also previously served on the editorial boards of the Journal of Real Estate Finance and Economics, the Journal of Property Research, and the Journal of Real Estate Finance. 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 Graaskamp, and a Ph.D. in finance and real estate from the University of Texas under Dr. Vijay Bawa. Adam D. Nowak is an associate professor of economics at West Virginia University. He earned degrees in mathematics and economics at Indiana University Bloomington in 26 and a degree in near-east languages and cultures that same year. He received a Ph.D. from Arizona State University. Nowak taught an introduction to macroeconomics course and a survey of international economics at Arizona State. 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 the Journal of Urban Economics, Regional Science and Urban Economics, Journal of Applied Econometrics, Real Estate Economics, and the 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 concerning 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 Economist, Forbes, New York Times, and Financial Times, among others. He is the 214 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 25, Institutional Investor s 2 Rising Stars of Real Estate in 26, and Real Estate Forum s 1 CEOs to Watch in 27. 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. His research has been published in the 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 University 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

5 COrnell Center for Real Estate and finance Report cornell hotel indices Cornell Hotel Indices: Fourth Quarter 218: David Hotels Continue to Dominate the Goliaths Crocker H. Liu, Adam D. Nowak, and Robert M. White, Jr. Analysis of Indices through Q4, 218 Gateway Cities Continue to Outshine Non-Gateway Cities. Hotels in gateway cities continue to outperform properties in non-gateway cities, as shown in Exhibit 1. The price of hotels in gateway cities rose 25 percent year over year (and 7.5 percent quarter over quarter) in this period, compared to 16 percent year over year (and.5 percent quarter over quarter) in the previous period. In contrast, hotel prices in non-gateway cities rose 8 percent year over year (6 percent quarter over quarter), compared to 2.3 percent year over year (1.5 percent quarter over quarter) in the prior period. Exhibit 1 Hotel performance for gateway cities versus non-gateway cities Gateway Non-gateway Sources: Cornell Center for Real Estate and Finance, CoStar, Real Capital Analytics CREF Hotel Indices January Vol. 8 No. 1 3

6 Exhibit 2 Economic value added (EVA) for hotels.6.4 E V A S p r e a d ( R O I C W A C C ) Sources: ACLI, Cornell Center for Real Estate and Finance, NAREIT, Federal Reserve Hotel Investment Based on Operating Performance Is Still in the Black (Breakeven). Our economic value added (EVA) indicator shown in Exhibit 2 has continued to remain positive at.1 (same value as in the previous quarter). For all practical purposes, the EVA indicator has continued to hover around zero since the second quarter of 216. Although the cost of debt financing declined to 5.63 percent in 218Q3 (from 6.66 percent in 218Q2), the ACLI hotel cap rate has also fallen, from 7.5 percent (218Q2) to 7 percent (218Q3). Thus, as suggested in Exhibit 3, positive leverage continued to be the norm for hotel deals in 218Q3 (the latest quarter for which ACLI data exists). This means that penciling feasible deals was easier to achieve as a result of a widening of the spread between the cap rate and the cost of debt financing. Intuitively, the investor should receive a higher return than his or her borrowing cost. The Median Price of Hotels Rose on a Quarterly as well as a Year-over-Year Basis. The median price of hotels rose approximately 26 percent from the previous quarter ($6.445M versus $5.125M). However, the total volume of all hotel transactions (both large hotels and small hotels combined) fell 16.2 percent (28 transactions this period as compared to 334 transactions previously), as reported in Exhibit 4 (including Exhibits 4a, 4b, and 4c). 4 The Center for Real Estate and Finance Cornell University

7 Exhibit 3 Return on investment capital versus cost of debt financing.3 R O I C a n d C o s t o f D e b t ROIC Cost of debt 218Q2 218Q3 ROIC 7.52% 7.2% Cost of debt 6.66% 5.63% Sources: ACLI, Cornell Center for Real Estate and Finance 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 $1 million or more), a small hotel index (hotels under $1 million), and a repeat sales index (RSI) that tracks actual hotel transactions. These indices are constructed 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 218Q1 issue, we introduced the Gateway Cities Index as a new metric in our hotel analytics arsenal. 1 Cities that we define as gateway cities are Boston, Chicago, Honolulu, Los Angeles, Miami, New York, San Francisco, and Washington, D.C. In this issue, we present updates and revisions to our hotel indices along with commentary and supporting evidence from the real estate market. 1 For a general discussion on what constitutes a gateway city, see: Corgel, J.B. (212), What Is a Gateway City?: A Hotel Market Perspective, Center for Real Estate and Finance Reports, Cornell University School of Hotel Administration. The study of Corgel, J. B., Liu, C., & White, R. M. (215). Determinants of hotel property prices. Journal of Real Estate Finance and Economics, 51, 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 January Vol. 8 No. 1 5

8 Exhibit 4a Transaction volume (obs) and median sale price (part 1: ) 6 The Center for Real Estate and Finance Cornell University

9 Exhibit 4b Transaction volume (obs) and median sale price (part 2: ) CREF Hotel Indices January Vol. 8 No. 1 7

10 Exhibit 4c Transaction volume (obs) and median sale price (part 3: 215 current) Year over year (217Q4 versus 218Q4), the median price of hotels rose 43.2 percent, while the volume of hotel transactions also increased, by 5.7 percent. A comparison of large hotels relative to smaller hotels on a year-over-year basis reveals that the median price of large hotels fell 26.8 percent on stronger volume (+59%), while in contrast the median price of small hotels rose 14.8 percent on weaker volume (-12%). 1 By comparison, the price change for 1 Note that the number of transactions is limited to the sales that are included in the hedonic index. As such, it should not be construed as being the total market activity. hotels sold in gateway cities increased 18 percent on stronger volume (+27%). A similar situation exists on a quarter-over-quarter basis for large hotels, with the median sale price of large hotels falling 3.6 percent on stronger transaction volume (+27%). The median sale price of smaller hotels also declined by 11 percent, albeit on weaker volume (-3%). The only bright spot was found in hotels in gateway cities, which rose in price (+8%) on stronger transaction volume (+5%). Exhibit 5 and Exhibit 6 show this year-over-year trend in the number of transactions for large hotels and small hotels. 8 The Center for Real Estate and Finance Cornell University

11 Exhibit 5 Median sale price and number of sales for high-price (large) hotels (sale prices of $1 million or more) 14 6 Number of transactions Median sale price 12 5 Number of transactions Median sale price ($millions) 2 1 Sources: CoStar, Real Capital Analytics Exhibit 6 Median sale price and number of sales for low-price (small) hotels (sale prices of less than $1 million) 35 Number of transactions Median sale price $ Number of transactions Median sale price ($millions) 5.5 Sources: CoStar, Real Capital Analytics CREF Hotel Indices January Vol. 8 No. 1 9

12 Exhibit 7 Hotel indices through 218, quarter 4 1 The Center for Real Estate and Finance Cornell University

13 Exhibit 8 Hedonic hotel indices for large and small hotel transactions Low-price (small) hotels (<$1 $1 MM) High-price (large) hotels (>$1 million) MM) Quarter Sources: Cornell Center for Real Estate and Finance, CoStar, Real Capital Analytics Our Moving Average Trendlines and Our Standardized Unexpected Price (SUP) Performance Metrics Both Point to Negative Price Momentum for Large Hotels, with Small Hotels Continuing to Experience Positive Price Performance in General. Exhibit 8, which graphs the prices reported in Exhibit 7, shows that the prices of large hotels continue their downward trend, falling 2 percent this quarter compared to a.4-percent decline last quarter. On the other hand, smaller hotels continued to post positive price performance, rising 2.9 percent this quarter compared to a 1.8-percent rise last quarter. In contrast, Exhibit 9 CREF Hotel Indices January Vol. 8 No. 1 11

14 Exhibit 9 Year-over-year change in high-price (large) hotel index, with moving-average trend line 8% 6% Year over year change in large-hotel index 4% 2% -2% -4% Sources: Cornell Center for Real Estate and Finance, CoStar, Real Capital Analytics shows that on a year-over-year basis, prices for large hotels inched up.86 percent (217Q4-218Q4), far less than the 5.5-percent jump recorded in the prior yearover-year period (217Q3-218Q3). Exhibit 1 shows that prices for smaller hotels rose 5.4 percent (217Q4-218Q4), a stronger performance than the 3.4-percent increase in the prior period (217Q3-218Q3). Thus, we conclude that the price of large hotels increased at a decreasing rate, while the price of small hotels increased at an increasing rate on a year-over-year basis. Our moving average trend lines for large hotels (in Exhibit 11) show that the price for large hotels is now approaching its short-term moving average trend line, albeit remaining above its longer term moving average trend line. In contrast to this, Exhibit 12 shows that the price for smaller hotels not only continues 12 The Center for Real Estate and Finance Cornell University

15 Exhibit 1 Year-over-year change in small-hotel index, with moving-average trend line 2% 15% Year-over-year change in small-hotel index 1% 5% % -5-1% -15% -2% Sources: Cornell Center for Real Estate and Finance, CoStar, Real Capital Analytics Exhibit 11 Moving average trend line for large-hotel index Large-hotel Hedonic Index High-price hotels (>$1MM) Moving average (12 quarters, 3 years) Moving average (2 quarters, 5 years) Sources: Cornell Center for Real Estate and Finance, CoStar, Real Capital Analytics CREF Hotel Indices January Vol. 8 No. 1 13

16 Exhibit 12 Moving average trend line for small-hotel index Small-hotel Hedonic Index Low-price hotels (<$1MM) Moving average (12 quarters, 3 years) Moving average (2 quarters, 5 years) Sources: Cornell Center for Real Estate and Finance, CoStar, Real Capital Analytics to exceed both its short-term and long-term moving average trend lines, but we also see that the spread between the price and these trend lines is widening. Based on our moving average indicators, positive momentum continues to persist for small properties but not large hotels this quarter. This indicates a continued signal that small hotels are still a buy and hold. Larger hotels however are moving towards a sell signal. Our Standardized Unexpected Price (SUP) metrics in Exhibit 13 show that the price of large hotels continued its descent this quarter, in contrast to the statistically significant and positive price momentum of smaller hotels, as depicted in Exhibit 14 (observe that both metrics are above the upper significance band). Repeat Sales: Prices Are Rising at a Decreasing Rate. Similar to small hotels, our repeat sale indicator 14 The Center for Real Estate and Finance Cornell University

17 Exhibit 13 Standardized unexpected price (SUP) for high-price hotel index 3 Critical value (9%) Price surprise indicator: High-price hotels (12 quarters, 3 yrs) Critical value (9%) Price surprise indicator: High-price hotels (2 quarters, 5 yrs) 2 1 Standarzied Unexp;ected Price Sources: Cornell Center for Real Estate and Finance, CoStar, Real Capital Analytics Exhibit 14 Standardized unexpected price (SUP) for small-hotel index 3 2 Standard Unexpected Price Critical value (9%, 1.65) Price surprise indicator: Low-price hotels (12 quarters, 3 yrs) Critical value (9%, -1.65) Price surprise indicator: Low-price hotels (2 quarters, 5 yrs) Sources: Cornell Center for Real Estate and Finance, CoStar, Real Capital Analytics CREF Hotel Indices January Vol. 8 No. 1 15

18 Exhibit 15 Moving average trend line for repeat sale-hotel index Repeat-sale Index Repeat sales Moving average (12 quarters, 3 years) Moving average (2 quarters, 5 years) Sources: Cornell Center for Real Estate and Finance, CoStar, Real Capital Analytics for the moving average trendline in Exhibit 15 indicates a continuation of the positive price momentum. 2 The price of hotels that have sold more than once (repeat sales) is still higher than either its short-term and long-term moving average. Our SUP performance metric (in Exhibit 16) indicates that although standardized prices continued to demonstrate positive strength 2 We report two repeat sale indices. The repeat sale full sample index uses all repeat sale pairs, whereas the repeat sale index with a base of 1 at 2Q1 uses only those sales that occurred on or after the first quarter of 2, but not those before that time. As such, if a hotel sold in 1995 and then sold again in 212, it would be included in the full sample, index but it would not be included in the repeat sale index that includes data from 2 onward. this quarter, they still remain below the upper significance line, in contrast to the situation with small hotels (referring to Exhibit 14). Exhibit 17 shows that the repeat sale price index is increasing at a decreasing rate, as follows. The repeat sale price index rose 3 percent year over year (217Q4 to 218Q4), down from a 4.6-percent price rise year over year (217Q3 to 218Q3) in the previous period. It also increased about 1 percent quarter over quarter (218Q3-218Q4), a smaller increase than the 2.6 percent rise recorded in the previous quarter (218Q2-218Q3). Mortgage Financing Volume for Hotels Declined Year over Year and Also Quarter over Quarter. Ex- 16 The Center for Real Estate and Finance Cornell University

19 Exhibit 16 Standardized Unexpected Price (SUP) for hotel repeat sale index (full sample) 3 2 Standard Unexpected Price Critical value (9%) Price surprise indicator: Low-price hotels (12 quarters, 3 yrs) Critical value (9%) Price surprise indicator: Low-price hotels (2 quarters, 5 yrs) -3 Sources: Cornell Center for Real Estate and Finance, CoStar, Real Capital Analytics Exhibit 17 Year-over-year change in repeat-sale index, with moving-average trend line 4% Year-over-year change in repeat-sales index 3% 2% 1% % -1% -2% -3% Sources: Cornell Center for Real Estate and Finance, CoStar, Real Capital Analytics CREF Hotel Indices January Vol. 8 No. 1 17

20 Exhibit 18 Mortgage origination volume versus loan-to-value ratio for hotels 35 8% MBAA Hotel Mortgage Origination Volume Index MBAA Hotel origination volume index (21 avg quarter = 1) CWSG Max loan-to-value (full-service hotels) 7% 6% 5% 4% 3% 2% Minimum Hotel Loan-to-Value Ratio (LTVR) 5 1% Sources: Cornell Center for Real Estate and Finance, Mortgage Bankers Association hibit 18 shows that mortgage origination volume for hotels as reported for 218Q3 is 19-percent lower on a year-over-year basis (217Q3-218Q3), 3 and even lower (-3%) on a quarter-over-quarter basis (218Q3 compared to 218Q2). The maximum loan-to-value (LTV) ratio for hotels remained at 7 percent. The Cost of Hotel Debt Financing Dipped Slightly with No Change in the Relative Risk Premium for Hotels. The cost of obtaining hotel debt financing, as reported by Cushman Wakefield Sonnenblick Goldman, dipped slightly this quarter for both Class A and Class B and C Hotels compared to the previous quarter. 4 Exhibit 19 shows that interest rates on Class A and Class B and C hotel deals de- 3 This is the latest information reported by the Mortgage Bankers Association as of the writing of this report. 4 The interest rate reported by Cushman Wakefield Sonnenblick Goldman (CWSG) differs from the interest rate used to calculate our EVA metric, which is based on the interest rate reported by the American Council of Life Insurers (ACLI). The ACLI interest rate reflects what life insurers are charging for institutional-size hotel deals. Our EVA calculation is based on property-specific cap rates and the associated financing terms. The CWSG interest rate is based on deals that CWSG has brokered, as well as their survey of rates on hotel deals. Those deals are not necessarily similar to deals that are reported by ACLI. clined relative to the previous quarter, but rose on a year-over-year basis a statistic that is relatively more worrisome. Interest rates were 5.9 percent for Class A properties and 5.29 percent for Class B and C deals in the fourth quarter of 218 (as of December), compared to 5.24 percent for Class A deals and 5.44 percent for Class B and C properties in the previous quarter (September 218). Year over year, however, interest rates increased 6 percent for financing on both Class A deals (from 4.8% to 5.9%) and also on Class B and C hotels (from 5% to 5.3%). 5 This upward trend in interest rates started in July 216. Exhibit 2 and Exhibit 21 depict interest rate spreads relative to different benchmarks. Exhibit 2 shows the spread between interest rates on Class A full-service hotels (as well as B and C) over the ten-year Treasury bond. On this metric, interest rate spreads have risen 25 basis points for both Class A and Class B and C hotels in the current quarter relative to the prior quarter (Class A spread: 2.45% versus 5 The biggest advantage of year-over-year comparisons relative to quarter-over-quarter comparisons is that they mitigate the effect of seasonality in addition to smoothing out any volatility in quarter-over quarter numbers. It s a good idea to look at quarterto-quarter as well to get the full picture. 18 The Center for Real Estate and Finance Cornell University

21 Exhibit 19 Interest rates on Class A hotels versus Class B and C properties 1% 9% 8% 7% Interest-rates 6% 5% 4% 3% 2% Class A interest rate Class B and C interest rate 1% Source: Cushman Wakefield Sonnenblick Goldman Exhibit 2 Interest-rate spreads of hotels versus U.S. Treasury ten-year bonds 7% 6% Interest-rate spread (Hotel - 1-year Tbond) 5% 4% 3% 2% 1% Class A interest-rate spread (Hotel 1-year Tbond) Class B & C interest-rate spread (Hotel 1-year Tbond) Source: Cushman Wakefield Sonnenblick Goldman CREF Hotel Indices January Vol. 8 No. 1 19

22 Exhibit 21 Interest-rate spreads of hotels versus non-hotel commercial real estate 1.6% 1.4% Interest-rate spread (Hotel Commercial real estate) 1.2% 1.%.8%.6%.4%.2% -.2% Class A interest-rate spread (Hotel CRE) Class B and C interest-rate spread (Hotel CRE) -.4% Source: Cushman Wakefield Sonnenblick Goldman 2.2%; Class B spread: 2.65% versus 2.4%). The rise in interest rate spreads signals that lenders view hotels as relatively more risky relative to our last report, and as such lenders compensation for risk associated with hotel loans has increased. Exhibit 21 shows the spread between the interest rate on Class A full-service hotels (and B and C properties) over the interest rate corresponding to non-hotel commercial real estate. This spread represents the hotel real estate premium. 6 The monthly hotel real estate premiums for both higher quality (Class A) and lower quality (Class B and C) hotels have remained constant relative to the prior quarter. In the current quarter (218Q4), the hotel real estate premium averaged.43 percent (.53% for B and C hotels), exactly the same as the premiums in the previous quarter (218Q3). This is a signal that the perceived default risk for hotel properties relative to other commercial real estate (office, retail, industrial, 6 The interest rate on hotel properties is generally higher than that for apartment, industrial, office, and retail properties in part because hotels cash flow is commonly more volatile than that of other commercial properties. and apartments) has remained the same this quarter compared to the previous quarter. Cost of Equity Financing Remained Relatively Constant, although the Riskiness of Hotels Relative to Other Types of Commercial Real Estate Has Risen. The cost of using equity financing for hotels as measured using the Capital Asset Pricing Model (CAPM) on hotel REIT returns, as shown in Exhibit 22, increased imperceptibly this quarter. We found that the cost of using equity funds was 8.26 percent for 218Q4, compared to 8.23 percent in the previous quarter. The cost of borrowing equity capital has thus remained relatively stable. In terms of total risk (systematic risk + risk that is unique to hotel REITs), Exhibit 23 shows that the total risk of hotel REITs rose this past quarter relative to the total risk of equity REITs as a whole. 7 This is at odds with Exhibit 21, which shows that the perceived default risk for hotels has remained constant (stable) relative to other types of commercial real es- 7 We calculate the total risk for hotel REITs using a twelvemonth rolling window of monthly return on hotel REITs. 2 The Center for Real Estate and Finance Cornell University

23 Exhibit 22 Cost of equity financing using the Capital Asset Pricing Model and hotel REITs % 14% Hotel REIT Beta Cost of equity (NAREIT) Beta (lodging) 12% 1% 8% 6% 4% 2% Cost of equity (measured using Hotel REITs) Sources: Cornell Center for Real Estate and Finance, NAREIT Exhibit 23 Risk differential between hotel REITs and equity REITs 12 Differential: [σ (Hotel REIT Returns) - σ (Equity REIT Returns)] Sources: Cornell Center for Real Estate and Finance, NAREIT CREF Hotel Indices January Vol. 8 No. 1 21

24 Exhibit 24 Hotel repeat sales index versus NAREIT lodging/resort price index 3 25 Repeat sales (full sample) NAREIT Lodging/Resort Price Index 12-period moving average repeat sales (full sample) 12-period moving average NAREIT Index 2 Price index Sources: Cornell Center for Real Estate and Finance, NAREIT tate, as we noted above. Expect higher borrowing costs for hotel loans to ensue given these metrics. Expect the Prices of Large Hotels and Small Hotels to Fall, Based on Our Reading of the Tea Leaves. Exhibit 24 compares the performance of the repeat sales index relative to the NAREIT Lodging/Resort Price Index. Note that the repeat sales index tends to lag the NAREIT index by at least one quarter or more. 8 Looking ahead, the NAREIT lodging index declined almost 22 percent this quarter compared to no change in the prior quarter, while it also decreased 17.5 percent year over year. We see then that the moving average NAREIT Lodging/Resort trendline has started to edge down, signaling a negative price momentum. The architecture billings index (ABI) for commercial and industrial property, which represents another forward-looking metric, declined this quarter from the previous quarter, as shown in Exhibit 25 (48.9 versus 8 This is consistent with prior academic studies which find that securitized real estate is leading indicator of underlying real estate performance, since the stock market is forward looking or efficient. 5.8). 9 The ABI metric provides confirmatory evidence that we should expect decreasing price momentum. The National Association of Purchasing Managers (NAPM) index shown in Exhibit 26, which is an indicator of anticipated business confidence and thus business traveler demand, decreased 9.4 percent year over year (-9.5 percent on a quarter-over-quarter basis) compared to a 2-percent increase in the prior yearover-year period (217Q3-218Q3). 1 Based on the moving average trendline for NAPM index, we expect 9 As of the time of this writing, only the October 218 AIA Billings Index has been reported. The December index was scheduled for release on January 23, 219. See: economics/aias The ISM: Purchasing Managers Index, (Diffusion index, SA), also known as the National Association of Purchasing Managers (NAPM) index, is based on a survey of over 25 companies within twenty-one industries covering all 5 states. It not only measures the health of the manufacturing sector but is a proxy for the overall economy. It is calculated by surveying purchasing managers for data about new orders, production, employment, deliveries, and inventory, in descending order of importance. A reading over 5% indicates that manufacturing is growing, while a reading below 5% means it is shrinking.

25 Exhibit 25 Hotel repeat sales index versus architecture billings index Repeat sales (full sample) Repeat sales Architecture Billings Index 4-period average moving average (architectural billings) Architecture Billings Index (ABI) Sources: Cornell Center for Real Estate and Finance, American Institute of Architects Exhibit 26 Business confidence index (National Association of Purchasing Managers) and high-price hotel index High-price hotels hedonic index High-price hotels ISM Purchasing Managers Index (Diffusion index, SA) Four-period moving average, ISM Purchasing Managers Index ISM Purchasing Managers Index Sources: Cornell Center for Real Estate and Finance, Institute for Supply Management (ISM)

26 Exhibit 27 Consumer confidence index and low-price hotel index Low-price hotels hedonic index Low-price hotels Consumer confidence index Four-period moving average of consumer confidence index Consumer confidence index Sources: Cornell Center for Real Estate and Finance, Conference Board the price of large hotels to fall over the next quarter. The Consumer Confidence Index from the Conference Board, graphed in Exhibit 27, which we use as a proxy for anticipated consumer demand for leisure travel and a leading indicator of the hedonic index for low priced hotels, rose 5 percent year over year (9.5 percent quarter over quarter) continuing its positive trend from the previous period (15.5%), although the rate of increase has declined. We expect the price momentum for small hotels to moderate at best and decline at worst in the next quarter. Hotel Valuation Model (HOTVAL) Has Been Updated. We have updated our hotel valuation regression model to include the transaction data used to generate this report. We provide this user-friendly hotel valuation model in an Excel spreadsheet entitled HOTVAL Toolkit as a complement to this report. The tool is available for download from our CREF website. 24 The Center for Real Estate and Finance Cornell University

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 Q = (A Q m Q )/s Q where SUE Q = quarter Q standardized unexpected earnings, A Q = quarter Q actual earnings per share reported by the firm, m Q = quarter Q consensus earnings per share forecasted by analysts in quarter Q-1, and s Q = quarter Q standard deviation of earnings estimates. Quarter SUP data and σ calculation for high-price hotels (12 quarters/3 years) High-price hotels m Moving average σ Price surprise indicator (SUP) From statistics, the SUE Q is normally distributed with a mean of zero and a standard deviation of one (~N(,1)). This calculation shows an earnings surprise when earnings are statistically significant, when SUE Q exceeds either ±1.645 (9% significant) or ±1.96 (95% significant). The earnings surprise is positive when SUE Q > 1.645, which is statistically significant at the 9% level assuming a two-tailed distribution. Similarly, if SUE Q < then earnings are negative, which is statistically significant at the 9% 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 fiveyear 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 fiveyear 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) = ( ) 12 = Standard Deviation (σ) = Standardized Unexp Price (SUP) = ( ) = CREF Hotel Indices January Vol. 8 No. 1 25

28 CREF Advisory Board Center for Real Estate and Finance Reports Vol. 8 No. 1 (January 219) Arthur Adler 78, P 16 Chairman, Americas Jones Lang LaSalle Steven M. Angel Principal Fulcrum Hospitality LLC Richard Baker 88 Governor and Chief Executive Officer HBC Michael Barnello A&S 87 President & Chief Executive Officer LaSalle Hotel Properties Robert Buccini 9 Co-president The Buccini/Pollin Group Marty Burger P 17, P 2 Chief Executive Officer Silverstein Properties, Inc. Adam Burinescu CALS 3 Managing Director Centerbridge Partners, LP Rodney Clough 94 Managing Partner HVS Howard Cohen 89 President & Chief Executive Officer Atlantic Pacific Companies Navin Dimond P 14, P 19 President & Chief Executive Officer Stonebridge Companies Joel Eisemann, MPS 8 Chief Development Officer, Americas InterContinental Hotels Group Habib Enayetullah SVP for Real Estate and Asset Management Hilton Worldwide Russell Galbut 74 Managing Principal Crescent Heights Nolan Hecht 97 Senior Managing Director Square Mile Capital Kate Henrikson 96 SVP Investment and Portfolio Analysis RLJ Lodging Trust Kenneth Himmel 7 President and CEO Related Urban Co-Managing Partner Gulf Related Jeff Horwitz Partner, Head of M&A Private Equity Real Estate, Head of Private Equity Lodging and Gaming, and Corporate Governance International Practice Group Proskauer David Jubitz 4 Principal Clearview Hotel Capital Alan Kanders 87 Principal Three Wall Capital Rob Kline 84 President & Co-Founder The Chartres Lodging Group Neil Luthra Principal Highgate Jay Mantz P 21 President, New York Rialto Alfonso Munk 96 Americas Chief Investment Officer and Head of Latin America Prudential Real Estate Investors Chip Ohlsson Executive Vice President and Chief Development Officer, North America Wyndham Hotel Group Mark Owens EVP and Head of Hospitality Capital Markets CBRE Daniel Peek 92 Senior Managing Director HFF David Pollin 9 Co-founder and President The Buccini/Pollin Group Ray Potter CALS 87, MBA 92 Founder and Managing Partner R3 Funding Michael Profenius, P 15, 17 Senior Partner, Head of Business Development Northwood Investors David Rosenberg P 11, P 13, P 19 Chief Executive Officer Sawyer Realty Holdings Chuck Rosenzweig ILR 85, JD 88 Founder and Managing Partner Criterion Real Estate Capital Ben Rowe 96 Founder and Managing Partner KHP Capital Partners Nirav Shah 5 Vice President, Development Hyatt Hotels Corporation Seth Singerman 99 Managing Partner Singerman Real Estate ( SRE ) Jackie Soffer P 2 Chairman & CEO Turnberry 219 Cornell University. This report may not be reproduced or distributed without the express permission of the publisher. The CREF Report series is produced for the benefit of the hospitality real estate and finance industries by The Center for Real Estate and Finance at Cornell University Daniel Quan, Arthur Adler 78 and Karen Newman Adler 78 Academic Director Cristina Carter, Assistant Program Manager Glenn Withiam, Contributing Editor Kate Walsh, Dean, E.M. Statler Professor, School of Hotel Administration Center for Real Estate and Finance Cornell University Cornell SC Johnson College of Business School of Hotel Administration Statler Hall Ithaca, NY Robert Springer 99 Executive Vice President, Chief Investment Officer Sunstone Hotel Investors Alan Tantleff 87 Senior Managing Director Corporate Finance/Restructuring Practice Leader, Hospitality Gaming and Leisure FTI Consulting Dan Unger 97 Chief Development Officer Tishman Robert White President Real Capital Analytics Jon S. Wright President and CEO Access Point Financial Lanhee Yung 97 Managing Director of Global Fundraising and Investor Relations Starwood Capital Group 26 The Center for Real Estate and Finance Cornell University