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Pricing Extreme Attributes in Commercial Real Estate: the Case of Hotel Transactions

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Abstract

We show that conventional hedonic models for commercial real estate prices ignore the utility investors derive from a building’s extreme attributes. Analyzing geo-enriched data on nearly 4,800 hotel transactions in the United States, we find that the relative positioning of an asset’s attributes – particularly at the extremes – has a significant impact on transaction prices. We also detect separating equilibria for extreme attributes across the premium and discount hotel segments. Extreme attributes “stand out” and are value enhancing in premium hotel segments. In contrast, extreme attributes are value diminishing in the discount hotel segment. The relative degree to which the asset’s attributes are extreme is important. Being a locally largest asset has a negative effect on price, however the negative effect is more than offset if the hotel is among the largest hotels nationally. The results suggest that locally extreme assets, unless also nationally extreme, are considered atypical and trade at a discount.

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Notes

  1. Investors’ desire to conspicuously stand out is particularly true in the hotel segment:

    (1) https://www.hotel-online.com/Trends/Andersen/Trophy_Investments.html

    (2) http://www.wsj.com/articles/SB107706340089632085

    (3) http://www.wsj.com/articles/waldorf-astoria-hotel-sale-completed-1423705536,

    (4) http://www.shanghaidaily.com/supplement/real-estate/Huge-jump-in-trophy-hotels-changing-hands/shdaily.shtml

  2. We provide a comprehensive review of the literature later in the paper in the background section.

  3. For more details, refer to: http://www.esri.com/landing-pages/tapestry

  4. See “2013 UNITED STATES HOTEL FRANCHISE FEE GUIDE” by HVS

  5. E.g. Accor North America Corp versus Accor North America

  6. E.g. Apple REIT companies versus Apple REIT Eight

  7. https://www.costar.com/about/support/costar-glossary#go_m

  8. Anecdotal evidence suggests an average 18mph driving speed in US urban areas. Thus, a 20-min driving distance would roughly imply a 6-mile radius, on average.

  9. The neighboring hotels includes sold and non-sold hotels

  10. Here, i indexes a subject hotel; \( \overset{-}{k}\ \mathrm{and} \) σ k   denote mean and standard deviation of the attribute k among all hotels within the trade area of a subject hotel. In all Z-Statistic calculations, groups with zero standard deviation are assigned a Z-statistic of zero.

  11. such that Z k = Age  ≥ 2

  12. Of the 55,000 hotels in the STR’s census, we have transaction details on approximately 4800 hotels. 5% of these sold hotels equal \( 0.45\%=\frac{5{\%}^{\ast }4,800}{55,000} \) of the population.

  13. 0.035∗FLOORS − 0.001 FLOORS2 = 0.306 − 0.001∗(FLOORS − 17.5)2 ; Also, \( \frac{0.306}{17.5}\approx 2\% \)

  14. http://www.npr.org/2011/11/07/141858484/how-the-worlds-tallest-skyscrapers-work

  15. \( -{0.008}^{\ast}\mathrm{AGE}+{0.00004}^{\ast }{\mathrm{AGE}}^2=-0.4+{0.00004}^{\ast }{\left(\mathrm{AGE}-100\right)}^{;2}\ Also,\frac{0.4}{100}\approx 0.4\% \)

  16. Based on their coefficients, top-10 and bottom-10 markets are listed in Appendix Table 10.

  17. e 0.111 − 1 = 11.7%

  18. Skewness = −0.44, kurtosis = 5.6

  19. For further details, see Appendix Table 11

  20. All assets which do not have an extreme attribute will have another asset with the locally extreme attribute. To avoid the issue of almost perfect multicollinearity, we specifically include the neighboring assets whose extreme attributes exceed at least one standard deviation from the local mean (i.e. Z≥1)

  21. The net effect is 1.36 × 0.88 = 1.20

References

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Acknowledgements

This manuscript has benefitted greatly from the anonymous referees of the journal. The authors are thankful to the following individuals for their contributions to this study: Duane Vinson and Steve Hood (STR Global), Jamie Alcock, Gabrielle Bodenmann, Yong Chen, Steffen Raub, Sean Way, Karthik Namasevayam, Emmanuel Jurczenko, Ramya Aroul, Jonathan Humphries, Frederick Delley, Marc Steirand, Oliver Judge, Adnan Shamim, and many others.

Author information

Authors and Affiliations

  1. Ecole hôtelière de Lausanne, HES.SO // University of Applied Sciences Western Switzerland, route de Cojonnex 18, 1000, Lausanne, Switzerland

    Prashant Das

  2. Fowler College of Business Administration, San Diego State University, 5500 Campanile Drive, San Diego, CA, 92182-8230, USA

    Patrick Smith

  3. Henley Business School, University of Reading, Reading, RG6 6UD, UK

    Paul Gallimore

Authors
  1. Prashant Das
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  2. Patrick Smith
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  3. Paul Gallimore
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Corresponding author

Correspondence to Prashant Das.

Additional information

An earlier version of the paper received the best paper award in real estate category at the Asian Real Estate Society Conference, Bangalore- 2016.

Appendix

Appendix

Table 10

Table 10 Top and bottom submarkets for hotel pricing
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Table 11

Table 11 Distribution of extreme attributes across premium and discount assets
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Fig. 6

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Enhanced Quantile Regression Estimates with the conspicuity level of Z = 2. Notes: This quantile regression plot depicts how the extreme attributes (specified plot titles) are differently associated with the dependent variable (∈) across its quantiles. ∈ is the residual extracted from a baseline hedonic model of the natural log of sale price. The analysis is applied to hotel sales in the U.S. from 1991 to 2013 for a sample of nearly 4800 hotel transactions. Dashed horizontal lines are the confidence interval for the baseline hedonic model applied to the same data. The dotted curve is the coefficient estimate from the quantile regression. Its confidence intervals are depicted by the grey band. Trade area refers to the 20-min driving distance radius surrounding a subject hotel. Nationally oldest, largest and tallest categories are based on the top 1% hotels based on the hotel age, number of rooms and number of floors respectively. Z2 prefix signifies that the extreme attributes are also characterized by being at least two standard deviations higher than the trade-area mean. XZ2 prefix signifies that the specified extreme attributes (of another hotel in the neighborhood relative to the subject hotel) exceeded two standard-deviations from the mean of the trade-area hotels

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Fig. 7

Fig. 7
figure 7

Enhanced Quantile Regression Estimates with the conspicuity level of Z = 3. Notes: This quantile regression plot depicts how the extreme attributes (specified plot titles) are differently associated with the dependent variable (∈) across its quantiles. ∈ is the residual extracted from a baseline hedonic model of the natural log of sale price. The analysis is applied to hotel sales in the U.S. from 1991 to 2013 for a sample of nearly 4800 hotel transactions. Dashed horizontal lines are the confidence interval for the baseline hedonic model applied to the same data. The dotted curve is the coefficient estimate from the quantile regression. Its confidence intervals are depicted by the grey band. Trade area refers to the 20-min driving distance radius surrounding a subject hotel. Nationally oldest, largest and tallest categories are based on the top 1% hotels based on the hotel age, number of rooms and number of floors respectively. Z3 prefix signifies that the extreme attributes are also characterized by being at least three standard deviations higher than the trade-area mean. XZ3 prefix signifies that the specified extreme attributes (of another hotel in the neighborhood relative to the subject hotel) exceeded three standard-deviations from the mean of the trade-area hotels

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Fig. 8

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figure 8

Quarterly Price Trend in the Baseline Model

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Das, P., Smith, P. & Gallimore, P. Pricing Extreme Attributes in Commercial Real Estate: the Case of Hotel Transactions. J Real Estate Finan Econ 57, 264–296 (2018). https://doi.org/10.1007/s11146-017-9621-4

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  • DOI: https://doi.org/10.1007/s11146-017-9621-4

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