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

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.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Notes

  1. 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. 2.

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

  3. 3.

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

  4. 4.

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

  5. 5.

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

  6. 6.

    E.g. Apple REIT companies versus Apple REIT Eight

  7. 7.

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

  8. 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. 9.

    The neighboring hotels includes sold and non-sold hotels

  10. 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. 11.

    such that Z k = Age  ≥ 2

  12. 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. 13.

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

  14. 14.

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

  15. 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. 16.

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

  17. 17.

    e 0.111 − 1 = 11.7%

  18. 18.

    Skewness = −0.44, kurtosis = 5.6

  19. 19.

    For further details, see Appendix Table 11

  20. 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. 21.

    The net effect is 1.36 × 0.88 = 1.20

References

  1. Abadie, A., & Dermisi, S. (2008). Is terrorism eroding agglomeration economies in central business districts? Lessons from the office real estate market in downtown Chicago. Journal of Urban Economics, 64(2), 451–463. doi:10.1016/j.jue.2008.04.002.

    Article  Google Scholar 

  2. Ackert, L. F., & Church, B. K. (2006). Firm image and individual investment decisions. Journal of Behavioral Finance, 7(3), 155–167. doi:10.1207/s15427579jpfm0703_4.

    Article  Google Scholar 

  3. Bagwell, L. S., & Bernheim, B. D. (1996). Veblen effects in a theory of conspicuous consumption. The American Economic Review, 86(3), 349–373. doi:10.1126/science.151.3712.867-a.

    Google Scholar 

  4. Bénabou, R., & Tirole, J. (2009). Over my dead body: bargaining and the price of dignity. American Economic Review, 99(2), 459–465.

    Article  Google Scholar 

  5. Billett, M. T., Jiang, Z., & Rego, L. L. (2014). Glamour brands and glamour stocks. Journal of Economic Behavior & Organization, 107, 744–759.

    Article  Google Scholar 

  6. Blal, I., & Graf, N. S. (2013). The discount effect of non-normative physical characteristics on the price of lodging properties. International Journal of Hospitality Management, 34, 413–422.

    Article  Google Scholar 

  7. Barr, J. (2010a). Skyscraper height. The Journal of Real Estate Finance and Economics, 45(3), 723–753. doi:10.1007/s11146-010-9274-z.

    Article  Google Scholar 

  8. Barr, J. (2010b). Skyscrapers and the skyline: Manhattan, 1895-2004. Real Estate Economics, 38(3), 567–597. doi:10.1111/j.1540-6229.2010.00277.x.

    Article  Google Scholar 

  9. Brennan, T. P., Cannaday, R. E., & Colwell, P. F. (1984). Office rent in the chicago CBD. Real Estate Economics, 12(3), 243–260 Retrieved from http://ideas.repec.org/a/bla/reesec/v12y1984i3p243-260.html.

    Article  Google Scholar 

  10. Campbell, J. D. (2014). Localized price promotions as a quality signal in a publicly observable network. Quantitative Marketing and Economics, 13(1), 27–57. doi:10.1007/s11129-014-9153-7.

    Article  Google Scholar 

  11. Capozza, D. R., Israelsen, R. D., & Thomson, T. A. (2005). Appraisal, agency and atypicality: evidence from manufactured homes. Real Estate Economics, 33(3), 509–537 Retrieved from http://www.scopus.com/scopus/inward/record.url?eid=2-s2.0-17544363804&partnerID=40.

    Article  Google Scholar 

  12. Corgel, J. B., & deRoos, J. A. (1994). Buying high and selling low in the lodging-property market. Cornell Hotel and Restaurant Administration Quarterly, 35(6), 33–38. doi:10.1177/001088049403500610.

    Article  Google Scholar 

  13. Corgel, J. B., Liu, C., & White, R. M. (2015). Determinants of hotel property prices. The Journal of Real Estate Finance and Economics. doi:10.1007/s11146-015-9494-3.

  14. Corneo, G., & Jeanne, O. (1997). Conspicuous consumption, snobbism and conformism. Journal of Public Economics, 66(1), 55–71. doi:10.1016/S0047-2727(97)00016-9.

    Article  Google Scholar 

  15. Das, P. (2015). Revisiting the hotel capitalization rate. International Journal of Hospitality Management, 46, 151–160. doi:10.1016/j.ijhm.2015.02.003.

    Article  Google Scholar 

  16. Das, P., & Wiley, J. a. (2014). Determinants of premia for energy-efficient design in the office market. Journal of Property Research, 31(1), 64–86. doi:10.1080/09599916.2013.788543.

    Article  Google Scholar 

  17. Eichholtz, P., Kok, N., & Quigley, J. M. (2010). Doing well by doing good ? Green office buildings. The American Economic Reviews, 100(5), 2492–2509.

    Article  Google Scholar 

  18. Ellingsen, T., & Johannesson, M. (2008). Pride and prejudice: the human side of incentive theory. American Economic Review, 98(3), 990–1008. doi:10.1257/aer.98.3.990.

    Article  Google Scholar 

  19. Esrig, D., Hudgins, M. C., & Cerreta, L. (2011). Revisiting the impact of large assets on real estate portfolio returns. The Journal of Portfolio Management, 35(5), 125–136. doi:10.3905/jpm. 2011.35.5.125.

    Article  Google Scholar 

  20. Gat, D. (1998). Toward a theory of the intraurban market for hotel services. Journal of Real Estate Finance and Economics, 211, 199–212.

    Article  Google Scholar 

  21. Ghysels, E., Plazzi, A., & Valkanov, R. (2007). Valuation in US commercial real estate. European Financial Management, 13(3), 472–497. doi:10.1111/j.1468-036X.2007.00369.x.

    Article  Google Scholar 

  22. Gifford, R. (2007). The consequences of living in high-rise buildings. Architectural Science Review, 50(1), 2. doi:10.3763/asre.2007.5002.

    Article  Google Scholar 

  23. Harrington Jr., J. E. (2009). Games, strategies, and decision making (2nd ed.). New York: Worth.

    Google Scholar 

  24. Heffetz, O. (2011). A test of conspicuous consumption: visibility and income elasticities. Review of Economics and Statistics, 93(4), 1101–1117. doi:10.1162/REST_a_00116.

    Article  Google Scholar 

  25. Helsley, R. W., & Strange, W. C. (2008). A game-theoretic analysis of skyscrapers. Journal of Urban Economics, 64(1), 49–64. doi:10.1016/j.jue. 2007.08.004.

    Article  Google Scholar 

  26. Hung, W. T., Shang, J. K., & Wang, F. C. (2010). Pricing determinants in the hotel industry: quantile regression analysis. International Journal of Hospitality Management, 29(3), 378–384. doi:10.1016/j.ijhm.2009.09.001.

    Article  Google Scholar 

  27. Jeary, A. P., Morris, R. G., & Tomlinson, R. W. (1988). Perception of vibration - tests in a tall building. Journal of Wind Engineering and Industrial Aerodynamics, 29, 361–370. doi:10.1016/0167-6105(88)90132-8.

    Article  Google Scholar 

  28. Kallberg, J. G., Liu, C. H., & Greig, D. W. (1996). The role of real estate in the portfolio allocation process. Real Estate Economics, 24(3), 359–377. doi:10.1111/1540-6229.00695.

    Article  Google Scholar 

  29. Kimes, S. E., & Fitzsimmons, J. a. (1990). Selecting profitable hotel sites at la Quinta motor inns. Interfaces, 20(2), 12–20. doi:10.1287/inte.20.2.12.

    Article  Google Scholar 

  30. Lee, K. O., & Mori, M. (2016). Do conspicuous consumers pay higher housing premiums? Spatial and temporal variation in the United States. Real Estate Economics, 44(3), 726–763.

    Article  Google Scholar 

  31. Lee, S. K., & Jang, S. (2012). Premium or discount in hotel room rates? The dual effects of a central downtown location. Cornell Hospitality Quarterly, 53(2), 165–173. doi:10.1177/1938965512441056.

    Article  Google Scholar 

  32. Leguizamon, S. (2010). The influence of reference group house size on house price. Real Estate Economics, 38(3), 507–527. doi:10.1111/j.1540-6229.2010.00275.x.

    Article  Google Scholar 

  33. Lin, Z., Rosenblatt, E., & Yao, V. W. (2009). Spillover effects of foreclosures on neighborhood property values. The Journal of Real Estate Finance and Economics, 38(4), 387–407. doi:10.1007/s11146-007-9093-z.

    Article  Google Scholar 

  34. Lizieri, C., & Pain, K. (2013). International office investment in global cities: the production of financial space and systemic risk. Regional Studies, 48(3), 439–455. doi:10.1080/00343404.2012.753434.

    Article  Google Scholar 

  35. Lockwood, L. J., & Rutherford, R. C. (1996). Determinants of industrial property value. Real Estate Economics, 24(2), 257–272.

    Article  Google Scholar 

  36. McDonald, J., & Dermisi, S. (2008). Capitalization rates, discount rates, and net operating income: the case of downtown Chicago office buildings. Journal of Real Estate Portfolio Management, 14(4), 363–374.

    Google Scholar 

  37. McGrath, K. M. (2013). The effects of eco-certification on office properties: a cap rates-based analysis. Journal of Property Research, 30(4), 345–365. doi:10.1080/09599916.2012.762034.

    Article  Google Scholar 

  38. McMillen, D. P. (2008). Changes in the distribution of house prices over time: structural characteristics, neighborhood, or coefficients? Journal of Urban Economics, 64(3), 573–589. doi:10.1016/j.jue.2008.06.002.

    Article  Google Scholar 

  39. Malmendier, U., & Tate, G. (2005). CEO overconfidence and corporate investment. The Journal of Finance, 60(6), 2661–2700.

    Article  Google Scholar 

  40. Nesbitt, S., & Phen. (1995). Buy high, sell low: timing errors in mutual fund allocation. Journal of Portfolio Management, 22(1), 57–60.

    Article  Google Scholar 

  41. Pai, A., & Geltner, D. (2007). Stocks are from Mars, real estate is from Venus. The. Journal of Portfolio Management, 33(5), 134–144. doi:10.3905/jpm. 2007.698912.

    Article  Google Scholar 

  42. Pauls, J. (1987). Calculating evacuation times for tall buildings. Fire Safety Journal, 12(3), 213–236. doi:10.1016/0379-7112 (87)90007-5.

    Article  Google Scholar 

  43. Peng, L. (2013). Finding cap rates: a property level analysis of commercial real estate pricing. University of Colorado at Boulder Leeds School of Business. Retrieved from http://www.reri.org

  44. Plazzi, A., Torous, W., & Valkanov, R. (2011). Exploiting property characteristics in commercial real estate portfolio allocation. The. Journal of Portfolio Management, 37(5), 39–50. doi:10.3905/jpm. 2011.37.5.039.

    Article  Google Scholar 

  45. Robinson, S., Singh, A. J., & Das, P. (2016). Financial impact of LEED and energy star certifications on hotel revenues. The Journal of Hospitality Financial Management, 24(2), 110–126. doi:10.1080/10913211.2016.1236567.

    Article  Google Scholar 

  46. Rosen, S. (1974). Hedonic prices and implicit markets: product differentiation in pure competition. Journal of Political Economy, 82(1), 34. doi:10.1086/260169.

    Article  Google Scholar 

  47. Rushmore, S. (2012). Hotel market analysis and valuation international issues and software applications. Appraisal Institute.

  48. Slade, B. (2000). Office rent determinants during market decline and recovery. Journal of Real Estate Research, 20(3), 357–380.

    Google Scholar 

  49. Smith, B. C. (2004). Economic depreciation of residential real estate: microlevel space and time analysis. Real Estate Economics, 32(1), 161–180. doi:10.1111/j.1080-8620.2004.00087.x.

    Article  Google Scholar 

  50. Thrall, G. I. (2002). Business geography and new real estate market analysis (spatial information systems). New York: Oxford University Press.

  51. Titman, S., & Twite, G. (2013). Urban density, law and the duration of real estate leases. Journal of Urban Economics, 74, 99–112. doi:10.1016/j.jue. 2012.10.003.

    Article  Google Scholar 

  52. Utts, J. M. (1982). The rainbow test for lack of fit in regression. Communications in Statistics-Theory and Methods, 11(24), 2801–2815. doi:10.1080/03610928208828423.

    Article  Google Scholar 

  53. Zahirovich-Herbert, V., & Chatterjee, S. (2011). What is the value of a name? Conspicuous consumption and house prices. Journal of Real Estate. Research, 33(1), 105–125.

    Google Scholar 

  54. Ziering, B., & McIntosh, W. (1999). Property size and risk: why bigger is not always better. Journal of Real Estate Portfolio Management, 5(2), 105–112.

    Google Scholar 

  55. Zietz, J., Zietz, E. N., & Sirmans, G. S. (2008). Determinants of house prices: a quantile regression approach. Journal of Real Estate Finance and Economics, 37(4), 317–333. doi:10.1007/s11146-007-9053-7.

    Article  Google Scholar 

Download references

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

Affiliations

Authors

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

Table 11

Table 11 Distribution of extreme attributes across premium and discount assets

Fig. 6

Fig. 6
figure6

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

Fig. 7

Fig. 7
figure7

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

Fig. 8

Fig. 8
figure8

Quarterly Price Trend in the Baseline Model

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

Keywords

  • Hedonic models
  • Price premium
  • Veblen effect
  • GIS
  • Commercial real estate
  • Hotels

JEL Classification

  • A14
  • G12
  • M31
  • R32