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Real Estate Market Segmentation: Hotels as Exemplar

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Abstract

Market segmentation for hotel properties is quantitatively assessed. Results indicate that the hotel property market is segmented by hotel class. The results are robust to model specification including general economic conditions, property performance measured by market level RevPAR, ADR, standard deviation in ADR, occupancy and standard deviation in occupancy and room count. The results strongly suggest segmented hotel property markets. By showing that hotel properties are not drawn from a single property population, the results advance the notion that aggregate property type pricing models may provide biased estimators.

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Notes

  1. Much of the property level data are proprietary making comparison difficult. Only a small portion of data publically available or from data vendors is sufficient to generate EGI, NOI and other property performance measures. Lease maturity is also unreported in much of the existing literature and in public disclosure of transaction price. This makes it difficult to get market and sub-market level data such as actual rents and occupancy. In much of the literature, submarket dummy and location designation (such as latitude and longitude) are used to proxy market conditions including rents. The critique is one related to sparse or limited data limiting investigation. The fall back has been a reliance on hedonic characteristics to model price. This contrasts with standard valuation and investment tools that focus on cash flows, DCF and financial performance.

  2. Even as the private sector extensively uses some type of segmentation such as “A”, “B”, “4-Star” or some other delineation which is likely related to rents and value.

  3. The segmentation is driven by brand management, franchisors, and web-based rating systems. There are internal and external validating actions. Physical quality, overall amenities and services are used.

  4. Contrasting somewhat with Corgel et al. (2015), the present study assumes that property class reflects amenity and quality differentiation that impacts rents.

  5. One reason appraisals are useful and become part of the due diligence and fund management processes of commercial real estate is that valuers/appraisers spend time obtaining and verifying property level data for the appraised property and comparable properties. This is a costly task. For example, discussions with an institutional level “value-add” fund manager points to as much as 3% of the purchase price being spent on due diligence. This can be a multimillion dollar capital expenditure for a large property inclusion of review of financial performance, environmental, legal and physical property risks.

  6. It must be acknowledged that price or valuation focused research is lacking for the hotel class. Present studies are limited to case studies or look at related issues like dividend policies (Mooradian and Yang (2001)), potential benefits to brand, flag or management in operations (Brady and Conlin (2004)), economic clustering (Gallagher and Mansour (2000)), and market shocks (Corgel (2002)).

  7. Smith Travel Research (STR) Global provides this level of detail, although extensive granular data is less available since the individual hotels providing data desire anonymized reporting.

  8. One of the “short comings” often mentioned with hotel investment is the high degree of market risk for the property type along with higher levels of operational management.

  9. We do not provide a comprehensive study of all property type specific value or price models. Our intent is to highlight the lack of direct rent data at the property and market levels used in existing studies of transaction prices. We speculate that this limitation is a result of data constraints and the imposition of the hedonic models that dominate residential pricing models.

  10. One reason the multifamily property class is used is that rents are re-priced quickly relative to other asset classes making the market proxy variables more meaningful as controls. Acknowledgement is again made to limitations in data. The general hypothesis proffered is that rental market data when available would make studies more complete.

  11. This is as opposed to a residual land approach which would have explicitly address property level rents and improvement costs.

  12. This would be related to re-branding and or maintaining a brand within a market or submarket.

  13. Corgel et al. (2015) proffer a DCF model, but are stymied in assessment due to limited actual property level net operating income data. The present research similarly uses market level performance data.

  14. The initial complete set of transactions totaled 2688. The final sample used is smaller due to missing price data on 337 properties and the loss of 94 properties given the use of a 12-month lag period in calculating some model variables.

  15. We use Honolulu county population for Oahu.

  16. We use land elasticity of Los Angeles for Anaheim.

  17. An additional work by Saiz (2010) provides a refined structure for defining and investigating geographically related land elasticities for housing.

  18. The wage categories from BLS for 2010 are: 11–9081(Lodging Managers), 35–0000 (Food Preparation and Serving Related Occupations), 37–2012 (Maids and Housekeeping Cleaners), 43–4081 (Hotel, Motel, and Resort Desk Clerks), 49–0000 (Installation, Maintenance, and Repair Occupations). While these cost measures are broad, they systematically address operating costs and reflect relative costs across cities.

  19. If a city in our sample is missing from the census tables, we replace it with a nearby metropolitan area. (This is true for Norfolk Virginia, Anaheim, New Orleans, and Oahu – replaced by indexes from Hampton, VA, Orange, CA, Baton Rouge, LA, and Honolulu, HI, respectively).

  20. Corgel et al. (2015) use city wide NOI and estimated property level. In the present model, we use ADR and occupancy.

  21. The STR data available for the period has very limited actual NOI data. For a subset of properties with full data, we found that measures of room rate performance (RevPar and Occupancy, for example) in fixed effects models (class, market, year and location) provided a better model fit than net operating income (NOI). NOI includes non-rental revenues (food and beverage as the prime example). When one conditions on property class, neither the NOI nor the food and beverage variables are statistically significant. Hotel class is a primary determinant of rent and the presence of food and beverage income.

  22. The results are quantitatively and qualitatively the same using unadjusted data.

  23. A separate test, not provided, indicates that LSE, Wage and Utility variables are correlated.

  24. Another way to look at this is to say that the coefficient on Luxury is 33.57% larger that the coefficient on Low/Mid.

  25. The recession variable is an indicator equals to 1 if the transaction took place during the recession years (2001, 2008, and 2009) and 0 otherwise.

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Authors and Affiliations

  1. The Hollo School of Real Estate, College of Business, Florida International University, 1101 Brickell Avenue, Suite 1100S, Miami, FL, 33131, USA

    Eli Beracha & William G. Hardin III

  2. Department of Finance and Real Estate, College of Business, Colorado State University, 324 Rockwell Hall, 1201 Campus Delivery, Fort Collins, CO, 80523, USA

    Hilla Maaria Skiba

Authors
  1. Eli Beracha
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  2. William G. Hardin III
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  3. Hilla Maaria Skiba
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Corresponding author

Correspondence to William G. Hardin III.

Appendix: Selected market characteristics

Appendix: Selected market characteristics

Table 6 Appendix shows selected market characteristics for the 25 markets included in the analysis. These variables include land supply elasticity, index value for average cost of utilities (2010), and average hospitality wage from hotel managers, food services, maids, desk clerks, and maintenance and repair workers.
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Beracha, E., Hardin, W.G. & Skiba, H.M. Real Estate Market Segmentation: Hotels as Exemplar. J Real Estate Finan Econ 56, 252–273 (2018). https://doi.org/10.1007/s11146-017-9598-z

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