17 resultados para Hotels -- Installations

em The Scholarly Commons | School of Hotel Administration


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[Excerpt] The hotel business has become a business of brands. Price Waterhouse Coopers estimates that there are over 300 hotel brands today with no one brand dominating the market. Every major brand management issue (brand extensions, global brand expansion, re-branding, un-branding, co-branding, brand portfolio development, brand acquisitions, new brand development, etc.) is being explored. An understanding of the competitive context and intra-and inter-brand dynamics will help owners, operators, asset managers, suppliers and litigators, as well as new entrants into the business make better and more informed brand management decisions.

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Our Standardized Unexpected Price (SUP) metric continues to show a decline in the price of large hotels, and now also the price of small hotels has eased—even though hotel transaction volume has increased. Although debt and equity financing for hotels remain relatively inexpensive, we are concerned that the total volatility of hotel returns is greater relative to the return volatility for other commercial real estate. If this trend continues, lenders will eventually start to tighten hotel lending standards. Our early warning indicators all continue to suggest that the downward trend in hotel prices should continue into the next quarter. This is report number 19 of the index series.

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Our Standardized Unexpected Price (SUP) metric showed an uptick in the price of large hotels during the third quarter of 2016, with a continued decline in the price of small hotels. Although debt and equity financing for hotels were still relatively inexpensive during this quarter, we remain concerned that the increasing relative riskiness of hotels compared to other commercial real estate suggests that lenders will eventually start to tighten hotel lending standards if this trend continues. Our early warning indicators continue to suggest an eventual downward trend in large hotel prices. This is report number 20 of the index series.

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Yield management helps hotels more profitably manage the capacity of their rooms. Hotels tend to have two types of business: transient and group. Yield management research and systems have been designed for transient business in which the group forecast is taken as a given. In this research, forecast data from approximately 90 hotels of a large North American hotel chain were used to determine the accuracy of group forecasts and to identify factors associated with accurate forecasts. Forecasts showed a positive bias and had a mean absolute percentage error (MAPE) of 40% at two months before arrival; 30% at one month before arrival; and 10-15% on the day of arrival. Larger hotels, hotels with a higher dependence on group business, and hotels that updated their forecasts frequently during the month before arrival had more accurate forecasts.

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In the hotel industry, undistributed operating expenses represent a significant portion of the operating costs for a hotel. Exactly how most of these expenses arise is not well understood. Using data from more than 40 hotels operated by a major chain, the authors examine the links between the variety of a hotel’s products and customers and its undistributed operating expenses and revenues. Their findings show that undistributed operating expenses are related to the extent of the property’s business and product-services mix. The results suggest that although increasing a property's product-service mix results in higher undistributed operating expenses, the incremental costs are compensated for by higher revenues. However, increasing business mix while increasing undistributed operating expenses does not result in higher revenues.

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[Updated August 2016] The Hotel Valuation Software, freely available from Cornell’s Center for Hospitality Research, has been updated to reflect the many changes in the 11th Edition of the Uniform System of Accounts for the Lodging Industry (USALI). Version 4.0 of the Hotel Valuation Software provides numerous enhancements over the original tool from 2011. In addition to a significant increase in functionality and an update to reflect the 11th edition of the USALI, Version 4.0 takes advantage of the power of the latest release of Microsoft Excel®. Note that Version 4.0 works only on a PC running Microsoft Windows, it does not work on a Mac running OS X. Users desiring an OS X compatible version should click here (Labeled as Version 2.5). 酒店评估软件手册和三个程序(点击这里 ) Users desiring a Mandarin version of the Hotel Valuation Software should click here The Hotel Valuation Software remains the only non-proprietary computer software designed specifically to assist in the preparation of market studies, forecasts of income and expense, and valuations for lodging property. The software provides an accurate, consistent, and cost-effective way for hospitality professionals to forecast occupancy, revenues and expenses and to perform hotel valuations. Version 4.0 of the Hotel Valuation Software includes the following upgrades – a complete update to reflect the 11th edition of the USALI – the most significant change to the chart of accounts in a generation, an average daily rate forecasting tool, a much more sophisticated valuation module, and an optional valuation tool useful in periods of limited capital liquidity. Using established methodology, the Hotel Valuation Software is a sophisticated tool for lodging professionals. The tool consists of three separate software programs written as Microsoft Excel files and a software users' guide. The tool is provided through the generosity of HVS and the School of Hotel Administration. The three software modules are: Room Night Analysis and Average Daily Rate: Enables the analyst to evaluate the various competitive factors such as occupancy, average room rate, and market segmentation for competitive hotels in a local market. Calculates the area-wide occupancy and average room rate, as well as the competitive market mix. Produce a forecast of occupancy and average daily rate for existing and proposed hotels in a local market. The program incorporates such factors as competitive occupancies, market segmentation, unaccommodated demand, latent demand, growth of demand, and the relative competitiveness of each property in the local market. The program outputs include ten-year projections of occupancy and average daily rate. Fixed and Variable Revenue and Expense Analysis: The key to any market study and valuation is a supportable forecast of revenues and expenses. Hotel revenue and expenses are comprised of many different components that display certain fixed and variable relationships to each other. This program enables the analyst to input comparable financial operating data and forecast a complete 11-year income and expense statement by defining a small set of inputs: The expected future occupancy levels for the subject hotel Base year operating data for the subject hotel Fixed and variable relationships for revenues and expenses Expected inflation rates for revenues and expenses Hotel Capitalization Software: A discounted cash flow valuation model utilizing the mortgage-equity technique forms the basis for this program. Values are produced using three distinct underwriting criteria: A loan-to-value ratio, in which the size of the mortgage is based on property value. A debt coverage ratio (also known as a debt-service coverage ratio), in which the size of the mortgage is based on property level cash flow, mortgage interest rate, and mortgage amortization. A debt yield, in which the size of the mortgage is based on property level cash flow. By entering the terms of typical lodging financing, along with a forecast of revenue and expense, the program determines the value that provides the stated returns to the mortgage and equity components. The program allows for a variable holding period from four to ten years The program includes an optional model useful during periods of capital market illiquidity that assumes a property refinancing during the holding period

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As many as one-third of U.S. hotels have been converted from one brand to another in recent years, a process that frequently improves the hotel’s financial performance—although that is not always the case. Using data collected between 1994 and 2012 from PKF Hospitality Research, an analysis of brand conversions by 260 hotels shows that hotels moving downscale generally improved their occupancy, and thus their top-line revenue and profit ratios, compared to a control group of 2,750 hotels that did not change brands. However, hotels that moved upscale did not see notable changes in revenue or profit, nor did hotels that moved across their tier, especially when they stayed within their brand family. Two factors seem to drive the financial results for converted hotels—the relative strength of the brand and the fit between the brand and the property.

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China has embarked on the largest program of new hotel construction the world has ever seen. Even though the nation’s growth rate has eased somewhat in the past year, China’s hotel development continues at a pace that would see at least three new 150+ room hotels open every day for the next 25 years.1 Even if the industry does not continue to expand at this rate, China’s hotel growth carries substantial consequences in terms of increases in energy and water consumption, and an expanding carbon footprint. In this paper, we outline the dimensions of this issue, and we urge hotel developers to heed the national government’s push for greater sustainability.

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Purpose – The objective of this exploratory study is to investigate the “flow-through” or relationship between top-line measures of hotel operating performance (occupancy, average daily rate and revenue per available room) and bottom-line measures of profitability (gross operating profit and net operating income), before and during the recent great recession. Design/methodology/approach – This study uses data provided by PKF Hospitality Research for the period from 2007-2009. A total of 714 hotels were analyzed and various top-line and bottom-line profitability changes were computed using both absolute levels and percentages. Multiple regression analysis was used to examine the relationship between top and bottom line measures, and to derive flow-through ratios. Findings – The results show that average daily rate (ADR) and occupancy are significantly and positively related to gross operating profit per available room (GOPPAR) and net operating income per available room (NOIPAR). The evidence indicates that ADR, rather than occupancy, appears to be the stronger predictor and better measure of RevPAR growth and bottom-line profitability. The correlations and explained variances are also higher than those reported in prior research. Flow-through ratios range between 1.83 and 1.91 for NOIPAR, and between 1.55 and 1.65 for GOPPAR, across all chain-scales. Research limitations/implications – Limitations of this study include the limited number of years in the study period, limited number of hotels in a competitive set, and self-selection of hotels by the researchers. Practical implications – While ADR and occupancy work in combination to drive profitability, the authors' study shows that ADR is the stronger predictor of profitability. Hotel managers can use flow-through ratios to make financial forecasts, or use them as inputs in valuation models, to forecast future profitability. Originality/value – This paper extends prior research on the relationship between top-line measures and bottom-line profitability and serves to inform lodging owners, operators and asset managers about flow-through ratios, and how these ratios impact hotel profitability.

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Hardly a day goes by without the release of a handful of news stories about autonomous vehicles (or AVs for short). The proverbial “tipping point” of awareness has been reached in the public consciousness as AV technology is quickly becoming the new focus of firms from Silicon Valley to Detroit and beyond. Automation has, and will continue to have far-reaching implications for many human activities, but for driving, the technology is here. Google has been in talks with automaker Ford (1), Elon Musk has declared that Tesla will have the appropriate technology in two years (2), GM is paired-up with Lyft (3), Uber is in development-mode (4), Microsoft and Volvo have announced a partnership (5), Apple has been piloting its top-secret project “Titan” (6), Toyota is working on its own technology (7), as is BMW (8). Audi (9) made a splash by sending a driverless A7 concept car 550 miles from San Francisco to Las Vegas just in time to roll-into the 2016 Consumer Electronics Show. Clearly, the race is on.

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Reno, Nevada, one of the nation’s original gaming meccas, is in the midst of reinventing itself and dramatically broadening its economic base. The timing is portentous, as a soft market for casino gaming prevails, largely due to nationwide expansion. Several Reno casinos have recently folded, and several more are facing difficulties.

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This article introduces the concept of error recovery performance, followed by the development and validation of an instrument to measure it. The first objective of this article is to broaden the current concept of service recovery to be relevant to the back-of-house operations. The second objective is to examine the influence of leader behavioral integrity (BI) on error recovery performance. Moreover, the study examines the mediating effect of job satisfaction between BI and error recovery performance. Finally, the study links error management performance with work-unit effectiveness. Data for Study 1 were collected from 369 hotel employees in Turkey. The same relationships were tested again in Study 2 to validate the findings of Study 1 with a different sample. Data for Study 2 were collected from 33 departmental managers from the same hotels. Linear regression analysis was used to test the direct effects. The mediating effects were tested using the mediation test suggested by Preacher and Hayes. In addition, in Study 2, general managers of the hotels were asked to rate the effectiveness of each manager and their respective department. Results from Study 1 indicate that BI drives error recovery performance, and this impact is mediated by employee job satisfaction. Results of Study 2 confirm this model and finds further that managers’ self-rated error recovery performance was associated with their general managers’ assessment of their deliverables and of their department’s overall performance.

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Research has shown that performance differences exist between brand-affiliated hotels and unaffiliated properties. However, the extant empirical results are mixed. Some research has shown that brands outperform unaffiliated hotels on various metrics, whereas other research has shown the opposite. This article analyzes this issue using a matched-pair approach where we compare the performance differences of brand-affiliated and unaffiliated properties between 1998 and 2010. The matched-pair approach ensures that local competitive conditions as well as hotel characteristics are the same across the comparison pair. In addition, all potential omitted-variable bias and model misspecifications are avoided. Thus, to address our research question, we compare branded hotels with unaffiliated properties that are identical in age, market segment, location, and duration of operation, as well as having a similar number of rooms. Our analysis shows that performance differentials are present, albeit not systematic. We found no consistent advantages in all segments for either the affiliated hotels or the comparable unaffiliated properties, taking into account our comparison factors. That said, the methodology of our approach yields results that are more informative to the affiliation choice of owners and to the growth strategies of hotel brand–owner companies than those of previous empirical studies.

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Hotel chains have access to a treasure trove of “big data” on individual hotels’ monthly electricity and water consumption. Benchmarked comparisons of hotels within a specific chain create the opportunity to cost-effectively improve the environmental performance of specific hotels. This paper describes a simple approach for using such data to achieve the joint goals of reducing operating expenditure and achieving broad sustainability goals. In recent years, energy economists have used such “big data” to generate insights about the energy consumption of the residential, commercial, and industrial sectors. Lessons from these studies are directly applicable for the hotel sector. A hotel’s administrative data provide a “laboratory” for conducting random control trials to establish what works in enhancing hotel energy efficiency.

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Several studies have been undertaken or attempted by industry and academe to address the need for lodging industry carbon benchmarking. However, these studies have focused on normalizing resource use with the goal of rating or comparing all properties based on multivariate regression according to an industry-wide set of variables, with the result that data sets for analysis were limited. This approach is backward, because practical hotel industry benchmarking must first be undertaken within a specific location and segment.1 Therefore, the CHSB study’s goal is to build a representative database providing raw benchmarks as a base for industry comparisons.2 These results are presented in the CHSB2016 Index, through which a user can obtain the range of benchmarks for energy consumption, water consumption, and greenhouse gas emissions for hotels within specific segments and geographic locations.