Room Prices Research Articles

Short-term hotel room price effects of sporting events

A difference-in-differences analysis is used to investigate the short-term price effects of eight sporting events in Finnish Lapland. Data consist of 220,000 room bookings from the reservation system of a nine-hotels chain. Treated hotels are those located within an area where sporting events are regularly held. The control group consists of hotels further away that are not affected by the event. Robust regressions show that hotel room prices rise by 14% on average during the event, when booking and guest specific factors are held constant. For the pre-event period, no significant positive price effect can be detected, and for the post-event period, there is even a significant negative effect of 6%, on average. In addition, there is a large variation in the price effects across the different sporting events, with the highest for the Levi FIS Alpine Ski World Cup competition (60%) and no effect for some small-scale events. Quantile regressions show that price effects are slightly higher for high-priced than for low-priced rooms.

Measuring Tourism Seasonality Across Selected Mediterranean Countries

Seasonality, as one of the key features of tourist activity, represents an unavoidable topic in scientific and professional analyses, whose aim it is to determine its causal effects. The economic consequences of seasonality are the kind of consequences that are most often emphasized and considered in discussions. They include inefficiencies in resource use, income variability, changes in the employment level, etc. However, only a few analyses have provided a more in-depth discussion of the common problems of seasonality among the Mediterranean countries. The most commonly used method of calculating the Gini coefficient will be used in order to estimate seasonality. This coefficient is characterized by certain advantages, such as taking into account the distribution asymmetry and the relative insensitivity to extreme values, as well as the stability indication of overnight stays distribution in a single year. Based on individual calculations of the seasonality index, this paper will provide a unique view of the similarities and differences between countries with regards to seasonal tourism oscillations. The analysis will cover the total number of tourists’ overnight stays in hotels and similar accommodation facilities, RevPAR revenue, and the average room price – ADR per month between 2007 and 2017. This paper will focus on determining the seasonal characteristics of the selected Mediterranean countries. The quantitative seasonality calculation will be a key indicator for seasonality measurements across different research areas. The contribution of this paper is reflected in the application of financial and non-financial indicators in hotel business that has not yet been implemented.
 Keywords: Tourism seasonality, RevPAR, ADR, gini index

Gaussian processes for daily demand prediction in tourism planning

AbstractThis study proposes Gaussian processes to forecast daily hotel occupancy at a city level. Unlike other studies in the tourism demand prediction literature, the hotel occupancy rate is predicted on a daily basis and 45 days ahead of time using online hotel room price data. A predictive framework is introduced that highlights feature extraction and selection of the independent variables. This approach shows that the dependence on internal hotel occupancy data can be removed by making use of a proxy measure for hotel occupancy rate at a city level. Six forecasting methods are investigated, including linear regression, autoregressive integrated moving average and recent machine learning methods. The results indicate that Gaussian processes offer the best tradeoff between accuracy and interpretation by providing prediction intervals in addition to point forecasts. It is shown how the proposed framework improves managerial decision making in tourism planning.

Pregled razlike cena među hotelima pozicioniranim u istoj kategoriji

Hotels have a variety of pricing methods at their disposal when defining adequate pricing strategy. Nevertheless, irrespective of the complexity and the manner in which room prices are calculated all methods are based on certain factors which have an influence on the final room rate. Accordingly, the perception regarding the impact of each factor along with the expected value provided to the customer will cause for hotels to charge different prices. The prices offered on Booking.com for hotels falling in different categories allow examination of the potential price differences among five, four, three and two stars hotels in multiple cities in Europe. The findings suggest that even though two hotels may belong to the same category, their prices may differ few hundreds of per cents which implies that the level of value offered to guests can vary significantly.

OYO Hotels USA: Coming to America

This case studies the growth of OYO Hotels (OYO) to illustrate the operational processes necessary to succeed in the service sector. The case allows for a discussion of employee- and customer-management systems, tech-driven solutions, and profit drivers. The material unfolds OYO's growth and its solution for making economy hotels discoverable and bookable online. The case raises a series of questions around OYO's business model, its ability to translate across global markets, and growth potential. It has been successfully taught in a second-year MBA class on the management of service operations. Excerpt UVA-OM-1641 Rev. May 1, 2020 OYO Hotels USA: Coming to America Jakayla Michel, a second-year MBA student, stood in the hallway gazing at her smartphone. She flicked her finger across the screen, reading through the job postings on Indeed.com for OYO Hotels USA, the new American arm of the fast-growing Oravel Stays Private Ltd. (OYO) that had been launched in February 2019. OYO had grown in just six years from a single room listing in India to one million rooms spanning the globe—one of the largest hotel chains in the world. The company boasted a tech-driven solution for making economy hotels predictable, discoverable, and bookable online that had attracted leading investors like SoftBank, Sequoia Capital, Lightspeed Venture Partners, and Airbnb, attaining a heady $ 10 billion valuation. OYO's 25-year-old wunderkind founder, Ritesh Agarwal, had produced what the Wall Street Journal was calling an emerging-market unicorn among unicorns. After first becoming the leader of hotel rooms in India, OYO had begun in 2017 to export its formula into Malaysia, Nepal, China, Indonesia, and the United Arab Emirates (UAE). In fall 2018, OYO entered its first developed market, the United Kingdom. And now it was spending $ 300million to enter the United States. Michel, who had been a junior investment banker for four years after college, was looking for a career shift. She didn't mind taking a short-term step back in pay to join a company with growth potential, but wanted to make sure the opportunity was real. OYO seemed to be hiring for every type of position in the United States: human resources director, field sales director, territory operations leader, and business development manager. She spotted opportunities in Dallas, Texas; Los Angeles, California; Portland, Oregon; Las Vegas, Nevada; Miami, Florida; and Myrtle Beach, South Carolina—and even near her family's home in Washington, DC. With a few taps, Michel saved the positions that seemed most interesting to her (Exhibit 1), made a note to herself to further research the company later, and found a seat in her operations class. When she got home, she looked through OYO's website, which described OYO's secret sauce as design cool, in spaces whenever you need them, at fantastic prices. The company seemed to be operating a franchise model wherein it spent hundreds of thousands of dollars to upgrade small, unbranded hotels, hang the OYO shingle, and charge the hotel owner around 20% per transaction. The company's team of engineers created apps to streamline such things as customer procurement, front desk management, room pricing, food and beverage offerings, and even room cleaning. The company said its upgrades increased hotel utilization and profitability. . . .

An empirical analysis of Lisbon hotel room rates determinants

This study investigates the impact of several characteristics and attributes of hotels on room rates in one of the most visited cities in Europe. The research allows to compare, in a transparent way, findings of previous studies and promotes added value using for the first time the hedonic price model (HPM) in the hospitality industry in Lisbon, Portugal. Although hotel room price determinants have already been studied, in this study it was introduced physical variables and location variables all together, as well as it was added the variables relating to certain services offered. A significant impact on hotel prices by official stars and OTAs punctuation has been confirmed. In line with previous studies, an additional star and belonging to a chain allow the hotel to increase the price. In addition, other attributes, such as the distance of the hotel from the airport and the centre, have impact on hotel prices. The evidence that the existence of a spa and a pool influences hotel prices will support stakeholders in investment and management decision.

Peer-to-peer-travel: is Airbnb a friend or foe to hotels?

Purpose The sharing economy has caught great attention from researchers and policymakers. However, due to the dearth of available data, not much empirical evidence has been provided. This paper aims to empirically assess the impacts of Airbnb on hotel performances in South Africa. Design/methodology/approach Using South Africa as a case study, the study measures the impacts of Airbnb on hotel performances on three key metrics, namely, room prices, occupancy and Revenue per available room (RevPAR). A difference-in-difference model is estimated using a population-based data set of 809 hotels from 2016 to 2018. Findings The results reveal that despite Airbnb significantly and negatively impacting on hotel occupancies it has a non-significant effect on hotel prices and RevPAR. Although from the theoretical perspective a disruptive innovation business model such as Airbnb can possibly have a negligible effect on hotel performances because it may attract a different group of customers and create a new market, the empirical findings of this study fail to support this theoretical hypothesis. Consequently, the findings diverge with newly developed knowledge in other markets and point to nuanced and contextual complementary effects. Research limitations/implications Although some interesting findings are revealed into his study, some caveats remain. For instance, the study relied on data from hotels not from Airbnb. If the data of Airbnb can become available, it would be interesting to further examine whether the aggregated RevPAR of Airbnb can compensate for the aggregated loss of hotel RevPAR. This type of analysis could provide a broader evaluation scope regarding the overall effect of Airbnb on hotel performances. Moreover, if a longer time series data set of hotels in the post-Airbnb time period could become available, it would be interesting to further investigate the time-varying dynamic effects of Airbnb on hotel performances. Practical implications While hotels have launched a campaign to portray Airbnb as being commercial operators looking to compete illegally with hotels for the same segment of customers, this study shows that the rhetoric has been exaggerated. Airbnb, and more broadly, vacation rentals do not represent a war with hotels. They represent an answer to a different need. Indeed, the study reveals that Airbnb’s offer is a mere supplement to the market contrary to media rhetoric that it is meant to substitute hotels. The study has several implications for practitioners. First, these results are important because they serve as evidence against news articles that claim Airbnb is driving hotels out of business. They also show that if current trends continue, employees in the hotel industry in South Africa do not need to be concerned about losing their jobs because of Airbnb’s emergence. It is also important information for investors who may be concerned that Airbnb is hurting the hotel industry’s bottom line. Second, as the share of Airbnb listings on the accommodation market varies dramatically between cities, it is likely that eventual regulations/restrictions should be introduced in the provincial levels, while most of the cities continue benefiting from the increasing number of Airbnb visitors. Originality/value To the best of the author’s knowledge, this study is the first in South Africa to provide empirical evidence that Airbnb is significantly changing consumption patterns in the hotel industry, as opposed to generating purely incremental economic activity.

A meta-regression on the effect of online ratings on hotel room rates

Purpose Online ratings (review valence) have been found to exert a strong influence on hotel room prices. This study aims to systematically synthesize research estimating the impact of online ratings on room rates using a meta-analytical method. Design/methodology/approach From major academic databases, a total of 163 estimates of the effects of online ratings on room rates were coded from 22 studies across different countries through a systematic review of relevant literature. All estimates were converted into elasticity-type effect sizes, and a hierarchical linear meta-regression was used to investigate factors explaining variations in the effect sizes. Findings The median elasticity of online ratings on hotel room rates was estimated to be 0.851. Meta-regression results highlighted four categories of factors moderating the size of this elasticity: data characteristics, research settings, variable measures and publication outlet. Among sub-ratings, results revealed value rating and room rating to exert the largest impact on room rates, whereas staff and cleanliness ratings demonstrated non-significant impacts. Practical implications This study provides practical implications on the relative importance of different types of online ratings for online reputation and revenue management. Originality/value This study represents the first research effort to understand factors moderating the effects of online ratings on hotel room rates based on a quantitative review of the literature. Moreover, this study provides beneficial insights into the specification of empirical hedonic pricing models and data-collection strategies, such as the selection of price variables and choices of model functional forms.

Forecasting hotel room prices in selected GCC cities using deep learning

This paper extends the research on hotel room prices using traditional and non-traditional statistical models following the analysis by Karathanasopoulos and Shehhi (2018), which discusses how hotel prices can be easily predicted. In this paper, we employed four forecasting models: the seasonal autoregressive integrated moving average (SARIMA) model, the restricted Boltzmann machine as a deep belief network model, the polynomial smooth support vector machine model, and finally, the adaptive network fuzzy interference system (ANFIS) model. Research data was obtained from Smith Travel Research.In this study, we apply advanced forecasting models based on machine learning and artificial intelligence to the hospitality sector. Some of the models used in this study, such as the ANFIS model, contribute to the research conducted in GCC region. The goal of the research was to contribute to the academic literature and assist hotel operators and decision-makers in setting appropriate strategies.

Effect of guest demographics on perception of hotel room price

Purpose A review of past scholarly work on pricing issues in hospitality has revealed a lack of focus on customers’ demographic profiles. However, research in other disciplines reveals that understanding price perception differences among groups of customers with different demographics, including culture, is an important consideration when offering pricing strategies. The purpose of this paper is to contribute to the body of pricing research by exploring the effect of hotel guests’ demographics on their perception of hotel room prices. Design/methodology/approach Through Qualtrics, data were collected from 414 respondents who stayed at a mid-scale hotel within the past 24 months. The respondents’ perceived value (PV), perceived fairness (PF) and willingness to pay (WTP) for hotel rooms were examined with MANOVA and ANOVA tests to determine the effects of customer demographics on these variables. Findings Age, gender and marital status showed a significant effect on PV while age, gender and culture significantly affected PF. However, none of these variables significantly affected WTP. The culture of origin and the culture raised-in influenced PV, PF and WTP similarly. Originality/value This study reconciles several divergent results from previous studies and extends the scope of others by introducing different scenarios to each of the three dependent variables. To the best of the authors’ knowledge, it is also the first research study on this subject to evaluate more than two cultures and their effects on the independent variables.

Hotel retailing with attribute-based room pricing and inventory control

Attribute-based pricing is the new era of retailing in the hospitality industry. This approach to room pricing improves customer satisfaction, since customers can purchase exactly the type of room they want for a memorable stay. For hoteliers, attribute-based pricing provides an opportunity to overhaul existing legacy rate structures with rate simplification and also generate incremental revenues since an infinite number of options can be inventory controlled on the price demand curve.

Research on the principal factors and indicators of urban MICE competitiveness from the perspective of supply

Purpose Urban MICE competitiveness research consists of two clusters, one that is public-statistics-based and another that is questionnaire-based. Supply-side research on urban MICE competitiveness is rare. Based on the findings of Chen (2014) and other scholars, the purpose of this paper is to design counterpart statistical indicators to empirically analyze CMCA member cities. Design/methodology/approach After calculating the standardized Z value of the original statistical data for 17 CMCA member cities, the authors conducted confirmatory factor analysis for the first-level principal components, based on which hierarchical clustering was performed; then, regression analysis was conducted with the MICE profit factor as the dependent variable and the cost factor, tight support factor and facilitating factor as the independent variables to support publishing articles. Findings The confirmatory factor analysis showed that the urban MICE competitiveness indicators from the supply-side perspective include the profit factor, cost factor, tight support factor and facilitating factor. Research limitations/implications On the basis of research findings from the demand perspective and the literature review, the authors constructed an urban MICE competitiveness indicator system from the perspective of the supply side and conducted principal component analysis. However, because of the inaccessibility of panel data, the current data were only sufficient to conduct the research. If panel data could be acquired, further research could be conducted to perfect the current indicator system for urban MICE competitiveness. Practical implications The findings suggest that tourism total income, tourism foreign exchange income, inbound tourist number, number of exhibitions, exhibition area, number of UFI member cities and number of ICCA member cities were the main reason for the gap between different cities’ competitiveness and the reform focus for improving urban MICE competitiveness. The cost factor had a significantly negative influence on urban MICE competitiveness, implying that the higher the average hotel room price and revenue per available room, the less competitive the MICE host city is. Social implications The tight support factor exerts a significant positive influence on urban MICE competitiveness from the supply-side perspective, while the cost factor exerts a significant negative influence. The findings suggest that the tourism total income, tourism foreign exchange income, inbound tourist number, number of exhibitions, exhibition area, number of UFI member cities and number of ICCA member cities were the main reason for the gap between different cities’ competitiveness and the reform focus for improving urban MICE competitiveness. The cost factor had a significantly negative influence on urban MICE competitiveness, implying that the higher the average hotel room price and revenue per available room, the less competitive the MICE host city is. Originality/value The research bridge the empirical statistics and the questionnaire-based perception study on urban MICE tourism image, and advance to construct an empirical statistics based indicator system for urban MICE tourism image.

Hotels to OTAs: “Hands off my rates!” The economic consequences of the rate parity legislative actions in Europe and the US

The objective of this research is to analyze the impact of the legislative actions regarding rate parity agreements on the market value of hotels and online travel agents in Europe and the USA. Rate parity clauses (uniform hotel room pricing across channels), have been solved differently depending on the countries: while several European countries have outlawed rate parity clauses, the legal status of rate parity has been reaffirmed by courts in the United States. However, whether rate parity is good for hotel and OTA performance is not completely understood. Drawing from Rochet and Tirole's (2003) theory of two-sided markets and using a market value based measure of performance, this study examines in a 2 × 2 framework the effects of the contrasting legislative outcomes in Europe and the United States on hotels and OTAs. The results show coherent outcomes in both continents: rate parity is beneficial to OTAs but not to hotels.

The Signalling Theory: The Key Role of Quality Standards in the Hotels Performance

ABSTRACTThe application of the Signalling Theory to the case of the tourism standards of quality are established. An empirical research study on the performance of a wide range of hotels that have evolved from not possessing certification to being accredited by some form of quality standard (regional, national or international) during a period of seven years. The results provide evidence on the usefulness of quality certifications as signals to the market to achieve increases in sales, gains in market share, increased occupancy and average room price, and therefore an improvement in profitability per room and overall profit for the hotels.

Mining significant fuzzy association rules with differential evolution algorithm

This article presents a new differential evolution (DE) algorithm for mining optimized statistically significant fuzzy association rules that are abundant in number and high in rule interestingness measure (RIM) values, with strict control over the risk of spurious rules. The risk control over spurious rules, as the most distinctive feature of the proposed DE compared with existing evolutionary algorithms (EAs) for association rule mining (ARM), is realized via two new statistically sound significance tests on the rules. The two tests, in the experimentwise and generationwise adjustment approach, can respectively limit the familywise error rate (the probability that any spurious rules occur in the ARM result) and percentage of spurious rules upon the user specified level. Experiments on variously sized data show that the proposed DE can keep the risk of spurious rules well below the user specified level, which is beyond the ability of existing EA-based ARM. The new method also carries forward the advantages of EA-based ARM and distinctive merits of DE in optimizing the rules: it can obtain several times as many rules and as high RIM values as conventional non-evolutionary ARM, and even more informative rules and better RIM values than genetic-algorithm-based ARM. Case studies on hotel room price determinants and wildfire risk factors demonstrate the practical usefulness of the proposed DE.

How important is F&B operation in the hotel industry? Empirical evidence in the U.S. market

In full-service hotels, the increase in resource (i.e., salary in this study) of the F&B department relative to that of the rooms department had a positive effect on the rooms' operational performance indicators including ADR (average daily rate), RevPAR (revenue per available room), GOPPAR (gross operating profit per available room), and gross operating profit ratio of the rooms department. Although it did not increase the demands for rooms, the improved room price (i.e., ADR) had a positive impact on their room revenue, as a result. Among the three major categories of full-service hotels, the positive impact of F&B offering on operational performance was observed significantly in the upscale category such as increased occupancy, ADR, RevPAR, GOPPAR, and even gross operating profit ratio of the rooms department, but not in luxury and upper upscale categories. The findings indicate that there are substantial customers’ needs for F&B offering in upscale hotels.

Determinants of online hotel room prices: comparing supply-side and demand-side decisions

Purpose This study aims to examine the effect of hotels’ star ratings and customer ratings on online hotel prices from both supply- and demand-side perspectives. Design/methodology/approach To compile the supply-side data, a Web crawler was designed and implemented to read online prices and characteristics of available hotels from Trivago. Demand-side data were compiled from surveys conducted using the Amazon Mechanical Turk portal. Data were analyzed with an array of advanced machine learning regression models. Findings The results show that while a star rating is the most important predictor of price from both supply- and demand-side perspectives, customer rating influences the price much more significantly on the demand-side. Customers showed a tendency to overestimate the room price of three- and four-star hotels and underestimate the price of five-star hotels. Customers placed a heavier weight on customer ratings when estimating prices particularly when the average rating was above 7.5 (out of 10). The study also confirms the strong effect of price adjustment for customers when they were exposed to the prices of other similar hotels. Finally, the study examines the impact of demographics on the perceived hotel value. Age, ethnicity, education and income are shown to be the most significant demographic characteristics. Originality/value The results are valuable from a research perspective because they demonstrate how to price rooms more effectively based on their perceived value from consumers’ perspectives. From a practical standpoint, the findings provide useful managerial tools for pricing in competitive environments.

Winescape’s Aesthetic Impact on Lodging Room Prices: A Spatial Analysis of the Douro Region

ABSTRACTThis study estimates the local economic effects on lodging establishments’ room prices of the winescape’s aesthetic attributes, mostly shaped by the wine filière, combined with other fundamental situation, site-specific and online reputation factors. A semi-parametric geographically weighted regression model was applied to examine how lodging establishments’ room prices vary in space. The hedonic pricing model was based on Booking.com’s room rates of lodging establishments located in the Douro region in Portugal, a monocultural environment, which is predominantly rural and dominated by vineyards, combining small towns with a hybrid urban-rural character. The results suggest that landscape factors, which encapsulate structural features of the Douro wine region, exert a statistically significant effect on lodging establishments’ room prices.

Do airbnb host listing attributes influence room pricing homogenously?

This research primarily contributes to the identification of the important variables that significantly influence room pricing on the Airbnb rental platform. The study adopted a comparative approach by using three different methods—OLS, random forest, and decision tree—and applied it to a vast amount of data from the Airbnb listing dataset of 11 US cities. Each individual amenity mentioned in the listing in the textual format was used as an independent variable. We also added six other common listing variables to obtain interesting insights into the influence of these variables from the perspective of the host, guest, traveler, and tourist. Apart from identifying city-specific variable importance using different models, we estimated a composite score of variable importance that may be helpful to generalize the influence of amenities and other explanatory variables in the presence of any city-specific regional heterogeneity on the shared rental platform.

Modeling dynamic price dispersion of hotel rooms in a spatially agglomerated tourism city for weekend and midweek stays

In spite of the abundant evidence suggesting the existence of spatial agglomeration in the hotel industry and the potential for spatial econometric methods to contribute to the understanding on the effect of spatial competition on room pricing, limited research has been conducted in this regard. To contribute to this area of research, this study applied spatial models to examine online pricing data of hotels in Hong Kong to determine the effect of spatial agglomeration on dynamic or intertemporal price dispersion. The findings revealed that the magnitude of dynamic price dispersion is not only influenced by demand but also the pricing of neighboring hotels and hotel-specific attributes, such as number of rooms, star rating, chain affiliation, and scale. A major implication of this finding is that real-time tracking and analysis of neighboring hotels’ prices could be an effective strategy to stay competitive in a spatially agglomerated environment.