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The authors investigate the effect of location on the nightly hotel room rates charged in Las Vegas. Using a hedonic estimation approach, the authors control for room amenities and hotel and time characteristics. Including 6087 hotel room nights for hotels located near the Las Vegas Strip in two different years (2012 and 2017), the authors estimate...
Contexts in source publication
Context 1
... The sample includes all of the major hotels within a five-mile radius of the Center of the Las Vegas Strip (which we define geographically, based on vegas.com map provided in Figure 1, as the intersection of Flamingo Road and Las Vegas Boulevard). Distance measures were determined using Google Maps' address-to-address walking distance. ...
Context 2
... The sample includes all of the major hotels within a five-mile radius of the Center of the Las Vegas Strip (which we define geographically, based on vegas.com map provided in Figure 1, as the intersection of Flamingo Road and Las Vegas Boulevard). Distance measures were determined using Google Maps' address-to-address walking distance. ...
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Citations
... Previous researchers have put forward their own views on hotel pricing. Some studies do their research on the basis of hedonism pricing (Rosen 1974;Wang et al. 2019;Castro et al. 2016;Chen and Rothschild 2010;Conroy et al. 2020; Arora and Mathur 2020) which presumes a linear relationship between room rates and the various attributes of the hotel service product (Chen and Rothschild 2010;Rosen 1974). Arora and Mathur (2020) have demonstrated that there is a positive correlation between rental premiums and star ratings in emerging markets, and this relationship is particularly stable in developed markets. ...
... Raya (2011) found that changes in location, hotel category, and market share on the Catalan coast can have the largest marginal effect on hotel prices. Conroy et al. (2020) also find out the evidence of the effect of location on hotel prices that in Las Vegas, compared to the hotels beyond 2.25 miles, a center-of-strip premium of 70.23% for hotels located within 0.72 miles, 36.98% for the next 0.75 miles and 18.89% for the next 0.75 miles. Some studies have expanded the scope of the attributes that will have an impact on hotel rates. ...
The outbreak of the COVID-19 pandemic has imposed substantial impacts on the global hotel industry. This study employed a two-tier stochastic frontier model to investigate the dynamics of transaction information distribution within the tourist hotel lodging prices in Taiwan from July 2019 to November 2020. The primary objective was to analyze the disparities in price information between consumers and hoteliers before and after the outbreak of COVID-19. Empirical findings reveal a notable reduction in the divergence of lodging price information between consumers and hoteliers post the COVID-19 outbreak, particularly in metropolitan areas. The shift in consumer group structures within hotel operations induced by the pandemic has, in turn, exerted an influence on the pricing strategies adopted by hoteliers. The empirical results underscore that the outbreak of COVID-19 has contributed to a more transparent and comprehensive market price information environment, consequently diminishing the asymmetry in information between hoteliers and consumers.
... Location-specific variables such as the distance to a beach, distance to the city centre, the airport, subway, or metro stations and distance to tourist attractions were also studied in literature (e.g. Coenders et al., 2003;Conroy et al., 2020;Tung et al., 2011). For example, Thrane (2007) reviewed the effect of distance to the city centre on hotel rates in Oslo. ...
Purpose
The purpose of this study is to examine the influence of key hotel attributes on the room rates of selected hotels in the Greater Gaborone Region, Botswana.
Design/methodology/approach
Using hedonic pricing analysis, the effect of eight attributes collected from 80 standard double rooms on Booking.com in the area was analysed using quantile regression.
Findings
The estimated results from quantile regression suggested the importance of the 10th quantile as the best predictor of hotel room price distribution. Overall, the presence of a fitness centre and the availability of meeting and conference facilities were positively significant for the lowest- and premium-priced hotels, respectively.
Research limitations/implications
The study advanced the literature in hedonic pricing models by confirming the applicability of hotel room rate attribute research in unexplored environments.
Practical implications
Hotel managers should be aware of the influence of key attributes, such as meeting and conference space availability and locational factors, on the pricing decisions of room rates in the Greater Gaborone Region. The study also presented opportunities for business-to-business marketing between hotel and tour operators in the region.
Originality/value
The study is one of the few that uses quantile regression in the hedonic pricing analysis of hotel room rates.
... According to [19,45], we chose the distance to the road and sea. Road sea data were obtained using OpenStreetMap software. ...
The goal of investors in the hotel business is to maximize profits, and the price is an important means of achieving this goal. This has attracted many scholars to study the spatiotemporal relationship between hotel room prices and their possible influencing factors from different perspectives. However, most existing studies adopt the linear assumption of the hedonic model, with limited features and a lack of feature selection procedures. Additionally, there are few forecasts of hotel pricing from a spatial perspective. To overcome these gaps, this study adopts linear and nonlinear machine learning methods based on the “big data” of Sanya City to explore the influencing factors of budget hotel pricing. Based on the spatial perspective, 81 potential factors were considered. They are further selected using a feature extraction model called recursive feature elimination. Six machine-learning algorithms were evaluated and compared: random forest, extreme gradient boosting, multi-linear regression, support vector regression, multilayer perceptron regression, and K-nearest neighbor regression. The optimal value was used to further calculate the feature importance. They disclosed 40 important impact characteristics and predicted the spatial distribution of hotel pricing.
Previous studies have extensively investigated the effects of eWOM (online word-of-mouth) factors such as volume and valence on product sales. Studies of the effect of eWOM on product prices are, however, lacking. It is necessary to examine how various eWOM factors can explain or affect product prices. The objective of this study is to suggest explanatory and predictive analytics using a regression analysis and ensemble-based machine learning methods for eWOM and hotels booking prices. This study utilizes publicly available data from a hotel booking site to build a sample of eWOM factors. The final study sample comprises 927 hotels. The important eWOM factors suggested as affecting hotel prices are the review depth and the review rating, and these are moderated by the number of reviews to affect prices. The effect of the number of positive words is moderated by review helpfulness to affect the price. The review depth and rating along with the number of reviews should be considered in the design of hotel services, as these provide the rationale for adjusting the prices of various aspects of hotel services. Further, the comparison results when applying various ensemble-based machine learning methods to predict prices using eWOM based on a 46-fold cross-validation partition method indicate that ensemble methods (bagging and boosting) based on decision trees outperform ensemble methods based on k-nearest neighbor methods and neural networks. This shows that bagging and boosting methods are effective ways to improve prediction performance outcomes when using decision trees. The explanatory and predictive analytics using eWOM for hotel booking prices offers a better understanding in terms of how the accommodation prices of hotel services can be explained and predicted by eWOM.
Information asymmetry in different service products is lacking research. Tourism e-commerce as a dynamic financial market driven by advanced information technology deepens the concern for asymmetry brought to hotel consumers. COVID-19 provides a natural intervention on market asymmetry in the hotel price information, which effects are worth evaluating. This study therefore evaluates the degree of information asymmetry in terms of the lodging price of international tourist hotels (ITHs). Through applying the concept of stochastic frontier approach (SFA), this study estimates the degree of information asymmetry and the inefficiencies. The estimation results indicate that the hotel location, traveler type, operation type may cause different information asymmetry. By comparing with the asymmetry before the pandemic, it is found that the asymmetry in lodging price information decreased since the outbreak of the COVID-19 pandemic. Among the six regions studied, the mean range of information equity degree increased from the original 0.634-0.832 to 0.7633-0.866. It indicates that the outbreak of COVID-19 changed the structure of the consumer groups in hotel operations and then affect the hotelier's pricing strategy.
This study applies a distance‐based measurement outlining market boundaries for competition in the lodging industry and investigates the effects of competition across a range of distances. Predetermined administrative boundaries have been the conventional metric used when estimating competition effects in a given geographical area in the lodging industry. For this study, distances are calculated using the location information of 45,623 hotels in the United States extracted from the Smith Travel Research Hotel Census. Our findings suggest a U‐shaped relationship: hotel room prices fall as relative distance increases, due to increasing negative price pressure, until a threshold distance is reached (a radius of 20 km), after which price pressure falls and prices begin to increase. These research findings have implications for hotel managers with respect to competitor management and for hotel investors interested in locating and developing future accommodation establishments.
