Why are hotel room prices different? Exploring spatially varying relationships between room price and hotel attributes

Despite abundant research on modeling hotel room prices, traditional hedonic pricing models (HPMs) have failed to consider spatial variations in the relationships among hotel room price and attribute variables. This study demonstrates the utility of a spatial HPM (s-HPM) using a geographically weighted regression analysis of 387 hotels in the Chicago area. Specifically, this study explored spatial variations in modeling hotel room prices and further identified spatial clustering patterns of relationships between room price and hotel attributes across market segments. The findings reveal that the s-HPM successfully identified spatially varying relationships between room price and hotel attributes, such as site attributes – size, age, class and service quality – and situation attributes – distances to airports, highways and tourist attractions – across the study area. This study contributes to a better understanding of local patterns of modeling room prices, ultimately providing guidelines for effective location-based hotel room pricing strategies

Examining the Determinants of Location Attributes and their Effect on Hotel Pricing in the Period of the Covid-19 Pandemic in an Emerging Market

The emergence of COVID-19 and the consequent travel restrictions have led to a decrease in the patronage of hotel services in nearly all economies in the world. In this circumstance, location attributes have become even more important in hotel pricing and investment decision-making. It is even more interesting to see how this plays out in emerging economies such as Ghana. The study assesses the effect of location attributes on hotel pricing during the COVID-19 pandemic period in Tamale. A sequential mixed research design including Mixed Spatial Hedonic Price Approach, Exploratory Factor Analysis and key informant interviews was employed. A sample of 815 tourists and 163 hotels was used. Hotel class, road accessibility, age of building, and hotel rate are the key determinants of hotel pricing. Among these, the hotel class showed more significance in influencing pricing decisions in the COVID-19 period. The models show that the hotel class with positive coefficients are located outside the city centre of Tamale. This has resulted in increased Yield To Maturity because the hotels located outside the city centre received more clients, with grade one hotels showing a huge net income and good post-COVID-19 investment drive. The results show that potential hotel investors should consider hotel class as a major entry decision factor during and after periods of the pandemic

How does AirBnb affect local Spanish tourism markets?

This paper analyses the effects of AirBnb on the size of local tourism markets using AirBnb occupancy rates and hotel overnight stays in order to explore the causal relationship in several Spanish cities. A dynamic panel data model is applied at the city level (2014–2017). Our findings show a positive relationship between the increase in the number of properties offered on AirBnb and the implicit volume of tourists received by each city, specifically in two large cities (Madrid and Barcelona), due to higher AirBnb occupancy rate.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This study was partially funded by Fundación de Estudios de Economía Aplicada (grant 2019/04)

Tourism price normalities in two Adriatic east coast ’euro’ countries

This debut work offers a stunning look at real vs nominal prices that consider more than just inflation. The inadequate examination of hospitality price comparison is investigated between two non-neighbouring Adriatic east coast countries – Slovenia and Montenegro – using the euro. Hospitality prices are an essential indicator in hospitality markets, destination marketing and management planning. Using 73 monthly time-series data for the economic crisis period from December 2008 to December 2014, this period covers one shock in a series. One of the key managerial features of cointegrated spatial hospitality price spread was that Montenegro followed Slovenian hospitality prices. Hospitality prices in Montenegro and Slovenia tend to be weakly integrated into the long term and seasonally driven in the short term. In addition, the econometric experimentation has given a theoretical novelty for underpinned and undermined tourism economy modelling in normalities. This state-of-the-art econometric feature is included in a customary vector error correction model (VECM). Robust applied results recognise that hospitality prices in Montenegro are domestic driven and in Slovenia Eurozone driven. This finding is relevant for applied economics on obtaining a normally distributed price model. Its theoretical and managerial implications are vital for hospitality economics, marketing and tourism management

Real Estate Market Efficiency: A Survey of Literature

In this paper, we discuss the question whether or not the real estate market is efficient. We define market efficiency and the efficient market hypothesis as it had been developed in the literature on financial markets. Then, we discuss the empirical evidence that exists concerning the efficiency or inefficiency of financial markets, usually seen as the reference markets as far as market efficiency is concerned. In a separate section, we turn to the real estate market. There, we define the real estate market and discuss various aspects that are decisive for the efficiency of that market. As it turns out, the result found in the literature is inconclusive. Majority of studies provide evidence supporting inefficiency of the real estate market while several studies maintain the notion of real estate market efficiency.

What Hotel Attributes Matter? Understanding the Price Determinants in the Lodging Industry

The major purposes of the empirical study are to investigate the price determinants in the lodging industry (i.e., hotel class, hotel operation, location, size, age, and seasonality) and to examine how these price determinants affect financial performance of the hotel. A quantitative method was used in this study, with 6,732 valid observations collected and analyzed. The findings suggested that hotel class, hotel operation, location, size, and seasonality are significant factors determining hotels’ average daily rate (ADR) and revenue per available room (RevPAR). The results have both theoretical and practical contributions. They not only broaden the existing lodging research but can also help hotel managers better understand pricing determinants

Spatial density, average prices and price dispersion. Evidence from the Spanish hotel industry

Based on the assumption that location is especially relevant in the lodging industry, we exploit a dataset of Spanish hotels to examine the relationship between spatial competition and retail price level and dispersion. Our results support the hypothesis that a greater density of competitors implies both a lower level and less dispersion of retail prices. We find that close competitors, in terms of hotel category and distance, have a stronger effect on price setting behavior. Moreover, we report weak evidence that the relationship between spatial competition and price level depends on whether the day considered belongs to the midweek or the weekend. Therefore, variation in the type of consumers seems to play quite an important role in explaining the relationship. Partiendo del supuesto de que la localización es especialmente relevante para el sector del alojamiento, utilizamos una base de datos de hoteles españoles para examinar la relación entre competencia espacial y el nivel y la dispersión de los precios de las habitaciones. Nuestros resultados confirman la hipótesis de que una mayor densidad de competidores implica niveles de precios menores y menor dispersión de precios. Los competidores cercanos, ya lo sean en términos de categoría hotelera como de distancia, tienen una mayor influencia sobre la fijación de precios. Adicionalmente, encontramos evidencia débil acerca de que la relación entre competencia espacial y el nivel de precios depende de si el día considerado es laborable o corresponde al fin de semana. Por tanto, las variaciones en el tipo de consumidores parecen tener un papel importante en la explicación de esta relación.Nivel de precios, dispersión de precios, competencia espacial, sector hotelero. Price level, price dispersion, spatial competition, hotel industry

Predicting and explaining Airbnb prices in Lisbon : machine learning approach

Airbnb is an online platform that provides listing and arrangement for short-term local home renting services. Since its establishment in 2008, it has offered 7 million homes and rooms in more than 81,000 cities throughout 191 countries. Airbnb price prediction is a valuable and important task both for guests and hosts. Overall, for practical applications, these models can give a host an optimal price they should charge for their new listing. On the consumer side, this will help travellers determine whether the listing price they see is fair. Much research has been done in this field; however, the longitude and latitude of Airbnb listings are often disregarded. This project focuses on Airbnb price prediction using the most recent (Sep 2021) Airbnb data in Lisbon. Using Google Maps API, the original dataset was enriched with information on the number of ATMs, metro stations, bars and discos within a maximum radius of 1 km. Also, using the geodesic distance, the distance to the airport and the nearest attraction were computed for each listing. A Linear Regression and a Gradient Boosting algorithm were compared based on the original Airbnb dataset and the extended dataset to examine the impact of new features that have been identified. According to the results, all models perform better when the new features are included. The best results are achieved with the Gradient Boosting with the extended data, with an MAE of 0. 3102 and an adjusted R-squared of 0.4633.O Airbnb é uma plataforma online que fornece alojamento de curto prazo. Desde a sua criação em 2008, já ofereceu 7 milhões de residências e quartos em mais de 81.000 cidades, em 191 países. A previsão de preços do Aibnb é uma tarefa valiosa tanto para hóspedes como para anfitriões. No geral, estes modelos de previsão podem oferecer ao anfitrião o preço ideal que deve ser cobrado pelo alojamento. Do lado do consumidor, ajudará os hóspedes a determinar se o preço do anúncio é justo. Muitos estudos já abordaram este tema, no entanto, a longitude e a latitude são frequentemente desconsideradas. Este projeto foca-se na previsão de preços do Airbnb em Lisboa usando os dados mais recentes (setembro de 2021). Usando a API do Google Maps, o conjunto de dados original foi ampliado adicionando colunas com o número de ATMs, estações de metro, bares e discotecas num raio máximo de 1 km. Além disso, usando a distância geodésica, a distância até o aeroporto e até à atração mais próxima foram calculadas. Os resultados de uma regressão linear e de um Gradient Boosting, com base no conjunto de dados original do Airbnb e no conjunto de dados alargado são comparados para examinar o impacto das novas variáveis. De acordo com os resultados, todos os modelos apresentam melhor desempenho quando as novas variáveis são incluídas. Os melhores resultados são obtidos com o Gradient Boosting com os dados alargados, com um MAE 0,3102 e um adjusted R-squared de 0,4633