Hotel dynamic pricing, stochastic demand and covid-19

Guizzardi, Andrea; Ballestra, Luca Vincenzo; D'Innocenzo, Enzo

doi:10.1016/j.annals.2022.103495

Cited by 9 publications

(2 citation statements)

References 57 publications

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“…Furthermore, Gričar and Bojnec (2019) used a vector autoregressive model to demonstrate the importance of overnight stays by foreign tourists regarding their price behaviour. The second dimension is the time of booking or the booking horizon (Solano-Sánchez et al, 2023;Guizzardi et al, 2022;Sánchez-Lozano et al, 2021).…”

Section: Research On Price Determinantsmentioning

confidence: 99%

“…After the impact of the Covid-19 pandemic on tourism in 2020, there was a rapid investigation into price responses in this context. Boto-García (2022b) studied the price response to the Covid-19 pandemic low demand among Airbnb hosts, Guizzardi et al (2022) found that hotels adopted price discounting strategies during the pandemic and Luo et al (2021) studied the impact of price strategies for tourism market recovery in China.…”

Section: Research On Price Determinantsmentioning

confidence: 99%

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Focal Points in Recent Tourism Price Research

Nemec Rudež

2024

Tourism (Zagreb, Online)

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The article aims to provide a comprehensive overview of recent academic studies on pricing in tourism and to deepen the understanding of the recently prevailing research streams on this topic. This article reviews research articles on tourism pricing published in journals in the Scopus database between 2017 and early 2023. An analysis of the selected literature identifies three research streams on pricing: research on price determinants, price development and customers’ price decisions. In terms of methodology, empirical studies predominate, and in terms of period, the distribution over the years shows a stable and consistent level of research activity. The article is essential for further research in the field by presenting a synthesis of recent academic work.

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Section: Research On Price Determinantsmentioning

confidence: 99%

Section: Research On Price Determinantsmentioning

confidence: 99%

Focal Points in Recent Tourism Price Research

Nemec Rudež

2024

Tourism (Zagreb, Online)

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Reverse engineering the last-minute on-line pricing practices: an application to hotels

Guizzardi,

Ballestra,

D’Innocenzo

2024

Stat Methods Appl

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We suggest a nonlinear time series methodology to model the (last-minute) price adjustments that hotels active in the online market make to adapt their early-booking rates in response to unpredictable fluctuations in demand. We use this approach to reverse-engineer the pricing strategies of six hotels in Milan, Italy, each with different features and services. The results reveal that the hotels’ ability to align last-minute adjustments with early-booking decisions and account for stochastic demand seasonality varies depending on factors such as size, star rating, and brand affiliation. As a primary empirical finding, we show that the autocorrelations of the first four moments of the last-minute price adjustment can be used to gain crucial insights into the hoteliers’ pricing strategies. Scaling up this approach has the potential to equip policymakers in smart destinations with a reliable and transparent tool for the real-time monitoring of demand dynamics.

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