Forecasting hotel room demand using search engine data

Abstract

He has a background in economics with research interests including tourism demand forecasting and tourism impact assessment.

New developments in tourism and hotel demand modeling and forecasting

“…Pan et al (2012) used search volume data from Google Trends on five related queries to predict the demand for hotel rooms in a specific city. An accuracy comparison between three autoregressive moving average (ARMA) family models and their ARMAX counterparts (i.e.…”

Similarities and correlation between resident tourist overnights and Google Trends information in Portugal and its tourism regions

“…For instance, Dehkordy et al (2014) selected the terms using the Google keyword tool (Google Adwords), Pan et al (2012) used the five keywords they consider "the most relevant and unique when tourists search for a destination city in the USA" (p. 200), Saidi et al (2010) and Artola and Galán (2012) used only one keyword "Dubai" and "Spain holiday", respectively. A limitation of the study appointed by Pan et al (2012) is precisely the low number of tourism-related queries and Varian (2014) refers that given there are billions of queries the big challenge is to "determine exactly which queries are the most predictive for a particular purpose".…”

“…Most publications fall under the healthcare (see for example Ginsberg et al, 2009;Yang et al, 2011;Dehkordy et al, 2014) and economy (see for example Choi & Varian, 2009ª;Schmidt & Vosen, 2009;Baker & Fradkin, 2011;Bughin, 2015) fields, but work was also performed in finance (Smith, 2012), communication and marketing (Granka, 2010); religion (Scheitle, 2011); education (Vaughan-Frias et al, 2013), and cinema (Judge & Hand, 2010). The hospitality and tourism sector was analysed in the studies developed by Chamberlin, 2010;Choi and Varian, 2009b;Shimshoni et al, 2009;Suhoy, 2009;Smith and White, 2011;Artola and Galán, 2012;Gawlik et al, 2011;Saidi et al, 2010;Pan et al, 2012;Concha et al, 2015;Kallasidis, 2015;Bangwayo-Skeete and Skeete (2015); Dinis et al 2013Dinis et al , 2015); Jackman and Naitram, 2015); Yang et al (2015); Rivera (2016);and Li et al, 2017).…”

Identifying the Optimal Combination of Hotel Room Distribution Channels: A DEA Analysis with a Balanced Scorecard Approach

“…Similarly, in their study, El Gayar, et al (2011, p. 88) found that forecasting was susceptible to frequent mistakes and that "a 20% reduction of forecast error can translate into a 1% incremental increase in revenue." While more traditional approaches to forecasting utilized historical data to predict future demand (Pan, Wu, & Song, 2012), more recent approaches also included forward looking data, channel use and customer sensitivity.…”

“…If a room has not sold for that night, the revenue was lost and cannot be recovered. Therefore, the number of rooms sold every night remains critical to overall success (Kimes, 1989;Pan, et al, 2012).…”