Modelling and prediction of a destination’s monthly average daily rate and occupancy rate based on hotel room prices offered online

Abstract

Tourism metrics are essential for managing a destination. Hotel performance metrics such as average daily rate and occupancy rate are two of the most prominent metrics for the industry. The authors’ research group works on developing methods for estimating tourism metrics based on digital footprint. Data available publicly on the Internet, including hotel room prices, are collected daily. This article shows that the prices offered online have a high positive correlation with those reported by official statistics at the Nomenclature of Units for Territorial Statistics 2 level after the online prices have been preprocessed and, thus, the relevance of this data source is established. This article then presents a model for explaining and predicting mean hotel occupancy rates by destination based on these prices. The results are very promising, the fit is excellent and the predictions are also good. In summary, prices have moved from reflecting the expected demand to reflecting the actual demand and occupancy rate.

Keywords

average daily rate dynamic prices hotel performance metrics occupancy rate virtual channel closures

Get full access to this article

View all access options for this article.

References

Abrate

Fraquelli

Viglia

(2012) Dynamic pricing strategies: Evidence from European hotels. International Journal of Hospitality Management 31(1): 160–168.

Athanasopoulos

Hyndman

(2008) Modelling and forecasting Australian domestic tourism. Tourism Management 29(1): 19–31.

Chen

K-Y

(2011) Combining linear and nonlinear model in forecasting tourism demand. Expert Systems with Applications 38(8): 10368–10376.

Chu

F-L

(2009) Forecasting tourism demand with ARMA-based methods. Tourism Management 30(5): 740–751.

Haensel

Koole

(2011) Booking horizon forecasting with dynamic updating: a case study of hotel reservation data. International Journal of Forecasting 27(3): 942–960.

Heerschap

Ortega

Priem

. (2014) Innovation of tourism statistics through the use of new big data sources. Paper presented at the 12th Global Forum of Tourism Statistics, Prague, 15-16 May.

Instituto Nacional de Estadística (n.d.) Hotel occupancy survey. Available at: http://www.ine.es/jaxi/menu.do?L=1&type=pcaxis&path=%2Ft11/e162eoh&file=inebase (accessed 12 December 2014).

Instituto Nacional de Estadística (2008) Indicators on the profitability of the hotel sector. Available at: http://www.ine.es/jaxiT3/Tabla.htm?t=2057 (accessed 10 February 2015).

Instituto Nacional de Estadística (2013a) Hotel occupancy survey 2013: methodology. Available at: http://www.ine.es/en/daco/daco42/ocuphotel/notaeoh_13_en.pdf (accessed 12 December 2014).

10.

Instituto Nacional de Estadística (2013b) Hotel price index (hpi). Base 2008 (as of January 2014) methodological note. Available at: http://www.ine.es/en/daco/daco42/prechote/meto_iph_base2008_en.pdf (accessed 12 March 2015).

11.

Kulendran

Witt

(2003) Leading indicator tourism forecasts. Tourism Management 24(5): 503–510.

12.

Magnini

Honeycutt

Hodge

(2007) Data mining for hotel firms: use and limitations. In: Rutherford

O’Fallon

(eds) Hotel Management and Operations. 4th edn. New York: Van Nostrand Rheinhold, pp. 399–414.

13.

Massieu

(2001) A System of Tourism Statistics (STS): scope and content. In: Lennon

(ed) Tourism Statistics: International Perspectives and Current Issues. London: Continuum, pp. 3–13.

14.

Oses

Gerrikagoitia

Alzua

(2015) Dynamic pricing patterns on an internet distribution channel: the case study of Bilbao’s hotels in 2013. In: Enter Conference on Information and Communication Technologies in Tourism. Lugano, Switzerland, 3–6 February 2015, Springer International Publishing.

15.

PKF Hospitality Research (2010) Assessing accuracy: hotel horizons forecasts. Available at: http://www.pkfc.com/en/pkfhome/nism/accuracyassessment/PKFAccuracy0610.pdf (accessed 15 December 2010).

16.

PwC Hospitality Directions Europe (2010) UK hotels forecast 2011 and 2012: how big a party for hotels in 2012? Available at: http://www.pwc.co.uk/hospitalitydirections (accessed 15 December 2010).

17.

Roman

(2012) Measuring the influence of events in hotel room prices. Unpublished Master’s thesis, Universidad del País Vasco-Euskal Herriko Unibertsitatea.

18.

Shen

Song

(2011) Combination forecasts of international tourism demand. Annals of Tourism Research 38(1): 72–89.

19.

Song

(2008) Tourism demand modelling and forecasting – a review of recent research. Tourism Management 29(2): 203–220.

20.

Song

Witt

Jensen

(2003) Tourism forecasting: accuracy of alternative econometric models. International Journal of Forecasting 19(1): 123–141.

21.

UNWTO (2008a) The conceptual framework for tourism statistics – international recommendations for tourism statistics 2008 (IRTS 2008). Available at: http://statistics.unwto.org/content/irts-2008 (accessed 2 February 2015).

22.

UNWTO (2008b) The conceptual framework for TSA – tourism satellite account: recommended methodological framework 2008 (TSA: RMF 2008). Available at: http://statistics.unwto.org/content/tsarmf-2008 (accessed 2 February 2015).

23.

Wall

McFeely

(2012) Ireland case study: measuring and analysing regional tourism. In: UNWTO/InRoute 1st Seminar on Regional Tourism: setting the focus. Venice, Italy, 5–6 July 2012. Available at: http://home.gwu.edu/~frechtli/material/6INRouTE%20EconTools7-12R.pdf.

24.

Witt

(1995) Forecasting tourism demand: a review of empirical research. International Journal of Forecasting 11(3): 447–475.

25.

Yap

Allen

(2011) Investigating other leading indicators influencing Australian domestic tourism demand. Mathematics and Computers in Simulation 81(7): 1365–1374.

26.

Yüksel

(2007) An integrated forecasting approach to hotel demand. Mathematical and Computer Modelling 46(7–8): 1063–1070.