Internet Search and Hotel Revenues Prashant Das, Ph.D. Assistant Professor of Real Estate Finance Ecole hôtelière de Lausanne, HES-SO // University of.

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Presentation transcript:

Internet Search and Hotel Revenues Prashant Das, Ph.D. Assistant Professor of Real Estate Finance Ecole hôtelière de Lausanne, HES-SO // University of Applied Sciences Western Switzerland Route de Cojonnex 18, 1000 Lausanne, Switzerland

Research Questions 1.Are Hotel Searches on Google Fundamentally Associated with Hotel Demand? – Do they offer additional information about demand? 2.Can Hotel Searches on Google help with Forecasting Hotel Demand? Motivation Issues with information on market fundamentals: – Infrequent release – Lagged release

Product Demand versus Information Demand Stocks – Da et al (2011): abnormal returns – Bank & Larch (2011), Drake et al (2012): liquidity – Vlastakis & Markellos (2012): volatility – Das et al. (2015a, b): stock returns Real Estate – Wu and Brynjolfsson (2009): house price index – Hohenstatt et al. (2011): endonegeneity with home prices – Beracha & Wintoki (2013): causality in abnormal return in home prices – Das et al. (2015a): occupancy, asset return, REIT returns

Google Trends Data Raw data versus search index – Cross sectional standardization – Time-series standardization – Sensitivity to time frame Relevance of Search – Search intent – Search Geography Vs

Data Summary of the Monthly Data UnitMeanMinMaxSDKPSSPPADF GOOG % <0.010 DEM Room nights (mi) >0.100<0.010 R$ADR $ OCC % <0.010 R$GDP $(Tr) Summary of the Weekly Data MeanMinMaxSDKPSSPPADF GOOG % <0.01 OCC % <0.01 DEM Room nights <0.01 Sources: Smith Travel Research (STR) Global Google Trends Macroeconomic Advisors

Methodology Structural Model for Hotel Demand Demand (Wheaton & Rossoff, 1998) Target DemandModeration Eefect Endogeneity

Results Demand (Room Night Stays) (1)(2)(3)(4) Ln(DEM t-1 ) *** *** *** *** (0.086)(0.093)(0.085)(0.092) Ln(DEM t-2 ) *** *** *** *** (0.088)(0.091)(0.088)(0.090) Ln(R$GDP) * * * * (2.139)(2.118)(2.102)(2.071) R$ADR ** * ** * (0.184)(0.183)(0.181)(0.179) GOOG * (0.0001) GOOG t (0.0001) GOOG t ** (0.0001) Ln(R$GDP)xR$ADR ** * ** * (0.019) (0.018) Constant * * ** * (20.388)(20.180)(20.033)(19.754) Trend Included Monthly Dummies Included Observations 102 R2R Adjusted R F Statistic *** *** *** *** Note: * p<0.1; ** p<0.05; *** p<0.01 Unit root problem with GDP

Methodology Structural Modeling Demand Supply Change in hotel room supply, Wheaton & Rossoff (1998) : Gap between demand and supply should reflect the vacancy rate

Methodology Forecasting Yang, Pan and Song (2014) and Weatherford & Keims (2003) – Hotel revenue forecasting models are dominantly univariate – ARIMA forecasting models for OCC and Ln(DEM), weekly basis Despite popularity of univariate forecasting models, fundamentals endogenously evolve here in a multivariate setting. – E.g. OCC and DEM may simultaneously impact each other as well as be impacted by exogenous factors. Besides, GOOG will primarily influence these variables in a lagged form ARIMAX model – Up to four lags of GOOG and a linear trend term as exogenous variables

Results Forecasting Weekly Room Nights Demand Using ARIMAX Models (1)(2)(3) AR t ***0.7801***0.0483*** (0.032)(0.0492)(0.0483) MA t *** *** * (0.0549)(0.0648)(0.0762) MA t * ** (0.0497)(0.0502)(0.0528) MA t ** (0.0384)(0.042)(0.0427) MA t ***0.3349***0.3868*** (0.0461)(0.05)(0.051) GOOG t *** (0.0007) GOOG t ***0.003*** (0.0007) GOOG t (0.0008) GOOG t (0.0007) ADR t ** (0.0016) Constant ***14.183*** *** (0.0256)(0.1218)(0.1851) Trend Included Log likelihood ME RMSE MAE MPE MAPE MASE Note: Dependent Variable: Ln(DEM). DEM is the weekly average of room nights demand aggregated for US. All models are of seasonally adjusted ARIMA(1,0,4) type.

Results Forecasting Weekly Occupancy Rate Using ARIMAX Models (1)(2)(3) AR t ***0.7915***0.7966*** (0.0324)(0.0486)(0.045) MA t *** *** * (0.0538)(0.0629)(0.0709) MA t * ** (0.0484)(0.05)(0.053) MA t *0.1025**0.0822* (0.041)(0.0452)(0.0466) MA t ***0.2759***0.3283*** (0.0446)(0.0498)(0.0502) GOOG t ***0.2009*** (0.0421)(0.0408) GOOG t ***0.1517*** (0.0421)(0.0425) GOOG t (0.0442)(0.0439) GOOG t (0.0426)(0.0418) ADR t *** (0.0845) Constant *** *** *** (1.3894)(7.3846)( ) Trend Included Log likelihood ME RMSE MAE MPE MAPE MASE Note: Dependent Variable: Occupancy rate. All models are of ARIMA(1,0,4) type.

Conclusion On a monthly frequency, global search trends are significantly reflective of future demand after controlling for known determinants of these variables. On a weekly basis, domestic trends significantly improve the weekly forecasts of demand and occupancy rates.

Questions / Suggestions? Thank you