1. 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 prashant.das@ehl.ch

2. 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

3. 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

4. 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

5. Data Summary of the Monthly Data UnitMeanMinMaxSDKPSSPPADF GOOG %-23.92-52.7513.0016.15 0.037<0.010 DEM Room nights (mi) 85.37 64.09105.910.89>0.100<0.010 R$ADR $ 110.8 101.3121.94.943 0.010 0.478 0.787 OCC % 60.31 43.5872.117.140 0.066<0.010 R$GDP $(Tr) 16.17 15.5816.910.317 0.010 0.608 0.900 Summary of the Weekly Data MeanMinMaxSDKPSSPPADF GOOG %-21.34-57.0020.0017.64 0.02<0.01 OCC % 60.43 33.6875.378.101 0.03<0.01 DEM Room nights19.57211.11625.5012.702 0.07<0.01 Sources: Smith Travel Research (STR) Global Google Trends Macroeconomic Advisors

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

7. Results Demand (Room Night Stays) (1)(2)(3)(4) Ln(DEM t-1 ) 0.387 *** 0.322 *** 0.406 *** 0.332 *** (0.086)(0.093)(0.085)(0.092) Ln(DEM t-2 ) 0.586 *** 0.635 *** 0.556 *** 0.614 *** (0.088)(0.091)(0.088)(0.090) Ln(R$GDP) -3.933 * -3.657 * -4.103 * -3.765 * (2.139)(2.118)(2.102)(2.071) R$ADR -0.369 ** -0.345 * -0.383 ** -0.354 * (0.184)(0.183)(0.181)(0.179) GOOG 0.0001 * (0.0001) GOOG t-1 -0.00002 (0.0001) GOOG t-2 0.0001 ** (0.0001) Ln(R$GDP)xR$ADR 0.038 ** 0.035 * 0.039 ** 0.036 * (0.019) (0.018) Constant 38.747 * 36.336 * 40.568 ** 37.561 * (20.388)(20.180)(20.033)(19.754) Trend Included Monthly Dummies Included Observations 102 R2R2 0.9910.992 Adjusted R 2 0.990 F Statistic 573.565 *** 555.644 *** 562.428 *** 524.785 *** Note: * p<0.1; ** p<0.05; *** p<0.01 Unit root problem with GDP

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

9. 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

10. Results Forecasting Weekly Room Nights Demand Using ARIMAX Models (1)(2)(3) AR t-1 0.8534***0.7801***0.0483*** (0.032)(0.0492)(0.0483) MA t-1 -0.2312***-0.2723***-0.1487* (0.0549)(0.0648)(0.0762) MA t-2 -0.0824*-0.0757-0.1279** (0.0497)(0.0502)(0.0528) MA t-3 0.05080.0826**0.0672 (0.0384)(0.042)(0.0427) MA t-4 0.3347***0.3349***0.3868*** (0.0461)(0.05)(0.051) GOOG t-1 0.004*** (0.0007) GOOG t-2 0.0026***0.003*** (0.0007) GOOG t-3 0.00090.0011 (0.0008) GOOG t-4 0.00050.0003 (0.0007) ADR t-1 -0.0037** (0.0016) Constant 14.8365***14.183***14.4917*** (0.0256)(0.1218)(0.1851) Trend Included Log likelihood 550.88571.3573.91 ME 0.00070.0004 RMSE 0.07870.07550.0751 MAE 0.05820.05540.0551 MPE 0.0016-0.00010.0002 MAPE 0.39370.37480.3727 MASE 0.92890.88450.8797 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.

11. Results Forecasting Weekly Occupancy Rate Using ARIMAX Models (1)(2)(3) AR t-1 0.8532***0.7915***0.7966*** (0.0324)(0.0486)(0.045) MA t-1 -0.2049***-0.2445***-0.1174* (0.0538)(0.0629)(0.0709) MA t-2 -0.0862*-0.079-0.136** (0.0484)(0.05)(0.053) MA t-3 0.0723*0.1025**0.0822* (0.041)(0.0452)(0.0466) MA t-4 0.2876***0.2759***0.3283*** (0.0446)(0.0498)(0.0502) GOOG t-1 0.2069***0.2009*** (0.0421)(0.0408) GOOG t-2 0.1305***0.1517*** (0.0421)(0.0425) GOOG t-3 0.02980.0383 (0.0442)(0.0439) GOOG t-4 0.0205-0.0004 (0.0426)(0.0418) ADR t-1 -0.2304*** (0.0845) Constant 60.619***32.6026***53.1911*** (1.3894)(7.3846)(10.8144) Trend Included Log likelihood -1412.24-1396.92-1393.44 ME 0.02120.01900.0219 RMSE 4.28704.15694.1266 MAE 3.27493.15353.1075 MPE -0.5675-0.5902-0.5708 MAPE 5.76465.54235.4487 MASE 0.92540.89110.8782 Note: Dependent Variable: Occupancy rate. All models are of ARIMA(1,0,4) type.

12. 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.

13. Questions / Suggestions? Thank you