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
Sincere thanks to Steve Hood and Duane Vinson of STR Global for their generous data support.

2. Research Questions Motivation
Are Hotel Searches on Google Fundamentally Associated with Hotel Demand?
Do they offer additional information about demand?
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 Sources: Smith Travel Research (STR) Global Google Trends
Summary of the Monthly Data
Unit
Mean
Min
Max SD
KPSS PP
ADF
GOOG %
-23.92
-52.75
13.00
16.15
0.037
<0.010
DEM
Room nights (mi)
85.37
64.09
105.9
10.89
>0.100
R$ADR $
110.8
101.3
121.9
4.943
0.010
0.478
0.787
OCC
60.31
43.58
72.11
7.140
0.066
R$GDP
$(Tr)
16.17
15.58
16.91
0.317
0.608
0.900
Summary of the Weekly Data
Mean
Min
Max SD
KPSS PP
ADF
GOOG %
-21.34
-57.00
20.00
17.64
0.02
<0.01
OCC
60.43
33.68
75.37
8.101
0.03
DEM
Room nights
19.572
11.116
25.501
2.702
0.07
Sources:
Smith Travel Research (STR) Global
Google Trends
Macroeconomic Advisors

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

7. Demand (Room Night Stays)
Results
Demand (Room Night Stays)
(1)
(2)
(3)
(4)
Ln(DEMt-1)
0.387***
0.322***
0.406***
0.332***
(0.086)
(0.093)
(0.085)
(0.092)
Ln(DEMt-2)
0.586***
0.635***
0.556***
0.614***
(0.088)
(0.091)
(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)
GOOGt-1
GOOGt-2
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
Observations
102 R2
0.991
0.992
Adjusted R2
0.990
F Statistic
***
***
***
***
Unit root problem with GDP
Note: *p<0.1; **p<0.05; ***p<0.01

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. Forecasting Weekly Room Nights Demand Using ARIMAX Models
Results
Forecasting Weekly Room Nights Demand Using ARIMAX Models
(1)
(2)
(3)
ARt-1
0.8534***
0.7801***
0.0483***
(0.032)
(0.0492)
(0.0483)
MAt-1
***
*** *
(0.0549)
(0.0648)
(0.0762)
MAt-2 * **
(0.0497)
(0.0502)
(0.0528)
MAt-3
0.0508
0.0826**
0.0672
(0.0384)
(0.042)
(0.0427)
MAt-4
0.3347***
0.3349***
0.3868***
(0.0461)
(0.05)
(0.051)
GOOGt-1
0.004***
(0.0007)
GOOGt-2
0.0026***
0.003***
GOOGt-3
0.0009
0.0011
(0.0008)
GOOGt-4
0.0005
0.0003
ADRt-1 **
(0.0016)
Constant
***
14.183***
***
(0.0256)
(0.1218)
(0.1851)
Trend
Included
Log likelihood
550.88
571.3
573.91 ME
0.0007
0.0004
RMSE
0.0787
0.0755
0.0751
MAE
0.0582
0.0554
0.0551
MPE
0.0016
0.0002
MAPE
0.3937
0.3748
0.3727
MASE
0.9289
0.8845
0.8797
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. Forecasting Weekly Occupancy Rate Using ARIMAX Models
Results
Forecasting Weekly Occupancy Rate Using ARIMAX Models
(1)
(2)
(3)
ARt-1
0.8532***
0.7915***
0.7966***
(0.0324)
(0.0486)
(0.045)
MAt-1
***
*** *
(0.0538)
(0.0629)
(0.0709)
MAt-2 *
-0.079
-0.136**
(0.0484)
(0.05)
(0.053)
MAt-3
0.0723*
0.1025**
0.0822*
(0.041)
(0.0452)
(0.0466)
MAt-4
0.2876***
0.2759***
0.3283***
(0.0446)
(0.0498)
(0.0502)
GOOGt-1
0.2069***
0.2009***
(0.0421)
(0.0408)
GOOGt-2
0.1305***
0.1517***
(0.0425)
GOOGt-3
0.0298
0.0383
(0.0442)
(0.0439)
GOOGt-4
0.0205
(0.0426)
(0.0418)
ADRt-1
***
(0.0845)
Constant
60.619***
***
***
(1.3894)
(7.3846)
( )
Trend
Included
Log likelihood ME
0.0212
0.0190
0.0219
RMSE
4.2870
4.1569
4.1266
MAE
3.2749
3.1535
3.1075
MPE
MAPE
5.7646
5.5423
5.4487
MASE
0.9254
0.8911
0.8782
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