Google Trends Predicting Present v2

8/22/2019 Google Trends Predicting Present v2

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Google Confidential and Proprietary 1

Predicting the PresentWith Google Trends

Hyunyoung Choi

Hal Varian

June 2009


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Google Confidential and Proprietary 2 2

Problem statement

Government agencies and other organizations produce monthly reports on economic activity

Retail Sales

House Sales

Automotive Sales

Unemployment

Problems with reports

Compilation delay of several weeks Subsequent revisions

Sample size may be small

Not available at all geographic levels

Google Trends releases daily and weekly index of search queries by industry vertical

Real time data

No revisions (but some sampling variation)

Large samples

Available by country, state and city

Can Google Trends data help predict currenteconomic activity?

Before release of preliminary statistics

Before release of final revision


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Google Confidential and Proprietary

3

Categories in Google Trends by Query Shares

Note: Queries from 2009-01-01 to 2009-04-30 & Growth Comparison w/ the same time window


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Real Estate


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Geography

Category

Time window


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Real Estate Agencies

Rental Listings & Referrals

Home Insurance

Home Inspections

& Appraisal

P

roperty

M

anagement

Home Financing

6

Subcategories under Real Estate by Query Shares


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Search on Real Estate Agencies



Searches on Rental Listings & Referrals


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Depicting trends

Google Trends measures normalizedquery

share of particular category of queries controls for overall growth

Often useful to look at year-on-year changes

to eliminate seasonality.

Illustrate correlations and covariates.

Improving predictions

Forecast time series using its own lagged

values and add Trends data as a predictor.

Statistical significance?

Improved fit?

Improved forecasts?

Identify turning points?

9

2006 2007 2008

30

20

10

0

10

20

Real Estate Agencies Query Index

Oct Jan Apr Jul20

15

10

5

0

5

Real Estate Agencies YOY Growth Index



15 yr Mortgage Rate vs. Home Financing



Forecasting primer

Basic forecasting models

Autoregressive: value at time t depends on

Value at time t-1

Seasonal adjustment: value at time t depends on

Value at time t-12

For monthly data

Transfer function: value at time t depends on

Other contemporaneous or lagging variables

Seasonal autoregressive transfer model: Value at time t depends on

Value at time t-12 (seasonality)

Value at time t-1 (recent behavior)

Other lagging or contemporaneous variables (such as Google Trends data)

Typical question of interest

How much more accurate forecasts can you get from additional variables over and above the accuracy

you get with the history of the time series itself?


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New Home Sales

Model

Recent Trend with NewHome Sales at t-1

Seasonality with NewHome Sales at t-12

Recent Search Activity on



Home Inspections &Appraisal

Property Management

Home Insurance

Home Financing

Time Series Google Trends

Housing affordabilitywith Average/MedianHome Price

Exogenous Variables



Predicting the present

Monthly release 24 28 days after the

month

Seasonally adjusted

National and Regional aggregate

Home Inspections & Appraisal

Home Insurance

Home Financing

Property Management



New Residential Sales from US Census Google Trends Real Estate by Category


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New House Sales vs. Real Estate Google Trends


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Model:

Yt = 446.1 + 0.864 * Yt - 1 4.340 * us378.1 + 4.198 * us96.2 0.001 * AvgPt 1

Yt : New house sold at t-th month

AvgPt 1: Average Sales Price of New One-Family Houses Sold at (t-1)-th month

us378.1 : Google Trend of vertical id = 378 (Rental Listings & Referrals ) at t-th month 1st week

us96.2 : Google Trend of vertical id = 96 (Real Estate Agent) at t-th month 2nd week

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Analysis and Forecasting

July 2008

Actual = 515K

Predicted = 442.98K

Z-score = 2.53

August 2008 Prediction = 417.52K


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Observations

Since 2005 new house sales have been decreasing, with little seasonality

Google Trends captures seasonality & recent trends

Positive association with Real Estate Agencies (96)

Negative association with Rental Listings & Referrals (378) and Average Price


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Travel


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Hotels & Accommodations

Attractions & Activities

Air Travel

Bus & Rail

Cruises &

Charters

A

dventure

Travel

Car Rental

& Taxi Services

Vacation Destinations

18

Subcategories under Travel by Query Shares


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Travel to Hong Kong

Monthly summaries release with 1

month lag

Reports Country/Territory of Residence

of visitors

Data available 2004-2008

Hotels & Accommodations

Air Travel

Car Rental & Taxi Services

Cruises & Charters

Attractions & Activities

Vacation Destinations

Australia

Caribbean Islands

Hawaii

Hong Kong

Las Vegas Mexico

New York City

Orlando

Adventure Travel

Bus & Rail

Google Trends Travel by CategoryVisitors Arrival Statistics from Hong

Kong Tourism Board


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Visitors Arrival Statistics vs. Google Trends


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Model:

log(Yi,t) = 0.664 + 0.113 * log(Yi,t-1) + 0.828 * log(Yi,t-12) + 0.001 * Xi,t,2 + 0.001 * Xi,t,3

+ 0.005 * FXrate i,t + i, + ei,t

ei,t ~ N(0, 0.09382), i ~ N(0, 0.0228

2)

Yi,t = Arrival to Hong Kong at month t and from i-th country

Xi,t,1 = Google Trend Search at 1st week of month t and from i-th country

Xi,t,2 = Google Trend Search at 2nd week of month t and from i-th country

Xi,t,3 = Google Trend Search at 3rd week of month t and from i-th country

FXrate i,t = Hong Kong Dollar per one unit of i-th countrys local currency at month t. Average of first

weeks FX rate is used as a proxy to FX rate per each month.


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Visitor Arrival Statistics - Actual & Fitted


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Conclusion

Arrival at time t is positively associated with arrival at time t-1 and arrival at time t-12.

It shows strong seasonality and autocorrelation

Arrival at time t is positively associated with searches on [Hong Kong].

Arrival at time t is positively associated with FX rates.

When the local currency appreciates relative to Hong Kong Dollar, visitors to Hong Kong increase.


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Automobiles


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US Auto Sales by Make

Monthly summaries released 1 week

after end of month

Data available by Car Sales, Truck

Sales and Total Sales for each make

Data available from 2003-2008

Source:Automotive News Data Center

Google Trends subcategory Vehicle

Brands.

Weekly Search query index

Total 31 verticals in this subcategory

27 verticals matching to Monthly Sales

available

Google Trends under Vehicle Brands

CategoryUS Auto Sales by Make


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Google Categories under Vehicle Brands

NOTE: Area represents the queries volume from first half year 2008 and the color represents queries yearly growth rate


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Auto Sales by Make (Top 9 Make by Sales)Monthly Sales vs. Google Trends at Second Week of each month


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Fixed effects model:

log(Yi,t) = 2.4276 + 0.2552 * log(Yi,t-1) + 0.4930 * log(Yi,t-12)

+ 0.0005 * Xi,t,2 + 0.0014 * Xi,t,2 + ai * Makei + ei,t

ei,t ~ N(0, 0.13472) , Adjusted R2 = 0.9829

Yi,t = Auto Sales of i-th Make at month t

Xi,t,1 = Google Trend Search at 1st week of month t and from i-th make

Xi,t,2

= Google Trend Search at 2nd week of month t and from i-th make

Makei =Dummy variable for Auto Make

ai = Coefficient to capture the mean level of Auto Sales by Make

ANOVA Table

Df Sum Sq Mean Sq F value Pr(>F)trends1 1 12.89 12.89 710.3542 < 2e-16 ***

trends2 1 0.05 0.05 2.7987 0.09455 .

log(s1) 1 1532.95 1532.95 84452.7530 < 2e-16 ***

log(s12) 1 24.07 24.07 1325.9741 < 2e-16 ***

as.factor(brand) 26 3.34 0.13 7.0696 < 2e-16 ***

Residuals 1480 26.86 0.02


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Actual vs. Fitted Sales (Top 9 Make by Sales)


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Conclusion

Sales at time t are positively associated with Sales at time t-1 and Sales at time t-12.

Sales show strong seasonality and autocorrelation

Monthly Sales are positively correlated to the first and second weeks search volume of each

month.

If the search volume increase by 1%, the sales volume will increase by an average of 0.19%.


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Unemployment


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YoY Growth in Initial Claims & Google Search

According to the NBER, the current recession started December 2007.

National unemployment rate passed 5% in mid 2008 and search queries on [Welfare

and Unemployment] also increased at same time.


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Initial claims is an important leading indicator

Google Trends data [Search Insights screenshot]


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Google Trends data [Search Insights screenshot]


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Initial Claims and Google Trends

Month May 2009

Week3/15/09 -

3/21/09

3/22/09 -

3/28/09

3/29/09 -

4/4/09

4/5/09 -

4/11/09

4/12/09 -

4/18/09

4/19/09 -

4/25/09

4/26/09 -

5/2/09

Initial Claims 81,236 74,179 69,471 75,875 84,410Continued Claims 859,561 826,924 866,734 834,569 846,477

Covered Employment 15,395,215 15,395,215 15,395,215 15,356,117 15,356,117

Insured Unemployment Rate 5.58 5.37 5.63 5.43 5.51

Jobs 9% 6% 2% 0% 1% -9% -11%

Welfare & Unemployment -2% -9% -13% -12% -6% -9% -10%

California

March 2009 April 2009

Release at

5/7/09

Release at

5/14/09

Google

Trends

US Dept of

Labor


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Strong Autocorrelation in Initial Claims

Time Series Autocorrelation Function


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Initial Claims Before/After Recession Started

California New York


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Time Window for Analysis

Window For Long Term Model

Window For Short Term Model

Recession Starts


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Model

Reference ARIMA(0,1,1) X (1,0,0)12 Model

ARIMA(0,1,1) X (1,0,0)12 Model With Google Trends

Model Fit improved significantlysmaller Standard deviation, high log likelihood and smaller AIC

Initial Claims are positively correlated with searches on Jobs and Welfare.

Sigmalog

likelihoodAIC Sigma

log

likelihoodAIC

LT Model -0.755 *** 0.619 *** 0.086 268.85 -531.69 -0.725 *** 0.565 *** 0.004 ** 0.003 ** 0.083 285.96 -561.91

ST Model -0.691 *** 0.463 *** 0.098 99.04 -192.08 -0.657 *** 0.359 ** 0.002 0.007 *** 0.088 114.19 -218.38

Reference Model Model with Google Trends

Theta Phi Theta Phi Jobs Welfare

Signif. codes: 0.001 ***0.05 ** 0.01 *


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Long Term Model: Prediction Comparison with MAE

With Google Trends, the out-of-sample prediction MAE decreases by 16.84%.

Prediction with rolling window from 1/11/2009 to 4/12/2009

Prediction Error at t:

Mean Absolute Error:


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Short Term Model: Prediction Comparison with MAE

With Google Trends, the out-of-sample prediction MAE decreases by 19.23%.

Prediction errors are within the same range as LT Model.

Fit improvement is better with ST Model.


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Summary

Google Trends significantly improves out-of-sample prediction of state unemployment, up

to 18 days in advance of data release.

Mean absolute error for out-of-sample predictions declines by 16.84% for LT Model and19.23% for ST Model.

Further work

Can examine metro level data

Other local data (real estate)

Combine with other predictors

Detect turning points?

Google Trends Predicting Present v2

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