Predicting the Present

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Predicting the Present

• With Google Trends

Hyunyoung Choi Hal Varian June 2009
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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 current
  economic activity?
• Before release of preliminary statistics
• Before release of final revision

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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
Time window
Category
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Subcategories under Real Estate by Query Shares
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Search on Real Estate Agencies
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Searches on Rental Listings Referrals
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Depicting trends

• Google Trends measures normalized query 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?

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15 yr Mortgage Rate vs. Home Financing
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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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Model
New Home Sales

• Recent Search Activity on
• Real Estate Agencies
• Rental Listings Referrals
• Home Inspections Appraisal
• Property Management
• Home Insurance
• Home Financing

Housing affordability with Average/Median Home
Price
Exogenous Variables
Recent Trend with New Home Sales at
t-1 Seasonality with New Home Sales at t-12
Time Series
Google Trends
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Predicting the present
New Residential Sales from US Census
Google Trends Real Estate by Category

• Monthly release 24 28 days after the month
• Seasonally adjusted
• National and Regional aggregate

• Home Inspections Appraisal
• Home Insurance
• Home Financing
• Property Management
• Rental Listings Referrals
• Real Estate Agencies

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New House Sales vs. Real Estate Google Trends
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Analysis and Forecasting
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
July 2008 Actual 515K Predicted
442.98K Z-score 2.53
August 2008 Prediction 417.52K
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Analysis and Forecasting

• 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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Subcategories under Travel by Query Shares
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Travel to Hong Kong
Google Trends Travel by Category
Visitors Arrival Statistics from Hong Kong
Tourism Board

• 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

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Visitors Arrival Statistics vs. Google Trends
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Analysis and Forecasting
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, 0.02282)? 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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Analysis and Forecasting

• 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
Google Trends under Vehicle Brands Category
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

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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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Analysis and Forecasting
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(gtF) trends1 1
12.89 12.89 710.3542 lt 2e-16 trends2
1 0.05 0.05 2.7987 0.09455 .
log(s1) 1 1532.95 1532.95 84452.7530
lt 2e-16 log(s12) 1 24.07 24.07
1325.9741 lt 2e-16 as.factor(brand) 26
3.34 0.13 7.0696 lt 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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Analysis and Forecasting

• 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
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Google Trends data Search Insights screenshot
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Initial Claims and Google Trends
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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
Recession Starts
Window For Long Term Model
Window For Short Term Model
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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 significantly smaller
Standard deviation, high log likelihood and
smaller AIC Initial Claims are positively
correlated with searches on Jobs and Welfare.
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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 and 19.23 for ST
  Model.
• Further work
• Can examine metro level data
• Other local data (real estate)?
• Combine with other predictors
• Detect turning points?