Automotive Marketing; Predicting The Present

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Automotive Marketing; Predicting The Present

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Automotive Marketing; Predicting The Present

  1. 1. Google Confidential and Proprietary1 State of the Economy Hal Varian Chief Economist 22 April 2009
  2. 2. Google Confidential and Proprietary2 The bad news 2 • Homes: U.S. home prices have fallen 27% since peak. Pending sales fell 7.7% in January (though up slightly in west.) • 2008 CPI: Full-year changes of +0.1% overall, +1.8% core. Dramatic deceleration Q4 due to falling aggregate demand. • March unemployment rate now at 8.5%, • Manufacturing-Activity Index: Currently at 28-year low. • Stock market: Down by 45% from peak. • Bottom Line: The financial crisis contributed to an already weak U.S. economy that officially entered a recession in 12/07. As of April, it is now the the longest U.S. recession since the Great Depression.
  3. 3. Google Confidential and Proprietary3 The good news
  4. 4. Google Confidential and Proprietary4 No, really... • Asset prices are low •Houses – pending sales up 2.1% in February. •Mortgages – conventional loans to qualified borrowers available •Stocks – up 20% since March low • Stimulus plan started in April •Payroll tax cut started April 1 (up to $400 per person) •Tax refunds larger •Home buyer, auto purchase credit •Some accelerated depreciation now available • Inventories are being depleted, albeit slowly •Housing •Goods
  5. 5. Google Confidential and Proprietary5 What happens in a recession? • Delay everything that can be delayed –Business investment –State and local spending (due to tax receipts) –Consumer durable purchase –However, “consumer staples” usually see much smaller hit •Government actions –Want to avoid downward spiral •Drop in demand … lay off workers … spending falls •Need to stabilize demand: consumption, govn't, investment –Trying a multipronged attack
  6. 6. Google Confidential and Proprietary6 Google Query Trends
  7. 7. Google Confidential and Proprietary7 Signs of hope • Good news? • Macroeconomics –Financial situation stabilizing •Particularly important for this recession –Market volatility coming down •VIX index – volatility index though back up again recently •Ted Spread – gap between LIBOR and T-bill rate –Keep a close eye on these metrics, as they are good leading indicators
  8. 8. Google Confidential and Proprietary8 Two sectors to watch: Real Estate and Autos • Mortgage money available • Auto loans to follow • Real estate shows signs of stabilizing – Queries showing usual seasonal uplift – May see further activity in Spring • Automotive sector is depressed – Expect to see very attractive terms offered – Also typical seasonal uplift
  9. 9. Google Confidential and Proprietary9 Implications for retail • Q1 has been slow, but not as bad as Q4 for economy –Impacted verticals –Real estate, auto, appliances, furniture, travel, luxury items –Less sensitive –Low end shopping, health, local spending •Areas to watch as leading indicators –Automotive, real estate –TED spread = 3 month Treasury bill rate – 3 month LIBOR –Watch the VIX! •Consumers are hunting for value –Classic, reliable, solid...
  10. 10. Google Confidential and Proprietary10 Everybody talks about the economy... • Can Google queries help forecast economy activity? •Government data released with a lag •Google data is real time •Appears to be correlated with current level of activity •May be helpful in “predicting the present” •This is still 4-6 weeks before official data release
  11. 11. 11Google Confidential and Proprietary Observing Query Growth with Google Trends
  12. 12. 12Google Confidential and Proprietary Observing Traffic with Google Trends DEMO
  13. 13. 13Google Confidential and Proprietary Google Categories under Vehicle Brands NOTE: Area represents the queries volume from first half year 2008 and the color represents queries yearly growth rate
  14. 14. 14Google Confidential and Proprietary 14 Model with Panel Data Model: log(Yi,t) = 1.681 + 0.3618 * log(Yi,t-1) + 0.4621 * log(Yi,t-12) + 0.0014 * Xi,t,2 + 0.0020 * Xi,t,2 + ai * Makei + ei,t ei,t ~ N(0, 0.14972) , Adjusted R2 = 0.9791 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 to indicate 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 7.48 7.48 333.8334 < 2.2e-16 *** trends2 1 1.71 1.71 76.2150 < 2.2e-16 *** log(s1) 1 1609.52 1609.52 71826.7401 < 2.2e-16 *** log(s12) 1 20.24 20.24 903.2351 < 2.2e-16 *** as.factor(brand) 26 2.11 0.08 3.6301 2.36e-09 *** Residuals 1535 34.40 0.02
  15. 15. 15Google Confidential and Proprietary Actual vs. Fitted Sales (Top 9 Make by Sales)
  16. 16. 16Google Confidential and Proprietary 16 Model with Univariate Time Series Model: log(Yi,t) = 3.0343 + 0.2054 * log(Yi,t-1) + 0.5396 * log(Yi,t-12) + 0.0034 * Xi,t,1 + ei,t ei,t ~ N(0, 0.10512) , Adjusted R2 = 0.5804 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 country Makei = Dummy variable to indicate Auto Make ANOVA Table Df Sum Sq Mean Sq F value Pr(>F) s1 1 0.23366 0.23366 21.151 2.603e-05 *** log(s1) 1 0.36614 0.36614 33.142 4.171e-07 *** log(s12) 1 0.30421 0.30421 27.537 2.651e-06 *** Residuals 54 0.59657 0.01105
  17. 17. 17Google Confidential and Proprietary Toyota Sales 1st Week of Month
  18. 18. Google Confidential and Proprietary18 Other interesting things 18 • Government statistics • automobile sales • home sales • retail sales • travel • Can look at state and city level data • Geographic variation is often quite striking • Great viz: http://www.slate.com/id/2216238/
  19. 19. Google Confidential and Proprietary Large differences in state patterns of unemployment claims Time Series Autocorrelation Function
  20. 20. Google Confidential and Proprietary Model Fit and Prediction
  21. 21. Google Confidential and Proprietary21 Useful sites • Economics blogs –Hall-Woodward (policy): http://woodwardhall.wordpress.com/ –Mankiw (right): http://gregmankiw.blogspot.com/ –Delong (left): http://delong.typepad.com/ –Thoma (mid): http://economistsview.typepad.com/ –Hamilton (tech): http://www.econbrowser.com/

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