1. What decides the price of used cars? Group 1 Jessica Aguirre Keith Cody Rui Feng Jennifer Griffeth Joonhee Lee Hans-Jakob Lothe Teng Wang

2. How we got data Collected from kbb.com (Kelley Blue Book) Used random number generator First collected 140 sets of data from various types of cars Then collected 160 sets of data from Toyota Camrys

3. Brand Population

4. Models

5. Average Selling Price by Brand

6. Assumptions Random sample is representative of population All prices are the selling price Residuals are homoskedastic Residuals are normally distributed The variables we choose affect the price of used cars: age, color, etc

7. Preparations Created dummy variables e.g. Transmission, automatic = 0, manual = 1 Color Type Engine (V4 = 4, V8 = 8, etc)

8. All Cars: Regression of price against independent variables (age, color, engine, miles and transmission) Dependent Variable: PRICE Method: Least Squares Date: 11/29/10 Time: 16:43 Sample: 1 140 Included observations: 140 VariableCoefficientStd. Errort-StatisticProb. AGE-671.2805191.5316-3.5048030.0006 COLOR151.6366156.63860.9680670.3348 ENGINE1793.689292.12686.1401050.0000 MILES-3798.259590.6794-6.4303230.0000 TRANSMISSION1462.7021248.1291.1719160.2433 C48055.056425.4177.4789000.0000 R-squared0.555991 Mean dependent var16859.54 Adjusted R-squared0.539424 S.D. dependent var6831.670 S.E. of regression4636.365 Akaike info criterion19.76316 Sum squared resid2.88E+09 Schwarz criterion19.88923 Log likelihood-1377.421 F-statistic33.55917 Durbin-Watson stat1.676795 Prob(F-statistic)0.000000

9. All Cars: Regression of price against significant independent variables (p<0.05) Dependent Variable: PRICE Method: Least Squares Date: 11/29/10 Time: 16:40 Sample: 1 140 Included observations: 140 VariableCoefficientStd. Errort-StatisticProb. AGE-630.8791190.0333-3.3198340.0012 ENGINE1751.229289.77286.0434570.0000 LNMILE-4013.936576.0638-6.9678660.0000 C51372.116106.1508.4131750.0000 R-squared0.547257 Mean dependent var16859.54 Adjusted R-squared0.537271 S.D. dependent var6831.670 S.E. of regression4647.190 Akaike info criterion19.75407 Sum squared resid2.94E+09 Schwarz criterion19.83812 Log likelihood-1378.785 F-statistic54.79716 Durbin-Watson stat1.655862 Prob (F-statistic)0.000000 Price = -631.9880*AGE + 949.8378* ENGINE -0.051251* MILEAGE + 1977.688*TRIM + 18866.11

10. Some reasons why this model fails Color is randomly assigned a number (red = 9, blue = 7, etc) Engines: e.g. 4 cylinder = 4, V8 = 8  assumes the V8 is twice the price of 4 cylinder We suspect that many models leads to low R-Square

11. Our solution: New model New model where we look at one model and brand (Toyota Camry), only two engines (4 cylinder and 6 cylinder), and disregard color Dummy variable for engine: 6 cylinder = 1, 4 cylinder = 0 We also introduce a new variable called trim Dummy variable for trim: luxury = 1, standard = 0 Toyota Camry o Most Popular Car in America* * Motor Trend http://www.motortrend.com/features/auto_news/2010/112_1004_america_top_10_best_selling_vehicle_comparison_2009_2000/index.html

12. Camry Price Histogram

13. Toyota Camry: Regression of price against independent variables (age, engine, mileage, trim and transmission) Dependent Variable: PRICE Method: Least Squares Date: 11/29/10 Time: 20:09 Sample: 1 160 Included observations: 160 VariableCoefficientStd. Errort-StatisticProb. AGE-625.432864.45118-9.7039780.0000 ENGINE917.0942324.95082.8222560.0054 MILEAGE-0.0510270.005406-9.4386890.0000 TRIM1972.208309.73516.3674000.0000 TRANSMISSION967.94151104.7420.8761700.3823 C17888.661141.74015.667890.0000 R-squared0.828216 Mean dependent var14937.87 Adjusted R-squared0.822638 S.D. dependent var3587.486 S.E. of regression1510.845 Akaike info criterion17.51551 Sum squared resid3.52E+08 Schwarz criterion17.63082 Log likelihood-1395.240 F-statistic148.4947 Durbin-Watson stat1.275033 Prob(F-statistic)0.000000

14. Toyota Camry: Regression of price against independent variables (age, engine, mileage and trim) Dependent Variable: PRICE Method: Least Squares Date: 11/29/10 Time: 20:10 Sample: 1 160 Included observations: 160 VariableCoefficientStd. Errort-StatisticProb. AGE-631.988063.96748-9.8798330.0000 ENGINE949.8378322.55272.9447530.0037 MILEAGE-0.0512510.005396-9.4979410.0000 TRIM1977.688309.43986.3911890.0000 C18866.11242.718177.728500.0000 R-squared0.827359 Mean dependent var14937.87 Adjusted R-squared0.822904 S.D. dependent var3587.486 S.E. of regression1509.713 Akaike info criterion17.50798 Sum squared resid3.53E+08 Schwarz criterion17.60408 Log likelihood-1395.638 F-statistic185.7048 Durbin-Watson stat1.286429 Prob(F-statistic)0.000000 Price = -631.9880 * AGE + 949.8378 * ENGINE -0.051251 * MILEAGE + 1977.688 * TRIM + 18866.11

15. All Cars: mileage against price R-Square ≈ 22%

16. Toyota Camrys: mileage against price R Square ≈ 66%

17. Alternative Model PRICE^(1/2) = -0.0002263673136*MILEAGE + 4.59824795*ENGINE - 2.952776402*AGE + 7.704044111*TRIM + 139.1536581 Dependent Variable: NewPRICE Method: Least Squares Date: 11/30/10 Time: 12:16 Sample: 1 160 Included observations: 160 VariableCoefficientStd. Errort-StatisticProb. MILEAGE-0.0002262.23E-05-10.164260.0000 ENGINE4.5982481.3312623.4540510.0007 AGE-2.9527760.264011-11.184290.0000 TRIM7.7040441.2771426.0322530.0000 C139.15371.001764138.90870.0000 R-squared0.871118 Mean dependent var121.1827 Adjusted R-squared0.847276 S.D. dependent var15.94425 S.E. of regression6.230994 Akaike info criterion6.527700 Sum squared resid6017.920 Schwarz criterion6.623799 Log likelihood-517.2160 F-statistic221.5239 Durbin-Watson stat1.222753 Prob(F-statistic)0.000000

18. New Price vs Original Price

19. Conclusions As expected, older, higher mileage cars are worth less than newer cars. Bigger engines and nicer levels of trim cost more Our model explains 82% of price variations

20. What we learned from this project Communication can be difficult EViews is amazingly fun and can be useful in analyzing social and economic phenomena Thanks!