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

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Presentation transcript:

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

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

Brand Population

Models

Average Selling Price by Brand

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

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

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: Included observations: 140 VariableCoefficientStd. Errort-StatisticProb. AGE COLOR ENGINE MILES TRANSMISSION C R-squared Mean dependent var Adjusted R-squared S.D. dependent var S.E. of regression Akaike info criterion Sum squared resid2.88E+09 Schwarz criterion Log likelihood F-statistic Durbin-Watson stat Prob(F-statistic)

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: Included observations: 140 VariableCoefficientStd. Errort-StatisticProb. AGE ENGINE LNMILE C R-squared Mean dependent var Adjusted R-squared S.D. dependent var S.E. of regression Akaike info criterion Sum squared resid2.94E+09 Schwarz criterion Log likelihood F-statistic Durbin-Watson stat Prob (F-statistic) Price = *AGE * ENGINE * MILEAGE *TRIM

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

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

Camry Price Histogram

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: Included observations: 160 VariableCoefficientStd. Errort-StatisticProb. AGE ENGINE MILEAGE TRIM TRANSMISSION C R-squared Mean dependent var Adjusted R-squared S.D. dependent var S.E. of regression Akaike info criterion Sum squared resid3.52E+08 Schwarz criterion Log likelihood F-statistic Durbin-Watson stat Prob(F-statistic)

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: Included observations: 160 VariableCoefficientStd. Errort-StatisticProb. AGE ENGINE MILEAGE TRIM C R-squared Mean dependent var Adjusted R-squared S.D. dependent var S.E. of regression Akaike info criterion Sum squared resid3.53E+08 Schwarz criterion Log likelihood F-statistic Durbin-Watson stat Prob(F-statistic) Price = * AGE * ENGINE * MILEAGE * TRIM

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

Toyota Camrys: mileage against price R Square ≈ 66%

Alternative Model PRICE^(1/2) = *MILEAGE *ENGINE *AGE *TRIM Dependent Variable: NewPRICE Method: Least Squares Date: 11/30/10 Time: 12:16 Sample: Included observations: 160 VariableCoefficientStd. Errort-StatisticProb. MILEAGE E ENGINE AGE TRIM C R-squared Mean dependent var Adjusted R-squared S.D. dependent var S.E. of regression Akaike info criterion Sum squared resid Schwarz criterion Log likelihood F-statistic Durbin-Watson stat Prob(F-statistic)

New Price vs Original Price

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

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!