factors determining the price of used mid-compact size vehicles

Factors Determining the Factors Determining the Price Of Used Mid-Compact Price Of Used Mid-Compact

Size VehiclesSize Vehicles

Team 4Team 4

INTRODUCTIONINTRODUCTION

Used least squares regression analysis to Used least squares regression analysis to determine the factors that affect mid-compact determine the factors that affect mid-compact size vehicle price.size vehicle price.

By determining these factors manufacturers, By determining these factors manufacturers, dealerships, rental agencies, and consumers dealerships, rental agencies, and consumers can incorporate these economic indicators into can incorporate these economic indicators into their decision making processes and operationstheir decision making processes and operations

What?

Why?

Identified and defined dependent variable Identified and defined dependent variable (Price of Mid-Compact Size Cars) (Price of Mid-Compact Size Cars)Collected sufficient data on potential Collected sufficient data on potential indicators/independent variablesindicators/independent variablesDeveloped regression model by Developed regression model by considering different model types and considering different model types and variable interactionsvariable interactionsDiagnosed and refined model taking into Diagnosed and refined model taking into consideration performance parametersconsideration performance parameters

How?

Independent VariablesIndependent Variables

SupplySupply - - Used cars available in a particular monthUsed cars available in a particular month FleetFleet - Percentage of supply of vehicles sold to public - Percentage of supply of vehicles sold to public

agencies (police department, government offices)agencies (police department, government offices) LeaseLease - Percentage of total supply of cars leased.- Percentage of total supply of cars leased. IncentivesIncentives - Rebates, APR, etc. dollar value($) - Rebates, APR, etc. dollar value($) PIPI - - MonthlyMonthly National Personal Income in National Personal Income in

Billions of dollarsBillions of dollars MonthMonth - - Month in which Price was recorded. Month in which Price was recorded. YearYear - -Year in which Price was recorded.Year in which Price was recorded.

Single Variable RegressionsSingle Variable Regressions

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Y=.0557x+6046.7 R^2=.1743 Y=-4031.1x+7730.3 R^2=.3143

Y=4850.4.4x+6088.3 R^2=.2421 Y=-.7584x+7895.1 R^2=.3742

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Y=-69.184x+7326

R^2=.0491

Y=-98.334x+7136.3

R^2=.0163

PRICE SUPPLY MONTH YEAR LEASE INCENTIVE FLEET

PRICE 1.0000 0.4175 -0.2216 -0.1278 0.4921 -0.6117 -0.5606

SUPPLY 0.4175 1.0000 -0.1115 0.0869 -0.0825 -0.4844 -0.3933

MONTH -0.2216 -0.1115 1.0000 -0.2223 -0.0368 0.1127 -0.1625

YEAR -0.1278 0.0869 -0.2223 1.0000 -0.1179 0.0332 0.0661

LEASE 0.4921 -0.0825 -0.0368 -0.1179 1.0000 -0.0422 -0.1142

INCENTIVE -0.6117 -0.4844 0.1127 0.0332 -0.0422 1.0000 0.5116

FLEET -0.5606 -0.3933 -0.1625 0.0661 -0.1142 0.5116 1.0000

Correlation MatrixCorrelation Matrix

By looking at the Correlation Matrix we see some fairly high By looking at the Correlation Matrix we see some fairly high correlations between independent variables and that indicates a correlations between independent variables and that indicates a potential problem with multicollinearitypotential problem with multicollinearity

Developing ModelsDeveloping Models (EQ 1) (EQ 1)

R-squared 0.7221 Mean dependent var 6833.9815

Adjusted R-squared 0.7068 S.D. dependent var 1028.1981

S.E. of regression 556.7549 Akaike info criterion 15.5396

Sum squared resid 39366959.4514 Schwarz criterion 15.7117

Log likelihood -1040.9202 F-statistic 47.1450

Durbin-Watson stat 0.6384 Prob(F-statistic) 0.0000

Variable Coefficient Std. Error t-Statistic Prob.

SUPPLY 0.0200 0.0075 2.6817 0.0083

FLEET -2146.8892 425.2621 -5.0484 0.0000

INCENTIVE -0.4330 0.0761 -5.6936 0.0000

LEASE 4240.7041 474.7231 8.9330 0.0000

MONTH -96.6401 25.9345 -3.7263 0.0003

YEAR -458.4911 297.1605 -1.5429 0.1253

PI 0.8325 0.6548 1.2714 0.2059

C 2494.8234 4111.2844 0.6068 0.5451

Developing ModelsDeveloping Models (EQ 2) (EQ 2)Variable Coefficient Std. Error t-Statistic Prob.

SUPPLY 0.0208 0.0075 2.7853 0.0062

FLEET -2231.5649 421.0242 -5.3003 0.0000

INCENTIVE -0.4093 0.0739 -5.5381 0.0000

LEASE 4281.7972 474.7604 9.0189 0.0000

MONTH -70.4489 15.7922 -4.4610 0.0000

YEAR -83.7062 37.5382 -2.2299 0.0275

C 7709.2045 285.1369 27.0369 0.0000







Testing Variable InteractionsTesting Variable Interactions

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Price Vs Year*Supply

Price Vs Incentive*Fleet

Y=.0071x+6517.7

R^2=.0548

Y=-1.8835x+7533.3

R^2=.3377

Developing ModelsDeveloping Models (EQ 3) (EQ 3)Variable Coefficient Std. Error t-Statistic Prob.

SUPPLY -0.0680 0.0158 -4.2942 0.0000

MONTH -66.2268 13.9278 -4.7550 0.0000

YEAR -477.4664 71.9577 -6.6354 0.0000

LEASE 5719.1001 478.8719 11.9429 0.0000

INCENTIVE -0.4027 0.0651 -6.1848 0.0000

FLEET -2100.0570 371.4849 -5.6531 0.0000

YEAR*SUPPLY 0.0289 0.0047 6.1611 0.0000

C 8602.7106 290.0318 29.6613 0.0000






Durbin-Watson stat 0.867002592 Prob(F-statistic) 0

Final ModelFinal ModelVariable Coefficient Std. Error t-Statistic Prob.

SUPPLY -0.0642 0.0145 -4.4258 0.0000

MONTH -57.8940 12.8331 -4.5113 0.0000

YEAR -498.9848 65.8981 -7.5721 0.0000

LEASE 5495.1686 439.8410 12.4935 0.0000

INCENTIVE -0.9477 0.1222 -7.7527 0.0000

FLEET -4523.0306 583.6174 -7.7500 0.0000

YEAR*SUPPLY 0.0284 0.0043 6.6105 0.0000

INCENTIVE*FLEET 2.3145 0.4534 5.1042 0.0000

C 9055.9077 279.5399 32.3958 0.0000







DiagnosticsDiagnostics

Residuals Vs predicted

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Final EquationFinal EquationPRICE = -0.06417398414*SUPPLY - 57.89403046*MONTH - 498.984817*YEAR + 5495.168601*LEASE - 0.9477265548*INCENTIVE - 4523.030592*FLEET + 0.02838232606*(YEAR*SUPPLY) + 2.314465082*(INCENTIVE*FLEET) + 9055.90772

dPrice/dSupply= -.064+.028(Year)

dPrice/dMonth= -57.89

dPrice/dYear= -498.98 + .028(Supply)

dPrice/dLease= 5495.17

dPrice/dIncentive= -.9477+2.31(Fleet)

dPrice/dFleet= -4523.03+2.31(Incentive)

ConclusionsConclusions

The month and lease % variables have the The month and lease % variables have the most significant impact on price.most significant impact on price.The effect of incentives on price cannot be The effect of incentives on price cannot be considered without looking at fleet %considered without looking at fleet %The effect of supply on price also cannot The effect of supply on price also cannot be considered without looking at yearbe considered without looking at yearAn informed buyer or seller of mid-An informed buyer or seller of mid-compact sized vehicles should consider compact sized vehicles should consider these implications before actingthese implications before acting

factors determining the price of used mid-compact size vehicles

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