The purpose of the dealer of Toyota Astra Motor is estimating the price of used Toyota Kijang Innova. It obtained data sample of sales during 2004 till 2010 within Jakarta area. In this project, the estimation has the following variables that dealers’ manager believe are associated with the price (dependent variable) of used Toyota Kijang Innova:  Age of car (getting from year 2004 – 2010)  Mileage (unit of kilometer)  Transmission (dummy variable, which are Automatic and Manual)  Type (dummy variable, which are type of V, G, and E) Dealers’ manager would like to have a model that would indicate whether and how these variables are related to the price. Another, this estimation would help them to set the price and maximize profit.

1. HEDONIC ANALYSIS TOWARD PRICING OF USED CAR TOYOTA KIJANG
INNOVA IN JAKARTA
Model Explanation
REGRESSION RESULT
STATE THE QUESTION
By using multiple regression model, it can be drawn a model as:
The purpose of the dealer of Toyota Astra Motor is estimating the price of
used Toyota Kijang Innova. It obtained data sample of sales during 2004 till
2010 within Jakarta area. In this project, the estimation has the following
variables that dealers’ manager believe are associated with the price
(dependent variable) of used Toyota Kijang Innova:
With the dummy codes:




The results of the multiple regression model given as:
̂ = ̂ 0 + ̂ 1 Age + ̂ 2 Mileage + ̂ 3 Transmission + ̂ 4 Type +
𝑌
𝛽
𝛽
𝛽
𝛽
𝛽
̂ 5 (Age x Transmission) + ̂ 6 (Age x Type) + Ɛ
𝛽
𝛽
Age of car (getting from year 2004 – 2010)
Mileage (unit of kilometer)
Transmission (dummy variable, which are Automatic and Manual)
Type (dummy variable, which are type of V, G, and E)
Dealers’ manager would like to have a model that would indicate whether
and how these variables are related to the price. Another, this estimation
would help them to set the price and maximize profit.
DATA SOURCES
The sample consists of 124 used Kijang Innova taken from
www.mobil123.com and www.mobil.kapanlagi.com on May, 2011 which
both of these sources provides the up to date data of selling used Toyota
Kijang Innova. This sample randomly collected of used
Toyota Kijang Innova over the seven-year period from 2004 till 2010 in
Jakarta area.
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2. The following variables that included in the model are the most relevant
variable that cause price. The adjusted R-square, as a reason, provides a
good sense that the variance of price can be highly clarified from the
estimation. Another reason comes from the significance from each
variable that have stated earlier.
REVERSE CAUSALITY BIAS
According to the figures above, the regression results a high adjusted Rsquare given by 0.879, meaning that the estimation explains 87.9% of the
variation of Y. In other words, these results can be way off by 12.1%.
Since the research used cross sectional data, the possibility appearing the
reverse causality bias is very small. In this research, there is no supporting
theory that tells the both conditions such as Xs affect Y and the contrary, Y
affect Xs will happen at the same time. It doesn’t make sense that the
price will affect one of the explanatory variable whether mileage, age of
car, transmission and type.
Consider on the significance of the variables, 95% Confidence Interval gives
for each ̂ of variable are not include zero, meaning that all the following
𝛽
variable are significant. It’s also supported from the amount of each t-stat
of variable. All of t-stat is higher than the t-table, which is 1.984 for total
sample equal to 124.
FUNCTIONAL FORM
This research applies linear as the most appropriate functional form in
order to estimate the price. Theoretically, if the age of car is older, the
price will decrease. Still in the same theory, the higher the mileage, the
lower car’s price will be. In addition, the price of car using automatic
transmission will has higher price than manual transmission. The price of
car with type of V has the most expensive price, followed by type of G and
type of E. Therefore, based on these theories and also the technical
reason, the linear is the best functional form to associate the explanatory
variables towards price of used Toyota Kijang Innova.
Model Evaluation
OMITTED VARIABLE BIAS
Theoretically, there might be some variables that affect to the price of
used Toyota Kijang Innova, such as the condition of car, color, and car
modification (e.g. audio and racing velg). However, there is no precise
measurement and hard to justify. In addition, the data are randomly
selected therefore the omitted ought not to distort our estimates. This
research concluded that there is no omitted variable from the model since
there is no variable that can affect both of dependent variable and one of
the independent variables.
MULTICOLLINEARITY
This research faced the imperfect multicollinearity, given high number of
VIF on certain variables, transmission and dummy of type V. However,
both of these variables (transmission and type) give strong contribution in
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3.  Another way to develop more accurate estimates is to increase the
sample size. It will deduct the variance of the estimated coefficients,
thereby reducing the impact of multicollinearity.
explaining the variance of price. If these variables excluded from the
model, it will reduce much of adjusted R-square.
Another reason why these variables still included in the model is to avoid
the omitted variable bias that can violate in the third classical assumption.
These kinds of improvements perhaps support the dealers’ manager to
improve their model, so they can set the price of used Toyota Kijang
Innova precisely and obtain higher positive profit.
SERIAL CORRELATION
The results of this research have no serial correlation. The first reason,
according to the calibration plot, the residual plots spread out of the lines.
In this case, different observations of the error term are completely
uncorrelated with each other. The second reason, the number of DurbinWatson test of 2.132 would indicate no serial correlation. Hence, with
given these reasons, the results are satisfied with the fourth classical
assumption.
Model Improvement
Based on model evaluation above, there are some improvement that
should be applied with the aim of get the better model and good
estimation, such:
 The estimation can be way off by 12.1%. To deal with this condition, this
research should be considered to other variable than influence to price,
particularly for color and car modification.
 This research has high number of VIF for variable of transmission and
type. However, in order to avoid omitted variable bias, it’s better to do
nothing; it’s better to keep these variables since these variables have
strong contribution to explain the variation of price.
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