Factors Affecting the Notebook Computer Prices in Turkey: A Hedonic Analysis

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The Empirical Economics Letters, 9(6): (June 2010)                           ISSN 1681 8997

Factors Affecting the Notebook Computer Prices in Turkey:
                    A Hedonic Analysis
                       İsmail Şentürk* and Cumhur Erdem**
     Department of Economics, Faculty of Economics and Administrative Sciences
                  Gaziosmanpaşa University, Tokat-Turkey, 60250
      Abstract: This study examines the factors affecting notebook computer prices by the
      hedonic regression technique. The data include 706 computers’ characteristics. The
      estimations were conducted with linear, semi-log, log-linear and Box-Cox transformation
      methods. Results show that Asus, Dell and MSI brands, screen size and webcam have
      negative effect and RAM and hard disc capacity, Bluetooth, processor speed and Sony
      brand have positive effect on prices.
      Keywords: Notebook Computer, Hedonic Regression, Computer Prices, Turkey

1. Introduction
Information and computer technology is one of the most developing fields in our age.
Consumers and business firms’ demand for computers have been enormously increased in
recent years. To meet this increasing demand, many giant corporations in electronics sector
have substantially increased their investment in computer production. With increasing
demand and R&Ds computer technology is making significant progress in the last quarter
century in terms of speed, capacity, size and features.

The computer market has been significantly growing in Turkey similar to the rest of the
world. Besides, this market is in a transformation stage nowadays. The transformation
appears to be switching from desktop computers to laptop computers. For instance, while
desktop computer sales decreased 1.3 percent, laptop computer sales increased 40 percent
and amount of laptop sales exceeded desktop sales in third quarter of 2008 (iSuppli, 2008).
Increase in the importance of portability, grow up of wireless communication and other
new features make important contributions to the tendency to laptop computers. In Turkey,
approximately 20 computer manufacturers are active in the market. Domestic computer
production has also increased in recent years. Casper (a domestic producer) is the market
leader with 15% share in desktop PC and HP is the notebook market leader with 19.2%
share in February 2009 (IDC, 2009).
Since there are different brands in the market, it is expected that there exist price difference
among the computers due to disparity among the computers in terms of quality, features,
brand loyalty, technical services and support and other features. It is important to identify

*
    Corresponding Author. Email: ismailsenturk@gop.edu.tr. ** Email: cumhur_erdem@yahoo.com
The Empirical Economics Letters, 9(6): (June 2010)                                          546

whether these differences among the computers are taken into account during the pricing
decision of the products in the retail market or not.
The aim of this study is to determine which characteristics affect laptop computer prices by
using data obtained from e-commerce sites. E-commerce is becoming common and taking
remarkable trade volume and computer products have an important share in e-commerce
firms’ sales in Turkey. Hedonic pricing methods were used to meet this purpose. The
findings of this study are expected to make important contribution to the literature by
filling the gap in this research area since to the best of our knowledge, this is the first study
conducted to determine the factors affecting laptop computer prices in Turkey. The
remainder of the paper is organized as the literature review, data and methodology,
empirical findings and summary and conclusion.

2. Literature Review
In general, hedonic pricing method is applied to assess the housing and residential property
values. It is also used to evaluate a variety of products’ value. The studies and their
findings are summarized as follows.
Baker (1997) examined laptop computer prices in U.S. with hedonic pricing methods and
found that RAM, maximum RAM, hard disc capacity, display size, operating system,
weight, volume and brand have effect on laptop computer prices. In his study, Chwelos
(2003) constituted hedonic price index for laptop computers in U.S. In addition to finding
similar results with Baker’s (1997) study, he found that display type, modem speed, battery
type and discounts have impact on computer prices.
By using the 1997-1999 time period data for Germany and France, Moch and Triplett
(2002) investigated personal computers prices and found that speed, capacity, cd-rom and
ram have significance and positive effect on computer prices. Also Moch (2001) examined
computer prices in Germany with a data set of 1985-1994 period and found that processor
speed, hard disc capacity, cd-rom and display size have positive effect on computer prices,
but CPU cache memory has no significant effect. Parkhomenko et. al. (2007) investigated
personal computer prices to constitute a hedonic price index in Russia. They found that PC
prices are falling with a significant growth in characteristics and quality.
There are studies about other IT products such as mobile phones and Personal Digital
Assistants (PDAs). Chwelos et al. (2008) implemented the model for Personal Digital
Assistants (PDAs), using data on prices and characteristics of 203 models sold by 12
manufacturers and found that prices are related to processor generation and clock speed,
memory capacity, screen size and quality, digital camera and wireless capability.
Dewenter et al. (2007) analyzed hedonic price of mobile telephones for the German
market, based on data of 302 different handsets from 25 manufacturers over the period of
The Empirical Economics Letters, 9(6): (June 2010)                                       547

May 1998-November 2003. Results show that while volume has a negative effect on the
prices, the number of ringtones and the talk time battery life relative to the handset’s
weight have positive effect on the price of mobile phones.
There are studies in the literature related to hedonic analysis for different products such as
cars (Pazarlıoğlu and Güneş, 2000; Andersson, 2005; Ginter et al., 1987; German Federal
Statistical Office, 2003; Erdem and Şentürk, 2009), coffee (Maietta, 2003), tomatoes
(Huang and Lin, 2007) and bottled water (He et al., 2007).

3. Data and Methodology
The data used in the study were obtained in August 2008 from five web sites which are
popular, reliable and which have more trade volume than others. All of the notebook
computers data were obtained from these sites. Number of observations used in the analysis
is 706. Descriptive statistics of the variables are presented in Table (1).
 Table 1: Descriptive Statistics of the Variables
 Variables                           Description                     Mean          St. Dev.
 PRICE                  Price of notebook computer (USD)            1764.100       813.850
 SITE1                  Sale website (1= site1, 0= if not)            0.317         0.466
 SITE2                  Sale website (1= site2, 0= if not)            0.296         0.457
 SITE3                  Sale website (1= site3, 0= if not)            0.178         0.383
 SITE4                  Sale website (1= site4, 0= if not)            0.208         0.406
 ACER                   Brand (1= Acer, 0= if not)                   0.189          0.392
 ASUS                   Brand (1= Asus, 0= if not)                   0.115          0.319
 DELL                   Brand (1= Dell, 0= if not)                   0.108          0.310
 HP                     Brand (1= HP, 0= if not)                     0.191          0.394
 MSI                    Brand (1= MSI, 0= if not)                    0.104          0.305
 SONY                   Brand (1= Sony, 0= if not)                   0.078          0.268
 TOSHIBA                Brand (1= Toshiba, 0= if not)                0.216          0.412
 SPEED                  Processor speed (GHz)                         1.960         0.302
 SCRSIZE                Display size (inch)                          14.829         1.505
 GRAPHCAP               Graphic card capacity (MB)                  278.830        142.880
 RAM                    Memory (RAM) (GB)                            1.779          0.733
 HARDSC                 Hard disc (GB)                              180.660        81.758
 BLUETH                 Bluetooth (1=yes, 0=no)                       0.763         0.426
 USB                    Number of USB port(s)                        3.336          0.755
 WCAM                   Integrated web cam (1=yes, 0=no)              0.661         0.474
 CARDREAD               Integrated card reader (1=yes, 0=no)          0.583         0.493
The Empirical Economics Letters, 9(6): (June 2010)                                      548

Hedonic pricing method (HPM) has been derived from value theory developed by
Lancaster (1966) and Rosen (1974). According to Rosen (1974) hedonic prices are defined
as the implicit prices of attributes and are revealed to economic agents from observed
prices of differentiated products and the specific amounts of characteristics associated with
them.
The main idea of this study is that when an individual goes to the computer market to buy a
laptop computer, he or she makes his/her decision based on characteristics of a laptop
computer such as CPU, Hard Disk capacity, RAM, display size, brand, size, Bluetooth, and
wireless connection. In HPM literature, there is no suggestion for the most appropriate
functional form to determine the relationship between price and attributes (Rasmussen and
Zuehlke, 1990). Generally used functional forms for a hedonic model are linear, semi-
logarithmic, log-linear, Box-Cox transformation. All of the four regression models were
used in this study.
Model 1 – Linear (e.g., Chan et. al., 2008; Matas and Raymond, 2008):
                           n                 m
         Pricei = α 0 + ∑ α k X ik + ∑ β j Dij + ε i                            (1)
                          k =1               j =1

Model 2 - Semi-logarithmic (e.g., Matas and Raymond, 2008; Kolodinsky, 2008;
Andersson, 2008):
                                        n            m
         Ln( Price)i = α 0 + ∑ α k X ik + ∑ β j Dij + ε i                       (2)
                                      k =1          j =1

Model 3 – Log-Log or log-linear (e.g., Chan et. al., 2008; Matas and Raymond, 2008;
Andersson, 2008):
                                        n                       m
         Ln( Price)i = α 0 + ∑ α k Ln( X )ik + ∑ β j Dij + ε i                  (3)
                                      k =1                      j =1

Model 4 – Box-Cox transformation (e.g., Spritzer, 1982; Garrod and Willis, 1992; Chan et.
al., 2008; Matas and Raymond, 2008; Kolodinsky, 2008; Maurer et. al., 2004; Snyder et. al.
2008; Huang and Lin, 2007; Andersson, 2008);
The results of limited simulations of Cropper, Deck and McConnell (1988) show that linear
Box-Cox function appears to be the functional form of choice to estimate hedonic price
functions. Box-Cox transformation (Box-Cox, 1964) model is specified as follows;
                                  n                      m
         Pricei (λ ) = α 0 + ∑ α k X ik (λ ) + ∑ β j Dij + ε i                  (4)
                                 k =1                    j =1

         Pricei( λ ) = ( Priceiλ − 1) / λ           if          λ≠0
                                                                       and
                    = ln( Price)i                   if          λ =0
The Empirical Economics Letters, 9(6): (June 2010)                                                 549

          X i( λ ) = ( X iλ − 1) / λ      if     λ≠0
                  = ln( X )i              if     λ =0
In the equations (1), (2), (3) and (4) X l s are quantitative variables and D j s are qualitative
variables represented in Table (1).       λ    is Box-Cox transformation parameter,     αl   and   βj
are coefficients and    ε   is error term which is assumed to be normally distributed with zero
mean and constant variance (0, σ 2 ).
The empirical model is specified as follows:

PRICE i = α 0 + α 1 SITE 2i + α 2 SITE 3i + α 3 SITE 4i + α 4 ACERi + α 5 ASUS i
         + α 6 DELLi + α 7 HPi + α 8 MSI i + α 9 SONYi + α 10 SPEEDi + α 11 SCRSIZE i              (5)
         + α 12 GRAPHCAPi + α 13 RAM i + α 14 HARDSC i + α 15 BLUETH i + α 16 USBi
         + α 17 WCAM i + α 18 CARDREADi + ε i

4. Empirical Findings
Table 2 shows the findings of linear, semi-logarithmic, log-linear and Box-Cox
transformation regression models. Results show that SITE4, ASUS, DELL, MSI, SONY,
SCRSIZE, RAM, HARDSC, BLUETH and WCAM variables’ coefficients are statistically
significant at 1,5 and 10 percent significance level in all four models. The SPEED
variable’s coefficient was found to be statistically significant in semi-log, log-linear and
Box-Cox regressions, the ACER variable’s coefficient was found to be statistically
significant in log-linear and Box-Cox regressions and the CARDREAD variable’s
coefficient is statistically significant only in log-linear regression. The SITE4 variable has a
negative coefficient in every model. The finding shows that the web site has a lower price
than SITE1 which is the base category. Toshiba is selected as the base category in brand
variables. Asus, Dell and MSI have lower prices but Sony has higher prices than Toshiba.
Notebook’s screen size (SCRSIZE) has a negative effect on prices. Memory (RAM) has a
positive coefficient in linear, log-linear and Box-Cox regressions and negative coefficient
in semi-log regression. The HARDSC variable’s coefficient shows that the larger the hard
disc drive capacity, the higher the price of notebook. The notebooks with Bluetooth feature
have higher prices than those without this feature. The results show that web camera
feature has a negative effect on computer prices. The CARDREAD variable’s coefficient is
statistically significant only in log-linear regression and positively related to prices.
The interpretations of the regression models’ findings can be presented by giving an
example of the SCRSIZE variable. For instance in linear regression an increase in screen
size of the notebook computer decreases price by 97.830 USD. In semi-log regression
The Empirical Economics Letters, 9(6): (June 2010)                                              550

model one unit increase in screen size decreases the price of notebook by 5.5 percent. For
the log-linear model one percent increase in the screen size results in a 0.66 percent
decrease on the price of notebook. The elasticities were calculated to find the marginal
contributions of variables in Box-Cox model. According to the elasticity values shown in
Table 2, the contribution provided by screen size of notebook was -0.674 USD.
 Table 2: Results for Linear, Semi-logarithmic, Log-linear and Box-Cox Regression
                                                         Log-Log
                          Linear        Semi-log
                                                       (log-linear)       Box-Cox (λ=-0.25)ª

                       Coefficient     Coefficient     Coefficient     Coefficient    Elasticity●
 SITE2                    65.308           0.029          0.033           0.004           0.009
 SITE3                    -42.682         -0.034         -0.026          -0.003          -0.005
 SITE4                  -417.670*         -0.253*        -0.255*         -0.033*         -0.061
 ACER                    -109.850         -0.055        -0.068***       -0.009***        -0.014
 ASUS                   -181.330**        -0.112*        -0.134*         -0.018*         -0.017
 DELL                   -318.680*         -0.186*        -0.220*         -0.030*         -0.026
 HP                       -83.860         -0.028         -0.050          -0.006          -0.010
 MSI                    -581.160*         -0.375*        -0.403*         -0.054*         -0.046
 SONY                    873.920*         0.385*         0.351*          0.040*           0.026
 SPEED                   143.300          0.171*         0.252*          0.044*           0.294
 SCRSIZE                 -97.830*         -0.055*        -0.660*         -0.176*         -0.674
 GRAPHCAP                  0.168          0.0001         -0.009          -0.009          -0.015
 RAM                      49.971*        -0.007***       0.130*          0.019*           0.127
 HARDSC                    2.632*         0.002*         0.208*          0.101*           0.192
 BLUETH                  639.600*         0.423*         0.416*          0.055*           0.342
 USB                      33.794          0.0001         -0.035          -0.009          -0.053
 WCAM                   -231.340*        -0.060**        -0.095*         -0.010*         -0.052
 CARDREAD                  -0.285         0.0001        0.0001***        0.0001          -0.001
 CONSTANT               2058.400*         7.421*         7.833*          3.157*          25.762
 R-Square                  0.684           0.600          0.597           0.606
Note: *, ** and *** shows that the coefficients are statistically significant at 1, 5 and 10 percent
significance levels. a- Same value (-0.25) is used to transform dependent variable and quantitative
independent variables. ● Elasticity values were calculated at mean values.

5. Summary and Conclusion
The Empirical Economics Letters, 9(6): (June 2010)                                      551

This study aims to identify the determinants of notebook prices. A hedonic price function is
employed with a data set obtained from five leader e-shopping websites. Four models,
linear, semi-logarithmic, log-linear and Box-Cox transformation are used.
We find that screen size, Bluetooth, memory and hard disc drive capacity have positive
effect on notebook prices. An interesting finding of the study is to have a negative
relationship between web camera feature and notebook prices. In addition, price
differences among websites and commercial brands are determined as important findings.
This study can make some contributions to consumers, producers and retailers. Brand of
notebook computer has a significant effect on prices. Therefore, it can be suggested to
producers to invest more for their commercial brands. It is found that there exist price
differences among e-shopping websites; therefore, it can be concluded that it is beneficial
for consumers to search all websites for the lowest price. Since our results show that screen
size, Bluetooth and processor speed make relatively higher contributions to the price,
manufacturers can attach more importance to these specialties/features.

References
Andersson, H., 2005, The Value of Safety as Revealed in the Swedish Car Market: An
Application of the Hedonic Pricing Approach, The Journal of Risk and Uncertainty,
Vol.30, No.3, pp.211–239.
Andersson, D.E., O.F. Shyr, J. Fu, 2008. Does high-speed rail accessibility influence
residential       property       prices?,  Journal      Transport          Geography
doi:10.1016/j.jtrangeo.2008.10.012
Baker, T. A., 1997, Quality-adjusted price indexes for portable computers, Applied
Economics, 29(9): 1115-1123
Box, G.E.P. and Cox, D.R., 1964, An Analysis of Transformations, Journal of Royal
Statistical Society, Series Vol.B, No.26, pp.211-243.
Chan, E. H. W., H. M. So, B. S. Tang, W. S. Wong, 2008, Private space, shared space and
private housing prices in Hong Kong: An exploratory study, Habitat International, 32: 336–
348
Chwelos, P., 2003, Approaches to Performance Measurement in Hedonic Analysis: Price
Indexes for Laptop Computers in the 1990s, Economics of Innovation and New
Technology, 12(3):199-224
Chwelos, P., E. R. Berndt and I. M. Cockburn, 2008, Faster, Smaller, Cheaper: An Hedonic
Price Analysis of PDAs, Applied Economics, 40(22): 2839–2856.
The Empirical Economics Letters, 9(6): (June 2010)                                     552

Cropper, M.L., L.B. Deck and K.E. McConnell, 1988, On the Choice of Functional Form
for Hedonic Price Function, The Review of Economics and Statistics, Vol.70, No.4,
pp.668-675.
Dewenter, R., J. Haucap, R. Luther, P. Rötzel, 2007, Hedonic Prices in the German Market
for Mobile Phones, Telecommunications Policy, 31, 4-13.
Erdem, C. and İ. Şentürk, 2009, A Hedonic Analysis of Used Car Prices in Turkey,
International Journal of Economic Perspectives, Volume 3, Issue 2, 141-149.
Garrod, G.D., K.G. Willis, 1992, Valuing goods’ characteristics: an application of the
hedonic price method to environmental attributes, Journal of Environmental Management
34, 59–76.
German Federal Statistical Office, 2003, Hedonic Methods of Price Measurement for Used
Cars,.http://www.destatis.de/jetspeed/portal/cms/Sites/destatis/Internet/EN/Content/Statisti
cs/Preise/HedonicUsedCars,property=file.pdf
Ginter, J.L., A.M. Young, and P.R. Dickson, 1987, A Market Efficiency Study of Used Car
Reliability and Prices, The Journal of Consumer Affairs, Vol.21, No.2, pp.258-276.
He, S., J. Jordan and K. Paudel, 2008, Economic Evaluation of Bottled Water Consumption
as an Averting Means: Evidence from a Hedonic Price Analysis, Applied Economics
Letters, Forthcoming.
Huang C. L. and B. H. Lin, 2007, A Hedonic Analysis of Fresh Tomato Prices among
Regional Markets, Review of Agricultural Economics, Vol.29, No.4, pp.1-18.
IDC, 2009, Turkey-Full Year 2009 Preliminary Results, February.
iSuppli (2008) http://www.isuppli.com/News/Pages/Notebook-PC-Shipments-Exceed-
Desktops-for-First-Time-in-Q3.aspx? (accesed in November 2009)
Kolodinsky, J., 2008, Affect or Information? Labeling Policy and Consumer Valuation of
rBST Free and Organic Characteristics of Milk, Food Policy, 33: 616–623.
Lancaster, K.J., 1966, A New Approach to Consumer Theory, The Journal of Political
Economy, Vol.74, No.2, pp. 132-157
Maietta, O. W., 2003, The Hedonic Price of Fair-trade Coffee for the Italian Consumer,
International Conference Agricultural policy reform and the WTO: where are we heading?
Capri, Italy, June 23-26.
Matas, A., Raymond, J. L., 2008, Hedonic prices for cars: an application to the Spanish car
market, 1981–2005, Applied Economics, doi:10.1080/00036840701720945.
The Empirical Economics Letters, 9(6): (June 2010)                                  553

Maurer, R., M. Pitzer and S. Sebastian, 2004, Hedonic price indices for the Paris housing
market, Allgemeines Statistisches Archiv, No.88, pp.303-326.
Moch, D., 2001, Price Indices for Information and Communication Technology Industries:
An Application to the German PC Market, Center for European Economic Research
(ZEW) Discussion Paper, No. 01-20, Mannheim, Germany
Moch, D. and J. E. Triplett, 2002, International Comparisons of Hedonic Price Indexes for
Computers: A Preliminary Examination, Draft. August 2002.
Parkhomenko, A., A. Redkina and O. Maslivets, 2007, Econometric Estimates of Hedonic
Price Indexes for Personal Computers (PC) in Russia, Available at SSRN:
http://ssrn.com/abstract=1008011 (accessed in July 2008)
Pazarlıoğlu, V.M. and M. Güneş, 2000, The Hedonic Price Model for Fusion on Car
Market International Conference of Information Fusion, Paris, France, pp. 4-13.
Rasmussen, D.W. and T.W. Zuehlke, 1990, On the Choice of functional from for hedonic
price functions, Applied Economics, No.22, pp. 431-38.
Rosen, S., 1974, Hedonic Prices and Implicit Markets: Product Differentiation in Pure
Competition, The Journal of Political Economy, No.82, pp. 34-55.
Snyder, S.A., M. A. Kilgore, R. Hudson, J. Donnay, 2008, Influence of purchaser
perceptions and intentions on price for forest land parcels: A hedonic pricing approach,
Journal of Forest Economics, 14: 47–72.
Spritzer, J., 1982, A primer on Box–Cox estimation, Review of Economics and Statistics
64 (2), 307–313.
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