Abstract
Increasing hand overs
in second hand car market makes it necessary to investigate the market
dynamics. Moreover, increased computer technology enables collecting huge
datasets. The purpose of this study is to decrease the number of features by
applying explanatory factor analysis to a large dataset gathered with web
scraping technique from Turkey’s second-hand car market. In the study, the data
set of 1,5277 vehicles obtained from the second-hand market were combined with
the web scraping technique. For each car, the following variables are
available: motor power, maximum speed and whether there is an mp3 player, cassette
player or cd player as well as the information whether the car has original,
painted or changed parts. The 11 features transformed to 4 new features after
applying factor analysis and these factors explains the variation at 62,423%
degree. Cronbach Alpha reliability values are computed, and the lowest
coefficient is calculated as 0,571. As a result, it is understood that, the
similar features are combined in the same factor.