Detail práce

Price Determinants of Art Photography at Auctions

Autor: Mgr. Veronika Habalová
Rok: 2018 - zimní
Typ práce: Diplomová
Ekonomická teorie
Jazyk: Anglicky
Stránky: 70
Abstrakt: In the recent years, prices of art have repeatedly broken records, and
the interest in investing in fine art photography has been growing. Although
there is plenty of research dedicated to studying prices of paintings, fine art
photography has been largely overlooked. This thesis aims to shed light on
identifying price determinants for this particular medium. A new data set
is collected from sold lot archives of Sotheby’s and Phillips auction houses,
which also provide images of some of the sold items. These images are then
used to create new variables describing visual attributes of the artworks. In
order to inspect the effect of color-related predictors on price, four different
methods are discussed. Color is found to be significant in OLS model, but
the effect diminishes when model averaging is applied. Machine learning algorithms
- regression trees and random forests - suggest that the importance
of color is relatively low. The thesis also shows that expert estimates can
improved by incorporating available information and using random forests
for prediction. The fact that the expert estimates are not very accurate suggest
that they either do not use all the available information or they do not
process it efficiently.


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