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CC BY V4.0, Open Access
Thesis
An investigation on the pricing of virtual items & digital commodities: evidence from the Counter-Strike: Global Offensive market
Washington State University
Master of Science (MS), Washington State University
2022
DOI:
https://doi.org/10.7273/000004977
Abstract
Counter-Strike : Global Offensive (CS:GO) is a popular first-person shooter video game with a unique feature: its out-of-game economy. CS:GO players purchase virtual cosmetic items called “skins” that decorate their weapons in-game yet provide no functional utility. Despite this, skins frequently sell for hundreds of US dollars. This paper investigates the nature of the CS:GO skins market and asks whether skins are more similar to consumer goods or more similar to speculative virtual assets like cryptocurrencies. I summarize the literature on virtual item pricing as well as literature on price prediction of cryptocurrencies. In order to assess the skins market quantitatively, I implement tree-based machine learning models to estimate the price of CS:GO skins based on their observable attributes and based on their historical price data. The attribute-based models reveal that observable attributes have little predictive power, while the historical price data models perform extremely well. I argue that because there is no clear relationship between skin attributes and their prices and because skin prices are well-described by methods used in cryptocurrency price prediction, CS:GO skins might be more like virtual commodities than traditional consumer goods.
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Details
- Title
- An investigation on the pricing of virtual items & digital commodities
- Creators
- Emma Taylor
- Contributors
- Alejandro Prera (Advisor)H Alan Love (Committee Member)Jia Yan (Committee Member)
- Awarding Institution
- Washington State University
- Academic Unit
- Economic Sciences, School of
- Theses and Dissertations
- Master of Science (MS), Washington State University
- Publisher
- Washington State University
- Number of pages
- 47
- Identifiers
- 99901019235801842
- Language
- English
- Resource Type
- Thesis