Valuing Patents with Linear Regression : Identifying value indicators and using a linear regression model to value  patents

Sammanfattning: This thesis consist of two parts. The first part of the thesis will conduct a multiple regression on a data-set obtained from the Ocean Tomo’s auction results between 2006 to 2008 with the purpose to identify key value indicators and investigate to what extent it is possible to predict the value of a patent. The final regression model consist of the following covariates Average number of citings per year, share of active family members, age of the patent, average invested USD per year, and nine CPC’s as dummy variables. The second part of the thesis will investigate why it is difficult to value a patent and the different factors and changes that have contributed to a growing importance of patent valuation by applying theories from knowledge-based economy and industrial change. This is done by conducting a literature review and interviews. The results of this thesis states that it is only possible to construct a model that has an explanation degree of 50.21%. The complexity of a patents value derives from uncertainties about future context of the patent and non-quantifiable parameters of the patent. Furthermore we find evidence of a shift from tangible assets to intangible assets in industrial nations which motivates the growing importance of patent valuation.