  While startup valuations are influenced by revenues, risks, age, and
macroeconomic conditions, specific causality is traditionally a black box.
Because valuations are not disclosed, roles played by other factors (industry,
geography, and intellectual property) can often only be guessed at. VC
valuation research indicates the importance of establishing a factor-hierarchy
to better understand startup valuations and their dynamics, suggesting the
wisdom of hiring data-scientists for this purpose. Bespoke understanding can be
established via construction of hierarchical prediction models based on
decision trees and random forests. These have the advantage of understanding
which factors matter most. In combination with OLS, the also tell us the
circumstances of when specific causalities apply. This study explores the
deterministic role of categorical variables on the valuation of start-ups (i.e.
the joint-combination geographic, urban, and sectoral denomination-variables),
in order to be able to build a generalized valuation scorecard approach. Using
a dataset of 1,091 venture-capital investments, containing 1,044 unique EU and
EEA, this study examines microeconomic, sectoral, and local-level impacts on
startup valuation. In principle, the study relies on Fixedeffects and
Joint-fixed-effects regressions as well as the analysis and exploration of
divergent micropopulations and fault-lines by means of non-parametric
approaches combining econometric and machinelearning techniques.
