Archaeological looting correlates with a number of problems, including the destruction of stratigraphic data and the damage and loss of artifacts. Looting is also understood to generate revenue, but systematic analysis of this issue is challenged by its opacity: how can we study the economic effects of archaeological looting when the practice is rarely directly observable? To address this problem, we estimate the market value of archaeological sites where artifacts have been previously excavated and documented, using a machine-learning approach. The first step uses 41,587 sales of objects from 33 firms to train an algorithm to predict the distribution channel, lot packaging, and estimated sale price of objects based on their observable characteristics. The second step uses the trained algorithm to estimate the value of sites in which a large number of artifacts have been legally excavated and documented. We make an out-of-sample prediction on two Syrian sites, Tell Bi’a and Dura Europos.
