Often, a relatively small group of trades causes the major part of the trading costs on an investment portfolio. Consequently, reducing the trading costs of comparatively few expensive trades would already result in substantial savings on total trading costs. Since trading costs depend to some extent on steering variables, investors can try to lower trading costs by carefully controlling these factors. As a first step in this direction, this paper focuses on the identification of expensive trades before actual trading takes place. However, forecasting market impact costs appears notoriously difficult and traditional methods fail. Therefore, we propose two alternative methods to form expectations about future trading costs. Applied to the equity trades of the world's second largest pension fund, both methods succeed in filtering out a considerable number of trades with high trading costs and substantially outperform no-skill prediction methods.