Mortgage overage pricing is little understood by consumers and has received little academic scrutiny. We consider the impact of the market power of individual loan officers on overages paid by borrowers, particularly minorities. We include numerous borrower and lender characteristics unavailable previously. We find that minorities who purchase homes pay larger overages than whites, but our evidence suggests that this traces to differences in the pools of borrowers rather than to racial discrimination. We conclude that a more effective way to eliminate racial differences in overages is to increase minorities’ ability to bargain rather than to enact additional anti-discrimination laws.

Using European stock data from two different venues and time periods for which we can identify each trade's aggressor, we test the performance of the bulk volume classification (Easley et al. (2016); BVC) algorithm. BVC is data efficient, but may identify trade aggressors less accurately than “bulk” versions of traditional trade-level algorithms. BVC-estimated trade flow is the only algorithm related to proxies of informed trading, however. This is because traditional algorithms are designed to find individual trade aggressors, but we find that trade aggressor no longer captures information. Finally, we find that after calibrating BVC to trading characteristics in out-of-sample data, it is better able to detect information and to identify trade aggressors. In the new era of fast trading, sophisticated investors, and smart order execution, BVC appears to be the most versatile algorithm.