This special feature examines policy-assignment dilemmas facing macroprudential authorities when housing markets boom: which instruments work best, on which objectives, and in combination with which other tools? It does so by revitalising Mundell’s Principle of Effective Market Classification, the policy-space analogue of Ricardo’s comparative advantage principle, and by applying it to macroprudential policy. The analysis uses a novel G-search literature-search algorithm and an AI-supported, replicable data-extraction system to assemble estimates of policy-impact parameters from the empirical literature. It then distinguishes standard, instrument-by-instrument evidence, from jointly estimated policy-impact parameters, which are needed to account for rival instruments acting in the same empirical setting. Three findings emerge. First, the results confirm earlier meta-analytic evidence that macroprudential policy moderates household credit growth more clearly than it does house price growth, that tightening has more visible effects than loosening, and that instruments differ in their strengths and weaknesses. Second, joint estimates sharpen policy-assignment analysis by revealing how relative effects change when instruments are assessed together rather than alone. Third, applying the Mundell framework identifies instrument pairings that satisfy necessary conditions for substitutability or complementarity. Overall, the menu of options available to effectively tame housing market booms is wide, provided instruments are assigned to objectives by their relative – not absolute – effectiveness.
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