Our statistical model would have predicted a correction by mid-2006
on shares and bonds, but one asset class is bigger than the two combined. Put together, the world’s homes are worth over $200trn. House prices are crucial harbingers of economic trends: the last time they fell across the rich world, it set off the deepest downturn in decades.
The impact of each of these variables often depends on the others. To combine them, we used a machine-learning algorithm called a random forest. This method creates a “forest” of “decision trees”, each containing a series of yes/no choices such as “Hasbeen rising?” or “Are price-to-rent ratios below the long-run average?”, and averages the output of each tree.
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