We study risk premiums in the U.S. Treasury bond market from the perspective of a Bayesian econometrician BLwho learns in real time from disagreement among investors about future bond yields. Notably, disagreement has substantial predictive power for yields, and BL's risk premiums are less volatile than those in the analogous model without learning. BL's forecasts are substantially more accurate than the consensus forecasts of market professionals, particularly following U.S. recessions. The predictive power of disagreement is distinct from the (much weaker) one of inflation and output growth. Rather, it appears to reflect uncertainty about future fiscal policy.

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This paper studies the idiosyncratic risk component of individual house capital gains using data on resales and intermediate capital investments. The idiosyncratic component is large; its dynamics do not follow a random walk; and its magnitude is associated with proxies of information quality and market liquidity at the level of individual properties. Accounting for idiosyncratic risk substantially changes the assessment of the risk-return trade-off for housing: it reduces Sharpe ratios and makes them holding period dependent. I use a simple quantitative portfolio model to show that homeowners may be willing to make significant payments to insure against idiosyncratic housing risk.

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