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.

We study the effects of underquoting, the practice of setting listing prices below sellers’ reservation values, on housing auctions. Laws introduced in Australia to deter underquoting lead to higher listing prices, but also to declines in sales prices and sales probabilities. We develop a quantitative model to formalize predictions under different assumptions about bidders’ information and rationality. The effects of the laws are matched by a version of the model in which participating bidders overbid. While both behavioral biases arising during the auction and switching costs can explain overbidding, empirical and survey evidence points to behavioral biases as the main mechanism.

Landlords appear to use stale information when setting rents. Among over 43,000 California rental houses in 2018–2019, those last purchased during 2005–2007 (the peak) rent for 2–3% more than those purchased during 2008–2010 (bust). Neither house nor landlord characteristics explain this “peak-bust rental spread.” To clarify the mechanism, we test cross-sectional predictions from a simple theory of rent-setting. We find empirical support for both reference dependence and distorted beliefs. In the first, monthly payments establish (recurring) reference points, against which gains or losses are measured. In the second, past sales prices distort landlords’ current estimates of house values/rents.

Homeowners who originally bought when marketwide price levels were high (low) fetch high (low) sales prices and rents, even decades later. We study the propagation of reference-dependence to neighboring listings. The “spillover” reference point effect is about one-half as large as the “own” reference point effect. Neither house quality nor location appears capable of explaining the result. Using a simple model to provide empirical predictions, we find support for a competition-based mechanism. We quantify the aggregate effect of own and spillover reference point effects on aggregate prices and/or rents at the ZIP code level.

We study risk premiums in the U.S. Treasury bond market from the perspective of a Bayesian econometrician who learns in real time from disagreement among investors about future bond yields. Notably, disagreement has substantial predictive power for yields, and 's risk premiums are less volatile than those in the analogous model without learning. '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.

Recent local price growth explains differences in search behavior across prospective homebuyers. Those experiencing higher growth in their postcode of residence search more broadly across locations and house characteristics, without changing attention devoted to individual sales listings, and have shorter search duration. Effects are stronger for homeowners, in particular those living in less wealthy areas and looking for a new primary residence. We use reduced‐form analysis and a quantitative equilibrium model to show that the expansion of search breadth translates into widespread spillovers onto house sales prices and inventories of listings across postcodes within a metropolitan area.