All‐cash homebuyers account for one‐third of U.S. home purchases between 1980 and 2017. We use multiple data sets and research designs to robustly estimate that mortgaged buyers pay an 11% premium over all‐cash buyers to compensate home sellers for mortgage transaction frictions. A dynamic, representative‐seller model implies only a 3% premium, which would suggest an 8% puzzle. Accounting for heterogeneity in selling conditions explains half of this difference, but a puzzle holds in conditions with high transaction risk. An experimental survey of U.S. homeowners replicates these patterns and suggests that belief distortions can explain the puzzle in these high‐risk states.

We investigate how access to robo-advisors impacts the financial investment and welfare of less-wealthy investors. We leverage a quasi-experiment where a major U.S. robo-advisor significantly expands access by reducing its account minimum, increasing participation by middle-class investors but not the poor. A benchmark model calibrated to portfolio-level data rationalizes this increase: middle-class investors want sophisticated investing but cannot achieve it themselves. Their welfare rises moderately, driven by advanced features like multi-dimensional glide-paths and additional priced risk factors. Middle-age investors gain three times more than millennials. Our results reveal novel margins of demand for robo-advisors, helping explain their sustained growth.