Understanding Rationality and Disagreement in House Price Expectations

Abstract

Professional house price forecast data are consistent with a rational model where agents must learn about the parameters of the house price growth process and the underlying state of the housing market. Slow learning about the long-run mean can generate forecast bias, a response of forecasts to lagged realizations, sluggish response of forecasts to contemporaneous realizations, and over-reaction to forecast revisions. Introducing behavioral biases, either over-confidence or diagnostic expectations, helps the model further improve its predictions for short-horizon over-reaction and dispersion. Using panel data for a cross-section of forecasters and a term structure of forecasts are important for generating these results.

Keywords: forecasts, survey data, learning models, behavioral biases

JEL Classification: D83, E13, E32, E43, E37, G12.