Understanding Rationality and Disagreement in House Price Expectations

Zigang Li, Stijn Van Nieuwerburgh and Wang Renxuan

No 31516, NBER Working Papers from National Bureau of Economic Research, Inc

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.

JEL-codes: E37 E7 G40 G50 R32 (search for similar items in EconPapers)
Date: 2023-07
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