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Understanding Rationality and Disagreement in House Price Expectations

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Listed:
  • Zigang Li
  • Stijn Van Nieuwerburgh
  • Wang Renxuan

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.

Suggested Citation

  • Zigang Li & Stijn Van Nieuwerburgh & Wang Renxuan, 2023. "Understanding Rationality and Disagreement in House Price Expectations," NBER Working Papers 31516, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:31516
    Note: AP EFG ME
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    More about this item

    JEL classification:

    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • E7 - Macroeconomics and Monetary Economics - - Macro-Based Behavioral Economics
    • G40 - Financial Economics - - Behavioral Finance - - - General
    • G50 - Financial Economics - - Household Finance - - - General
    • R32 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Other Spatial Production and Pricing Analysis

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