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Understanding Rationality and Disagreement in House Price Expectations

Author

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 generates overreaction to forecast revisions and a modest response of forecasts to lagged realizations. Heterogeneity in signals and priors about the long-run mean helps the model account for cross-sectional dispersion in forecasts. Introducing behavioral frictions helps improve the model’s predictions for short-horizon overreaction and dispersion. Using a cross-section of forecasters and a term structure of forecasts are crucial for inference.

Suggested Citation

  • Zigang Li & Stijn Van Nieuwerburgh & Wang Renxuan, 2026. "Understanding Rationality and Disagreement in House Price Expectations," The Review of Financial Studies, Society for Financial Studies, vol. 39(2), pages 297-342.
    • Handle: RePEc:oup:rfinst:v:39:y:2026:i:2:p:297-342.
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    File URL: http://hdl.handle.net/10.1093/rfs/hhaf073
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    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • G41 - Financial Economics - - Behavioral Finance - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making in Financial Markets
    • 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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