Working Paper: NBER ID: w26907
Authors: Zhenyu Gao; Michael Sockin; Wei Xiong
Abstract: We develop a model to analyze information aggregation and learning in housing markets. In the presence of pervasive informational frictions, housing prices serve as important signals to households and capital producers about the economic strength of a neighborhood. Our model provides a novel mechanism for amplification through learning in which noise from the housing market can propagate to the local economy, distorting not only migration into the neighborhood, but also the supply of capital and labor. We provide consistent evidence of our model implications for housing price volatility and new construction using data from the recent U.S. housing cycle.
Keywords: Informational Frictions; Housing Markets; Learning; Expectations
JEL Codes: E22; E44; G1; R31
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Informational frictions (D89) | Learning about economic strength of a neighborhood (R20) |
| Learning about economic strength of a neighborhood (R20) | Housing demand (R21) |
| Learning about economic strength of a neighborhood (R20) | Local investment decisions (F21) |
| Noise in housing prices (R31) | Expectations of households and capital producers (D84) |
| Expectations of households and capital producers (D84) | Amplified housing cycles (E32) |
| Supply elasticity (H31) | Effects of noise on housing prices (R31) |
| Consumption complementarity (D10) | Learning effects (C92) |
| Learning effects (C92) | Magnitudes of housing price booms and busts (E32) |
| Housing prices (R31) | Migration decisions (F22) |
| Housing prices (R31) | Local investment decisions (F21) |