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Learning about the Neighborhood

Zhenyu Gao, Michael Sockin and Wei Xiong ()

No 26907, NBER Working Papers from National Bureau of Economic Research, Inc

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.

JEL-codes: E22 E44 G1 R3 R31 (search for similar items in EconPapers)
Date: 2020-03
New Economics Papers: this item is included in nep-mac and nep-ure
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