Learning about the Neighborhood
研究了住房市场中信息聚合与学习机制，房价作为信号影响家庭和资本生产者对邻里的判断，噪音通过家庭决策互补性放大并扭曲迁移与要素供给。
Abstract We develop a model to analyze information aggregation and learning in housing markets. Households enter a neighborhood by buying houses and consuming each other’s final goods. In the presence of pervasive informational frictions, housing prices serve as important signals to households and capital producers about the neighborhood’s economic strength. Our model provides a novel amplification mechanism in which noise from housing markets propagates throughout the local economy via learning because of the complementarity in households’ decisions, distorting migration into the neighborhood and the supply of capital and labor. We provide consistent evidence based on the recent U.S. housing cycle.