全球智库信息集成服务系统(G2TT): Learning about the Neighborhood

G2TT

来源类型	Working Paper
规范类型	报告
DOI	10.3386/w26907
来源ID	Working Paper 26907
	Learning about the Neighborhood
	Zhenyu Gao; Michael Sockin; Wei Xiong
发表日期	2020-03-30
出版年	2020
语种	英语
摘要	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.
主题	Macroeconomics ; Consumption and Investment ; Money and Interest Rates ; Financial Economics ; Financial Markets ; Regional and Urban Economics ; Real Estate
URL	https://www.nber.org/papers/w26907
来源智库	National Bureau of Economic Research (United States)
引用统计
资源类型	智库出版物
条目标识符	http://119.78.100.153/handle/2XGU8XDN/584580
推荐引用方式 GB/T 7714	Zhenyu Gao,Michael Sockin,Wei Xiong. Learning about the Neighborhood. 2020.