On the Nature of Self‐Assessed House Prices
利用房地产繁荣与萧条期数据，发现家庭自评房价变化与最优过滤房价数据的模型预测高度一致，自评房价在繁荣期涨幅和萧条期跌幅均小于房价指数，卡尔曼滤波模型几乎完美复现了这些数据。
In models of optimal household behavior, the value of housing affects consumption, savings and other variables. But homeowners do not know the value of their house for certain until they sell, so while they live in their home they must rely on local house price data to estimate its value. This article uses data from the recent housing boom and bust to demonstrate that changes in households' self‐assessed home values are strongly consistent with the predictions of a model in which households optimally filter available house price data. Specifically, we show that self‐assessed house prices did not increase as rapidly as house price indexes during the boom and did not decline as severely during the bust. A Kalman filter model nearly perfectly replicates these data. These findings have direct implications for economists studying asking prices during booms and busts, optimal default decisions and other key housing‐related phenomena.