Chinese housing prices rose by over 10 percent per year in real terms between 2003 and 2014, and are now between two and ten times higher than the construction cost of apartments. At the same time, Chinese developers built 100 billion square feet of residential real estate. This boom has been accompanied by a large increase in the number of vacant homes, held by both developers and households. This boom may turn out to be a housing bubble followed by a crash, yet that future is far from certain. The demand for real estate in China is so strong that current prices might be sustainable, especially given the sparse alternative investments for Chinese households, so long as the level of new supply is radically curtailed. Whether that happens depends on the policies of the Chinese government, which must weigh the benefits of price stability against the costs of restricting urban growth.
We present a model of stereotypes based on Kahneman and Tversky’s representativeness heuristic. A decision maker assesses a target group by overweighting its representative types, defined as the types that occur more frequently in that group than in a baseline reference group. Stereotypes formed this way contain a ‘‘kernel of truth’’: they are rooted in true differences between groups. Because stereotypes focus on differences, they cause belief distortions, particularly when groups are similar. Stereotypes are also context dependent: beliefs about a group depend on the characteristics of the reference group. In line with our predictions, beliefs in the lab about abstract groups and beliefs in the field about political groups are context dependent and distorted in the direction of representative types. JEL Codes: D03, D83, D84, C91.
We revisit La Porta’s (1996) finding that returns on stocks with the most optimistic analyst long term earnings growth forecasts are substantially lower than those for stocks with the most pessimistic forecasts. We document that this finding still holds, and present several further facts about the joint dynamics of fundamentals, expectations, and returns for these portfolios. We explain these facts using a new model of belief formation based on a portable formalization of the representativeness heuristic. In this model, analysts forecast future fundamentals from the history of earnings growth, but they over-react to news by exaggerating the probability of states that have become objectively more likely. Intuitively, fast earnings growth predicts future Googles but not as many as analysts believe. We test predictions that distinguish this mechanism from both Bayesian learning and adaptive expectations, and find supportive evidence. A calibration of the model offers a satisfactory account of the key patterns in fundamentals, expectations, and returns.
We evaluate Eugene Fama’s claim that stock prices do not exhibit price bubbles. Based on US industry returns 1926-2014 and international sector returns 1985-2014, we present four findings: (1) Fama is correct in that a sharp price increase of an industry portfolio does not, on average, predict unusually low returns going forward; (2) such sharp price increases predict a substantially heightened probability of a crash; (3) attributes of the price run-up, including volatility, turnover, issuance, and the price path of the run-up can all help forecast an eventual crash and future returns; and (4) some of these characteristics can help investors earn superior returns by timing the bubble. Results hold similarly in US and international samples.
of episodic memory, especially interference. In a new laboratory experiment on cued
recall, participants are shown two groups of images with different distributions of
colors. We find that i) decreasing the frequency of a given color in one group significantly
increases the recalled frequency of that color in the other group, ii) for a
fixed set of images, different cues for the same objective distribution entail different
interference patterns and different probabilistic assessments. Selective retrieval
and interference may offer a foundation for the representativeness heuristic, but
more generally for understanding the formation of probability judgments from experienced