Long-run expectations, learning and the U.S. housing market

In this paper I examine several key facts about the aggregate housing market of the United States. For example, the price to rent ratio is highly volatile and significantly autocorrelated. Returns on housing are positively autocorrelated. The price to rent ratio is negatively correlated with future returns on housing and future rent growth as well. Finally, housing returns exhibit time varying volatility. Given the similarity of these facts to those previously documented in U.S. equity markets, I begin by showing that a benchmark asset pricing model is inconsistent with these facts. I then modify the model in two ways. First, I allow for a sticky price mechanism that allows prices to adjust slowly to their fundamental value. Second, I develop a novel learning mechanism where I assume the agent does not know if housing fundamentals are trend stationary or difference stationary and therefore must form beliefs about the likelihood that each model is true. As a result, the agent has changing beliefs depending on how well each model fits recent data. These modifications allow the model to increase the volatility of the price to rent ratio and to match the auto-correlation of housing returns. The price to rent ratio then negatively forecasts returns and rent growth. Finally the model generates time varying volatility. The paper is consistent with recent evidence on economic expectations that overly optimistic expectations about housing fundamentals fueled the recent boom and bust in housing prices. Specifically, consistent with the empirical evidence, long-run expectations are the key drivers of the housing booms and busts.