[A dividend-ratio model is introduced here that makes the log of the dividend-price ratio on a stock linear in optimally forecast future one-period real discount rates and future one-period growth rates of real dividends. If ex post discount rates are observable, this model can be tested by using vector autoregressive methods. Four versions of the linearized model, differing in the measure of discount rates, are tested for U.S. time series 1871-1986 and 1926-1986: a version that imposes constant real discount rates, and versions that measure discount rates from real interest rate data, aggregate real consumption data, and return variance data. The results yield a metric to judge the relative importance of real dividend growth, measured real discount rates, and unexplained factors in determining the dividend-price ratio.]

A linearization of a rational expectations present value model for corporate stock prices produces a simple relation between the log dividend-price ratio and mathematical expectations of future log real dividend changes and future real discount rates. This relation can be tested using vector autoregressive methods. Three versions of the linearized model, differing in the measure of discount rates, are tested for U. S. time series 1871-1986: versions using real interest rate data, aggregate real consumption data, and return variance data. The results yield a metric to judge the relative importance of real dividend growth, measured real discount rates and unexplained factors in determining the dividend-price ratio.

Tests of weak-form efficiency of the market for single family homes are performed using data on repeat sales prices of 39, 210 individual homes, each for two sales dates. Tests were done for Atlanta, Chicago, Dallas, and San Francisco/Oakland for 1970-86. While evidence for seasonality in real housing prices is weak, we do find some evidence of inertia in housing prices. A city-wide real log price index change in a given year tends to be followed by a city-wide real log price index change in the same direction (and between a quarter to a half as large in magnitude) in the subsequent year. However, the inertia cannot account for much of the variation in individual housing real price changes. There is so much noise in individual housing prices relative to city-wide price index changes that the R[squared] in forecasting regressions for annual real price change in individual homes is never more than .04.

Long historical averages of real earnings help forecast present values of future real dividends. With aggregate U.S. stock market data (1871–1986), a vector‐autoregressive forecast of the present value of future dividends is, for each year, roughly a weighted average of moving‐average earnings and current real price, with between two thirds and three fourths of the weight on the earnings measure. We develop the implications of this for the present‐value model of stock prices and for recent results that long‐horizon stock returns are highly forecastable.