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 R2 in forecasting regressions for annual real price change in individual homes is never more than .04.

The popular press is full of speculation that the United States, as well as other countries, is in a "housing bubble" that is about to burst. Barrons, Money magazine, and The Economist have all run recent feature stories about the irrational run-up in home prices and the potential for a crash. The Economist has published a series of articles with titles like "Castles in Hot Air," "House of Cards," "Bubble Trouble," and "Betting the House." These accounts have necessarily raised concerns among the general public. But how do we know if the housing market is in a bubble? The term "bubble" is widely used but rarely clearly defined. We believe that in its widespread use the term refers to a situation in which excessive public expectations of future price increases cause prices to be temporarily elevated. During a housing price bubble, homebuyers think that a home that they would normally consider too expensive for them is now an acceptable purchase because they will be compensated by significant further price increases. They will not need to save as much as they otherwise might, because they expect the increased value of their home to do the saving for them. First-time homebuyers may also worry during a housing bubble that if they do not buy now, they will not be able to afford a home later. Furthermore, the expectation of large price increases may have a strong impact on demand if people think that home prices are very unlikely to fall, and certainly not likely to fall for long, so that there is little perceived risk associated with an investment in a home.

We examine the link between increases in housing wealth, financial wealth, and consumer spending. We rely upon a panel of 14 countries observed annually for various periods during the past 25 years and a panel of U.S. states observed quarterly during the 1980s and 1990s. We impute the aggregate value of owner-occupied housing, the value of financial assets, and measures of aggregate consumption for each of the geographic units over time.We estimate regression models in levels, first differences and in error-correction form, relating consumption to income and wealth measures. We find a statistically significant and rather large effect of housing wealth upon household consumption.

This paper uses data on nearly a million homes sold in four metropolitan areas-Atlanta, Chicago, Dallas and San Francisco-to construct quarterly indexes of existing home prices between 1970 and 1986. We propose and apply a new method of constructing such indexes which we call the weighted repeat sales method (WRS). We believe the results give an accurate picture of the actual rate of appreciation in home prices in the four cities. The paper explains the construction of the index, discusses the results and compares them with the National Association of Realtors data on the median price of existing single family homes for the period 1981-1986.

We examine the link between increases in housing wealth, financial wealth, and consumer spending. We rely upon a panel of 14 countries observed annually for various periods during the past 25 years and a panel of U.S. states observed quarterly during the 1980s and 1990s. We impute the aggregate value of owner-occupied housing, the value of financial assets, and measures of aggregate consumption for each of the geographic units over time. We estimate regressions relating consumption to income and wealth measures, finding a statistically significant and rather large effect of housing wealth upon household consumption.