The growing empirical literature testing informational efficiency of real estate markets uses data from various contexts and at different levels of aggregation. The results of these studies are mixed. We use a distinctive meta-analysis to examine whether some of these study characteristics and contexts lead to a significantly higher chance for identification of an efficient real estate market. The results generated through meta-regression suggest that use of stock market data and individual level data, rather than aggregate data, significantly improves the probability of a study concluding efficiency. Additionally, the findings neither provide support for the suspicion that the view of market efficiency has significantly changed over the years nor do they indicate a publication bias resulting from such a view. The statistical insignificance of other study characteristics suggests that the outcome concerning efficiency is a context-specific random manifestation for the most part. (authors' abstract)

Few areas of research in the finance literature have received greater attention than the efficient market hypothesis. Much of the research has been directed toward the securities market while very little research has been done in the real estate markets. The existing research on real estate market efficiency has been either descriptive or illustrative with very little empirical testing being performed. The major reason for the lack of empirical testing has been the inability to develop an adequate data base. The results of the empirical work that has been done do not support the widely held belief that real estate markets are inefficient. This study, using the autoregressive-integrative-moving average (ARIMA) time series analysis technique, tests the weak-form efficiency of the Dallas-Fort Worth office properties real estate market. According to the weak-form efficient market hypothesis, all price information should be capitalized into current real estate prices and not provide the basis for earning abnormal returns in trading. Price data formed from office building sales dating from January, 1979 to January, 1985 are used to test the market. The data was gathered from the files of several professional appraisal firms located in the Dallas-Fort Worth area. The transaction information includes (1) transaction price; (2) location of the property; (3) net rentable area; (4) gross income multiplier (GIM); (5) net income multiplier (NIM); and (6) net operating income. The results of the study indicate a lack of significant autocorrelation. This suggests that the Dallas-Fort Worth office properties real estate market is weak-form efficient. As further evidence of weak-form market efficiency, ARIMA models are estimated to predict future sales prices but they are unable to outperform a simple mean series forecast. The results indicate that a change in traditional real estate theory concerning market efficiency may be warranted.