Securitized real estate returns have traditionnally been forecasted using economic variables. However, no consenus exists regarding the variables to use. Financial and real estate factors have recently emerged as an alternative set of variables useful in forecasting securitized real estate returns. This paper examines whether the predictive ability of the two sets of variables differs. We use fractional cointegration analysis to identify whether long-run nonlinear relations exist between securitized real estate and each of the two sets of forecasting variables. That is, we examine whether such relationships are characterized by long memory, short memory, mean reversion (no long-run effects) or no mean reversion (no long-run equilibrium). Empirical analyses are conducted using data for the U.S., the U.K., and Australia. The results show that nancial and real estate factors generally outperform economic variables in forecasting securitized real estate returns. Long memory (long-range dependence) is generally found between securitized real estate returns and stocks, bonds, and direct real estate returns, while only short memory is found between securitized real estate returns and the economic variables. Such results imply that to forecast securitized real estate returns, it may not be necessary to identify the economic variables that are related to changing economic trends and business conditions.