In this article, we investigate the commonly used autoregressive filter method of adjusting appraisal-based real estate returns to correct for the perceived biases induced in the appraisal process. Many articles have been written on appraisal smoothing but remarkably few have considered the relationship between smoothing at the individual property level and the amount of persistence in the aggregate appraisal-based index. To investigate this issue we analyze a large sample of appraisal data at the individual property level from the Investment Property Databank. We find that commonly used unsmoothing estimates at the index level overstate the extent of smoothing that takes place at the individual property level. There is also strong support for an ARFIMA representation of appraisal returns at the index level and an ARMA model at the individual property level.The treatment of appraisal-based returns has received significant attention in real estate research. Evidence from a review of real estate articles suggests that research on this topic dominates the citation list in real estate journals (Dombrow and Turnbull 2004). While an emerging strand of research has focused on transaction-based returns series (see Fisher, Geltner and Pollakowski 2007), the use of appraisal-based returns remains common in the academic literature 1 and is still widely present in commercial research applications.There is a widespread belief among academics that such appraisal-based returns do not accurately represent the underlying dynamics of commercial real estate returns because biases are introduced in the valuation process by appraisers. As explained in Geltner (1997) and Bowles, McAllister and Tarbert (2001), appraisers tend to review past estimates and embed that old information