Commercial Real Estate Returns: An Anatomy of Smoothing in Asset and Index Returns

Abstract

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

The long-term linkages between direct and indirect property in Australia

“…Moreover, valuations of different properties in the indices are often conducted over different times of the year, so when they are compiled into an index, there will be additional issues of nonsynchronous pricing and over-averaging of the smoothing bias amongst index constituents. Bond and Hwang (2007) and Bond et al (2012) provide evidence of this from examining property indices at the aggregate and individual levels of the USA and UK. They apply the Autoregressive Fractionally Integrated Moving Average (ARFIMA) methodology to address these issues.…”

“…There are similarities between the characteristics of the data used in these studies with real estate data. To our knowledge, the only studies adopting this method for the use of property data are from Bond and Hwang (2007) and Bond et al (2012) for the USA and the UK and so we consider this opportunity to apply the method onto the Australian property market.…”

The Dynamics of Appraisal Smoothing

“…Many different methods have been proposed for the estimation of (average) smoothing level in appraisal‐based index returns. For example, see Geltner, MacGregor and Schwann () for a survey of earlier studies, as well as Edelstein and Quan (), Bond and Hwang () and Bond, Hwang and Marcato (). Estimating time‐varying smoothing is more complicated, because the dynamics of smoothing is a latent process that needs to be estimated together with other parameters in the model.…”

Transaction-Based and Appraisal-Based Capitalization Rate Determinants

“…Cheng et al (2011) demonstrate that the degree of heterogeneity of appraisers will determine whether the appraisal-based variance is smoothed or exceed the true variance. This has been further analyzed by Bond et al (2013), who use a large sample of appraisal data at the individual property level to empirically estimate the smoothing at both the individual property and Despite the abundant literature, the discussion about potential mismatches between valuations and transaction prices in general and appraisal smoothing in particular has not reached a consensus. Given that (1) indices -whether smoothed or not -are either based on valuations or transactions of individual properties, and (2) that there is some evidence of a mismatch between valuations and transaction prices, we maintain that it is important to improve the understanding of the similarities and differences between the driving forces of those valuations and transactions.…”