The Magnitude of Random Appraisal Error in Commercial Real Estate Valuation
Graff, Richard A., Young, Michael S., The Journal of Real Estate Research
Abstract. Analysis of more than seven hundred pairs of simultaneous independent appraisals of institutional-grade commercial properties shows that the standard deviation of the random component of appraisal error is approximately 2%. Random appraisal error appears constant across both time and the institutional-grade investment universe, except during infrequent periods of real estate market gridlock. Most appraisal error is deterministic in nature, even though it usually appears random in routine cross-sectional analysis. Such appraisal error can be constrained and reduced by investment management control systems.
A decade ago, real estate researchers began to consider the possibility that investment statistics derived from appraisal-based commercial real estate valuations might be much less satisfactory measures of real estate investment performance than corresponding statistics derived from transactions in the United States stock and bond markets. The main source for concern was considered to be random appraisal error, since earlier studies of appraisal error in the residential housing market had suggested that typical appraisal error is at least 10% of asset capitalization and that most residential appraisal error is random.1
Since researchers did not have access to large numbers of commercial real estate appraisals to aid in formulating and testing hypotheses about appraisal valuation behavior, empirical investigation of commercial real estate appraisal error sources and their effect on real estate investment statistics was not possible. Accordingly, researchers used accepted beliefs about residential real estate appraisal accuracy and aggregate National Council of Real Estate Investment Fiduciaries (NCREIF) statistics to justify assumptions about the magnitude and random behavior of commercial real estate appraisal error.2
Unfortunately, in the case of institutional-grade real estate these assumptions neglect two major differences between commercial property and residential housing: institutions purchase property primarily for cash flow and secondarily for capital appreciation, so the hedonic measures institutions apply to value commercial property are more uniform than the hedonic measures by which homeowners value residential housing; and institutional real estate managers usually make real estate investment decisions on behalf of institutional investors, raising the possibility of agency-based (nonrandom) contributions to appraisal error usually absent in the case of residential housing.3
This suggests two likely consequences: commercial real estate appraisal error contains both random and nonrandom components, and the average magnitude of random appraisal error in the case of commercial real estate is smaller than the average magnitude of random residential housing appraisal error. It follows that any description of the effect of appraisal error on investment statistics should be more complicated than suggested by previous studies, since random and nonrandom appraisal error components affect sample means, variances and correlations in very different ways.
In the absence of access to a fairly large database of commercial real estate appraisals, it has been impossible for most researchers to determine whether the magnitude of random appraisal error is large enough to have a material impact on sample real estate investment statistics. However, even if such access were available, it would only enable researchers to estimate the magnitude of total appraisal error. Access to appraisal data alone would not enable researchers to compare the relative importance of contributions from random and nonrandom error components.
The magnitude of random appraisal error can be determined empirically if an appraisal database for institutional-grade commercial real estate can be located that includes at least two simultaneous independent appraisals whenever an appraisal valuation is updated. …