Gerald R. Brown

Sticky Valuations, Aggregation Effects and Property Indices

Previous studies on real estate smoothing have generally focused on the second moment of returns for individual properties. Although this body of research has developed plausible reasons for explaining the observed lower risk associated with real estate, no explanation has, however, been offered to account for the large difference in serial correlation at the individual property level compared with the index level. This article addresses this issue and also offers an explanation for the difference in serial correlation observed with different frequency real estate indices. Employing the framework developed by Holbrook Working (1960), we argue that the high levels of serial correlation typically observed in real estate indices results from a combination of random and sticky appraisals that induce cross-correlations between the component returns. Using the concept of sticky values we question the results of Lai and Wang (1998) in which they argue that the variance of appraisal-based returns should always be greater than true returns. We argue that a pragmatic conclusion regarding volatility should be conditioned on the underlying stochastic processes. We draw a distinction between serial cross-sectional and temporal sticky appraisal processes that influence smoothing at the index and individual property levels. Our results indicate that smoothing does not appear to be a serious issue at the individual property level. However, when different appraisal processes are aggregated into an index the underlying cross-correlation between those processes can induce high levels of smoothing.

Valuation uncertainty and the Mallinson Report

This paper addresses uncertainty in valuations in relation to the issues raised by the Mallinson Report . It shows that valuers have approximately 1 chance in 10 of achieving values within +/- 5% of the mean value. This improves to 1 in 5 if the range is increased to +/- 10%. These figures are, however, based on average properties within each sector and are probably much lower than generally believed. Valuers may, however, achieve better or worse performance when considering individual properties. In an ex ante framework it is shown that the main influence on variability in valuations switches from growth to total returns as capital values decline. Improving the standard error of forecasts can lead to a significant improvement in the uncertainty of valuations. This points to the need for better research. The levels of valuation uncertainty reported in this paper do not imply any inefficiency in the way valuations are prepared. Differences of opinion are important in order to encourage the development of an active property market. Valuation uncertainty as identified in the Mallinson Report is not, therefore, a serious issue. A more serious problem concerns errors in valuation.

A Time-Varying Analysis of Abnormal Performance of UK Property Companies

The investment selection ability of property company managers is investigated. The specialized nature of commercial property portfolio holdings presents the opportunity for added value and, therefore, for abnormal performance. It is argued that property company share investment performance should be undertaken using time-varying abnormal performance measures; in particular, a time-varying measure of Jenens excess performance. Furthermore, because of the changing structure of both the composition of the underlying property portfolios held by property companies and the levels of gearing, the analytical framework should also employ a time-varying beta measure. Over the period of analysis, 1980-1995, the majority of property companies analysed exhibited an enduring risk-adjusted underperformance profile, although this was not found to be statistically distinguishable from zero. For the few companies delivering a positive abnormal performance it did not prove statistically significant. The implication is that the total returns delivered by property companies are not significantly different from a random buy-and-hold strategy. This may be indicative of the indifferent performance of the underlying direct property portfolio holdings, with the implication that property company managers have not demonstrated any property selection skills by exploiting their specialist knowledge in identifying underpriced investment opportunities. If property selection ability does exist in the direct commercial property market then, in the main, it is not reflected in risk-adjusted outperformance in property company share prices. If investors believe there are inefficiencies in the direct commercial property market, and therefore opportunities for outperformance, it does not appear to be possible to exploit such inefficiencies indirectly by investing in property company shares.

Value weighting and real estate portfolio risk

This paper extends the discussion concerning the value weighting and variability of portfolio returns. In particular, it critically analyses the work of Morrell (1993) in light of previous research and the theory of portfolio strategy. It shows that his conclusions are only valid under restrictive conditions concerning asset variance and the pairwise correlation structure of returns that are typical of naive investment strategies. A revised formulation of Morrells coefficient of value skewness (CVS) is derived which is more general in application, while caveats are place on its interpretation. The strategic implications of value weighting are then discussed in the light of this analysis, raising questions for further research.

A note on the periodic conversion of measures of risk

The conversion of monthly or quarterly standard deviations to their annual equivalent presents few problems if the underlying returns series is serially uncorrelated. If however the same procedure is used to convert serially correlated returns then the annual equivalent risk measures may be seriously understated. Using continuous rates of return this paper presents a compact solution which makes allowance for serial correlation and can be used for making the conversion over any interval.

Equilibrium time on the market (ETOM) for commercial real estate in the UK

Time on the market (TOM) has been widely tested in the US real estate literature using listing and selling data of houses captured in the multiple listing services (MLSs). Unfortunately in the UK there are no MLSs so it is not possible to undertake similar analyses. The approach adopted in this paper differs from traditional TOM analyses in that it focuses on the speed or time the market takes to correct for information differences between open market valuations and traded prices. In this context the paper introduces the concept of equilibrium time on the market (ETOM). The study therefore adopts a different approach to estimating TOM and in addition also examines the phenomenon within the UK commercial real estate sector. Based on a simple present value model, the time taken for the difference between an appraisers estimate of open market value and known selling prices define our time on the market under equilibrium market conditions. Using the annualised UK Investment Property Databank all‐property total return index for a sample period of 17 years between 1983 and 1999, the average ETOM was estimated to be 8.4 months. This figure, however, varied and depended on market conditions.

A note on the effects of serial cross correlation

Given a large sample of properties with time series returns extending over a number of periods it can be shown that the average cross correlation coefficient between the properties increases with the reporting interval. This paper offers an explanation for why this phenomenon exists and shows that, in addition to the contemporaneous cross correlation, the impact of serial cross correlation plays an important role. By contrast, smoothing has little impact. It is further shown that at the portfolio level the distribution of cross correlation coefficients is positively skewed for monthly returns. As the reporting interval increases the distribution becomes more normal. This has important implications at two levels. First, behavioural effects are likely to be more pervasive at the monthly level so that a large proportion of monthly valued properties will exhibit high serial cross correlation. Second, high serial cross correlation will induce high serial correlation in an index of returns. As the reporting interval increases this effect diminishes