A Further Examination of Appraisal Data and the Potential Bias in Real Estate Return Indexes: Comment and Clarification

This comment points out a flaw in Gau and Wang's recent empirical analysis of appraisal return bias and clarifies a point in Geltner's previous article that may have misled Gau and Wang as well as others. In examining appraisal bias in returns it is important to distinguish between cross-sectional versus time-series moments. In dealing with time-series data, both the arithmetic mean and the geometric mean returns offer useful information and can complement each other in correcting for appraisal bias in the first moment of returns series.     

Temporal Aggregation in Real Estate Return Indices

Temporal aggregation is defined as the use of spot valuations of properties occuring over an interval of time to impute the spot value of a property or of a real estate value index as of a single point in time. Temporal aggregation may characterize not only appraisal-based indices but also indices based directly on transaction prices, such as the National Real Estate Index (NREI) and regression-based indices such as hedonic or repeat-sales indices. This paper analyzes the effect of temporal aggregation on the smoothing of the time series second moments in the resulting real estate return index. Assuming true spot returns are uncorrelated, temporal aggregation-induced smoothing will cause the empirically observed real estate index to understate the own-variance by one-third and the beta by one-half. This amount of bias in the second moments can have major implications for the real estate share in an optimal portfolio. Thus, empirical-based investment analysis could be led astray by smoothing even if the real estate return index is "transaction-based" rather than "appraisal-based."     

What Does the Stock Market Tell Us About Real Estate Returns?

This paper analyzes the risks and returns of different types of real estate-related firms traded on the New York and American stock exchanges (NYSE and AMEX). We examine the relation between real estate stock portfolio returns and returns on a standard appraisal-based index, and find that lagged values of traded real estate portfolio returns can predict returns on the appraisal-based index after controlling for persistence in the appraisal series. The stock market reflects information about real estate markets that is later imbedded in infrequent property appraisals. Additional analysis suggests that the differences in the return and risk characteristics across different types of traded real estate firms can be explained in part by appealing to real estate market fundamentals relating to the degree of dependence of the real estate firm upon rental cash flows from existing buildings. These findings highlight the heterogeneity of securitized real estate-related firms.     

Errors in Variables, Links between Variables and Recovery of Volatility Information in Appraisal-Based Real Estate Return Indexes

The present article proposes a multivariate approach to unsmoothing appraisal-based real estate return indexes to recover the true market volatility information in real estate returns. It scrutinizes the role played by errors in variables, in conjunction with an analysis of other economic activities relevant to real estate returns, to exploit the functional relationship and the mechanism of interactions between real estate returns and these economic activities. Appraisal smoothing can therefore be detected and corrected properly and efficiently, without presuming a weakly efficient real estate market. The approach is then applied to U.K. real estate indexes as empirical examples. The results suggest a reasonable volatility in U.K. real estate investment that is close to reality. It is found that the volatility of the true market return on real estate is 1.5404–1.9282 times that of the return on the appraisal-based indexes, in contrast to figures of 2.4862–5.8720 produced by the fully unsmoothing procedure.     

Appraisal Smoothing: The Other Side of the Story

Appraisal smoothing has been widely accepted as an important factor to consider when analyzing real estate returns using appraisal-based data. In this paper, we demonstrate that the general applicability of the appraisal-smoothing arguments developed so far in the literature is limited by the assumptions upon which the arguments are based. We further show that the use of appraisal-based data can result in a higher (not lower) variance than that of true returns. Given this, it might be more fruitful to analyze the unique characteristics of real estate markets as possible explanations for the seemingly low variance observed in appraisal-based (or transaction-based) return indexes.     

A Further Examination of Appraisal Data and the Potential Bias in Real Estate Return Indexes

Recent articles by Giliberto [2] and Geltner [1] examine the biases inherent in the use of appraisal data in real estate performance measurement. This note takes another look at the direction and magnitude of any bias in holding period returns. Using appraisal data from a commingled real estate fund, we show that in actual application the size of the holding period return bias can be quite small and this bias may have no appreciable effect on real estate return indexes.     

Institutional Portfolios: Diversification through Farmland Investment

This article demonstrates that farmland can enhance the overall performance of institutional portfolios which are currently dominated by stocks, bonds, and business real estate. Unlike previous articles on farmland returns, this article addresses the issue of "smoothing bias" associated with appraisal-based farmland returns. Improved measures of income returns to farmland are also used in developing the estimates of optimal portfolios. Parametric testing revealed that farmland continues to enter the optimal portfolios even for large increases in the variance or for large reductions in the annual returns to farmland.     

A Measure of Fundamental Volatility in the Commercial Property Market

The low level of volatility observed in appraisal-based commercial property indices relative to other asset classes has been frequently noted and extensively commented on in the real estate finance literature. However, the volatility of such commercial property indices is only one source of information on the second moment of commercial property returns. The volatility of securitized property returns forms another potential source of information, though there is some uncertainty about how closely the volatility of securitized returns may match the volatility of the underlying asset. Each measure of volatility has a potential source of noise associated with it. This paper proposes a fundamental measure of volatility for the commercial property market by using a stochastic volatility model to filter out the signal in the different sources of volatility information. This allows for different measures of volatility to be decomposed into transitory noise and unobserved fundamental volatility. The suitability of such an approach and the properties of the underlying fundamental volatility series are analyzed using data from the U.K. property market.     

The Substitutability of Real Estate Assets

This paper investigates the degree of substitutability between securitized real estate assets and real estate assets whose prices are appraisal-based. Given the insensitivity of unsecuritized asset's returns to the returns on stock market indices, equilibrium asset pricing models cannot be used to compare these two avenues of investment. Two assets are deemed substitutable if the information sets underlying unbiased, minimum error variance estimates of their pricing parameters are identical. The empirical evidence shows that the prices of the transactions-based assets—real estate investment trusts and the stock price index of the home building industry—follow a random walk while the prices of the appraisal-based assets—FRC/NCREIF indices—do not. The variance decompositions of the vector autoregressions also show that the level of economic activity helps predict the price indices of appraisal-based assets while the stock market index and the term structure of interest rates are better predictors of the prices of transactions-based assets     

The persistence of abnormal returns

The time-series behavior of ROI is examined to assess a central element of competitive markets, the lack of persistence of abnormal profits. The analysis first determines the aggregate dynamic process of ROI and then examines how strategic and market factors influence this process. Consistent with abnormal returns resulting from a disequilibrium phenomenon, a mean reverting time-series process approximates the behavior of ROI. While a variety of factors influence the persistence of return, the conditions under which market forces do not drive return back to its competitive rate seem remote, if present at all. Nonetheless, these factors can insulate a firm from competitive forces and so result in longer-term abnormal profits.     

The Long-Run Performance of REIT Stock Repurchases

This study investigates the long-horizon performance of open-market stock repurchases for real estate investment trusts (REITs). We develop a new methodology to model the autocorrelation of monthly returns into long-horizon buy-and-hold abnormal return estimators. Serial correlation can introduce bias (autocorrelation bias) because the bid-ask bounce may affect monthly returns for sample firms and non-sample firms in a different fashion. Previous long-horizon event studies have overlooked this source of bias. There is compelling evidence that the market underreacts to the stock repurchase announcements. The evidence holds for different measures of the variance and the effects of cross-correlation of abnormal returns. Results are also robust to the traditional buy-and-hold abnormal return and the wealth relative estimators. We investigate the nature of the underreaction and find strong support for the undervaluation hypothesis.     

The Bias of the RSR Estimator and the Accuracy of Some Alternatives

This paper analyzes the implications of cross-sectional heteroskedasticity in the repeat sales regression (RSR). RSR estimators are essentially geometric averages of individual asset returns because of the logarithmic transformation of price relatives. We show that the cross-sectional variance of asset returns affects the magnitude of the bias in the average return estimate for each period, while reducing the bias for the surrounding periods. It is not easy to use an approximation method to correct the bias problem. We suggest an unbiased maximum likelihood alternative to the RSR that directly estimates index returns, which we term MLRSR. The unbiased MLRSR estimators are analogous to the RSR estimators but are arithmetic averages of individual asset returns. Simulations show that these estimators are robust to time-varying cross-sectional variance and that the MLRSR may be more accurate than RSR and some alternative methods.     

The Integration of Commercial Real Estate Markets and Stock Markets

This paper tests whether commercial real estate markets (both exchange-traded and non-exchange-traded) are integrated with stock markets using multifactor asset pricing models. The results support the hypothesis that the market for exchange-traded real estate companies, including REITs, is integrated with the market for exchange-traded (non-real-estate) stocks. Moreover, the degree of integration has significantly increased during the 1990s. However, when appraisal-based returns (adjusted for smoothing) are used to construct real estate portfolio returns, the results fail to support the integration hypothesis, although this may reflect the inability of these estimated private market returns to accurately proxy for commercial real estate returns. Interestingly, the growth rate in real per capita consumption is consistently priced in both commercial real estate markets and stock markets, whereas previous studies have found mixed evidence on the role of consumption in explaining ex ante stock returns.     

Smoothing, Nonsynchronous Appraisal and Cross-Sectional Aggregation in Real Estate Price Indices

In this article three econometric issues related to private-equity return indices, such as real estate indices, are explored (smoothing, nonsynchronous appraisal and cross-sectional aggregation). Under certain assumptions, it is found that index returns based on appraisals follow an ARFIMA(1, d , 1) (autoregressive fractionally integrated moving average) process, where the long memory parameter ( d ) explains the level of smoothing and the AR and MA parameters represent the level of persistence in marketwide fundamentals and the nonsynchronous appraisal, respectively. The empirical results show that: (1) the level of smoothing in appraisal-based real estate indices is far less than assumed in many academic studies (2) there is weak evidence of nonsynchronous appraisal in the UK, IPD (Investment Property Databank) index and (3) marketwide fundamentals are highly persistent for the IPD index returns. On the other hand, there is no evidence of nonsynchronous appraisal or a persistent common factor in the U.S. NCREIF (National Council of Real Estate Investment Fiduciaries) index.     

Cross-return predictability in Pacific Basin stock markets

In this article we examine the return transmission across the stock markets of the Pacific Basin. Using the multiple time-series approach we identify explicitly the interaction among national markets and investigate the predictability of return on a post-sample basis. The in-sample estimation results show that the markets do not generally follow a random walk and significant multivariate transmission structures are present. On the post-sample prediction basis, there is no evidence to suggest that using only a country's own past returns can outperform the naive forecast. Incorporating other countries' past returns can, however, improve the forecast performance, and this improvement is clearer in the post-crash period.The authors are Senior Lecturer and Associate Professor respectively at the Department of Economics & Statistics, National University of Singapore.     

Measuring the Employer's Return on Investments in Training: Evidence from the Literature

Three components of the literature on measuring the employer's rate of return to investments in employee training are reviewed: (1) studiesthat use large samples of firm-level or establishment-level data collected through mail or phone surveys, (2) studies that use data from one or two companies to conduct an 'econometric' case study, and (3) company-sponsored case studies. The strengths and weaknesses of each of these approaches are evaluated and the estimated returns on investments (ROIs) are compared. The analysis indicates that the employer's return on investments in training may be much higher than previously believed. In order to obtainaccurate information on the employer's ROI from training, researchers should be encouraged to gain access to company databases and to supplement them with data-gathering efforts to collect information on variables needed to isolate the effect of training.     

Privatization and production efficiency in Taiwan's telecommunications industry

Many managers, economists, and policy-makers have long believed that the production of a private enterprise is more efficient than that of a public one. This paper investigated whether it was good policy to privatize Taiwan's telecommunications industry by comparing the changes in efficiency in Chunghwa Telecom Company (CHT Co.) before and after privatization. The data envelopment analysis (DEA) model was used to evaluate their operational performance both pre- and post-privatization. The technical efficiency (CRS) computed with the CCR model assuming constant returns to scale, the technical efficiency (VRS), and the scale efficiency (VRS) were obtained with the BCC model assuming variable returns to scale, using the time-series data from CHT. The findings of this study show that the production efficiencies of CHT, both pre- and post-privatization are inefficient since all of the technical efficiencies are smaller than one.     

House Prices and Inflation

The present paper examines the long-run impact of inflation on homeowner equity by investigating the relationship between house prices and the prices of nonhousing goods and services, rather than return series and inflation rates as in previous empirical studies on the inflation hedging ability of real estate. There are two reasons for this methodological departure from prior practice: (1) while the total return on housing cannot be accurately measured, the total return on housing is fully reflected in housing prices, and (2) given that using returns or differencing a time series leads to a loss of long-run information contained in the series, valuable long-run information can be captured by using prices. Also, unlike previous related studies, we exclude housing costs from goods and services prices to avoid potential bias in estimating how inflation affects housing prices. Monthly data series are collected for existing and for new house prices as well as the consumer price index excluding housing costs for the period 1968–2000. Based on both autoregressive distributed lag (ARDL) models and recursive regressions, the empirical results yield estimated Fisher coefficients that are consistently greater than one over the sample period. Thus, we infer that house prices are a stable inflation hedge in the long run.     

Return Risk and Cash Flow Risk with Long-term Riskless Leases in Commercial Real Estate

This paper presents a conceptual analysis of some of the key fundamentals that underlie the risk characteristics of commercial real estate returns. In particular, the relationship between the property's return risk and its cash flow risk is explored. This relationship is important because it is the return risk that should matter most to investors, yet it is the cash flow risk or market risk about which we may have the most objective information and the most intuition. This is because real estate assets are generally unsecuritized and trade too infrequently to observe time series of returns (including appreciation) that could be used to directly study the risk characteristics of the returns. By explicitly incorporating the possibility of cash flows governed by riskless long-term leases, this paper also explores the relationship between lease term and both cash flow risk and return risk.     

Forecasting Prices and Excess Returns in the Housing Market

The paper uses quarterly indexes of existing single-family home prices estimated with microdata on properties that sold more than once to estimate excess returns to investment in owner-occupied housing. Housing prices and excess returns are estimated over the period 1970:1 to 1986:3 for Atlanta, Chicago, Dallas, San Francisco. Using time-series cross-section regressions we test for the forecastability of prices and excess returns using a number of independent variables. Price changes in one year tend to continue for more than one year in the same direction. The ratio of construction costs to price, changes in adult population and increases in real per capita income all are positively related to excess returns or price changes over the subsequent year. The results add weight to the argument that the market for single-family homes is inefficient.