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."     

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.     

A Simulation Approach to the Appraisal of Income Producing Real Estate

The income approach to appraising real property has been criticized in at least three general areas: the use of point estimates for input variables and market value, the failure to use an after-tax valuation model, and the ability of a single valuation model to capture the market for a given property. The objectives of this study are the development of an analytical framework and computer model to overcome these criticisms. The computer model is developed using Monte Carlo simulation and allows the appraiser to consider uncertainty, after-tax cash flows and numerous valuation models. The ability of appraisers to generate the necessary inputs and the interpretation of the output from the model are demonstrated using a case study.     

试论房地产评估中的建筑物的损耗浅析

章阐述房屋建筑损耗的概念及分类,提出计算损耗的见解。     

替代原理在房地产估价中运用

在简要介绍替代原理及其作用的基础上,分析了替代原理在房地产估价各种办法中运用情况。指出替代原理是指导房地产估价的普遍性原理,是房地产估价的重要原则,在房地产估价中具有广泛的应用。     

房地产项目经济评价中基准收益率设定的探讨

目前我国在项目投资评价中仍采用在经济条件下制定的基准收益率,这已经不符合我国目前市场经济的要求。房地产作为我国的支柱性产业,更不适合采用此基准收益率。借鉴目前国际上使用较为广泛成熟的加权平均资本成本方法,提出适合我国国情的基准收益率的确定方法。     

The Risk and Return of Commercial Real Estate: A Property Level Analysis

I compare the performance of the index‐based time series approach and the cross‐sectional approach in estimating factor loadings of nontraded assets, and show that the latter likely provides less biased and more efficient estimates. I then use the cross‐sectional approach to estimate the loadings of privately owned commercial real estate on the Fama and French (1993) factors, the Pastor and Stambaugh (2003) liquidity factor, and two bond market factors, using a sample of 14,115 properties in the 1977–2012 period. I find statistically significant loadings, of which the signs seem consistent across property types, but the magnitude varies. Using the time series approach on the same data, I find insignificant loadings on virtually all factors. To investigate the sources of the weak results from the time series approach, I conduct a Monte Carlo simulation in which both approaches are correctly specified and indices can be estimated perfectly. Simulation results suggest that the cross‐sectional approach provides more accurate estimates under reasonable market conditions.     

A Note on the use of Appraisal Data in Indexes of Performance Measurement

This paper demonstrates that even when unbiased appraisals of market value are used in measuring the investment performance of real estate portfolios, a bias in the rate of return or index is present. Further, in the case where the appraisal errors are serially independent, the bias is always positive. The potential for bias in a standard rate of return formula is described. Some implications for portfolio formation and investment manager comparisons are discussed.     

Institutional Capital Flows and Return Dynamics in Private Commercial Real Estate Markets

This article examines the short- and long-run dynamics among institutional capital flows and returns in private real estate markets. At the aggregate U.S. level, we find evidence that lagged institutional flows significantly influence subsequent returns. When disaggregating by property type at the national level, we find that capital flows predict subsequent returns in the apartment and office sectors, but not in the retail and industrial markets. At the metropolitan level, we find that the flows help explain subsequent returns in a limited number of core business statistical areas (CBSAs), although these CBSAs collectively represent about 30% of institutional capital. We find no evidence that institutional returns are predictive of future capital flows at the national or CBSA level, suggesting that institutional investors are not chasing returns.     

Risk and Return on Real Estate: Evidence from Equity REITs

We analyze monthly returns on an equally weighted index of eighteen to twenty-three equity (real property) real estate investment trusts (REITs) that were traded on major stock exchanges over the 1973–87 period. We employ a multifactor Arbitrage Pricing Model using prespecified macroeconomic factors. We also test whether equity REIT returns are related to changes in the discount on closed-end stock funds, which seems plausible given the closed-end nature of REITs.
Three factors, and the percentage change in the discount on closed-end stock funds, consistently drive equity REIT returns: unexpected inflation and changes in the risk and term structures of interest rates. The impacts of these variables on equity REIT returns is around 60% of the impacts on corporate stock returns generally. As expected, the impacts are greater for more heavily levered REITs than for less levered REITs. Real estate, at least as measured by the return performance of equity REITs, is less risky than stocks generally, but does not offer a superior risk-adjusted return and is not a hedge against unexpected inflation.     

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.     

An Empirical Analysis of Hedonic Regression and Grid-Adjustment Techniques in Real Estate Appraisal

Multiple regression analysis has become increasingly popular when appraising residential properties for tax purposes. Alternatively, most fee appraisers and real estate brokers use the traditional sales comparison approach. This study combines the two techniques and uses multiple regression to generate the adjustment coefficients used in the grid adjustment method. The study compares the combined grid-regression method with ordinary regression and defines the market conditions under which each method is likely to be more effective. The grid-regression method is found to be more accurate for relatively homogeneous housing markets, and the multiplicative percentage adjustment method (MPAM) the preferred approach.     

房地产三种评估价格的比较分析

简要的阐述了比准价格、收益价格、积算价格三种房地产评估价格的形成机理及理论依据，并从对一种观点的局限性分析出发，综合运用市场供求理论、均衡理论以及短边原则，对不同市场背景下三种估价方法得出的评估价格进行经济比较分析。     

Analyzing Real Estate Data Problems Using the Gibbs Sampler

Real estate data are often characterized by data irregularities: missing data, censoring or truncation, measurement error, etc. Practitioners often discard missing- or censored-data cases and ignore measurement error. We argue here that an attractive remedy for these irregularity problems is simulation-based model fitting using the Gibbs sampler. The style of the paper is primarily pedagogic, employing a simple illustration to convey the essential ideas, unobscured by implementation complications. Focusing on the missing-data problem, we show dramatic improvement in inference by retaining rather than deleting cases of partially observed data. We also detail Gibbs-sampler usage for other data problems.     

The Role of Uncertainty in Investment: An Examination of Competing Investment Models Using Commercial Real Estate Data

Neoclassical investment decision criteria suggest that only the systematic component of total risk affects the rate of investment, as channeled through the built-asset price. Alternatively, option-based investment models suggest a direct role for total uncertainty in investment decisionmaking. To sort out uncertainty's role in investment, we specify and empirically estimate a structural model of asset-market equilibrium. Commercial real estate time-series data with two distinct measures of asset price and uncertainty are used to assess the competing investment models. Empirical results generally favor predictions of the option-based model and hence suggest that irreversibility and delay are important considerations to investors. Our findings also have implications for macroeconomic policy and for forecasts of cyclical investment activity.