商业房地产评估：基础投资者情感对战【外文翻译】.doc

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1、本科毕业论文（设计）外 文 翻 译原文：Commercial Real Estate Valuation: Fundamentals Versus Investor SentimentBackground and Previous LiteratureBoth sentiment and limits to arbitrage are necessary conditions for the existence of mispricing. More specifically, in a market characterized by heterogeneous investors, the

2、existence of short sale constraints can generate deviations in asset prices from fundamental values. Optimistic investors take long positions, while pessimistic investors would like to take short positions. Short-sale constraints, however, may inhibit the ability of rational investors to eliminate o

3、verpricing, even over sustained time periods. Therefore, rational investors may sit on the sidelines when they believe prices are too high relative to fundamentals, leaving market clearing prices to be determined, at the margin, by overly optimistic investors as in Baker and Stein (2004).Most behavi

4、oral finance research has followed a “bottom up” microeconomic approach that appeals to biases in individual investor psychology to explain how and why investors might overreact or under-react to past returns and information about market fundamentals. Brown and Cliff (2004, 2005) and Baker and Wurgl

5、er (2006, 2007) offer a new “top down” macroeconomic approach, the first step of which is to derive measures of aggregate investor sentiment for stocks. Brown and Cliff (2004, 2005) employ both survey measures of investor sentiment as well as sentiment measures derived from a principal component ana

6、lysis of a set of potential sentiment proxies. They find that investor sentiment is highly correlated with contemporaneous stock returns but has little short-run predictive power (Brown and Cliff 2004). However, taking a longer term perspective (2 to 3 years), periods of high sentiment are followed

7、by low returns as the market mean reverts (Brown and Cliff 2005).Baker and Wurgler (2006, 2007) also employ principal component analysis to construct a sentiment measure, and they extend the literature by quantifying the differential effect of sentiment on the cross-section of stock returns by ident

8、ifying which stocks are likely to be more affected by sentiment. Consistent with model predictions, their results suggest that when beginning-of-period proxies for investor sentiment are high (low), subsequent returns are relatively low (high) for stocks that are either more speculative in nature or

9、 for which arbitrage tends to be particularly risky.Real estate investors monitor market sentiment in several ways. First, they may subscribe to data services that provide regular survey-based information about investment sentiment (such as the quarterly RERC Real Estate Report used in this paper).

10、Many investors also monitor variables related to “capital flows” into the real estate sector. For example, they may track data on mortgage flows, the dollar volume and number of properties sold, and capital flowing into real estate investment vehicles (e.g., commingled funds for institutional and hi

11、gh net worth investors) under the belief that there is a common sentiment component embedded in these investor activity variables.Although regarded as important by practitioners, there has been relatively little academic work aimed at understanding the role of fundamentals versus investor sentiment

12、and capital flows in real estate pricing dynamics. A contemporaneous correlation between capital flows and cap rates does not by itself imply causation. Capital flows and property prices (and hence cap rates) might both respond in a similar fashion to fundamental economic variables and risk factors,

13、 such as unexpected inflation, changes in real interest rates, or revisions in risk premiums. For example, if both capital flows and property prices increase when positive economic news is released, then a negative contemporaneous correlation between capital flows and cap rates does not prove that c

14、apital flows cause or predict cap rates.The lack of research examining the role of fundamentals versus sentiment and capital flows in real estate markets is partly due to data limitations. Ling and Naranjo (2003, 2006) examine the dynamics of commercial real estate capital flows and returns. Their w

15、ork provides evidence that capital flows into public (i.e., securitized) real estate markets do not predict subsequent returns, but that returns do affect subsequent capital flows into these securitized real estate markets. Fisher et al. (2007) extend the work of Ling and Naranjo (2003, 2006) by inv

16、estigating the short and long-run dynamics among institutional capital flows and property returns in the largest US metropolitan areas. The authors find some evidence that lagged institutional capital flows influence current returns at the aggregate level, but the evidence is less convincing when di

17、saggregated by metropolitan area and property type. These papers provide useful empirical characterizations of the dynamics of real estate capital flows and pricing, and therefore provide a solid foundation on which additional research can build. However, their results do not directly address the ro

18、le sentiment plays in real estate markets, as they do not explicitly relate capital flows to investor sentiment within a model of property pricing.Shilling and Sing (2007) examine the rationality of investors expected income growth rates and total return forecasts in private commercial real estate m

19、arkets. Their findings are consistent with models of investor irrationality. Furthermore, Shilling and Sing find evidence that investors act overly optimistic and that they generally anchor their expectations to the previous period. Finally, Ling (2005) provides preliminary unvaried evidence consist

20、ent with real estate pricing being driven at times by investor sentiment. Modeling Prices and Cap RatesArcher and Ling (1997) argue that three “markets” play a role in determining commercial real estate prices: space markets, capital markets, and property markets. Local market rents are determined i

21、n the space market (i.e., the market for leasable space). Required risk premiums for assets with varying profiles of cash flow risk are determined in the capital market. Finally, property markets are where asset-specific discount rates, property values, and cap rates are determined.It is important t

22、o note that the level of NOI has no impact on the cap rate. Rather, it is the excepted change in NOI that affects the price investors are willing to pay per dollar of first year NOI. Of course, it is unlikely that NOI growth rates and future discount rates are expected to be constant forever. Nevert

23、heless, EQ is an approximation that motivates our empirical cap rate specification and is consistent with a more general present value model that allows for time variation in NOI growth and the discount rate to impact commercial property valuation and hence the cap rate.The risk-adjusted discount ra

24、te has two components: RFt, the rate of return available on a risk-free. Treasury bond with a maturity equal to the expected holding period of the property; and RFt, the required risk premium, which is property, market, and time dependent. Clearly, RFt, is determined outside local space and property

25、 markets, as yields on Treasury securities are determined by the bid and ask prices of Treasury market investors from around the world.What about the determinants of RFt? In the capital markets, commercial real estate competes with all other assets for a place in investors portfolios. According to c

26、lassical portfolio theory, investors will select a mix of investments based on the variances and co variances of the returns among the possible assets. As investors bid for their optimal portfolio mix, their bidding simultaneously determines the required risk premiums for the universe of investments

27、 according to their risk (variance and covariance) profiles. Thus, the pricing of risk depends on risk preferences articulated in the broader capital as well as the specific risk profile of the investment, which is determined by current and expected future conditions in the space market in which the

28、 property is located.The Dynamic Nature of Real Estate Pricing and Cap RatesIn highly liquid public securities markets, asset prices are believed to adjust quickly to changes in market fundamentals such as interest rates, inflation expectations, and national and local market conditions. However, in

29、private, commercial real estate markets, observed cap rates may adjust more gradually to the arrival of new information because of numerous property market inefficiencies, such as high transaction costs, lengthy decision making processes and due-diligence periods, and informational inefficiencies. A

30、 number of authors have estimated structural models derived from theoretical cap rate models to investigate property price dynamics (Sivitanides et al. 2001; Hendershott and MacGregor 2005a, b; Chen et al. 2004; Plazzi et al. 2004, Chichernea et al. 2008; Sivitanidou and Sivitanides 1999).To capture

31、 both long-run and short-run cap rate dynamics, we employ an error correction model (ECM) similar to Hendershott and MacGregor (2005a). This framework allows us to model cap rates as an adjustment process around equilibrium values. Error correction models are based on the idea that two or more time

32、series exhibit a long-run time-varying equilibrium to which the system tends to converge. The long-run influence in the error correction model is achieved through negative feedback and error correction, and this influence measures the degree to which long run equilibrium forces drive short-run price

33、 dynamics (see, for example, Engle and Granger 1987 and Hamilton 1994).DataOur primary data source is the Real Estate Research Corporation (RERC). Founded in 1931 in Chicago, RERC is nationally known for its research, analysis, and investment criteria. Published quarterly in the Real Estate Report,

34、the RERC Real Estate Investment Survey summarizes information on current investment criteria such as going-in (acquisition) cap rates, terminal cap rates, unlevered required rates of return on equity, expected rental growth rates, and investment conditions provided by a sample of institutional inves

35、tors and managers throughout the USA9 According to RERC, the survey results are used by investors, developers, appraisers, and financial institutions to “monitor changing market conditions and to forecast financial performance.” As a robustness check, we also employ survey data from Korpacz Price Wa

36、terhouse Coopers.Ideally, our cap rate data would be based on a large number of constant-quality (including location) properties with identical lease terms. Such data do not exist. The RERC data, however, represent the cap rates respondents are currently observing in the market for notional investme

37、nt grade properties of constant quality. Thus, these data are well-suited to our task, except they are not based on actual transactions.Recall from EQ that equilibrium going-in cap rates Ret are a function of unlevered discount rates (rt) and expected growth rates in net rental income (gt). However,

38、 rt and gt cannot be directly observed. Thus, in prior cap rate studies, proxies for these variables, or their component parts, were estimated. One attraction of the RERC data is that expected rental growth rates and required equity returns are two of the survey questions. In addition, survey respon

39、dents are asked to rank the “investment conditions” of various property types and markets. These ranking of investment conditions directly measure investor sentiment.We focus first on the going-in capitalization rates reported by RERC for nine property types: apartment, hotel, industrial research an

40、d development, industrial warehouse, central business district (CBD) office, suburban office, neighborhood retail, power shopping centers, and regional malls. Survey cap rates for the nine property types are displayed in Fig. During the first half of the 1996:Q12007:Q2 sample period, cap rates remai

41、ned relatively stable. However, beginning in 2002, cap rates on all property types began to decline. For example, apartment cap rates stood at 8.7% in 2002:Q1; by 2007:Q2 they had declined 300 basis points to 5.7%.To address potential concerns about the survey-based nature of our cap rate data, we c

42、ompare RERC cap rates, by property type, to cap rates obtained from two other sources, the National Council of Real Estate Investment Fiduciaries (NCREIF) and Real Capital Analytics (RCA). NCREIF cap rates represent averages derived from valuations of institutional class properties held by firms tha

43、t are contributing members to the NCREIF Property Index (NPI). RCA cap rates are averages derived from a much larger, but more heterogeneous, population, coming from the sales of all properties of $5 million or more. NCREIF cap rates are appraisal-based , and hence potentially backward looking. RCA

44、cap rates are transaction-based but potentially noisy because they are not constant quality. NCREIF data extend back to 1990, whereas RCA data begin in 2001. Correlations between RERC and NCREIF cap rate levels over the 1996:1 to 2007:2 period, and RERC and RCA cap rates over the 2001:1 to 2007:2 pe

45、riod, exceed 90% for all nine property types. Moreover, regressions of RERC cap rates on NCREIF (RCA) cap rates yield highly significant slope coefficients of 0.80 (0.90) and above and R2s of 85% (90%) and above. In fact, regressions of RERC cap rates on both NCREIF and RCA cap rates together result

46、 in adjusted R2s above 95% in almost all nine cases. The tight connection between RERC cap rates and these two alternative series indicates that our survey based cap rates are tracking pricing dynamics in commercial real estate markets very well.Table 1 contains summary statistics, by property type,

47、 for our key RERC regression variables. The top panel contains means, standard deviations, minimums, maximums, and serial correlations of levels and changes for capitalization rates, expected rental growth rates, required equity returns, and investment conditions. Mean expected rent growth ranges fr

48、om 2.3% (annually) for power centers to 2.9% for apartments. The levels of expected rent growth display substantial positive serial correlation across quarters. However, changes in expected rental growth rates display significant negative serial correlation, with the exception of apartments and hote

49、ls.Source: Jim Clayton, David C. Ling, Andy Naranjo, 2008. “Commercial Real Estate Valuation: Fundamentals Versus Investor Sentiment” . Springer Science, vol.17, July, pp.5-37.译文：商业房地产评估：基础投资者情感对战研究背景和文献这两种情绪和套利限制是为表示定价而存在的必要条件。具体而言，在投资者的异质性为特征的市场，卖空限制的存在可以产生从资产价格偏离得基本价值。乐观的投资者采取多头头寸，而悲观的投资者想借淡仓。但是卖空限制可能会抑制理性投资者能够消除甚至超过过高的持续时间期间。因此，理性的投资者认为相对于基本面价格太高时，可能会坐在场边时，使市场清算价格确定，在过分乐观的投资者保证金（贝克和斯坦，2004）。行为金融学的研究大多遵循“自下而上”的微观经济的方法，对个别投资者的心理偏差需要解释，为什么投资者可能反应过度或不足的反应，过去的回报和对市场基础，（布朗和克里夫，2004，2005）和（贝克和伍格儿，2006，