institutional capital flows and return dynamics in private commercial real estate markets

2009 V37 1: pp. 85–116

REAL ESTATE

ECONOMICS

Institutional Capital Flows and ReturnDynamics in Private Commercial RealEstate MarketsJeffrey Fisher,∗ David C. Ling∗∗ and Andy Naranjo∗∗∗

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

Assuming investor rationality and costless arbitrage, asset prices in publicmarkets are not affected by capital flows or trading activity; the current marketvalue of an asset is simply the present value of the expected future cash flows.However, a growing behavioral finance literature is critical of the assumptionsof investor rationality and costless arbitrage implied by the efficient markethypothesis. Numerous theoretical models have been developed that recognizelimits to arbitrage (supply inelasticities) and/or heterogeneous investor beliefs(“noise traders”). In these models, investor sentiment, capital flows and tradingvolume can have a role in the determination of asset prices independent ofmarket fundamentals.1

The role of capital flows in public market asset pricing has also been the subjectof numerous empirical investigations, including analyses of the price impact

∗Center for Real Estate Studies, Kelley School of Business, Indiana University,Bloomington, IN 47405 or [email protected].

∗∗Department of Finance, Insurance and Real Estate, Center for Real Estate Studies,Warrington College of Business, University of Florida, Gainesville, FL 32611-7168or [email protected].

∗∗∗Department of Finance, Insurance and Real Estate, Warrington College of Business,University of Florida, Gainesville, FL 32611-7168 or [email protected].

1 See Clayton (2003) for a review of the literature that posits and tests for supply anddemand effects in the determination of asset prices.

C© 2009 American Real Estate and Urban Economics Association

86 Fisher, Ling and Naranjo

associated with a stock’s inclusion in the S&P 500 or other stock indices, theeffects of increased foreign capital flows on stock price valuations in developingcountries and the role of dedicated mutual fund capital flows in the asset pricemovements of individual securities and indices.2 Although the issue is far fromsettled, several empirical studies suggest a role for capital flows in the pricingof publicly traded stocks and bonds.

In the public commercial real estate sector, Ling and Naranjo (2003) analyzethe influence of total equity flows into the real estate investment trust (REIT)sector on aggregate REIT prices and returns and, simultaneously, the influenceof past industry-level returns on subsequent capital flows into the REIT sector.Ling and Naranjo (2006) examine the interrelationships and short- and long-run dynamics between aggregate capital flows to dedicated REIT mutual fundsand industry-level REIT returns. No consistent evidence that capital flows arepredictive of subsequent returns in the REIT sector is uncovered in either study.In this article, we examine institutional capital flows and return dynamics inprivate commercial real estate markets, which is an important yet unexaminedresearch area to date.

The focus of prior stock, bond and real estate capital flow studies on publiclytraded securities is not surprising, given the availability of flow and return datain public markets and the corresponding lack of such data in private markets.However, the absence of a rigorous analysis of the dynamics of capital flowsand returns in private real estate markets is a serious void in the literaturefor several reasons. First, approximately 90% of investible commercial realestate in the United States is owned and traded in private markets.3 Second, theconventional wisdom among real estate practitioners is that capital flows dodirectly impact asset prices. In fact, the significant reduction in capitalizationrates (initial yields) that occurred in most commercial real estate markets during2002–2007 is largely, if not entirely, attributed to the surge in real estate capitalflows that occurred during this period (e.g., House 2004, Downs 2007).

However, a contemporaneous correlation between asset prices and capital flowsis not sufficient to conclude that flows affect valuations and returns. An alter-native explanation is that changes in fundamental economic variables and risk

2 Prior studies that have examined the various effects of capital flows in asset pricinginclude the following: for stock index studies, see Cha and Lee (2001) and Shleifer(1986); for the effects of foreign capital flows, see Choe, Kho and Stulz (1999), Bekaertand Harvey (2000), Bekaert, Harvey and Lumsdaine (2002), Brennan and Cao (1997),Stulz (1999) and Tesar and Werner (1995); for the role of mutual fund flows, seeEdelen and Warner (2001), Fortune (1998), Karceski (2002), Remolona, Kleiman andGruenstein (1997), Sirri and Tufano (1998) and Warther (1995).3 See Ling and Archer (2008), Chapter 18, p. 453.

Institutional Capital Flows and Return Dynamics 87

factors—such as per capita income, employment and interest rates—producechanges in expected property-level cash flows or required rates of returns,which, in turn, lead to both higher asset prices and increased capital flowsand transaction activity (Clayton 2003, Ling and Naranjo 2006). Therefore, anobserved contemporaneous correlation between property price movements andcapital flows does not imply causality.

Why might capital flows influence returns in private real estate markets? Unlikethe exchange-listed shares of firms “for which close substitutes exist eitherdirectly or indirectly” (Scholes 1972), the unique location and other attributesof commercial real estate assets creates highly segmented markets, which, inturn, severely restrict an investor’s set of acceptable investment substitutes.This segmentation may create markets in which aggregate demand (capitalflows) affects valuations—in addition to the marginal investor’s assessment offundamental value (Gompers and Lerner 2000). For example, an increase inthe target allocations of pension funds to commercial real estate may result ingreater buy-side competition in some metropolitan areas for existing properties,leading in turn to higher prices and short-term returns. This “price pressure”will likely be greatest when pension funds are aggressively growing their realestate portfolios. In addition, the lead time required to bring new commercialproperties to the market to meet an increased demand is likely to be significantlylonger than the time required to increase the supply of venture capital firmsstudied by Gompers and Lerner (2000) or other financial assets. The increasedprice sensitivity to demand shocks that results from an inelastic supply ofproperties, at least in the short run, would be observed regardless of whetherchanging demand is driven by rational or irrational forces.

Although increased capital flows may create price pressure in segmented realestate markets, shifts in capital flows may also reflect changes in investor ex-pectations. That is, increased capital flows into a sector could signal a revisionupward in investors’ expectations regarding future income streams and/or de-creases in investors’ required returns—both of which should lead to higher assetprices. In fact, Fisher et al. (2003) argue that transaction volume is predictiveof the conditions of local, regional and national economies. In addition to re-flecting changes in investors’ rational assessments of fundamental variables,variations in capital flows may, at times, be driven by (irrational) investorsentiment. In short, even in the absence of price pressure, capital flows maycapture variation in investor expectations and, therefore, be predictive of futurereturns.4

4 We are not able to separate the price pressure effects of capital flows from signalingeffects. For an investigation of the relative pricing roles of fundamentals and investorsentiment in private commercial real estate markets, see Clayton, Ling and Naranjo(2008).


This article examines the extent to which institutional capital flows are asso-ciated with subsequent returns in a cross-section of U.S. commercial propertysectors and geographic markets. Simultaneously, we examine whether the re-turns earned by institutional investors in private real estate markets impact theirsubsequent acquisitions and dispositions. Our capital flows and returns are ob-tained from data supplied by the National Council of Real Estate InvestmentFiduciaries (NCREIF). The focus on institutional investors reflects both dataavailability and the widely held belief that institutional buyers and sellers havedifferent investment motivations and constraints than noninstitutional investorsand that institutional investors often move into, or out of, markets in herds.5

Although there may be periods during which the returns of institutional in-vestors are driven, at the margin, by the investment activities of other investorsegments, such as REITs or private buyers, the objective of this article is toquantify the dynamic relation between institutional capital flows and returns.

The main tool of analysis that we employ is a vector autoregressive (VAR)regression model in which both institutional capital flows and returns are spec-ified as endogenous variables in a two-equation system. We begin with ananalysis using national-level institutional capital flows and returns. However,because increases or decreases in institutional capital flows may have differ-ential pricing effects across property types and geographic segments of theprivate real estate market, we also disaggregate our analysis by property typeand major metropolitan areas.

Our primary findings can be summarized as follows. At the aggregate U.S.level, where net institutional flows reflect the amount of capital flowing intothe private institutional sector from the noninstitutional sector, we find evi-dence that the impact of institutional capital flows on subsequent institutionalreturns is both statistically and economically significant. When disaggregatingby property type at the national level, we find that the positive capital flow–return relationship is driven by the office and apartment sectors; we detect nosuch relation in retail and industrial markets. Furthermore, our metropolitan-level analysis reveals that the positive capital flow–return dynamic uncovered atthe aggregate U.S. level appears to be driven by a limited number of core busi-ness statistical areas (CBSAs), although these CBSAs collectively representapproximately 30% of invested institutional capital. Finally, the influence ofour control variables is far from consistent across property types and metropoli-tan areas. This suggests that models intended to predict returns in private real

5 Several stock market studies find institutions to be informed investors “smart money”(e.g., Chakravarty 2001, Jones and Lipson 2004, Sias, Starks and Titman 2006). How-ever, this evidence is tempered by studies suggesting that institutions do not outperformindividual investors (e.g., Nofsinger and Sias 1999, Kaniel, Saar and Titman 2005).


estate market returns must be estimated using data disaggregated to, at least,the metropolitan level.

The remainder of the article proceeds as follows. In the next section, the VARmethodology that we employ to examine the conditional covariation of in-stitutional capital flows and returns is described. We then discuss our datasources and provide a discussion of the descriptive statistics. In the followingsection, we present our aggregate and disaggregate VAR results. We then dis-cuss the results from various alternative VAR specifications, demonstrating therobustness of our major findings. Our conclusions are presented in the finalsection.

Research Methodology

We employ VAR models to examine the relationships among institutionalinvestor real estate capital flows and property returns and seek to answer twoquestions.6 First, do NCREIF Property Index (NPI) capital flows predict NPIreturns over and above the predictions of lagged returns? Second, do NPIreturns predict flows over and above the predictions of lagged flows?

In its simplest form, a VAR model is composed of a system of regressions wherea set of dependent variables are expressed as linear functions of their own andeach other’s lagged values and possibly some other exogenous variables. Inmore technical terms, a VAR model is the unconstrained reduced form of adynamic simultaneous equations model. An unrestricted pth-order GaussianVAR model can be represented as:

Yt = μ + �1Yt−1 + �2Yt−2 + · · · + �kYt−p + et ,

where Yt is a vector of variables, μ is a p × 1 vector of intercepts, �1,�2 , . . . , �k are p × p matrices of parameters with all eigenvalues of � havingmoduli less than 1 so that the VAR is stationary and et is a vector of uncorrelatedstructural shocks (∼NID(0, �)). In a bivariate framework of only flows andreturns, the diagonal coefficients of � represent conditional momentum in flowsand returns, whereas the off-diagonal coefficients of � represent conditionalpositive feedback trading (flows following returns) and conditional anticipationeffects (returns following flows). The off-diagonal elements of � capture theprice impact effect of flows on returns.

We obtain the maximum likelihood estimates of � and � using iterated leastsquares. The lag length of the VAR is chosen by looking at the Akaike

6 VAR methods have proven successful for forecasting systems of interrelated timeseries variables (see Sims 1980).


Information Criterion and the likelihood ratio for various choices of p. Wefind that four lags provide the best fit. It is important to note that the laggedreturns in the return equation control for the well-documented autoregressivenature of NCREIF returns (i.e., the smoothing bias). Thus, the reported flowcoefficient estimates are marginal effects.

The above unconstrained VAR system is estimated at the aggregate U.S. level,at the national level by property type and for a large sample of CBSAs over the1983:3–2005:2 period using quarterly data aggregated across all property types.We then estimate property-specific equations at the CBSA level in those CBSAswith adequate data for the apartment, retail, office and industrial property types.By separately examining the CBSAs, we are able to disentangle the influenceof CBSA location on the conditional dynamics of institutional capital flowsand returns.

Risk and Macroeconomic Control Variables

To control for other potential sources of variation in our quarterly return andflow equations, we also include lagged values of the three Fama–French riskfactors: MKT , SMB and HML. MKT is the total return on the value-weightedstock market portfolio, as measured by the Center for Research in SecuritiesPricing (CRSP), minus the corresponding quarterly return on U.S. Treasurysecurities from the CRSP. SMB is defined as the total return on a portfolioof small-cap stocks in excess of the return on a portfolio of large-cap stocks.Finally, HML is the total return on stocks with high ratios of book-to-marketvalue in excess of the returns on a portfolio of stocks with low book-to-marketratios.7

The majority of the total return typically earned by commercial real estateinvestors is provided by current income, not capital gains. Thus, commercialreal estate assets can generally be characterized as “value” assets with highbook-to-market value ratios. We therefore posit a positive relation betweenHML and NPI returns, that is, as stock investors rotate out of “growth” stocks

7 Fama and French (1996) show that the cross-sectional variation in expected stockreturns can be explained by their three-factor model. They argue that SMB and HMLare state variables in an intertemporal asset pricing model, although rational assetpricing theories do not clearly show how SMB and HML are related to the un-derlying undiversifiable macroeconomic risks. However, there is some recent em-pirical and theoretical work that links the Fama–French factors to undiversifiablemacroeconomic risks (Liew and Vassalou 2000, Lettau and Ludvigson 2001). Thequarterly Fama–French risk factors were obtained from Ken French’s Web site athttp://mba.tuck.dartmouth.edu/pages/faculty/ken.french/index.html.


(with low book-to-market value ratios) into value assets, we expect the privatecommercial real estate sector to benefit from this capital rotation.

The use of the NPI income return as an explanatory variable in our analysisis motivated by the work of Bekaert and Harvey (2003), who argue that, ina rational pricing model, current (dividend) yields will be decreasing in thegrowth rate of dividends and increasing in the discount rate. Therefore, dividendyields may be useful in capturing permanent price effects induced by a changein a sector’s cost of capital. Ghysels, Plazzi and Valkanov (2007) provideempirical evidence that the income return (i.e., cap rate) is related to futurecommercial real estate returns. To control for long-term interest rate levels andtrends, we also include the lagged quarterly yield on constant maturity, 10-yearTreasury securities (TRYLD). All else being equal, increases in the cost of debtare expected to decrease transaction frequency and property prices, at least inthe short run.

Government intervention at the federal, state and local levels often plays asignificant role in real estate markets. For example, growth management andeconomic development initiatives can vary significantly over time and acrosslocal markets. Therefore, we also include several variables intended to con-trol for the economic condition and regulatory environment of the market. Inparticular, following Fisher et al. (2004) and others, we include the change innonagricultural employment (EMPLOY) and the change in per capita incomeover the prior four quarters (INCOME) to control for the general economiccondition of the market. In addition, we include the change in the number ofmultifamily housing starts (MULTI) and the change in the number of single-family housing starts over the prior four quarters (SINGLE). These latter twovariables are expected to proxy for the condition of the local real estate mar-ket, including the cross-CBSA variation in growth prospects driven by theavailability of developable land.

Using the risk and macro control variables, we estimate three unrestrictedVAR models: a bivariate model, a 7-factor model and an 11-factor model. Thebivariate model consists of total NPI capital flows and aggregate NPI returns.Next, using our 7-factor model, we examine whether the relations we uncoverin our bivariate model are robust to the addition of controls for lagged Treasuryyield levels, lagged income returns and the lagged Fama–French risk factors(MKT, SMB and HML). Finally, following Fisher et al. (2004), our 11-factormodel also includes the change in nonagricultural employment, the changein per capita income over the prior four quarters, the change in the numberof multifamily housing starts and the change in the number of single-familyhousing starts over the prior four quarters.


Data and Descriptive Statistics

Data Sources and Definitions

The NCREIF is a not-for-profit institutional real estate industry association. Es-tablished in 1982, it serves the real estate investment industry by collecting, pro-cessing, validating and then disseminating information on the risk/return char-acteristics of commercial real estate assets owned by institutional—primarilypension fund—investors. NCREIF’s flagship index, the NPI, tracks the quar-terly total return performance of a large pool of individual commercial realestate properties acquired in the private market for investment purposes only.8

To be included in the NPI, the data contributing member’s asset must be anexisting property, be at least 60% leased and be wholly owned or in a jointventure structure. If in a joint venture, the data are reported so that the returnscan still be calculated on the entire property—not just on the joint ventureinterest. Although levered properties are included in the NPI, investment per-formance is reported on an unlevered basis. The property composition of theNPI changes quarterly as data-contributing NCREIF members buy and sellproperties. However, all historical data remain in the database and in the index.

The NPI is compiled from the quarterly returns of individual properties beforethe deduction of portfolio-level management fees, but inclusive of property-level management fees. Each property’s quarterly return is weighted by itsmarket value relative to the total market value of the properties that constitutethe NPI. In addition to total returns, the income and capital gain componentsof the total return are separately reported.

The NCREIF NPI is the only source of consistently collected information onthe total returns earned by investors in private U.S. commercial real estatemarkets and is therefore widely used as a benchmark of commercial real estatereturns. Nevertheless, the NPI has several shortcomings.9 Of primary concernin the current study is that firms that contribute return data to the NPI areexclusively institutional investors. Although the acquisition and dispositionactivities of NCREIF members clearly impact subsequent NPI returns, theinvestment performance of institutional quality properties may also be affectedby the investment decisions of other investor types, such as foreign investorsand private buyers and sellers.

8 Detailed information on the NCREIF and the NPI is available at www.ncreif.com.9 For a detailed discussion of the characteristics of NCREIF’s NPI, see Geltner and Ling(2007).


Table 1 � Percentage of total Real Capital Analytics acquisitions by buyer type.

Buyer AverageClassification 2001 2002 2003 2004 2005 2006 2001–2006

Unclassified 6.3% 3.8% 4.8% 5.4% 4.0% 3.6% 4.7%User/other 9.6% 5.4% 5.5% 4.1% 3.3% 3.3% 5.2%Condo converter 0.9% 1.0% 2.4% 6.7% 11.2% 3.2% 4.2%Foreign 3.9% 7.5% 7.5% 7.5% 7.2% 5.2% 6.5%Private in-state 19.6% 17.9% 20.6% 18.8% 17.3% 19.1% 18.9%Private out-of-state 16.1% 13.4% 14.2% 12.4% 12.3% 17.9% 14.4%Syndicator 1.0% 2.4% 4.4% 4.0% 3.6% 2.7% 3.0%Fund 7.2% 8.0% 6.7% 6.5% 8.1% 15.0% 8.6%REIT/REOC 13.0% 23.3% 20.3% 17.6% 14.5% 9.4% 16.3%Institutional 22.4% 17.2% 13.6% 16.9% 18.5% 20.7% 18.2%

Total 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0%

Data obtained from Real Capital Analytics (RCA), a national real estate data vendorspecializing in tracking commercial real estate transaction activity and prices. TheRCA database contains monthly observations since 2001 on average transaction-basedcapitalization rates, average per square foot sale prices and sales volume (both dollaramount and number of properties) for all transactions in excess of $5.0 million inthe approximately 45 major metropolitan areas they follow. RCA disaggregates allacquisitions and dispositions by investor type: institutional, foreign, public real estateinvestment trust (REIT), syndicator, fund, local private investor and national privateinvestor. REOC = real estate operating company.

Table 1 provides information on the relative importance of institutional in-vestors in private commercial real estate markets. These data were obtainedfrom Real Capital Analytics (RCA), a national real estate data vendor special-izing in tracking commercial real estate transaction activity and prices. TheRCA database contains monthly observations on the average transaction-basedcapitalization rates, average per square foot sale prices and sales volume (bothdollar amount and number of properties). Unlike the NCREIF database, whichtracks the investment activity and return performance on “institutional” qualityproperties (generally with market values in excess of $10 million), the RCAdatabase captures all transactions in excess of $5.0 million. The RCA dataare available since 2001 for approximately 45 major CBSAs by property type.RCA disaggregates all acquisitions and dispositions by investor type: institu-tional, foreign, public REIT, syndicator, fund, local private investor and nationalprivate investor.

Over the 2001–2006 period, institutional investors have accounted for 18.2%of all acquisitions of properties with sale prices greater than $5 million. Onlythe category of private, in-state buyers have accounted for a larger share of thetotal dollar value of acquisitions. REITs and real estate-operating companies


(REOCs) have accounted for 16.2%, on average, of all acquisitions. Clearly,institutional investors are a major source of equity capital in private commercialreal estate markets.

Although the NPI is available beginning in the first quarter of 1978, the lim-ited number of data-contributing members and constituent properties in theearly years of the NPI does not support CBSA- and sub-CBSA-level analyses.Therefore, to minimize noise in the construction of our NPI return series, webegin our sample period in the third quarter of 1983. To further ensure that anadequate number of properties are included in the calculation of our CBSA-level indices, we imposed the condition that the average number of constituentproperties over the 1983:3–2005:2 sample period must be equal to or greaterthan 10 and that there must be at least four properties in the NPI in any quar-ter. These screens reduced the number of usable CBSAs to 43. We also havesufficient CBSA-level data to construct apartment returns for 19 CBSAs. Thecorresponding number of available CBSAs for our retail, office and industrialanalyses are 12, 20 and 28, respectively.

In each quarter, the market value of properties in the NPI changes for severalreasons: (1) capital appreciation, (2) net capital flows from NCREIF memberacquisitions and dispositions and (3) properties added to the database fromnew members. We remove the effects of (1) and (3) from the net change inmarket value to capture the change in market value due to a flow of fundsfrom net acquisitions. This raw capital flow variable is defined as FLOWS andis constructed for the United States as a whole, for each of the four majorproperty types at the aggregate U.S. level, for each of our 43 CBSAs and foreach of our sub-CBSA property-type indices.10

Finally, Froot, O’Connell and Seasholes (2001) and Ling and Naranjo (2003,2006) argue that the impact of capital flows on returns is likely to be conditionalon the size of the market. On the basis of this argument, we create an additionalflow variable, RFLOWS, for use in our regressions that is defined as the rawquarterly capital flow in a sector divided by the corresponding total marketvalue of NPI properties in that sector at the beginning of the quarter.

Aggregate U.S. Summary Statistics

Descriptive statistics from the aggregate U.S. data set are presented inTable 2. The mean and standard deviation of our quarterly data are presentedin the first two columns, followed by the minimum and maximum values foreach variable.

10 We did not include capital expenditures as capital flows under the assumption thatcapital expenditures are intended to preserve the existing capital investment.


Table 2 � Descriptive statistics for aggregate U.S. variables: 1983:3–2005:2.

Variables Mean Std. Dev. Minimum Maximum

NPIMV (in billions of 59.2 41.6 8.6 165.92005 U.S. dollars)

NUMPROP 2,322 939 989 4,554INCRET 1.92% 0.19% 1.56% 2.24%RET 2.03% 1.60% −5.33% 5.34%FLOWS (in billions of 1.5 2.5 −2.3 14.1

2005 U.S. dollars)RFLOWS 3.04% 3.01% −4.20% 10.99%MKT 1.96 8.42 −24.32 20.65SMB 0.26 5.35 −10.84 19.10HML 0.97 6.91 −32.02 20.59TRYLD 7.01 2.23 3.33 13.56EMPLOY 289,138 33,292 223,704 334,840INCOME 23,069 6,201 12,718 34,550MULTI 1,110 450 399 2,158SINGLE 3,546 592 2,109 5,114

NPIMV = total market value of properties that constitute the NPI; NUMPROP =number of properties that constitute the NPI; INCRET = income return on the NPI;RET = total return on the NPI; FLOWS = market value of properties added to, ordeleted from, the NPI; RFLOWS = FLOWS divided by the market value of the NPIlagged by one quarter; MKT = total return on Center for Research in SecuritiesPricing (CRSP)’s value-weighted market index minus the CRSP three-month U.S.Treasury return; SMB = Fama–French small-firm minus big-firm return factor;HML = Fama–French high book-to-market return factor minus low book-to-market re-turn factor; TRYLD = constant maturity yield on 10-year Treasury security; EMPLOY =nonagricultural employment in thousands; INCOME = per capita income; MULTI =number of multifamily housing starts in thousands, annualized; SINGLE = number ofsingle-family housing starts in thousands, annualized.

The market value of the aggregate NPI averaged $59.2 billion (in 2005:2 dollars)over the 1983:3 to 2005:2 time period, ranging from a low of $8.6 billion in1983:3 to a high of $165.9 billion in 2005:2. This increase in aggregate marketvalue over the sample period was largely driven by an increase in constituentproperties and contributing members.

Aggregate quarterly NPI returns, RET , averaged 2.03% over the sample period,ranging from a low of −5.33% (1991:4) to a high of 5.34% (2005:2). Theincome component of the total return, INCRET , averaged 1.92% per quarter,or 94% of the average total return. With a standard deviation of just 0.19%,however, the income component has exhibited significantly less volatility thanthe total return.


NPI flows (FLOWS) for all NCREIF markets averaged $1.5 billion per quar-ter. RFLOWS averaged 3.09% per quarter and displayed substantial volatility,ranging from a low of −4.20% to a high of 10.99% over the study period.

The stock market risk premium (MKT) averaged 1.96% per quarter and dis-played significant volatility, ranging from a low of −24.32% to a high of20.65%. SMB and HML averaged 0.26% and 0.97%, respectively, and theyhave also displayed substantial volatility over the sample period. The constantmaturity annual yield on 10-year Treasury securities averaged 7.01% over thesample period.

Panel A of Table 3 contains the contemporaneous correlations among thevariables used in our analysis, along with their statistical significance. Thefirst column in panel A reveals that aggregate NPI total returns (RET)are positively correlated (ρ = 0.193) with aggregate NPI flows (FLOWS).

Table 3 � Correlations of aggregate variables.

Panel A: Contemporaneous Correlations

RET FLOWS RFLOWS MKT SMB HML TRYLD

RET 1.000FLOWS 0.193∗ 1.000RFLOWS 0.018 0.579∗∗ 1.000MKT −0.047 0.022 0.091 1.000SMB −0.142 0.305∗∗ 0.188∗ 0.437∗∗ 1.000HML 0.085 0.115 0.047 −0.399∗∗ −0.130 1.000TRYLD −0.005 −0.440∗∗ 0.137 −0.046 −0.190∗ 0.037 1.000

Panel B: Lead and Lag Correlations

RETt −1 RET RETt +1 RFLOWSt −1 RFLOWS RFLOWS t+1

RETt −1 1.000RET 0.686∗∗ 1.000RETt +1 0.716∗∗ 0.686∗∗ 1.000RFLOWSt −1 0.018 0.027 0.089 1.000RFLOWS 0.053 0.018 0.027 0.093 1.000RFLOWSt +1 0.164 0.053 0.018 −0.107 0.093 1.000

Notes: ∗∗ and ∗ represent 5% and 10% significance levels, respectively.RET = total return on the NPI; FLOWS = market value of properties added to, ordeleted from, the NPI; RFLOWS = FLOWS divided by the market value of the NPIlagged by one quarter; MKT = total return on Center for Research in SecuritiesPricing (CRSP)’s value-weighted market index minus the CRSP three-month U.S.Treasury return; SMB = Fama–French small-firm minus big-firm return factor; HML= Fama–French high book-to-market return factor minus low book-to-market returnfactor; TRYLD = constant maturity yield on 10-year Treasury security.


Figure 1 � National Council of Real Estate Investment Fiduciaries (NCREIF) relativecapital flows and total returns.

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1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003

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Capital Flows

NCREIF Total Returns

However, RET displays no significant contemporaneous correlation (ρ = 0.018)with RFLOWS. Prior empirical studies suggest that commercial real estate re-turns are likely to be positively correlated with the contemporaneous premiumsearned by value stocks. However, the correlation of RET with HML (ρ = 0.085)is not statistically significant. Moreover, the correlations among RET and MKT ,RET and SMB and RET and TRYLD cannot be distinguished from 0.

The second column in panel A of Table 3 reveals that aggregate NPI flowsare highly correlated with our constructed measure of relative fund flows(ρ = 0.579). NPI capital flows are also positively correlated with SMB, butthey are negatively correlated with TRYLD (ρ = −0.440). The correlations be-tween RFLOWS and the three Fama–French risk variables are largely consistentwith the FLOWS correlations.

Panel B of Table 3 documents evidence of simple univariate relations betweenthe lead and lagged values of our two endogenous regression variables, NPIreturns and flows. Aggregate U.S. NPI returns are highly correlated with returnsin both prior and subsequent quarters. The high degree of autocorrelation isattributable, at least in part, to the temporal lag bias (i.e., “smoothing”) associ-ated with NCREIF returns, which we control for in our conditional regressionanalysis. In sharp contrast, RFLOWS displays no significant correlation withRFLOWS in the prior or subsequent quarters.

What about the lead–lag relation between NPI capital flows and returns? InFigure 1, we graph RET and RFLOWS over the sample period. Relative capitalflows are measured on the left vertical axis, and NPI total returns are measuredon the right. Inspection of Figure 1 reveals little evidence of a consistent


univariate relation between capital flows and returns over the full sample period.This impression is largely confirmed by the correlations reported in panel B ofTable 3. Current-quarter NPI returns are not significantly correlated with eithercontemporaneous or lagged flows.

Disaggregated Data and Statistics

Table 4 provides the mean values of selected variables for each of our 43CBSAs, aggregated across all property types, as well as the mean, standarddeviation and range of the CBSA mean values. The mean market valueof NPI properties in our 43 CBSA sample (in 2005:2 dollars) averaged$1,145 million over the 1983:3–2005:2 sample period, with a low of $189million in Camden, New Jersey, and a high of $4,311 million in Los Angeles.The corresponding mean number of properties constituting the NPI averaged44 across the 43 CBSAs, ranging from a minimum of 13 in Camden to a highof 144 in Chicago.

The mean NPI income returns averaged 2.04% across the 43 markets, with astandard deviation of just 0.11%. In fact, the mean quarterly income returnsranged from a low of 1.84% to a high of just 2.29%. This 45-basis-point rangein average quarterly income returns reveals that the average income return(dividend yield) on core institutional properties varied little across CBSAsduring our more than 20-year sample period. The mean NPI total returns (RET)averaged 2.13% across the 43 markets, with a standard deviation of just 0.38%.Nominal quarterly capital flows (FLOWS) averaged $36.6 million; RFLOWSaveraged 4.4% across the 43 CBSAs.

VAR Results

Aggregate Results

In this section, we examine the conditional covariation results using returnand flow data aggregated across all CBSAs and property types. The first twocolumns in Table 5 contain the results for the bivariate model. Turning first tothe return (RET) equations, we find that aggregate NPI returns are influencedby returns in previous quarters. Although not separately reported, the sum ofthe four lagged coefficients on RET is 0.926, with a p value of 0.000, indicatinga strong relation between contemporaneous and lagged institutional returnsover the prior four quarters. Given the widely documented autocorrelation (i.e.,smoothing) in NPI return series, this result was expected.11

11 See Fisher and Geltner (2000) and the references therein for a discussion of appraisalsmoothing in the context of the NCREIF Index.


Tabl

e4

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valu

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able

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–200

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ketV

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me

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.


Controlling for the smoothed nature of NCREIF returns, lagged institutionalcapital flows have no impact on current returns; in fact, even the sum of the flowcoefficients over the prior four quarters is not statistically significant (p value =0.24) in the aggregate RET equation. Thus, in the absence of additional controlvariables, institutional capital flows are not predictive of future NPI returns.

Table 5 � Aggregate vector autoregressive model estimates: 1983:3–2005:2(quarterly).

Bivariate Model 7-Factor Model 11-Factor Model

Variables RET RFLOWS RET RFLOWS RET RFLOWS

Constant −0.014 2.074∗∗ −2.791∗ 9.646∗∗ 2.410 8.919(−0.056) (2.683) (−1.956) (2.125) (1.435) (1.280)

RETt −1 0.351∗∗ −0.183 0.237∗∗ −0.110 −0.004 −0.187(3.322) (−0.562) (2.208) (−0.323) (−0.042) (−0.437)

RETt −2 0.295∗∗ 0.284 0.302∗∗ 0.419 0.110 0.299(2.638) (0.826) (2.689) (1.177) (1.098) (0.724)

RETt −3 −0.103 −0.274 −0.064 −0.258 −0.062 −0.291(−0.908) (−0.780) (−0.570) (−0.724) (0.669) (−0.757)

RETt −4 0.382∗∗ 0.414 0.402∗∗ 0.336 0.550∗∗ 0.486(3.625) (1.271) (3.904) (1.030) (6.012) (1.279)

RFLOWSt− 1 0.007 0.113 0.023 0.042 0.039 0.043(0.203) (1.027) (0.644) (0.361) (1.297) (0.3429)

RFLOWSt− 2 0.039 −0.085 0.053∗ −0.093 0.074∗∗ −0.125(1.457) (−1.026) (1.934) (−1.060) (2.429) (−0.993)

RFLOWSt− 3 0.019 0.003 0.046∗ −0.015 0.016 0.087(0.700) (0.041) (1.640) (−0.176) (0.516) (0.683)

RFLOWSt −4 0.001 0.144∗ 0.027 0.098 0.063∗∗ 0.139(0.031) (1.73) (0.969) (1.104) (2.106) (1.116)

INCRET 1.514∗∗ −3.791∗ −0.407 −3.384(2.327) (−1.840) (−0.594) (−1.190)

MKT 0.015 0.080∗ 0.002 0.094∗

(1.065) (1.721) (0.210) (1.778)SMB 0.001 −0.010 0.017 −0.021

(0.035) (−0.136) (0.867) (−0.251)HML 0.024 0.086∗ 0.025∗ 0.089

(1.419) (1.611) (1.799) (1.511)TRYLD −0.052 −0.048 −0.369∗∗ −0.117

(−0.925) (−0.271) (−4.546) (−0.348)MULTI 0.010∗∗ −0.001

(3.560) (−0.076)SINGLE −0.007 −0.002

(−0.922) (−0.065)EMPLOY 0.267∗∗ 0.072

(2.369) (0.153)

(Continued)


Table 5 � Continued.

Bivariate Model 7-Factor Model 11-Factor Model

Variables RET RFLOWS RET RFLOWS RET RFLOWS

INCOME 0.137∗ 0.017(1.880) (0.056)

Adj. R2 0.620 0.012 0.652 0.043 0.777 0.014

Notes: t statistics in parentheses; ∗∗ and ∗ represent 5% and 10% significance levels,respectively.We estimate the following unrestricted vector autoregressive (VAR) model:

Yt = μ + �1Yt−1 + �2Yt−2 + · · · + �kYt−p + et ,

where Yt is a vector of variables, μ is a p × 1 vector of intercepts, �1, �2 , . . . , �k

are p × p matrices of parameters with all eigenvalues of � having moduli less than1 so that the VAR is stationary and et is a vector of uncorrelated structural shocks(∼NID(0, �)). We obtain the maximum likelihood parameter estimates using iteratedleast squares. The three models that we estimate are a bivariate model, a 7-factormodel and an 11-factor model. The bivariate model consists of total NCREIF PropertyIndex (NPI) capital flows and aggregate NPI returns. For the 7-factor model, weadd 10-year Constant Maturity Treasury yields, NCREIF income returns and thethree Fama–French risk factors (MKT, SMB and HML) to the bivariate model.For the 11-factor model, we estimate an augmented version of the 7-factor modelthat also includes the change in the number of multifamily housing starts in theUnited States over the prior four quarters (MULTI), the change in the number ofsingle-family housing starts over the prior four quarters (SINGLE), the change innonagricultural employment of the prior four quarters (EMPLOY) and the changein per capita income over the prior four quarters (INCOME). The exogenous variablesin the various VAR specifications are lagged.

Overall, the simple bivariate model is able to explain 62% of the variation incurrent NPI returns.

The 7-factor model (column 3) includes five additional control variables: theincome return on the aggregate NPI in the prior quarter (INCRET), the threeFama–French risk factors (MKT , SMB and HML) and lagged Treasury ratelevels (TRYLD). Similar to our bivariate results, we find a strong relationbetween RETt and returns in quarters t – 1, t – 2 and t – 4. The sum of thefour lagged coefficients on RET is 0.878, with a p value of 0.000. Unlike ourbivariate results, however, we now find evidence that flows in quarters t – 2 andt – 3 positively impact current returns. Moreover, the sum of the lagged flowcoefficients is 0.15, with a p value of 0.024, indicating that the cumulative effectof lagged flows on returns is positive and statistically significant. Although theestimated coefficient on INCRET is positive and statistically significant, theestimated coefficients on MKT , SMB, HML and TRYLD cannot be distinguishedfrom 0. The adjusted R2 for the 7-factor total return model is 0.652, a modestincrease relative to the bivariate model.


Finally, in column 5 of Table 5, we report the results of further augmenting theaggregate RET equation with our four demographic/market variables: MULTI,SINGLE, EMPLOY and INCOME. Including these additional control variablesin the estimation increases the adjusted R2 to 0.777 from 0.652 and producessome interesting results. First, the estimated coefficients on MULTI, EMPLOYand INCOME are all positive and statistically significant. However, the ad-dition of these four variables causes the coefficient on INCRET to becomeinsignificant. In addition, the strong positive relation between RET and returnsin quarters t – 1 and t – 2 found in the first two specifications no longer exists.However, the explanatory power of RETt–4 actually increases (coefficient of0.550, t statistic of 6.012). The sum of the lagged coefficients on RFLOWS inthe 11-factor return model is 0.193, with an associated p value of 0.004.

To examine whether the statistically significant flow effect on returns is alsoeconomically significant, we set the independent variables used in the returnequation equal to their mean values over the study period. We then multi-ply the estimated coefficients by these mean values to produce a “predicted”aggregate quarterly return of 2.01% (the mean RET reported in Table 2 is2.03%). Finally, we shock RFLOWS by one standard deviation. This producesa 58-basis-point increase (decrease) in the predicted quarterly return, or 231basis points on an annual basis. Thus, our results suggest that lagged institu-tional capital flows play an economically significant role in explaining the timevariation in national-level NPI returns. This result is consistent with the widelyheld belief among practitioners that capital flows in private real estate marketsare predictive of subsequent returns.

We now turn to the capital flow equations estimated simultaneously with thereturn equations. In our bivariate model, RFLOWS displays no relation to laggedreturns. That is, the NCREIF investors do not appear to chase NPI returns—atleast at the aggregate level. Moreover, RFLOWS is not associated with flowsover the prior three quarters, although the coefficient on RFLOWSt–4 is positiveand marginally significant. The adjusted R2 of the bivariate estimation of theflows equation is just 0.012.

When INCRET , MKT, SMB and HML are added to the RFLOWS equation (col-umn 4), none of the lagged flow (or return) variables are statistically significant.However, current NPI flows are weakly positively associated with MKT andHML. Nevertheless, the significance of MKT and HML in the RFLOWS equa-tion is not robust to the inclusion of MULTI, SINGLE, EMPLOY and INCOME(column 6). Overall, the 11-factor RFLOWS model is able to explain just 1.4%of the variation in current flows. Clearly, the prediction of institutional capitalflows is difficult at the aggregate U.S. level.


U.S. Results Disaggregated by Property Type

In this section, we examine the conditional covariation results using data ag-gregated across all CBSAs but disaggregated by property type. The first twocolumns in Table 6 contain results for the U.S. apartment sample using the11-factor model. The corresponding results for retail, office and industrialproperties are presented in the remaining six columns.

Consistent with the aggregate results presented in Table 5, property-level returnsare influenced by lagged returns. In the apartment sample, we also find a statis-tically significant relationship between RET and RFLOWt–1 and RFLOWSt–2.In terms of economic significance, we find that a one-standard-deviation shockto the apartment capital flows produces an economically significant 42-basis-point change in the predicted quarterly return (164 basis points annually). Inthe office sample, RFLOWt–1 and RFLOWt–4 have a statistically and econom-ically significant effect on current returns. In contrast, none of the individuallagged flow coefficients are significant in the retail or industrial RET equations,suggesting that the significant flow–return relations uncovered in the aggregateU.S. regressions are driven primarily by apartment and office properties.

The estimated coefficients on the lagged NPI income return and the Fama–French factors are not significant in any of the four property-type VAR spec-ifications. However, the returns are negatively and significantly related to thelagged Treasury yield. Moreover, MULTI and EMPLOY are positively andsignificantly related to the returns in all but the retail RET estimation.

An examination of the RFLOWS estimations confirms our prior finding ofno significant role for lagged flows or lagged returns in explaining currentflows. Although the adjusted R2 of the apartment RFLOWS equation is 0.182,institutional capital flows remain difficult to predict at the aggregate U.S. level.

Results Disaggregated by CBSA

As reported above, we find conditional evidence using national-level data thatboth lagged returns and capital flows predict subsequent returns to institutionalreal estate investors. We also find evidence that lagged returns and flows influ-ence subsequent returns for the apartment and office properties, although notfor the industrial and retail properties. These results suggest that the effectsof capital flows on returns may depend on the motivations for institutionalinvestors to purchase properties in a particular property sector. The next ques-tion that we examine is whether the institutional capital flow–return dynamicvaries across CBSAs. To answer this question, we separately estimate our 11-factor VAR model for each of the 43 CBSAs in our sample. In Table 7, we


Tabl

e6

�A

ggre

gate

prop

erty

-lev

elve

ctor

auto

regr

essi

ve(V

AR

)m

odel

estim

ates

:198

3:3–

2005

:2(q

uart

erly

).

Apa

rtm

ents

Ret

ail

Offi

ceIn

dust

rial

Var

iabl

eR

ET

RF

LO

WS

RE

TR

FL

OW

SR

ET

RF

LO

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RE

TR

FL

OW

S

Con

stan

t2.

429∗

1.71

5∗2.

910

1.27

0∗−1

.502

1.13

31.

854

1.86

6∗∗

(1.8

58)

(1.7

70)

(1.5

74)

(1.6

52)

(−0.

861)

(1.4

38)

(1.2

95)

(2.0

43)

RE

Tt−

10.

202∗

−0.6

750.

071

−0.2

10−0

.004

0.34

9−0

.037

−0.0

20(1

.759

)(−

0.79

5)(0

.549

)(−

0.38

9)(−

0.04

4)(0

.807

)(−

0.26

4)(−

0.02

2)R

ET

t−2

0.14

00.

457

0.49

0∗∗0.

226

0.08

20.

627

0.33

3∗∗0.

469

(1.1

78)

(0.5

19)

(3.8

09)

(0.4

21)

(0.8

47)

(1.4

29)

(2.3

69)

(0.5

23)

RE

Tt−

3−0

.041

−1.5

20∗

0.19

−0.1

25−0

.068

−0.7

37∗

−0.0

860.

746

(−0.

346)

(−1.

719)

(0.1

39)

(−0.

213)

(−0.

756)

(−1.

808)

(−0.

588)

(0.7

99)

RE

Tt−

40.

296∗∗

0.34

10.

583∗∗

0.42

40.

583∗∗

0.23

70.

481∗∗

0.26

0(2

.550

)(0

.397

)(4

.341

)(0

.758

)(6

.778

)(0

.610

)(3

.726

)(0

.315

)R

FL

OW

S t−

10.

036∗∗

−0.1

220.

031

−0.0

900.

048∗

−0.0

690.

027

−0.0

57(2

.328

)(−

1.06

3)(1

.122

)(−

0.77

3)(1

.842

)(−

0.58

7)(1

.404

)(−

0.46

1)R

FL

OW

S t−

20.

029∗

−0.1

340.

002

0.04

70.

034

−0.0

670.

029

−0.2

05∗

(1.8

08)

(−1.

149)

(0.1

94)

(0.9

78)

(1.5

34)

(−0.

669)

(1.5

11)

(−1.

670)

RF

LO

WS t

−3

−0.0

010.

022

0.00

9−0

.022

0.02

6−0

.055

0.01

4−0

.131

(−0.

064)

(0.1

83)

(0.9

57)

(−0.

548)

(1.2

91)

(−0.

607)

(0.7

62)

(−1.

154)

RF

LO

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−4

−0.0

060.

249∗∗

0.01

10.

014

0.04

0∗∗−0

.697

0.02

9−0

.018

(−0.

397)

(2.1

58)

(1.1

10)

(0.3

51)

(2.0

08)

(−0.

781)

(1.6

30)

(−0.

155)

INC

RE

T−0

.351

−6.3

43∗

−0.7

89−4

.680

1.24

1∗−4

.065

−0.2

14−8

.120

∗∗

(−0.

698)

(−1.

703)

(−1.

099)

(−1.

567)

(1.8

13)

(−1.

314)

(−0.

370)

(−2.

205)

MK

T−0

.017

0.20

3∗−0

.021

0.15

7∗∗0.

027

0.08

30.

009

0.06

1(−

1.13

4)(1

.815

)(−

1.14

0)(2

.025

)(1

.599

)(1

.069

)(0

.745

)(0

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)SM

B0.

022

−0.1

20−0

.002

−0.1

560.

015

0.13

60.

021

0.00

5(0

.971

)(−

0.71

1)(−

0.05

2)(−

1.29

4)(0

.530

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)

(Con

tinu

ed)

106 Fisher, Ling and NaranjoTa

ble

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tinue

d.

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rtm

ents

Ret

ail

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ceIn

dust

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−0.3

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407

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401)

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MU

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SIN

GL

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290.

012

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11−0

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90(1

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MP

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.


report the percentage of CBSA regressions in which a particular coefficient issignificant—either positively or negatively—at the 10% level or greater.12

The first two rows in the top panel of Table 7 contain our “All property type”results for the return equations. For example, the estimated coefficient onRETt–1 is positive and significant in 19% of the CBSA return equations. Innone of the return equations was the estimated coefficient on RETt–1 negativeand significant. NPI returns are also strongly positively influenced by returnsin quarters t – 2, t – 3 and t – 4. In fact, the estimated coefficient on RETt–4 waspositive and significant in 53% of the “All property types” return regressions.The sum of the estimated coefficients on lagged NPI returns is statisticallysignificant in 91% of the CBSA return regressions.

The national-level regressions reported in Table 5 indicate that a significant roleis played by lagged capital flows in explaining the variation in the aggregateNPI return index over time. However, the results reported in Table 7 suggestthat this aggregate result may be driven by a limited number of the 43 CBSAs,although as noted below, these CBSAs represent about 30% of the sample. Morespecifically, the estimated coefficients on the four lagged values of RFLOWSare positive and significant in just 9%, 12%, 14% and 7%, respectively, of the43 return regressions. Looking at the cumulative four-quarter effect of flowson returns, we find that returns in just 23% of the CBSAs are influenced bythe cumulative lagged flows. Taken together, these results reveal a great dealof variation across CBSAs in the impact of NPI capital flows on subsequentreturns. It is notable that several of the CBSAs where we do find a relationshipbetween lagged capital flows and returns were those CBSAs that represent asignificant proportion of the institutional capital in the NCREIF database.

What about the influence of our other control variables on NPI returns? Theestimated coefficients on INCRET , MKT and SMB are positive and statisticallysignificant in less than 10% of the 43 CBSA return regressions. The estimatedcoefficients on MULTI and SINGLE are significant in 16% and 12%, respec-tively, of the return equations. The estimated coefficient on TRYLD is negativeand significant in 70% of the return regressions, suggesting that CBSA-levelreturns are strongly inversely related to lagged interest rates. Finally, the coeffi-cients on EMPLOY and INCOME are positive and significant in 44% and 26%,respectively, of the return regressions, indicating a prominent role for changes

12 Although the use of a 5% significance level would be more restrictive, we chose a 10%significance level to increase the likelihood of finding a statistically significant relationbetween flows and returns, particularly in light of our results suggesting that there isa somewhat limited effect. If we instead use 5% significance levels, the proportionatesignificant coefficient effects are approximately 80% of those reported in Table 7.


in employment and per capita income in explaining NPI returns. Nevertheless,it is clear that the influence of our control variables is far from consistent acrossmetropolitan areas. This suggests that models intended to predict private marketreturns must be estimated using data disaggregated to, at least, the metropolitanlevel.

Corresponding results for the CBSA-level flow equations aggregated across allproperty types are reported immediately below the RET results in the top panelof Table 7. In general, our model variables are statistically significant in a verylimited number of the RFLOWS equations, although the sum of the four laggedflow coefficients is significant in 28% of the CBSA-level estimates. Overall, theCBSA-level flow results suggest that the prediction of CBSA-level institutionalcapital flows is a difficult task.

Results Disaggregated by Property Type at the CBSA Level

It is possible that our CBSA-level results are masking significant variation byproperty type within a given metropolitan area. To examine this possibility, weseparately estimate the 11-factor VAR specification by major property type inall CBSAs where sufficient NCREIF data are available. More specifically, weidentified 10 CBSAs with sufficient data to estimate our dynamic VAR modelfor apartments. Similarly, we identified 12, 20 and 28 CBSAs, respectively, withadequate data to estimate separate retail, office and industrial VAR models.

For all four property types, lagged returns play a significant role in explain-ing current NPI returns. This result, however, is most pronounced for retailproperties, where the accumulated return coefficients over the prior year aresignificant in 100% of the CBSA regressions. It is worth noting that RETt–4 isconsistently the most significant of the lagged returns, a somewhat surprisingresult. The absence of a significant role for lagged capital flows in explainingcurrent returns is also evident in the property-level RET equations. Clearly, thelack of explanatory power displayed by NPI capital flows in the CBSA-levelregressions is robust with respect to disaggregation by property type. Overall,the property-level regressions provide evidence of significant variation acrossproperty types in the dynamic relation between capital flows and returns and inthe importance of CBSA control variables.

Some Additional Robustness Checks

We performed various robustness checks on the conditional relation betweenNPI flows and subsequent returns. In particular, we examined the stationarityof the variables, various structural VARs, the time variation of our results, theinfluence of public real estate market returns, the measurement of flows relative


to the flows across all of the institutional markets, the effects of replacingNCREIF total returns with the portion attributable to nominal price appreciation(i.e., the capital gain), the effects of using the transaction-based index (TBI)of NCREIF returns produced by MIT in place of the NPI, the effects of usingalternative market risk factors and lags and potential asymmetric and nonlineareffects. Though not tabulated, we describe below the results corresponding tothese various robustness checks.

To insure we are using the appropriate dynamic model, we examined whetherour capital flow and return time series variables are stationary, as nonstationaritywould require the use of a vector error correction model.13 Unit root testsindicated that the various series were stationary.

We also estimated various structural VAR models (i.e., restricted VARs) wherewe impose various identifying restrictions, including specifying current NPI re-turns related to current flows (as well as both past returns and flows) and currentflows related to current NPI returns. We find that current flows explain currentreturns and that current returns explain current flows. However, contempora-neous flow–return effects are highly sensitive to model specification (variablesincluded and restrictions), the sample period, the estimation algorithm and thestarting values used. Although contemporaneous flow–return effects are unsta-ble, the estimated coefficients on the remaining variables are similar to thosereported in Table 5.

To assess the robustness of our results with respect to time, we separated theanalysis into two nonoverlapping subperiods, 1983–1993 and 1994–2005, aswell as into a 15-year subperiod from 1990 to 2005. Overall, we find littletime variation in the influence of NPI flows on NPI returns and vice versa. Inparticular, we find a very small decrease in the number of CBSAs in which NPIreturns influence returns, flows influence returns and returns influence flows.The frequency of flows influencing flows remains constant over each of thesubperiods.

The potential influence of lagged REIT returns on NPI returns and flows is alsoexamined. The REIT returns, as captured by the National Association of RealEstate Investment Trust (NAREIT) Equity Index, have an insignificant effecton both institutional flows and returns. More specifically, the t statistics on

13 In general, a series is nonstationary if its mean, autocovariances or other highermoments are time dependent. For example, if the mean of a series varies with respectto time, it is likely to be nonstationary. Simply stated, the test for a unit root (i.e.,nonstationarity) in a time series is the test that a regression of a series on itself lagged byone period yields a coefficient of 1. This test is complicated by several features arisingfrom the nonstationarity of the series under the null hypothesis.


the NAREIT coefficients were insignificant in the aggregate U.S. specification.Moreover, the inclusion of NAREIT returns did not affect the influence of othervariables in the specification. At the CBSA level, the inclusion of NAREITreturns also did not change our reported results.14

We also examined the effect of measuring flows relative to the NCREIF capitalflows across all of the CBSA markets (instead of relative to flows within asingle market). The use of this alternative flow variable yields similar resultsto those reported above. For example, in Table 7, the summed effect of flowson returns is significant in 23% of the CBSAs, whereas it is significant in 20%of the CBSAs using the across-market relative specification.

Examination of Table 2 reveals that the primary driver of NCREIF total returnsis the income component. Because of the existence of multiple-year leases onmost property types, it could be argued that the income component is relativelysticky and not affected by capital flows and trading intensity, at least in theshort run. Thus, any price pressure caused by capital flows would presumablyinfluence only transaction prices. To address this issue, we redid our analysisusing the appreciation component of the NPI total return in place of the totalreturn. The results are very similar to those reported in the article. There is,however, a small increase (less than 10%) in the number of times the cumulativelagged flows are significant in the CBSA-level return equation. It is important tonote that the lagged income return is included as an explanatory variable in ourpreferred VAR specifications. Thus, by controlling for the income return, ourestimation framework picks up the effects of capital flows and other exogenousvariables on the appreciation return component.

We argue above that, by including four quarters of lagged NPI returns in ourreturn and capital flow equations, we are effectively controlling for the well-documented appraisal smoothing/lagging nature of NCREIF returns. As anadditional robustness check, however, we reran our national/aggregate analysisusing the TBI of NCREIF returns produced by MIT’s Center for Real Estate(CRE) in place of the NPI. The MIT/CRE TBI is constructed to measure marketmovements and returns based on transaction prices of properties sold from theNCREIF Index database. Therefore, it can be argued that the TBI may providea more accurate picture of price movements.15 Using the TBI produces similar

14 This result is consistent with many prior analyses of the relation between returnsin public and private commercial real estate markets. See, for example, Clayton andMacKinnon (2003) and the references cited therein.15 The TBI estimates quarterly market price changes based on the verifiable sales pricesof all properties sold from the NPI database in each quarter. The TBI is a hedonic priceindex that controls for differences in the properties that are sold in each period, for


results, although the statistical significance of RFLOWS in the aggregate returnequation declines somewhat.

We also examined the residuals from an AR(1,4) of NPI returns in place of NPIreturns. It is important to note that our VAR estimation, in effect, captures thesescaled returns because we use four lags in our VAR analysis. However, if weinstead use the residuals from an AR(1,4) process in place of lagged returns,the statistical significance of lagged flows in the return regressions declinesmodestly.

Our selection of MKT as a control variable is based on the current state ofthe art in empirical asset pricing research, as is our use of the Fama–Frenchfactors. However, to further address the robustness of our primary results, wetried various alternative specifications of the excess market return variable.In particular, we examined both recent and cumulative market returns, laggedmarket returns, the change in market returns over the year and the volatility ofexcess market returns. The only alternative specification that was marginallysignificant (at the 10% level) was lagged market returns, although it did notmaterially influence any of the reported results.

To test for nonlinearities in the relation between flows and returns, we addedsquared capital flows and squared returns to the 11-factor model. We findthat the squared variables are significant in less than 8 of the 43 CBSAs, andtheir inclusion did not affect the significance of the other variables. To testfor potential asymmetric effects, we also separately examined the influenceof positive and negative flows (returns) and changes in relative flows greaterthan 5% (returns). The inclusion of these asymmetric variables also yieldssimilar results to those reported. Additionally, the asymmetric flow and returneffects on returns are significant in less than 5 of the 43 CBSAs for each ofthe asymmetric variables, whereas they are significant in the flows specifica-tion for less than 10 of the CBSAs. Overall, the nonlinear and asymmetriceffects are of greater importance for the flows specification than the returnsspecification.

Summary and Conclusion

Real estate practitioners have long argued that the relation between capitalflows and asset prices is more pronounced in private markets that generallylack the liquidity and supply elasticity of public securities markets. Althoughthe importance of capital flows in asset pricing has received significant attention

transaction sample selection bias and for estimation error noise. The details of the indexmethodology are described in Fisher, Geltner and Pollakowski (2007).


in public securities markets, a rigorous analysis of the dynamics of capital flowsand returns in private real estate markets has not yet been undertaken. This is asignificant void in the literature because of the size of the private commercialreal estate market and because the supply inelasticities inherent in commercialreal estate markets should make asset prices and returns in these markets moresusceptible to exogenous demand shocks.

This article examines the short- and long-run dynamics among institutionalcapital flows and returns in private real estate markets. The main tool of analysisis a VAR regression model in which both institutional capital flows and returnsare specified as endogenous variables in a two-equation simultaneous system.We also include other exogenous variables such as lagged interest rates, theFama–French factors, building starts and changes in employment and per capitaincome in an attempt to purge the capital flow and return equations of anyrelationship that may exist because of their mutual relation to these exogenousvariables and risk factors.

We first estimate our VARs using capital flow and return data from the NCREIFaggregated across all U.S. metropolitan areas and property types. However,because increases or decreases in capital flows may have different effects indifferent segments of the private real estate market, we also disaggregate ournational analysis by property type. Next, we examine whether net new capitalinvested in 43 metropolitan areas by members of the NCREIF is associated withhigher, or lower, institutional returns and capital flows in subsequent periods.Simultaneously, we examine whether quarterly real estate returns in CBSAs,as measured by the NCREIF, are predictive of future institutional returns andcapital flows by NCREIF members in these CBSAs. As a further robustnesscheck, we also examine the short- and long-run dynamics among CBSA-levelcapital flows and returns disaggregated by the four major property types: office,industrial, retail and apartment.

At the aggregate U.S. level, where net institutional capital flows reflect capitalbeing added to the private institutional sector from the noninstitutional sector,we find evidence that institutional capital flows have a statistically and econom-ically significant association with subsequent returns. This result is consistentwith the widely held belief among practitioners that capital flows in privatereal estate markets are predictive of subsequent returns. When disaggregatingby property type at the national level, we find that institutional capital flowspredict subsequent returns in the apartment and office properties sectors, butnot in the retail and industrial markets. This may reflect differences by propertytype as to whether institutional or noninstitutional investors were the “marginalinvestor” during the time period studied.


When evaluating the results for individual CBSAs, we find evidence of flowspredicting subsequent returns in only a limited number of CBSAs, althoughthese CBSAs collectively represented about 30% of the market value of theNCREIF database. Because some of the net capital flows into property sectorsand CBSAs represent movement of institutional capital from one propertysector or CBSA to another, these flows may not have the same impact on pricesas new institutional capital coming into the market from the noninstitutionalsector.

We find no evidence that returns are predictive of future institutional capitalflows. We also document that institutional capital flows are not generally pre-dictive of future capital flows. Thus, institutional investors do not appear tosystematically chase returns or capital flows of other institutional investors.

Clearly, additional work on the return–flow relation in private real estate marketsis warranted. In particular, it would be interesting to test for a return–flowrelationship using a measure of capital flows broader than the institutionalcapital flows measured in this article with the NCREIF data. The capital flowdata available since 2001 from RCA may prove to be helpful in this effort.

We thank the Real Estate Research Institute for providing partial fundingfor this project and Mark Zandi of Economy.com for providing metro-leveleconomic data. We also thank Crocker Liu (the Editor), two anonymous refer-ees and participants at the 2006 RERI Research Conference in Chicago, the2006 AREUEA International meeting in Vancouver, the 2007 AFA/AREUEAsession at the ASSA meetings in Chicago and the 2007 ERES meeting inLondon for helpful comments and suggestions.

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